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431 results on '"Rusinek, H."'

1. Reduced glucose uptake and Aβ in brain regions with hyperintensities in connected white matter

Author

Glodzik, L, Kuceyeski, A, Rusinek, H, Tsui, W, Mosconi, L, Li, Y, Osorio, RS, Williams, S, Randall, C, Spector, N, McHugh, P, Murray, J, Pirraglia, E, Vallabhajosula, S, Raj, A, and de Leon, MJ

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Clinical Research, Neurosciences, Biomedical Imaging, Neurological, Aged, Amyloid beta-Peptides, Aniline Compounds, Blood Glucose, Brain, Female, Fluorodeoxyglucose F18, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Positron-Emission Tomography, Thiazoles, White Matter, Aging, Amyloid beta, Cerebrovascular disorders, Magnetic resonance imaging, Positron emission tomography, Medical and Health Sciences, Psychology and Cognitive Sciences, Neurology & Neurosurgery, Biomedical and clinical sciences, Health sciences
Abstract

Interstitial concentration of amyloid beta (Aß) is positively related to synaptic activity in animal experiments. In humans, Aß deposition in Alzheimer's disease overlaps with cortical regions highly active earlier in life. White matter lesions (WML) disrupt connections between gray matter (GM) regions which in turn changes their activation patterns. Here, we tested if WML are related to Aß accumulation (measured with PiB-PET) and glucose uptake (measured with FDG-PET) in connected GM. WML masks from 72 cognitively normal (age 61.7 ± 9.6 years, 71% women) individuals were obtained from T2-FLAIR. MRI and PET images were normalized into common space, segmented and parcellated into gray matter (GM) regions. The effects of WML on connected GM regions were assessed using the Change in Connectivity (ChaCo) score. Defined for each GM region, ChaCo is the percentage of WM tracts connecting to that region that pass through the WML mask. The regional relationship between ChaCo, glucose uptake and Aß was explored via linear regression. Subcortical regions of the bilateral caudate, putamen, calcarine, insula, thalamus and anterior cingulum had WM connections with the most lesions, followed by frontal, occipital, temporal, parietal and cerebellar regions. Regional analysis revealed that GM with more lesions in connecting WM and thus impaired connectivity had lower FDG-PET (r = 0.20, p

Published
2014

7. Imaging Alzheimer's Disease: The Evolution of Biomarkers

Author

de Leon, M.J., primary, Glodzik, L., additional, Mosconi, L., additional, Osorio, R., additional, Kamer, A., additional, De Santi, S., additional, Shulman, M., additional, Li, Y., additional, Tsui, W., additional, Butler, T., additional, Okamura, N., additional, and Rusinek, H., additional

Published
2015
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8. The Early Detection of Brain Pathology in Alzheimer’s Disease

Author

de Leon, M. J., Smith, G., Convit, A., George, A. E., Golomb, J., Flicker, C., McRae, T., Kluger, A., Tsui, W., Rusinek, H., Ferris, S. H., Reisberg, B., Ciaravino, J., Wegiel, J., Narkiewicz, O., Wisniewski, H., Wolf, A. P., Christen, Yves, editor, and Churchland, Patricia S., editor

Published
1992
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9. Development of a Deep Learning Model for Early Alzheimer’s Disease Detection from Structural MRIs and External Validation on an Independent Cohort

Author

Fernandez-Granda C, Zhang B, Zhu W, Narges Razavian, Chen J, Masurkar Av, Rusinek H, and Liu S

Subjects
medicine.medical_specialty, Disease detection, business.industry, Deep learning, External validation, Disease, medicine.disease, Clinical trial, Physical medicine and rehabilitation, Neuroimaging, Cohort, medicine, Dementia, Artificial intelligence, business
Abstract

Early diagnosis of Alzheimer’s disease plays a pivotal role in patient care and clinical trials. In this study, we have developed a new approach based on 3D deep convolutional neural networks to accurately differentiate mild Alzheimer’s disease dementia from mild cognitive impairment and cognitively normal individuals using structural MRIs. For comparison, we have built a reference model based on the volumes and thickness of previously reported brain regions that are known to be implicated in disease progression. We validate both models on an internal held-out cohort from The Alzheimer’s Disease Neuroimaging Initiative (ADNI) and on an external independent cohort from The National Alzheimer’s Coordinating Center (NACC). The deep-learning model is more accurate and significantly faster than the volume/thickness model. The model can also be used to forecast progression: subjects with mild cognitive impairment misclassified as having mild Alzheimer’s disease dementia by the model were faster to progress to dementia over time. An analysis of the features learned by the proposed model shows that it relies on a wide range of regions associated with Alzheimer’s disease. These findings suggest that deep neural networks can automatically learn to identify imaging biomarkers that are predictive of Alzheimer’s disease, and leverage them to achieve accurate early detection of the disease.

Published
2021

11. MR spectroscopy indicates diffuse multiple sclerosis activity during remission

Author

Kirov, I.I., Patil, V., Babb, J.S., Rusinek, H., Herbert, J., and Gonen, O.

Subjects
Multiple sclerosis -- Development and progression, Multiple sclerosis -- Research, Nuclear magnetic resonance spectroscopy -- Usage, Nervous system -- Degeneration, Nervous system -- Research, Health, Psychology and mental health
Published
2009

12. Standardized Brain MRI Acquisition Protocols Improve Statistical Power in Multicenter Quantitative Morphometry Studies

Author

George, A, Kuzniecky, R, Rusinek, H, Pardoe, HR, French, J, Lowenstein, D, Cristofaro, S, McKenna, K, Mays, V, Shack, D, Barnard, S, Burke, C, Hegde, M, Glauser, T, O'Brien, T, Pollard, J, Ting, T, Meador, K, Darby, D, Morrison, C, Penovich, P, Schembri, A, Kanner, A, Altilab, HH, Barry, J, Hesdorffer, D, Hope, O, Nadkarni, S, Sperling, M, Winawer, M, Dlugos, D, Beal, J, Boro, A, Herman, S, Singh, R, Halford, J, Thio, LL, Pardoe, H, Cascino, G, Glynn, S, Jackson, G, Knowlton, R, Gidal, B, Abou-Khalil, B, Alldredge, B, Faught, E, Ficker, D, Klein, P, Mintzer, S, Detyniecki, K, Haut, S, Hixson, J, Holmes, M, Kalviainen, R, Widdess-Walsh, P, Krishnamurthy, K, Park, K, Gelfand, M, Kang, J, Krauss, G, Cole, A, Atkinson, P, Trinka, E, Kirschner, M, Schmid, E, Somerville, E, Zentner, C, Laue-Gizzi, H, Rodriguez, A, Devinsky, O, Nadkarni, M, Cook, M, Berkovic, S, Bebin, M, Szaflarsk, J, Szabo, C, Burneo, J, Abou-Khalil, BW, Weisenberg, J, Altilab, H, George, A, Kuzniecky, R, Rusinek, H, Pardoe, HR, French, J, Lowenstein, D, Cristofaro, S, McKenna, K, Mays, V, Shack, D, Barnard, S, Burke, C, Hegde, M, Glauser, T, O'Brien, T, Pollard, J, Ting, T, Meador, K, Darby, D, Morrison, C, Penovich, P, Schembri, A, Kanner, A, Altilab, HH, Barry, J, Hesdorffer, D, Hope, O, Nadkarni, S, Sperling, M, Winawer, M, Dlugos, D, Beal, J, Boro, A, Herman, S, Singh, R, Halford, J, Thio, LL, Pardoe, H, Cascino, G, Glynn, S, Jackson, G, Knowlton, R, Gidal, B, Abou-Khalil, B, Alldredge, B, Faught, E, Ficker, D, Klein, P, Mintzer, S, Detyniecki, K, Haut, S, Hixson, J, Holmes, M, Kalviainen, R, Widdess-Walsh, P, Krishnamurthy, K, Park, K, Gelfand, M, Kang, J, Krauss, G, Cole, A, Atkinson, P, Trinka, E, Kirschner, M, Schmid, E, Somerville, E, Zentner, C, Laue-Gizzi, H, Rodriguez, A, Devinsky, O, Nadkarni, M, Cook, M, Berkovic, S, Bebin, M, Szaflarsk, J, Szabo, C, Burneo, J, Abou-Khalil, BW, Weisenberg, J, and Altilab, H

