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1. Brain charts for the human lifespan

Author: Bethlehem, RAI, Seidlitz, J, White, SR, Vogel, JW, Anderson, KM, Adamson, C, Adler, S, Alexopoulos, GS, Anagnostou, E, Areces-Gonzalez, A, Astle, DE, Auyeung, B, Ayub, M, Bae, J, Ball, G, Baron-Cohen, S, Beare, R, Bedford, SA, Benegal, V, Beyer, F, Blangero, J, Blesa Cábez, M, Boardman, JP, Borzage, M, Bosch-Bayard, JF, Bourke, N, Calhoun, VD, Chakravarty, MM, Chen, C, Chertavian, C, Chetelat, G, Chong, YS, Cole, JH, Corvin, A, Costantino, M, Courchesne, E, Crivello, F, Cropley, VL, Crosbie, J, Crossley, N, Delarue, M, Delorme, R, Desrivieres, S, Devenyi, GA, Di Biase, MA, Dolan, R, Donald, KA, Donohoe, G, Dunlop, K, Edwards, AD, Elison, JT, Ellis, CT, Elman, JA, Eyler, L, Fair, DA, Feczko, E, Fletcher, PC, Fonagy, P, Franz, CE, Galan-Garcia, L, Gholipour, A, Giedd, J, Gilmore, JH, Glahn, DC, Goodyer, IM, Grant, PE, Groenewold, NA, Gunning, FM, Gur, RE, Gur, RC, Hammill, CF, Hansson, O, Hedden, T, Heinz, A, Henson, RN, Heuer, K, Hoare, J, Holla, B, Holmes, AJ, Holt, R, Huang, H, Im, K, Ipser, J, Jack, CR, Jackowski, AP, Jia, T, Johnson, KA, Jones, PB, Jones, DT, Kahn, RS, Karlsson, H, Karlsson, L, Kawashima, R, Kelley, EA, Kern, S, Kim, KW, Kitzbichler, MG, Kremen, WS, Lalonde, F, and Landeau, B
Subjects: Biological Psychology, Psychology, Aging, Clinical Research, Neurosciences, Brain Disorders, Biomedical Imaging, Neurological, Mental health, Body Height, Brain, Humans, Longevity, Magnetic Resonance Imaging, Neuroimaging, 3R-BRAIN, AIBL, Alzheimer’s Disease Neuroimaging Initiative, Alzheimer’s Disease Repository Without Borders Investigators, CALM Team, Cam-CAN, CCNP, COBRE, cVEDA, ENIGMA Developmental Brain Age Working Group, Developing Human Connectome Project, FinnBrain, Harvard Aging Brain Study, IMAGEN, KNE96, Mayo Clinic Study of Aging, NSPN, POND, PREVENT-AD Research Group, VETSA, General Science & Technology
Abstract: Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify individual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight1. Here we assemble an interactive open resource to benchmark brain morphology derived from any current or future sample of MRI data ( http://www.brainchart.io/ ). With the goal of basing these reference charts on the largest and most inclusive dataset available, acknowledging limitations due to known biases of MRI studies relative to the diversity of the global population, we aggregated 123,984 MRI scans, across more than 100 primary studies, from 101,457 human participants between 115 days post-conception to 100 years of age. MRI metrics were quantified by centile scores, relative to non-linear trajectories2 of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones3, showed high stability of individuals across longitudinal assessments, and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared with non-centiled MRI phenotypes, and provided a standardized measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In summary, brain charts are an essential step towards robust quantification of individual variation benchmarked to normative trajectories in multiple, commonly used neuroimaging phenotypes.
Published: 2022

2. Widespread white matter microstructural differences in schizophrenia across 4322 individuals: results from the ENIGMA Schizophrenia DTI Working Group

Author: Kelly, S, Jahanshad, N, Zalesky, A, Kochunov, P, Agartz, I, Alloza, C, Andreassen, OA, Arango, C, Banaj, N, Bouix, S, Bousman, CA, Brouwer, RM, Bruggemann, J, Bustillo, J, Cahn, W, Calhoun, V, Cannon, D, Carr, V, Catts, S, Chen, J, Chen, J-X, Chen, X, Chiapponi, C, Cho, Kl K, Ciullo, V, Corvin, AS, Crespo-Facorro, B, Cropley, V, De Rossi, P, Diaz-Caneja, CM, Dickie, EW, Ehrlich, S, Fan, F-M, Faskowitz, J, Fatouros-Bergman, H, Flyckt, L, Ford, JM, Fouche, J-P, Fukunaga, M, Gill, M, Glahn, DC, Gollub, R, Goudzwaard, ED, Guo, H, Gur, RE, Gur, RC, Gurholt, TP, Hashimoto, R, Hatton, SN, Henskens, FA, Hibar, DP, Hickie, IB, Hong, LE, Horacek, J, Howells, FM, Hulshoff Pol, HE, Hyde, CL, Isaev, D, Jablensky, A, Jansen, PR, Janssen, J, Jönsson, EG, Jung, LA, Kahn, RS, Kikinis, Z, Liu, K, Klauser, P, Knöchel, C, Kubicki, M, Lagopoulos, J, Langen, C, Lawrie, S, Lenroot, RK, Lim, KO, Lopez-Jaramillo, C, Lyall, A, Magnotta, V, Mandl, RCW, Mathalon, DH, McCarley, RW, McCarthy-Jones, S, McDonald, C, McEwen, S, McIntosh, A, Melicher, T, Mesholam-Gately, RI, Michie, PT, Mowry, B, Mueller, BA, Newell, DT, O'Donnell, P, Oertel-Knöchel, V, Oestreich, L, Paciga, SA, Pantelis, C, Pasternak, O, Pearlson, G, Pellicano, GR, Pereira, A, and Pineda Zapata, J
Subjects: Neurosciences, Brain Disorders, Mental Health, Serious Mental Illness, Biomedical Imaging, Schizophrenia, Clinical Research, Mental health, Adult, Aged, Aged, 80 and over, Brain, Cohort Studies, Corpus Callosum, Diffusion Magnetic Resonance Imaging, Diffusion Tensor Imaging, Female, Humans, Image Processing, Computer-Assisted, Male, Middle Aged, White Matter, Young Adult, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract: The regional distribution of white matter (WM) abnormalities in schizophrenia remains poorly understood, and reported disease effects on the brain vary widely between studies. In an effort to identify commonalities across studies, we perform what we believe is the first ever large-scale coordinated study of WM microstructural differences in schizophrenia. Our analysis consisted of 2359 healthy controls and 1963 schizophrenia patients from 29 independent international studies; we harmonized the processing and statistical analyses of diffusion tensor imaging (DTI) data across sites and meta-analyzed effects across studies. Significant reductions in fractional anisotropy (FA) in schizophrenia patients were widespread, and detected in 20 of 25 regions of interest within a WM skeleton representing all major WM fasciculi. Effect sizes varied by region, peaking at (d=0.42) for the entire WM skeleton, driven more by peripheral areas as opposed to the core WM where regions of interest were defined. The anterior corona radiata (d=0.40) and corpus callosum (d=0.39), specifically its body (d=0.39) and genu (d=0.37), showed greatest effects. Significant decreases, to lesser degrees, were observed in almost all regions analyzed. Larger effect sizes were observed for FA than diffusivity measures; significantly higher mean and radial diffusivity was observed for schizophrenia patients compared with controls. No significant effects of age at onset of schizophrenia or medication dosage were detected. As the largest coordinated analysis of WM differences in a psychiatric disorder to date, the present study provides a robust profile of widespread WM abnormalities in schizophrenia patients worldwide. Interactive three-dimensional visualization of the results is available at www.enigma-viewer.org.
Published: 2018

3. Prefrontal cortical thinning links to negative symptoms in schizophrenia via the ENIGMA consortium.

Author: Walton, E, Hibar, DP, van Erp, TGM, Potkin, SG, Roiz-Santiañez, R, Crespo-Facorro, B, Suarez-Pinilla, P, van Haren, NEM, de Zwarte, SMC, Kahn, RS, Cahn, W, Doan, NT, Jørgensen, KN, Gurholt, TP, Agartz, I, Andreassen, OA, Westlye, LT, Melle, I, Berg, AO, Morch-Johnsen, L, Færden, A, Flyckt, L, Fatouros-Bergman, H, Karolinska Schizophrenia Project Consortium (KaSP), Jönsson, EG, Hashimoto, R, Yamamori, H, Fukunaga, M, Jahanshad, N, De Rossi, P, Piras, F, Banaj, N, Spalletta, G, Gur, RE, Gur, RC, Wolf, DH, Satterthwaite, TD, Beard, LM, Sommer, IE, Koops, S, Gruber, O, Richter, A, Krämer, B, Kelly, S, Donohoe, G, McDonald, C, Cannon, DM, Corvin, A, Gill, M, Di Giorgio, A, Bertolino, A, Lawrie, S, Nickson, T, Whalley, HC, Neilson, E, Calhoun, VD, Thompson, PM, Turner, JA, and Ehrlich, S
Subjects: Karolinska Schizophrenia Project Consortium, Prefrontal Cortex, Humans, Magnetic Resonance Imaging, Linear Models, Schizophrenia, Psychiatric Status Rating Scales, Schizophrenic Psychology, Internationality, Image Processing, Computer-Assisted, Adult, Female, Male, Functional Laterality, Cortical thickness, ENIGMA, FreeSurfer, MRI, PANSS, SANS, medial orbitofrontal cortex, negative symptoms, schizophrenia, Image Processing, Computer-Assisted, Psychiatry, Psychology, Public Health and Health Services, Neurosciences
Abstract: BackgroundOur understanding of the complex relationship between schizophrenia symptomatology and etiological factors can be improved by studying brain-based correlates of schizophrenia. Research showed that impairments in value processing and executive functioning, which have been associated with prefrontal brain areas [particularly the medial orbitofrontal cortex (MOFC)], are linked to negative symptoms. Here we tested the hypothesis that MOFC thickness is associated with negative symptom severity.MethodsThis study included 1985 individuals with schizophrenia from 17 research groups around the world contributing to the ENIGMA Schizophrenia Working Group. Cortical thickness values were obtained from T1-weighted structural brain scans using FreeSurfer. A meta-analysis across sites was conducted over effect sizes from a model predicting cortical thickness by negative symptom score (harmonized Scale for the Assessment of Negative Symptoms or Positive and Negative Syndrome Scale scores).ResultsMeta-analytical results showed that left, but not right, MOFC thickness was significantly associated with negative symptom severity (β std = -0.075; p = 0.019) after accounting for age, gender, and site. This effect remained significant (p = 0.036) in a model including overall illness severity. Covarying for duration of illness, age of onset, antipsychotic medication or handedness weakened the association of negative symptoms with left MOFC thickness. As part of a secondary analysis including 10 other prefrontal regions further associations in the left lateral orbitofrontal gyrus and pars opercularis emerged.ConclusionsUsing an unusually large cohort and a meta-analytical approach, our findings point towards a link between prefrontal thinning and negative symptom severity in schizophrenia. This finding provides further insight into the relationship between structural brain abnormalities and negative symptoms in schizophrenia.
Published: 2018

4. Positive symptoms associate with cortical thinning in the superior temporal gyrus via the ENIGMA Schizophrenia consortium

Author: Walton, E, Hibar, DP, Erp, TGM, Potkin, SG, Roiz‐Santiañez, R, Crespo‐Facorro, B, Suarez‐Pinilla, P, Van Haren, NEM, Zwarte, SMC, Kahn, RS, Cahn, W, Doan, NT, Jørgensen, KN, Gurholt, TP, Agartz, I, Andreassen, OA, Westlye, LT, Melle, I, Berg, AO, Mørch‐Johnsen, L, Færden, A, Flyckt, L, Fatouros‐Bergman, H, Consortium, Karolinska Schizophrenia Project, Jönsson, EG, Hashimoto, R, Yamamori, H, Fukunaga, M, Preda, A, De Rossi, P, Piras, F, Banaj, N, Ciullo, V, Spalletta, G, Gur, RE, Gur, RC, Wolf, DH, Satterthwaite, TD, Beard, LM, Sommer, IE, Koops, S, Gruber, O, Richter, A, Krämer, B, Kelly, S, Donohoe, G, McDonald, C, Cannon, DM, Corvin, A, Gill, M, Di Giorgio, A, Bertolino, A, Lawrie, S, Nickson, T, Whalley, HC, Neilson, E, Calhoun, VD, Thompson, PM, Turner, JA, and Ehrlich, S
Subjects: Schizophrenia, Mental Health, Brain Disorders, Mental health, Good Health and Well Being, Adult, Brain Mapping, Female, Humans, Magnetic Resonance Imaging, Male, Prospective Studies, Psychiatric Status Rating Scales, Schizophrenic Psychology, Temporal Lobe, schizophrenia, positive symptoms, superior temporal gyrus, cortical thickness, ENIGMA, FreeSurfer, MRI, scale for the assessment of positive symptoms, positive and negative syndrome scale, Karolinska Schizophrenia Project Consortium, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract: ObjectiveBased on the role of the superior temporal gyrus (STG) in auditory processing, language comprehension and self-monitoring, this study aimed to investigate the relationship between STG cortical thickness and positive symptom severity in schizophrenia.MethodThis prospective meta-analysis includes data from 1987 individuals with schizophrenia collected at seventeen centres around the world that contribute to the ENIGMA Schizophrenia Working Group. STG thickness measures were extracted from T1-weighted brain scans using FreeSurfer. The study performed a meta-analysis of effect sizes across sites generated by a model predicting left or right STG thickness with a positive symptom severity score (harmonized SAPS or PANSS-positive scores), while controlling for age, sex and site. Secondary models investigated relationships between antipsychotic medication, duration of illness, overall illness severity, handedness and STG thickness.ResultsPositive symptom severity was negatively related to STG thickness in both hemispheres (left: βstd = -0.052; P = 0.021; right: βstd = -0.073; P = 0.001) when statistically controlling for age, sex and site. This effect remained stable in models including duration of illness, antipsychotic medication or handedness.ConclusionOur findings further underline the important role of the STG in hallmark symptoms in schizophrenia. These findings can assist in advancing insight into symptom-relevant pathophysiological mechanisms in schizophrenia.
Published: 2017

5. Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ): Rationale and Study Design of the Largest Global Prospective Cohort Study of Clinical High Risk for Psychosis

