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105 results on '"Pirpamer, L."'

1. The Meta VCI Map consortium for meta‐analyses on strategic lesion locations for vascular cognitive impairment using lesion‐symptom mapping: Design and multicenter pilot study

Author: Weaver, Nick A, Zhao, Lei, Biesbroek, J Matthijs, Kuijf, Hugo J, Aben, Hugo P, Bae, Hee‐Joon, Caballero, Miguel ÁA, Chappell, Francesca M, Chen, Christopher PLH, Dichgans, Martin, Duering, Marco, Georgakis, Marios K, van der Giessen, Ruben S, Gyanwali, Bibek, Hamilton, Olivia KL, Hilal, Saima, Hofe, Elise M vom, de Kort, Paul LM, Koudstaal, Peter J, Lam, Bonnie YK, Lim, Jae‐Sung, Makin, Stephen DJ, Mok, Vincent CT, Shi, Lin, Hernández, Maria C Valdés, Venketasubramanian, Narayanaswamy, Wardlaw, Joanna M, Wollenweber, Frank A, Wong, Adrian, Xin, Xu, consortium, Meta VCI Map, DeCarli, C, Fletcher, EA, Maillard, P, Barnes, J, Sudre, CH, Schott, JM, Ikram, MA, Papma, JM, Steketee, RME, Vernooij, MW, Bordet, R, Lopes, R, Huang, C‐W, Frayne, R, McCreary, CR, Smith, EE, group, Calgary Normative Study, Backes, W, Köhler, S, van Oostenbrugge, RJ, Staals, J, Verhey, F, Cheng, CY, Kalaria, RN, Werring, D, Hsu, JL, Huang, K‐L, van der Grond, J, Jukema, JW, van der Mast, RC, Nijboer, TCW, group, Framingham Heart Study, Yu, K‐H, group, The Dutch Parelsnoer Institute–Neurodegenerative diseases, Schmidt, R, Pirpamer, L, MacIntosh, BJ, Robertson, AD, de Leeuw, F‐E, Tuladhar, AM, Chaturvedi, N, Tillin, T, Brodaty, H, Sachdev, P, group, TABASCO, Barkhof, F, van der Flier, WM, Kappelle, LJ, and Biessels, Geert Jan
Subjects: Biomedical and Clinical Sciences, Clinical Sciences, Biomedical Imaging, Atherosclerosis, Clinical Research, Brain Disorders, Acquired Cognitive Impairment, Stroke, Neurosciences, Meta VCI Map consortium, Cerebrovascular disease, Consortium, Data harmonization, Lesion location, Lesion-symptom mapping, Small vessel disease, Support vector regression, Vascular cognitive impairment, Genetics, Biological psychology
Abstract: IntroductionThe Meta VCI Map consortium performs meta-analyses on strategic lesion locations for vascular cognitive impairment using lesion-symptom mapping. Integration of data from different cohorts will increase sample sizes, to improve brain lesion coverage and support comprehensive lesion-symptom mapping studies.MethodsCohorts with available imaging on white matter hyperintensities or infarcts and cognitive testing were invited. We performed a pilot study to test the feasibility of multicenter data processing and analysis and determine the benefits to lesion coverage.ResultsForty-seven groups have joined Meta VCI Map (stroke n = 7800 patients; memory clinic n = 4900; population-based n = 14,400). The pilot study (six ischemic stroke cohorts, n = 878) demonstrated feasibility of multicenter data integration (computed tomography/magnetic resonance imaging) and achieved marked improvement of lesion coverage.DiscussionMeta VCI Map will provide new insights into the relevance of vascular lesion location for cognitive dysfunction. After the successful pilot study, further projects are being prepared. Other investigators are welcome to join.
Published: 2019

2. The Meta VCI Map consortium for meta-analyses on strategic lesion locations for vascular cognitive impairment using lesion-symptom mapping: Design and multicenter pilot study

Author: DeCarli, C., Fletcher, E.A., Maillard, P., Barnes, J., Sudre, C.H., Schott, J.M., Ikram, M.A., Papma, J.M., Steketee, R.M.E., Vernooij, M.W., Bordet, R., Lopes, R., Huang, C.-W., Frayne, R., McCreary, C.R., Smith, E.E., Backes, W., Köhler, S., van Oostenbrugge, R.J., Staals, J., Verhey, F., Cheng, C.Y., Kalaria, R.N., Werring, D., Hsu, J.L., Huang, K.-L., van der Grond, J., Jukema, J.W., van der Mast, R.C., Nijboer, T.C.W., Yu, K.-H., Schmidt, R., Pirpamer, L., MacIntosh, B.J., Robertson, A.D., de Leeuw, F.-E., Tuladhar, A.M., Chaturvedi, N., Tillin, T., Brodaty, H., Sachdev, P., Barkhof, F., van der Flier, W.M., Kappelle, L.J., Weaver, Nick A., Zhao, Lei, Biesbroek, J. Matthijs, Kuijf, Hugo J., Aben, Hugo P., Bae, Hee-Joon, Caballero, Miguel Á.A., Chappell, Francesca M., Chen, Christopher P.L.H., Dichgans, Martin, Duering, Marco, Georgakis, Marios K., van der Giessen, Ruben S., Gyanwali, Bibek, Hamilton, Olivia K.L., Hilal, Saima, vom Hofe, Elise M., de Kort, Paul L.M., Koudstaal, Peter J., Lam, Bonnie Y.K., Lim, Jae-Sung, Makin, Stephen D.J., Mok, Vincent C.T., Shi, Lin, Valdés Hernández, Maria C., Venketasubramanian, Narayanaswamy, Wardlaw, Joanna M., Wollenweber, Frank A., Wong, Adrian, Xin, Xu, and Biessels, Geert Jan
Published: 2019
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3. Genetic architecture of orbital telorism

Author: Knol, M.J., Pawlak, Marta E., Lamballais, S., Terzikhan, N., Hofer, E., Xiong, Z., Klaver, C.C.W., Pirpamer, L., Vernooij, M.W., Ikram, M.Arfan, Schmidt, R., Kayser, M., Evans, T.E., Adams, H.H.H., Knol, M.J., Pawlak, Marta E., Lamballais, S., Terzikhan, N., Hofer, E., Xiong, Z., Klaver, C.C.W., Pirpamer, L., Vernooij, M.W., Ikram, M.Arfan, Schmidt, R., Kayser, M., Evans, T.E., and Adams, H.H.H.
Abstract: Contains fulltext : 251528.pdf (Publisher’s version ) (Open Access), The interocular distance, or orbital telorism, is a distinctive craniofacial trait that also serves as a clinically informative measure. While its extremes, hypo- and hypertelorism, have been linked to monogenic disorders and are often syndromic, little is known about the genetic determinants of interocular distance within the general population. We derived orbital telorism measures from cranial magnetic resonance imaging by calculating the distance between the eyeballs' centre of gravity, which showed a good reproducibility with an intraclass correlation coefficient of 0.991 (95% confidence interval 0.985-0.994). Heritability estimates were 76% (standard error = 12%) with a family-based method (N = 364) and 39% (standard error = 2.4%) with a single nucleotide polymorphism-based method (N = 34 130) and were unaffected by adjustment for height (model II) and intracranial volume (model III) or head width (model IV). Genome-wide association studies in 34 130 European individuals identified 56 significantly associated genomic loci (P < 5 × 10-8) across four different models of which 46 were novel for facial morphology, and overall these findings replicated in an independent sample (N = 10 115) with telorism-related horizontal facial distance measures. Genes located nearby these 56 identified genetic loci were 4.9-fold enriched for Mendelian hypotelorism and hypertelorism genes, underlining their biological relevance. This study provides novel insights into the genetic architecture underlying interocular distance in particular, and the face in general, and explores its potential for applications in a clinical setting.
Published: 2022

4. Prediction of dementia using diffusion tensor MRI measures: the OPTIMAL collaboration

Author: Egle, M., Hilal, S., Tuladhar, A.M., Pirpamer, L., Hofer, E., Duering, M., Wason, J., Morris, R.G.M., Dichgans, M., Schmidt, R., Tozer, D., Chen, C, Leeuw, F.E. de, Markus, H.S., Egle, M., Hilal, S., Tuladhar, A.M., Pirpamer, L., Hofer, E., Duering, M., Wason, J., Morris, R.G.M., Dichgans, M., Schmidt, R., Tozer, D., Chen, C, Leeuw, F.E. de, and Markus, H.S.
Abstract: Item does not contain fulltext, OBJECTIVES: It has been suggested that diffusion tensor imaging (DTI) measures sensitive to white matter (WM) damage may predict future dementia risk not only in cerebral small vessel disease (SVD), but also in mild cognitive impairment. To determine whether DTI measures were associated with cognition cross-sectionally and predicted future dementia risk across the full range of SVD severity, we established the International OPtimising mulTImodal MRI markers for use as surrogate markers in trials of Vascular Cognitive Impairment due to cerebrAl small vesseL disease collaboration which included six cohorts. METHODS: Among the six cohorts, prospective data with dementia incidences were available for three cohorts. The associations between six different DTI measures and cognition or dementia conversion were tested. The additional contribution to prediction of other MRI markers of SVD was also determined. RESULTS: The DTI measure mean diffusivity (MD) median correlated with cognition in all cohorts, demonstrating the contribution of WM damage to cognition. Adding MD median significantly improved the model fit compared to the clinical risk model alone and further increased in all single-centre SVD cohorts when adding conventional MRI measures. Baseline MD median predicted dementia conversion. In a study with severe SVD (SCANS) change in MD median also predicted dementia conversion. The area under the curve was best when employing a multimodal MRI model using both DTI measures and other MRI measures. CONCLUSIONS: Our results support a central role for WM alterations in dementia pathogenesis in all cohorts. DTI measures such as MD median may be a useful clinical risk predictor. The contribution of other MRI markers varied according to disease severity.
Published: 2022

5. RELEVANCE-GUIDED DEEP LEARNING FOR FEATURE IDENTIFICATION IN R2* MAPS IN ALZHEIMER��S DISEASE CLASSIFICATION

Author: Tinauer, C., Heber, S., Pirpamer, L., Damulina, A., Soellradl, M., Sackl, M., and Hofer, E.
Published: 2021
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6. Determinants of brain swelling in pediatric and adult cerebral malaria

Author: Sahu, PK, Duffy, FJ, Dankwa, S, Vishnyakova, M, Majhi, M, Pirpamer, L, Vigdorovich, V, Bage, J, Maharana, S, Mandala, W, Rogerson, SJ, Seydel, KB, Taylor, TE, Kim, K, Sather, DN, Mohanty, A, Mohanty, RR, Pattnaik, R, Aitchison, JD, Hoffman, A, Mohanty, S, Smith, JD, Bernabeu, M, Wassmer, SC, Sahu, PK, Duffy, FJ, Dankwa, S, Vishnyakova, M, Majhi, M, Pirpamer, L, Vigdorovich, V, Bage, J, Maharana, S, Mandala, W, Rogerson, SJ, Seydel, KB, Taylor, TE, Kim, K, Sather, DN, Mohanty, A, Mohanty, RR, Pattnaik, R, Aitchison, JD, Hoffman, A, Mohanty, S, Smith, JD, Bernabeu, M, and Wassmer, SC
Abstract: Cerebral malaria (CM) affects children and adults, but brain swelling is more severe in children. To investigate features associated with brain swelling in malaria, we performed blood profiling and brain MRI in a cohort of pediatric and adult patients with CM in Rourkela, India, and compared them with an African pediatric CM cohort in Malawi. We determined that higher plasma Plasmodium falciparum histidine rich protein 2 (PfHRP2) levels and elevated var transcripts that encode for binding to endothelial protein C receptor (EPCR) were linked to CM at both sites. Machine learning models trained on the African pediatric cohort could classify brain swelling in Indian children CM cases but had weaker performance for adult classification, due to overall lower parasite var transcript levels in this age group and more severe thrombocytopenia in Rourkela adults. Subgrouping of patients with CM revealed higher parasite biomass linked to severe thrombocytopenia and higher Group A-EPCR var transcripts in mild thrombocytopenia. Overall, these findings provide evidence that higher parasite biomass and a subset of Group A-EPCR binding variants are common features in children and adult CM cases, despite age differences in brain swelling.
Published: 2021

7. Association of common genetic variants with brain microbleeds

Author: Knol, M.J., Lu, D.W., Traylor, M., Adams, H.H.H., Romero, J.R.J., Smith, A.V., Fornage, M., Hofer, E., Liu, J.F., Hostettler, I.C., Luciano, M., Trompet, S., Giese, A.K., Hilal, S., Akker, E.B. van den, Vojinovic, D., Li, S., Sigurdsson, S., Lee, S.J. van der, Jack, C.R., Wilson, D., Yilmaz, P., Satizabal, C.L., Liewald, D.C.M., Grond, J. van der, Chen, C., Saba, Y., Lugt, A. van der, Bastin, M.E., Windham, B.G., Cheng, C.Y., Pirpamer, L., Kantarci, K., Himali, J.J., Yang, Q., Morris, Z., Beiser, A.S., Tozer, D.J., Vernooij, M.W., Amin, N., Beekman, M., Koh, J.Y., Stott, D.J., Houlden, H., Schmidt, R., Gottesman, R.F., MacKinnon, A.D., DeCarli, C., Gudnason, V., Deary, I.J., Duijn, C.M. van, Slagboom, P.E., Wong, T.Y., Rost, N.S., Jukema, J.W., Mosley, T.H., Werring, D.J., Schmidt, H., Wardlaw, J.M., Ikram, M.A., Seshadri, S., Launer, L.J., Markus, H.S., Neurology, Amsterdam Neuroscience - Neurodegeneration, Epidemiology, Neurosciences, Clinical Genetics, and Radiology & Nuclear Medicine
Subjects: 0301 basic medicine, medicine.medical_specialty, education.field_of_study, business.industry, Population, Genome-wide association study, Single-nucleotide polymorphism, Odds ratio, Gastroenterology, Hyperintensity, 03 medical and health sciences, 030104 developmental biology, 0302 clinical medicine, Internal medicine, Genetic predisposition, medicine, Neurology (clinical), Allele, business, education, 030217 neurology & neurosurgery, Genetic association
Abstract: ObjectiveTo identify common genetic variants associated with the presence of brain microbleeds (BMBs).MethodsWe performed genome-wide association studies in 11 population-based cohort studies and 3 case–control or case-only stroke cohorts. Genotypes were imputed to the Haplotype Reference Consortium or 1000 Genomes reference panel. BMBs were rated on susceptibility-weighted or T2*-weighted gradient echo MRI sequences, and further classified as lobar or mixed (including strictly deep and infratentorial, possibly with lobar BMB). In a subset, we assessed the effects of APOE ε2 and ε4 alleles on BMB counts. We also related previously identified cerebral small vessel disease variants to BMBs.ResultsBMBs were detected in 3,556 of the 25,862 participants, of which 2,179 were strictly lobar and 1,293 mixed. One locus in the APOE region reached genome-wide significance for its association with BMB (lead single nucleotide polymorphism rs769449; odds ratio [OR]any BMB [95% confidence interval (CI)] 1.33 [1.21–1.45]; p = 2.5 × 10−10). APOE ε4 alleles were associated with strictly lobar (OR [95% CI] 1.34 [1.19–1.50]; p = 1.0 × 10−6) but not with mixed BMB counts (OR [95% CI] 1.04 [0.86–1.25]; p = 0.68). APOE ε2 alleles did not show associations with BMB counts. Variants previously related to deep intracerebral hemorrhage and lacunar stroke, and a risk score of cerebral white matter hyperintensity variants, were associated with BMB.ConclusionsGenetic variants in the APOE region are associated with the presence of BMB, most likely due to the APOE ε4 allele count related to a higher number of strictly lobar BMBs. Genetic predisposition to small vessel disease confers risk of BMB, indicating genetic overlap with other cerebral small vessel disease markers.
Published: 2020

8. Cereb Cortex

Author: Shin, J., Ma, S., Hofer, E., Patel, Y., VOSBERG, D. E., TILLEY, S., ROSHCHUPKIN, G. V., SOUSA, A. M. M., JIAN, X., GOTTESMAN, R., MOSLEY, T. H., FORNAGE, M., SABA, Y., PIRPAMER, L., Schmidt, R., Schmidt, H., CARRION-CASTILLO, A., Crivello, F., Mazoyer, B., BIS, J. C., Li, S., Yang, Q., LUCIANO, M., Karama, S., Lewis, L., BASTIN, M. E., HARRIS, M. A., WARDLAW, J. M., DEARY, I. E., Scholz, M., Loeffler, M., WITTE, A. V., Beyer, F., VILLRINGER, A., ARMSTRONG, N. J., MATHER, K. A., Ames, D., Jiang, J., KWOK, J. B., SCHOFIELD, P. R., THALAMUTHU, A., TROLLOR, J. N., WRIGHT, M. J., BRODATY, H., Wen, W., SACHDEV, P. S., TERZIKHAN, N., EVANS, T. E., ADAMS, Hhhh, Ikram, M. A., FRENZEL, S., AUWERA-PALITSCHKA, S. V., WITTFELD, K., BULOW, R., Grabe, H. J., Tzourio, Christophe, Mishra, Aniket, MAINGAULT, S., Debette, Stéphanie, GILLESPIE, N. A., FRANZ, C. E., KREMEN, W. S., Ding, L., JAHANSHAD, N., Sestan, N., PAUSOVA, Z., SESHADRI, S., PAUS, T., Bordeaux population health (BPH), and Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM)
Subjects: endocrine system, VINTAGE, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie
Abstract: We have carried out meta-analyses of genome-wide association studies (GWAS) (n = 23 784) of the first two principal components (PCs) that group together cortical regions with shared variance in their surface area. PC1 (global) captured variations of most regions, whereas PC2 (visual) was specific to the primary and secondary visual cortices. We identified a total of 18 (PC1) and 17 (PC2) independent loci, which were replicated in another 25 746 individuals. The loci of the global PC1 included those associated previously with intracranial volume and/or general cognitive function, such as MAPT and IGF2BP1. The loci of the visual PC2 included DAAM1, a key player in the planar-cell-polarity pathway. We then tested associations with occupational aptitudes and, as predicted, found that the global PC1 was associated with General Learning Ability, and the visual PC2 was associated with the Form Perception aptitude. These results suggest that interindividual variations in global and regional development of the human cerebral cortex (and its molecular architecture) cascade—albeit in a very limited manner—to behaviors as complex as the choice of one’s occupation.
Published: 2020

9. Genetic correlations and genome-wide associations of cortical structure in general population samples of 22,824 adults

Author: Hofer, E., Roshchupkin, G.V., Adams, H.H.H., Knol, M.J., Lin, H., Li, S., Zare, H., Ahmad, S., Armstrong, N.J., Satizabal, C.L., Bernard, M., Bis, J.C., Gillespie, N.A., Luciano, M., Mishra, A., Scholz, M., Teumer, A., Xia, R., Jian, X., Mosley, T.H., Saba, Y., Pirpamer, L., Seiler, S., Becker, J.T., Carmichael, O., Rotter, J.I., Psaty, B.M., Lopez, O.L., Amin, N., van der Lee, S.J., Yang, Q., Himali, J.J., Maillard, P., Beiser, A.S., DeCarli, C., Karama, S., Lewis, L., Harris, M., Bastin, M.E., Deary, I.J., Veronica Witte, A., Beyer, F., Loeffler, M., Mather, K.A., Schofield, P.R., Thalamuthu, A., Kwok, J.B., Wright, M.J., Ames, D., Trollor, J., Jiang, J., Brodaty, H., Wen, W., Vernooij, M.W., Hofman, A., Uitterlinden, A.G., Niessen, W.J., Wittfeld, K., Bülow, R., Völker, U., Pausova, Z., Bruce Pike, G., Maingault, S., Crivello, F., Tzourio, C., Amouyel, P., Mazoyer, B., Neale, M.C., Franz, C.E., Lyons, M.J., Panizzon, M.S., Andreassen, O.A., Dale, A.M., Logue, M.A., Grasby, K.L., Jahanshad, N., Painter, J.N., Colodro-Conde, L., Bralten, J., Hibar, D.P., Lind, P.A., Pizzagalli, F., Stein, J.L., Thompson, P.M., Medland, S.E., Sachdev, P.S., Kremen, W.S., Wardlaw, J.M., Villringer, A., van Duijn, C.M., Grabe, H.J., Longstreth, W.T., Fornage, M., Paus, T., Debette, S., Arfan Ikram, M., Schmidt, H., Schmidt, R., Seshadri, S., Hofer, E., Roshchupkin, G.V., Adams, H.H.H., Knol, M.J., Lin, H., Li, S., Zare, H., Ahmad, S., Armstrong, N.J., Satizabal, C.L., Bernard, M., Bis, J.C., Gillespie, N.A., Luciano, M., Mishra, A., Scholz, M., Teumer, A., Xia, R., Jian, X., Mosley, T.H., Saba, Y., Pirpamer, L., Seiler, S., Becker, J.T., Carmichael, O., Rotter, J.I., Psaty, B.M., Lopez, O.L., Amin, N., van der Lee, S.J., Yang, Q., Himali, J.J., Maillard, P., Beiser, A.S., DeCarli, C., Karama, S., Lewis, L., Harris, M., Bastin, M.E., Deary, I.J., Veronica Witte, A., Beyer, F., Loeffler, M., Mather, K.A., Schofield, P.R., Thalamuthu, A., Kwok, J.B., Wright, M.J., Ames, D., Trollor, J., Jiang, J., Brodaty, H., Wen, W., Vernooij, M.W., Hofman, A., Uitterlinden, A.G., Niessen, W.J., Wittfeld, K., Bülow, R., Völker, U., Pausova, Z., Bruce Pike, G., Maingault, S., Crivello, F., Tzourio, C., Amouyel, P., Mazoyer, B., Neale, M.C., Franz, C.E., Lyons, M.J., Panizzon, M.S., Andreassen, O.A., Dale, A.M., Logue, M.A., Grasby, K.L., Jahanshad, N., Painter, J.N., Colodro-Conde, L., Bralten, J., Hibar, D.P., Lind, P.A., Pizzagalli, F., Stein, J.L., Thompson, P.M., Medland, S.E., Sachdev, P.S., Kremen, W.S., Wardlaw, J.M., Villringer, A., van Duijn, C.M., Grabe, H.J., Longstreth, W.T., Fornage, M., Paus, T., Debette, S., Arfan Ikram, M., Schmidt, H., Schmidt, R., and Seshadri, S.
Abstract: Cortical thickness, surface area and volumes vary with age and cognitive function, and in neurological and psychiatric diseases. Here we report heritability, genetic correlations and genome-wide associations of these cortical measures across the whole cortex, and in 34 anatomically predefined regions. Our discovery sample comprises 22,824 individuals from 20 cohorts within the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the UK Biobank. We identify genetic heterogeneity between cortical measures and brain regions, and 160 genome-wide significant associations pointing to wnt/β-catenin, TGF-β and sonic hedgehog pathways. There is enrichment for genes involved in anthropometric traits, hindbrain development, vascular and neurodegenerative disease and psychiatric conditions. These data are a rich resource for studies of the biological mechanisms behind cortical development and aging.
Published: 2020

