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1. Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures.

Author

Belov, V, Erwin-Grabner, T, Aghajani, M, Aleman, A, Amod, AR, Basgoze, Z, Benedetti, F, Besteher, B, Bülow, R, Ching, CRK, Connolly, CG, Cullen, K, Davey, CG, Dima, D, Dols, A, Evans, JW, Fu, CHY, Gonul, AS, Gotlib, IH, Grabe, HJ, Groenewold, N, Hamilton, JP, Harrison, BJ, Ho, TC, Mwangi, B, Jaworska, N, Jahanshad, N, Klimes-Dougan, B, Koopowitz, S-M, Lancaster, T, Li, M, Linden, DEJ, MacMaster, FP, Mehler, DMA, Melloni, E, Mueller, BA, Ojha, A, Oudega, ML, Penninx, BWJH, Poletti, S, Pomarol-Clotet, E, Portella, MJ, Pozzi, E, Reneman, L, Sacchet, MD, Sämann, PG, Schrantee, A, Sim, K, Soares, JC, Stein, DJ, Thomopoulos, SI, Uyar-Demir, A, van der Wee, NJA, van der Werff, SJA, Völzke, H, Whittle, S, Wittfeld, K, Wright, MJ, Wu, M-J, Yang, TT, Zarate, C, Veltman, DJ, Schmaal, L, Thompson, PM, Goya-Maldonado, R, ENIGMA Major Depressive Disorder working group, Belov, V, Erwin-Grabner, T, Aghajani, M, Aleman, A, Amod, AR, Basgoze, Z, Benedetti, F, Besteher, B, Bülow, R, Ching, CRK, Connolly, CG, Cullen, K, Davey, CG, Dima, D, Dols, A, Evans, JW, Fu, CHY, Gonul, AS, Gotlib, IH, Grabe, HJ, Groenewold, N, Hamilton, JP, Harrison, BJ, Ho, TC, Mwangi, B, Jaworska, N, Jahanshad, N, Klimes-Dougan, B, Koopowitz, S-M, Lancaster, T, Li, M, Linden, DEJ, MacMaster, FP, Mehler, DMA, Melloni, E, Mueller, BA, Ojha, A, Oudega, ML, Penninx, BWJH, Poletti, S, Pomarol-Clotet, E, Portella, MJ, Pozzi, E, Reneman, L, Sacchet, MD, Sämann, PG, Schrantee, A, Sim, K, Soares, JC, Stein, DJ, Thomopoulos, SI, Uyar-Demir, A, van der Wee, NJA, van der Werff, SJA, Völzke, H, Whittle, S, Wittfeld, K, Wright, MJ, Wu, M-J, Yang, TT, Zarate, C, Veltman, DJ, Schmaal, L, Thompson, PM, Goya-Maldonado, R, and ENIGMA Major Depressive Disorder working group

Abstract

Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects.

Published
2024

2. Volume of subcortical brain regions in social anxiety disorder: mega-analytic results from 37 samples in the ENIGMA-Anxiety Working Group.