Author: Wannan, CMJ, Nelson, B, Addington, J, Allott, K, Anticevic, A, Arango, C, Baker, JT, Bearden, CE, Billah, T, Bouix, S, Broome, MR, Buccilli, K, Cadenhead, KS, Calkins, ME, Cannon, TD, Cecci, G, Chen, EYH, Cho, KIK, Choi, J, Clark, SR, Coleman, MJ, Conus, P, Corcoran, CM, Cornblatt, BA, Diaz-Caneja, CM, Dwyer, D, Ebdrup, BH, Ellman, LM, Fusar-Poli, P, Galindo, L, Gaspar, PA, Gerber, C, Glenthoj, LB, Glynn, R, Harms, MP, Horton, LE, Kahn, RS, Kambeitz, J, Kambeitz-Ilankovic, L, Kane, JM, Kapur, T, Keshavan, MS, Kim, S-W, Koutsouleris, N, Kubicki, M, Kwon, JS, Langbein, K, Lewandowski, KE, Light, GA, Mamah, D, Marcy, PJ, Mathalon, DH, McGorry, PD, Mittal, VA, Nordentoft, M, Nunez, A, Pasternak, O, Pearlson, GD, Perez, J, Perkins, DO, Powers, AR, Roalf, DR, Sabb, FW, Schiffman, J, Shah, JL, Smesny, S, Spark, J, Stone, WS, Strauss, GP, Tamayo, Z, Torous, J, Upthegrove, R, Vangel, M, Verma, S, Wang, J, Winter-van Rossum, I, Wolf, DH, Wolff, P, Wood, SJ, Yung, AR, Agurto, C, Alvarez-Jimenez, M, Amminger, P, Armando, M, Asgari-Targhi, A, Cahill, J, Carrion, RE, Castro, E, Cetin-Karayumak, S, Chakravarty, MM, Cho, YT, Cotter, D, D'Alfonso, S, Ennis, M, Fadnavis, S, Fonteneau, C, Gao, C, Gupta, T, Gur, RE, Gur, RC, Hamilton, HK, Hoftman, GD, Jacobs, GR, Jarcho, J, Ji, JL, Kohler, CG, Lalousis, PA, Lavoie, S, Lepage, M, Liebenthal, E, Mervis, J, Murty, V, Nicholas, SC, Ning, L, Penzel, N, Poldrack, R, Polosecki, P, Pratt, DN, Rabin, R, Eichi, HR, Rathi, Y, Reichenberg, A, Reinen, J, Rogers, J, Ruiz-Yu, B, Scott, I, Seitz-Holland, J, Srihari, VH, Srivastava, A, Thompson, A, Turetsky, BI, Walsh, BC, Whitford, T, Wigman, JTW, Yao, B, Yuen, HP, Ahmed, U, Byun, AJS, Chung, Y, Do, K, Hendricks, L, Huynh, K, Jeffries, C, Lane, E, Langholm, C, Lin, E, Mantua, V, Santorelli, G, Ruparel, K, Zoupou, E, Adasme, T, Addamo, L, Adery, L, Ali, M, Auther, A, Aversa, S, Baek, S-H, Bates, K, Bathery, A, Bayer, JMM, Beedham, R, Bilgrami, Z, Birch, S, Bonoldi, I, Borders, O, Borgatti, R, Brown, L, Bruna, A, Carrington, H, Castillo-Passi, RI, Chen, J, Cheng, N, Ching, AE, Clifford, C, Colton, B-L, Contreras, P, Corral, S, Damiani, S, Done, M, Estrade, A, Etuka, BA, Formica, M, Furlan, R, Geljic, M, Germano, C, Getachew, R, Goncalves, M, Haidar, A, Hartmann, J, Jo, A, John, O, Kerins, S, Kerr, M, Kesselring, I, Kim, H, Kim, N, Kinney, K, Krcmar, M, Kotler, E, Lafanechere, M, Lee, C, Llerena, J, Markiewicz, C, Matnejl, P, Maturana, A, Mavambu, A, Mayol-Troncoso, R, McDonnell, A, McGowan, A, McLaughlin, D, McIlhenny, R, McQueen, B, Mebrahtu, Y, Mensi, M, Hui, CLM, Suen, YN, Wong, SMY, Morrell, N, Omar, M, Partridge, A, Phassouliotis, C, Pichiecchio, A, Politi, P, Porter, C, Provenzani, U, Prunier, N, Raj, J, Ray, S, Rayner, V, Reyes, M, Reynolds, K, Rush, S, Salinas, C, Shetty, J, Snowball, C, Tod, S, Turra-Farina, G, Valle, D, Veale, S, Whitson, S, Wickham, A, Youn, S, Zamorano, F, Zavaglia, E, Zinberg, J, Woods, SW, Shenton, ME, Wannan, CMJ, Nelson, B, Addington, J, Allott, K, Anticevic, A, Arango, C, Baker, JT, Bearden, CE, Billah, T, Bouix, S, Broome, MR, Buccilli, K, Cadenhead, KS, Calkins, ME, Cannon, TD, Cecci, G, Chen, EYH, Cho, KIK, Choi, J, Clark, SR, Coleman, MJ, Conus, P, Corcoran, CM, Cornblatt, BA, Diaz-Caneja, CM, Dwyer, D, Ebdrup, BH, Ellman, LM, Fusar-Poli, P, Galindo, L, Gaspar, PA, Gerber, C, Glenthoj, LB, Glynn, R, Harms, MP, Horton, LE, Kahn, RS, Kambeitz, J, Kambeitz-Ilankovic, L, Kane, JM, Kapur, T, Keshavan, MS, Kim, S-W, Koutsouleris, N, Kubicki, M, Kwon, JS, Langbein, K, Lewandowski, KE, Light, GA, Mamah, D, Marcy, PJ, Mathalon, DH, McGorry, PD, Mittal, VA, Nordentoft, M, Nunez, A, Pasternak, O, Pearlson, GD, Perez, J, Perkins, DO, Powers, AR, Roalf, DR, Sabb, FW, Schiffman, J, Shah, JL, Smesny, S, Spark, J, Stone, WS, Strauss, GP, Tamayo, Z, Torous, J, Upthegrove, R, Vangel, M, Verma, S, Wang, J, Winter-van Rossum, I, Wolf, DH, Wolff, P, Wood, SJ, Yung, AR, Agurto, C, Alvarez-Jimenez, M, Amminger, P, Armando, M, Asgari-Targhi, A, Cahill, J, Carrion, RE, Castro, E, Cetin-Karayumak, S, Chakravarty, MM, Cho, YT, Cotter, D, D'Alfonso, S, Ennis, M, Fadnavis, S, Fonteneau, C, Gao, C, Gupta, T, Gur, RE, Gur, RC, Hamilton, HK, Hoftman, GD, Jacobs, GR, Jarcho, J, Ji, JL, Kohler, CG, Lalousis, PA, Lavoie, S, Lepage, M, Liebenthal, E, Mervis, J, Murty, V, Nicholas, SC, Ning, L, Penzel, N, Poldrack, R, Polosecki, P, Pratt, DN, Rabin, R, Eichi, HR, Rathi, Y, Reichenberg, A, Reinen, J, Rogers, J, Ruiz-Yu, B, Scott, I, Seitz-Holland, J, Srihari, VH, Srivastava, A, Thompson, A, Turetsky, BI, Walsh, BC, Whitford, T, Wigman, JTW, Yao, B, Yuen, HP, Ahmed, U, Byun, AJS, Chung, Y, Do, K, Hendricks, L, Huynh, K, Jeffries, C, Lane, E, Langholm, C, Lin, E, Mantua, V, Santorelli, G, Ruparel, K, Zoupou, E, Adasme, T, Addamo, L, Adery, L, Ali, M, Auther, A, Aversa, S, Baek, S-H, Bates, K, Bathery, A, Bayer, JMM, Beedham, R, Bilgrami, Z, Birch, S, Bonoldi, I, Borders, O, Borgatti, R, Brown, L, Bruna, A, Carrington, H, Castillo-Passi, RI, Chen, J, Cheng, N, Ching, AE, Clifford, C, Colton, B-L, Contreras, P, Corral, S, Damiani, S, Done, M, Estrade, A, Etuka, BA, Formica, M, Furlan, R, Geljic, M, Germano, C, Getachew, R, Goncalves, M, Haidar, A, Hartmann, J, Jo, A, John, O, Kerins, S, Kerr, M, Kesselring, I, Kim, H, Kim, N, Kinney, K, Krcmar, M, Kotler, E, Lafanechere, M, Lee, C, Llerena, J, Markiewicz, C, Matnejl, P, Maturana, A, Mavambu, A, Mayol-Troncoso, R, McDonnell, A, McGowan, A, McLaughlin, D, McIlhenny, R, McQueen, B, Mebrahtu, Y, Mensi, M, Hui, CLM, Suen, YN, Wong, SMY, Morrell, N, Omar, M, Partridge, A, Phassouliotis, C, Pichiecchio, A, Politi, P, Porter, C, Provenzani, U, Prunier, N, Raj, J, Ray, S, Rayner, V, Reyes, M, Reynolds, K, Rush, S, Salinas, C, Shetty, J, Snowball, C, Tod, S, Turra-Farina, G, Valle, D, Veale, S, Whitson, S, Wickham, A, Youn, S, Zamorano, F, Zavaglia, E, Zinberg, J, Woods, SW, and Shenton, ME
Abstract: This article describes the rationale, aims, and methodology of the Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ). This is the largest international collaboration to date that will develop algorithms to predict trajectories and outcomes of individuals at clinical high risk (CHR) for psychosis and to advance the development and use of novel pharmacological interventions for CHR individuals. We present a description of the participating research networks and the data processing analysis and coordination center, their processes for data harmonization across 43 sites from 13 participating countries (recruitment across North America, Australia, Europe, Asia, and South America), data flow and quality assessment processes, data analyses, and the transfer of data to the National Institute of Mental Health (NIMH) Data Archive (NDA) for use by the research community. In an expected sample of approximately 2000 CHR individuals and 640 matched healthy controls, AMP SCZ will collect clinical, environmental, and cognitive data along with multimodal biomarkers, including neuroimaging, electrophysiology, fluid biospecimens, speech and facial expression samples, novel measures derived from digital health technologies including smartphone-based daily surveys, and passive sensing as well as actigraphy. The study will investigate a range of clinical outcomes over a 2-year period, including transition to psychosis, remission or persistence of CHR status, attenuated positive symptoms, persistent negative symptoms, mood and anxiety symptoms, and psychosocial functioning. The global reach of AMP SCZ and its harmonized innovative methods promise to catalyze the development of new treatments to address critical unmet clinical and public health needs in CHR individuals.
Published: 2024

6. Subcortical brain volume abnormalities in 2028 individuals with schizophrenia and 2540 healthy controls via the ENIGMA consortium

Author: van Erp, TGM, Hibar, DP, Rasmussen, JM, Glahn, DC, Pearlson, GD, Andreassen, OA, Agartz, I, Westlye, LT, Haukvik, UK, Dale, AM, Melle, I, Hartberg, CB, Gruber, O, Kraemer, B, Zilles, D, Donohoe, G, Kelly, S, McDonald, C, Morris, DW, Cannon, DM, Corvin, A, Machielsen, MWJ, Koenders, L, de Haan, L, Veltman, DJ, Satterthwaite, TD, Wolf, DH, Gur, RC, Gur, RE, Potkin, SG, Mathalon, DH, Mueller, BA, Preda, A, Macciardi, F, Ehrlich, S, Walton, E, Hass, J, Calhoun, VD, Bockholt, HJ, Sponheim, SR, Shoemaker, JM, van Haren, NEM, Pol, HEH, Ophoff, RA, Kahn, RS, Roiz-Santiañez, R, Crespo-Facorro, B, Wang, L, Alpert, KI, Jönsson, EG, Dimitrova, R, Bois, C, Whalley, HC, McIntosh, AM, Lawrie, SM, Hashimoto, R, Thompson, PM, and Turner, JA
Subjects: Biological Psychology, Pharmacology and Pharmaceutical Sciences, Biomedical and Clinical Sciences, Psychology, Serious Mental Illness, Brain Disorders, Schizophrenia, Clinical Research, Neurosciences, Mental Health, Biomedical Imaging, Rare Diseases, Aetiology, 2.1 Biological and endogenous factors, Mental health, Good Health and Well Being, Adult, Brain, Brain Mapping, Case-Control Studies, Female, Functional Laterality, Humans, Image Processing, Computer-Assisted, Longitudinal Studies, Magnetic Resonance Imaging, Male, Middle Aged, Neuroimaging, Prospective Studies, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Clinical sciences, Biological psychology, Clinical and health psychology
Abstract: The profile of brain structural abnormalities in schizophrenia is still not fully understood, despite decades of research using brain scans. To validate a prospective meta-analysis approach to analyzing multicenter neuroimaging data, we analyzed brain MRI scans from 2028 schizophrenia patients and 2540 healthy controls, assessed with standardized methods at 15 centers worldwide. We identified subcortical brain volumes that differentiated patients from controls, and ranked them according to their effect sizes. Compared with healthy controls, patients with schizophrenia had smaller hippocampus (Cohen's d=-0.46), amygdala (d=-0.31), thalamus (d=-0.31), accumbens (d=-0.25) and intracranial volumes (d=-0.12), as well as larger pallidum (d=0.21) and lateral ventricle volumes (d=0.37). Putamen and pallidum volume augmentations were positively associated with duration of illness and hippocampal deficits scaled with the proportion of unmedicated patients. Worldwide cooperative analyses of brain imaging data support a profile of subcortical abnormalities in schizophrenia, which is consistent with that based on traditional meta-analytic approaches. This first ENIGMA Schizophrenia Working Group study validates that collaborative data analyses can readily be used across brain phenotypes and disorders and encourages analysis and data sharing efforts to further our understanding of severe mental illness.
Published: 2016

7. Attention/vigilance in schizophrenia: Performance results from a large multi-site study of the Consortium on the Genetics of Schizophrenia (COGS)

Author: Nuechterlein, KH, Green, MF, Calkins, ME, Greenwood, TA, Gur, RE, Gur, RC, Lazzeroni, LC, Light, GA, Radant, AD, Seidman, LJ, Siever, LJ, Silverman, JM, Sprock, J, Stone, WS, Sugar, CA, Swerdlow, NR, Tsuang, DW, Tsuang, MT, Turetsky, BI, and Braff, DL
Subjects: Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract: Attention/vigilance impairments are present in individuals with schizophrenia across psychotic and remitted states and in their first-degree relatives. An important question is whether deficits in attention/vigilance can be consistently and reliably measured across sites varying in many participant demographic, clinical, and functional characteristics, as needed for large-scale genetic studies of endophenotypes. We examined Continuous Performance Test (CPT) data from phase 2 of the Consortium on the Genetics of Schizophrenia (COGS-2), the largest-scale assessment of cognitive and psychophysiological endophenotypes relevant to schizophrenia. The CPT data from 2251 participants from five sites were examined. A perceptual-load vigilance task (the Degraded Stimulus CPT or DS-CPT) and a memory-load vigilance task (CPT-Identical Pairs or CPT-IP) were utilized. Schizophrenia patients performed more poorly than healthy comparison subjects (HCS) across sites, despite significant site differences in participant age, sex, education, and racial distribution. Patient-HCS differences in signal/noise discrimination (d') in the DS-CPT varied significantly across sites, but averaged a medium effect size. CPT-IP performance showed large patient-HCS differences across sites. Poor CPT performance was independent of or weakly correlated with symptom severity, but was significantly associated with lower educational achievement and functional capacity. Current smoking was associated with poorer CPT-IP d'. Patients taking both atypical and typical antipsychotic medication performed more poorly than those on no or atypical antipsychotic medications, likely reflecting their greater severity of illness. We conclude that CPT deficits in schizophrenia can be reliably detected across sites, are relatively independent of current symptom severity, and are related to functional capacity.
Published: 2015

8. Verbal working memory in schizophrenia from the Consortium on the Genetics of Schizophrenia (COGS) Study: The moderating role of smoking status and antipsychotic medications

Author: Lee, J, Green, MF, Calkins, ME, Greenwood, TA, Gur, RE, Gur, RC, Lazzeroni, LC, Light, GA, Nuechterlein, KH, Radant, AD, Seidman, LJ, Siever, LJ, Silverman, JM, Sprock, J, Stone, WS, Sugar, CA, Swerdlow, NR, Tsuang, DW, Tsuang, MT, Turetsky, BI, and Braff, DL
Subjects: Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract: Objectives: Working memory impairment has been extensively studied in schizophrenia, but less is known about moderators of the impairment. Using the Consortium on the Genetics of Schizophrenia case-control study (COGS-2), we examined smoking status, types of antipsychotic medication, and history of substance as moderators for working memory impairment in schizophrenia. Methods: From 5 sites, 1377 patients with schizophrenia or schizoaffective, depressed type and 1037 healthy controls completed the letter-number span (LNS) task. The LNS uses intermixed letter and digit stimuli that increase from 2 up to 8 stimuli. In the forward condition, participants repeated the letters and numbers in the order they were presented. In the reorder condition, participants repeated the digits in ascending order followed by letters in alphabetical order. Results: Schizophrenia patients performed more poorly than controls, with a larger difference on reorder than forward conditions. Deficits were associated with symptoms, functional capacity, and functional outcome. Patients who smoked showed larger impairment than nonsmoking patients, primarily due to deficits on the reorder condition. The impairing association of smoking was more pronounced among patients taking first-generation than those taking second-generation antipsychotic medications. Correlations between working memory and community functioning were stronger for nonsmokers. History of substance use did not moderate working memory impairment. Conclusions: Results confirm the working memory impairment in schizophrenia, and indicate smoking status as an important moderator for these deficits. The greater impairment in smokers may reflect added burden of smoking on general health or that patients with greater deficits are more likely to smoke.
Published: 2015

9. California verbal learning test-ii performance in schizophrenia as a function of ascertainment strategy: Comparing the first and second phases of the consortium on the genetics of schizophrenia (COGS)

Author: Stone, WS, Mesholam-Gately, RI, Braff, DL, Calkins, ME, Freedman, R, Green, MF, Greenwood, TA, Gur, RE, Gur, RC, Lazzeroni, LC, Light, GA, Nuechterlein, KH, Olincy, A, Radant, AD, Siever, LJ, Silverman, JM, Sprock, J, Sugar, CA, Swerdlow, NR, Tsuang, DW, Tsuang, MT, Turetsky, BI, and Seidman, LJ
Subjects: Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract: The first phase of the Consortium on the Genetics of Schizophrenia (COGS-1) showed performance deficits in learning and memory on the California Verbal Learning Test, Second Edition (CVLT-II) in individuals with schizophrenia (SZ), compared to healthy comparison subjects (HCS). A question is whether the COGS-1 study, which used a family study design (i.e. studying relatively intact families), yielded "milder" SZ phenotypes than those acquired subsequently in the COGS-2 case-control design that did not recruit unaffected family members. CVLT-II performance was compared for the COGS-1 and COGS-2 samples. Analyses focused on learning, recall and recognition variables, with age, gender and education as covariates. Analyses of COGS-2 data explored effects of additional covariates and moderating factors in CVLT-II performance. 324 SZ subjects and 510 HCS had complete CVLT-II and covariate data in COGS-1, while 1356 SZ and 1036 HCS had complete data in COGS-2. Except for recognition memory, analysis of covariance showed significantly worse performance in COGS-2 on all CVLT-II variables for SZ and HCS, and remained significant in the presence of the covariates. Performance in each of the 5 learning trials differed significantly. However, effect sizes comparing cases and controls were comparable across the two studies. COGS-2 analyses confirmed SZ performance deficits despite effects of multiple significant covariates and moderating factors. CVLT-II performance was worse in COGS-2 than in COGS-1 for both the SZ and the HCS in this large cohort, likely due to cohort effects. Demographically corrected data yield a consistent pattern of performance across the two studies in SZ.
Published: 2015

10. Factor structure and heritability of endophenotypes in schizophrenia: Findings from the Consortium on the Genetics of Schizophrenia (COGS-1)

Author: Seidman, LJ, Hellemann, G, Nuechterlein, KH, Greenwood, TA, Braff, DL, Cadenhead, KS, Calkins, ME, Freedman, R, Gur, RE, Gur, RC, Lazzeroni, LC, Light, GA, Olincy, A, Radant, AD, Siever, LJ, Silverman, JM, Sprock, J, Stone, WS, Sugar, C, Swerdlow, NR, Tsuang, DW, Tsuang, MT, Turetsky, BI, and Green, MF
Subjects: Psychiatry, Medical and Health Sciences, Psychology and Cognitive Sciences
Abstract: Background: Although many endophenotypes for schizophrenia have been studied individually, few studies have examined the extent to which common neurocognitive and neurophysiological measures reflect shared versus unique endophenotypic factors. It may be possible to distill individual endophenotypes into composite measures that reflect dissociable, genetically informative elements. Methods: The first phase of the Consortium on the Genetics of Schizophrenia (COGS-1) is a multisite family study that collected neurocognitive and neurophysiological data between 2003 and 2008. For these analyses, participants included schizophrenia probands (n=. 83), their nonpsychotic siblings (n=. 151), and community comparison subjects (n=. 209) with complete data on a battery of 12 neurocognitive tests (assessing domains of working memory, declarative memory, vigilance, spatial ability, abstract reasoning, facial emotion processing, and motor speed) and 3 neurophysiological tasks reflecting inhibitory processing (P50 gating, prepulse inhibition and antisaccade tasks). Factor analyses were conducted on the measures for each subject group and across the entire sample. Heritability analyses of factors were performed using SOLAR. Results: Analyses yielded 5 distinct factors: 1) Episodic Memory, 2) Working Memory, 3) Perceptual Vigilance, 4) Visual Abstraction, and 5) Inhibitory Processing. Neurophysiological measures had low associations with these factors. The factor structure of endophenotypes was largely comparable across probands, siblings and controls. Significant heritability estimates for the factors ranged from 22% (Episodic Memory) to 39% (Visual Abstraction). Conclusions: Neurocognitive measures reflect a meaningful amount of shared variance whereas the neurophysiological measures reflect largely unique contributions as endophenotypes for schizophrenia. Composite endophenotype measures may inform our neurobiological and genetic understanding of schizophrenia.
Published: 2015