10. Genetic correlations and genome-wide associations of cortical structure in general population samples of 22,824 adults

Author: Hofer, E, Roshchupkin, GV, Adams, HHH, Knol, MJ, Lin, H, Li, S, Zare, H, Ahmad, S, Armstrong, NJ, Satizabal, CL, Bernard, M, Bis, JC, Gillespie, NA, Luciano, M, Mishra, A, Scholz, M, Teumer, A, Xia, R, Jian, X, Mosley, TH, Saba, Y, Pirpamer, L, Seiler, S, Becker, JT, Carmichael, O, Rotter, JI, Psaty, BM, Lopez, OL, Amin, N, van der Lee, SJ, Yang, Q, Himali, JJ, Maillard, P, Beiser, AS, Decarli, C, Karama, S, Lewis, L, Harris, M, Bastin, ME, Deary, IJ, Witte, AV, Beyer, F, Loeffler, M, Mather, KA, Schofield, PR, Thalamuthu, A, Kwok, JB, Wright, MJ, Ames, D, Trollor, J, Jiang, J, Brodaty, H, Wen, W, Vernooij, MW, Hofman, A, Uitterlinden, AG, Niessen, WJ, Wittfeld, K, Bülow, R, Völker, U, Pausova, Z, Pike, GB, Maingault, S, Crivello, F, Tzourio, C, Amouyel, P, Mazoyer, B, Neale, MC, Franz, CE, Lyons, MJ, Panizzon, MS, Andreassen, OA, Dale, AM, Logue, M, Grasby, KL, Jahanshad, N, Painter, JN, Colodro-Conde, L, Bralten, J, Hibar, DP, Lind, PA, Pizzagalli, F, Stein, JL, Thompson, PM, Medland, SE, Sachdev, PS, Kremen, WS, Wardlaw, JM, Villringer, A, van Duijn, CM, Grabe, HJ, Longstreth, WT, Fornage, M, Paus, T, Debette, S, Ikram, MA, Schmidt, H, Schmidt, R, Seshadri, S, Ching, CRK, Hofer, E, Roshchupkin, GV, Adams, HHH, Knol, MJ, Lin, H, Li, S, Zare, H, Ahmad, S, Armstrong, NJ, Satizabal, CL, Bernard, M, Bis, JC, Gillespie, NA, Luciano, M, Mishra, A, Scholz, M, Teumer, A, Xia, R, Jian, X, Mosley, TH, Saba, Y, Pirpamer, L, Seiler, S, Becker, JT, Carmichael, O, Rotter, JI, Psaty, BM, Lopez, OL, Amin, N, van der Lee, SJ, Yang, Q, Himali, JJ, Maillard, P, Beiser, AS, Decarli, C, Karama, S, Lewis, L, Harris, M, Bastin, ME, Deary, IJ, Witte, AV, Beyer, F, Loeffler, M, Mather, KA, Schofield, PR, Thalamuthu, A, Kwok, JB, Wright, MJ, Ames, D, Trollor, J, Jiang, J, Brodaty, H, Wen, W, Vernooij, MW, Hofman, A, Uitterlinden, AG, Niessen, WJ, Wittfeld, K, Bülow, R, Völker, U, Pausova, Z, Pike, GB, Maingault, S, Crivello, F, Tzourio, C, Amouyel, P, Mazoyer, B, Neale, MC, Franz, CE, Lyons, MJ, Panizzon, MS, Andreassen, OA, Dale, AM, Logue, M, Grasby, KL, Jahanshad, N, Painter, JN, Colodro-Conde, L, Bralten, J, Hibar, DP, Lind, PA, Pizzagalli, F, Stein, JL, Thompson, PM, Medland, SE, Sachdev, PS, Kremen, WS, Wardlaw, JM, Villringer, A, van Duijn, CM, Grabe, HJ, Longstreth, WT, Fornage, M, Paus, T, Debette, S, Ikram, MA, Schmidt, H, Schmidt, R, Seshadri, S, and Ching, CRK
Abstract: Cortical thickness, surface area and volumes vary with age and cognitive function, and in neurological and psychiatric diseases. Here we report heritability, genetic correlations and genome-wide associations of these cortical measures across the whole cortex, and in 34 anatomically predefined regions. Our discovery sample comprises 22,824 individuals from 20 cohorts within the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the UK Biobank. We identify genetic heterogeneity between cortical measures and brain regions, and 160 genome-wide significant associations pointing to wnt/β-catenin, TGF-β and sonic hedgehog pathways. There is enrichment for genes involved in anthropometric traits, hindbrain development, vascular and neurodegenerative disease and psychiatric conditions. These data are a rich resource for studies of the biological mechanisms behind cortical development and aging.
Published: 2020

11. Association of common genetic variants with brain microbleeds: A genome-wide association study

Author: Knol, M.J. (Maria J.), Lu, D. (Dongwei), Traylor, M. (Matthew), Adams, H.H.H. (Hieab), Romero, J.R.J. (José Rafael J), Smith, A.V. (Albert), Fornage, M. (Myriam), Hofer, E. (Edith), Liu, J. (Junfeng), Hostettler, I.C. (Isabel C.), Luciano, M. (Michelle), Trompet, S. (Stella), Giese, A.-K. (Anne-Katrin), Hilal, S. (Saima), Akker, E.B. (Erik) van den, Vojinović, D. (Dina), Li, S. (Shuo), Sigurdsson, S. (Stefan), Lee, S.J. (Sven) van der, Jack Jr., C.R. (Clifford), Wilson, D. (Duncan), Yilmaz, P. (Pinar), Satizabal, C.L. (Claudia), Liewald, D.C.M. (David C M), van der Grond, J. (Jeroen), Chen, C. (Christopher), Saba, Y. (Yasaman), Lugt, A. (Aad) van der, Bastin, M.E. (Mark), Windham, B.G. (Gwen), Cheng, C.Y. (Ching Yu), Pirpamer, L. (Lukas), Kantarci, K. (Kejal), Himali, J.J. (Jayandra), Yang, Q. (Qiong), Morris, Z. (Zoe), Beiser, A. (Alexa), Tozer, D.J. (Daniel J.), Vernooij, M.W. (Meike), Amin, N. (Najaf), Beekman, M. (Marian), Koh, J.Y. (Jia Yu), Stott, D.J. (David. J.), Houlden, H. (Henry), Schmidt, R. (Reinhold), Gottesman, R.F. (Rebecca), MacKinnon, A.D. (Andrew D.), DeCarli, C. (Charles), Gudnason, V. (Vilmundur), Deary, I.J. (Ian), Duijn, C.M. (Cornelia) van, Slagboom, P.E. (Eline), Wong, T.Y. (Tien Yin), Rost, N.S. (Natalia), Jukema, J.W. (Jan Wouter), Mosley, T.H. (Thomas H.), Werring, D.J. (David), Schmidt, H. (Helena), Wardlaw, J.M. (J.), Ikram, M.A. (Arfan), Seshadri, S. (Sudha), Launer, L.J. (Lenore), Markus, H.S. (Hugh), Knol, M.J. (Maria J.), Lu, D. (Dongwei), Traylor, M. (Matthew), Adams, H.H.H. (Hieab), Romero, J.R.J. (José Rafael J), Smith, A.V. (Albert), Fornage, M. (Myriam), Hofer, E. (Edith), Liu, J. (Junfeng), Hostettler, I.C. (Isabel C.), Luciano, M. (Michelle), Trompet, S. (Stella), Giese, A.-K. (Anne-Katrin), Hilal, S. (Saima), Akker, E.B. (Erik) van den, Vojinović, D. (Dina), Li, S. (Shuo), Sigurdsson, S. (Stefan), Lee, S.J. (Sven) van der, Jack Jr., C.R. (Clifford), Wilson, D. (Duncan), Yilmaz, P. (Pinar), Satizabal, C.L. (Claudia), Liewald, D.C.M. (David C M), van der Grond, J. (Jeroen), Chen, C. (Christopher), Saba, Y. (Yasaman), Lugt, A. (Aad) van der, Bastin, M.E. (Mark), Windham, B.G. (Gwen), Cheng, C.Y. (Ching Yu), Pirpamer, L. (Lukas), Kantarci, K. (Kejal), Himali, J.J. (Jayandra), Yang, Q. (Qiong), Morris, Z. (Zoe), Beiser, A. (Alexa), Tozer, D.J. (Daniel J.), Vernooij, M.W. (Meike), Amin, N. (Najaf), Beekman, M. (Marian), Koh, J.Y. (Jia Yu), Stott, D.J. (David. J.), Houlden, H. (Henry), Schmidt, R. (Reinhold), Gottesman, R.F. (Rebecca), MacKinnon, A.D. (Andrew D.), DeCarli, C. (Charles), Gudnason, V. (Vilmundur), Deary, I.J. (Ian), Duijn, C.M. (Cornelia) van, Slagboom, P.E. (Eline), Wong, T.Y. (Tien Yin), Rost, N.S. (Natalia), Jukema, J.W. (Jan Wouter), Mosley, T.H. (Thomas H.), Werring, D.J. (David), Schmidt, H. (Helena), Wardlaw, J.M. (J.), Ikram, M.A. (Arfan), Seshadri, S. (Sudha), Launer, L.J. (Lenore), and Markus, H.S. (Hugh)
Abstract: OBJECTIVE: To identify common genetic variants associated with the presence of brain microbleeds (BMBs). METHODS: We performed genom
Published: 2020
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12. Age-Related Changes of Peak Width Skeletonized Mean Diffusivity (PSMD) Across the Adult Lifespan: A Multi-Cohort Study

Author: Beaudet, G, Tsuchida, A, Petit, L, Tzourio, C, Caspers, S, Schreiber, J, Pausova, Z, Patel, Y, Paus, T, Schmidt, R, Pirpamer, L, Sachdev, PS, Brodaty, H, Kochan, N, Trollor, J, Wen, W, Armstrong, NJ, Deary, IJ, Bastin, ME, Wardlaw, JM, Munõz Maniega, S, Witte, AV, Villringer, A, Duering, M, Debette, S, Mazoyer, B, Beaudet, G, Tsuchida, A, Petit, L, Tzourio, C, Caspers, S, Schreiber, J, Pausova, Z, Patel, Y, Paus, T, Schmidt, R, Pirpamer, L, Sachdev, PS, Brodaty, H, Kochan, N, Trollor, J, Wen, W, Armstrong, NJ, Deary, IJ, Bastin, ME, Wardlaw, JM, Munõz Maniega, S, Witte, AV, Villringer, A, Duering, M, Debette, S, and Mazoyer, B
Abstract: Parameters of water diffusion in white matter derived from diffusion-weighted imaging (DWI), such as fractional anisotropy (FA), mean, axial, and radial diffusivity (MD, AD, and RD), and more recently, peak width of skeletonized mean diffusivity (PSMD), have been proposed as potential markers of normal and pathological brain ageing. However, their relative evolution over the entire adult lifespan in healthy individuals remains partly unknown during early and late adulthood, and particularly for the PSMD index. Here, we gathered and analyzed cross-sectional diffusion tensor imaging (DTI) data from 10 population-based cohort studies in order to establish the time course of white matter water diffusion phenotypes from post-adolescence to late adulthood. DTI data were obtained from a total of 20,005 individuals aged 18.1 to 92.6 years and analyzed with the same pipeline for computing skeletonized DTI metrics from DTI maps. For each individual, MD, AD, RD, and FA mean values were computed over their FA volume skeleton, PSMD being calculated as the 90% peak width of the MD values distribution across the FA skeleton. Mean values of each DTI metric were found to strongly vary across cohorts, most likely due to major differences in DWI acquisition protocols as well as pre-processing and DTI model fitting. However, age effects on each DTI metric were found to be highly consistent across cohorts. RD, MD, and AD variations with age exhibited the same U-shape pattern, first slowly decreasing during post-adolescence until the age of 30, 40, and 50 years, respectively, then progressively increasing until late life. FA showed a reverse profile, initially increasing then continuously decreasing, slowly until the 70s, then sharply declining thereafter. By contrast, PSMD constantly increased, first slowly until the 60s, then more sharply. These results demonstrate that, in the general population, age affects PSMD in a manner different from that of other DTI metrics. The constant incre
Published: 2020

13. Common genetic variation indicates separate causes for periventricular and deep white matter hyperintensities

Author: Armstrong, N.J., Mather, K.A., Sargurupremraj, M., Knol, M.J., Malik, R., Satizabal, C.L., Yanek, L.R., Wen, W., Gudnason, V.G., Dueker, N.D., Elliott, L.T., Hofer, E., Bis, J., Jahanshad, N., Li, S., Logue, M.A., Luciano, M., Scholz, M., Smith, A.V., Trompet, S., Vojinovic, D., Xia, R., Alfaro-Almagro, F., Ames, D., Amin, N., Amouyel, P., Beiser, A.S., Brodaty, H., Deary, I.J., Fennema-Notestine, C., Gampawar, P.G., Gottesman, R., Griffanti, L., Jack, C.R., Jenkinson, M., Jiang, J., Kral, B.G., Kwok, J.B., Lampe, L., Liewald, D.C.M., Maillard, P., Marchini, J., Bastin, M.E., Mazoyer, B., Pirpamer, L., Romero, J.R., Roshchupkin, G.V., Schofield, P.R., Schroeter, M.L., Stott, D.J., Thalamuthu, A., Trollor, J., Tzourio, C., Van der Grond, J., Vernooij, M.W., Witte, V.A., Wright, M.J., Yang, Q., Morris, Z., Siggurdsson, S., Psaty, B.M., Villringer, A., Schmidt, H., Håberg, A.K., van Duijn, C.M., Jukema, J.W., Dichgans, M., Sacco, R.L., Wright, C.B., Kremen, W.S., Becker, L.C., Thompson, P.M., Mosley, T.H., Wardlaw, J.M., Ikram, M.A., Adams, H.H.H., Seshadri, S., Sachdev, P.S., Smith, S.M., Launer, L., Longstreth, W.T., DeCarli, C., Schmidt, R., Fornage, M., Debette, S., Nyquist, P.A., Armstrong, N.J., Mather, K.A., Sargurupremraj, M., Knol, M.J., Malik, R., Satizabal, C.L., Yanek, L.R., Wen, W., Gudnason, V.G., Dueker, N.D., Elliott, L.T., Hofer, E., Bis, J., Jahanshad, N., Li, S., Logue, M.A., Luciano, M., Scholz, M., Smith, A.V., Trompet, S., Vojinovic, D., Xia, R., Alfaro-Almagro, F., Ames, D., Amin, N., Amouyel, P., Beiser, A.S., Brodaty, H., Deary, I.J., Fennema-Notestine, C., Gampawar, P.G., Gottesman, R., Griffanti, L., Jack, C.R., Jenkinson, M., Jiang, J., Kral, B.G., Kwok, J.B., Lampe, L., Liewald, D.C.M., Maillard, P., Marchini, J., Bastin, M.E., Mazoyer, B., Pirpamer, L., Romero, J.R., Roshchupkin, G.V., Schofield, P.R., Schroeter, M.L., Stott, D.J., Thalamuthu, A., Trollor, J., Tzourio, C., Van der Grond, J., Vernooij, M.W., Witte, V.A., Wright, M.J., Yang, Q., Morris, Z., Siggurdsson, S., Psaty, B.M., Villringer, A., Schmidt, H., Håberg, A.K., van Duijn, C.M., Jukema, J.W., Dichgans, M., Sacco, R.L., Wright, C.B., Kremen, W.S., Becker, L.C., Thompson, P.M., Mosley, T.H., Wardlaw, J.M., Ikram, M.A., Adams, H.H.H., Seshadri, S., Sachdev, P.S., Smith, S.M., Launer, L., Longstreth, W.T., DeCarli, C., Schmidt, R., Fornage, M., Debette, S., and Nyquist, P.A.
Abstract: Background and Purpose: Periventricular white matter hyperintensities (WMH; PVWMH) and deep WMH (DWMH) are regional classifications of WMH and reflect proposed differences in cause. In the first study, to date, we undertook genome-wide association analyses of DWMH and PVWMH to show that these phenotypes have different genetic underpinnings. Methods: Participants were aged 45 years and older, free of stroke and dementia. We conducted genome-wide association analyses of PVWMH and DWMH in 26,654 participants from CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology), ENIGMA (Enhancing Neuro-Imaging Genetics Through Meta-Analysis), and the UKB (UK Biobank). Regional correlations were investigated using the genome-wide association analyses -pairwise method. Cross-trait genetic correlations between PVWMH, DWMH, stroke, and dementia were estimated using LDSC. Results: In the discovery and replication analysis, for PVWMH only, we found associations on chromosomes 2 (NBEAL), 10q23.1 (TSPAN14/FAM231A), and 10q24.33 (SH3PXD2A). In the much larger combined meta-analysis of all cohorts, we identified ten significant regions for PVWMH: chromosomes 2 (3 regions), 6, 7, 10 (2 regions), 13, 16, and 17q23.1. New loci of interest include 7q36.1 (NOS3) and 16q24.2. In both the discovery/replication and combined analysis, we found genome-wide significant associations for the 17q25.1 locus for both DWMH and PVWMH. Using gene-based association analysis, 19 genes across all regions were identified for PVWMH only, including the new genes: CALCRL (2q32.1), KLHL24 (3q27.1), VCAN (5q27.1), and POLR2F (22q13.1). Thirteen genes in the 17q25.1 locus were significant for both phenotypes. More extensive genetic correlations were observed for PVWMH with small vessel ischemic stroke. There were no associations with dementia for either phenotype. Conclusions: Our study confirms these phenotypes have distinct and also shared genetic architectures. Genetic analyses indicated PVWMH was mo
Published: 2020