Abstract

BACKGROUND AND PURPOSE: In this study, we used power analysis to calculate required sample sizes to detect group-level changes in quantitative neuroanatomical estimates derived from MRI scans obtained from multiple imaging centers. Sample size estimates were derived from (i) standardized 3T image acquisition protocols and (ii) nonstandardized clinically acquired images obtained at both 1.5 and 3T as part of the multicenter Human Epilepsy Project. Sample size estimates were compared to assess the benefit of standardizing acquisition protocols. METHODS: Cortical thickness, hippocampal volume, and whole brain volume were estimated from whole brain T1-weighted MRI scans processed using Freesurfer v6.0. Sample sizes required to detect a range of effect sizes were calculated using (i) standard t-test based power analysis methods and (ii) a nonparametric bootstrap approach. RESULTS: A total of 32 participants were included in our analyses, aged 29.9 ± 12.62 years. Standard deviation estimates were lower for all quantitative neuroanatomical metrics when assessed using standardized protocols. Required sample sizes per group to detect a given effect size were markedly reduced when using standardized protocols, particularly for cortical thickness changes <.2 mm and hippocampal volume changes <10%. CONCLUSIONS: The use of standardized protocols yielded up to a five-fold reduction in required sample sizes to detect disease-related neuroanatomical changes, and is particularly beneficial for detecting subtle effects. Standardizing image acquisition protocols across scanners prior to commencing a study is a valuable approach to increase the statistical power of multicenter MRI studies.

Published
2020

16. A blood-based signature of cerebrospinal fluid A beta(1-42) status

Author

Goudey, B., Fung, B.J., Schieber, C., Faux, N.G., Weiner, M.W., Aisen, P., Petersen, R., Jack, C.R., Jagust, W., Trojanowki, J.Q., Toga, A.W., Beckett, L., Green, R.C., Saykin, A.J., Morris, J., Shaw, L.M., Kaye, J., Quinn, J., Silbert, L., Lind, B., Carter, R., Dolen, S., Schneider, L.S., Pawluczyk, S., Beccera, M., Teodoro, L., Spann, B.M., Brewer, J., Vanderswag, H., Fleisher, A., Heidebrink, J.L., Lord, J.L., Mason, S.S., Albers, C.S., Knopman, D., Johnson, K., Doody, R.S., Villanueva-Meyer, J., Chowdhury, M., Rountree, S., Dang, M., Stern, Y., Honig, L.S., Bell, K.L., Ances, B., Morris, J.C., Carroll, M., Creech, M.L., Franklin, E., Mintun, M.A., Schneider, S., Oliver, A., Marson, D., Griffth, R., Clark, D., Geldmacher, D., Brockington, J., Roberson, E., Natelson Love, M., Grossman, H., Mitsis, E., Shah, R.C., deToledo-Morrell, L., Duara, R., Varon, D., Greig, M.T., Roberts, P., Albert, M., Onyike, C., D'Agostino, D., Kielb, S., Galvin, J.E., Cerbone, B., Michel, C.A., Pogorelec, D.M., Rusinek, H., Leon, M.J. de, Glodzik, L., De Santi, S., Doraiswamy, P.M., Petrella, J.R., Borges-Neto, S., Wong, T.Z., Coleman, E., Smith, C.D., Jicha, G., Hardy, P., Sinha, P., Oates, E., Conrad, G., Porsteinsson, A.P., Goldstein, B.S., Martin, K., Makino, K.M., Ismail, M.S., Brand, C., Mulnard, R.A., Thai, G., McAdams-Ortiz, C., Womack, K., Mathews, D., Quiceno, M., Levey, A.I., Lah, J.J., Cellar, J.S., Burns, J.M., Swerdlow, R.H., Brooks, W.M., Apostolova, L., Tingus, K., Woo, E., Silverman, D.H.S., Lu, P.H., Bartzokis, G., Graff-Radford, N.R., Parftt, F., Kendall, T., Johnson, H., Farlow, M.R., Hake, A.M., Matthews, B.R., Brosch, J.R., Herring, S., Hunt, C., Dyck, .H. van, Carson, R.E., MacAvoy, M.G., Varma, P., Chertkow, H., Bergman, H., Hosein, C., Black, S., Stefanovic, B., Caldwell, C., Hsiung, Ging-Yuek Robin, Feldman, H., Mudge, B., Assaly, M., Finger, E., Pasternack, S., Rachisky, I., Trost, D., Kertesz, A., Bernick, C., Munic, D., Mesulam, M.-M., Lipowski, K., Weintraub, S., Bonakdarpour, B., Kerwin, D., Wu, C.-K., Johnson, N., Sadowsky, C., Villena, T., Turner, R.S., Reynolds, B., Sperling, R.A., Johnson, K.A., Marshall, G., Yesavage, J., Taylor, J.L., Lane, B., Rosen, A., Tinklenberg, J., Sabbagh, M.N., Belden, C.M., Jacobson, S.A., Sirrel, S.A., Kowall, N., Killiany, R., Budson, A.E., Norbash, A., Johnson, P.L., Obisesan, T.O., Wolday, S., Allard, J., Lerner, A., Ogrocki, P., Tatsuoka, C., Fatica, P., Fletcher, E., Maillard, P., Olichney, J., DeCarli, C., Carmichael, O., Kittur, S., Borrie, M., Lee, T.-Y., Bartha, R., Johnson, S., Asthana, S., Carlsson, C.M., Potkin, S.G., Preda, A., Nguyen, D., Tariot, P., Burke, A., Trncic, N., Reeder, S., Bates, V., Capote, H., Rainka, M., Scharre, D.W., Kataki, M., Adeli, A., Zimmerman, E.A., Celmins, D., Brown, A.D., Pearlson, G.D., Blank, K., Anderson, K., Flashman, L.A., Seltzer, M., Hynes, M.L., Santulli, R.B., Sink, K.M., Gordineer, L., Williamson, J.D., Garg, P., Watkins, F., Ott, B.R., Querfurth, H., Tremont, G., Salloway, S., Malloy, P., Correia, S., Rosen, H.J., Miller, B.L., Perry, D., Mintzer, J., Spicer, K., Bachman, D., Pomara, N., Hernando, R., Sarrael, A., Relkin, N., Chaing, G., Lin, M., Ravdin, L., Smith, A., Raj, B.A., Fargher, K., Saykin, A., Nho, K., Kling, M., Toledo, J., Shaw, L., Trojanowski, J., Farrer, L., Kastsenmueller, G., Arnold, M., Wishart, D., Wurtz, P., Bhattcharyya, S., Duijin, C. van, Mangravite, L., Han, X., Hankemeier, T., Fiehn, O., Barupal, D., Thiele, I., Heinken, A., Meikle, P., Price, N., Funk, C., Jia, W., Kueider-Paisley, A., Tenebaum, J., Black, C., Moseley, A., Thompson, W., Mahmoudiandehkorki, S., Baillie, R., Welsh-Bohmer, K., Plassman, B., and Epidemiology

Subjects
Male, 0301 basic medicine, Apolipoprotein E, Oncology, medicine.medical_specialty, Amyloid, Amyloid beta, lcsh:Medicine, Article, 03 medical and health sciences, Apolipoproteins E, 0302 clinical medicine, Cerebrospinal fluid, Alzheimer Disease, Predictive Value of Tests, Internal medicine, medicine, Humans, Dementia, Cognitive decline, lcsh:Science, Aged, Aged, 80 and over, Amyloid beta-Peptides, Multidisciplinary, biology, Chemokine CCL26, business.industry, lcsh:R, Alzheimer’s Disease Metabolomics Consortium, Alzheimer’s Disease Neuroimaging Initiative, medicine.disease, Peptide Fragments, 3. Good health, 030104 developmental biology, biology.protein, Chromogranin A, Female, lcsh:Q, Alzheimer's disease, business, Biomarkers, 030217 neurology & neurosurgery, Alzheimer's Disease Neuroimaging Initiative
Abstract

It is increasingly recognized that Alzheimer’s disease (AD) exists before dementia is present and that shifts in amyloid beta occur long before clinical symptoms can be detected. Early detection of these molecular changes is a key aspect for the success of interventions aimed at slowing down rates of cognitive decline. Recent evidence indicates that of the two established methods for measuring amyloid, a decrease in cerebrospinal fluid (CSF) amyloid β1−42 (Aβ1−42) may be an earlier indicator of Alzheimer’s disease risk than measures of amyloid obtained from Positron Emission Tomography (PET). However, CSF collection is highly invasive and expensive. In contrast, blood collection is routinely performed, minimally invasive and cheap. In this work, we develop a blood-based signature that can provide a cheap and minimally invasive estimation of an individual’s CSF amyloid status using a machine learning approach. We show that a Random Forest model derived from plasma analytes can accurately predict subjects as having abnormal (low) CSF Aβ1−42 levels indicative of AD risk (0.84 AUC, 0.78 sensitivity, and 0.73 specificity). Refinement of the modeling indicates that only APOEε4 carrier status and four plasma analytes (CGA, Aβ1−42, Eotaxin 3, APOE) are required to achieve a high level of accuracy. Furthermore, we show across an independent validation cohort that individuals with predicted abnormal CSF Aβ1−42 levels transitioned to an AD diagnosis over 120 months significantly faster than those with predicted normal CSF Aβ1−42 levels and that the resulting model also validates reasonably across PET Aβ1−42 status (0.78 AUC). This is the first study to show that a machine learning approach, using plasma protein levels, age and APOEε4 carrier status, is able to predict CSF Aβ1−42 status, the earliest risk indicator for AD, with high accuracy.