Author

Groenewold, NA, Bas-Hoogendam, JM, Amod, AR, Laansma, MA, Van Velzen, LS, Aghajani, M, Hilbert, K, Oh, H, Salas, R, Jackowski, AP, Pan, PM, Salum, GA, Blair, JR, Blair, KS, Hirsch, J, Pantazatos, SP, Schneier, FR, Talati, A, Roelofs, K, Volman, I, Blanco-Hinojo, L, Cardoner, N, Pujol, J, Beesdo-Baum, K, Ching, CRK, Thomopoulos, SI, Jansen, A, Kircher, T, Krug, A, Nenadić, I, Stein, F, Dannlowski, U, Grotegerd, D, Lemke, H, Meinert, S, Winter, A, Erb, M, Kreifelts, B, Gong, Q, Lui, S, Zhu, F, Mwangi, B, Soares, JC, Wu, M-J, Bayram, A, Canli, M, Tükel, R, Westenberg, PM, Heeren, A, Cremers, HR, Hofmann, D, Straube, T, Doruyter, AGG, Lochner, C, Peterburs, J, Van Tol, M-J, Gur, RE, Kaczkurkin, AN, Larsen, B, Satterthwaite, TD, Filippi, CA, Gold, AL, Harrewijn, A, Zugman, A, Bülow, R, Grabe, HJ, Völzke, H, Wittfeld, K, Böhnlein, J, Dohm, K, Kugel, H, Schrammen, E, Zwanzger, P, Leehr, EJ, Sindermann, L, Ball, TM, Fonzo, GA, Paulus, MP, Simmons, A, Stein, MB, Klumpp, H, Phan, KL, Furmark, T, Månsson, KNT, Manzouri, A, Avery, SN, Blackford, JU, Clauss, JA, Feola, B, Harper, JC, Sylvester, CM, Lueken, U, Veltman, DJ, Winkler, AM, Jahanshad, N, Pine, DS, Thompson, PM, Stein, DJ, Van der Wee, NJA, Groenewold, NA, Bas-Hoogendam, JM, Amod, AR, Laansma, MA, Van Velzen, LS, Aghajani, M, Hilbert, K, Oh, H, Salas, R, Jackowski, AP, Pan, PM, Salum, GA, Blair, JR, Blair, KS, Hirsch, J, Pantazatos, SP, Schneier, FR, Talati, A, Roelofs, K, Volman, I, Blanco-Hinojo, L, Cardoner, N, Pujol, J, Beesdo-Baum, K, Ching, CRK, Thomopoulos, SI, Jansen, A, Kircher, T, Krug, A, Nenadić, I, Stein, F, Dannlowski, U, Grotegerd, D, Lemke, H, Meinert, S, Winter, A, Erb, M, Kreifelts, B, Gong, Q, Lui, S, Zhu, F, Mwangi, B, Soares, JC, Wu, M-J, Bayram, A, Canli, M, Tükel, R, Westenberg, PM, Heeren, A, Cremers, HR, Hofmann, D, Straube, T, Doruyter, AGG, Lochner, C, Peterburs, J, Van Tol, M-J, Gur, RE, Kaczkurkin, AN, Larsen, B, Satterthwaite, TD, Filippi, CA, Gold, AL, Harrewijn, A, Zugman, A, Bülow, R, Grabe, HJ, Völzke, H, Wittfeld, K, Böhnlein, J, Dohm, K, Kugel, H, Schrammen, E, Zwanzger, P, Leehr, EJ, Sindermann, L, Ball, TM, Fonzo, GA, Paulus, MP, Simmons, A, Stein, MB, Klumpp, H, Phan, KL, Furmark, T, Månsson, KNT, Manzouri, A, Avery, SN, Blackford, JU, Clauss, JA, Feola, B, Harper, JC, Sylvester, CM, Lueken, U, Veltman, DJ, Winkler, AM, Jahanshad, N, Pine, DS, Thompson, PM, Stein, DJ, and Van der Wee, NJA

Abstract

There is limited convergence in neuroimaging investigations into volumes of subcortical brain regions in social anxiety disorder (SAD). The inconsistent findings may arise from variations in methodological approaches across studies, including sample selection based on age and clinical characteristics. The ENIGMA-Anxiety Working Group initiated a global mega-analysis to determine whether differences in subcortical volumes can be detected in adults and adolescents with SAD relative to healthy controls. Volumetric data from 37 international samples with 1115 SAD patients and 2775 controls were obtained from ENIGMA-standardized protocols for image segmentation and quality assurance. Linear mixed-effects analyses were adjusted for comparisons across seven subcortical regions in each hemisphere using family-wise error (FWE)-correction. Mixed-effects d effect sizes were calculated. In the full sample, SAD patients showed smaller bilateral putamen volume than controls (left: d = -0.077, pFWE = 0.037; right: d = -0.104, pFWE = 0.001), and a significant interaction between SAD and age was found for the left putamen (r = -0.034, pFWE = 0.045). Smaller bilateral putamen volumes (left: d = -0.141, pFWE < 0.001; right: d = -0.158, pFWE < 0.001) and larger bilateral pallidum volumes (left: d = 0.129, pFWE = 0.006; right: d = 0.099, pFWE = 0.046) were detected in adult SAD patients relative to controls, but no volumetric differences were apparent in adolescent SAD patients relative to controls. Comorbid anxiety disorders and age of SAD onset were additional determinants of SAD-related volumetric differences in subcortical regions. To conclude, subtle volumetric alterations in subcortical regions in SAD were detected. Heterogeneity in age and clinical characteristics may partly explain inconsistencies in previous findings. The association between alterations in subcortical volumes and SAD illness progression deserves further investigation, especially from adolescence into adulthood.