14. Global and regional development of the human cerebral cortex: Molecular architecture and occupational aptitudes

Author: Paus, T., Seshadri, S., Pausova, Z., Sestan, N., Jahanshad, N., Ding, L., Kremen, W.S., Franz, C.E., Gillespie, N.A., Debette, S., Maingault, S., Mishra, A., Tzourio, C., Grabe, H.J., Bülow, R., Wittfeld, K., van der -Palitschka, S., Frenzel, S., Ikram, M.A., Adams, H.H.H.H., Evans, T.E., Terzikhan, N., Sachdev, P.S., Wen, W., Brodaty, H., Wright, M.J., Trollor, J.N., Thalamuthu, A., Schofield, P.R., Kwok, J.B., Jiang, J., Ames, D., Mather, K.A., Armstrong, N.J., Villringer, A., Beyer, F., Witte, A.V., Loeffler, M., Scholz, M., Deary, I.E., Wardlaw, J.M., Harris, M.A., Bastin, M.E., Lewis, L., Karama, S., Luciano, M., Yang, Q., Li, S., Bis, J.C., Mazoyer, B., Crivello, F., Carrion-Castillo, A., Schmidt, H., Schmidt, R., Pirpamer, L., Saba, Y., Fornage, M., Mosley, T.H., Gottesman, R., Jian, X., Sousa, A.M.M., Roshchupkin, G.V., Tilley, S., Vosberg, D.E., Patel, Y., Hofer, E., Ma, S., Shin, J., Paus, T., Seshadri, S., Pausova, Z., Sestan, N., Jahanshad, N., Ding, L., Kremen, W.S., Franz, C.E., Gillespie, N.A., Debette, S., Maingault, S., Mishra, A., Tzourio, C., Grabe, H.J., Bülow, R., Wittfeld, K., van der -Palitschka, S., Frenzel, S., Ikram, M.A., Adams, H.H.H.H., Evans, T.E., Terzikhan, N., Sachdev, P.S., Wen, W., Brodaty, H., Wright, M.J., Trollor, J.N., Thalamuthu, A., Schofield, P.R., Kwok, J.B., Jiang, J., Ames, D., Mather, K.A., Armstrong, N.J., Villringer, A., Beyer, F., Witte, A.V., Loeffler, M., Scholz, M., Deary, I.E., Wardlaw, J.M., Harris, M.A., Bastin, M.E., Lewis, L., Karama, S., Luciano, M., Yang, Q., Li, S., Bis, J.C., Mazoyer, B., Crivello, F., Carrion-Castillo, A., Schmidt, H., Schmidt, R., Pirpamer, L., Saba, Y., Fornage, M., Mosley, T.H., Gottesman, R., Jian, X., Sousa, A.M.M., Roshchupkin, G.V., Tilley, S., Vosberg, D.E., Patel, Y., Hofer, E., Ma, S., and Shin, J.
Abstract: We have carried out meta-analyses of genome-wide association studies (GWAS) (n = 23 784) of the first two principal components (PCs) that group together cortical regions with shared variance in their surface area. PC1 (global) captured variations of most regions, whereas PC2 (visual) was specific to the primary and secondary visual cortices. We identified a total of 18 (PC1) and 17 (PC2) independent loci, which were replicated in another 25 746 individuals. The loci of the global PC1 included those associated previously with intracranial volume and/or general cognitive function, such as MAPT and IGF2BP1. The loci of the visual PC2 included DAAM1, a key player in the planar-cell-polarity pathway. We then tested associations with occupational aptitudes and, as predicted, found that the global PC1 was associated with General Learning Ability, and the visual PC2 was associated with the Form Perception aptitude. These results suggest that interindividual variations in global and regional development of the human cerebral cortex (and its molecular architecture) cascade—albeit in a very limited manner—to behaviors as complex as the choice of one’s occupation.
Published: 2020

15. Global and Regional Development of the Human Cerebral Cortex: Molecular Architecture and Occupational Aptitudes

Author: Shin, J, Ma, S, Hofer, E, Patel, Y, Vosberg, DE, Tilley, S, Roshchupkin, G, Sousa, AMM, Jian, X, Gottesman, R, Mosley, TH, Fornage, M, Saba, Y, Pirpamer, L, Schmidt, R, Schmidt, H, Carrion-Castillo, A, Crivello, F, Mazoyer, B, Bis, JC, Li, S, Yang, Q, Luciano, M, Karama, S, Lewis, L, Bastin, ME, Harris, MA, Wardlaw, JM, Deary, IE, Scholz, M, Loeffler, M, Witte, AV, Beyer, F, Villringer, A, Armstrong, NJ, Mather, KA, Ames, D, Jiang, J, Kwok, JB, Schofield, PR, Thalamuthu, A, Trollor, JN, Wright, MJ, Brodaty, H, Wen, W, Sachdev, PS, Terzikhan, N, Evans, TE, Adams, HHHH, Ikram, MA, Frenzel, S, Van der Auwera-Palitschka, S, Wittfeld, K, Bulow, R, Grabe, HJ, Tzourio, C, Mishra, A, Maingault, S, Debette, S, Gillespie, NA, Franz, CE, Kremen, WS, Ding, L, Jahanshad, N, Sestan, N, Pausova, Z, Seshadri, S, Paus, T, Shin, J, Ma, S, Hofer, E, Patel, Y, Vosberg, DE, Tilley, S, Roshchupkin, G, Sousa, AMM, Jian, X, Gottesman, R, Mosley, TH, Fornage, M, Saba, Y, Pirpamer, L, Schmidt, R, Schmidt, H, Carrion-Castillo, A, Crivello, F, Mazoyer, B, Bis, JC, Li, S, Yang, Q, Luciano, M, Karama, S, Lewis, L, Bastin, ME, Harris, MA, Wardlaw, JM, Deary, IE, Scholz, M, Loeffler, M, Witte, AV, Beyer, F, Villringer, A, Armstrong, NJ, Mather, KA, Ames, D, Jiang, J, Kwok, JB, Schofield, PR, Thalamuthu, A, Trollor, JN, Wright, MJ, Brodaty, H, Wen, W, Sachdev, PS, Terzikhan, N, Evans, TE, Adams, HHHH, Ikram, MA, Frenzel, S, Van der Auwera-Palitschka, S, Wittfeld, K, Bulow, R, Grabe, HJ, Tzourio, C, Mishra, A, Maingault, S, Debette, S, Gillespie, NA, Franz, CE, Kremen, WS, Ding, L, Jahanshad, N, Sestan, N, Pausova, Z, Seshadri, S, and Paus, T
Abstract: We have carried out meta-analyses of genome-wide association studies (GWAS) (n = 23 784) of the first two principal components (PCs) that group together cortical regions with shared variance in their surface area. PC1 (global) captured variations of most regions, whereas PC2 (visual) was specific to the primary and secondary visual cortices. We identified a total of 18 (PC1) and 17 (PC2) independent loci, which were replicated in another 25 746 individuals. The loci of the global PC1 included those associated previously with intracranial volume and/or general cognitive function, such as MAPT and IGF2BP1. The loci of the visual PC2 included DAAM1, a key player in the planar-cell-polarity pathway. We then tested associations with occupational aptitudes and, as predicted, found that the global PC1 was associated with General Learning Ability, and the visual PC2 was associated with the Form Perception aptitude. These results suggest that interindividual variations in global and regional development of the human cerebral cortex (and its molecular architecture) cascade-albeit in a very limited manner-to behaviors as complex as the choice of one's occupation.
Published: 2020

16. Age-Related changes of Peak Width Skeletonized Mean Diffusivity (PSMD) across the adult lifespan: A Multi-Cohort Study

Author: Beaudet, G., Tsuchida, A., Petit, L., Tzourio, C., Caspers, S., Schreiber, J., Pausova, Z., Patel, Y., Paus, T., Schmidt, R., Pirpamer, L., Sachdev, P.S., Brodaty, H., Kochan, N., Trollor, J., Wen, W., Armstrong, N.J., Deary, I.J., Bastin, M.E., Wardlaw, J.M., Munoz Maniega, S., Witte, A.V., Villringer, A., Duering, M., Debette, S., Mazoyer, B., Beaudet, G., Tsuchida, A., Petit, L., Tzourio, C., Caspers, S., Schreiber, J., Pausova, Z., Patel, Y., Paus, T., Schmidt, R., Pirpamer, L., Sachdev, P.S., Brodaty, H., Kochan, N., Trollor, J., Wen, W., Armstrong, N.J., Deary, I.J., Bastin, M.E., Wardlaw, J.M., Munoz Maniega, S., Witte, A.V., Villringer, A., Duering, M., Debette, S., and Mazoyer, B.
Abstract: Parameters of water diffusion in white matter derived from diffusion-weighted imaging (DWI), such as fractional anisotropy (FA), mean, axial, and radial diffusivity (MD, AD, and RD), and more recently, peak width of skeletonized mean diffusivity (PSMD), have been proposed as potential markers of normal and pathological brain ageing. However, their relative evolution over the entire adult lifespan in healthy individuals remains partly unknown during early and late adulthood, and particularly for the PSMD index. Here, we gathered and analyzed cross-sectional diffusion tensor imaging (DTI) data from 10 population-based cohort studies in order to establish the time course of white matter water diffusion phenotypes from post-adolescence to late adulthood. DTI data were obtained from a total of 20,005 individuals aged 18.1 to 92.6 years and analyzed with the same pipeline for computing skeletonized DTI metrics from DTI maps. For each individual, MD, AD, RD, and FA mean values were computed over their FA volume skeleton, PSMD being calculated as the 90% peak width of the MD values distribution across the FA skeleton. Mean values of each DTI metric were found to strongly vary across cohorts, most likely due to major differences in DWI acquisition protocols as well as pre-processing and DTI model fitting. However, age effects on each DTI metric were found to be highly consistent across cohorts. RD, MD, and AD variations with age exhibited the same U-shape pattern, first slowly decreasing during post-adolescence until the age of 30, 40, and 50 years, respectively, then progressively increasing until late life. FA showed a reverse profile, initially increasing then continuously decreasing, slowly until the 70s, then sharply declining thereafter. By contrast, PSMD constantly increased, first slowly until the 60s, then more sharply. These results demonstrate that, in the general population, age affects PSMD in a manner different from that of other DTI metrics. The constant incre
Published: 2020

17. Association of common genetic variants with brain microbleeds: A genome-wide association study

Author: Knol, Maria, Lu, Difei, Traylor, M, Adams, Hieab, Romero, JR, Smith, AV, Fornage, M, Hofer, E, Liu, Jun, Hostettler, IC, Luciano, M, Trompet, S, Giese, A K, Hilal, S, van den Akker, EB, Vojinovic, Dina, Li, S, Sigurdsson, S, van der Lee, Sven, Jack, CR, Wilson, D, Yilmaz, Pinar, Satizabal, CL, Liewald, DCM, van der Grond, J, Chen, C, Saba, Y, van der Lugt, Aad, Bastin, ME, Windham, BG, Cheng, CY (Ching-Yu), Pirpamer, L, Kantarci, K, Himali, JJ, Yang, Q, Morris, Z, Beiser, AS, Tozer, DJ, Vernooij, Meike, Amin, Najaf, Beekman, M, Koh, JY, Stott, DJM, Houlden, H, Schmidt, R, Gottesman, RF, MacKinnon, AD, DeCarli, C, Gudnason, V, Deary, IJ, Duijn, Cornelia, Slagboom, PE, Wong, TY, Rost, NS, Jukema, JW, Mosley, TH, Jr, Werring, DJ, Schmidt, H, Wardlaw, JM, Ikram, Arfan, Seshadri, S, Launer, LJ, Markus, HS, Knol, Maria, Lu, Difei, Traylor, M, Adams, Hieab, Romero, JR, Smith, AV, Fornage, M, Hofer, E, Liu, Jun, Hostettler, IC, Luciano, M, Trompet, S, Giese, A K, Hilal, S, van den Akker, EB, Vojinovic, Dina, Li, S, Sigurdsson, S, van der Lee, Sven, Jack, CR, Wilson, D, Yilmaz, Pinar, Satizabal, CL, Liewald, DCM, van der Grond, J, Chen, C, Saba, Y, van der Lugt, Aad, Bastin, ME, Windham, BG, Cheng, CY (Ching-Yu), Pirpamer, L, Kantarci, K, Himali, JJ, Yang, Q, Morris, Z, Beiser, AS, Tozer, DJ, Vernooij, Meike, Amin, Najaf, Beekman, M, Koh, JY, Stott, DJM, Houlden, H, Schmidt, R, Gottesman, RF, MacKinnon, AD, DeCarli, C, Gudnason, V, Deary, IJ, Duijn, Cornelia, Slagboom, PE, Wong, TY, Rost, NS, Jukema, JW, Mosley, TH, Jr, Werring, DJ, Schmidt, H, Wardlaw, JM, Ikram, Arfan, Seshadri, S, Launer, LJ, and Markus, HS
Published: 2020

18. The Meta VCI Map consortium for meta‐analyses on strategic lesion locations for vascular cognitive impairment using lesion‐symptom mapping: design and multicenter pilot study

Author: Weaver, Nick A., Zhao, Lei, Biesbroek, J. Matthijs, Kuijf, Hugo J., Aben, Hugo P., Bae, Hee‐Joon, Caballero, Miguel Á.A., Chappell, Francesca M., Chen, Christopher P.L.H., Dichgans, Martin, Duering, Marco, Georgakis, Marios K., Giessen, Ruben S., Gyanwali, Bibek, Hamilton, Olivia K.L., Hilal, Saima, Hofe, Elise M., Kort, Paul L.M., Koudstaal, Peter J., Lam, Bonnie Y.K., Lim, Jae‐Sung, Makin, Stephen D.J., Mok, Vincent C.T., Shi, Lin, Valdés Hernández, Maria C., Venketasubramanian, Narayanaswamy, Wardlaw, Joanna M., Wollenweber, Frank A., Wong, Adrian, Xin, Xu, DeCarli, C., Fletcher, E.A., Maillard, P., Barnes, J., Sudre, C.H., Schott, J.M., Ikram, M.A., Papma, J.M., Steketee, R.M.E., Vernooij, M.W., Bordet, R., Lopes, R., Huang, C.‐W., Frayne, R., McCreary, C.R., Smith, E.E., Backes, W., Köhler, S., Oostenbrugge, R.J., Staals, J., Verhey, F., Cheng, C.Y., Kalaria, R.N., Werring, D., Hsu, J.L., Huang, K.‐L., Grond, J., Jukema, J.W., Mast, R.C., Nijboer, T.C.W., Yu, K.‐H., Schmidt, R., Pirpamer, L., MacIntosh, B.J., Robertson, A.D., Leeuw, F.‐E., Tuladhar, A.M., Chaturvedi, N., Tillin, T., Brodaty, H., Sachdev, P., Barkhof, F., Flier, W.M., Kappelle, L.J., and Biessels, Geert Jan
Abstract: Introduction: \ud The Meta VCI Map consortium performs meta-analyses on strategic lesion locations for vascular cognitive impairment using lesion-symptom mapping. Integration of data from different cohorts will increase sample sizes, to improve brain lesion coverage and support comprehensive lesion-symptom mapping studies.\ud \ud Methods: \ud Cohorts with available imaging on white matter hyperintensities or infarcts and cognitive testing were invited. We performed a pilot study to test the feasibility of multicenter data processing and analysis and determine the benefits to lesion coverage.\ud \ud Results: \ud Forty-seven groups have joined Meta VCI Map (stroke n = 7800 patients; memory clinic n = 4900; population-based n = 14,400). The pilot study (six ischemic stroke cohorts, n = 878) demonstrated feasibility of multicenter data integration (computed tomography/magnetic resonance imaging) and achieved marked improvement of lesion coverage.\ud \ud Discussion: \ud Meta VCI Map will provide new insights into the relevance of vascular lesion location for cognitive dysfunction. After the successful pilot study, further projects are being prepared. Other investigators are welcome to join.
Published: 2019

19. Microstructural Tissue Changes in Alzheimer Disease Brains: Insights from Magnetization Transfer Imaging

Author: Colonna, I., primary, Koini, M., additional, Pirpamer, L., additional, Damulina, A., additional, Hofer, E., additional, Schwingenschuh, P., additional, Enzinger, C., additional, Schmidt, R., additional, and Ropele, S., additional
Published: 2021
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20. Specific patterns of thalamic functional connectivity changes related to disability and cognitive impairment in multiple sclerosis

Author: Schoonheim, M. M., Pinter, D., Meijer, K. A., Eijlers, A. J. C., Pirpamer, L., Khalil, M., Pichler, A., Fuchs, S., Fazekas, F., Uitdehaag, B. M. J., Barkhof, F., Enzinger, C., Geurts, J. J. G., Amsterdam Neuroscience - Brain Imaging, Amsterdam Neuroscience - Neuroinfection & -inflammation, Anatomy and neurosciences, Clinical chemistry, Plastic, Reconstructive and Hand Surgery, Neurology, CCA - Imaging and biomarkers, Radiology and nuclear medicine, Amsterdam Neuroscience - Cellular & Molecular Mechanisms, Amsterdam Neuroscience - Neurodegeneration, and CCA - Cancer biology and immunology
Published: 2018

21. Novel genetic loci associated with hippocampal volume

Author: Hibar, DP, Adams, HHH, Jahanshad, N, Chauhan, G, Stein, JL, Hofer, E, Renteria, ME, Bis, JC, Arias-Vasquez, A, Ikram, MK, Desrivières, S, Vernooij, MW, Abramovic, L, Alhusaini, S, Amin, N, Andersson, M, Arfanakis, K, Aribisala, BS, Armstrong, NJ, Athanasiu, L, Axelsson, T, Beecham, AH, Beiser, A, Bernard, M, Blanton, SH, Bohlken, MM, Boks, MP, Bralten, J, Brickman, AM, Carmichael, O, Chakravarty, MM, Chen, Q, Ching, CRK, Chouraki, V, Cuellar-Partida, G, Crivello, F, Den Braber, A, Doan, NT, Ehrlich, S, Giddaluru, S, Goldman, AL, Gottesman, RF, Grimm, O, Griswold, ME, Guadalupe, T, Gutman, BA, Hass, J, Haukvik, UK, Hoehn, D, Holmes, AJ, Hoogman, M, Janowitz, D, Jia, T, Jørgensen, KN, Karbalai, N, Kasperaviciute, D, Kim, S, Klein, M, Kraemer, B, Lee, PH, Liewald, DCM, Lopez, LM, Luciano, M, MacAre, C, Marquand, AF, Matarin, M, Mather, KA, Mattheisen, M, McKay, DR, Milaneschi, Y, Muñoz Maniega, S, Nho, K, Nugent, AC, Nyquist, P, Loohuis, LMO, Oosterlaan, J, Papmeyer, M, Pirpamer, L, Pütz, B, Ramasamy, A, Richards, JS, Risacher, SL, Roiz-Santiañez, R, Rommelse, N, Ropele, S, Rose, EJ, Royle, NA, Rundek, T, Sämann, PG, Saremi, A, Satizabal, CL, Schmaal, L, Schork, AJ, Shen, L, Shin, J, Shumskaya, E, Smith, AV, Sprooten, E, Strike, LT, Teumer, A, Hibar, DP, Adams, HHH, Jahanshad, N, Chauhan, G, Stein, JL, Hofer, E, Renteria, ME, Bis, JC, Arias-Vasquez, A, Ikram, MK, Desrivières, S, Vernooij, MW, Abramovic, L, Alhusaini, S, Amin, N, Andersson, M, Arfanakis, K, Aribisala, BS, Armstrong, NJ, Athanasiu, L, Axelsson, T, Beecham, AH, Beiser, A, Bernard, M, Blanton, SH, Bohlken, MM, Boks, MP, Bralten, J, Brickman, AM, Carmichael, O, Chakravarty, MM, Chen, Q, Ching, CRK, Chouraki, V, Cuellar-Partida, G, Crivello, F, Den Braber, A, Doan, NT, Ehrlich, S, Giddaluru, S, Goldman, AL, Gottesman, RF, Grimm, O, Griswold, ME, Guadalupe, T, Gutman, BA, Hass, J, Haukvik, UK, Hoehn, D, Holmes, AJ, Hoogman, M, Janowitz, D, Jia, T, Jørgensen, KN, Karbalai, N, Kasperaviciute, D, Kim, S, Klein, M, Kraemer, B, Lee, PH, Liewald, DCM, Lopez, LM, Luciano, M, MacAre, C, Marquand, AF, Matarin, M, Mather, KA, Mattheisen, M, McKay, DR, Milaneschi, Y, Muñoz Maniega, S, Nho, K, Nugent, AC, Nyquist, P, Loohuis, LMO, Oosterlaan, J, Papmeyer, M, Pirpamer, L, Pütz, B, Ramasamy, A, Richards, JS, Risacher, SL, Roiz-Santiañez, R, Rommelse, N, Ropele, S, Rose, EJ, Royle, NA, Rundek, T, Sämann, PG, Saremi, A, Satizabal, CL, Schmaal, L, Schork, AJ, Shen, L, Shin, J, Shumskaya, E, Smith, AV, Sprooten, E, Strike, LT, and Teumer, A
Abstract: The hippocampal formation is a brain structure integrally involved in episodic memory, spatial navigation, cognition and stress responsiveness. Structural abnormalities in hippocampal volume and shape are found in several common neuropsychiatric disorders. To identify the genetic underpinnings of hippocampal structure here we perform a genome-wide association study (GWAS) of 33,536 individuals and discover six independent loci significantly associated with hippocampal volume, four of them novel. Of the novel loci, three lie within genes (ASTN2, DPP4 and MAST4) and one is found 200 kb upstream of SHH. A hippocampal subfield analysis shows that a locus within the MSRB3 gene shows evidence of a localized effect along the dentate gyrus, subiculum, CA1 and fissure. Further, we show that genetic variants associated with decreased hippocampal volume are also associated with increased risk for Alzheimer's disease (rg =-0.155). Our findings suggest novel biological pathways through which human genetic variation influences hippocampal volume and risk for neuropsychiatric illness.
Published: 2017