11. The utility of P300 as a schizophrenia endophenotype and predictive biomarker: Clinical and socio-demographic modulators in COGS-2

Author: Turetsky, BI, Dress, EM, Braff, DL, Calkins, ME, Green, MF, Greenwood, TA, Gur, RE, Gur, RC, Lazzeroni, LC, Nuechterlein, KH, Radant, AD, Seidman, LJ, Siever, LJ, Silverman, JM, Sprock, J, Stone, WS, Sugar, CA, Swerdlow, NR, Tsuang, DW, Tsuang, MT, and Light, G
Subjects: Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract: Reduced auditory P300 amplitude is a robust schizophrenia deficit exhibiting the qualities of a viable genetic endophenotype. These include heritability, test-retest reliability, and trait-like stability. Recent evidence suggests that P300 may also serve as a predictive biomarker for transition to psychosis during the schizophrenia prodrome. Historically, the utility of the P300 has been limited by its clinical nonspecificity, cross-site measurement variability, and required EEG expertise. The Consortium on the Genetics of Schizophrenia (COGS-2) study provided an opportunity to examine the consistency of the measure across multiple sites with varying degrees of EEG experience, and to identify important modulating factors that contribute to measurement variability. Auditory P300 was acquired from 649 controls and 587 patients at 5 sites. An overall patient deficit was observed with effect size 0.62. Each site independently observed a significant patient deficit, but site differences also existed. In patients, site differences reflected clinical differences in positive symptomatology and functional capacity. In controls, site differences reflected differences in racial stratification, smoking and substance use history. These factors differentially suppressed the P300 response, but only in control subjects. This led to an attenuated patient-control difference among smokers and among African Americans with history of substance use. These findings indicate that the P300 can be adequately assessed quantitatively, across sites, without substantial EEG expertise. Measurements are suitable for both genetic endophenotype analyses and studies of psychosis risk and conversion. However, careful attention must be given to selection of appropriate comparison samples to avoid misleading false negative results.
Published: 2015

12. Large- scale analysis of structural brain asymmetries in schizophrenia via the ENIGMA consortium

Author: Schijven, D, Postema, MC, Fukunaga, M, Matsumoto, J, Miura, K, de Zwarte, SMC, van Haren, NEM, Cahn, W, Pol, HEH, Kahn, RS, Ayesa-Arriola, R, de la Foz, VO-G, Tordesillas-Gutierrez, D, Vazquez-Bourgon, J, Crespo-Facorro, B, Alnaes, D, Dahl, A, Westlye, LT, Agartz, I, Andreassen, OA, Jonsson, EG, Kochunov, P, Bruggemann, JM, Catts, SV, Michie, PT, Mowry, BJ, Quide, Y, Rasser, PE, Schall, U, Scott, RJ, Carr, VJ, Green, MJ, Henskens, FA, Loughland, CM, Pantelis, C, Weickert, CS, Weickert, TW, De Haan, L, Brosch, K, Pfarr, J-K, Ringwald, KG, Stein, F, Jansen, A, Kircher, TTJ, Nenadic, I, Kramer, B, Gruber, O, Satterthwaite, TD, Bustillo, J, Mathalon, DH, Preda, A, Calhoun, VD, Ford, JM, Potkin, SG, Chen, J, Tan, Y, Wang, Z, Xiang, H, Fan, F, Bernardoni, F, Ehrlich, S, Fuentes-Claramonte, P, Garcia-Leon, MA, Guerrero-Pedraza, A, Salvador, R, Sarro, S, Pomarol-Clotet, E, Ciullo, V, Piras, F, Vecchio, D, Banaj, N, Spalletta, G, Michielse, S, van Amelsvoort, T, Dickie, EW, Voineskos, AN, Sim, K, Ciufolini, S, Dazzan, P, Murray, RM, Kim, W-S, Chung, Y-C, Andreou, C, Schmidt, A, Borgwardt, S, McIntosh, AM, Whalley, HC, Lawrie, SM, Du Plessis, S, Luckhoff, HK, Scheffler, F, Emsley, R, Grotegerd, D, Lencer, R, Dannlowski, U, Edmond, JT, Rootes-Murdy, K, Stephen, JM, Mayer, AR, Antonucci, LA, Fazio, L, Pergola, G, Bertolino, A, Diaz-Caneja, CM, Janssen, J, Lois, NG, Arango, C, Tomyshev, AS, Lebedeva, I, Cervenkav, S, Sellgrenv, CM, Georgiadis, F, Kirschner, M, Kaiser, S, Hajek, T, Skoch, A, Spaniel, F, Kim, M, Bin Kwak, Y, Oh, S, Kwon, JS, James, A, Bakker, G, Knochel, C, Stablein, M, Oertel, V, Uhlmann, A, Howells, FM, Stein, DJ, Temmingh, HS, Diaz-Zuluaga, AM, Pineda-Zapata, JA, Lopez-Jaramillo, C, Homan, S, Ji, E, Surbeck, W, Homan, P, Fishera, SE, Franke, B, Glahn, DC, Gur, RC, Hashimoto, R, Jahanshad, N, Luders, E, Medland, SE, Thompson, PM, Turner, JA, van Erp, TGM, Francks, C, Schijven, D, Postema, MC, Fukunaga, M, Matsumoto, J, Miura, K, de Zwarte, SMC, van Haren, NEM, Cahn, W, Pol, HEH, Kahn, RS, Ayesa-Arriola, R, de la Foz, VO-G, Tordesillas-Gutierrez, D, Vazquez-Bourgon, J, Crespo-Facorro, B, Alnaes, D, Dahl, A, Westlye, LT, Agartz, I, Andreassen, OA, Jonsson, EG, Kochunov, P, Bruggemann, JM, Catts, SV, Michie, PT, Mowry, BJ, Quide, Y, Rasser, PE, Schall, U, Scott, RJ, Carr, VJ, Green, MJ, Henskens, FA, Loughland, CM, Pantelis, C, Weickert, CS, Weickert, TW, De Haan, L, Brosch, K, Pfarr, J-K, Ringwald, KG, Stein, F, Jansen, A, Kircher, TTJ, Nenadic, I, Kramer, B, Gruber, O, Satterthwaite, TD, Bustillo, J, Mathalon, DH, Preda, A, Calhoun, VD, Ford, JM, Potkin, SG, Chen, J, Tan, Y, Wang, Z, Xiang, H, Fan, F, Bernardoni, F, Ehrlich, S, Fuentes-Claramonte, P, Garcia-Leon, MA, Guerrero-Pedraza, A, Salvador, R, Sarro, S, Pomarol-Clotet, E, Ciullo, V, Piras, F, Vecchio, D, Banaj, N, Spalletta, G, Michielse, S, van Amelsvoort, T, Dickie, EW, Voineskos, AN, Sim, K, Ciufolini, S, Dazzan, P, Murray, RM, Kim, W-S, Chung, Y-C, Andreou, C, Schmidt, A, Borgwardt, S, McIntosh, AM, Whalley, HC, Lawrie, SM, Du Plessis, S, Luckhoff, HK, Scheffler, F, Emsley, R, Grotegerd, D, Lencer, R, Dannlowski, U, Edmond, JT, Rootes-Murdy, K, Stephen, JM, Mayer, AR, Antonucci, LA, Fazio, L, Pergola, G, Bertolino, A, Diaz-Caneja, CM, Janssen, J, Lois, NG, Arango, C, Tomyshev, AS, Lebedeva, I, Cervenkav, S, Sellgrenv, CM, Georgiadis, F, Kirschner, M, Kaiser, S, Hajek, T, Skoch, A, Spaniel, F, Kim, M, Bin Kwak, Y, Oh, S, Kwon, JS, James, A, Bakker, G, Knochel, C, Stablein, M, Oertel, V, Uhlmann, A, Howells, FM, Stein, DJ, Temmingh, HS, Diaz-Zuluaga, AM, Pineda-Zapata, JA, Lopez-Jaramillo, C, Homan, S, Ji, E, Surbeck, W, Homan, P, Fishera, SE, Franke, B, Glahn, DC, Gur, RC, Hashimoto, R, Jahanshad, N, Luders, E, Medland, SE, Thompson, PM, Turner, JA, van Erp, TGM, and Francks, C
Abstract: Left-right asymmetry is an important organizing feature of the healthy brain that may be altered in schizophrenia, but most studies have used relatively small samples and heterogeneous approaches, resulting in equivocal findings. We carried out the largest case-control study of structural brain asymmetries in schizophrenia, with MRI data from 5,080 affected individuals and 6,015 controls across 46 datasets, using a single image analysis protocol. Asymmetry indexes were calculated for global and regional cortical thickness, surface area, and subcortical volume measures. Differences of asymmetry were calculated between affected individuals and controls per dataset, and effect sizes were meta-analyzed across datasets. Small average case-control differences were observed for thickness asymmetries of the rostral anterior cingulate and the middle temporal gyrus, both driven by thinner left-hemispheric cortices in schizophrenia. Analyses of these asymmetries with respect to the use of antipsychotic medication and other clinical variables did not show any significant associations. Assessment of age- and sex-specific effects revealed a stronger average leftward asymmetry of pallidum volume between older cases and controls. Case-control differences in a multivariate context were assessed in a subset of the data (N = 2,029), which revealed that 7% of the variance across all structural asymmetries was explained by case-control status. Subtle case-control differences of brain macrostructural asymmetry may reflect differences at the molecular, cytoarchitectonic, or circuit levels that have functional relevance for the disorder. Reduced left middle temporal cortical thickness is consistent with altered left-hemisphere language network organization in schizophrenia.
Published: 2023

13. Greater male than female variability in regional brain structure across the lifespan

Author: Wierenga, LM, Doucet, GE, Dima, D, Agartz, I, Aghajani, M, Akudjedu, TN, Albajes-Eizagirre, A, Alnaes, D, Alpert, K, Andreassen, OA, Anticevic, A, Asherson, P, Banaschewski, T, Bargallo, N, Baumeister, S, Baur-Streubel, R, Bertolino, A, Bonvino, A, Boomsma, D, Borgwardt, S, Bourque, J, den Braber, A, Brandeis, D, Breier, A, Brodaty, H, Brouwer, RM, Buitelaar, JK, Busatto, GF, Calhoun, VD, Canales-Rodriguez, EJ, Cannon, DM, Caseras, X, Castellanos, FX, Chaim-Avancini, TM, Ching, CRK, Clark, VP, Conrod, PJ, Conzelmann, A, Crivello, F, Davey, CG, Dickie, EW, Ehrlich, S, Van't Ent, D, Fisher, SE, Fouche, J-P, Franke, B, Fuentes-Claramonte, P, de Geus, EJC, Di Giorgio, A, Glahn, DC, Gotlib, IH, Grabe, HJ, Gruber, O, Gruner, P, Gur, RE, Gur, RC, Gurholt, TP, de Haan, L, Haatveit, B, Harrison, BJ, Hartman, CA, Hatton, SN, Heslenfeld, DJ, van den Heuvel, OA, Hickie, IB, Hoekstra, PJ, Hohmann, S, Holmes, AJ, Hoogman, M, Hosten, N, Howells, FM, Pol, HEH, Huyser, C, Jahanshad, N, James, AC, Jiang, J, Jonsson, EG, Joska, JA, Kalnin, AJ, Klein, M, Koenders, L, Kolskar, KK, Kramer, B, Kuntsi, J, Lagopoulos, J, Lazaro, L, Lebedeva, IS, Lee, PH, Lochner, C, Machielsen, MWJ, Maingault, S, Martin, NG, Martinez-Zalacain, I, Mataix-Cols, D, Mazoyer, B, McDonald, BC, McDonald, C, McIntosh, AM, McMahon, KL, McPhilemy, G, van der Meer, D, Menchon, JM, Naaijen, J, Nyberg, L, Oosterlaan, J, Paloyelis, Y, Pauli, P, Pergola, G, Pomarol-Clotet, E, Portella, MJ, Radua, J, Reif, A, Richard, G, Roffman, JL, Rosa, PGP, Sacchet, MD, Sachdev, PS, Salvador, R, Sarro, S, Satterthwaite, TD, Saykin, AJ, Serpa, MH, Sim, K, Simmons, A, Smoller, JW, Sommer, IE, Soriano-Mas, C, Stein, DJ, Strike, LT, Szeszko, PR, Temmingh, HS, Thomopoulos, S, Tomyshev, AS, Trollor, JN, Uhlmann, A, Veer, IM, Veltman, DJ, Voineskos, A, Volzke, H, Walter, H, Wang, L, Wang, Y, Weber, B, Wen, W, West, JD, Westlye, LT, Whalley, HC, Williams, SCR, Wittfeld, K, Wolf, DH, Wright, MJ, Yoncheva, YN, Zanetti, M, Ziegler, GC, de Zubicaray, G, Thompson, PM, Crone, EA, Frangou, S, Tamnes, CK, Wierenga, LM, Doucet, GE, Dima, D, Agartz, I, Aghajani, M, Akudjedu, TN, Albajes-Eizagirre, A, Alnaes, D, Alpert, K, Andreassen, OA, Anticevic, A, Asherson, P, Banaschewski, T, Bargallo, N, Baumeister, S, Baur-Streubel, R, Bertolino, A, Bonvino, A, Boomsma, D, Borgwardt, S, Bourque, J, den Braber, A, Brandeis, D, Breier, A, Brodaty, H, Brouwer, RM, Buitelaar, JK, Busatto, GF, Calhoun, VD, Canales-Rodriguez, EJ, Cannon, DM, Caseras, X, Castellanos, FX, Chaim-Avancini, TM, Ching, CRK, Clark, VP, Conrod, PJ, Conzelmann, A, Crivello, F, Davey, CG, Dickie, EW, Ehrlich, S, Van't Ent, D, Fisher, SE, Fouche, J-P, Franke, B, Fuentes-Claramonte, P, de Geus, EJC, Di Giorgio, A, Glahn, DC, Gotlib, IH, Grabe, HJ, Gruber, O, Gruner, P, Gur, RE, Gur, RC, Gurholt, TP, de Haan, L, Haatveit, B, Harrison, BJ, Hartman, CA, Hatton, SN, Heslenfeld, DJ, van den Heuvel, OA, Hickie, IB, Hoekstra, PJ, Hohmann, S, Holmes, AJ, Hoogman, M, Hosten, N, Howells, FM, Pol, HEH, Huyser, C, Jahanshad, N, James, AC, Jiang, J, Jonsson, EG, Joska, JA, Kalnin, AJ, Klein, M, Koenders, L, Kolskar, KK, Kramer, B, Kuntsi, J, Lagopoulos, J, Lazaro, L, Lebedeva, IS, Lee, PH, Lochner, C, Machielsen, MWJ, Maingault, S, Martin, NG, Martinez-Zalacain, I, Mataix-Cols, D, Mazoyer, B, McDonald, BC, McDonald, C, McIntosh, AM, McMahon, KL, McPhilemy, G, van der Meer, D, Menchon, JM, Naaijen, J, Nyberg, L, Oosterlaan, J, Paloyelis, Y, Pauli, P, Pergola, G, Pomarol-Clotet, E, Portella, MJ, Radua, J, Reif, A, Richard, G, Roffman, JL, Rosa, PGP, Sacchet, MD, Sachdev, PS, Salvador, R, Sarro, S, Satterthwaite, TD, Saykin, AJ, Serpa, MH, Sim, K, Simmons, A, Smoller, JW, Sommer, IE, Soriano-Mas, C, Stein, DJ, Strike, LT, Szeszko, PR, Temmingh, HS, Thomopoulos, S, Tomyshev, AS, Trollor, JN, Uhlmann, A, Veer, IM, Veltman, DJ, Voineskos, A, Volzke, H, Walter, H, Wang, L, Wang, Y, Weber, B, Wen, W, West, JD, Westlye, LT, Whalley, HC, Williams, SCR, Wittfeld, K, Wolf, DH, Wright, MJ, Yoncheva, YN, Zanetti, M, Ziegler, GC, de Zubicaray, G, Thompson, PM, Crone, EA, Frangou, S, and Tamnes, CK
Abstract: For many traits, males show greater variability than females, with possible implications for understanding sex differences in health and disease. Here, the ENIGMA (Enhancing Neuro Imaging Genetics through Meta-Analysis) Consortium presents the largest-ever mega-analysis of sex differences in variability of brain structure, based on international data spanning nine decades of life. Subcortical volumes, cortical surface area and cortical thickness were assessed in MRI data of 16,683 healthy individuals 1-90 years old (47% females). We observed significant patterns of greater male than female between-subject variance for all subcortical volumetric measures, all cortical surface area measures, and 60% of cortical thickness measures. This pattern was stable across the lifespan for 50% of the subcortical structures, 70% of the regional area measures, and nearly all regions for thickness. Our findings that these sex differences are present in childhood implicate early life genetic or gene-environment interaction mechanisms. The findings highlight the importance of individual differences within the sexes, that may underpin sex-specific vulnerability to disorders.
Published: 2022