Published
2023

3. A large-scale ENIGMA multisite replication study of brain age in depression

Author

Han, LKM, Dinga, R, Leenings, R, Hahn, T, Cole, JH, Aftanas, LI, Amod, AR, Besteher, B, Colle, R, Corruble, E, Couvy-Duchesne, B, Danilenko, KV, Fuentes-Claramonte, P, Gonul, AS, Gotlib, IH, Goya-Maldonado, R, Groenewold, NA, Hamilton, P, Ichikawa, N, Ipser, JC, Itai, E, Koopowitz, S-M, Li, M, Okada, G, Okamoto, Y, Churikova, OS, Osipov, EA, Penninx, BWJH, Pomarol-Clotet, E, Rodríguez-Cano, E, Sacchet, MD, Shinzato, H, Sim, K, Stein, DJ, Uyar-Demir, A, Veltman, DJ, Schmaal, L, Han, LKM, Dinga, R, Leenings, R, Hahn, T, Cole, JH, Aftanas, LI, Amod, AR, Besteher, B, Colle, R, Corruble, E, Couvy-Duchesne, B, Danilenko, KV, Fuentes-Claramonte, P, Gonul, AS, Gotlib, IH, Goya-Maldonado, R, Groenewold, NA, Hamilton, P, Ichikawa, N, Ipser, JC, Itai, E, Koopowitz, S-M, Li, M, Okada, G, Okamoto, Y, Churikova, OS, Osipov, EA, Penninx, BWJH, Pomarol-Clotet, E, Rodríguez-Cano, E, Sacchet, MD, Shinzato, H, Sim, K, Stein, DJ, Uyar-Demir, A, Veltman, DJ, and Schmaal, L

Abstract

Background: Several studies have evaluated whether depressed persons have older appearing brains than their nondepressed peers. However, the estimated neuroimaging-derived “brain age gap” has varied from study to study, likely driven by differences in training and testing sample (size), age range, and used modality/features. To validate our previously developed ENIGMA brain age model and the identified brain age gap, we aim to replicate the presence and effect size estimate previously found in the largest study in depression to date (N = 2126 controls & N = 2675 cases; +1.08 years [SE 0.22], Cohen’s d = 0.14, 95% CI: 0.08–0.20), in independent cohorts that were not part of the original study. Methods: A previously trained brain age model (www.photon-ai.com/enigma_brainage) based on 77 FreeSurfer brain regions of interest was used to obtain unbiased brain age predictions in 751 controls and 766 persons with depression (18–75 years) from 13 new cohorts collected from 20 different scanners. Meta-regressions were used to examine potential moderating effects of basic cohort characteristics (e.g., clinical and scan technical) on the brain age gap. Results: Our ENIGMA MDD brain age model generalized reasonably well to controls from the new cohorts (predicted age vs. age: r = 0.73, R2 = 0.47, MAE = 7.50 years), although the performance varied from cohort to cohort. In these new cohorts, on average, depressed persons showed a significantly higher brain age gap of +1 year (SE 0.35) (Cohen’s d = 0.15, 95% CI: 0.05–0.25) compared with controls, highly similar to our previous finding. Significant moderating effects of FreeSurfer version 6.0 (d = 0.41, p = 0.007) and Philips scanner vendor (d = 0.50, p < 0.0001) were found, leading to more positive effect size estimates. Conclusions: This study further validates our previously developed ENIGMA brain age algorithm. Importantly, we replicated the brain age gap in depression with a comparable effect size. Thus, two large-scale indep