22. Novel genetic loci associated with hippocampal volume

Author: Hibar, D.P. (Derrek), Adams, H.H.H. (Hieab), Jahanshad, N. (Neda), Chauhan, G. (Ganesh), Stein, J.L., Hofer, E. (Edith), Rentería, M.E. (Miguel), Bis, J.C. (Joshua), Arias-Vásquez, A. (Alejandro), Ikram, M.K. (M. Kamran), Desrivières, S. (Sylvane), Vernooij, M.W. (Meike), Abramovic, L. (Lucija), Alhusaini, S. (Saud), Amin, N. (Najaf), Andersson, M. (Micael), Arfanakis, K. (Konstantinos), Aribisala, B. (Benjamin), Armstrong, N.J. (Nicola J.), Athanasiu, L. (Lavinia), Axelsson, T. (Tomas), Beecham, A.H. (Ashley), Beiser, A. (Alexa), Bernard, M. (Manon), Blanton, S.H. (Susan H.), Bohlken, M.M. (Marc M.), Boks, M.P.M. (Marco), Bralten, L.B.C. (Linda), Brickman, A.M. (Adam M.), Carmichael, O. (Owen), Chakravarty, M.M. (M. Mallar), Chen, Q. (Qiang), Ching, C.R.K. (Christopher), Chouraki, V. (Vincent), Cuellar-Partida, G. (Gabriel), Crivello, F. (Fabrice), Braber, A. (Anouk) den, Doan, N.T. (Nhat Trung), Ehrlich, S.M. (Stefan), Giddaluru, S. (Sudheer), Goldman, A.L. (Aaron L.), Gottesman, R.F. (Rebecca), Grimm, O. (Oliver), Griswold, M.D. (Michael), Guadalupe, T. (Tulio), Gutman, B.A. (Boris A.), Hass, J. (Johanna), Haukvik, U.K. (Unn), Hoehn, D. (David), Holmes, A.J. (Avram), Hoogman, M. (Martine), Janowitz, D. (Deborah), Jia, T. (Tianye), Jørgensen, K.N. (Kjetil N.), Karbalai, N. (Nazanin), Kasperaviciute, D. (Dalia), Kim, S. (Shinseog), Klein, M. (Marieke), Kraemer, B. (Bernd), Lee, P.H. (Phil), Liewald, D.C.M. (David), Lopez, L.M. (Lorna), Luciano, M. (Michelle), MacAre, C. (Christine), Marquand, A.F. (Andre F.), Matarin, M. (Mar), Mather, R., Mattheisen, M. (Manuel), McKay, D.R. (David R.), Milaneschi, Y. (Yuri), Muñoz Maniega, S. (Susana), Nho, K. (Kwangsik), Nugent, A.C. (Allison), Nyquist, P. (Paul), Loohuis, L.M.O. (Loes M. Olde), Oosterlaan, J. (Jaap), Papmeyer, M. (Martina), Pirpamer, L. (Lukas), Pütz, B. (Benno), Ramasamy, A. (Adaikalavan), Richards, J.S. (Jennifer S.), Risacher, S.L. (Shannon), Roiz-Santiañez, R. (Roberto), Rommelse, N. (Nanda), Ropele, S. (Stefan), Rose, E.J. (Emma), Royle, N.A. (Natalie), Rundek, T. (Tatjana), Sämann, P.G. (Philipp), Saremi, A. (Arvin), Satizabal, C.L. (Claudia), Schmaal, L. (Lianne), Schork, N.J. (Nicholas), Shen, L. (Li), Shin, J. (Jean), Shumskaya, E. (Elena), Smith, A.V. (Albert Vernon), Sprooten, R. (Roy), Strike, L.T. (Lachlan), Teumer, A. (Alexander), Tordesillas-Gutierrez, D. (Diana), Toro, R. (Roberto), Trabzuni, D. (Danyah), Trompet, S. (Stella), Vaidya, D. (Dhananjay), van der Grond, J. (Jeroen), Lee, S.J. (Sven) van der, Van Der Meer, D. (Dennis), Van Donkelaar, M.M.J. (Marjolein M. J.), Eijk, K.R. (Kristel) van, Erp, T.G.M. (Theo G.) van, Van Rooij, D. (Daan), Walton, E. (Esther), Westlye, L.T. (Lars), Whelan, C.D. (Christopher), Windham, B.G. (Gwen), Winkler, A.M. (Anderson), Wittfeld, K. (Katharina), Woldehawariat, G. (Girma), Björnsson, A. (Asgeir), Wolfers, T. (Thomas), Yanek, L.R. (Lisa), Yang, J. (Jingyun), Zijdenbos, A.P., Zwiers, M.P. (Marcel), Agartz, I. (Ingrid), Almasy, L. (Laura), Ames, D.J. (David), Amouyel, P. (Philippe), Andreassen, O.A. (Ole), Arepalli, S. (Sampath), Assareh, A.A., Barral, S. (Sandra), Bastin, M.E. (Mark), Becker, D.M. (Diane M.), Becker, J.T. (James), Bennett, D.A. (David A.), Blangero, J. (John), Bokhoven, H. (Hans) van, Boomsma, D.I. (Dorret), Brodaty, H. (Henry), Brouwer, R.M. (Rachel), Brunner, H.G., Buckner, M., Buitelaar, J.K. (Jan), Bulayeva, K. (Kazima), Cahn, W. (Wiepke), Calhoun, V.D. (Vince), Cannon, D.M. (Dara), Cavalleri, G. (Gianpiero), Cheng, C.-Y. (Ching-Yu), Cichon, S. (Sven), Cookson, M.R. (Mark), Corvin, A. (Aiden), Crespo-Facorro, B. (Benedicto), Curran, J.E. (Joanne), Czisch, M. (Michael), Dale, A.M. (Anders), Davies, G.E. (Gareth), Craen, A.J. (Anton) de, Geus, E.J.C. (Eco) de, Jager, P.L. (Philip) de, Zubicaray, G.I. (Greig) de, Deary, I.J. (Ian), Debette, S. (Stéphanie), DeCarli, C. (Charles), Delanty, N., Depondt, C. (Chantal), DeStefano, A.L. (Anita), Dillman, A. (Allissa), Djurovic, S. (Srdjan), Donohoe, D.J. (Dennis), Drevets, D.A. (Douglas), Duggirala, R. (Ravi), Dyer, M.D. (Matthew), Enzinger, C. (Christian), Erk, S., Espeseth, T. (Thomas), Fedko, I.O. (Iryna O.), Fernández, G. (Guillén), Ferrucci, L. (Luigi), Fisher, S.E. (Simon), Fleischman, D. (Debra), Ford, I. (Ian), Fornage, M. (Myriam), Foroud, T. (Tatiana), Fox, P.T. (Peter), Francks, C. (Clyde), Fukunaga, M. (Masaki), Gibbs, J.R. (J. Raphael), Glahn, D.C. (David), Gollub, R.L. (Randy), Göring, H.H.H. (Harald H.), Green, R.C. (Robert C.), Gruber, O. (Oliver), Gudnason, V. (Vilmundur), Guelfi, S. (Sebastian), Håberg, A.K. (Asta K.), Hansell, N.K. (Narelle), Hardy, J. (John), Hartman, C.A. (C.), Hashimoto, R. (Ryota), Hegenscheid, K. (Katrin), Heinz, J. (Judith), Le Hellard, S. (Stephanie), Hernandez, D.G. (Dena), Heslenfeld, D.J. (Dirk), Ho, B.-C. (Beng-Choon), Hoekstra, P.J. (Pieter), Hoffmann, W. (Wolfgang), Hofman, A. (Albert), Holsboer, F. (Florian), Homuth, G. (Georg), Hosten, N. (Norbert), Hottenga, J.J. (Jouke Jan), Huentelman, M.J. (Matthew), Pol, H.H., Ikeda, M. (Masashi), Jack, C.R. (Clifford R.), Jenkinson, S. (Sarah), Johnson, R. (Robert), Jönsson, E.G. (Erik G.), Jukema, J.W., Kahn, R. (René), Kanai, R. (Ryota), Kloszewska, I. (Iwona), Knopman, D.S. (David S.), Kochunov, P. (Peter), Kwok, J.B. (John B.), Lawrie, S. (Stephen), Lemaître, H. (Herve), Liu, X. (Xinmin), Longo, D.L. (Dan L.), Lopez, O.L. (Oscar L.), Lovestone, S. (Simon), Martinez, O. (Oliver), Martinot, J.-L. (Jean-Luc), Mattay, V.S. (Venkata S.), McDonald, C. (Colm), McIntosh, A.M. (Andrew), McMahon, F.J. (Francis J.), McMahon, K.L. (Katie L.), Mecocci, P. (Patrizia), Melle, I. (Ingrid), Meyer-Lindenberg, A. (Andreas), Mohnke, S. (Sebastian), Montgomery, G.W. (Grant W.), Morris, D.W. (Derek W), Mosley, T.H. (Thomas H.), Mühleisen, T.W. (Thomas), Müller-Myhsok, B. (B.), Nalls, M.A. (Michael), Nauck, M. (Matthias), Nichols, T.E. (Thomas), Niessen, W.J. (Wiro), Nöthen, M.M. (Markus), Nyberg, L. (Lars), Ohi, K. (Kazutaka), Olvera, R.L. (Rene), Ophoff, R.A. (Roel), Pandolfo, M. (Massimo), Paus, T. (Tomas), Pausova, Z. (Zdenka), Penninx, B.W.J.H. (Brenda), Pike, G.B. (G. Bruce), Potkin, S.G. (Steven), Psaty, B.M. (Bruce), Reppermund, S., Rietschel, M. (Marcella), Roffman, J.L. (Joshua), Seiferth, N. (Nina), Rotter, J.I. (Jerome I.), Ryten, M. (Mina), Sacco, R.L. (Ralph L.), Sachdev, P.S. (Perminder), Saykin, A.J. (Andrew), Schmidt, R. (Reinhold), Schmidt, H. (Helena), Schofield, C.J. (Christopher), Sigursson, S. (Sigurdur), Simmons, A. (Andrew), Singleton, A. (Andrew), Sisodiya, S.M. (Sanjay), Smith, C. (Colin), Smoller, J.W., Soininen, H. (H.), Steen, V.M. (Vidar), Stott, D.J. (David J.), Sussmann, J. (Jessika), Thalamuthu, A. (Anbupalam), Toga, A.W. (Arthur W.), Traynor, B. (Bryan), Troncoso, J.C. (Juan), Tsolaki, M. (Magda), Tzourio, C. (Christophe), Uitterlinden, A.G. (André), Hernández, M.C.V. (Maria C. Valdés), Brug, M.P. (Marcel) van der, Lugt, A. (Aad) van der, Wee, N.J. (Nic) van der, Haren, N.E.M. (Neeltje E.) van, Ent, D. (Dennis) van 't, Tol, M.J.D. (Marie-José) van, Vardarajan, B.N. (Badri), Vellas, B. (Bruno), Veltman, D.J. (Dick), Völzke, H. (Henry), Walter, H.J. (Henrik), Wardlaw, J. (Joanna), Wassink, A.M.J. (Annemarie), Weale, M.E. (Michael), Weinberger, D.R. (Daniel R.), Weiner, M.W. (Michael W.), Wen, W. (Wei), Westman, E. (Eric), White, T.J.H. (Tonya), Wong, T.Y. (Tien Y.), Wright, C.B. (Clinton B.), Zielke, R.H. (Ronald H.), Zonderman, A.B., Martin, N.G. (Nicholas), Duijn, C.M. (Cornelia) van, Wright, M.J. (Margaret), Longstreth Jr, W.T., Schumann, G. (Gunter), Grabe, H.J. (Hans Jörgen), Franke, B. (Barbara), Launer, L.J. (Lenore), Medland, S.E. (Sarah), Seshadri, S. (Sudha), Thompson, P.M. (Paul), Ikram, M.K. (Kamran), Hibar, D.P. (Derrek), Adams, H.H.H. (Hieab), Jahanshad, N. (Neda), Chauhan, G. (Ganesh), Stein, J.L., Hofer, E. (Edith), Rentería, M.E. (Miguel), Bis, J.C. (Joshua), Arias-Vásquez, A. (Alejandro), Ikram, M.K. (M. Kamran), Desrivières, S. (Sylvane), Vernooij, M.W. (Meike), Abramovic, L. (Lucija), Alhusaini, S. (Saud), Amin, N. (Najaf), Andersson, M. (Micael), Arfanakis, K. (Konstantinos), Aribisala, B. (Benjamin), Armstrong, N.J. (Nicola J.), Athanasiu, L. (Lavinia), Axelsson, T. (Tomas), Beecham, A.H. (Ashley), Beiser, A. (Alexa), Bernard, M. (Manon), Blanton, S.H. (Susan H.), Bohlken, M.M. (Marc M.), Boks, M.P.M. (Marco), Bralten, L.B.C. (Linda), Brickman, A.M. (Adam M.), Carmichael, O. (Owen), Chakravarty, M.M. (M. Mallar), Chen, Q. (Qiang), Ching, C.R.K. (Christopher), Chouraki, V. (Vincent), Cuellar-Partida, G. (Gabriel), Crivello, F. (Fabrice), Braber, A. (Anouk) den, Doan, N.T. (Nhat Trung), Ehrlich, S.M. (Stefan), Giddaluru, S. (Sudheer), Goldman, A.L. (Aaron L.), Gottesman, R.F. (Rebecca), Grimm, O. (Oliver), Griswold, M.D. (Michael), Guadalupe, T. (Tulio), Gutman, B.A. (Boris A.), Hass, J. (Johanna), Haukvik, U.K. (Unn), Hoehn, D. (David), Holmes, A.J. (Avram), Hoogman, M. (Martine), Janowitz, D. (Deborah), Jia, T. (Tianye), Jørgensen, K.N. (Kjetil N.), Karbalai, N. (Nazanin), Kasperaviciute, D. (Dalia), Kim, S. (Shinseog), Klein, M. (Marieke), Kraemer, B. (Bernd), Lee, P.H. (Phil), Liewald, D.C.M. (David), Lopez, L.M. (Lorna), Luciano, M. (Michelle), MacAre, C. (Christine), Marquand, A.F. (Andre F.), Matarin, M. (Mar), Mather, R., Mattheisen, M. (Manuel), McKay, D.R. (David R.), Milaneschi, Y. (Yuri), Muñoz Maniega, S. (Susana), Nho, K. (Kwangsik), Nugent, A.C. (Allison), Nyquist, P. (Paul), Loohuis, L.M.O. (Loes M. Olde), Oosterlaan, J. (Jaap), Papmeyer, M. (Martina), Pirpamer, L. (Lukas), Pütz, B. (Benno), Ramasamy, A. (Adaikalavan), Richards, J.S. (Jennifer S.), Risacher, S.L. (Shannon), Roiz-Santiañez, R. (Roberto), Rommelse, N. (Nanda), Ropele, S. (Stefan), Rose, E.J. (Emma), Royle, N.A. (Natalie), Rundek, T. (Tatjana), Sämann, P.G. (Philipp), Saremi, A. (Arvin), Satizabal, C.L. (Claudia), Schmaal, L. (Lianne), Schork, N.J. (Nicholas), Shen, L. (Li), Shin, J. (Jean), Shumskaya, E. (Elena), Smith, A.V. (Albert Vernon), Sprooten, R. (Roy), Strike, L.T. (Lachlan), Teumer, A. (Alexander), Tordesillas-Gutierrez, D. (Diana), Toro, R. (Roberto), Trabzuni, D. (Danyah), Trompet, S. (Stella), Vaidya, D. (Dhananjay), van der Grond, J. (Jeroen), Lee, S.J. (Sven) van der, Van Der Meer, D. (Dennis), Van Donkelaar, M.M.J. (Marjolein M. J.), Eijk, K.R. (Kristel) van, Erp, T.G.M. (Theo G.) van, Van Rooij, D. (Daan), Walton, E. (Esther), Westlye, L.T. (Lars), Whelan, C.D. (Christopher), Windham, B.G. (Gwen), Winkler, A.M. (Anderson), Wittfeld, K. (Katharina), Woldehawariat, G. (Girma), Björnsson, A. (Asgeir), Wolfers, T. (Thomas), Yanek, L.R. (Lisa), Yang, J. (Jingyun), Zijdenbos, A.P., Zwiers, M.P. (Marcel), Agartz, I. (Ingrid), Almasy, L. (Laura), Ames, D.J. (David), Amouyel, P. (Philippe), Andreassen, O.A. (Ole), Arepalli, S. (Sampath), Assareh, A.A., Barral, S. (Sandra), Bastin, M.E. (Mark), Becker, D.M. (Diane M.), Becker, J.T. (James), Bennett, D.A. (David A.), Blangero, J. (John), Bokhoven, H. (Hans) van, Boomsma, D.I. (Dorret), Brodaty, H. (Henry), Brouwer, R.M. (Rachel), Brunner, H.G., Buckner, M., Buitelaar, J.K. (Jan), Bulayeva, K. (Kazima), Cahn, W. (Wiepke), Calhoun, V.D. (Vince), Cannon, D.M. (Dara), Cavalleri, G. (Gianpiero), Cheng, C.-Y. (Ching-Yu), Cichon, S. (Sven), Cookson, M.R. (Mark), Corvin, A. (Aiden), Crespo-Facorro, B. (Benedicto), Curran, J.E. (Joanne), Czisch, M. (Michael), Dale, A.M. (Anders), Davies, G.E. (Gareth), Craen, A.J. (Anton) de, Geus, E.J.C. (Eco) de, Jager, P.L. (Philip) de, Zubicaray, G.I. (Greig) de, Deary, I.J. (Ian), Debette, S. (Stéphanie), DeCarli, C. (Charles), Delanty, N., Depondt, C. (Chantal), DeStefano, A.L. (Anita), Dillman, A. (Allissa), Djurovic, S. (Srdjan), Donohoe, D.J. (Dennis), Drevets, D.A. (Douglas), Duggirala, R. (Ravi), Dyer, M.D. (Matthew), Enzinger, C. (Christian), Erk, S., Espeseth, T. (Thomas), Fedko, I.O. (Iryna O.), Fernández, G. (Guillén), Ferrucci, L. (Luigi), Fisher, S.E. (Simon), Fleischman, D. (Debra), Ford, I. (Ian), Fornage, M. (Myriam), Foroud, T. (Tatiana), Fox, P.T. (Peter), Francks, C. (Clyde), Fukunaga, M. (Masaki), Gibbs, J.R. (J. Raphael), Glahn, D.C. (David), Gollub, R.L. (Randy), Göring, H.H.H. (Harald H.), Green, R.C. (Robert C.), Gruber, O. (Oliver), Gudnason, V. (Vilmundur), Guelfi, S. (Sebastian), Håberg, A.K. (Asta K.), Hansell, N.K. (Narelle), Hardy, J. (John), Hartman, C.A. (C.), Hashimoto, R. (Ryota), Hegenscheid, K. (Katrin), Heinz, J. (Judith), Le Hellard, S. (Stephanie), Hernandez, D.G. (Dena), Heslenfeld, D.J. (Dirk), Ho, B.-C. (Beng-Choon), Hoekstra, P.J. (Pieter), Hoffmann, W. (Wolfgang), Hofman, A. (Albert), Holsboer, F. (Florian), Homuth, G. (Georg), Hosten, N. (Norbert), Hottenga, J.J. (Jouke Jan), Huentelman, M.J. (Matthew), Pol, H.H., Ikeda, M. (Masashi), Jack, C.R. (Clifford R.), Jenkinson, S. (Sarah), Johnson, R. (Robert), Jönsson, E.G. (Erik G.), Jukema, J.W., Kahn, R. (René), Kanai, R. (Ryota), Kloszewska, I. (Iwona), Knopman, D.S. (David S.), Kochunov, P. (Peter), Kwok, J.B. (John B.), Lawrie, S. (Stephen), Lemaître, H. (Herve), Liu, X. (Xinmin), Longo, D.L. (Dan L.), Lopez, O.L. (Oscar L.), Lovestone, S. (Simon), Martinez, O. (Oliver), Martinot, J.-L. (Jean-Luc), Mattay, V.S. (Venkata S.), McDonald, C. (Colm), McIntosh, A.M. (Andrew), McMahon, F.J. (Francis J.), McMahon, K.L. (Katie L.), Mecocci, P. (Patrizia), Melle, I. (Ingrid), Meyer-Lindenberg, A. (Andreas), Mohnke, S. (Sebastian), Montgomery, G.W. (Grant W.), Morris, D.W. (Derek W), Mosley, T.H. (Thomas H.), Mühleisen, T.W. (Thomas), Müller-Myhsok, B. (B.), Nalls, M.A. (Michael), Nauck, M. (Matthias), Nichols, T.E. (Thomas), Niessen, W.J. (Wiro), Nöthen, M.M. (Markus), Nyberg, L. (Lars), Ohi, K. (Kazutaka), Olvera, R.L. (Rene), Ophoff, R.A. (Roel), Pandolfo, M. (Massimo), Paus, T. (Tomas), Pausova, Z. (Zdenka), Penninx, B.W.J.H. (Brenda), Pike, G.B. (G. Bruce), Potkin, S.G. (Steven), Psaty, B.M. (Bruce), Reppermund, S., Rietschel, M. (Marcella), Roffman, J.L. (Joshua), Seiferth, N. (Nina), Rotter, J.I. (Jerome I.), Ryten, M. (Mina), Sacco, R.L. (Ralph L.), Sachdev, P.S. (Perminder), Saykin, A.J. (Andrew), Schmidt, R. (Reinhold), Schmidt, H. (Helena), Schofield, C.J. (Christopher), Sigursson, S. (Sigurdur), Simmons, A. (Andrew), Singleton, A. (Andrew), Sisodiya, S.M. (Sanjay), Smith, C. (Colin), Smoller, J.W., Soininen, H. (H.), Steen, V.M. (Vidar), Stott, D.J. (David J.), Sussmann, J. (Jessika), Thalamuthu, A. (Anbupalam), Toga, A.W. (Arthur W.), Traynor, B. (Bryan), Troncoso, J.C. (Juan), Tsolaki, M. (Magda), Tzourio, C. (Christophe), Uitterlinden, A.G. (André), Hernández, M.C.V. (Maria C. Valdés), Brug, M.P. (Marcel) van der, Lugt, A. (Aad) van der, Wee, N.J. (Nic) van der, Haren, N.E.M. (Neeltje E.) van, Ent, D. (Dennis) van 't, Tol, M.J.D. (Marie-José) van, Vardarajan, B.N. (Badri), Vellas, B. (Bruno), Veltman, D.J. (Dick), Völzke, H. (Henry), Walter, H.J. (Henrik), Wardlaw, J. (Joanna), Wassink, A.M.J. (Annemarie), Weale, M.E. (Michael), Weinberger, D.R. (Daniel R.), Weiner, M.W. (Michael W.), Wen, W. (Wei), Westman, E. (Eric), White, T.J.H. (Tonya), Wong, T.Y. (Tien Y.), Wright, C.B. (Clinton B.), Zielke, R.H. (Ronald H.), Zonderman, A.B., Martin, N.G. (Nicholas), Duijn, C.M. (Cornelia) van, Wright, M.J. (Margaret), Longstreth Jr, W.T., Schumann, G. (Gunter), Grabe, H.J. (Hans Jörgen), Franke, B. (Barbara), Launer, L.J. (Lenore), Medland, S.E. (Sarah), Seshadri, S. (Sudha), Thompson, P.M. (Paul), and Ikram, M.K. (Kamran)
Abstract: The hippocampal formation is a brain structure integrally involved in episodic memory, spatial navigation, cognition and stress responsiveness. Structural abnormalities in hippocampal volume and shape are found in several common neuropsychiatric disorders. To identify the genetic underpinnings of hippocampal structure here we perform a genome-wide association study (GWAS) of 33,536 individuals and discover six independent loci significantly associated with hippocampal volume, four of them novel. Of the novel loci, three lie within genes (ASTN2, DPP4 and MAST4) and one is found 200 kb upstream of SHH. A hippocampal subfield analysis shows that a locus within the MSRB3 gene shows evidence of a localized effect along the dentate gyrus, subiculum, CA1 and fissure. Further, we show that genetic variants associated with decreased hippocampal volume are also associated with increased risk for Alzheimer's disease (rg =-0.155). Our findings suggest novel biological pathways through which human genetic variation influences hippocampal volume and risk for neuropsychiatric illness.
Published: 2017
Full Text: View/download PDF