14. Subcortical volumes across the lifespan: Data from 18,605 healthy individuals aged 3-90 years

Author: Dima, D, Modabbernia, A, Papachristou, E, Doucet, GE, Agartz, I, Aghajani, M, Akudjedu, TN, Albajes-Eizagirre, A, Alnaes, D, Alpert, K, Andersson, M, Andreasen, NC, Andreassen, OA, Asherson, P, Banaschewski, T, Bargallo, N, Baumeister, S, Baur-Streubel, R, Bertolino, A, Bonvino, A, Boomsma, D, Borgwardt, S, Bourque, J, Brandeis, D, Breier, A, Brodaty, H, Brouwer, RM, Buitelaar, JK, Busatto, GF, Buckner, RL, Calhoun, V, Canales-Rodriguez, EJ, Cannon, DM, Caseras, X, Castellanos, FX, Cervenka, S, Chaim-Avancini, TM, Ching, CRK, Chubar, V, Clark, VP, Conrod, P, Conzelmann, A, Crespo-Facorro, B, Crivello, F, Crone, EA, Dale, AM, Davey, C, de Geus, EJC, de Haan, L, de Zubicaray, G, den Braber, A, Dickie, EW, Di Giorgio, A, Nhat, TD, Dorum, ES, Ehrlich, S, Erk, S, Espeseth, T, Fatouros-Bergman, H, Fisher, SE, Fouche, J-P, Franke, B, Frodl, T, Fuentes-Claramonte, P, Glahn, DC, Gotlib, IH, Grabe, H-J, Grimm, O, Groenewold, NA, Grotegerd, D, Gruber, O, Gruner, P, Gur, RE, Gur, RC, Harrison, BJ, Hartman, CA, Hatton, SN, Heinz, A, Heslenfeld, DJ, Hibar, DP, Hickie, IB, Ho, B-C, Hoekstra, PJ, Hohmann, S, Holmes, AJ, Hoogman, M, Hosten, N, Howells, FM, Pol, HEH, Huyser, C, Jahanshad, N, James, A, Jernigan, TL, Jiang, J, Jonsson, EG, Joska, JA, Kahn, R, Kalnin, A, Kanai, R, Klein, M, Klyushnik, TP, Koenders, L, Koops, S, Kraemer, B, Kuntsi, J, Lagopoulos, J, Lazaro, L, Lebedeva, I, Lee, WH, Lesch, K-P, Lochner, C, Machielsen, MWJ, Maingault, S, Martin, NG, Martinez-Zalacain, I, Mataix-Cols, D, Mazoyer, B, McDonald, C, McDonald, BC, McIntosh, AM, McMahon, KL, McPhilemy, G, Menchon, JM, Medland, SE, Meyer-Lindenberg, A, Naaijen, J, Najt, P, Nakao, T, Nordvik, JE, Nyberg, L, Oosterlaan, J, Ortiz-Garcia De la Foz, V, Paloyelis, Y, Pauli, P, Pergola, G, Pomarol-Clotet, E, Portella, MJ, Potkin, SG, Radua, J, Reif, A, Rinker, DA, Roffman, JL, Rosa, PGP, Sacchet, MD, Sachdev, PS, Salvador, R, Sanchez-Juan, P, Sarro, S, Satterthwaite, TD, Saykin, AJ, Serpa, MH, Schmaal, L, Schnell, K, Schumann, G, Sim, K, Smoller, JW, Sommer, I, Soriano-Mas, C, Stein, DJ, Strike, LT, Swagerman, SC, Tamnes, CK, Temmingh, HS, Thomopoulos, S, Tomyshev, AS, Tordesillas-Gutierrez, D, Trollor, JN, Turner, JA, Uhlmann, A, van den Heuvel, OA, van den Meer, D, van der Wee, NJA, van Haren, NEM, Van't Ent, D, van Erp, TGM, Veer, IM, Veltman, DJ, Voineskos, A, Voelzke, H, Walter, H, Walton, E, Wang, L, Wang, Y, Wassink, TH, Weber, B, Wen, W, West, JD, Westlye, LT, Whalley, H, Wierenga, LM, Williams, SCR, Wittfeld, K, Wolf, DH, Worker, A, Wright, MJ, Yang, K, Yoncheva, Y, Zanetti, M, Ziegler, GC, Thompson, PM, Frangou, S, Dima, D, Modabbernia, A, Papachristou, E, Doucet, GE, Agartz, I, Aghajani, M, Akudjedu, TN, Albajes-Eizagirre, A, Alnaes, D, Alpert, K, Andersson, M, Andreasen, NC, Andreassen, OA, Asherson, P, Banaschewski, T, Bargallo, N, Baumeister, S, Baur-Streubel, R, Bertolino, A, Bonvino, A, Boomsma, D, Borgwardt, S, Bourque, J, Brandeis, D, Breier, A, Brodaty, H, Brouwer, RM, Buitelaar, JK, Busatto, GF, Buckner, RL, Calhoun, V, Canales-Rodriguez, EJ, Cannon, DM, Caseras, X, Castellanos, FX, Cervenka, S, Chaim-Avancini, TM, Ching, CRK, Chubar, V, Clark, VP, Conrod, P, Conzelmann, A, Crespo-Facorro, B, Crivello, F, Crone, EA, Dale, AM, Davey, C, de Geus, EJC, de Haan, L, de Zubicaray, G, den Braber, A, Dickie, EW, Di Giorgio, A, Nhat, TD, Dorum, ES, Ehrlich, S, Erk, S, Espeseth, T, Fatouros-Bergman, H, Fisher, SE, Fouche, J-P, Franke, B, Frodl, T, Fuentes-Claramonte, P, Glahn, DC, Gotlib, IH, Grabe, H-J, Grimm, O, Groenewold, NA, Grotegerd, D, Gruber, O, Gruner, P, Gur, RE, Gur, RC, Harrison, BJ, Hartman, CA, Hatton, SN, Heinz, A, Heslenfeld, DJ, Hibar, DP, Hickie, IB, Ho, B-C, Hoekstra, PJ, Hohmann, S, Holmes, AJ, Hoogman, M, Hosten, N, Howells, FM, Pol, HEH, Huyser, C, Jahanshad, N, James, A, Jernigan, TL, Jiang, J, Jonsson, EG, Joska, JA, Kahn, R, Kalnin, A, Kanai, R, Klein, M, Klyushnik, TP, Koenders, L, Koops, S, Kraemer, B, Kuntsi, J, Lagopoulos, J, Lazaro, L, Lebedeva, I, Lee, WH, Lesch, K-P, Lochner, C, Machielsen, MWJ, Maingault, S, Martin, NG, Martinez-Zalacain, I, Mataix-Cols, D, Mazoyer, B, McDonald, C, McDonald, BC, McIntosh, AM, McMahon, KL, McPhilemy, G, Menchon, JM, Medland, SE, Meyer-Lindenberg, A, Naaijen, J, Najt, P, Nakao, T, Nordvik, JE, Nyberg, L, Oosterlaan, J, Ortiz-Garcia De la Foz, V, Paloyelis, Y, Pauli, P, Pergola, G, Pomarol-Clotet, E, Portella, MJ, Potkin, SG, Radua, J, Reif, A, Rinker, DA, Roffman, JL, Rosa, PGP, Sacchet, MD, Sachdev, PS, Salvador, R, Sanchez-Juan, P, Sarro, S, Satterthwaite, TD, Saykin, AJ, Serpa, MH, Schmaal, L, Schnell, K, Schumann, G, Sim, K, Smoller, JW, Sommer, I, Soriano-Mas, C, Stein, DJ, Strike, LT, Swagerman, SC, Tamnes, CK, Temmingh, HS, Thomopoulos, S, Tomyshev, AS, Tordesillas-Gutierrez, D, Trollor, JN, Turner, JA, Uhlmann, A, van den Heuvel, OA, van den Meer, D, van der Wee, NJA, van Haren, NEM, Van't Ent, D, van Erp, TGM, Veer, IM, Veltman, DJ, Voineskos, A, Voelzke, H, Walter, H, Walton, E, Wang, L, Wang, Y, Wassink, TH, Weber, B, Wen, W, West, JD, Westlye, LT, Whalley, H, Wierenga, LM, Williams, SCR, Wittfeld, K, Wolf, DH, Worker, A, Wright, MJ, Yang, K, Yoncheva, Y, Zanetti, M, Ziegler, GC, Thompson, PM, and Frangou, S
Abstract: Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine age-related trajectories inferred from cross-sectional measures of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3-90 years. All subcortical structure volumes were at their maximum value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age. The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter-individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age-related morphometric patterns.
Published: 2022

15. Cortical thickness across the lifespan: Data from 17,075 healthy individuals aged 3-90 years

Author: Frangou, S, Modabbernia, A, Williams, SCR, Papachristou, E, Doucet, GE, Agartz, I, Aghajani, M, Akudjedu, TN, Albajes-Eizagirre, A, Alnaes, D, Alpert, K, Andersson, M, Andreasen, NC, Andreassen, OA, Asherson, P, Banaschewski, T, Bargallo, N, Baumeister, S, Baur-Streubel, R, Bertolino, A, Bonvino, A, Boomsma, D, Borgwardt, S, Bourque, J, Brandeis, D, Breier, A, Brodaty, H, Brouwer, RM, Buitelaar, JK, Busatto, GF, Buckner, RL, Calhoun, V, Canales-Rodriguez, EJ, Cannon, DM, Caseras, X, Castellanos, FX, Cervenka, S, Chaim-Avancini, TM, Ching, CRK, Chubar, V, Clark, VP, Conrod, P, Conzelmann, A, Crespo-Facorro, B, Crivello, F, Crone, EA, Dale, AM, Davey, C, de Geus, EJC, de Haan, L, de Zubicaray, G, den Braber, A, Dickie, EW, Di Giorgio, A, Nhat, TD, Dorum, ES, Ehrlich, S, Erk, S, Espeseth, T, Fatouros-Bergman, H, Fisher, SE, Fouche, J-P, Franke, B, Frodl, T, Fuentes-Claramonte, P, Glahn, DC, Gotlib, IH, Grabe, H-J, Grimm, O, Groenewold, NA, Grotegerd, D, Gruber, O, Gruner, P, Gur, RE, Gur, RC, Harrison, BJ, Hartman, CA, Hatton, SN, Heinz, A, Heslenfeld, DJ, Hibar, DP, Hickie, IB, Ho, B-C, Hoekstra, PJ, Hohmann, S, Holmes, AJ, Hoogman, M, Hosten, N, Howells, FM, Pol, HEH, Huyser, C, Jahanshad, N, James, A, Jernigan, TL, Jiang, J, Jonsson, EG, Joska, JA, Kahn, R, Kalnin, A, Kanai, R, Klein, M, Klyushnik, TP, Koenders, L, Koops, S, Kraemer, B, Kuntsi, J, Lagopoulos, J, Lazaro, L, Lebedeva, I, Lee, WH, Lesch, K-P, Lochner, C, Machielsen, MWJ, Maingault, S, Martin, NG, Martinez-Zalacain, I, Mataix-Cols, D, Mazoyer, B, McDonald, C, McDonald, BC, McIntosh, AM, McMahon, KL, McPhilemy, G, Menchon, JM, Medland, SE, Meyer-Lindenberg, A, Naaijen, J, Najt, P, Nakao, T, Nordvik, JE, Nyberg, L, Oosterlaan, J, de la Foz, VO-G, Paloyelis, Y, Pauli, P, Pergola, G, Pomarol-Clotet, E, Portella, MJ, Potkin, SG, Radua, J, Reif, A, Rinker, DA, Roffman, JL, Rosa, PGP, Sacchet, MD, Sachdev, PS, Salvador, R, Sanchez-Juan, P, Sarro, S, Satterthwaite, TD, Saykin, AJ, Serpa, MH, Schmaal, L, Schnell, K, Schumann, G, Sim, K, Smoller, JW, Sommer, I, Soriano-Mas, C, Stein, DJ, Strike, LT, Swagerman, SC, Tamnes, CK, Temmingh, HS, Thomopoulos, S, Tomyshev, AS, Tordesillas-Gutierrez, D, Trollor, JN, Turner, JA, Uhlmann, A, van den Heuvel, OA, van den Meer, D, van der Wee, NJA, van Haren, NEM, van't Ent, D, van Erp, TGM, Veer, IM, Veltman, DJ, Voineskos, A, Voelzke, H, Walter, H, Walton, E, Wang, L, Wang, Y, Wassink, TH, Weber, B, Wen, W, West, JD, Westlye, LT, Whalley, H, Wierenga, LM, Wittfeld, K, Wolf, DH, Worker, A, Wright, MJ, Yang, K, Yoncheva, Y, Zanetti, M, Ziegler, GC, Thompson, PM, Dima, D, Frangou, S, Modabbernia, A, Williams, SCR, Papachristou, E, Doucet, GE, Agartz, I, Aghajani, M, Akudjedu, TN, Albajes-Eizagirre, A, Alnaes, D, Alpert, K, Andersson, M, Andreasen, NC, Andreassen, OA, Asherson, P, Banaschewski, T, Bargallo, N, Baumeister, S, Baur-Streubel, R, Bertolino, A, Bonvino, A, Boomsma, D, Borgwardt, S, Bourque, J, Brandeis, D, Breier, A, Brodaty, H, Brouwer, RM, Buitelaar, JK, Busatto, GF, Buckner, RL, Calhoun, V, Canales-Rodriguez, EJ, Cannon, DM, Caseras, X, Castellanos, FX, Cervenka, S, Chaim-Avancini, TM, Ching, CRK, Chubar, V, Clark, VP, Conrod, P, Conzelmann, A, Crespo-Facorro, B, Crivello, F, Crone, EA, Dale, AM, Davey, C, de Geus, EJC, de Haan, L, de Zubicaray, G, den Braber, A, Dickie, EW, Di Giorgio, A, Nhat, TD, Dorum, ES, Ehrlich, S, Erk, S, Espeseth, T, Fatouros-Bergman, H, Fisher, SE, Fouche, J-P, Franke, B, Frodl, T, Fuentes-Claramonte, P, Glahn, DC, Gotlib, IH, Grabe, H-J, Grimm, O, Groenewold, NA, Grotegerd, D, Gruber, O, Gruner, P, Gur, RE, Gur, RC, Harrison, BJ, Hartman, CA, Hatton, SN, Heinz, A, Heslenfeld, DJ, Hibar, DP, Hickie, IB, Ho, B-C, Hoekstra, PJ, Hohmann, S, Holmes, AJ, Hoogman, M, Hosten, N, Howells, FM, Pol, HEH, Huyser, C, Jahanshad, N, James, A, Jernigan, TL, Jiang, J, Jonsson, EG, Joska, JA, Kahn, R, Kalnin, A, Kanai, R, Klein, M, Klyushnik, TP, Koenders, L, Koops, S, Kraemer, B, Kuntsi, J, Lagopoulos, J, Lazaro, L, Lebedeva, I, Lee, WH, Lesch, K-P, Lochner, C, Machielsen, MWJ, Maingault, S, Martin, NG, Martinez-Zalacain, I, Mataix-Cols, D, Mazoyer, B, McDonald, C, McDonald, BC, McIntosh, AM, McMahon, KL, McPhilemy, G, Menchon, JM, Medland, SE, Meyer-Lindenberg, A, Naaijen, J, Najt, P, Nakao, T, Nordvik, JE, Nyberg, L, Oosterlaan, J, de la Foz, VO-G, Paloyelis, Y, Pauli, P, Pergola, G, Pomarol-Clotet, E, Portella, MJ, Potkin, SG, Radua, J, Reif, A, Rinker, DA, Roffman, JL, Rosa, PGP, Sacchet, MD, Sachdev, PS, Salvador, R, Sanchez-Juan, P, Sarro, S, Satterthwaite, TD, Saykin, AJ, Serpa, MH, Schmaal, L, Schnell, K, Schumann, G, Sim, K, Smoller, JW, Sommer, I, Soriano-Mas, C, Stein, DJ, Strike, LT, Swagerman, SC, Tamnes, CK, Temmingh, HS, Thomopoulos, S, Tomyshev, AS, Tordesillas-Gutierrez, D, Trollor, JN, Turner, JA, Uhlmann, A, van den Heuvel, OA, van den Meer, D, van der Wee, NJA, van Haren, NEM, van't Ent, D, van Erp, TGM, Veer, IM, Veltman, DJ, Voineskos, A, Voelzke, H, Walter, H, Walton, E, Wang, L, Wang, Y, Wassink, TH, Weber, B, Wen, W, West, JD, Westlye, LT, Whalley, H, Wierenga, LM, Wittfeld, K, Wolf, DH, Worker, A, Wright, MJ, Yang, K, Yoncheva, Y, Zanetti, M, Ziegler, GC, Thompson, PM, and Dima, D
Abstract: Delineating the association of age and cortical thickness in healthy individuals is critical given the association of cortical thickness with cognition and behavior. Previous research has shown that robust estimates of the association between age and brain morphometry require large-scale studies. In response, we used cross-sectional data from 17,075 individuals aged 3-90 years from the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to infer age-related changes in cortical thickness. We used fractional polynomial (FP) regression to quantify the association between age and cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma method. Interindividual variability was estimated using meta-analysis and one-way analysis of variance. For most regions, their highest cortical thickness value was observed in childhood. Age and cortical thickness showed a negative association; the slope was steeper up to the third decade of life and more gradual thereafter; notable exceptions to this general pattern were entorhinal, temporopolar, and anterior cingulate cortices. Interindividual variability was largest in temporal and frontal regions across the lifespan. Age and its FP combinations explained up to 59% variance in cortical thickness. These results may form the basis of further investigation on normative deviation in cortical thickness and its significance for behavioral and cognitive outcomes.
Published: 2022

16. Deep Generative Medical Image Harmonization for Improving Cross-Site Generalization in Deep Learning Predictors

Author: Bashyam, VM, Doshi, J, Erus, G, Srinivasan, D, Abdulkadir, A, Singh, A, Habes, M, Fan, Y, Masters, CL, Maruff, P, Zhuo, C, Voelzke, H, Johnson, SC, Fripp, J, Koutsouleris, N, Satterthwaite, TD, Wolf, DH, Gur, RE, Gur, RC, Morris, JC, Albert, MS, Grabe, HJ, Resnick, SM, Bryan, NR, Wittfeld, K, Bulow, R, Wolk, DA, Shou, H, Nasrallah, IM, Davatzikos, C, Bashyam, VM, Doshi, J, Erus, G, Srinivasan, D, Abdulkadir, A, Singh, A, Habes, M, Fan, Y, Masters, CL, Maruff, P, Zhuo, C, Voelzke, H, Johnson, SC, Fripp, J, Koutsouleris, N, Satterthwaite, TD, Wolf, DH, Gur, RE, Gur, RC, Morris, JC, Albert, MS, Grabe, HJ, Resnick, SM, Bryan, NR, Wittfeld, K, Bulow, R, Wolk, DA, Shou, H, Nasrallah, IM, and Davatzikos, C
Abstract: BACKGROUND: In the medical imaging domain, deep learning-based methods have yet to see widespread clinical adoption, in part due to limited generalization performance across different imaging devices and acquisition protocols. The deviation between estimated brain age and biological age is an established biomarker of brain health and such models may benefit from increased cross-site generalizability. PURPOSE: To develop and evaluate a deep learning-based image harmonization method to improve cross-site generalizability of deep learning age prediction. STUDY TYPE: Retrospective. POPULATION: Eight thousand eight hundred and seventy-six subjects from six sites. Harmonization models were trained using all subjects. Age prediction models were trained using 2739 subjects from a single site and tested using the remaining 6137 subjects from various other sites. FIELD STRENGTH/SEQUENCE: Brain imaging with magnetization prepared rapid acquisition with gradient echo or spoiled gradient echo sequences at 1.5 T and 3 T. ASSESSMENT: StarGAN v2, was used to perform a canonical mapping from diverse datasets to a reference domain to reduce site-based variation while preserving semantic information. Generalization performance of deep learning age prediction was evaluated using harmonized, histogram matched, and unharmonized data. STATISTICAL TESTS: Mean absolute error (MAE) and Pearson correlation between estimated age and biological age quantified the performance of the age prediction model. RESULTS: Our results indicated a substantial improvement in age prediction in out-of-sample data, with the overall MAE improving from 15.81 (±0.21) years to 11.86 (±0.11) with histogram matching to 7.21 (±0.22) years with generative adversarial network (GAN)-based harmonization. In the multisite case, across the 5 out-of-sample sites, MAE improved from 9.78 (±6.69) years to 7.74 (±3.03) years with histogram normalization to 5.32 (±4.07) years with GAN-based harmonization. DATA CONCLUSION: While
Published: 2022