Published
2022

4. Genetic variants for head size share genes and pathways with cancer

Author

Shihua Li, Melissa J. Green, Nagata M, Miyamoto S, Le Grand Q, Pourcain Bs, Grabe Hj, Catherine Helmer, William R. Reay, Nele Friedrich, Mazoyer B, Seshadri S, Per Hoffmann, Simon Haworth, Veronica Witte A, Yang Q, Max Lam, Teunissen Mw, Fang-Chi Hsu, Debette S, Murray J. Cairns, Amod Ar, Setoh K, Windham Bg, Shareefa Dalvie, Georg Homuth, Charlie S. DeCarli, Gennady V. Roshchupkin, Céline S. Reinbold, Mühleisen Tw, Nakahara S, Van der Auwera S, Xueqiu Jian, Cecil Ca, Jean-François Dartigues, Alexa S. Beiser, Stein Jl, Houlden H, Christiane Jockwitz, Moebus S, Claudia L. Satizabal, Tavia E. Evans, Matsuda F, Cora E. Lewis, Wittfeld K, Calhoun Vd, van der Lugt A, Teumer A, Pausova Z, W. T. Longstreth, Aniket Mishra, Tomáš Paus, Vernooij Mw, Medland Se, Palmer Nd, Ahmad R. Hariri, Adrian Preda, Clifford R. Jack, van Erp Tg, Amaia Carrion-Castillo, Werring Dj, Gwénaëlle Catheline, Asta Håberg, Robin Bülow, Sven Cichon, Yann Quidé, Muetzel R, Tabara Y, Sidney S, Cheung C, Brunner Hg, Annchen R. Knodt, Mosley Th, van Dam-Nolen Dh, Hostettler Ic, Jean Shin, Shapland Cy, Reut Avinun, Crivello F, Mäkitie O, Fornage M, Yoshida K, Villringer A, Lim K, Schreiner Pj, Bowden Dw, Thompson Pm, Daniel Bos, Uhlmann A, Enlund-Cerullo M, Schmidt R, Hieab H.H. Adams, Neda Jahanshad, Frauke Beyer, Gloria H.Y. Li, Fabio Macciardi, Lamballais S, Kämpe A, Judith M. Ford, Poot Ra, J. C. Bis, Yoichiro Kamatani, Ami Tsuchida, Markus Scholz, Ikram Ma, Andersson S, Lenore J. Launer, Stein Dj, Koudstaal Pj, Barry I. Freedman, Barbara Franke, Ikram Mk, Sim K, Marie-Gabrielle Duperron, Bryan Rn, Svenja Caspers, Pawlak Ma, Tonya White, Maria J. Knol, Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium, and Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Consortium

Subjects
Genetics, 0303 health sciences, education.field_of_study, biology, Population, Wnt signaling pathway, Macrocephaly, Cancer, medicine.disease, Biological pathway, 03 medical and health sciences, 0302 clinical medicine, medicine, biology.protein, PTEN, Human height, medicine.symptom, education, Gene, 030217 neurology & neurosurgery, 030304 developmental biology
Abstract

The size of the human head is determined by growth in the first years of life, while the rest of the body typically grows until early adulthood1. Such complex developmental processes are regulated by various genes and growth pathways2. Rare genetic syndromes have revealed genes that affect head size3, but the genetic drivers of variation in head size within the general population remain largely unknown. To elucidate biological pathways underlying the growth of the human head, we performed the largest genome-wide association study on human head size to date (N = 79,107). We identified 67 genetic loci, 50 of which are novel, and found that these loci are preferentially associated with head size and mostly independent from height. In subsequent neuroimaging analyses, the majority of genetic variants demonstrated widespread effects on the brain, whereas the effects of 17 variants could be localized to one or two specific brain regions. Through hypothesis-free approaches, we find a strong overlap of head size variants with both cancer pathways and cancer genes. Gene set analyses showed enrichment for different types of cancer and the p53, Wnt and ErbB signalling pathway. Genes overlapping or close to lead variants – such as TP53, PTEN and APC – were enriched for genes involved in macrocephaly syndromes (up to 37-fold) and high-fidelity cancer genes (up to 9-fold), whereas this enrichment was not seen for human height variants. This indicates that genes regulating early brain and cranial growth are associated with a propensity to neoplasia later in life, irrespective of height. Our results warrant further investigations of the link between head size and cancer, as well as its clinical implications in the general population.

Published
2020
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5. Genetic variants for head size share genes and pathways with cancer.