23. Novel genetic loci associated with hippocampal volume

Author: Hibar, D.P., Adams, H.H.H., Jahanshad, N., Chauhan, G., Stein, J.L., Hofer, E., Renteria, M.E., Bis, J.C., Arias-Vasquez, A., Ikram, M.K., Desrivières, S., Vernooij, M.W., Abramovic, L., Alhusaini, S., Amin, N., Andersson, M., Arfanakis, K., Aribisala, B.S., Armstrong, N.J., Athanasiu, L., Axelsson, T., Beecham, A.H., Beiser, A., Bernard, M., Blanton, S.H., Bohlken, M.M., Boks, M.P., Bralten, J., Brickman, A.M., Carmichael, O., Chakravarty, M.M., Chen, Q., Ching, C.R.K., Chouraki, V., Cuellar-Partida, G., Crivello, F., den Braber, A., Doan, N.T., Ehrlich, S., Giddaluru, S., Goldman, A.L., Gottesman, R.F., Grimm, O., Griswold, M.E., Guadalupe, T., Gutman, B.A., Hass, J., Haukvik, U.K., Hoehn, D., Holmes, A.J., Hoogman, M., Janowitz, D., Jia, T., Jørgensen, K.N., Karbalai, N., Kasperaviciute, D., Kim, S., Klein, M., Kraemer, B., Lee, P.H., Liewald, D.C.M., Lopez, L.M., Luciano, M., Macare, C., Marquand, A.F., Matarin, M., Mather, K.A., Mattheisen, M., McKay, D.R., Milaneschi, Y., Munoz Maniega, S., Nho, K., Nugent, A.C., Nyquist, P., Loohuis, L.M.O., Oosterlaan, J., Papmeyer, M., Pirpamer, L., Putz, B., Ramasamy, A., Richards, J.S., Risacher, S.L., Roiz-Santiañez, R., Rommelse, N., Ropele, S., Rose, E.J., Royle, N.A., Rundek, T., Sämann, P.G., Saremi, A., Satizabal, C.L., Schmaal, L., Schork, A.J., Shen, L., Shin, J., Shumskaya, E., Smith, A.V., Sprooten, E., Strike, L.T., Teumer, A., Tordesillas-Gutierrez, D., Toro, R., Trabzuni, D., Trompet, S., Vaidya, D., Van der Grond, J., van der Lee, S.J., van der Meer, D., van Donkelaar, M.M.J., van Eijk, K.R., Van Erp, T.G.M., Van Rooij, D., Walton, E., Westlye, L.T., Whelan, C.D., Windham, B.G., Winkler, A.M., Wittfeld, K., Woldehawariat, G., Wolf, C., Wolfers, T., Yanek, L.R., Yang, J., Zijdenbos, A., Zwiers, M.P., Agartz, I., Almasy, L., Ames, D., Amouyel, P., Andreassen, O.A., Arepalli, S., Assareh, A.A., Barral, S., Bastin, M.E., Becker, D.M., Becker, J.T., Bennett, D.A., Blangero, J., van Bokhoven, H., Boomsma, D.I., Brodaty, H., Brouwer, R.M., Brunner, H.G., Buckner, R.L., Buitelaar, J.K., Bulayeva, K.B., Cahn, W., Calhoun, V.D., Cannon, D.M., Cavalleri, G.L., Cheng, C-Y, Cichon, S., Cookson, M.R., Corvin, A., Crespo-Facorro, B., Curran, J.E., Czisch, M., Dale, A.M., Davies, G.E., de Craen, A.J.M., de Geus, E.J.C., De Jager, P.L., de Zubicaray, G.I., Deary, I.J., Debette, S., DeCarli, C., Delanty, N., Depondt, C., DeStefano, A.L., Dillman, A., Djurovic, S., Donohoe, G., Drevets, W.C., Duggirala, R., Dyer, T.D., Enzinger, C., Erk, S., Espeseth, T., Fedko, I.O., Fernández, G., Ferrucci, L., Fisher, S.E., Fleischman, D.A., Ford, I., Fornage, M., Foroud, T.M., Fox, P.T., Francks, C., Fukunaga, M., Gibbs, J.R., Glahn, D.C., Gollub, R.L., Göring, H.H.H., Green, R.C., Gruber, O., Gudnason, V., Guelfi, S., Håberg, A.K., Hansell, N.K., Hardy, J., Hartman, C.A., Hashimoto, R., Hegenscheid, K., Heinz, A., Le Hellard, S., Hernandez, D.G., Heslenfeld, D.J., Ho, B-C., Hoekstra, P.J., Hoffmann, W., Hofman, A., Holsboer, F., Homuth, G., Hosten, N., Hottenga, J-J, Huentelman, M., Pol, H.E.H., Ikeda, M., Jack Jr, C.R., Jenkinson, M., Johnson, R., Jönsson, E.G., Jukema, J.W., Kahn, R.S., Kanai, R., Kloszewska, I., Knopman, D.S., Kochunov, P., Kwok, J.B., Lawrie, S.M., Lemaitre, H., Liu, X., Longo, D.L., Lopez, O.L., Lovestone, S., Martinez, O.P., Martinot, J-L, Mattay, V.S., McDonald, C., McIntosh, A.M., McMahon, F.J., McMahon, K.L., Mecocci, P., Melle, I., Meyer-Lindenberg, A., Mohnke, S., Montgomery, G.W., Morris, D.W., Mosley, T.H., Mühleisen, T.W., Müller-Myhsok, B., Nalls, M.A., Nauck, M., Nichols, T.E., Niessen, W.J., Nöthen, M.M., Nyberg, L., Ohi, K., Olvera, R.L., Ophoff, R.A., Pandolfo, M., Paus, T., Pausova, Z., Penninx, B.W.J.H., Pike, G.B., Potkin, S.G., Psaty, B.M., Reppermund, S., Rietschel, M., Roffman, J.L., Romanczuk-Seiferth, N., Rotter, J.I., Ryten, M., Sacco, R.L., Sachdev, P.S., Saykin, A.J., Schmidt, R., Schmidt, H., Schofield, P.R., Sigursson, S., Simmons, A., Singleton, A., Sisodiya, S.M., Smith, C., Smoller, J.W., Soininen, H., Steen, V.M., Stott, D.J., Sussmann, J.E., Thalamuthu, A., Toga, A.W., Traynor, B.J., Troncoso, J., Tsolaki, M., Tzourio, C., Uitterlinden, A.G., Hernández, M.C.V., van der Brug, M., van der Lugt, A., van der Wee, N.J.A., van Haren, N.E.M., van ’t Ent, D., van Tol, M-J, Vardarajan, B.N., Vellas, B., Veltman, D.J., Volzke, H., Walter, H., Wardlaw, J.M., Wassink, T.H., Weale, M.E., Weinberger, D.R., Weiner, M.W., Wen, W., Westman, E., White, T., Wong, T.Y., Wright, C.B., Zielke, R.H., Zonderman, A.B., Martin, N.G., van Duijn, C.M., Wright, M.J., Longstreth, W.T., Schumann, G., Grabe, H.J., Franke, B., Launer, L.J., Medland, S.E., Seshadri, S., Thompson, P.M., Ikram, M.A., Hibar, D.P., Adams, H.H.H., Jahanshad, N., Chauhan, G., Stein, J.L., Hofer, E., Renteria, M.E., Bis, J.C., Arias-Vasquez, A., Ikram, M.K., Desrivières, S., Vernooij, M.W., Abramovic, L., Alhusaini, S., Amin, N., Andersson, M., Arfanakis, K., Aribisala, B.S., Armstrong, N.J., Athanasiu, L., Axelsson, T., Beecham, A.H., Beiser, A., Bernard, M., Blanton, S.H., Bohlken, M.M., Boks, M.P., Bralten, J., Brickman, A.M., Carmichael, O., Chakravarty, M.M., Chen, Q., Ching, C.R.K., Chouraki, V., Cuellar-Partida, G., Crivello, F., den Braber, A., Doan, N.T., Ehrlich, S., Giddaluru, S., Goldman, A.L., Gottesman, R.F., Grimm, O., Griswold, M.E., Guadalupe, T., Gutman, B.A., Hass, J., Haukvik, U.K., Hoehn, D., Holmes, A.J., Hoogman, M., Janowitz, D., Jia, T., Jørgensen, K.N., Karbalai, N., Kasperaviciute, D., Kim, S., Klein, M., Kraemer, B., Lee, P.H., Liewald, D.C.M., Lopez, L.M., Luciano, M., Macare, C., Marquand, A.F., Matarin, M., Mather, K.A., Mattheisen, M., McKay, D.R., Milaneschi, Y., Munoz Maniega, S., Nho, K., Nugent, A.C., Nyquist, P., Loohuis, L.M.O., Oosterlaan, J., Papmeyer, M., Pirpamer, L., Putz, B., Ramasamy, A., Richards, J.S., Risacher, S.L., Roiz-Santiañez, R., Rommelse, N., Ropele, S., Rose, E.J., Royle, N.A., Rundek, T., Sämann, P.G., Saremi, A., Satizabal, C.L., Schmaal, L., Schork, A.J., Shen, L., Shin, J., Shumskaya, E., Smith, A.V., Sprooten, E., Strike, L.T., Teumer, A., Tordesillas-Gutierrez, D., Toro, R., Trabzuni, D., Trompet, S., Vaidya, D., Van der Grond, J., van der Lee, S.J., van der Meer, D., van Donkelaar, M.M.J., van Eijk, K.R., Van Erp, T.G.M., Van Rooij, D., Walton, E., Westlye, L.T., Whelan, C.D., Windham, B.G., Winkler, A.M., Wittfeld, K., Woldehawariat, G., Wolf, C., Wolfers, T., Yanek, L.R., Yang, J., Zijdenbos, A., Zwiers, M.P., Agartz, I., Almasy, L., Ames, D., Amouyel, P., Andreassen, O.A., Arepalli, S., Assareh, A.A., Barral, S., Bastin, M.E., Becker, D.M., Becker, J.T., Bennett, D.A., Blangero, J., van Bokhoven, H., Boomsma, D.I., Brodaty, H., Brouwer, R.M., Brunner, H.G., Buckner, R.L., Buitelaar, J.K., Bulayeva, K.B., Cahn, W., Calhoun, V.D., Cannon, D.M., Cavalleri, G.L., Cheng, C-Y, Cichon, S., Cookson, M.R., Corvin, A., Crespo-Facorro, B., Curran, J.E., Czisch, M., Dale, A.M., Davies, G.E., de Craen, A.J.M., de Geus, E.J.C., De Jager, P.L., de Zubicaray, G.I., Deary, I.J., Debette, S., DeCarli, C., Delanty, N., Depondt, C., DeStefano, A.L., Dillman, A., Djurovic, S., Donohoe, G., Drevets, W.C., Duggirala, R., Dyer, T.D., Enzinger, C., Erk, S., Espeseth, T., Fedko, I.O., Fernández, G., Ferrucci, L., Fisher, S.E., Fleischman, D.A., Ford, I., Fornage, M., Foroud, T.M., Fox, P.T., Francks, C., Fukunaga, M., Gibbs, J.R., Glahn, D.C., Gollub, R.L., Göring, H.H.H., Green, R.C., Gruber, O., Gudnason, V., Guelfi, S., Håberg, A.K., Hansell, N.K., Hardy, J., Hartman, C.A., Hashimoto, R., Hegenscheid, K., Heinz, A., Le Hellard, S., Hernandez, D.G., Heslenfeld, D.J., Ho, B-C., Hoekstra, P.J., Hoffmann, W., Hofman, A., Holsboer, F., Homuth, G., Hosten, N., Hottenga, J-J, Huentelman, M., Pol, H.E.H., Ikeda, M., Jack Jr, C.R., Jenkinson, M., Johnson, R., Jönsson, E.G., Jukema, J.W., Kahn, R.S., Kanai, R., Kloszewska, I., Knopman, D.S., Kochunov, P., Kwok, J.B., Lawrie, S.M., Lemaitre, H., Liu, X., Longo, D.L., Lopez, O.L., Lovestone, S., Martinez, O.P., Martinot, J-L, Mattay, V.S., McDonald, C., McIntosh, A.M., McMahon, F.J., McMahon, K.L., Mecocci, P., Melle, I., Meyer-Lindenberg, A., Mohnke, S., Montgomery, G.W., Morris, D.W., Mosley, T.H., Mühleisen, T.W., Müller-Myhsok, B., Nalls, M.A., Nauck, M., Nichols, T.E., Niessen, W.J., Nöthen, M.M., Nyberg, L., Ohi, K., Olvera, R.L., Ophoff, R.A., Pandolfo, M., Paus, T., Pausova, Z., Penninx, B.W.J.H., Pike, G.B., Potkin, S.G., Psaty, B.M., Reppermund, S., Rietschel, M., Roffman, J.L., Romanczuk-Seiferth, N., Rotter, J.I., Ryten, M., Sacco, R.L., Sachdev, P.S., Saykin, A.J., Schmidt, R., Schmidt, H., Schofield, P.R., Sigursson, S., Simmons, A., Singleton, A., Sisodiya, S.M., Smith, C., Smoller, J.W., Soininen, H., Steen, V.M., Stott, D.J., Sussmann, J.E., Thalamuthu, A., Toga, A.W., Traynor, B.J., Troncoso, J., Tsolaki, M., Tzourio, C., Uitterlinden, A.G., Hernández, M.C.V., van der Brug, M., van der Lugt, A., van der Wee, N.J.A., van Haren, N.E.M., van ’t Ent, D., van Tol, M-J, Vardarajan, B.N., Vellas, B., Veltman, D.J., Volzke, H., Walter, H., Wardlaw, J.M., Wassink, T.H., Weale, M.E., Weinberger, D.R., Weiner, M.W., Wen, W., Westman, E., White, T., Wong, T.Y., Wright, C.B., Zielke, R.H., Zonderman, A.B., Martin, N.G., van Duijn, C.M., Wright, M.J., Longstreth, W.T., Schumann, G., Grabe, H.J., Franke, B., Launer, L.J., Medland, S.E., Seshadri, S., Thompson, P.M., and Ikram, M.A.
Abstract: The hippocampal formation is a brain structure integrally involved in episodic memory, spatial navigation, cognition and stress responsiveness. Structural abnormalities in hippocampal volume and shape are found in several common neuropsychiatric disorders. To identify the genetic underpinnings of hippocampal structure here we perform a genome-wide association study (GWAS) of 33,536 individuals and discover six independent loci significantly associated with hippocampal volume, four of them novel. Of the novel loci, three lie within genes (ASTN2, DPP4 and MAST4) and one is found 200 kb upstream of SHH. A hippocampal subfield analysis shows that a locus within the MSRB3 gene shows evidence of a localized effect along the dentate gyrus, subiculum, CA1 and fissure. Further, we show that genetic variants associated with decreased hippocampal volume are also associated with increased risk for Alzheimer’s disease (rg=−0.155). Our findings suggest novel biological pathways through which human genetic variation influences hippocampal volume and risk for neuropsychiatric illness.
Published: 2017

24. Lower Magnetization Transfer Ratio in the Forceps Minor Is Associated with Poorer Gait Velocity in Older Adults

Author: Seiler, S., primary, Pirpamer, L., additional, Gesierich, B., additional, Hofer, E., additional, Duering, M., additional, Pinter, D., additional, Jouvent, E., additional, Fazekas, F., additional, Mangin, J.-F., additional, Chabriat, H., additional, Ropele, S., additional, and Schmidt, R., additional
Published: 2016
Full Text: View/download PDF