Published
2019

21. Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference

Author

Young, A. L., Marinescu, R. V., Oxtoby, N. P., Bocchetta, M., Yong, K., Firth, N. C., Cash, D. M., Thomas, D. L., Dick, K. M., Cardoso, J., van Swieten, J., Borroni, B., Galimberti, D., Masellis, M., Tartaglia, M. C., Rowe, J. B., Graff, C., Tagliavini, F., Frisoni, G. B., Laforce, R., Finger, E., de Mendonca, A., Sorbi, S., Warren, J. D., Crutch, S., Fox, N. C., Ourselin, S., Schott, J. M., Rohrer, J. D., Alexander, D. C., Andersson, C., Archetti, S., Arighi, A., Benussi, L., Binetti, G., Black, S., Cosseddu, M., Fallstrom, M., Ferreira, C., Fenoglio, C., Freedman, M., Fumagalli, G. G., Gazzina, S., Ghidoni, R., Grisoli, M., Jelic, V., Jiskoot, L., Keren, R., Lombardi, G., Maruta, C., Meeter, L., Mead, S., van Minkelen, R., Nacmias, B., Oijerstedt, L., Padovani, A., Panman, J., Pievani, M., Polito, C., Premi, E., Prioni, S., Rademakers, R., Redaelli, V., Rogaeva, E., Rossi, G., Rossor, M., Scarpini, E., Tang-Wai, D., Thonberg, H., Tiraboschi, P., Verdelho, A., Weiner, M. W., Aisen, P., Petersen, R., Jack, C. R., Jagust, W., Trojanowki, J. Q., Toga, A. W., Beckett, L., Green, R. C., Saykin, A. J., Morris, J., Shaw, L. M., Khachaturian, Z., Sorensen, G., Kuller, L., Raichle, M., Paul, S., Davies, P., Fillit, H., Hefti, F., Holtzman, D., Mesulam, M. M., Potter, W., Snyder, P., Schwartz, A., Montine, T., Thomas, R. G., Donohue, M., Walter, S., Gessert, D., Sather, T., Jiminez, G., Harvey, D., Bernstein, M., Thompson, P., Schuff, N., Borowski, B., Gunter, J., Senjem, M., Vemuri, P., Jones, D., Kantarci, K., Ward, C., Koeppe, R. A., Foster, N., Reiman, E. M., Chen, K., Mathis, C., Landau, S., Cairns, N. J., Householder, E., Taylor-Reinwald, L., Lee, V., Korecka, M., Figurski, M., Crawford, K., Neu, S., Foroud, T. M., Potkin, S., Shen, L., Faber, K., Kim, S., Nho, K., Thal, L., Buckholtz, N., Albert, M., Frank, R., Hsiao, J., Kaye, J., Quinn, J., Lind, B., Carter, R., Dolen, S., Schneider, L. S., Pawluczyk, S., Beccera, M., Teodoro, L., Spann, B. M., Brewer, J., Vanderswag, H., Fleisher, A., Heidebrink, J. L., Lord, J. L., Mason, S. S., Albers, C. S., Knopman, D., Johnson, K., Doody, R. S., Villanueva-Meyer, J., Chowdhury, M., Rountree, S., Dang, M., Stern, Y., Honig, L. S., Bell, K. L., Ances, B., Carroll, M., Leon, S., Mintun, M. A., Schneider, S., Oliver, A., Marson, D., Griffith, R., Clark, D., Geldmacher, D., Brockington, J., Roberson, E., Grossman, H., Mitsis, E., de Toledo-Morrell, L., Shah, R. C., Duara, R., Varon, D., Greig, M. T., Roberts, P., Onyike, C., D'Agostino, D., Kielb, S., Galvin, J. E., Cerbone, B., Michel, C. A., Rusinek, H., de Leon, M. J., Glodzik, L., De Santi, S., Doraiswamy, P. M., Petrella, J. R., Wong, T. Z., Arnold, S. E., Karlawish, J. H., Wolk, D., Smith, C. D., Jicha, G., Hardy, P., Sinha, P., Oates, E., Conrad, G., Lopez, O. L., Oakley, M. A., Simpson, D. M., Porsteinsson, A. P., Goldstein, B. S., Martin, K., Makino, K. M., Ismail, M. S., Brand, C., Mulnard, R. A., Thai, G., Mc-Adams-Ortiz, C., Womack, K., Mathews, D., Quiceno, M., Diaz-Arrastia, R., King, R., Weiner, M., Martin-Cook, K., Devous, M., Levey, A. I., Lah, J. J., Cellar, J. S., Burns, J. M., Anderson, H. S., Swerdlow, R. H., Apostolova, L., Tingus, K., Woo, E., Silverman, D. H., P. H., Lu, Bartzokis, G., Graff-Radford, N. R., Parfitt, F., Kendall, T., Johnson, H., Farlow, M. R., Hake, A. M., Matthews, B. R., Herring, S., Hunt, C., van Dyck, C. H., Carson, R. E., Macavoy, M. G., Chertkow, H., Bergman, H., Hosein, C., Stefanovic, B., Caldwell, C., Hsiung, G. -Y. R., Feldman, H., Mudge, B., Assaly, M., Kertesz, A., Rogers, J., Bernick, C., Munic, D., Kerwin, D., Mesulam, M. -M., Lipowski, K., C. -K., Wu, Johnson, N., Sadowsky, C., Martinez, W., Villena, T., Turner, R. S., Reynolds, B., Sperling, R. A., Johnson, K. A., Marshall, G., Frey, M., Lane, B., Rosen, A., Tinklenberg, J., Sabbagh, M. N., Belden, C. M., Jacobson, S. A., Sirrel, S. A., Kowall, N., Killiany, R., Budson, A. E., Norbash, A., Johnson, P. L., Allard, J., Lerner, A., Ogrocki, P., Hudson, L., Fletcher, E., Carmichael, O., Olichney, J., Decarli, C., Kittur, S., Borrie, M., Lee, T. -Y., Bartha, R., Johnson, S., Asthana, S., Carlsson, C. M., Potkin, S. G., Preda, A., Nguyen, D., Tariot, P., Reeder, S., Bates, V., Capote, H., Rainka, M., Scharre, D. W., Kataki, M., Adeli, A., Zimmerman, E. A., Celmins, D., Brown, A. D., Pearlson, G. D., Blank, K., Anderson, K., Santulli, R. B., Kitzmiller, T. J., Schwartz, E. S., Sink, K. M., Williamson, J. D., Garg, P., Watkins, F., Ott, B. R., Querfurth, H., Tremont, G., Salloway, S., Malloy, P., Correia, S., Rosen, H. J., Miller, B. L., Mintzer, J., Spicer, K., Bachman, D., Pasternak, S., Rachinsky, I., Drost, D., Pomara, N., Hernando, R., Sarrael, A., Schultz, S. K., Ponto, L. L. B., Shim, H., Smith, K. E., Relkin, N., Chaing, G., Raudin, L., Smith, A., Fargher, K., Raj, B. A., Neylan, T., Grafman, J., Davis, M., Morrison, R., Hayes, J., Finley, S., Friedl, K., Fleischman, D., Arfanakis, K., James, O., Massoglia, D., Fruehling, J. J., Harding, S., Peskind, E. R., Petrie, E. C., Li, G., Yesavage, J. A., Taylor, J. L., and Furst, A. J.