17. Brain charts for the human lifespan (vol 604, pg 525, 2022)

Author: Bethlehem, RAI, Seidlitz, J, White, SR, Vogel, JW, Anderson, KM, Adamson, C, Adler, S, Alexopoulos, GS, Anagnostou, E, Areces-Gonzalez, A, Astle, DE, Auyeung, B, Ayub, M, Bae, J, Ball, G, Baron-Cohen, S, Beare, R, Bedford, SA, Benegal, V, Beyer, F, Blangero, J, Blesa Cabez, M, Boardman, JP, Borzage, M, Bosch-Bayard, JF, Bourke, N, Calhoun, VD, Chakravarty, MM, Chen, C, Chertavian, C, Chetelat, G, Chong, YS, Cole, JH, Corvin, A, Costantino, M, Courchesne, E, Crivello, F, Cropley, VL, Crosbie, J, Crossley, N, Delarue, M, Delorme, R, Desrivieres, S, Devenyi, GA, Di Biase, MA, Dolan, R, Donald, KA, Donohoe, G, Dunlop, K, Edwards, AD, Elison, JT, Ellis, CT, Elman, JA, Eyler, L, Fair, DA, Feczko, E, Fletcher, PC, Fonagy, P, Franz, CE, Galan-Garcia, L, Gholipour, A, Giedd, J, Gilmore, JH, Glahn, DC, Goodyer, IM, Grant, PE, Groenewold, NA, Gunning, FM, Gur, RE, Gur, RC, Hammill, CF, Hansson, O, Hedden, T, Heinz, A, Henson, RN, Heuer, K, Hoare, J, Holla, B, Holmes, AJ, Holt, R, Huang, H, Im, K, Ipser, J, Jack, CR, Jackowski, AP, Jia, T, Johnson, KA, Jones, PB, Jones, DT, Kahn, RS, Karlsson, H, Karlsson, L, Kawashima, R, Kelley, EA, Kern, S, Kim, KW, Kitzbichler, MG, Kremen, WS, Lalonde, F, Landeau, B, Lee, S, Lerch, J, Lewis, JD, Li, J, Liao, W, Liston, C, Lombardo, MV, Lv, J, Lynch, C, Mallard, TT, Marcelis, M, Markello, RD, Mathias, SR, Mazoyer, B, McGuire, P, Meaney, MJ, Mechelli, A, Medic, N, Misic, B, Morgan, SE, Mothersill, D, Nigg, J, Ong, MQW, Ortinau, C, Ossenkoppele, R, Ouyang, M, Palaniyappan, L, Paly, L, Pan, PM, Pantelis, C, Park, MM, Paus, T, Pausova, Z, Paz-Linares, D, Pichet Binette, A, Pierce, K, Qian, X, Qiu, J, Qiu, A, Raznahan, A, Rittman, T, Rodrigue, A, Rollins, CK, Romero-Garcia, R, Ronan, L, Rosenberg, MD, Rowitch, DH, Salum, GA, Satterthwaite, TD, Schaare, HL, Schachar, RJ, Schultz, AP, Schumann, G, Scholl, M, Sharp, D, Shinohara, RT, Skoog, I, Smyser, CD, Sperling, RA, Stein, DJ, Stolicyn, A, Suckling, J, Sullivan, G, Taki, Y, Thyreau, B, Toro, R, Traut, N, Tsvetanov, KA, Turk-Browne, NB, Tuulari, JJ, Tzourio, C, Vachon-Presseau, E, Valdes-Sosa, MJ, Valdes-Sosa, PA, Valk, SL, van Amelsvoort, T, Vandekar, SN, Vasung, L, Victoria, LW, Villeneuve, S, Villringer, A, Vertes, PE, Wagstyl, K, Wang, YS, Warfield, SK, Warrier, V, Westman, E, Westwater, ML, Whalley, HC, Witte, AV, Yang, N, Yeo, B, Yun, H, Zalesky, A, Zar, HJ, Zettergren, A, Zhou, JH, Ziauddeen, H, Zugman, A, Zuo, XN, Bullmore, ET, Alexander-Bloch, AF, Bethlehem, RAI, Seidlitz, J, White, SR, Vogel, JW, Anderson, KM, Adamson, C, Adler, S, Alexopoulos, GS, Anagnostou, E, Areces-Gonzalez, A, Astle, DE, Auyeung, B, Ayub, M, Bae, J, Ball, G, Baron-Cohen, S, Beare, R, Bedford, SA, Benegal, V, Beyer, F, Blangero, J, Blesa Cabez, M, Boardman, JP, Borzage, M, Bosch-Bayard, JF, Bourke, N, Calhoun, VD, Chakravarty, MM, Chen, C, Chertavian, C, Chetelat, G, Chong, YS, Cole, JH, Corvin, A, Costantino, M, Courchesne, E, Crivello, F, Cropley, VL, Crosbie, J, Crossley, N, Delarue, M, Delorme, R, Desrivieres, S, Devenyi, GA, Di Biase, MA, Dolan, R, Donald, KA, Donohoe, G, Dunlop, K, Edwards, AD, Elison, JT, Ellis, CT, Elman, JA, Eyler, L, Fair, DA, Feczko, E, Fletcher, PC, Fonagy, P, Franz, CE, Galan-Garcia, L, Gholipour, A, Giedd, J, Gilmore, JH, Glahn, DC, Goodyer, IM, Grant, PE, Groenewold, NA, Gunning, FM, Gur, RE, Gur, RC, Hammill, CF, Hansson, O, Hedden, T, Heinz, A, Henson, RN, Heuer, K, Hoare, J, Holla, B, Holmes, AJ, Holt, R, Huang, H, Im, K, Ipser, J, Jack, CR, Jackowski, AP, Jia, T, Johnson, KA, Jones, PB, Jones, DT, Kahn, RS, Karlsson, H, Karlsson, L, Kawashima, R, Kelley, EA, Kern, S, Kim, KW, Kitzbichler, MG, Kremen, WS, Lalonde, F, Landeau, B, Lee, S, Lerch, J, Lewis, JD, Li, J, Liao, W, Liston, C, Lombardo, MV, Lv, J, Lynch, C, Mallard, TT, Marcelis, M, Markello, RD, Mathias, SR, Mazoyer, B, McGuire, P, Meaney, MJ, Mechelli, A, Medic, N, Misic, B, Morgan, SE, Mothersill, D, Nigg, J, Ong, MQW, Ortinau, C, Ossenkoppele, R, Ouyang, M, Palaniyappan, L, Paly, L, Pan, PM, Pantelis, C, Park, MM, Paus, T, Pausova, Z, Paz-Linares, D, Pichet Binette, A, Pierce, K, Qian, X, Qiu, J, Qiu, A, Raznahan, A, Rittman, T, Rodrigue, A, Rollins, CK, Romero-Garcia, R, Ronan, L, Rosenberg, MD, Rowitch, DH, Salum, GA, Satterthwaite, TD, Schaare, HL, Schachar, RJ, Schultz, AP, Schumann, G, Scholl, M, Sharp, D, Shinohara, RT, Skoog, I, Smyser, CD, Sperling, RA, Stein, DJ, Stolicyn, A, Suckling, J, Sullivan, G, Taki, Y, Thyreau, B, Toro, R, Traut, N, Tsvetanov, KA, Turk-Browne, NB, Tuulari, JJ, Tzourio, C, Vachon-Presseau, E, Valdes-Sosa, MJ, Valdes-Sosa, PA, Valk, SL, van Amelsvoort, T, Vandekar, SN, Vasung, L, Victoria, LW, Villeneuve, S, Villringer, A, Vertes, PE, Wagstyl, K, Wang, YS, Warfield, SK, Warrier, V, Westman, E, Westwater, ML, Whalley, HC, Witte, AV, Yang, N, Yeo, B, Yun, H, Zalesky, A, Zar, HJ, Zettergren, A, Zhou, JH, Ziauddeen, H, Zugman, A, Zuo, XN, Bullmore, ET, and Alexander-Bloch, AF
Published: 2022

18. QSIPrep: an integrative platform for preprocessing and reconstructing diffusion MRI data

Author: Cieslak, M, Cook, PA, He, X, Yeh, F-C, Dhollander, T, Adebimpe, A, Aguirre, GK, Bassett, DS, Betzel, RF, Bourque, J, Cabral, LM, Davatzikos, C, Detre, JA, Earl, E, Elliott, MA, Fadnavis, S, Fair, DA, Foran, W, Fotiadis, P, Garyfallidis, E, Giesbrecht, B, Gur, RC, Gur, RE, Kelz, MB, Keshavan, A, Larsen, BS, Luna, B, Mackey, AP, Milham, MP, Oathes, DJ, Perrone, A, Pines, AR, Roalf, DR, Richie-Halford, A, Rokem, A, Sydnor, VJ, Tapera, TM, Tooley, UA, Vettel, JM, Yeatman, JD, Grafton, ST, Satterthwaite, TD, Cieslak, M, Cook, PA, He, X, Yeh, F-C, Dhollander, T, Adebimpe, A, Aguirre, GK, Bassett, DS, Betzel, RF, Bourque, J, Cabral, LM, Davatzikos, C, Detre, JA, Earl, E, Elliott, MA, Fadnavis, S, Fair, DA, Foran, W, Fotiadis, P, Garyfallidis, E, Giesbrecht, B, Gur, RC, Gur, RE, Kelz, MB, Keshavan, A, Larsen, BS, Luna, B, Mackey, AP, Milham, MP, Oathes, DJ, Perrone, A, Pines, AR, Roalf, DR, Richie-Halford, A, Rokem, A, Sydnor, VJ, Tapera, TM, Tooley, UA, Vettel, JM, Yeatman, JD, Grafton, ST, and Satterthwaite, TD
Abstract: Diffusion-weighted magnetic resonance imaging (dMRI) is the primary method for noninvasively studying the organization of white matter in the human brain. Here we introduce QSIPrep, an integrative software platform for the processing of diffusion images that is compatible with nearly all dMRI sampling schemes. Drawing on a diverse set of software suites to capitalize on their complementary strengths, QSIPrep facilitates the implementation of best practices for processing of diffusion images.
Published: 2021