25. Novel genetic loci underlying human intracranial volume identified through genome-wide association

Author: Adams, H.H.H., Hibar, D.P., Chouraki, V., Stein, J.L., Nyquist, P.A., Renteria, M.E., Trompet, S., Arias-Vasquez, A., Seshadri, S., Desrivières, S., Beecham, A.H., Jahanshad, N., Wittfeld, K., van der Lee, S.J., Abramovic, L., Alhusaini, S., Amin, N., Andersson, M., Arfanakis, K., Aribisala, B.S., Armstrong, N.J., Athanasiu, L., Axelsson, T., Beiser, A., Bernard, M., Bis, J.C., Blanken, L.M.E., Blanton, S.H., Bohlken, M.M., Boks, M.P., Bralten, J., Brickman, A.M., Carmichael, O., Chakravarty, M.M., Chauhan, G., Chen, Q., Ching, C.R.K., Cuellar-Partida, G., Braber, A.D., Doan, N.T., Ehrlich, S., Filippi, I., Ge, T., Giddaluru, S., Goldman, A.L., Gottesman, R.F., Greven, C.U., Grimm, O., Griswold, M.E., Guadalupe, T., Hass, J., Haukvik, U.K., Hilal, S., Hofer, E., Hoehn, D., Holmes, A.J., Hoogman, M., Janowitz, D., Jia, T., Kasperaviciute, D., Kim, S., Klein, M., Kraemer, B., Lee, P.H., Liao, J., Liewald, D.C.M., Lopez, L.M., Luciano, M., Macare, C., Marquand, A., Matarin, M., Mather, K.A., Mattheisen, M., Mazoyer, B., McKay, D.R., McWhirter, R., Milaneschi, Y., Mirza-Schreiber, N., Muetzel, R.L., Maniega, S.M., Nho, K., Nugent, A.C., Loohuis, L.M.O., Oosterlaan, J., Papmeyer, M., Pappa, I., Pirpamer, L., Pudas, S., Pütz, B., Rajan, K.B., Ramasamy, A., Richards, J.S., Risacher, S.L., Roiz-Santiañez, R., Rommelse, N., Rose, E.J., Royle, N.A., Rundek, T., Sämann, P.G., Satizabal, C.L., Schmaal, L., Schork, A.J., Shen, L., Shin, J., Shumskaya, E., Smith, A.V., Sprooten, E., Strike, L.T., Teumer, A., Thomson, R., Tordesillas-Gutierrez, D., Toro, R., Trabzuni, D., Vaidya, D., Van der Grond, J., van der Meer, D., van Donkelaar, M.M.J., van Eijk, K.R., Van Erp, T.G.M., Van Rooij, D., Walton, E., Westlye, L.T., Whelan, C.D., Windham, B.G., Winkler, A.M., Woldehawariat, G., Wolf, C., Wolfers, T., Xu, B., Yanek, L.R., Yang, J., Zijdenbos, A., Zwiers, M.P., Agartz, I., Aggarwal, N.T., Almasy, L., Ames, D., Amouyel, P., Andreassen, O.A., Arepalli, S., Assareh, A.A., Barral, S., Bastin, M.E., Becker, D.M., Becker, J.T., Bennett, D.A., Blangero, J., van Bokhoven, H., Boomsma, D.I., Brodaty, H., Brouwer, R.M., Brunner, H.G., Buckner, R.L., Buitelaar, J.K., Bulayeva, K.B., Cahn, W., Calhoun, V.D., Cannon, D.M., Cavalleri, G.L., Chen, C., Cheng, C-Y, Cichon, S., Cookson, M.R., Corvin, A., Crespo-Facorro, B., Curran, J.E., Czisch, M., Dale, A.M., Davies, G.E., De Geus, E.J.C., De Jager, P.L., de Zubicaray, G.I., Delanty, N., Depondt, C., DeStefano, A.L., Dillman, A., Djurovic, S., Donohoe, G., Drevets, W.C., Duggirala, R., Dyer, T.D., Erk, S., Espeseth, T., Evans, D.A., Fedko, I.O., Fernández, G., Ferrucci, L., Fisher, S.E., Fleischman, D.A., Ford, I., Foroud, T.M., Fox, P.T., Francks, C., Fukunaga, M., Gibbs, J.R., Glahn, D.C., Gollub, R.L., Göring, H.H.H., Grabe, H.J., Green, R.C., Gruber, O., Gudnason, V., Guelfi, S., Hansell, N.K., Hardy, J., Hartman, C.A., Hashimoto, R., Hegenscheid, K., Heinz, A., Le Hellard, S., Hernandez, D.G., Heslenfeld, D.J., Ho, B-C., Hoekstra, P.J., Hoffmann, W., Hofman, A., Holsboer, F., Homuth, G., Hosten, N., Hottenga, J-J, Pol, H.E.H., Ikeda, M., Ikram, M.K., Jack, C.R., Jenkinson, M., Johnson, R., Jönsson, E.G., Jukema, J.W., Kahn, R.S., Kanai, R., Kloszewska, I., Knopman, D.S., Kochunov, P., Kwok, J.B., Lawrie, S.M., Lemaitre, H., Liu, X., Longo, D.L., Longstreth, W.T., Lopez, O.L., Lovestone, S., Martinez, O.P., Martinot, J-L, Mattay, V.S., McDonald, C., McIntosh, A.M., McMahon, K.L., McMahon, F.J., Mecocci, P., Melle, I., Meyer-Lindenberg, A., Mohnke, S., Montgomery, G.W., Morris, D.W., Mosley, T.H., Mühleisen, T.W., Müller-Myhsok, B., Nalls, M.A., Nauck, M., Nichols, T.E., Niessen, W.J., Nöthen, M.M., Nyberg, L., Ohi, K., Olvera, R.L., Ophoff, R.A., Pandolfo, M., Paus, T., Pausova, Z., Penninx, B.W.J.H., Pike, G.B., Potkin, S.G., Psaty, B.M., Reppermund, S., Rietschel, M., Roffman, J.L., Romanczuk-Seiferth, N., Rotter, J.I., Ryten, M., Sacco, R.L., Sachdev, P.S., Saykin, A.J., Schmidt, R., Schofield, P.R., Sigurdsson, S., Simmons, A., Singleton, A., Sisodiya, S.M., Smith, C., Smoller, J.W., Soininen, H., Srikanth, V., Steen, V.M., Stott, D.J., Sussmann, J.E., Thalamuthu, A., Tiemeier, H., Toga, A.W., Traynor, B.J., Troncoso, J., Turner, J.A., Tzourio, C., Uitterlinden, A.G., Hernández, M.C.V., van der Brug, M., Van der Lugt, A., van der Wee, N.J.A., van Duijn, C.M., van Haren, N.E.M., Van′t Ent, D., van Tol, M-J, Vardarajan, B.N., Veltman, D.J., Vernooij, M.W., Volzke, H., Walter, H., Wardlaw, J.M., Wassink, T.H., Weale, M.E., Weinberger, D.R., Weiner, M.W., Wen, W., Westman, E., White, T., Wong, T.Y., Wright, C.B., Zielke, H.R., Zonderman, A.B., Deary, I.J., DeCarli, C., Schmidt, H., Martin, N.G., de Craen, A.J.M., Wright, M.J., Launer, L.J., Schumann, G., Fornage, M., Franke, B., Debette, S., Medland, S.E., Ikram, M.A., Thompson, P.M., Adams, H.H.H., Hibar, D.P., Chouraki, V., Stein, J.L., Nyquist, P.A., Renteria, M.E., Trompet, S., Arias-Vasquez, A., Seshadri, S., Desrivières, S., Beecham, A.H., Jahanshad, N., Wittfeld, K., van der Lee, S.J., Abramovic, L., Alhusaini, S., Amin, N., Andersson, M., Arfanakis, K., Aribisala, B.S., Armstrong, N.J., Athanasiu, L., Axelsson, T., Beiser, A., Bernard, M., Bis, J.C., Blanken, L.M.E., Blanton, S.H., Bohlken, M.M., Boks, M.P., Bralten, J., Brickman, A.M., Carmichael, O., Chakravarty, M.M., Chauhan, G., Chen, Q., Ching, C.R.K., Cuellar-Partida, G., Braber, A.D., Doan, N.T., Ehrlich, S., Filippi, I., Ge, T., Giddaluru, S., Goldman, A.L., Gottesman, R.F., Greven, C.U., Grimm, O., Griswold, M.E., Guadalupe, T., Hass, J., Haukvik, U.K., Hilal, S., Hofer, E., Hoehn, D., Holmes, A.J., Hoogman, M., Janowitz, D., Jia, T., Kasperaviciute, D., Kim, S., Klein, M., Kraemer, B., Lee, P.H., Liao, J., Liewald, D.C.M., Lopez, L.M., Luciano, M., Macare, C., Marquand, A., Matarin, M., Mather, K.A., Mattheisen, M., Mazoyer, B., McKay, D.R., McWhirter, R., Milaneschi, Y., Mirza-Schreiber, N., Muetzel, R.L., Maniega, S.M., Nho, K., Nugent, A.C., Loohuis, L.M.O., Oosterlaan, J., Papmeyer, M., Pappa, I., Pirpamer, L., Pudas, S., Pütz, B., Rajan, K.B., Ramasamy, A., Richards, J.S., Risacher, S.L., Roiz-Santiañez, R., Rommelse, N., Rose, E.J., Royle, N.A., Rundek, T., Sämann, P.G., Satizabal, C.L., Schmaal, L., Schork, A.J., Shen, L., Shin, J., Shumskaya, E., Smith, A.V., Sprooten, E., Strike, L.T., Teumer, A., Thomson, R., Tordesillas-Gutierrez, D., Toro, R., Trabzuni, D., Vaidya, D., Van der Grond, J., van der Meer, D., van Donkelaar, M.M.J., van Eijk, K.R., Van Erp, T.G.M., Van Rooij, D., Walton, E., Westlye, L.T., Whelan, C.D., Windham, B.G., Winkler, A.M., Woldehawariat, G., Wolf, C., Wolfers, T., Xu, B., Yanek, L.R., Yang, J., Zijdenbos, A., Zwiers, M.P., Agartz, I., Aggarwal, N.T., Almasy, L., Ames, D., Amouyel, P., Andreassen, O.A., Arepalli, S., Assareh, A.A., Barral, S., Bastin, M.E., Becker, D.M., Becker, J.T., Bennett, D.A., Blangero, J., van Bokhoven, H., Boomsma, D.I., Brodaty, H., Brouwer, R.M., Brunner, H.G., Buckner, R.L., Buitelaar, J.K., Bulayeva, K.B., Cahn, W., Calhoun, V.D., Cannon, D.M., Cavalleri, G.L., Chen, C., Cheng, C-Y, Cichon, S., Cookson, M.R., Corvin, A., Crespo-Facorro, B., Curran, J.E., Czisch, M., Dale, A.M., Davies, G.E., De Geus, E.J.C., De Jager, P.L., de Zubicaray, G.I., Delanty, N., Depondt, C., DeStefano, A.L., Dillman, A., Djurovic, S., Donohoe, G., Drevets, W.C., Duggirala, R., Dyer, T.D., Erk, S., Espeseth, T., Evans, D.A., Fedko, I.O., Fernández, G., Ferrucci, L., Fisher, S.E., Fleischman, D.A., Ford, I., Foroud, T.M., Fox, P.T., Francks, C., Fukunaga, M., Gibbs, J.R., Glahn, D.C., Gollub, R.L., Göring, H.H.H., Grabe, H.J., Green, R.C., Gruber, O., Gudnason, V., Guelfi, S., Hansell, N.K., Hardy, J., Hartman, C.A., Hashimoto, R., Hegenscheid, K., Heinz, A., Le Hellard, S., Hernandez, D.G., Heslenfeld, D.J., Ho, B-C., Hoekstra, P.J., Hoffmann, W., Hofman, A., Holsboer, F., Homuth, G., Hosten, N., Hottenga, J-J, Pol, H.E.H., Ikeda, M., Ikram, M.K., Jack, C.R., Jenkinson, M., Johnson, R., Jönsson, E.G., Jukema, J.W., Kahn, R.S., Kanai, R., Kloszewska, I., Knopman, D.S., Kochunov, P., Kwok, J.B., Lawrie, S.M., Lemaitre, H., Liu, X., Longo, D.L., Longstreth, W.T., Lopez, O.L., Lovestone, S., Martinez, O.P., Martinot, J-L, Mattay, V.S., McDonald, C., McIntosh, A.M., McMahon, K.L., McMahon, F.J., Mecocci, P., Melle, I., Meyer-Lindenberg, A., Mohnke, S., Montgomery, G.W., Morris, D.W., Mosley, T.H., Mühleisen, T.W., Müller-Myhsok, B., Nalls, M.A., Nauck, M., Nichols, T.E., Niessen, W.J., Nöthen, M.M., Nyberg, L., Ohi, K., Olvera, R.L., Ophoff, R.A., Pandolfo, M., Paus, T., Pausova, Z., Penninx, B.W.J.H., Pike, G.B., Potkin, S.G., Psaty, B.M., Reppermund, S., Rietschel, M., Roffman, J.L., Romanczuk-Seiferth, N., Rotter, J.I., Ryten, M., Sacco, R.L., Sachdev, P.S., Saykin, A.J., Schmidt, R., Schofield, P.R., Sigurdsson, S., Simmons, A., Singleton, A., Sisodiya, S.M., Smith, C., Smoller, J.W., Soininen, H., Srikanth, V., Steen, V.M., Stott, D.J., Sussmann, J.E., Thalamuthu, A., Tiemeier, H., Toga, A.W., Traynor, B.J., Troncoso, J., Turner, J.A., Tzourio, C., Uitterlinden, A.G., Hernández, M.C.V., van der Brug, M., Van der Lugt, A., van der Wee, N.J.A., van Duijn, C.M., van Haren, N.E.M., Van′t Ent, D., van Tol, M-J, Vardarajan, B.N., Veltman, D.J., Vernooij, M.W., Volzke, H., Walter, H., Wardlaw, J.M., Wassink, T.H., Weale, M.E., Weinberger, D.R., Weiner, M.W., Wen, W., Westman, E., White, T., Wong, T.Y., Wright, C.B., Zielke, H.R., Zonderman, A.B., Deary, I.J., DeCarli, C., Schmidt, H., Martin, N.G., de Craen, A.J.M., Wright, M.J., Launer, L.J., Schumann, G., Fornage, M., Franke, B., Debette, S., Medland, S.E., Ikram, M.A., and Thompson, P.M.
Abstract: Intracranial volume reflects the maximally attained brain size during development, and remains stable with loss of tissue in late life. It is highly heritable, but the underlying genes remain largely undetermined. In a genome-wide association study of 32,438 adults, we discovered five previously unknown loci for intracranial volume and confirmed two known signals. Four of the loci were also associated with adult human stature, but these remained associated with intracranial volume after adjusting for height. We found a high genetic correlation with child head circumference (ρgenetic = 0.748), which indicates a similar genetic background and allowed us to identify four additional loci through meta-analysis (Ncombined = 37,345). Variants for intracranial volume were also related to childhood and adult cognitive function, and Parkinson's disease, and were enriched near genes involved in growth pathways, including PI3K-AKT signaling. These findings identify the biological underpinnings of intracranial volume and their link to physiological and pathological traits.
Published: 2016

26. Clinical Implications of White Matter Lesions in Overweight Male Individuals with Bipolar Disorder

Author: Birner, A., primary, Seiler, S., additional, Lackner, N., additional, Bengesser, S., additional, Queissner, R., additional, Platzer, M., additional, Fellendorf, F., additional, Pirpamer, L., additional, Ropele, S., additional, Enzinger, C., additional, Kapfhammer, H.P., additional, Reininghaus, B., additional, and Reininghaus, E., additional
Published: 2015
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27. Strategic white matter tracts for processing speed deficits in age-related small vessel disease

Author: Duering, M., primary, Gesierich, B., additional, Seiler, S., additional, Pirpamer, L., additional, Gonik, M., additional, Hofer, E., additional, Jouvent, E., additional, Duchesnay, E., additional, Chabriat, H., additional, Ropele, S., additional, Schmidt, R., additional, and Dichgans, M., additional
Published: 2014
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28. Decreased T1 Contrast between Gray Matter and Normal-Appearing White Matter in CADASIL

Author: De Guio, F., primary, Reyes, S., additional, Duering, M., additional, Pirpamer, L., additional, Chabriat, H., additional, and Jouvent, E., additional
Published: 2013
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29. Longitudinal Assessment of Multiple Sclerosis with the Brain-Age Paradigm

Author: Hugo Vrenken, Lukas Pirpamer, Alex Rovira, Richard Nicholas, Frcp Olga Ciccarelli PhD, James H. Cole, Joel Raffel, Declan T. Chard, Serena Ruggieri, Nicola De Stefano, Christian Enzinger, Frederik Barkhof, Arman Eshaghi, Bernard M. J. Uitdehaag, Jaume Sastre-Garriga, Maria A. Rocca, Tim Friede, Maria Laura Stromillo, Fean Massimo Filippi Md, Fracp Wallace J. Brownlee PhD, Claudio Gasperini, Amsterdam Neuroscience - Neuroinfection & -inflammation, Neurology, Radiology and nuclear medicine, Cole, Jh, Raffel, J, Friede, T, Eshaghi, A, Brownlee, Wj, Chard, D, De Stefano, N, Enzinger, C, Pirpamer, L, Filippi, M, Gasperini, C, Rocca, Ma, Rovira, A, Ruggieri, S, Sastre-Garriga, J, Stromillo, Ml, Uitdehaag, Bmj, Vrenken, H, Barkhof, F, Nicholas, R, Ciccarelli, O, and MAGNIMS study, Group
Subjects: 0301 basic medicine, Adult, Male, medicine.medical_specialty, Aging, Multiple Sclerosis, Adolescent, 03 medical and health sciences, Disability Evaluation, Young Adult, 0302 clinical medicine, Atrophy, Internal medicine, medicine, Humans, Longitudinal Studies, 10. No inequality, Aged, Clinically isolated syndrome, Expanded Disability Status Scale, medicine.diagnostic_test, business.industry, Multiple sclerosis, Hazard ratio, Brain, Magnetic resonance imaging, Middle Aged, medicine.disease, Demyelinating Diseases, Disease Progression, Female, Confidence interval, Clinical trial, 030104 developmental biology, Neurology, Neurology (clinical), business, 030217 neurology & neurosurgery
Abstract: Objective: During the natural course of multiple sclerosis (MS), the brain is exposed to aging as well as disease effects. Brain aging can be modeled statistically; the so-called “brain-age” paradigm. Here, we evaluated whether brain-predicted age difference (brain-PAD) was sensitive to the presence of MS, clinical progression, and future outcomes. Methods: In a longitudinal, multicenter sample of 3,565 magnetic resonance imaging (MRI) scans, in 1,204 patients with MS and clinically isolated syndrome (CIS) and 150 healthy controls (mean follow-up time: patients 3.41 years, healthy controls 1.97 years), we measured “brain-predicted age” using T1-weighted MRI. We compared brain-PAD among patients with MS and patients with CIS and healthy controls, and between disease subtypes. Relationships between brain-PAD and Expanded Disability Status Scale (EDSS) were explored. Results: Patients with MS had markedly higher brain-PAD than healthy controls (mean brain-PAD +10.3 years; 95% confidence interval [CI] = 8.5–12.1] versus 4.3 years; 95% CI = 2.1 to 6.4; p < 0.001). The highest brain-PADs were in secondary-progressive MS (+13.3 years; 95% CI = 11.3–15.3). Brain-PAD at study entry predicted time-to-disability progression (hazard ratio 1.02; 95% CI = 1.01–1.03; p < 0.001); although normalized brain volume was a stronger predictor. Greater annualized brain-PAD increases were associated with greater annualized EDSS score (r = 0.26; p < 0.001). Interpretation: The brain-age paradigm is sensitive to MS-related atrophy and clinical progression. A higher brain-PAD at baseline was associated with more rapid disability progression and the rate of change in brain-PAD related to worsening disability. Potentially, “brain-age” could be used as a prognostic biomarker in early-stage MS, to track disease progression or stratify patients for clinical trial enrollment. ANN NEUROL 2020 ANN NEUROL 2020;88:93–105.
Published: 2019

30. A worldwide study of white matter microstructural alterations in people living with Parkinson's disease.

Author: Owens-Walton C, Nir TM, Al-Bachari S, Ambrogi S, Anderson TJ, Aventurato ÍK, Cendes F, Chen YL, Ciullo V, Cook P, Dalrymple-Alford JC, Dirkx MF, Druzgal J, Emsley HCA, Guimarães R, Haroon HA, Helmich RC, Hu MT, Johansson ME, Kim HB, Klein JC, Laansma M, Lawrence KE, Lochner C, Mackay C, McMillan CT, Melzer TR, Nabulsi L, Newman B, Opriessnig P, Parkes LM, Pellicano C, Piras F, Piras F, Pirpamer L, Pitcher TL, Poston KL, Roos A, Silva LS, Schmidt R, Schwingenschuh P, Shahid-Besanti M, Spalletta G, Stein DJ, Thomopoulos SI, Tosun D, Tsai CC, van den Heuvel OA, van Heese E, Vecchio D, Villalón-Reina JE, Vriend C, Wang JJ, Wu YR, Yasuda CL, Thompson PM, Jahanshad N, and van der Werf Y
Abstract: The progression of Parkinson's disease (PD) is associated with microstructural alterations in neural pathways, contributing to both motor and cognitive decline. However, conflicting findings have emerged due to the use of heterogeneous methods in small studies. Here we performed a large diffusion MRI study in PD, integrating data from 17 cohorts worldwide, to identify stage-specific profiles of white matter differences. Diffusion-weighted MRI data from 1654 participants diagnosed with PD (age: 20-89 years; 33% female) and 885 controls (age: 19-84 years; 47% female) were analyzed using the ENIGMA-DTI protocol to evaluate white matter microstructure. Skeletonized maps of fractional anisotropy (FA) and mean diffusivity (MD) were compared across Hoehn and Yahr (HY) disease groups and controls to reveal the profile of white matter alterations at different stages. We found an enhanced, more widespread pattern of microstructural alterations with each stage of PD, with eventually lower FA and higher MD in almost all regions of interest: Cohen's d effect sizes reached d = -1.01 for FA differences in the fornix at PD HY Stage 4/5. The early PD signature in HY stage 1 included higher FA and lower MD across the entire white matter skeleton, in a direction opposite to that typical of other neurodegenerative diseases. FA and MD were associated with motor and non-motor clinical dysfunction. While overridden by degenerative changes in the later stages of PD, early PD is associated with paradoxically higher FA and lower MD in PD, consistent with early compensatory changes associated with the disorder., (© 2024. The Author(s).)
Published: 2024
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31. Time is myelin: early cortical myelin repair prevents atrophy and clinical progression in multiple sclerosis.