Published
2018

22. The nonlinear relationship between cerebrospinal fluid A beta(42) and tau in preclinical Alzheimer's disease

Author

de Leon, MJ, Pirraglia, E, Osorio, RS, Glodzik, L, Saint-Louis, L, Kim, HJ, Fortea, J, Fossati, S, Laska, E, Siegel, C, Butler, T, Li, Y, Rusinek, H, Zetterberg, H, Blennow, K, Alzheimer's Dis Neuroimaging, and Natl Alzheimer's Coordinating Ctr

Abstract

Cerebrospinal fluid (CSF) studies consistently show that CSF levels of amyloid-beta 1-42 (A beta(42)) are reduced and tau levels increased prior to the onset of cognitive decline related to Alzheimer's disease (AD). However, the preclinical prediction accuracy for low CSF A beta(42) levels, a surrogate for brain A beta(42) deposits, is not high. Moreover, the pathology data suggests a course initiated by tauopathy contradicting the contemporary clinical view of an A beta initiated cascade. CSF A beta(42) and tau data from 3 normal aging cohorts (45-90 years) were combined to test both cross-sectional (n = 766) and longitudinal (n = 651) hypotheses: 1) that the relationship between CSF levels of A beta(42) and tau are not linear over the adult life-span; and 2) that non-linear models improve the prediction of cognitive decline. Supporting the hypotheses, the results showed that a u-shaped quadratic fit (A beta(2)) best describes the relationship for CSF A beta(42) with CSF tau levels. Furthermore we found that the relationship between A beta(42) and tau changes with age-between 45 and 70 years there is a positive linear association, whereas between 71 and 90 years there is a negative linear association between A beta(42) and tau. The quadratic effect appears to be unique to A beta(42), as A beta(38) and A beta(40) showed only positive linear relationships with age and CSF tau. Importantly, we observed the prediction of cognitive decline was improved by considering both high and low levels of A beta(42). Overall, these data suggest an earlier preclinical stage than currently appreciated, marked by CSF elevations in tau and accompanied by either elevations or reductions in A beta(42). Future studies are needed to examine potential mechanisms such as failing CSF clearance as a common factor elevating CSF A beta(xx) analyte levels prior to A beta(42) deposition in brain.

Published
2018

27. Cerebrospinal fluid clearance in Alzheimer disease measured with dynamic PET

Author

De Leon M.J., Li Y., Okamura N., Tsui W.H., Saint-Louis L.A., Glodzik L., Osorio R.S., Fortea J., Butler T., Pirraglia E., Fossati S., Kim H.-J., Carare R.O., Nedergaard M., Benveniste H., and Rusinek H.

Subjects
Male, aniline derivative, positron emission tomography, cerebrospinal fluid clearance, neuroanatomy, diagnostic imaging, very elderly, blood brain barrier, protein binding, Article, cerebrospinal fluid, N-methyl-2-(4'-methylaminophenyl)-6-hydroxybenzothiazole, 6-((3-fluoro-2-hydroxy)propoxy)-2-(4-methylaminophenyl)quinoline, image analysis, Alzheimer Disease, Humans, Pittsburgh compound B, controlled study, human, nervous system parameters, ventricular time activity curve, Benzothiazoles, signal processing, Aged, carbon Pittsburgh compound B c 11, Aged, 80 and over, clinical article, flourine thk 5117 f 18, Aniline Compounds, case control study, biological marker, benzothiazole derivative, unclassified drug, radiopharmaceutical agent, female, priority journal, concentration response, validation process, amyloid beta protein, Case-Control Studies, Positron-Emission Tomography, nose cavity, quinoline derivative, metabolism, Quinol
Abstract

Evidence supporting the hypothesis that reduced cerebrospinal fluid (CSF) clearance is involved in the pathophysiology of Alzheimer disease (AD) comes primarily from rodent models. However, unlike rodents, in which predominant extracranial CSF egress is via olfactory nerves traversing the cribriform plate, human CSF clearance pathways are not well characterized. Dynamic PET with 18F-THK5117, a tracer for tau pathology, was used to estimate the ventricular CSF time-activity as a biomarker for CSF clearance. We tested 3 hypotheses: extracranial CSF is detected at the superior turbinates; CSF clearance is reduced in AD; and CSF clearance is inversely associated with amyloid deposition. Methods: Fifteen subjects, 8 with AD and 7 normal control volunteers, were examined with 18F-THK5117. Ten subjects additionally underwent 11C-Pittsburgh compound B (11C-PiB) PET scanning, and 8 were 11C-PiB-positive. Ventricular time-activity curves of 18F-THK5117 were used to identify highly correlated time-activity curves from extracranial voxels. Results: For all subjects, the greatest density of CSF-positive extracranial voxels was in the nasal turbinates. Tracer concentration analyses validated the superior nasal turbinate CSF signal intensity. AD patients showed ventricular tracer clearance reduced by 23% and 66% fewer superior turbinate CSF egress sites. Ventricular CSF clearance was inversely associated with amyloid deposition. Conclusion: The human nasal turbinate is part of the CSF clearance system. Lateral ventricle and superior nasal turbinate CSF clearance abnormalities are found in AD. Ventricular CSF clearance reductions are associated with increased brain amyloid depositions. These data suggest that PET-measured CSF clearance is a biomarker of potential interest in AD and other neurodegenerative diseases. Copyright © 2017 by the Society of Nuclear Medicine and Molecular Imaging.

Published
2017

30. Generation of uniform high fields with magnetized wedges

Author

Abele, Manlio G., Jensen, Jens H., and Rusinek, H.

Subjects
Magnets, Permanent -- Design and construction, Magnetic materials -- Usage, Business, Electronics, Electronics and electrical industries
Abstract

The properties of permanent magnets composed of uniformly magnetized wedges are presented in this paper. The magnets are fully open structures capable of generating strong fields exceeding the remanence of the material.

Published
1997

31. Reply

Author

Thakur, S.K., primary, Serulle, Y., additional, Miskin, N.P., additional, Rusinek, H., additional, Golomb, J., additional, and George, A.E., additional

Published
2017
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32. Strapping techniques for permanent magnets

Author

Abele, M.G., Jensen, J.H., and Rusinek, H.

Subjects
Magnets, Permanent -- Design and construction, Magnetization -- Analysis, Magnetic fields -- Analysis, Ferromagnetic materials -- Usage, Business, Electronics, Electronics and electrical industries
Abstract

The magnetic field distortion due to magnetization tolerances, fabrication imperfections, and design alterations can be reduced by inserting into a permanent magnet structure thin straps of high permeability ferromagnetic material that lie along equipotential surfaces of the ideal field of a theoretical design. Numerical results are given for a simple model showing quantitatively the beneficial effect of strapping in improving the quality of the field.

Published
1996

33. Open C-shaped permanent magnets for NMR imaging

Author

Abele, M.G. and Rusinek, H.

Subjects
Magnets, Permanent -- Design and construction, Magnetic devices -- Design and construction, Magnetic resonance imaging -- Equipment and supplies, Business, Electronics, Electronics and electrical industries
Published
1995

34. Shimming of yokeless permanent magnets designed to generate uniform fields

Author

Bertora, F., Trequattrini, A., Abele, M.G., and Rusinek, H.

Subjects
Magnets, Permanent -- Research, Magnetic fields -- Research, Physics
Abstract

The effect of shimming treatments on the ability of yokeless permanent magnets to generate uniform magnetic fields was investigated. The results showed that shimming allowed fabrication and magnetization tolerances to be achieved. In addition, shimming compensated for residual inhomogeneities in the yokeless permanent magnet. The technique was able to achieve an improvement of more than an order of magnitude.

Published
1993

35. Optimization of multilayered magnetic structures

Author

Abele, M.G. and Rusinek, H.

Subjects
Magnets, Permanent -- Research, Magnetic alloys -- Research, Physics
Abstract

The optimization of a two-layered structure consisting of two materials with differing energy product values was studied. The results showed that using a material with lower remanence can significantly increase the efficiency of the magnetic structure. The superposition of two or more structures of such materials with similar geometries makes it possible to increase the field proportionally to the number of structures.