19. Virtual Histology of Cortical Thickness and Shared Neurobiology in 6 Psychiatric Disorders

Author: Patel, Y, Parker, N, Shin, J, Howard, D, French, L, Thomopoulos, SI, Pozzi, E, Abe, Y, Abe, C, Anticevic, A, Alda, M, Aleman, A, Alloza, C, Alonso-Lana, S, Ameis, SH, Anagnostou, E, McIntosh, AA, Arango, C, Arnold, PD, Asherson, P, Assogna, F, Auzias, G, Ayesa-Arriola, R, Bakker, G, Banaj, N, Banaschewski, T, Bandeira, CE, Baranov, A, Bargallo, N, Bau, CHD, Baumeister, S, Baune, BT, Bellgrove, MA, Benedetti, F, Bertolino, A, Boedhoe, PSW, Boks, M, Bollettini, I, del Mar Bonnin, C, Borgers, T, Borgwardt, S, Brandeis, D, Brennan, BP, Bruggemann, JM, Bulow, R, Busatto, GF, Calderoni, S, Calhoun, VD, Calvo, R, Canales-Rodriguez, EJ, Cannon, DM, Carr, VJ, Cascella, N, Cercignani, M, Chaim-Avancini, TM, Christakou, A, Coghill, D, Conzelmann, A, Crespo-Facorro, B, Cubillo, AI, Cullen, KR, Cupertino, RB, Daly, E, Dannlowski, U, Davey, CG, Denys, D, Deruelle, C, Di Giorgio, A, Dickie, EW, Dima, D, Dohm, K, Ehrlich, S, Ely, BA, Erwin-Grabner, T, Ethofer, T, Fair, DA, Fallgatter, AJ, Faraone, SV, Fatjo-Vilas, M, Fedor, JM, Fitzgerald, KD, Ford, JM, Frodl, T, Fu, CHY, Fullerton, JM, Gabel, MC, Glahn, DC, Roberts, G, Gogberashvili, T, Goikolea, JM, Gotlib, IH, Goya-Maldonado, R, Grabe, HJ, Green, MJ, Grevet, EH, Groenewold, NA, Grotegerd, D, Gruber, O, Gruner, P, Guerrero-Pedraza, A, Gur, RE, Gur, RC, Haar, S, Haarman, BCM, Haavik, J, Hahn, T, Hajek, T, Harrison, BJ, Harrison, NA, Hartman, CA, Whalley, HC, Heslenfeld, DJ, Hibar, DP, Hilland, E, Hirano, Y, Ho, TC, Hoekstra, PJ, Hoekstra, L, Hohmann, S, Hong, LE, Hoschl, C, Hovik, MF, Howells, FM, Nenadic, I, Jalbrzikowski, M, James, AC, Janssen, J, Jaspers-Fayer, F, Xu, J, Jonassen, R, Karkashadze, G, King, JA, Kircher, T, Kirschner, M, Koch, K, Kochunov, P, Kohls, G, Konrad, K, Kramer, B, Krug, A, Kuntsi, J, Kwon, JS, Landen, M, Landro, NI, Lazaro, L, Lebedeva, IS, Leehr, EJ, Lera-Miguel, S, Lesch, K-P, Lochner, C, Louza, MR, Luna, B, Lundervold, AJ, MacMaster, FP, Maglanoc, LA, Malpas, CB, Portella, MJ, Marsh, R, Martyn, FM, Mataix-Cols, D, Mathalon, DH, McCarthy, H, McDonald, C, McPhilemy, G, Meinert, S, Menchon, JM, Minuzzi, L, Mitchell, PB, Moreno, C, Morgado, P, Muratori, F, Murphy, CM, Murphy, D, Mwangi, B, Nabulsi, L, Nakagawa, A, Nakamae, T, Namazova, L, Narayanaswamy, J, Jahanshad, N, Nguyen, DD, Nicolau, R, O'Gorman Tuura, RL, O'Hearn, K, Oosterlaan, J, Opel, N, Ophoff, RA, Oranje, B, Garcia de la Foz, VO, Overs, BJ, Paloyelis, Y, Pantelis, C, Parellada, M, Pauli, P, Pico-Perez, M, Picon, FA, Piras, F, Plessen, KJ, Pomarol-Clotet, E, Preda, A, Puig, O, Quide, Y, Radua, J, Ramos-Quiroga, JA, Rasser, PE, Rauer, L, Reddy, J, Redlich, R, Reif, A, Reneman, L, Repple, J, Retico, A, Richarte, V, Richter, A, Rosa, PGP, Rubia, KK, Hashimoto, R, Sacchet, MD, Salvador, R, Santonja, J, Sarink, K, Sarro, S, Satterthwaite, TD, Sawa, A, Schall, U, Schofield, PR, Schrantee, A, Seitz, J, Serpa, MH, Setien-Suero, E, Shaw, P, Shook, D, Silk, TJ, Sim, K, Simon, S, Simpson, HB, Singh, A, Skoch, A, Skokauskas, N, Soares, JC, Soreni, N, Soriano-Mas, C, Spalletta, G, Spaniel, F, Lawrie, SM, Stern, ER, Stewart, SE, Takayanagi, Y, Temmingh, HS, Tolin, DF, Tomecek, D, Tordesillas-Gutierrez, D, Tosetti, M, Uhlmann, A, van Amelsvoort, T, van der Wee, NJA, van der Werff, SJA, van Haren, NEM, van Wingen, GA, Vance, A, Vazquez-Bourgon, J, Vecchio, D, Venkatasubramanian, G, Vieta, E, Vilarroya, O, Vives-Gilabert, Y, Voineskos, AN, Volzke, H, von Polier, GG, Walton, E, Weickert, TW, Weickert, CS, Weideman, AS, Wittfeld, K, Wolf, DH, Wu, M-J, Yang, TT, Yang, K, Yoncheva, Y, Yun, J-Y, Cheng, Y, Zanetti, MV, Ziegler, GC, Franke, B, Hoogman, M, Buitelaar, JK, van Rooij, D, Andreassen, OA, Ching, CRK, Veltman, DJ, Schmaal, L, Stein, DJ, van den Heuvel, OA, Turner, JA, van Erp, TGM, Pausova, Z, Thompson, PM, Paus, T, Patel, Y, Parker, N, Shin, J, Howard, D, French, L, Thomopoulos, SI, Pozzi, E, Abe, Y, Abe, C, Anticevic, A, Alda, M, Aleman, A, Alloza, C, Alonso-Lana, S, Ameis, SH, Anagnostou, E, McIntosh, AA, Arango, C, Arnold, PD, Asherson, P, Assogna, F, Auzias, G, Ayesa-Arriola, R, Bakker, G, Banaj, N, Banaschewski, T, Bandeira, CE, Baranov, A, Bargallo, N, Bau, CHD, Baumeister, S, Baune, BT, Bellgrove, MA, Benedetti, F, Bertolino, A, Boedhoe, PSW, Boks, M, Bollettini, I, del Mar Bonnin, C, Borgers, T, Borgwardt, S, Brandeis, D, Brennan, BP, Bruggemann, JM, Bulow, R, Busatto, GF, Calderoni, S, Calhoun, VD, Calvo, R, Canales-Rodriguez, EJ, Cannon, DM, Carr, VJ, Cascella, N, Cercignani, M, Chaim-Avancini, TM, Christakou, A, Coghill, D, Conzelmann, A, Crespo-Facorro, B, Cubillo, AI, Cullen, KR, Cupertino, RB, Daly, E, Dannlowski, U, Davey, CG, Denys, D, Deruelle, C, Di Giorgio, A, Dickie, EW, Dima, D, Dohm, K, Ehrlich, S, Ely, BA, Erwin-Grabner, T, Ethofer, T, Fair, DA, Fallgatter, AJ, Faraone, SV, Fatjo-Vilas, M, Fedor, JM, Fitzgerald, KD, Ford, JM, Frodl, T, Fu, CHY, Fullerton, JM, Gabel, MC, Glahn, DC, Roberts, G, Gogberashvili, T, Goikolea, JM, Gotlib, IH, Goya-Maldonado, R, Grabe, HJ, Green, MJ, Grevet, EH, Groenewold, NA, Grotegerd, D, Gruber, O, Gruner, P, Guerrero-Pedraza, A, Gur, RE, Gur, RC, Haar, S, Haarman, BCM, Haavik, J, Hahn, T, Hajek, T, Harrison, BJ, Harrison, NA, Hartman, CA, Whalley, HC, Heslenfeld, DJ, Hibar, DP, Hilland, E, Hirano, Y, Ho, TC, Hoekstra, PJ, Hoekstra, L, Hohmann, S, Hong, LE, Hoschl, C, Hovik, MF, Howells, FM, Nenadic, I, Jalbrzikowski, M, James, AC, Janssen, J, Jaspers-Fayer, F, Xu, J, Jonassen, R, Karkashadze, G, King, JA, Kircher, T, Kirschner, M, Koch, K, Kochunov, P, Kohls, G, Konrad, K, Kramer, B, Krug, A, Kuntsi, J, Kwon, JS, Landen, M, Landro, NI, Lazaro, L, Lebedeva, IS, Leehr, EJ, Lera-Miguel, S, Lesch, K-P, Lochner, C, Louza, MR, Luna, B, Lundervold, AJ, MacMaster, FP, Maglanoc, LA, Malpas, CB, Portella, MJ, Marsh, R, Martyn, FM, Mataix-Cols, D, Mathalon, DH, McCarthy, H, McDonald, C, McPhilemy, G, Meinert, S, Menchon, JM, Minuzzi, L, Mitchell, PB, Moreno, C, Morgado, P, Muratori, F, Murphy, CM, Murphy, D, Mwangi, B, Nabulsi, L, Nakagawa, A, Nakamae, T, Namazova, L, Narayanaswamy, J, Jahanshad, N, Nguyen, DD, Nicolau, R, O'Gorman Tuura, RL, O'Hearn, K, Oosterlaan, J, Opel, N, Ophoff, RA, Oranje, B, Garcia de la Foz, VO, Overs, BJ, Paloyelis, Y, Pantelis, C, Parellada, M, Pauli, P, Pico-Perez, M, Picon, FA, Piras, F, Plessen, KJ, Pomarol-Clotet, E, Preda, A, Puig, O, Quide, Y, Radua, J, Ramos-Quiroga, JA, Rasser, PE, Rauer, L, Reddy, J, Redlich, R, Reif, A, Reneman, L, Repple, J, Retico, A, Richarte, V, Richter, A, Rosa, PGP, Rubia, KK, Hashimoto, R, Sacchet, MD, Salvador, R, Santonja, J, Sarink, K, Sarro, S, Satterthwaite, TD, Sawa, A, Schall, U, Schofield, PR, Schrantee, A, Seitz, J, Serpa, MH, Setien-Suero, E, Shaw, P, Shook, D, Silk, TJ, Sim, K, Simon, S, Simpson, HB, Singh, A, Skoch, A, Skokauskas, N, Soares, JC, Soreni, N, Soriano-Mas, C, Spalletta, G, Spaniel, F, Lawrie, SM, Stern, ER, Stewart, SE, Takayanagi, Y, Temmingh, HS, Tolin, DF, Tomecek, D, Tordesillas-Gutierrez, D, Tosetti, M, Uhlmann, A, van Amelsvoort, T, van der Wee, NJA, van der Werff, SJA, van Haren, NEM, van Wingen, GA, Vance, A, Vazquez-Bourgon, J, Vecchio, D, Venkatasubramanian, G, Vieta, E, Vilarroya, O, Vives-Gilabert, Y, Voineskos, AN, Volzke, H, von Polier, GG, Walton, E, Weickert, TW, Weickert, CS, Weideman, AS, Wittfeld, K, Wolf, DH, Wu, M-J, Yang, TT, Yang, K, Yoncheva, Y, Yun, J-Y, Cheng, Y, Zanetti, MV, Ziegler, GC, Franke, B, Hoogman, M, Buitelaar, JK, van Rooij, D, Andreassen, OA, Ching, CRK, Veltman, DJ, Schmaal, L, Stein, DJ, van den Heuvel, OA, Turner, JA, van Erp, TGM, Pausova, Z, Thompson, PM, and Paus, T
Abstract: IMPORTANCE: Large-scale neuroimaging studies have revealed group differences in cortical thickness across many psychiatric disorders. The underlying neurobiology behind these differences is not well understood. OBJECTIVE: To determine neurobiologic correlates of group differences in cortical thickness between cases and controls in 6 disorders: attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD), major depressive disorder (MDD), obsessive-compulsive disorder (OCD), and schizophrenia. DESIGN, SETTING, AND PARTICIPANTS: Profiles of group differences in cortical thickness between cases and controls were generated using T1-weighted magnetic resonance images. Similarity between interregional profiles of cell-specific gene expression and those in the group differences in cortical thickness were investigated in each disorder. Next, principal component analysis was used to reveal a shared profile of group difference in thickness across the disorders. Analysis for gene coexpression, clustering, and enrichment for genes associated with these disorders were conducted. Data analysis was conducted between June and December 2019. The analysis included 145 cohorts across 6 psychiatric disorders drawn from the ENIGMA consortium. The numbers of cases and controls in each of the 6 disorders were as follows: ADHD: 1814 and 1602; ASD: 1748 and 1770; BD: 1547 and 3405; MDD: 2658 and 3572; OCD: 2266 and 2007; and schizophrenia: 2688 and 3244. MAIN OUTCOMES AND MEASURES: Interregional profiles of group difference in cortical thickness between cases and controls. RESULTS: A total of 12 721 cases and 15 600 controls, ranging from ages 2 to 89 years, were included in this study. Interregional profiles of group differences in cortical thickness for each of the 6 psychiatric disorders were associated with profiles of gene expression specific to pyramidal (CA1) cells, astrocytes (except for BD), and microglia (except for OCD); collectively, gene
Published: 2021

20. Genetic and clinical analyses of psychosis spectrum symptoms in a large multiethnic youth cohort reveal significant link with ADHD

Author: Loohuis, LMO, Mennigen, E, Ori, APS, Perkins, D, Robinson, E, Addington, J, Cadenhead, KS, Cornblatt, BA, Mathalon, DH, McGlashan, TH, Seidman, LJ, Keshavan, MS, Stone, WS, Tsuang, MT, Walker, EF, Woods, SW, Cannon, TD, Gur, RC, Gur, RE, Bearden, CE, Ophoff, Roel, Loohuis, LMO, Mennigen, E, Ori, APS, Perkins, D, Robinson, E, Addington, J, Cadenhead, KS, Cornblatt, BA, Mathalon, DH, McGlashan, TH, Seidman, LJ, Keshavan, MS, Stone, WS, Tsuang, MT, Walker, EF, Woods, SW, Cannon, TD, Gur, RC, Gur, RE, Bearden, CE, and Ophoff, Roel
Abstract: Psychotic symptoms are not only an important feature of severe neuropsychiatric disorders, but are also common in the general population, especially in youth. The genetic etiology of psychosis symptoms in youth remains poorly understood. To characterize genetic risk for psychosis spectrum symptoms (PS), we leverage a community-based multiethnic sample of children and adolescents aged 8–22 years, the Philadelphia Neurodevelopmental Cohort (n = 7225, 20% PS). Using an elastic net regression model, we aim to classify PS status using polygenic scores (PGS) based on a range of heritable psychiatric and brain-related traits in a multi-PGS model. We also perform univariate PGS associations and evaluate age-specific effects. The multi-PGS analyses do not improve prediction of PS status over univariate models, but reveal that the attention deficit hyperactivity disorder (ADHD) PGS is robustly and uniquely associated with PS (OR 1.12 (1.05, 1.18) P = 0.0003). This association is driven by subjects of European ancestry (OR = 1.23 (1.14, 1.34), P = 4.15 × 10−7) but is not observed in African American subjects (P = 0.65). We find a significant interaction of ADHD PGS with age (P = 0.01), with a stronger association in younger children. The association is independent of phenotypic overlap between ADHD and PS, not indirectly driven by substance use or childhood trauma, and appears to be specific to PS rather than reflecting general psychopathology in youth. In an independent sample, we replicate an increased ADHD PGS in 328 youth at clinical high risk for psychosis, compared to 216 unaffected controls (OR 1.06, CI(1.01, 1.11), P = 0.02). Our findings suggest that PS in youth may reflect a different genetic etiology than psychotic symptoms in adulthood, one more akin to ADHD, and shed light on how genetic risk can be investigated across early disease trajectories.
Published: 2021

21. Association among income loss, financial strain and depressive symptoms during COVID-19: evidence from two longitudinal studies

Author: Hertz-Palmor, N, primary, Moore, TM, additional, Gothelf, D, additional, DiDomenico, GE, additional, Dekel, I, additional, Greenberg, DM, additional, Brown, LA, additional, Matalon, N, additional, Visoki, E, additional, White, LK, additional, Himes, MH, additional, Schwartz-Lifshitz, M, additional, Gross, R, additional, Gur, RC, additional, Gur, RE, additional, Pessach, IM, additional, and Barzilay, R, additional
Published: 2020
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22. Reply to: New Meta- and Mega-analyses of Magnetic Resonance Imaging Findings in Schizophrenia: Do They Really Increase Our Knowledge About the Nature of the Disease Process?

Author: van Erp, TGM, Walton, E, Hibar, DP, Schmaal, L, Jiang, W, Glahn, DC, Pearlson, GD, Yao, N, Fukunaga, M, Hashimoto, R, Okada, N, Yamamori, H, Clark, VP, Mueller, BA, de Zwarte, SMC, Ophoff, RA, van Haren, NEM, Andreassen, OA, Gurholt, TP, Gruber, O, Kraemer, B, Richter, A, Calhoun, VD, Crespo-Facorro, B, Roiz-Santiañez, R, Tordesillas-Gutiérrez, D, Loughland, C, Catts, S, Fullerton, JM, Green, MJ, Henskens, F, Jablensky, A, Mowry, BJ, Pantelis, C, Quidé, Y, Schall, U, Scott, RJ, Cairns, MJ, Seal, M, Tooney, PA, Rasser, PE, Cooper, G, Shannon Weickert, C, Weickert, TW, Hong, E, Kochunov, P, Gur, RE, Gur, RC, Ford, JM, Macciardi, F, Mathalon, DH, Potkin, SG, Preda, A, Fan, F, Ehrlich, S, King, MD, De Haan, L, Veltman, DJ, Assogna, F, Banaj, N, de Rossi, P, Iorio, M, Piras, F, Spalletta, G, Pomarol-Clotet, E, Kelly, S, Ciufolini, S, Radua, J, Murray, R, Marques, TR, Simmons, A, Borgwardt, S, Schönborn-Harrisberger, F, Riecher-Rössler, A, Smieskova, R, Alpert, KI, Bertolino, A, Bonvino, A, Di Giorgio, A, Neilson, E, Mayer, AR, Yun, JY, Cannon, DM, Lebedeva, I, Tomyshev, AS, Akhadov, T, Kaleda, V, Fatouros-Bergman, H, Flyckt, L, Farde, L, Cervenka, S, Agartz, I, Collste, K, Victorsson, P, Engberg, G, Erhardt, S, Schwieler, L, Malmqvist, A, Hedberg, M, Orhan, F, van Erp, TGM, Walton, E, Hibar, DP, Schmaal, L, Jiang, W, Glahn, DC, Pearlson, GD, Yao, N, Fukunaga, M, Hashimoto, R, Okada, N, Yamamori, H, Clark, VP, Mueller, BA, de Zwarte, SMC, Ophoff, RA, van Haren, NEM, Andreassen, OA, Gurholt, TP, Gruber, O, Kraemer, B, Richter, A, Calhoun, VD, Crespo-Facorro, B, Roiz-Santiañez, R, Tordesillas-Gutiérrez, D, Loughland, C, Catts, S, Fullerton, JM, Green, MJ, Henskens, F, Jablensky, A, Mowry, BJ, Pantelis, C, Quidé, Y, Schall, U, Scott, RJ, Cairns, MJ, Seal, M, Tooney, PA, Rasser, PE, Cooper, G, Shannon Weickert, C, Weickert, TW, Hong, E, Kochunov, P, Gur, RE, Gur, RC, Ford, JM, Macciardi, F, Mathalon, DH, Potkin, SG, Preda, A, Fan, F, Ehrlich, S, King, MD, De Haan, L, Veltman, DJ, Assogna, F, Banaj, N, de Rossi, P, Iorio, M, Piras, F, Spalletta, G, Pomarol-Clotet, E, Kelly, S, Ciufolini, S, Radua, J, Murray, R, Marques, TR, Simmons, A, Borgwardt, S, Schönborn-Harrisberger, F, Riecher-Rössler, A, Smieskova, R, Alpert, KI, Bertolino, A, Bonvino, A, Di Giorgio, A, Neilson, E, Mayer, AR, Yun, JY, Cannon, DM, Lebedeva, I, Tomyshev, AS, Akhadov, T, Kaleda, V, Fatouros-Bergman, H, Flyckt, L, Farde, L, Cervenka, S, Agartz, I, Collste, K, Victorsson, P, Engberg, G, Erhardt, S, Schwieler, L, Malmqvist, A, Hedberg, M, and Orhan, F
Published: 2019

23. Randomized Controlled Trial of Imagery Rehearsal for Posttraumatic Nightmares in Combat Veterans

Author: Harb, GC, Cook, JM, Phelps, AJ, Gehrman, PR, Forbes, D, Localio, R, Harpaz-Rotem, I, Gur, RC, Ross, RJ, Harb, GC, Cook, JM, Phelps, AJ, Gehrman, PR, Forbes, D, Localio, R, Harpaz-Rotem, I, Gur, RC, and Ross, RJ
Abstract: STUDY OBJECTIVES: To examine the efficacy of imagery rehearsal (IR) combined with cognitive behavioral therapy for insomnia (CBT-I) compared to CBT-I alone for treating recurrent nightmares in military veterans with posttraumatic stress disorder (PTSD). METHODS: In this randomized controlled study, 108 male and female United States veterans of the Iraq and Afghanistan conflicts with current, severe PTSD and recurrent, deployment-related nightmares were randomized to six sessions of IR + CBT-I (n = 55) or CBT-I (n = 53). Primary outcomes were measured with the Nightmare Frequency Questionnaire (NFQ) and Nightmare Distress Questionnaire (NDQ). RESULTS: Improvement with treatment was significant (29% with reduction in nightmare frequency and 22% with remission). Overall, IR + CBT-I was not superior to CBT-I (NFQ: -0.12; 95% confidence interval = -0.87 to 0.63; likelihood ratio chi square = 4.7(3), P = .2); NDQ: 1.5, 95% confidence interval = -1.4 to 4.4; likelihood ratio chi square = 7.3, P = .06). CONCLUSIONS: Combining IR with CBT-I conferred no advantage overall. Further research is essential to examine the possibly greater benefit of adding IR to CBT-I for some subgroups of veterans with PTSD. CLINICAL TRIAL REGISTRATION: Registry: ClinicalTrials.gov; Title: Cognitive Behavioral Therapy (CBT) for Nightmares in Operation Enduring Freedom/Operation Iraqi Freedom (OEF/OIF) Veterans; Identifier: NCT00691626; URL: https://clinicaltrials.gov/ct2/show/NCT00691626.
Published: 2019

24. Cortical Brain Abnormalities in 4474 Individuals With Schizophrenia and 5098 Control Subjects via the Enhancing Neuro Imaging Genetics Through Meta Analysis (ENIGMA) Consortium