Author: Lazzarotto A, Hamzaoui M, Tonietto M, Dubessy AL, Khalil M, Pirpamer L, Ropele S, Enzinger C, Battaglini M, Stromillo ML, De Stefano N, Filippi M, Rocca MA, Gallo P, Gasperini C, Stankoff B, and Bodini B
Subjects: Humans, Myelin Sheath pathology, Disease Progression, Atrophy pathology, Multiple Sclerosis pathology, Multiple Sclerosis, Chronic Progressive diagnostic imaging, Multiple Sclerosis, Chronic Progressive pathology
Abstract: Cortical myelin loss and repair in multiple sclerosis (MS) have been explored in neuropathological studies, but the impact of these processes on neurodegeneration and the irreversible clinical progression of the disease remains unknown. Here, we evaluated in vivo cortical demyelination and remyelination in a large cohort of people with all clinical phenotypes of MS followed up for 5 years using magnetization transfer imaging (MTI), a technique that has been shown to be sensitive to myelin content changes in the cortex. We investigated 140 people with MS (37 clinically isolated syndrome, 71 relapsing-MS, 32 progressive-MS), who were clinically assessed at baseline and after 5 years and, along with 84 healthy controls, underwent a 3 T-MRI protocol including MTI at baseline and after 1 year. Changes in cortical volume over the radiological follow-up were computed with a Jacobian integration method. Magnetization transfer ratio was employed to calculate for each patient an index of cortical demyelination at baseline and of dynamic cortical demyelination and remyelination over the follow-up period. The three indices of cortical myelin content change were heterogeneous across patients but did not significantly differ across clinical phenotypes or treatment groups. Cortical remyelination, which tended to fail in the regions closer to CSF (-11%, P < 0.001), was extensive in half of the cohort and occurred independently of age, disease duration and clinical phenotype. Higher indices of cortical dynamic demyelination (β = 0.23, P = 0.024) and lower indices of cortical remyelination (β = -0.18, P = 0.03) were significantly associated with greater cortical atrophy after 1 year, independently of age and MS phenotype. While the extent of cortical demyelination predicted a higher probability of clinical progression after 5 years in the entire cohort [odds ratio (OR) = 1.2; P = 0.043], the impact of cortical remyelination in reducing the risk of accumulating clinical disability after 5 years was significant only in the subgroup of patients with shorter disease duration and limited extent of demyelination in cortical regions (OR = 0.86, P = 0.015, area under the curve = 0.93). In this subgroup, a 30% increase in cortical remyelination nearly halved the risk of clinical progression at 5 years, independently of clinical relapses. Overall, our results highlight the critical role of cortical myelin dynamics in the cascade of events leading to neurodegeneration and to the subsequent accumulation of irreversible disability in MS. Our findings suggest that early-stage myelin repair compensating for cortical myelin loss has the potential to prevent neuro-axonal loss and its long-term irreversible clinical consequences in people with MS., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Guarantors of Brain.)
Published: 2024
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32. Psychological factors and brain magnetic resonance imaging metrics associated with fatigue in persons with multiple sclerosis.

Author: Hechenberger S, Helmlinger B, Penner IK, Pirpamer L, Fruhwirth V, Heschl B, Ropele S, Wurth S, Damulina A, Eppinger S, Demjaha R, Khalil M, Pinter D, and Enzinger C
Subjects: Humans, Female, Depression, Brain diagnostic imaging, Brain pathology, Magnetic Resonance Imaging, Cognition, Multiple Sclerosis complications, Multiple Sclerosis diagnostic imaging, Multiple Sclerosis pathology
Abstract: Background: Besides demographics and clinical factors, psychological variables and brain-tissue changes have been associated with fatigue in persons with multiple sclerosis (pwMS). Identifying predictors of fatigue could help to improve therapeutic approaches for pwMS. Therefore, we investigated predictors of fatigue using a multifactorial approach., Methods: 136 pwMS and 49 normal controls (NC) underwent clinical, neuropsychological, and magnetic resonance imaging examinations. We assessed fatigue using the "Fatigue Scale for Motor and Cognitive Functions", yielding a total, motor, and cognitive fatigue score. We further analyzed global and subcortical brain volumes, white matter lesions and microstructural changes (examining fractional anisotropy; FA) along the cortico striatal thalamo cortical (CSTC) loop. Potential demographic, clinical, psychological, and magnetic resonance imaging predictors of total, motor, and cognitive fatigue were explored using multifactorial linear regression models., Results: 53% of pwMS and 20% of NC demonstrated fatigue. Besides demographics and clinical data, total fatigue in pwMS was predicted by higher levels of depression and reduced microstructural tissue integrity in the CSTC loop (adjusted R 2 = 0.52, p < 0.001). More specifically, motor fatigue was predicted by lower education, female sex, higher physical disability, higher levels of depression, and self-efficacy (adjusted R 2 = 0.54, p < 0.001). Cognitive fatigue was also predicted by higher levels of depression and lower self-efficacy, but in addition by FA reductions in the CSTC loop (adjusted R 2 = 0.45, p < 0.001)., Conclusions: Our results indicate that depression and self-efficacy strongly predict fatigue in MS. Incremental variance in total and cognitive fatigue was explained by microstructural changes along the CSTC loop, beyond demographics, clinical, and psychological variables., Competing Interests: Declaration of Competing Interest The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: S.H. has received speaking honoraria from Roche and Bristol-Myers Squibb. B.H. has received funding for travel from Janssen and speaking honoraria from Roche. I.K.P. has received honoraria for speaking at scientific meetings, serving at scientific advisory boards, and performing consulting activities, from Adamas Pharma, Almirall, Bayer Pharma, Biogen, BMS, Celgene, Desitin, Sanofi-Genzyme, Janssen, Merck, Novartis, Roche, and Teva. She received research support from the German MS Society, Celgene, Novartis, Roche, and Teva. L.P. has no disclosures. V.F. has no disclosures. B.H. has no disclosures. S.R. has no disclosures. S.W. has no disclosures. A.D. has received speaker honoraria from Sanofi-Aventis and travel funding from Novartis. S.E. has no disclosures. R.D. has no disclosures. M.K. has received funding for travel and speaker honoraria from Bayer Schering Pharma, Novartis Genzyme, Merck Serono, Biogen Idec and Teva Pharmaceutical Industries Ltd. And a research grant from Teva Pharmaceutical Industries Ltd. D.P. has received funding for travel from merck, Genzyme/sanofi-Aventis and Biogen, as well as speaking honoraria from Biogen, Novartis and Merck. C.E. received funding for traveling and speaker honoraria from Biogen Idec, Bayer Schering Pharma, Merck Serono, Novartis, Genzyme and Teva Pharmaceutical Industries Ltd./Sanofi-aventis, Shire; received research support from Merck Serono, Biogen Idec, and Teva Pharmaceutical Industries Ltd./Sanofi-aventis; and serves on scientific advisory boards for Bayer Schering Pharma, Biogen Idec, Merck Serono, Novartis, Genzyme, Roche, and Teva Pharmaceutical Industries Ltd./Sanofi- Aventis., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)
Published: 2023
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33. Arterial stiffness and its influence on cerebral morphology and cognitive function.

Author: Haidegger M, Lindenbeck S, Hofer E, Rodler C, Zweiker R, Perl S, Pirpamer L, Kneihsl M, Fandler-Höfler S, Gattringer T, Enzinger C, and Schmidt R
Abstract: Background: Recently, arterial stiffness has been associated with cerebral small vessel disease (SVD), brain atrophy and vascular dementia. Arterial stiffness is assessed via pulse wave velocity (PWV) measurement and is strongly dependent on arterial blood pressure. While circadian blood pressure fluctuations are important determinants of end-organ damage, the role of 24-h PWV variability is yet unclear., Objectives: We here investigated the association between PWV and its circadian changes on brain morphology and cognitive function in community-dwelling individuals., Design: Single-centre, prospective, community-based follow-up study., Methods: The study cohort comprised elderly community-based participants of the Austrian Stroke Prevention Family Study which was started in 2006. Patients with any history of cerebrovascular disease or dementia were excluded. The study consists of 84 participants who underwent ambulatory 24-h PWV measurement. White matter hyperintensity volume and brain volume were evaluated by 3-Tesla magnetic resonance imaging (MRI). A subgroup of patients was evaluated for cognitive function using an extensive neuropsychological test battery., Results: PWV was significantly related to reduced total brain volume ( p = 0.013), which was independent of blood pressure and blood pressure variability. We found no association between PWV with markers of cerebral SVD or impaired cognitive functioning. Only night-time PWV values were associated with global brain atrophy ( p = 0.005)., Conclusions: This study shows a relationship of arterial stiffness and reduced total brain volume. Elevations in PWV during night-time are of greater importance than day-time measures., Competing Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s), 2023.)
Published: 2023
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34. High serum neurofilament light chain levels correlate with brain atrophy and physical disability in multiple sclerosis.

Author: Buchmann A, Pirpamer L, Pinter D, Voortman M, Helmlinger B, Pichler A, Maceski AM, Benkert P, Bachmaier G, Ropele S, Reindl M, Leppert D, Kuhle J, Enzinger C, and Khalil M
Subjects: Humans, Female, Adult, Male, Cross-Sectional Studies, Intermediate Filaments, Brain diagnostic imaging, Brain pathology, Biomarkers, Neurofilament Proteins, Atrophy pathology, Multiple Sclerosis pathology, Central Nervous System Diseases, Neurodegenerative Diseases pathology
Abstract: Background and Purpose: Serum neurofilament light chain (sNfL) is a promising biomarker of neuroaxonal damage in persons with multiple sclerosis (pwMS). In cross-sectional studies, sNfL has been associated with disease activity and brain magnetic resonance imaging (MRI) changes; however, it is still unclear to what extent in particular high sNfL levels impact on subsequent disease evolution., Methods: sNfL was quantified by an ultrasensitive single molecule array (Simoa) in 199 pwMS (median age = 34.2 years, 64.3% female) and 49 controls. All pwMS underwent 3-T MRI to assess global and compartmental normalized brain volumes, T2-lesion load, and cortical mean thickness. Follow-up data and serum samples were available in 144 pwMS (median follow-up time = 3.8 years). Linear and binary logistic models were used to estimate the independent contribution of sNfL for changes in MRI and Expanded Disability Status Scale (EDSS). Age-corrected sNfL z-scores from a normative database of healthy controls were used for sensitivity analyses., Results: High sNfL levels at baseline were associated with atrophy measures of the whole brain (standardized beta coefficient βj = -0.352, p < 0.001), white matter (βj = -0.229, p = 0.007), thalamus (βj = -0.372, p = 0.004), and putamen (βj = -1.687, p = 0.012). pwMS with high levels of sNfL at baseline and follow-up had a greater risk of EDSS worsening (p = 0.007)., Conclusions: Already single time point elevation of sNfL has a distinct effect on brain volume changes over a short-term period, and repeated high levels of sNfL indicate accumulating physical disability. Serial assessment of sNfL may provide added value in the clinical management of pwMS., (© 2023 The Authors. European Journal of Neurology published by John Wiley & Sons Ltd on behalf of European Academy of Neurology.)
Published: 2023
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35. Interpretable brain disease classification and relevance-guided deep learning.

Author: Tinauer C, Heber S, Pirpamer L, Damulina A, Schmidt R, Stollberger R, Ropele S, and Langkammer C
Subjects: Humans, Middle Aged, Aged, Aged, 80 and over, Head, Brain diagnostic imaging, Atrophy, Alzheimer Disease diagnostic imaging, Deep Learning
Abstract: Deep neural networks are increasingly used for neurological disease classification by MRI, but the networks' decisions are not easily interpretable by humans. Heat mapping by deep Taylor decomposition revealed that (potentially misleading) image features even outside of the brain tissue are crucial for the classifier's decision. We propose a regularization technique to train convolutional neural network (CNN) classifiers utilizing relevance-guided heat maps calculated online during training. The method was applied using T1-weighted MR images from 128 subjects with Alzheimer's disease (mean age = 71.9 ± 8.5 years) and 290 control subjects (mean age = 71.3 ± 6.4 years). The developed relevance-guided framework achieves higher classification accuracies than conventional CNNs but more importantly, it relies on less but more relevant and physiological plausible voxels within brain tissue. Additionally, preprocessing effects from skull stripping and registration are mitigated. With the interpretability of the decision mechanisms underlying CNNs, these results challenge the notion that unprocessed T1-weighted brain MR images in standard CNNs yield higher classification accuracy in Alzheimer's disease than solely atrophy., (© 2022. The Author(s).)
Published: 2022
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36. Evidence of Brain Alterations in Noncerebral Falciparum Malaria.

Author: Mohanty S, Sahu PK, Pattnaik R, Majhi M, Maharana S, Bage J, Mohanty A, Mohanty A, Bendszus M, Patterson C, Gupta H, Dondorp AM, Pirpamer L, Hoffmann A, and Wassmer SC
Subjects: Adult, Brain diagnostic imaging, Brain pathology, Coma complications, Creatinine, Humans, Brain Edema diagnostic imaging, Brain Edema etiology, Brain Edema pathology, Malaria, Cerebral complications, Malaria, Falciparum complications
Abstract: Background: Cerebral malaria in adults is associated with brain hypoxic changes on magnetic resonance (MR) images and has a high fatality rate. Findings of neuroimaging studies suggest that brain involvement also occurs in patients with uncomplicated malaria (UM) or severe noncerebral malaria (SNCM) without coma, but such features were never rigorously characterized., Methods: Twenty patients with UM and 21 with SNCM underwent MR imaging on admission and 44-72 hours later, as well as plasma analysis. Apparent diffusion coefficient (ADC) maps were generated, with values from 5 healthy individuals serving as controls., Results: Patients with SNCM had a wide spectrum of cerebral ADC values, including both decreased and increased values compared with controls. Patients with low ADC values, indicating cytotoxic edema, showed hypoxic patterns similar to cerebral malaria despite the absence of deep coma. Conversely, high ADC values, indicative of mild vasogenic edema, were observed in both patients with SNCM and patients with UM. Brain involvement was confirmed by elevated circulating levels of S100B. Creatinine was negatively correlated with ADC in SNCM, suggesting an association between acute kidney injury and cytotoxic brain changes., Conclusions: Brain involvement is common in adults with SNCM and a subgroup of hospitalized patients with UM, which warrants closer neurological follow-up. Increased creatinine in SNCM may render the brain more susceptible to cytotoxic edema., (© The Author(s) 2021. Published by Oxford University Press for the Infectious Diseases Society of America.)
Published: 2022
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37. Free water diffusion MRI and executive function with a speed component in healthy aging.

Author: Berger M, Pirpamer L, Hofer E, Ropele S, Duering M, Gesierich B, Pasternak O, Enzinger C, Schmidt R, and Koini M
Subjects: Aged, Biomarkers, Diffusion Magnetic Resonance Imaging, Diffusion Tensor Imaging methods, Executive Function physiology, Humans, Water, Healthy Aging, Leukoaraiosis, White Matter diagnostic imaging
Abstract: Extracellular free water (FW) increases are suggested to better provide pathophysiological information in brain aging than conventional biomarkers such as fractional anisotropy. The aim of the present study was to determine the relationship between conventional biomarkers, FW in white matter hyperintensities (WMH), FW in normal appearing white matter (NAWM) and in white matter tracts and executive functions (EF) with a speed component in elderly persons. We examined 226 healthy elderly participants (median age 69.83 years, IQR: 56.99-74.42) who underwent brain MRI and neuropsychological examination. FW in WMH and in NAWM as well as FW corrected diffusion metrics and measures derived from conventional MRI (white matter hyperintensities, brain volume, lacunes) were used in partial correlation (adjusted for age) to assess their correlation with EF with a speed component. Random forest analysis was used to assess the relative importance of these variables as determinants. Lastly, linear regression analyses of FW in white matter tracts corrected for risk factors of cognitive and white matter deterioration, were used to examine the role of specific tracts on EF with a speed component, which were then ranked with random forest regression. Partial correlation analyses revealed that almost all imaging metrics showed a significant association with EF with a speed component (r = -0.213 - 0.266). Random forest regression highlighted FW in WMH and in NAWM as most important among all diffusion and structural MRI metrics. The fornix (R 2 =0.421, p = 0.018) and the corpus callosum (genu (R 2 = 0.418, p = 0.021), prefrontal (R 2 = 0.416, p = 0.026), premotor (R 2 = 0.418, p = 0.021)) were associated with EF with a speed component in tract based regression analyses and had highest variables importance. In a normal aging population FW in WMH and NAWM is more closely related to EF with a speed component than standard DTI and brain structural measures. Higher amounts of FW in the fornix and the frontal part of the corpus callosum leads to deteriorating EF with a speed component., Competing Interests: Declarations of Competing Interest None., (Copyright © 2022. Published by Elsevier Inc.)
Published: 2022
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38. Heritability of R2* iron in the basal ganglia and cortex.

Author: Hofer E, Pirpamer L, Langkammer C, Tinauer C, Seshadri S, Schmidt H, and Schmidt R
Subjects: Aged, Brain, Cerebral Cortex diagnostic imaging, Humans, Magnetic Resonance Imaging, Middle Aged, Basal Ganglia, Iron
Abstract: Background: While iron is essential for normal brain functioning, elevated concentrations are commonly found in neurodegenerative diseases and are associated with impaired cognition and neurological deficits. Currently, only little is known about genetic and environmental factors that influence brain iron concentrations., Methods: Heritability and bivariate heritability of regional brain iron concentrations, assessed by R2* relaxometry at 3 Tesla MRI, were estimated with variance components models in 130 middle-aged to elderly participants of the Austrian Stroke Prevention Family Study., Results: Heritability of R2* iron ranged from 0.46 to 0.82 in basal ganglia and from 0.65 to 0.76 in cortical lobes. Age and BMI explained up to 12% and 9% of the variance of R2* iron, while APOE ε4 carrier status, hypertension, diabetes, hypercholesterolemia, sex and smoking explained 5% or less. The genetic correlation of R2* iron among basal ganglionic nuclei and among cortical lobes ranged from 0.78 to 0.87 and from 0.65 to 0.97, respectively. R2* rates in basal ganglia and cortex were not genetically correlated., Conclusions: Regional brain iron concentrations are mainly driven by genetic factors while environmental factors contribute to a certain extent. Brain iron levels in the basal ganglia and cortex are controlled by distinct sets of genes.
Published: 2022
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39. Genetic architecture of orbital telorism.

Author: Knol MJ, Pawlak MA, Lamballais S, Terzikhan N, Hofer E, Xiong Z, Klaver CCW, Pirpamer L, Vernooij MW, Ikram MA, Schmidt R, Kayser M, Evans TE, and Adams HHH
Subjects: Genetic Loci, Humans, Polymorphism, Single Nucleotide genetics, Reproducibility of Results, Genome-Wide Association Study methods, Hypertelorism genetics
Abstract: The interocular distance, or orbital telorism, is a distinctive craniofacial trait that also serves as a clinically informative measure. While its extremes, hypo- and hypertelorism, have been linked to monogenic disorders and are often syndromic, little is known about the genetic determinants of interocular distance within the general population. We derived orbital telorism measures from cranial magnetic resonance imaging by calculating the distance between the eyeballs' centre of gravity, which showed a good reproducibility with an intraclass correlation coefficient of 0.991 (95% confidence interval 0.985-0.994). Heritability estimates were 76% (standard error = 12%) with a family-based method (N = 364) and 39% (standard error = 2.4%) with a single nucleotide polymorphism-based method (N = 34 130) and were unaffected by adjustment for height (model II) and intracranial volume (model III) or head width (model IV). Genome-wide association studies in 34 130 European individuals identified 56 significantly associated genomic loci (P < 5 × 10-8) across four different models of which 46 were novel for facial morphology, and overall these findings replicated in an independent sample (N = 10 115) with telorism-related horizontal facial distance measures. Genes located nearby these 56 identified genetic loci were 4.9-fold enriched for Mendelian hypotelorism and hypertelorism genes, underlining their biological relevance. This study provides novel insights into the genetic architecture underlying interocular distance in particular, and the face in general, and explores its potential for applications in a clinical setting., (© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)
Published: 2022
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40. Serum NfL in Alzheimer Dementia: Results of the Prospective Dementia Registry Austria.

Author: Kern D, Khalil M, Pirpamer L, Buchmann A, Hofer E, Dal-Bianco P, Stögmann E, Scherfler C, Benke T, Ransmayr G, and Schmidt R
Subjects: Austria epidemiology, Cross-Sectional Studies, Humans, Prospective Studies, Registries, Alzheimer Disease diagnostic imaging
Abstract: Background and Objectives: The neurofilament light chain (NfL) is a biomarker for neuro-axonal injury in various acute and chronic neurological disorders, including Alzheimer’s disease (AD). We here investigated the cross-sectional and longitudinal associations between baseline serum NfL (sNfL) levels and cognitive, behavioural as well as MR volumetric findings in the Prospective Dementia Registry Austria (PRODEM-Austria). Materials and Methods: All participants were clinically diagnosed with AD according to NINCDS-ADRDA criteria and underwent a detailed clinical assessment, cognitive testing (including the Mini Mental State Examination (MMSE) and the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD)), the neuropsychiatric inventory (NPI) and laboratory evaluation. A total of 237 patients were included in the study. Follow-up examinations were done at 6 months, 1 year and 2 years with 93.3% of patients undergoing at least one follow-up. We quantified sNfL by a single molecule array (Simoa). In a subgroup of 125 subjects, brain imaging data (1.5 or 3T MRI, with 1 mm isotropic resolution) were available. Brain volumetry was assessed using the FreeSurfer image analysis suite (v6.0). Results: Higher sNfL concentrations were associated with worse performance in cognitive tests at baseline, including CERAD (B = −10.084, SE = 2.999, p < 0.001) and MMSE (B = −3.014, SE = 1.293, p = 0.021). The sNfL levels also correlated with the presence of neuropsychiatric symptoms (NPI total score: r = 0.138, p = 0.041) and with smaller volumes of the temporal lobe (B = −0.012, SE = 0.003, p = 0.001), the hippocampus (B = −0.001, SE = 0.000201, p = 0.013), the entorhinal (B = −0.000308, SE = 0.000124, p = 0.014), and the parahippocampal cortex (B = −0.000316, SE = 0.000113, p = 0.006). The sNfL values predicted more pronounced cognitive decline over the mean follow-up period of 22 months, but there were no significant associations with respect to change in neuropsychiatric symptoms and brain volumetric measures. Conclusions: the sNfL levels relate to cognitive, behavioural, and imaging hallmarks of AD and predicts short term cognitive decline.
Published: 2022
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41. Transcellular blood-brain barrier disruption in malaria-induced reversible brain edema.