Published
1993

36. Quantitative mpMRI parameters and derived metrics to assess heterogeneity, aggressiveness of prostate cancer and predict targeted biopsy outcome

Author

Orczyk, C, Rusinek, H, Mikheev, A, Bazille, C, Villers, A, Valable, S, Imagerie et Stratégies Thérapeutiques des pathologies Cérébrales et Tumorales (ISTCT), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Centre National de la Recherche Scientifique (CNRS), and Brunaud, Carole

Subjects
[SDV] Life Sciences [q-bio], [SDV]Life Sciences [q-bio]
Abstract

CERVOXY; International audience

Published
2016

37. Sex-dependent association of common variants of microcephaly genes with brain structure

Author

Rimol, L. M., Agartz, I., Djurovic, S., Brown, A. A., Roddey, J. C., Kahler, A. K., Mattingsdal, M., Athanasiu, L., Joyner, A. H., Schork, N. J., Halgren, E., Sundet, K., Melle, I., Dale, A. M., Andreassen, O. A., Weiner, M., Thal, L., Petersen, R., Jack, C. R., Jagust, W., Trojanowki, J., Toga, A. W., Beckett, L., Green, R. C., Gamst, A., Potter, W. Z., Montine, T., Anders, D., Bernstein, M., Felmlee, J., Fox, N., Thompson, P., Schuff, N., Alexander, G., Bandy, D., Koeppe, R. A., Foster, N., Reiman, E. M., Chen, K., Shaw, L., Lee, V. M.- Y., Korecka, M., Crawford, K., Neu, S., Harvey, D., Kornak, J., Kachaturian, Z., Frank, R., Snyder, P. J., Molchan, S., Kaye, J., Vorobik, R., Quinn, J., Schneider, L., Pawluczyk, S., Spann, B., Fleisher, A. S., Vanderswag, H., Heidebrink, J. L., Lord, J. L., Johnson, K., Doody, R. S., Villanueva-Meyer, J., Chowdhury, M., Stern, Yaakov, Honig, L. S., Bell, K. L., Morris, J. C., Mintun, M. A., Schneider, S., Marson, D., Griffith, R., Badger, B., Grossman, H., Tang, C., Stern, J., deToledo-Morrell, L., Shah, R. C., Bach, J., Duara, R., Isaacson, R., Strauman, S., Albert, M. S., Pedroso, J., Toroney, J., Rusinek, H., de Leon, M. J., De Santi, S. M., Doraiswamy, P. M., Petrella, J. R., Aiello, M., Clark, C. M., Pham, C., Nunez, J., Smith, C. D., Given II, C. A., Hardy, P., DeKosky, S. T., Oakley, M., Simpson, D. M., Ismail, M. S., Porsteinsson, A., McCallum, C., Cramer, S. C., Mulnard, R. A., McAdams-Ortiz, C., Diaz-Arrastia, R., Martin-Cook, K., DeVous, M., Levey, A. I., Lah, J. J., Cellar, J. S., Burns, J. M., Anderson, H. S., Laubinger, M. M., Bartzokis, G., Silverman, D. H. S., Lu, P. H., Fletcher, R., Parfitt, F., Johnson, H., Farlow, M., Herring, S., Hake, A. M., van Dyck, C. H., MacAvoy, M. G., Bifano, L. A., Chertkow, H., Bergman, H., Hosein, C., Black, S., Graham, S., Caldwell, C., Feldman, H., Assaly, M., Hsiung, G.-Y. R., Kertesz, A., Rogers, J., Trost, D., Bernick, C., Gitelman, D., Johnson, N., Mesulam, M., Sadowsky, C., Villena, T., Mesner, S., Aisen, P. S., Johnson, K. B., Behan, K. E., Sperling, R. A., Rentz, D. M., Johnson, K. A., Rosen, A., Tinklenberg, J., Ashford, W., Sabbagh, M., Connor, D., Obradov, S., Killiany, R., Norbash, A., Obisesan, T. O., Jayam-Trouth, A., Wang, P., Auchus, A. P., Huang, J., Friedland, R. P., DeCarli, C., Fletcher, E., Carmichael, O., Kittur, S., Mirje, S., Johnson, S. C., Borrie, M., Lee, T.-Y., Asthana, S., Carlsson, C. M., Potkin, S. G., Highum, D., Preda, A., Nguyen, D., Tariot, P. N., Hendin, B. A., Scharre, D. W., Kataki, M., Beversdorf, D. Q., Zimmerman, E. A., Celmins, D., Brown, A. D., Gandy, S., Marenberg, M. E., Rovner, B. W., Pearlson, G., Blank, K., Anderson, K., Saykin, A. J., Santulli, R. B., Pare, N., Williamson, J. D., Sink, K. M., Potter, H., Ashok Raj, B., Giordano, A., Ott, B. R., Wu, C.-K., Cohen, R., Wilks, K. L., and Alzheimer's Disease Neuroimaging Initiative

Subjects
Adult, Male, Microcephaly, Molecular Sequence Data, Single-nucleotide polymorphism, Biology, Polymorphism, Single Nucleotide, Brain mapping, ASPM, Sex Factors, medicine, Animals, Humans, SNP, Genetic Predisposition to Disease, Genetics, Brain Mapping, Multidisciplinary, CDK5RAP2, Brain morphometry, Brain, Middle Aged, Biological Sciences, medicine.disease, Magnetic Resonance Imaging, Phenotype, Brain size, Female
Abstract

Loss-of-function mutations in the genes associated with primary microcephaly (MCPH) reduce human brain size by about two-thirds, without producing gross abnormalities in brain organization or physiology and leaving other organs largely unaffected [Woods CG, et al. (2005) Am J Hum Genet 76:717–728]. There is also evidence suggesting that MCPH genes have evolved rapidly in primates and humans and have been subjected to selection in recent human evolution [Vallender EJ, et al. (2008) Trends Neurosci 31:637–644]. Here, we show that common variants of MCPH genes account for some of the common variation in brain structure in humans, independently of disease status. We investigated the correlations of SNPs from four MCPH genes with brain morphometry phenotypes obtained with MRI. We found significant, sex-specific associations between common, nonexonic, SNPs of the genes CDK5RAP2 , MCPH1 , and ASPM , with brain volume or cortical surface area in an ethnically homogenous Norwegian discovery sample ( n = 287), including patients with mental illness. The most strongly associated SNP findings were replicated in an independent North American sample ( n = 656), which included patients with dementia. These results are consistent with the view that common variation in brain structure is associated with genetic variants located in nonexonic, presumably regulatory, regions.

Published
2009

39. Prediction of cognitive decline in normal elderly subjects with 2-[[sup.18]F]fluoro-2-deoxy-D-glucose/ positron-emission tomography (FDG/PET)

Author

de Leon, M. J., Convit, A., Wolf, O. T., Tarshish, C. Y., DeSanti, S., Rusinek, H., Tsui, W., Kandil, E., Scherer, A. J., Roche, A., Imossi, A., Thorn, E., Bobinski, M., Caraos, C., Lesbre, P., Schlyer, D., Poirier, J., Reisberg, B., and Fowler, J.

Subjects
Cognitive balance -- Evaluation, Aged -- Health aspects, Brain damage -- Testing, Cerebral cortex -- Physiological aspects, Science and technology
Abstract

Neuropathology studies show that patients with mild cognitive impairment (MCI) and Alzheimer's disease typically have lesions of the entorhinal cortex (EC), hippocampus (Hip), and temporal neocortex. Related observations with in vivo imaging have enabled the prediction of dementia from MCI. Although individuals with normal cognition may have focal EC lesions, this anatomy has not been studied as a predictor of cognitive decline and brain change. The objective of this MRI-guided 2-[[sup.18]F]fluoro-2-deoxy-D-glucose/ positron-emission tomography (FDG/PET) study was to examine the hypothesis that among normal elderly subjects, EC METglu reductions predict decline and the involvement of the Hip and neocortex. In a 3-year longitudinal study of 48 healthy normal elderly, 12 individuals (mean age 72) demonstrated cognitive decline (11 to MCI and 1 to Alzheimer's disease). Nondeclining controls were matched on apolipoprotein E genotype, age, education, and gender. At baseline, metabolic reductions in the EC accurately predicted the conversion from normal to MCI. Among those who declined, the baseline EC predicted longitudinal memory and temporal neocortex metabolic reductions. At follow-up, those who declined showed memory impairment and hypometabolism in temporal lobe neocortex and Hip. Among those subjects who declined, apolipoprotein E E4 carriers showed marked longitudinal temporal neocortex reductions. In summary, these data suggest that an EC stage of brain involvement can be detected in normal elderly that predicts future cognitive and brain metabolism reductions. Progressive E4-related hypometabolism may underlie the known increased susceptibility for dementia. Further study is required to estimate individual risks and to determine the physiologic basis for METglu changes detected while cognition is normal.