Author: van Erp, TGM, Walton, E, Hibar, DP, Schmaal, L, Jiang, W, Glahn, DC, Pearlson, GD, Yao, N, Fukunaga, M, Hashimoto, R, Okada, N, Yamamori, H, Bustillo, JR, Clark, VP, Agartz, I, Mueller, BA, Cahn, W, de Zwarte, SMC, Hulshoff Pol, HE, Kahn, RS, Ophoff, RA, van Haren, NEM, Andreassen, OA, Dale, AM, Doan, NT, Gurholt, TP, Hartberg, CB, Haukvik, UK, Jørgensen, KN, Lagerberg, TV, Melle, I, Westlye, LT, Gruber, O, Kraemer, B, Richter, A, Zilles, D, Calhoun, VD, Crespo-Facorro, B, Roiz-Santiañez, R, Tordesillas-Gutiérrez, D, Loughland, C, Carr, VJ, Catts, S, Cropley, VL, Fullerton, JM, Green, MJ, Henskens, FA, Jablensky, A, Lenroot, RK, Mowry, BJ, Michie, PT, Pantelis, C, Quidé, Y, Schall, U, Scott, RJ, Cairns, MJ, Seal, M, Tooney, PA, Rasser, PE, Cooper, G, Shannon Weickert, C, Weickert, TW, Morris, DW, Hong, E, Kochunov, P, Beard, LM, Gur, RE, Gur, RC, Satterthwaite, TD, Wolf, DH, Belger, A, Brown, GG, Ford, JM, Macciardi, F, Mathalon, DH, O'Leary, DS, Potkin, SG, Preda, A, Voyvodic, J, Lim, KO, McEwen, S, Yang, F, Tan, Y, Tan, S, Wang, Z, Fan, F, Chen, J, Xiang, H, Tang, S, Guo, H, Wan, P, Wei, D, Bockholt, HJ, Ehrlich, S, Wolthusen, RPF, King, MD, Shoemaker, JM, Sponheim, SR, De Haan, L, Koenders, L, van Erp, TGM, Walton, E, Hibar, DP, Schmaal, L, Jiang, W, Glahn, DC, Pearlson, GD, Yao, N, Fukunaga, M, Hashimoto, R, Okada, N, Yamamori, H, Bustillo, JR, Clark, VP, Agartz, I, Mueller, BA, Cahn, W, de Zwarte, SMC, Hulshoff Pol, HE, Kahn, RS, Ophoff, RA, van Haren, NEM, Andreassen, OA, Dale, AM, Doan, NT, Gurholt, TP, Hartberg, CB, Haukvik, UK, Jørgensen, KN, Lagerberg, TV, Melle, I, Westlye, LT, Gruber, O, Kraemer, B, Richter, A, Zilles, D, Calhoun, VD, Crespo-Facorro, B, Roiz-Santiañez, R, Tordesillas-Gutiérrez, D, Loughland, C, Carr, VJ, Catts, S, Cropley, VL, Fullerton, JM, Green, MJ, Henskens, FA, Jablensky, A, Lenroot, RK, Mowry, BJ, Michie, PT, Pantelis, C, Quidé, Y, Schall, U, Scott, RJ, Cairns, MJ, Seal, M, Tooney, PA, Rasser, PE, Cooper, G, Shannon Weickert, C, Weickert, TW, Morris, DW, Hong, E, Kochunov, P, Beard, LM, Gur, RE, Gur, RC, Satterthwaite, TD, Wolf, DH, Belger, A, Brown, GG, Ford, JM, Macciardi, F, Mathalon, DH, O'Leary, DS, Potkin, SG, Preda, A, Voyvodic, J, Lim, KO, McEwen, S, Yang, F, Tan, Y, Tan, S, Wang, Z, Fan, F, Chen, J, Xiang, H, Tang, S, Guo, H, Wan, P, Wei, D, Bockholt, HJ, Ehrlich, S, Wolthusen, RPF, King, MD, Shoemaker, JM, Sponheim, SR, De Haan, L, and Koenders, L
Abstract: Background: The profile of cortical neuroanatomical abnormalities in schizophrenia is not fully understood, despite hundreds of published structural brain imaging studies. This study presents the first meta-analysis of cortical thickness and surface area abnormalities in schizophrenia conducted by the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) Schizophrenia Working Group. Methods: The study included data from 4474 individuals with schizophrenia (mean age, 32.3 years; range, 11–78 years; 66% male) and 5098 healthy volunteers (mean age, 32.8 years; range, 10–87 years; 53% male) assessed with standardized methods at 39 centers worldwide. Results: Compared with healthy volunteers, individuals with schizophrenia have widespread thinner cortex (left/right hemisphere: Cohen's d = −0.530/−0.516) and smaller surface area (left/right hemisphere: Cohen's d = −0.251/−0.254), with the largest effect sizes for both in frontal and temporal lobe regions. Regional group differences in cortical thickness remained significant when statistically controlling for global cortical thickness, suggesting regional specificity. In contrast, effects for cortical surface area appear global. Case-control, negative, cortical thickness effect sizes were two to three times larger in individuals receiving antipsychotic medication relative to unmedicated individuals. Negative correlations between age and bilateral temporal pole thickness were stronger in individuals with schizophrenia than in healthy volunteers. Regional cortical thickness showed significant negative correlations with normalized medication dose, symptom severity, and duration of illness and positive correlations with age at onset. Conclusions: The findings indicate that the ENIGMA meta-analysis approach can achieve robust findings in clinical neuroscience studies; also, medication effects should be taken into account in future genetic association studies of cortical thickness in schizophrenia.
Published: 2018

25. Erratum: Subcortical brain volume abnormalities in 2028 individuals with schizophrenia and 2540 healthy controls via the ENIGMA consortium (Molecular Psychiatry (2015) DOI:10.1038/mp.2015.63)

Author: Van Erp, TGM, Hibar, DP, Rasmussen, JM, Glahn, DC, Pearlson, GD, Andreassen, OA, Agartz, I, Westlye, LT, Haukvik, UK, Dale, AM, Melle, I, Hartberg, CB, Gruber, O, Kraemer, B, Zilles, D, Donohoe, G, Kelly, S, Mcdonald, C, Morris, DW, Cannon, DM, Corvin, A, Machielsen, MWJ, Koenders, L, De Haan, L, Veltman, DJ, Satterthwaite, TD, Wolf, DH, Gur, RC, Gur, RE, Potkin, SG, Mathalon, DH, Mueller, BA, Preda, A, Macciardi, F, Ehrlich, S, Walton, E, Hass, J, Calhoun, VD, Bockholt, HJ, Sponheim, SR, Shoemaker, JM, Van Haren, NEM, Pol, HEH, Ophoff, RA, Kahn, RS, Roiz-Santiañez, R, Crespo-Facorro, B, Wang, L, Alpert, KI, Jönsson, EG, Dimitrova, R, Bois, C, Whalley, HC, Mcintosh, AM, Lawrie, SM, Hashimoto, R, Thompson, PM, and Turner, JA
Published: 2016

26. 1075 VERBAL MEMORY FUNCTIONING MODERATES RESPONSE TO TREATMENT FOR RECURRENT NIGHTMARES IN PTSD

Author: Scott, JC, primary, Harb, GC, additional, Brownlow, JA, additional, Greene, JL, additional, Gur, RC, additional, and Ross, RJ, additional
Published: 2017
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27. The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data

Author: Thompson, PM, Stein, JL, Medland, SE, Hibar, DP, Vasquez, AA, Renteria, ME, Toro, R, Jahanshad, N, Schumann, G, Franke, B, Wright, MJ, Martin, NG, Agartz, I, Alda, M, Alhusaini, S, Almasy, L, Almeida, J, Alpert, K, Andreasen, NC, Andreassen, OA, Apostolova, LG, Appel, K, Armstrong, NJ, Aribisala, B, Bastin, ME, Bauer, M, Bearden, CE, Bergmann, O, Binder, EB, Blangero, J, Bockholt, HJ, Boen, E, Bois, C, Boomsma, DI, Booth, T, Bowman, IJ, Bralten, J, Brouwer, RM, Brunner, HG, Brohawn, DG, Buckner, RL, Buitelaar, J, Bulayeva, K, Bustillo, JR, Calhoun, VD, Cannon, DM, Cantor, RM, Carless, MA, Caseras, X, Cavalleri, GL, Chakravarty, MM, Chang, KD, Ching, CRK, Christoforou, A, Cichon, S, Clark, VP, Conrod, P, Coppola, G, Crespo-Facorro, B, Curran, JE, Czisch, M, Deary, IJ, de Geus, EJC, den Braber, A, Delvecchio, G, Depondt, C, de Haan, L, de Zubicaray, GI, Dima, D, Dimitrova, R, Djurovic, S, Dong, H, Donohoe, G, Duggirala, R, Dyer, TD, Ehrlich, S, Ekman, CJ, Elvsashagen, T, Emsell, L, Erk, S, Espeseth, T, Fagerness, J, Fears, S, Fedko, I, Fernandez, G, Fisher, SE, Foroud, T, Fox, PT, Francks, C, Frangou, S, Frey, EM, Frodl, T, Frouin, V, Garavan, H, Giddaluru, S, Glahn, DC, Godlewska, B, Goldstein, RZ, Gollub, RL, Grabe, HJ, Grimm, O, Gruber, O, Guadalupe, T, Gur, RE, Gur, RC, Goering, HHH, Hagenaars, S, Hajek, T, Hall, GB, Hall, J, Hardy, J, Hartman, CA, Hass, J, Hatton, SN, Haukvik, UK, Hegenscheid, K, Heinz, A, Hickie, IB, Ho, B-C, Hoehn, D, Hoekstra, PJ, Hollinshead, M, Holmes, AJ, Homuth, G, Hoogman, M, Hong, LE, Hosten, N, Hottenga, J-J, Pol, HEH, Hwang, KS, Jack, CR, Jenkinson, M, Johnston, C, Joensson, EG, Kahn, RS, Kasperaviciute, D, Kelly, S, Kim, S, Kochunov, P, Koenders, L, Kraemer, B, Kwok, JBJ, Lagopoulos, J, Laje, G, Landen, M, Landman, BA, Lauriello, J, Lawrie, SM, Lee, PH, Le Hellard, S, Lemaitre, H, Leonardo, CD, Li, C-S, Liberg, B, Liewald, DC, Liu, X, Lopez, LM, Loth, E, Lourdusamy, A, Luciano, M, Macciardi, F, Machielsen, MWJ, MacQueen, GM, Malt, UF, Mandl, R, Manoach, DS, Martinot, J-L, Matarin, M, Mather, KA, Mattheisen, M, Mattingsdal, M, Meyer-Lindenberg, A, McDonald, C, McIntosh, AM, McMahon, FJ, McMahon, KL, Meisenzahl, E, Melle, I, Milaneschi, Y, Mohnke, S, Montgomery, GW, Morris, DW, Moses, EK, Mueller, BA, Munoz Maniega, S, Muehleisen, TW, Mueller-Myhsok, B, Mwangi, B, Nauck, M, Nho, K, Nichols, TE, Nilsson, L-G, Nugent, AC, Nyberg, L, Olvera, RL, Oosterlaan, J, Ophoff, RA, Pandolfo, M, Papalampropoulou-Tsiridou, M, Papmeyer, M, Paus, T, Pausova, Z, Pearlson, GD, Penninx, BW, Peterson, CP, Pfennig, A, Phillips, M, Pike, GB, Poline, J-B, Potkin, SG, Puetz, B, Ramasamy, A, Rasmussen, J, Rietschel, M, Rijpkema, M, Risacher, SL, Roffman, JL, Roiz-Santianez, R, Romanczuk-Seiferth, N, Rose, EJ, Royle, NA, Rujescu, D, Ryten, M, Sachdev, PS, Salami, A, Satterthwaite, TD, Savitz, J, Saykin, AJ, Scanlon, C, Schmaal, L, Schnack, HG, Schork, AJ, Schulz, SC, Schuer, R, Seidman, L, Shen, L, Shoemaker, JM, Simmons, A, Sisodiya, SM, Smith, C, Smoller, JW, Soares, JC, Sponheim, SR, Sprooten, E, Starr, JM, Steen, VM, Strakowski, S, Strike, L, Sussmann, J, Saemann, PG, Teumer, A, Toga, AW, Tordesillas-Gutierrez, D, Trabzuni, D, Trost, S, Turner, J, Van den Heuvel, M, van der Wee, NJ, van Eijk, K, van Erp, TGM, van Haren, NEM, van 't Ent, D, van Tol, M-J, Hernandez, MCV, Veltman, DJ, Versace, A, Voelzke, H, Walker, R, Walter, H, Wang, L, Wardlaw, JM, Weale, ME, Weiner, MW, Wen, W, Westlye, LT, Whalley, HC, Whelan, CD, White, T, Winkler, AM, Wittfeld, K, Woldehawariat, G, Wolf, C, Zilles, D, Zwiers, MP, Thalamuthu, A, Schofield, PR, Freimer, NB, Lawrence, NS, Drevets, W, Thompson, PM, Stein, JL, Medland, SE, Hibar, DP, Vasquez, AA, Renteria, ME, Toro, R, Jahanshad, N, Schumann, G, Franke, B, Wright, MJ, Martin, NG, Agartz, I, Alda, M, Alhusaini, S, Almasy, L, Almeida, J, Alpert, K, Andreasen, NC, Andreassen, OA, Apostolova, LG, Appel, K, Armstrong, NJ, Aribisala, B, Bastin, ME, Bauer, M, Bearden, CE, Bergmann, O, Binder, EB, Blangero, J, Bockholt, HJ, Boen, E, Bois, C, Boomsma, DI, Booth, T, Bowman, IJ, Bralten, J, Brouwer, RM, Brunner, HG, Brohawn, DG, Buckner, RL, Buitelaar, J, Bulayeva, K, Bustillo, JR, Calhoun, VD, Cannon, DM, Cantor, RM, Carless, MA, Caseras, X, Cavalleri, GL, Chakravarty, MM, Chang, KD, Ching, CRK, Christoforou, A, Cichon, S, Clark, VP, Conrod, P, Coppola, G, Crespo-Facorro, B, Curran, JE, Czisch, M, Deary, IJ, de Geus, EJC, den Braber, A, Delvecchio, G, Depondt, C, de Haan, L, de Zubicaray, GI, Dima, D, Dimitrova, R, Djurovic, S, Dong, H, Donohoe, G, Duggirala, R, Dyer, TD, Ehrlich, S, Ekman, CJ, Elvsashagen, T, Emsell, L, Erk, S, Espeseth, T, Fagerness, J, Fears, S, Fedko, I, Fernandez, G, Fisher, SE, Foroud, T, Fox, PT, Francks, C, Frangou, S, Frey, EM, Frodl, T, Frouin, V, Garavan, H, Giddaluru, S, Glahn, DC, Godlewska, B, Goldstein, RZ, Gollub, RL, Grabe, HJ, Grimm, O, Gruber, O, Guadalupe, T, Gur, RE, Gur, RC, Goering, HHH, Hagenaars, S, Hajek, T, Hall, GB, Hall, J, Hardy, J, Hartman, CA, Hass, J, Hatton, SN, Haukvik, UK, Hegenscheid, K, Heinz, A, Hickie, IB, Ho, B-C, Hoehn, D, Hoekstra, PJ, Hollinshead, M, Holmes, AJ, Homuth, G, Hoogman, M, Hong, LE, Hosten, N, Hottenga, J-J, Pol, HEH, Hwang, KS, Jack, CR, Jenkinson, M, Johnston, C, Joensson, EG, Kahn, RS, Kasperaviciute, D, Kelly, S, Kim, S, Kochunov, P, Koenders, L, Kraemer, B, Kwok, JBJ, Lagopoulos, J, Laje, G, Landen, M, Landman, BA, Lauriello, J, Lawrie, SM, Lee, PH, Le Hellard, S, Lemaitre, H, Leonardo, CD, Li, C-S, Liberg, B, Liewald, DC, Liu, X, Lopez, LM, Loth, E, Lourdusamy, A, Luciano, M, Macciardi, F, Machielsen, MWJ, MacQueen, GM, Malt, UF, Mandl, R, Manoach, DS, Martinot, J-L, Matarin, M, Mather, KA, Mattheisen, M, Mattingsdal, M, Meyer-Lindenberg, A, McDonald, C, McIntosh, AM, McMahon, FJ, McMahon, KL, Meisenzahl, E, Melle, I, Milaneschi, Y, Mohnke, S, Montgomery, GW, Morris, DW, Moses, EK, Mueller, BA, Munoz Maniega, S, Muehleisen, TW, Mueller-Myhsok, B, Mwangi, B, Nauck, M, Nho, K, Nichols, TE, Nilsson, L-G, Nugent, AC, Nyberg, L, Olvera, RL, Oosterlaan, J, Ophoff, RA, Pandolfo, M, Papalampropoulou-Tsiridou, M, Papmeyer, M, Paus, T, Pausova, Z, Pearlson, GD, Penninx, BW, Peterson, CP, Pfennig, A, Phillips, M, Pike, GB, Poline, J-B, Potkin, SG, Puetz, B, Ramasamy, A, Rasmussen, J, Rietschel, M, Rijpkema, M, Risacher, SL, Roffman, JL, Roiz-Santianez, R, Romanczuk-Seiferth, N, Rose, EJ, Royle, NA, Rujescu, D, Ryten, M, Sachdev, PS, Salami, A, Satterthwaite, TD, Savitz, J, Saykin, AJ, Scanlon, C, Schmaal, L, Schnack, HG, Schork, AJ, Schulz, SC, Schuer, R, Seidman, L, Shen, L, Shoemaker, JM, Simmons, A, Sisodiya, SM, Smith, C, Smoller, JW, Soares, JC, Sponheim, SR, Sprooten, E, Starr, JM, Steen, VM, Strakowski, S, Strike, L, Sussmann, J, Saemann, PG, Teumer, A, Toga, AW, Tordesillas-Gutierrez, D, Trabzuni, D, Trost, S, Turner, J, Van den Heuvel, M, van der Wee, NJ, van Eijk, K, van Erp, TGM, van Haren, NEM, van 't Ent, D, van Tol, M-J, Hernandez, MCV, Veltman, DJ, Versace, A, Voelzke, H, Walker, R, Walter, H, Wang, L, Wardlaw, JM, Weale, ME, Weiner, MW, Wen, W, Westlye, LT, Whalley, HC, Whelan, CD, White, T, Winkler, AM, Wittfeld, K, Woldehawariat, G, Wolf, C, Zilles, D, Zwiers, MP, Thalamuthu, A, Schofield, PR, Freimer, NB, Lawrence, NS, and Drevets, W
Abstract: The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Consortium is a collaborative network of researchers working together on a range of large-scale studies that integrate data from 70 institutions worldwide. Organized into Working Groups that tackle questions in neuroscience, genetics, and medicine, ENIGMA studies have analyzed neuroimaging data from over 12,826 subjects. In addition, data from 12,171 individuals were provided by the CHARGE consortium for replication of findings, in a total of 24,997 subjects. By meta-analyzing results from many sites, ENIGMA has detected factors that affect the brain that no individual site could detect on its own, and that require larger numbers of subjects than any individual neuroimaging study has currently collected. ENIGMA's first project was a genome-wide association study identifying common variants in the genome associated with hippocampal volume or intracranial volume. Continuing work is exploring genetic associations with subcortical volumes (ENIGMA2) and white matter microstructure (ENIGMA-DTI). Working groups also focus on understanding how schizophrenia, bipolar illness, major depression and attention deficit/hyperactivity disorder (ADHD) affect the brain. We review the current progress of the ENIGMA Consortium, along with challenges and unexpected discoveries made on the way.
Published: 2014

28. The Neural Correlates of Emotion Experience – Mood Induction with Facial Expressions and Classical Music

Author: Dyck, M, primary, Loughead, J, additional, Boers, F, additional, Kellermann, T, additional, Ruparel, K, additional, Gur, RC, additional, and Mathiak, K, additional
Published: 2009
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29. Effects of Explicit vs. Implicit Processing of Emotion on the Fusiform Face Response

Author: Monroe, JF, primary, Griffin, M, additional, Pinkham, A, additional, Loughead, J, additional, Gur, RC, additional, Roberts, TPL, additional, and Edgar, JC, additional
Published: 2009
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30. Sex differences in aging of the human frontal and temporal lobes

Author: Cowell, PE, primary, Turetsky, BI, additional, Gur, RC, additional, Grossman, RI, additional, Shtasel, DL, additional, and Gur, RE, additional
Published: 1994
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31. Neurocognitive endophenotypes in a multiplex multigenerational family study of schizophrenia.