Author

Knol MJ, Poot RA, Evans TE, Satizabal CL, Mishra A, Sargurupremraj M, van der Auwera S, Duperron MG, Jian X, Hostettler IC, van Dam-Nolen DHK, Lamballais S, Pawlak MA, Lewis CE, Carrion-Castillo A, van Erp TGM, Reinbold CS, Shin J, Scholz M, Håberg AK, Kämpe A, Li GHY, Avinun R, Atkins JR, Hsu FC, Amod AR, Lam M, Tsuchida A, Teunissen MWA, Aygün N, Patel Y, Liang D, Beiser AS, Beyer F, Bis JC, Bos D, Bryan RN, Bülow R, Caspers S, Catheline G, Cecil CAM, Dalvie S, Dartigues JF, DeCarli C, Enlund-Cerullo M, Ford JM, Franke B, Freedman BI, Friedrich N, Green MJ, Haworth S, Helmer C, Hoffmann P, Homuth G, Ikram MK, Jack CR Jr, Jahanshad N, Jockwitz C, Kamatani Y, Knodt AR, Li S, Lim K, Longstreth WT, Macciardi F, Mäkitie O, Mazoyer B, Medland SE, Miyamoto S, Moebus S, Mosley TH, Muetzel R, Mühleisen TW, Nagata M, Nakahara S, Palmer ND, Pausova Z, Preda A, Quidé Y, Reay WR, Roshchupkin GV, Schmidt R, Schreiner PJ, Setoh K, Shapland CY, Sidney S, St Pourcain B, Stein JL, Tabara Y, Teumer A, Uhlmann A, van der Lugt A, Vernooij MW, Werring DJ, Windham BG, Witte AV, Wittfeld K, Yang Q, Yoshida K, Brunner HG, Le Grand Q, Sim K, Stein DJ, Bowden DW, Cairns MJ, Hariri AR, Cheung CL, Andersson S, Villringer A, Paus T, Cichon S, Calhoun VD, Crivello F, Launer LJ, White T, Koudstaal PJ, Houlden H, Fornage M, Matsuda F, Grabe HJ, Ikram MA, Debette S, Thompson PM, Seshadri S, and Adams HHH

Subjects
Humans, Female, Male, Polymorphism, Single Nucleotide genetics, Genetic Variation, Organ Size genetics, Signal Transduction genetics, Adult, Genetic Predisposition to Disease, Genome-Wide Association Study, Head anatomy & histology, Neoplasms genetics, Neoplasms pathology
Abstract

The size of the human head is highly heritable, but genetic drivers of its variation within the general population remain unmapped. We perform a genome-wide association study on head size (N = 80,890) and identify 67 genetic loci, of which 50 are novel. Neuroimaging studies show that 17 variants affect specific brain areas, but most have widespread effects. Gene set enrichment is observed for various cancers and the p53, Wnt, and ErbB signaling pathways. Genes harboring lead variants are enriched for macrocephaly syndrome genes (37-fold) and high-fidelity cancer genes (9-fold), which is not seen for human height variants. Head size variants are also near genes preferentially expressed in intermediate progenitor cells, neural cells linked to evolutionary brain expansion. Our results indicate that genes regulating early brain and cranial growth incline to neoplasia later in life, irrespective of height. This warrants investigation of clinical implications of the link between head size and cancer., Competing Interests: Declaration of interests H.H. and I.C.H. received funding from Alzheimer’s Research UK and the Dunhill Medical Trust Foundation. M.A.P. reported receiving grants and personal and travel fees from Roche, Novartis, Merck, and Biogen outside the submitted work. M. Scholz receives funding from Pfizer Inc. for a project not related to this research. C.D. serves as a consultant of Novartis Pharmaceuticals. B.F. has received educational speaking fees from Medice. N.J. and P.M.T. are MPIs of a research grant from Biogen Inc. for work unrelated to the contents of this manuscript. D.J.W. received funding from the Stroke Foundation/British Heart Foundation. D.J.S. has received consultancy honoraria from Discovery Vitality, Johnson & Johnson, Kanna, L’Oreal, Lundbeck, Orion, Sanofi, Servier, Takeda, and Vistagen. H.H. received funding from MRC, Wellcome Trust, and NIHR UCLH BRC. H.J.G. has received travel grants and speaker’s honoraria from Fresenius Medical Care, Neuraxpharm, and Janssen Cilag as well as research funding from Fresenius Medical Care., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Published
2024
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6. Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures.