Published
2001

41. Genome-wide scan of healthy human connectome discovers SPON1 gene variant influencing dementia severity

Author

Jahanshad, N., Rajagopalan, P., Hua, X., Hibar, D. P., Nir, T. M., Toga, A. W., Jack, C. R., Saykin, A. J., Green, R. C., Weiner, M. W., Medland, S. E., Montgomery, G. W., Hansell, N. K., McMahon, K. L., de Zubicaray, G. I., Martin, N. G., Wright, M. J., Thompson, P. M., the Alzheimer's Disease Neuroimaging Initiative, Weiner, M., Aisen, P., Petersen, R., Jagust, W., Trojanowski, J. Q., Beckett, L., Morris, J., Liu, E., Montine, T., Gamst, A., Thomas, R. G., Donohue, M., Walter, S., Gessert, D., Sather, T., Harvey, D., Kornak, J., Dale, A., Bernstein, M., Felmlee, J., Fox, N., Thompson, P., Schuff, N., Alexander, G., DeCarli, C., Bandy, D., Koeppe, R. A., Foster, N., Reiman, E. M., Chen, K., Mathis, C., Cairns, N. J., Taylor-Reinwald, L., Trojanowki, J. Q., Shaw, L., Lee, V. M. Y., Korecka, M., Crawford, K., Neu, S., Foroud, T. M., Potkin, S., Shen, L., Khachaturian, Z., Frank, R., Snyder, P. J., Molchan, S., Kaye, J., Quinn, J., Lind, B., Dolen, S., Schneider, L. S., Pawluczyk, S., Spann, B. M., Brewer, J., Vanderswag, H., Heidebrink, J. L., Lord, J. L., Johnson, K., Doody, R. S., Villanueva-Meyer, J., Chowdhury, M., Stern, Yaakov, Honig, L. S., Bell, K. L., Morris, J. C., Ances, B., Carroll, M., Leon, S., Mintun, M. A., Schneider, S., Marson, D., Griffith, R., Clark, D., Grossman, H., Mitsis, E., Romirowsky, A., deToledo-Morrell, L., Shah, R. C., Duara, R., Varon, D., Roberts, P., Albert, M., Onyike, C., Kielb, S., Rusinek, H., de Leon, M. J., Glodzik, L., De Santi, S., Doraiswamy, P. M., Petrella, J. R., Coleman, R. E., Arnold, S. E., Karlawish, J. H., Wolk, D., Smith, C. D., Jicha, G., Hardy, P., Lopez, O. L., Oakley, M., Simpson, D. M., Porsteinsson, A. P., Goldstein, B. S., Martin, K., Makino, K. M., Ismail, M. S., Brand, C., Mulnard, R. A., Thai, G., Mc-Adams-Ortiz, C., Womack, K., Mathews, D., Quiceno, M., Diaz-Arrastia, R., King, R., Martin-Cook, K., DeVous, M., Levey, A. I., Lah, J. J., Cellar, J. S., Burns, J. M., Anderson, H. S., Swerdlow, R. H., Apostolova, L., Lu, P. H., Bartzokis, G., Silverman, D. H. S., Graff-Radford, N. R., Parfitt, F., Johnson, H., Farlow, M. R., Hake, A. M., Matthews, B. R., Herring, S., van Dyck, C. H., Carson, R. E., MacAvoy, M. G., Chertkow, H., Bergman, H., Hosein, C., Black, S., Stefanovic, B., Caldwell, C., Hsiung, G.-Y. R., Feldman, H., Mudge, B., Assaly, M., Kertesz, A., Rogers, J., Trost, D., Bernick, C., Munic, D., Kerwin, D., Mesulam, M.-M., Lipowski, K., Wu, C.-K., Johnson, N., Sadowsky, C., Martinez, W., Villena, T., Turner, R. S., Reynolds, B., Sperling, R. A., Johnson, K. A., Marshall, G., Frey, M., Yesavage, J., Taylor, J. L., Lane, B., Rosen, A., Tinklenberg, J., Sabbagh, M., Belden, C., Jacobson, S., Kowall, N., Killiany, R., Budson, A. E., Norbash, A., Johnson, P. L., Obisesan, T. O., Wolday, S., Bwayo, S. K., Lerner, A., Hudson, L., Ogrocki, P., Fletcher, E., Carmichael, O., Olichney, J., Kittur, S., Borrie, M., Lee, T.- Y., Bartha, R., Johnson, S., Asthana, S., Carlsson, C. M., Potkin, S. G., Preda, A., Nguyen, D., Tariot, P., Fleisher, A., Reeder, S., Bates, V., Capote, H., Rainka, M., Scharre, D. W., Kataki, M., Zimmerman, E. A., Celmins, D., Brown, A. D., Pearlson, G. D., Blank, K., Anderson, K., Santulli, R. B., Schwartz, E. S., Sink, K. M., Williamson, J. D., Garg, P., Watkins, F., Ott, B. R., Querfurth, H., Tremont, G., Salloway, S., Malloy, P., Correia, S., Rosen, H. J., Miller, B. L., Mintzer, J., Longmire, C. F., Spicer, K., Finger, E., Rachinsky, I., and Drost, D.

Published
2013
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42. Association of common genetic variants in GPCPD1 with scaling of visual cortical surface area in humans

Author

Bakken, TE, Roddey, JC, Djurovic, S, Akshoomoff, N, Amaral, DG, Bloss, CS, Casey, BJ, Chang, L, Ernst, TM, Gruen, JR, Jernigan, TL, Kaufmann, WE, Kenet, T, Kennedy, DN, Kuperman, JM, Murray, SS, Sowell, ER, Rimol, LM, Mattingsdal, M, Melle, I, Agartz, I, Andreassen, OA, Schork, NJ, Dale, AM, Alzheimer’s Disease Neuroimaging Initiative, Pediatric Imaging, Neurocognition, Genetics Study, Weiner, M, Aisen, P, Petersen, R, Jack, CR, Jr, Jagust, W, Trojanowki, JQ, Toga, AW, Beckett, L, Green, RC, Saykin, AJ, Morris, J, Liu, E, Montine, T, Gamst, A, Thomas, RG, Donohue, M, Walter, S, Gessert, D, Sather, T, Harvey, D, Kornak, J, Dale, A, Bernstein, M, Felmlee, J, Fox, N, Thompson, P, Schuff, N, Alexander, G, DeCarli, C, Bandy, D, Koeppe, RA, Foster, N, Reiman, EM, Chen, K, Mathis, C, Cairns, NJ, Taylor-Reinwald, L, Shaw, L, Lee, VM, Korecka, M, Crawford, K, Neu, S, Foroud, TM, Potkin, S, Shen, L, Kachaturian, Z, Frank, R, Snyder, PJ, Molchan, S, Kaye, J, Quinn, J, Lind, B, Dolen, S, Schneider, LS, Pawluczyk, S, Spann, BM, Brewer, J, Vanderswag, H, Heidebrink, JL, Lord, JL, Johnson, K, Doody, RS, Villanueva-Meyer, J, Chowdhury, M, Stern, Yaakov, Honig, LS, Bell, KL, Morris, JC, Ances, B, Carroll, M, Leon, S, Mintun, MA, Schneider, S, Marson, D, Griffith, R, Clark, D, Grossman, H, Mitsis, E, Romirowsky, A, deToledo-Morrell, L, Shah, RC, Duara, R, Varon, D, Roberts, P, Albert, M, Onyike, C, Kielb, S, Rusinek, H, de, Leon, MJ, Glodzik, L, De, Santi, S, Doraiswamy, PM, Petrella, JR, Coleman, RE, Arnold, SE, Karlawish, JH, Wolk, D, Smith, CD, Jicha, G, Hardy, P, Lopez, OL, Oakley, M, Simpson, DM, Porsteinsson, AP, Goldstein, BS, Martin, K, Makino, KM, Ismail, MS, Brand, C, Mulnard, RA, Thai, G, Mc-Adams-Ortiz, C, Womack, K, Mathews, D, Quiceno, M, Diaz-Arrastia, R, King, R, Martin-Cook, K, DeVous, M, Levey, AI, Lah, JJ, Cellar, JS, Burns, JM, Anderson, HS, Swerdlow, RH, Apostolova, L, Lu, PH, Bartzokis, G, Silverman, DH, Graff-Radford, NR, Parfitt, F, Johnson, H, Farlow, MR, Hake, AM, Matthews, BR, Herring, S, van, Dyck, CH, Carson, RE, MacAvoy, MG, Chertkow, H, Bergman, H, Hosein, C, Black, S, Stefanovic, B, Caldwell, C, Ging-Yuek, Hsiung, R, Feldman, H, Mudge, B, Assaly, M, Kertesz, A, Rogers, J, Trost, D, Bernick, C, Munic, D, Kerwin, D, Mesulam, MM, Lipowski, K, Wu, CK, Johnson, N, Sadowsky, C, Martinez, W, Villena, T, Turner, RS, Reynolds, B, Sperling, RA, Johnson, KA, Marshall, G, Frey, M, Yesavage, J, Taylor, JL, Lane, B, Rosen, A, Tinklenberg, J, Sabbagh, M, Belden, C, Jacobson, S, Kowall, N, Killiany, R, Budson, AE, Norbash, A, Johnson, PL, Obisesan, TO, Wolday, S, Bwayo, SK, Lerner, A, Hudson, L, Ogrocki, P, Fletcher, E, Carmichael, O, Olichney, J, Kittur, S, Borrie, M, Lee, TY, Bartha, R, Johnson, S, Asthana, S, Carlsson, CM, Potkin, SG, Preda, A, Nguyen, D, Tariot, P, Fleisher, A, Reeder, S, Bates, V, Capote, H, Rainka, M, Scharre, DW, Kataki, M, Zimmerman, EA, Celmins, D, Brown, AD, Pearlson, GD, Blank, K, Anderson, K, Santulli, RB, Schwartz, ES, Sink, KM, Williamson, JD, Garg, P, Watkins, F, Ott, BR, Querfurth, H, Tremont, G, Salloway, S, Malloy, P, Correia, S, Rosen, HJ, Miller, BL, Mintzer, J, Longmire, CF, Spicer, K, Finger, E, Rachinsky, I, Drost, D, Jernigan, T, McCabe, C, Grant, E, Ernst, T, Kuperman, J, Chung, Y, Murray, S, Bloss, C, Darst, B, Pritchett, L, Saito, A, Amaral, D, DiNino, M, Eyngorina, B, Sowell, E, Houston, S, Soderberg, L, Kaufmann, W, van, Zijl, P, Rizzo-Busack, H, Javid, M, Mehta, N, Ruberry, E, Powers, A, Rosen, B, Gebhard, N, Manigan, H, Frazier, J, Kennedy, D, Yakutis, L, Hill, M, Gruen, J, Bosson-Heenan, J, and Carlson, H