Author: Jin J, Ba MA, Wai CH, Mohanty S, Sahu PK, Pattnaik R, Pirpamer L, Fischer M, Heiland S, Lanzer M, Frischknecht F, Mueller AK, Pfeil J, Majhi M, Cyrklaff M, Wassmer SC, Bendszus M, and Hoffmann A
Subjects: Animals, Blood-Brain Barrier diagnostic imaging, Brain diagnostic imaging, Brain pathology, Edema pathology, Humans, Mice, Brain Edema diagnostic imaging, Brain Edema etiology, Brain Edema pathology, Malaria, Cerebral pathology
Abstract: Brain swelling occurs in cerebral malaria (CM) and may either reverse or result in fatal outcome. It is currently unknown how brain swelling in CM reverses, as brain swelling at the acute stage is difficult to study in humans and animal models with reliable induction of reversible edema are not known. In this study, we show that reversible brain swelling in experimental murine CM can be induced reliably after single vaccination with radiation-attenuated sporozoites as proven by in vivo high-field magnetic resonance imaging. Our results provide evidence that brain swelling results from transcellular blood-brain barrier disruption (BBBD), as revealed by electron microscopy. This mechanism enables reversal of brain swelling but does not prevent persistent focal brain damage, evidenced by microhemorrhages, in areas of most severe BBBD. In adult CM patients magnetic resonance imaging demonstrate microhemorrhages in more than one third of patients with reversible edema, emphasizing similarities of the experimental model and human disease. Our data suggest that targeting transcellular BBBD may represent a promising adjunct therapeutic approach to reduce edema and may improve neurological outcome., (© 2022 Jin et al.)
Published: 2022
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42. Kidney function, brain morphology and cognition in the elderly: sex differences in the Austrian Stroke Prevention Study.

Author: Kolland M, Hofer E, Pirpamer L, Eibl D, Enzinger C, Rosenkranz AR, and Schmidt R
Subjects: Aged, Austria epidemiology, Cross-Sectional Studies, Female, Humans, Independent Living, Male, Middle Aged, Brain anatomy & histology, Cognition physiology, Glomerular Filtration Rate physiology, Kidney physiology, Stroke prevention & control
Abstract: Impaired kidney function is associated with structural brain changes and cognitive dysfunction. In the aging kidney, hemodynamic and structural alterations reduce the glomerular filtration rate (eGFR). Little is known about differences between men and women regarding decline of kidney function and brain damage. In this community-based study, we assessed associations between the eGFR, focal and diffuse brain abnormalities and cognitive functions. Sex-specific effects were analyzed by interaction terms eGFR x sex on brain structure and cognition. Interactive effects were assessed using mixed-models -stratified by sex. Overall, 196 women and 129 men (median age 68 years and mean eGFR 73.8±14.9 ml/min/1.73m 2 ) were included. Significant associations existed between eGFR and cortical volume (β: 1.53E-04; SE: 6.72E-05; p=0.023 for neocortex). Sex exerted a significant interactive effect. Only in men, eGFR related to cortical volumes of all lobes and of deep gray matter structures (p= 0.001 for total gray matter, p=0.0004 for neocortex). In the whole group eGFR was not associated with cognition, but men with lower eGFR performed worse on tests for executive function, which, after FDR correction, was not significant. We conclude, that in community-dwelling middle-aged and elderly individuals, reduced eGFR relates to brain volume loss in men but not in women.
Published: 2022
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43. Periventricular magnetisation transfer abnormalities in early multiple sclerosis.

Author: Pirpamer L, Kincses B, Kincses ZT, Kiss C, Damulina A, Khalil M, Stollberger R, Schmidt R, Enzinger C, and Ropele S
Subjects: Brain, Cerebral Ventricles pathology, Humans, Magnetic Resonance Imaging, Demyelinating Diseases pathology, Multiple Sclerosis diagnostic imaging, Multiple Sclerosis pathology, Multiple Sclerosis, Relapsing-Remitting pathology, White Matter pathology
Abstract: Objective: Recent studies suggested that CSF-mediated factors contribute to periventricular (PV) T2-hyperintense lesion formation in multiple sclerosis (MS) and this in turn correlates with cortical damage. We thus investigated if such PV-changes are observable microstructurally in early-MS and if they correlate with cortical damage., Methods: We assessed the magnetisation transfer ratio (MTR) in PV normal-appearing white matter (NAWM) and in MS lesions in 44 patients with a clinically isolated syndrome (CIS) suggestive of MS and 73 relapsing-remitting MS (RRMS) patients. Band-wise MTR values were related to cortical mean thickness (CMT) and compared with 49 healthy controls (HCs). For each band, MTR changes were assessed relative to the average MTR values of all HCs., Results: Relative to HCs, PV-MTR was significantly reduced up to 2.63% in CIS and 5.37% in RRMS (p < 0.0001). The MTR decreased towards the lateral ventricles with 0.18%/mm in CIS and 0.31%/mm in RRMS patients, relative to HCs. In RRMS, MTR-values adjacent to the ventricle and in PV-lesions correlated positively with CMT and negatively with EDSS., Conclusion: PV-MTR gradients are present from the earliest stage of MS, consistent with more pronounced microstructural WM-damage closer to the ventricles. The positive association between reduced CMT and lower MTR in PV-NAWM suggests a common pathophysiologic mechanism. Together, these findings indicate the potential use of multimodal MRI as refined marker for MS-related tissue changes., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)
Published: 2022
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44. Disability in multiple sclerosis is related to thalamic connectivity and cortical network atrophy.

Author: Schoonheim MM, Pinter D, Prouskas SE, Broeders TA, Pirpamer L, Khalil M, Ropele S, Uitdehaag BM, Barkhof F, Enzinger C, and Geurts JJ
Subjects: Atrophy pathology, Humans, Magnetic Resonance Imaging, Thalamus diagnostic imaging, Thalamus pathology, Disabled Persons, Multiple Sclerosis diagnostic imaging, Multiple Sclerosis pathology
Abstract: Background: Thalamic atrophy is proposed to be a major predictor of disability progression in multiple sclerosis (MS), while thalamic function remains understudied., Objectives: To study how thalamic functional connectivity (FC) is related to disability and thalamic or cortical network atrophy in two large MS cohorts., Methods: Structural and resting-state functional magnetic resonance imaging (fMRI) was obtained in 673 subjects from Amsterdam (MS: N = 332, healthy controls (HC): N = 96) and Graz (MS: N = 180, HC: N = 65) with comparable protocols, including disability measurements in MS (Expanded Disability Status Scale, EDSS). Atrophy was measured for the thalamus and seven well-recognized resting-state networks. Static and dynamic thalamic FC with these networks was correlated with disability. Significant correlates were included in a backward multivariate regression model., Results: Disability was most strongly related (adjusted R 2 = 0.57, p < 0.001) to higher age, a progressive phenotype, thalamic atrophy and increased static thalamic FC with the sensorimotor network (SMN). Static thalamus-SMN FC was significantly higher in patients with high disability (EDSS ⩾ 4) and related to network atrophy but not thalamic atrophy or lesion volumes., Conclusion: The severity of disability in MS was related to increased static thalamic FC with the SMN. Thalamic FC changes were only related to cortical network atrophy, but not to thalamic atrophy.
Published: 2022
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45. Prediction of dementia using diffusion tensor MRI measures: the OPTIMAL collaboration.

Author: Egle M, Hilal S, Tuladhar AM, Pirpamer L, Hofer E, Duering M, Wason J, Morris RG, Dichgans M, Schmidt R, Tozer D, Chen C, de Leeuw FE, and Markus HS
Subjects: Cerebral Small Vessel Diseases diagnostic imaging, Cognition, Cognitive Dysfunction diagnostic imaging, Cohort Studies, Humans, Prospective Studies, White Matter diagnostic imaging, Dementia diagnostic imaging, Diffusion Tensor Imaging methods
Abstract: Objectives: It has been suggested that diffusion tensor imaging (DTI) measures sensitive to white matter (WM) damage may predict future dementia risk not only in cerebral small vessel disease (SVD), but also in mild cognitive impairment. To determine whether DTI measures were associated with cognition cross-sectionally and predicted future dementia risk across the full range of SVD severity, we established the International OPtimising mulTImodal MRI markers for use as surrogate markers in trials of Vascular Cognitive Impairment due to cerebrAl small vesseL disease collaboration which included six cohorts., Methods: Among the six cohorts, prospective data with dementia incidences were available for three cohorts. The associations between six different DTI measures and cognition or dementia conversion were tested. The additional contribution to prediction of other MRI markers of SVD was also determined., Results: The DTI measure mean diffusivity (MD) median correlated with cognition in all cohorts, demonstrating the contribution of WM damage to cognition. Adding MD median significantly improved the model fit compared to the clinical risk model alone and further increased in all single-centre SVD cohorts when adding conventional MRI measures. Baseline MD median predicted dementia conversion. In a study with severe SVD (SCANS) change in MD median also predicted dementia conversion. The area under the curve was best when employing a multimodal MRI model using both DTI measures and other MRI measures., Conclusions: Our results support a central role for WM alterations in dementia pathogenesis in all cohorts. DTI measures such as MD median may be a useful clinical risk predictor. The contribution of other MRI markers varied according to disease severity., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. No commercial re-use. See rights and permissions. Published by BMJ.)
Published: 2022
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46. Determining the OPTIMAL DTI analysis method for application in cerebral small vessel disease.

Author: Egle M, Hilal S, Tuladhar AM, Pirpamer L, Bell S, Hofer E, Duering M, Wason J, Morris RG, Dichgans M, Schmidt R, Tozer DJ, Barrick TR, Chen C, de Leeuw FE, and Markus HS
Subjects: Biomarkers, Cross-Sectional Studies, Diffusion Tensor Imaging methods, Humans, Cerebral Small Vessel Diseases complications, Dementia complications, White Matter diagnostic imaging
Abstract: Background: DTI is sensitive to white matter (WM) microstructural damage and has been suggested as a surrogate marker for phase 2 clinical trials in cerebral small vessel disease (SVD). The study's objective is to establish the best way to analyse the diffusion-weighted imaging data in SVD for this purpose. The ideal method would be sensitive to change and predict dementia conversion, but also straightforward to implement and ideally automated. As part of the OPTIMAL collaboration, we evaluated five different DTI analysis strategies across six different cohorts with differing SVD severity., Methods: Those 5 strategies were: (1) conventional mean diffusivity WM histogram measure (MD median), (2) a principal component-derived measure based on conventional WM histogram measures (PC1), (3) peak width skeletonized mean diffusivity (PSMD), (4) diffusion tensor image segmentation θ (DSEG θ) and (5) a WM measure of global network efficiency (Geff). The association between each measure and cognitive function was tested using a linear regression model adjusted by clinical markers. Changes in the imaging measures over time were determined. In three cohort studies, repeated imaging data together with data on incident dementia were available. The association between the baseline measure, change measure and incident dementia conversion was examined using Cox proportional-hazard regression or logistic regression models. Sample size estimates for a hypothetical clinical trial were furthermore computed for each DTI analysis strategy., Results: There was a consistent cross-sectional association between the imaging measures and impaired cognitive function across all cohorts. All baseline measures predicted dementia conversion in severe SVD. In mild SVD, PC1, PSMD and Geff predicted dementia conversion. In MCI, all markers except Geff predicted dementia conversion. Baseline DTI was significantly different in patients converting to vascular dementia than to Alzheimer' s disease. Significant change in all measures was associated with dementia conversion in severe but not in mild SVD. The automatic and semi-automatic measures PSMD and DSEG θ required the lowest minimum sample sizes for a hypothetical clinical trial in single-centre sporadic SVD cohorts., Conclusion: DTI parameters obtained from all analysis methods predicted dementia, and there was no clear winner amongst the different analysis strategies. The fully automated analysis provided by PSMD offers advantages particularly for large datasets., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)
Published: 2022
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47. Information processing speed as a prognostic marker of physical impairment and progression in patients with multiple sclerosis.

Author: Hechenberger S, Helmlinger B, Ropele S, Pirpamer L, Bachmaier G, Damulina A, Pichler A, Khalil M, Enzinger C, and Pinter D
Subjects: Adult, Cognition, Disability Evaluation, Disease Progression, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Neuropsychological Tests, Prognosis, Cognition Disorders, Multiple Sclerosis diagnostic imaging, Multiple Sclerosis, Relapsing-Remitting diagnostic imaging
Abstract: Background: Prediction of disability progression in patients with MS (pwMS) is challenging. So far, scarce evidence exists suggesting knowledge about how cognitive performance may potentially improve prediction of physical impairment and disability progression in MS. Therefore, we wanted to assess the prognostic value of cognitive performance regarding physical impairment and disability progression in pwMS., Methods: 85 patients (64% female; 60% relapse-remitting MS; mean age=36.78 ± 9.63 years) underwent clinical, neuropsychological (Brief Repeatable Battery for Neuropsychological Test (BRB-N)) and brain MRI (T1-weighted and T2-weighted FLAIR images) assessment at baseline and after an average of 7 years (SD=3.75) at follow-up. We assessed physical impairment and annualized disability progression (disability progression divided by follow-up duration) using the Expanded Disability Status Scale (EDSS). To compare patients with no or mild physical impairment (EDSS≤2.5) and patients with moderate to severe physical impairment (EDSS≥3.0), we used an EDSS score ≥3.0 as cut-off. Silent progression was defined by an EDSS worsening of at least 0.5 in the absence of relapses and inflammation in relapsing-remitting MS., Results: In hierarchical regression models (method "STEPWISE", forward) performance in information processing speed was a significant and independent predictor of physical impairment (EDSS≥3.0) at follow-up (model R²=0.671, b=-1.46, OR=0.23, p=0.001) and annualized disability progression (adjusted model R²=0.257, β=-0.26, 95% CI: -0.066, -0.008, p=0.012), in addition to demographics (age, education, individual follow-up time), clinical (EDSS, disease duration, clinical phenotype, annualized-relapse-rate) and MRI measures (brain volumes and T2-lesion load). In a MANCOVA controlled for age, disease duration and individual follow-up time, worse baseline performance in information processing speed was found in patients with higher EDSS at follow-up (m=-1.91, SD=1.18, p<0.001) and silent progression (m=-2.19, SD=1.01, p=0.038)., Conclusion: Performance in information processing speed might help to identify patients at risk for physical impairment. Therefore, neuropsychological assessment should be integrated in clinical standard care to support disease management in pwMS., (Copyright © 2021. Published by Elsevier B.V.)
Published: 2022
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48. Factors influencing serum neurofilament light chain levels in normal aging.

Author: Koini M, Pirpamer L, Hofer E, Buchmann A, Pinter D, Ropele S, Enzinger C, Benkert P, Leppert D, Kuhle J, Schmidt R, and Khalil M
Subjects: Adult, Age Factors, Aged, Austria, Axons physiology, Body Mass Index, Cohort Studies, Female, Humans, Male, Middle Aged, Aging physiology, Biomarkers blood, Healthy Volunteers statistics & numerical data, Intermediate Filaments, Neurofilament Proteins blood
Abstract: Objective: Serum neurofilament light (sNfL) is a promising marker for neuro-axonal damage and it is now well known that its levels also increase with higher age. However, the effect of other determinants besides age is still poorly investigated. We therefore aimed to identify factors influencing the sNfL concentration by analysing a large set of demographical, life-style and clinical variables in a normal aging cohort., Methods: sNfL was quantified by single molecule array (Simoa) assay in 327 neurologically inconspicuous individuals (median age 67.8±10.7 years, 192 female) who participated in the Austrian Stroke Prevention Family Study (ASPS-Fam). Random forest regression analysis was used to rank the association of included variables with sNfL in the entire cohort and in age-stratified subgroups. Linear regression then served to identify factors independently influencing sNfL concentration., Results: Age (β=0.513, p<0.001) was by far the most important factor influencing sNfL, which was mainly driven by individuals ≥60 years. In age stratified sub-groups, body mass index (BMI) (β=-0.298, p<0.001) independently predicted sNfL in individuals aged 38-60 years. In individuals ≥60 years, age (β=0.394, p<0.001), renal function (β=0.376, p<0.001), blood volume (β=-0.198, p=0.008) and high density lipoprotein (HDL) (β=0.149, p=0.013) were associated with sNfL levels., Conclusions: Age is the most important factor influencing sNfL concentrations, getting increasingly relevant in elderly people. BMI further influences sNfL levels, especially at younger age, whereas renal function gets increasingly relevant in the elderly.
Published: 2021
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49. Anatomically Standardized Detection of MRI Atrophy Patterns in Early-Stage Alzheimer's Disease.

Author: Lenhart L, Seiler S, Pirpamer L, Goebel G, Potrusil T, Wagner M, Dal Bianco P, Ransmayr G, Schmidt R, Benke T, and Scherfler C
Abstract: MRI studies have consistently identified atrophy patterns in Alzheimer's disease (AD) through a whole-brain voxel-based analysis, but efforts to investigate morphometric profiles using anatomically standardized and automated whole-brain ROI analyses, performed at the individual subject space, are still lacking. In this study we aimed (i) to utilize atlas-derived measurements of cortical thickness and subcortical volumes, including of the hippocampal subfields, to identify atrophy patterns in early-stage AD, and (ii) to compare cognitive profiles at baseline and during a one-year follow-up of those previously identified morphometric AD subtypes to predict disease progression. Through a prospectively recruited multi-center study, conducted at four Austrian sites, 120 patients were included with probable AD, a disease onset beyond 60 years and a clinical dementia rating of ≤1. Morphometric measures of T1-weighted images were obtained using FreeSurfer. A principal component and subsequent cluster analysis identified four morphometric subtypes, including (i) hippocampal predominant (30.8%), (ii) hippocampal-temporo-parietal (29.2%), (iii) parieto-temporal (hippocampal sparing, 20.8%) and (iv) hippocampal-temporal (19.2%) atrophy patterns that were associated with phenotypes differing predominately in the presentation and progression of verbal memory and visuospatial impairments. These morphologically distinct subtypes are based on standardized brain regions, which are anatomically defined and freely accessible so as to validate its diagnostic accuracy and enhance the prediction of disease progression.
Published: 2021
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50. Brain Magnetic Resonance Imaging Reveals Different Courses of Disease in Pediatric and Adult Cerebral Malaria.

Author: Sahu PK, Hoffmann A, Majhi M, Pattnaik R, Patterson C, Mahanta KC, Mohanty AK, Mohanty RR, Joshi S, Mohanty A, Bage J, Maharana S, Seitz A, Bendszus M, Sullivan SA, Turnbull IW, Dondorp AM, Gupta H, Pirpamer L, Mohanty S, and Wassmer SC
Subjects: Adult, Brain diagnostic imaging, Child, Humans, Lipocalin-2 blood, Magnetic Resonance Imaging, MicroRNAs blood, Brain Diseases diagnostic imaging, Brain Diseases parasitology, Malaria, Cerebral diagnostic imaging, Malaria, Falciparum diagnostic imaging
Abstract: Background: Cerebral malaria is a common presentation of severe Plasmodium falciparum infection and remains an important cause of death in the tropics. Key aspects of its pathogenesis are still incompletely understood, but severe brain swelling identified by magnetic resonance imaging (MRI) was associated with a fatal outcome in African children. In contrast, neuroimaging investigations failed to identify cerebral features associated with fatality in Asian adults., Methods: Quantitative MRI with brain volume assessment and apparent diffusion coefficient (ADC) histogram analyses were performed for the first time in 65 patients with cerebral malaria to compare disease signatures between children and adults from the same cohort, as well as between fatal and nonfatal cases., Results: We found an age-dependent decrease in brain swelling during acute cerebral malaria, and brain volumes did not differ between fatal and nonfatal cases across both age groups. In nonfatal disease, reversible, hypoxia-induced cytotoxic edema occurred predominantly in the white matter in children, and in the basal ganglia in adults. In fatal cases, quantitative ADC histogram analyses also demonstrated different end-stage patterns between adults and children: Severe hypoxia, evidenced by global ADC decrease and elevated plasma levels of lipocalin-2 and microRNA-150, was associated with a fatal outcome in adults. In fatal pediatric disease, our results corroborate an increase in brain volume, leading to augmented cerebral pressure, brainstem herniation, and death., Conclusions: Our findings suggest distinct pathogenic patterns in pediatric and adult cerebral malaria with a stronger cytotoxic component in adults, supporting the development of age-specific adjunct therapies., (© The Author(s) 2020. Published by Oxford University Press for the Infectious Diseases Society of America.)
Published: 2021
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