Author

Belov V, Erwin-Grabner T, Aghajani M, Aleman A, Amod AR, Basgoze Z, Benedetti F, Besteher B, Bülow R, Ching CRK, Connolly CG, Cullen K, Davey CG, Dima D, Dols A, Evans JW, Fu CHY, Gonul AS, Gotlib IH, Grabe HJ, Groenewold N, Hamilton JP, Harrison BJ, Ho TC, Mwangi B, Jaworska N, Jahanshad N, Klimes-Dougan B, Koopowitz SM, Lancaster T, Li M, Linden DEJ, MacMaster FP, Mehler DMA, Melloni E, Mueller BA, Ojha A, Oudega ML, Penninx BWJH, Poletti S, Pomarol-Clotet E, Portella MJ, Pozzi E, Reneman L, Sacchet MD, Sämann PG, Schrantee A, Sim K, Soares JC, Stein DJ, Thomopoulos SI, Uyar-Demir A, van der Wee NJA, van der Werff SJA, Völzke H, Whittle S, Wittfeld K, Wright MJ, Wu MJ, Yang TT, Zarate C, Veltman DJ, Schmaal L, Thompson PM, and Goya-Maldonado R

Subjects
Humans, Benchmarking, Brain diagnostic imaging, Neuroimaging methods, Machine Learning, Magnetic Resonance Imaging methods, Depressive Disorder, Major diagnostic imaging, Depressive Disorder, Major psychology
Abstract

Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects., (© 2024. The Author(s).)

Published
2024
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7. Volume of subcortical brain regions in social anxiety disorder: mega-analytic results from 37 samples in the ENIGMA-Anxiety Working Group.

Author

Groenewold NA, Bas-Hoogendam JM, Amod AR, Laansma MA, Van Velzen LS, Aghajani M, Hilbert K, Oh H, Salas R, Jackowski AP, Pan PM, Salum GA, Blair JR, Blair KS, Hirsch J, Pantazatos SP, Schneier FR, Talati A, Roelofs K, Volman I, Blanco-Hinojo L, Cardoner N, Pujol J, Beesdo-Baum K, Ching CRK, Thomopoulos SI, Jansen A, Kircher T, Krug A, Nenadić I, Stein F, Dannlowski U, Grotegerd D, Lemke H, Meinert S, Winter A, Erb M, Kreifelts B, Gong Q, Lui S, Zhu F, Mwangi B, Soares JC, Wu MJ, Bayram A, Canli M, Tükel R, Westenberg PM, Heeren A, Cremers HR, Hofmann D, Straube T, Doruyter AGG, Lochner C, Peterburs J, Van Tol MJ, Gur RE, Kaczkurkin AN, Larsen B, Satterthwaite TD, Filippi CA, Gold AL, Harrewijn A, Zugman A, Bülow R, Grabe HJ, Völzke H, Wittfeld K, Böhnlein J, Dohm K, Kugel H, Schrammen E, Zwanzger P, Leehr EJ, Sindermann L, Ball TM, Fonzo GA, Paulus MP, Simmons A, Stein MB, Klumpp H, Phan KL, Furmark T, Månsson KNT, Manzouri A, Avery SN, Blackford JU, Clauss JA, Feola B, Harper JC, Sylvester CM, Lueken U, Veltman DJ, Winkler AM, Jahanshad N, Pine DS, Thompson PM, Stein DJ, and Van der Wee NJA

Subjects
Adult, Adolescent, Humans, Magnetic Resonance Imaging methods, Brain, Anxiety, Neuroimaging methods, Phobia, Social
Abstract