Subjects
anatomy & histology, pathology [Visual Cortex], Adult, Diagnostic Imaging, Male, Linkage disequilibrium, Visual perception, genetic structures, Adolescent, Genotype, Imaging genetics, methods [Diagnostic Imaging], Single-nucleotide polymorphism, Genome-wide association study, Saccharomyces cerevisiae, Biology, Polymorphism, Single Nucleotide, Cohort Studies, methods [Brain Mapping], pathology [Brain], Cortex (anatomy), Genetic variation, Medicine and Health Sciences, medicine, Humans, genetics [Phosphoric Diester Hydrolases], Aged, Visual Cortex, Genetics, Brain Mapping, Multidisciplinary, Models, Genetic, Phosphoric Diester Hydrolases, metabolism [Saccharomyces cerevisiae], Brain, Genetic Variation, Genomics, Middle Aged, Biological Sciences, Visual cortex, medicine.anatomical_structure, Female, Genome-Wide Association Study
Abstract

Visual cortical surface area varies two- to threefold between human individuals, is highly heritable, and has been correlated with visual acuity and visual perception. However, it is still largely unknown what specific genetic and environmental factors contribute to normal variation in the area of visual cortex. To identify SNPs associated with the proportional surface area of visual cortex, we performed a genome-wide association study followed by replication in two independent cohorts. We identified one SNP (rs6116869) that replicated in both cohorts and had genome-wide significant association ( P combined = 3.2 × 10 −8 ). Furthermore, a metaanalysis of imputed SNPs in this genomic region identified a more significantly associated SNP (rs238295; P = 6.5 × 10 −9 ) that was in strong linkage disequilibrium with rs6116869. These SNPs are located within 4 kb of the 5′ UTR of GPCPD1 , glycerophosphocholine phosphodiesterase GDE1 homolog ( Saccharomyces cerevisiae ), which in humans, is more highly expressed in occipital cortex compared with the remainder of cortex than 99.9% of genes genome-wide. Based on these findings, we conclude that this common genetic variation contributes to the proportional area of human visual cortex. We suggest that identifying genes that contribute to normal cortical architecture provides a first step to understanding genetic mechanisms that underlie visual perception.

Published
2012

43. Detection of third and sixth cranial nerve palsies with a novel method for eye tracking while watching a short film clip

Author

Samadani, U, primary, Farooq, S, additional, Ritlop, R, additional, Warren, F, additional, Reyes, M, additional, Lamm, E, additional, Alex, A, additional, Nehrbass, E, additional, Kolecki, R, additional, Jureller, M, additional, Schneider, J, additional, Chen, A, additional, Shi, C, additional, Mendhiratta, N, additional, Huang, JH, additional, Qian, M, additional, Kwak, R, additional, Mikheev, A, additional, Rusinek, H, additional, George, A, additional, Fergus, R, additional, Kondziolka, D, additional, Huang, PP, additional, and Smith, RT, additional

Published
2015
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44. Apolipoprotein E (APOE) genotype has dissociable effects on memory and attentional-executive network function in Alzheimer's disease

Author

Wolk, D. A., Dickerson, B. C., the Alzheimer's Disease Neuroimaging Initiative, Weiner, M., Aiello, M., Aisen, P., Albert, M. S., Alexander, G., Anderson, H. S., Anderson, K., Apostolova, L., Arnold, S., Ashford, W., Assaly, M., Asthana, S., Bandy, D., Bartha, R., Bates, V., Beckett, L., Bell, K. L., Benincasa, A. L., Bergman, H., Bernick, C., Bernstein, M., Black, S., Blank, K., Borrie, M., Brand, C., Brewer, J., Brown, A. D., Burns, J. M., Cairns, N. J., Caldwell, C., Capote, H., Carlsson, C. M., Carmichael, O., Cellar, J. S., Celmins, D., Chen, K., Chertkow, H., Chowdhury, M., Clark, D., Connor, D., Correia, S., Crawford, K., Dale, A., de Leon, M. J., De Santi, S. M., DeCarli, C., deToledo-Morrell, L., DeVous, M., Diaz-Arrastia, R., Dolen, S., Donohue, M., Doody, R. S., Doraiswamy, P. M., Duara, R., Englert, J., Farlow, M., Feldman, H., Felmlee, J., Fleisher, A., Fletcher, E., Foroud, T. M., Foster, N., Fox, N., Frank, R., Gamst, A., Given, C. A., Graff-Radford, N. R., Green, R. C., Griffith, R., Grossman, H., Hake, A. M., Hardy, P., Harvey, D., Heidebrink, J. L., Hendin, B. A., Herring, S., Honig, L. S., Hosein, C., Robin Hsiung, G.-Y., Hudson, L., Ismail, M. S., Jack, C. R., Jacobson, S., Jagust, W., Jayam-Trouth, A., Johnson, K., Johnson, H., Johnson, N., Johnson, K. A., Johnson, S., Kachaturian, Z., Karlawish, J. H., Kataki, M., Kaye, J., Kertesz, A., Killiany, R., Kittur, S., Koeppe, R. A., Korecka, M., Kornak, J., Kozauer, N., Lah, J. J., Laubinger, M. M., Lee, V. M.- Y., Lee, T.- Y., Lerner, A., Levey, A. I., Longmire, C. F., Lopez, O. L., Lord, J. L., Lu, P. H., MacAvoy, M. G., Malloy, P., Marson, D., Martin-Cook, K., Martinez, W., Marzloff, G., Mathis, C., Mc-Adams-Ortiz, C., Mesulam, M., Miller, B. L., Mintun, M. A., Mintzer, J., Molchan, S., Montine, T., Morris, J., Mulnard, R. A., Munic, D., Nair, A., Neu, S., Nguyen, D., Norbash, A., Oakley, M., Obisesan, T. O., Ogrocki, P., Ott, B. R., Parfitt, F., Pawluczyk, S., Pearlson, G., Petersen, R., Petrella, J. R., Potkin, S., Potter, W. Z., Preda, A., Quinn, J., Rainka, M., Reeder, S., Reiman, E. M., Rentz, D. M., Reynolds, B., Richard, J., Roberts, P., Rogers, J., Rosen, A., Rosen, H. J., Rusinek, H., Sabbagh, M., Sadowsky, C., Salloway, S., Santulli, R. B., Saykin, A. J., Scharre, D. W., Schneider, L., Schneider, S., Schuff, N., Shah, R. C., Shaw, L., Shen, L., Silverman, D. H. S., Simpson, D. M., Sink, K. M., Smith, C. D., Snyder, P. J., Spann, B. M., Sperling, R. A., Spicer, K., Stefanovic, B., Stern, Yaakov, Stopa, E., Tang, C., Tariot, P., Taylor-Reinwald, L., Thai, G., Thomas, R. G., Thompson, P., Tinklenberg, J., Toga, A. W., Tremont, G., Trojanowki, J. Q., Trost, D., Turner, R. S., van Dyck, C. H., Vanderswag, H., Varon, D., Villanueva-Meyer, J., Villena, T., Walter, S., Wang, P., Watkins, F., Williamson, J. D., Wolk, D., Wu, C.-K., Zerrate, M., and Zimmerman., E. A.

Published
2010
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45. A commonly carried allele of the obesity-related FTO gene is associated with reduced brain volume in the healthy elderly

Author

Ho, A. J., Stein, J. L., Hua, X., Lee, S., Hibar, D. P., Leow, A. D., Dinov, I. D., Toga, A. W., Saykin, A. J., Shen, L., Foroud, T., Pankratz, N., Huentelman, M. J., Craig, D. W., Gerber, J. D., Allen, A. N., Corneveaux, J. J., Stephan, D. A., DeCarli, C. S., DeChairo, B. M., Potkin, S. G., Jack, C. R., Weiner, M. W., Raji, C. A., Lopez, O. L., Becker, J. T., Carmichael, O. T., Thompson, P. M., the Alzheimer's Disease Neuroimaging Initiative, Weiner, M., Thal, L., Petersen, R., Jagust, W., Trojanowki, J., Beckett, L., Green, R. C., Gamst, A., Potter, W. Z., Montine, T., Anders, D., Bernstein, M., Felmlee, J., Fox, N., Thompson, P., Schuff, N., Alexander, G., Bandy, D., Koeppe, R. A., Foster, N., Reiman, E. M., Chen, K., Shaw, L., Lee, V. M.- Y., Korecka, M., Crawford, K., Neu, S., Harvey, D., Kornak, J., Kachaturian, Z., Frank, R., Snyder, P. J., Molchan, S., Kaye, J., Vorobik, R., Quinn, J., Schneider, L., Pawluczyk, S., Spann, B., Fleisher, A. S., Vanderswag, H., Heidebrink, J. L., Lord, J. L., Johnson, K., Doody, R. S., Villanueva-Meyer, J., Chowdhury, M., Stern, Yaakov, Honig, L. S., Bell, K. L., Morris, J. C., Mintun, M. A., Schneider, S., Marson, D., Griffith, R., Badger, B., Grossman, H., Tang, C., Stern, J., deToledo-Morrell, L., Shah, R. C., Bach, J., Duara, R., Isaacson, R., Strauman, S., Albert, M. S., Pedroso, J., Toroney, J., Rusinek, H., de Leon, M. J., De Santi, S. M., Doraiswamy, P. M., Petrella, J. R., Aiello, M., Clark, C. M., Pham, C., Nunez, J., Smith, C. D., Given II, C. A., Hardy, P., DeKosky, S. T., Oakley, M., Simpson, D. M., Ismail, M. S., Porsteinsson, A., McCallum, C., Cramer, S. C., Mulnard, R. A., McAdams-Ortiz, C., Diaz-Arrastia, R., Martin-Cook, K., DeVous, M., Levey, A. I., Lah, J. J., Cellar, J. S., Burns, J. M., Anderson, H. S., Laubinger, M. M., Bartzokis, G., Silverman, D. H. S., Lu, P. H., Fletcher, R., Parfitt, F., Johnson, H., Farlow, M., Herring, S., Hake, A. M., van Dyck, C. H., MacAvoy, M. G., Bifano, L. A., Chertkow, H., Bergman, H., Hosein, C., Black, S., Graham, S., Caldwell, C., Feldman, H., Assaly, M., Hsiung, G.-Y. R., Kertesz, A., Rogers, J., Trost, D., Bernick, C., Gitelman, D., Johnson, N., Mesulam, M., Sadowsky, C., Villena, T., Mesner, S., Aisen, P. S., Johnson, K. B., Behan, K. E., Sperling, R. A., Rentz, D. M., Johnson, K. A., Rosen, A., Tinklenberg, J., Ashford, W., Sabbagh, M., Connor, D., Obradov, S., Killiany, R., Norbash, A., Obisesan, T. O., Jayam-Trouth, A., Wang, P., Auchus, A. P., Huang, J., Friedland, R. P., DeCarli, C., Fletcher, E., Carmichael, O., Kittur, S., Mirje, S., Johnson, S. C., Borrie, M., Lee, T.-Y., Asthana, S., Carlsson, C. M., Highum, D., Preda, A., Nguyen, D., Tariot, P. N., Hendin, B. A., Scharre, D. W., Kataki, M., Beversdorf, D. Q., Zimmerman, E. A., Celmins, D., Brown, A. D., Gandy, S., Marenberg, M. E., Rovner, B. W., Pearlson, G., Blank, K., Anderson, K., Santulli, R. B., Pare, N., Williamson, J. D., Sink, K. M., Potter, H., Ashok Raj, B., Giordano, A., Ott, B. R., Wu, C.-K., Cohen, R., and Wilks, K. L.

Published
2010
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46. Proton MR Spectroscopy and MRI-Volumetry in Mild Traumatic Brain Injury

Author

Cohen, B. A., Matilde Inglese, Rusinek, H., Babb, J. S., Grossman, I., and Gonen, O.

Subjects
Adult, Male, Neurons, Aspartic Acid, Magnetic Resonance Spectroscopy, Age Factors, Brain, Middle Aged, Axons, Brain Injuries, Humans, Female, Glasgow Coma Scale, Atrophy, Protons
Abstract

BACKGROUND AND PURPOSE: More than 85% of brain traumas are classified as “mild”; MR imaging findings are minimal if any and do not correspond to clinical symptoms. Our goal, therefore, was to quantify the global decline of the neuronal marker N-acetylaspartate (NAA), as well as gray (GM) and white matter (WM) atrophy after mild traumatic brain injury (mTBI). MATERIALS AND METHODS: Twenty patients (11 male, 9 female; age range, 19−57 years; median, 35 years) with mTBI (Glasgow Coma Scale score 13−15 with loss of consciousness for at least 30 seconds) and 19 age- and sex-matched control subjects were studied. Seven patients were studied within 9 days of TBI; the other 13 ranged from 1.2 months to 31.5 years (average and median of 4.6 and 1.7 years, respectively) after injury. Whole-brain NAA (WBNAA) concentration was obtained in all subjects with nonlocalizing proton MR spectroscopy. Brain volume and GM and WM fractions were segmented from T1-weighted MR imaging and normalized to the total intracranial volume, suitable for intersubject comparisons. The data were analyzed with least squares regression. RESULTS: Patients with mTBI exhibited, on average, a 12% WBNAA deficit that increased with age, compared with the control subjects (p < .05). Adjusted for age effects, patients also suffered both global atrophy (−1.09%/year; P = .029) and GM atrophy (−0.89%/year; P = .042). Patients with and without visible MR imaging pathology, typically punctate foci of suspected shearing injury, were indistinguishable in both atrophy and WBNAA. CONCLUSION: WBNAA detected neuronal/axonal injury beyond the minimal focal MR-visible lesions in mTBI. Combined with GM atrophy, the findings may provide further, noninvasive insight into the nature and progression of mTBI.

Published
2007

47. Optimum design of yokeless permanent magnets.

Author

Abele, M. G. and Rusinek, H.

Subjects
*PERMANENT magnets, *GEOMETRY
Abstract

Presents a study that discussed the selection of the design parameters and the optimum geometry of yokeless permanent magnets that generate a uniform field within an arbitrary closed cavity. Discussion of the figure of merit; Examples; Conclusion.

Published
1990
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