There is limited convergence in neuroimaging investigations into volumes of subcortical brain regions in social anxiety disorder (SAD). The inconsistent findings may arise from variations in methodological approaches across studies, including sample selection based on age and clinical characteristics. The ENIGMA-Anxiety Working Group initiated a global mega-analysis to determine whether differences in subcortical volumes can be detected in adults and adolescents with SAD relative to healthy controls. Volumetric data from 37 international samples with 1115 SAD patients and 2775 controls were obtained from ENIGMA-standardized protocols for image segmentation and quality assurance. Linear mixed-effects analyses were adjusted for comparisons across seven subcortical regions in each hemisphere using family-wise error (FWE)-correction. Mixed-effects d effect sizes were calculated. In the full sample, SAD patients showed smaller bilateral putamen volume than controls (left: d = -0.077, p FWE  = 0.037; right: d = -0.104, p FWE  = 0.001), and a significant interaction between SAD and age was found for the left putamen (r = -0.034, p FWE  = 0.045). Smaller bilateral putamen volumes (left: d = -0.141, p FWE  < 0.001; right: d = -0.158, p FWE  < 0.001) and larger bilateral pallidum volumes (left: d = 0.129, p FWE  = 0.006; right: d = 0.099, p FWE  = 0.046) were detected in adult SAD patients relative to controls, but no volumetric differences were apparent in adolescent SAD patients relative to controls. Comorbid anxiety disorders and age of SAD onset were additional determinants of SAD-related volumetric differences in subcortical regions. To conclude, subtle volumetric alterations in subcortical regions in SAD were detected. Heterogeneity in age and clinical characteristics may partly explain inconsistencies in previous findings. The association between alterations in subcortical volumes and SAD illness progression deserves further investigation, especially from adolescence into adulthood., (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)

Published
2023
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8. The association of peripheral immune markers with brain cortical thickness and surface area in South African people living with HIV.

Author

Williams ME, Joska JA, Amod AR, Paul RH, Stein DJ, Ipser JC, and Naudé PJW

Subjects
Adult, Biomarkers blood, CD4-Positive T-Lymphocytes immunology, CD4-Positive T-Lymphocytes virology, Case-Control Studies, Chemokine CCL2 genetics, Chemokine CCL2 immunology, Cognitive Dysfunction diagnostic imaging, Cognitive Dysfunction genetics, Cognitive Dysfunction psychology, Female, Gene Expression, HIV Infections diagnostic imaging, HIV Infections genetics, HIV Infections psychology, HIV-1 immunology, Humans, Lipocalin-2 immunology, Magnetic Resonance Imaging, Male, Matrix Metalloproteinase 9 genetics, Matrix Metalloproteinase 9 immunology, Middle Aged, Neuroimaging, Neuropsychological Tests, Prefrontal Cortex diagnostic imaging, Prefrontal Cortex virology, South Africa, Thymidine Phosphorylase genetics, Thymidine Phosphorylase immunology, Transforming Growth Factor beta1 genetics, Transforming Growth Factor beta1 immunology, Vascular Endothelial Growth Factor A genetics, Vascular Endothelial Growth Factor A immunology, Cognition, Cognitive Dysfunction immunology, HIV Infections immunology, HIV-1 pathogenicity, Lipocalin-2 genetics, Prefrontal Cortex immunology
Abstract

A spectrum of cognitive impairments known as HIV-associated neurocognitive disorders (HAND) are consequences of the effects of HIV-1 within the central nervous system. Regardless of treatment status, an aberrant chronic neuro-immune regulation is a crucial contributor to the development of HAND. However, the extent to which inflammation affects brain structures critical for cognitive status remains unclear. The present study aimed to determine associations of peripheral immune markers with cortical thickness and surface area. Participants included 65 treatment-naïve HIV-positive individuals and 26 HIV-negative controls. Thickness and surface area of all cortical regions were derived using automated parcellation of T1-weighted images acquired at 3 T. Peripheral immune markers included C-C motif ligand 2 (CCL2), matrix metalloproteinase 9 (MMP9), neutrophil gelatinase-associated lipocalin (NGAL), thymidine phosphorylase (TYMP), transforming growth factor (TGF)-β1, and vascular endothelial growth factor (VEGF), which were measured using enzyme-linked immunosorbent assays. Associations of these markers with thickness and surface area of cortical regions were evaluated. A mediation analysis examined whether associations of inflammatory markers with cognitive functioning were mediated by brain cortical thickness and surface area. After controlling for multiple comparisons, higher NGAL was associated with reduced thickness of the bilateral orbitofrontal cortex in HIV-positive participants. The association of NGAL with worse motor function was mediated by cortical thickness of the bilateral orbitofrontal region. Taken together, this study suggests that NGAL plays a potential role in the neuropathophysiology of neurocognitive impairments of HIV.

Published
2020
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