Author: Gur RE, Nimgaonkar VL, Almasy L, Calkins ME, Ragland JD, Pogue-Geile MF, Kanes S, Blangero J, and Gur RC
Abstract: OBJECTIVE: Genetic factors contribute to the development of schizophrenia where cognitive dysfunction is a hallmark. The purpose of this article was to examine computerized neurocognitive measures as candidate endophenotypic markers of liability for schizophrenia in a genetically informative cohort. METHOD: European Americans from 35 multiplex multigenerational families (N=349) and healthy participants (N=154) underwent clinical assessments and neurocognitive measurements and provided blood samples. The neurocognitive measures included performance (accuracy and speed) from a computerized battery that assessed abstraction/mental flexibility; attention; verbal, face, and spatial memory; spatial processing; sensorimotor processing; and emotion intensity discrimination. RESULTS: Probands, relatives, and comparison subjects differed from each other in performance. Probands demonstrated greatest impairment relative to comparison subjects, followed by family members. Liability for schizophrenia affected the speed-accuracy tradeoff differently for specific neurocognitive domains. Significant heritability estimates were obtained for accuracy of verbal, facial, and spatial memory and spatial and emotion processing. For speed, estimates of heritability were significant for abstraction/mental flexibility, attention, face memory, and spatial and sensorimotor processing. CONCLUSIONS: In a multigenerational multiplex design, the authors demonstrated that neurocognitive measures are associated with schizophrenia, differentiate unaffected relatives from comparison subjects, and may have significant presumed heritability. Therefore, they are endophenotypes suitable for genetic studies. Accuracy and speed can be differentially sensitive to presumed genetic liability. [ABSTRACT FROM AUTHOR]
Published: 2007
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32. Visual attention circuitry in schizophrenia investigated with oddball event-related functional magnetic resonance imaging.

Author: Gur RE, Turetsky BI, Loughead J, Snyder W, Kohler C, Elliott M, Pratiwadi R, Ragland JD, Bilker WB, Siegel SJ, Kanes SJ, Arnold SE, and Gur RC
Abstract: OBJECTIVE: Schizophrenia patients have problems directing attention. Sustained attention requires ensuring that brain resources are focused on a selected target (top-down task) while ignoring irrelevant distractors (bottom-up interference). Whether patients have too little ability to focus or too much interference from distraction has not been clarified. The oddball paradigm embeds infrequent targets and distractors into the stimulus train, and schizophrenia deficits have been linked to diminished responses to both. Cerebral activity underlying abnormal attention can be examined with event-related functional magnetic resonance imaging. METHOD: A visual oddball task was presented to 22 patients with schizophrenia and 28 comparison subjects. Statistical probability maps reflecting blood-oxygenation-level-dependent changes were generated for infrequent targets and novel distractors relative to frequent standard stimuli. Activation was related to performance and symptoms. RESULTS: Activation specific to targets and distractors was associated with faster performance. For targets, patients had diminished activation in superior temporal and frontal gyri, cingulate, thalamus, and basal ganglia. They had increased activation in right insula, mid-frontal gyrus, posterior cingulate, and left inferior parietal lobule. For distractors, patients showed less activation in occipital regions and left inferior parietal lobule but increased activation in parietal-occipital, right mid-frontal, and left inferior frontal gyri. Abnormal activation correlated with positive and negative symptoms. CONCLUSIONS: Abnormal activation in schizophrenia in response to attentional demands reflects both insufficient recruitment of brain systems required for target detection and overcommitment of resources for processing irrelevant distractors. Schizophrenia patients appear to have an inability both to focus on targets and ignore distraction. [ABSTRACT FROM AUTHOR]
Published: 2007
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33. Levels-of-processing effect on internal source monitoring in schizophrenia.

Author: Ragland DH, McCarthy E, Bilker WB, Brensinger CM, Valdez J, Kohler C, Gur RE, and Gur RC
Abstract: Background. Recognition can be normalized in schizophrenia by providing patients with semantic organizational strategies through a levels-of-processing (LOP) framework. However, patients may rely primarily on familiarity effects, making recognition less sensitive than source monitoring to the strength of the episodic memory trace. The current study investigates whether providing semantic organizational strategies can also normalize patients' internal source-monitoring performance.Method. Sixteen clinically stable medicated patients with schizophrenia and 15 demographically matched healthy controls were asked to identify the source of remembered words following an LOP-encoding paradigm in which they alternated between processing words on a 'shallow' perceptual versus a 'deep' semantic level. A multinomial analysis provided orthogonal measures of item recognition and source discrimination, and bootstrapping generated variance to allow for parametric analyses. LOP and group effects were tested by contrasting recognition and source-monitoring parameters for words that had been encoded during deep versus shallow processing conditions.Results. As in a previous study there were no group differences in LOP effects on recognition performance, with patients and controls benefiting equally from deep versus shallow processing. Although there were no group differences in internal source monitoring, only controls had significantly better performance for words processed during the deep encoding condition. Patient performance did not correlate with clinical symptoms or medication dose.Conclusions. Providing a deep processing semantic encoding strategy significantly improved patients' recognition performance only. The lack of a significant LOP effect on internal source monitoring in patients may reflect subtle problems in the relational binding of semantic information that are independent of strategic memory processes. [ABSTRACT FROM AUTHOR]
Published: 2006
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34. Measuring Hypnotic Susceptibility: A Guest Editorial

Author: Gur Rc
Subjects: Adult, Psychological Tests, Complementary and alternative medicine, Individuality, Humans, Hypnotic susceptibility, General Medicine, Child, Psychology, Hypnosis, Clinical psychology
Published: 1978

35. Executive and semantic system impairment during animal word list generation in schizophrenia.

Author: Moelter, ST, Hill, SK, Ragland, JD, Lunardelli, A, Gur, RC, Gur, RE, and Moberg, PJ
Published: 2000
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36. Neural correlates of depressive realism--an fMRI study on causal attribution in depression.

Author: Seidel EM, Satterthwaite TD, Eickhoff SB, Schneider F, Gur RC, Wolf DH, Habel U, Derntl B, Seidel, Eva-Maria, Satterthwaite, Theodore D, Eickhoff, Simon B, Schneider, Frank, Gur, Ruben C, Wolf, Daniel H, Habel, Ute, and Derntl, Birgit
Abstract: Background: Biased causal attribution is a critical factor in the cognitive model of depression. Whereas depressed patients interpret events negatively, healthy people show a self-serving bias (internal attribution of positive events and external attribution of negative events).Methods: Using fMRI, depressed patients (n=15) and healthy controls (n=15) were confronted with positive and negative social events and made causal attributions (internal vs. external). Functional data were analyzed using a mixed effects model.Results: Behaviourally, controls showed a self-serving bias, whereas patients demonstrated a balanced attributional pattern. Analysis of functional data revealed a significant group difference in a fronto-temporal network. Higher activation of this network was associated with non self-serving attributions in controls but self-serving attributions in patients. Applying a psycho-physiological interaction analysis, we observed reduced coupling between a dorsomedial PFC seed region and limbic areas during self-serving attributions in patients compared to controls.Limitations: Results of the PPI analysis are preliminary given the liberal statistical threshold.Conclusions: The association of the behaviourally less frequent attributional pattern with activation in a fronto-temporal network suggests that non self-serving responses may produce a self-related response conflict in controls, while self-serving responses produce this conflict in patients. Moreover, attribution-modulated coupling between the dorsomedial PFC and limbic regions was weaker in patients than controls. This preliminary finding suggests that depression may be associated with disturbances in fronto-limbic coupling during attributional decisions. Our results implicate that treatment of major depression may benefit from approaches that facilitate reinterpretation of emotional events in a more positive, more self-serving way. [ABSTRACT FROM AUTHOR]
Published: 2012
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37. The General Psychopathology 'p' Factor in Adolescence: Multi-Informant Assessment and Computerized Adaptive Testing.

Author: Jones JD, Boyd RC, Sandro AD, Calkins ME, Los Reyes A, Barzilay R, Young JF, Benton TD, Gur RC, Moore TM, and Gur RE
Subjects: Humans, Adolescent, Male, Female, Child, Mental Disorders diagnosis, Mental Disorders psychology, Psychometrics instrumentation, Psychometrics methods, Psychopathology methods, Parents psychology
Abstract: Accumulating evidence supports the presence of a general psychopathology dimension, the p factor ('p'). Despite growing interest in the p factor, questions remain about how p is assessed. Although multi-informant assessment of psychopathology is commonplace in clinical research and practice with children and adolescents, almost no research has taken a multi-informant approach to studying youth p or has examined the degree of concordance between parent and youth reports. Further, estimating p requires assessment of a large number of symptoms, resulting in high reporter burden that may not be feasible in many clinical and research settings. In the present study, we used bifactor multidimensional item response theory models to estimate parent- and adolescent-reported p in a large community sample of youth (11-17 years) and parents (N = 5,060 dyads). We examined agreement between parent and youth p scores and associations with assessor-rated youth global functioning. We also applied computerized adaptive testing (CAT) simulations to parent and youth reports to determine whether adaptive testing substantially alters agreement on p or associations with youth global functioning. Parent-youth agreement on p was moderate (r =.44) and both reports were negatively associated with youth global functioning. Notably, 7 out of 10 of the highest loading items were common across reporters. CAT reduced the average number of items administered by 57%. Agreement between CAT-derived p scores was similar to the full form (r =.40) and CAT scores were negatively correlated with youth functioning. These novel results highlight the promise and potential clinical utility of a multi-informant p factor approach., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)
Published: 2024
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38. Polygenic risk of social isolation behavior and its influence on psychopathology and personality.

Author: Socrates AJ, Mullins N, Gur RC, Gur RE, Stahl E, O'Reilly PF, Reichenberg A, Jones H, Zammit S, and Velthorst E
Subjects: Humans, Male, Female, Adolescent, Depressive Disorder, Major genetics, Child, Linkage Disequilibrium genetics, Autism Spectrum Disorder genetics, Autism Spectrum Disorder psychology, Schizophrenia genetics, Psychopathology, Loneliness psychology, Friends psychology, Risk Factors, Middle Aged, Mendelian Randomization Analysis methods, Neuroticism, Adult, Genome-Wide Association Study methods, Multifactorial Inheritance genetics, Genetic Predisposition to Disease genetics, Polymorphism, Single Nucleotide genetics, Social Isolation psychology, Personality genetics
Abstract: Social isolation has been linked to a range of psychiatric issues, but the behavioral component that drives it is not well understood. Here, a genome-wide associations study (GWAS) was carried out to identify genetic variants that contribute specifically to social isolation behavior (SIB) in up to 449,609 participants from the UK Biobank. 17 loci were identified at genome-wide significance, contributing to a 4% SNP-based heritability estimate. Using the SIB GWAS, polygenic risk scores (PRS) were derived in ALSPAC, an independent, developmental cohort, and used to test for association with self-reported friendship scores, comprising items related to friendship quality and quantity, at age 12 and 18 to determine whether genetic predisposition manifests during childhood development. At age 18, friendship scores were associated with the SIB PRS, demonstrating that the genetic factors can predict related social traits in late adolescence. Linkage disequilibrium (LD) score correlation using the SIB GWAS demonstrated genetic correlations with autism spectrum disorder (ASD), schizophrenia, major depressive disorder (MDD), educational attainment, extraversion, and loneliness. However, no evidence of causality was found using a conservative Mendelian randomization approach between SIB and any of the traits in either direction. Genomic Structural Equation Modeling (SEM) revealed a common factor contributing to SIB, neuroticism, loneliness, MDD, and ASD, weakly correlated with a second common factor that contributes to psychiatric and psychotic traits. Our results show that SIB contributes a small heritable component, which is associated genetically with other social traits such as friendship as well as psychiatric disorders., (© 2024. Crown.)
Published: 2024
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39. Investigating Mood and Cognition in People with Multiple Sclerosis: A Prospective Study Protocol.

Author: Cooper EC, Schindler MK, Bar-Or A, Brandstadter RB, Calkins ME, Gur RC, Jacobs DA, Markowitz CE, Moore TM, Naydovich LR, Perrone CM, Ruparel K, Spangler BC, Troyan S, Shinohara RT, Satterthwaite TD, and Baller EB
Abstract: Multiple sclerosis (MS) is an immune-mediated neurological disorder that affects one million people in the United States. Up to 50% of people with MS experience depression, yet the mechanisms of depression in MS remain under-investigated. Studies of medically healthy participants with depression have described associations between white matter variability and depressive symptoms, but frequently exclude participants with medical comorbidities and thus cannot be extrapolated to people with intracranial diseases. White matter lesions are a key pathologic feature of MS and could disrupt pathways involved in depression symptoms. The purpose of this study is to investigate the impact of brain network disruption on depression using MS as a model. We will obtain structured clinical and cognitive assessments from two hundred fifty participants with MS and prospectively evaluate white matter lesion burden as a predictor of depressive symptoms. Ethics approval was obtained from The University of Pennsylvania Institutional Review Board (Protocol #853883). The results of this study will be presented at scientific meetings and conferences and published in peer-reviewed journals., Competing Interests: COMPETING INTEREST STATEMENTS The authors declare no competing interests.
Published: 2024
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40. Similarity-based multimodal regression.

Author: Chen AA, Weinstein SM, Adebimpe A, Gur RC, Gur RE, Merikangas KR, Satterthwaite TD, Shinohara RT, and Shou H
Subjects: Humans, Regression Analysis, Data Interpretation, Statistical, Models, Statistical
Abstract: To better understand complex human phenotypes, large-scale studies have increasingly collected multiple data modalities across domains such as imaging, mobile health, and physical activity. The properties of each data type often differ substantially and require either separate analyses or extensive processing to obtain comparable features for a combined analysis. Multimodal data fusion enables certain analyses on matrix-valued and vector-valued data, but it generally cannot integrate modalities of different dimensions and data structures. For a single data modality, multivariate distance matrix regression provides a distance-based framework for regression accommodating a wide range of data types. However, no distance-based method exists to handle multiple complementary types of data. We propose a novel distance-based regression model, which we refer to as Similarity-based Multimodal Regression (SiMMR), that enables simultaneous regression of multiple modalities through their distance profiles. We demonstrate through simulation, imaging studies, and longitudinal mobile health analyses that our proposed method can detect associations between clinical variables and multimodal data of differing properties and dimensionalities, even with modest sample sizes. We perform experiments to evaluate several different test statistics and provide recommendations for applying our method across a broad range of scenarios., (Published by Oxford University Press 2023.)
Published: 2024
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41. Neural Responses to Auditory Food Stimuli Following Cognitive Behavioral Therapy for Binge-Eating Disorder.

Author: Chao AM, Agarwal K, Zhou Y, Grilo CM, Gur RC, Joseph P, Shinohara RT, Richmond TS, and Wadden TA
Subjects: Humans, Female, Adult, Brain diagnostic imaging, Brain physiopathology, Cues, Food, Treatment Outcome, Middle Aged, Reward, Acoustic Stimulation, Cognitive Behavioral Therapy, Binge-Eating Disorder therapy, Binge-Eating Disorder physiopathology, Magnetic Resonance Imaging
Abstract: Objective: Adults with binge-eating disorder (BED), compared with those without BED, demonstrate higher blood-oxygen-level-dependent (BOLD) response to food cues in reward-related regions of the brain. It is not known whether cognitive behavioral therapy (CBT) can reverse this reward system hyperactivation. This randomized controlled trial (RCT) assessed changes in BOLD response to binge-eating cues following CBT versus wait-list control (WLC)., Method: Females with BED (N = 40) were randomized to CBT or WLC. Participants completed assessments at baseline and 16 weeks including measures of eating and appetite and functional magnetic resonance imaging (fMRI) to measure BOLD response while listening to personalized scripts of binge-eating and neutral-relaxing cues. Data were analyzed using general linear models with mixed effects., Results: Overall retention rate was 87.5%. CBT achieved significantly greater reductions in binge-eating episodes than WLC (mean ± standard error decline of 14.6 ± 2.7 vs. 5.7 ± 2.8 episodes in the past 28 days, respectively; p = 0.03). CBT and WLC did not differ significantly in changes in neural responses to binge-eating stimuli during the fMRI sessions. Compared with WLC, CBT had significantly greater improvements in reward-based eating drive, disinhibition, and hunger as assessed by questionnaires (ps < 0.05)., Discussion: CBT was effective in reducing binge eating, but, contrary to our hypothesis, CBT did not improve BOLD response to auditory binge-eating stimuli in reward regions of the brain. Further studies are needed to assess mechanisms underlying improvements with CBT for BED., Trial Registration: ClinicalTrials.gov identifier: NCT03604172., (© 2024 The Author(s). International Journal of Eating Disorders published by Wiley Periodicals LLC.)
Published: 2024
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42. Omega-3 Supplementation Reduces Schizotypal Personality in Children: A Randomized Controlled Trial.

Author: Raine A, Gur RC, Gur RE, Richmond TS, Hibbeln J, and Liu J
Subjects: Humans, Male, Child, Female, Single-Blind Method, Combined Modality Therapy, Outcome Assessment, Health Care, Schizotypal Personality Disorder therapy, Fatty Acids, Omega-3 administration & dosage, Fatty Acids, Omega-3 pharmacology, Dietary Supplements, Cognitive Behavioral Therapy methods
Abstract: Background and Hypothesis: Based on a childhood intervention from ages 3 to 5 years that included additional fish consumption and which resulted in reduced schizotypal personality at age 23, we had previously hypothesized that omega-3 could reduce schizotypy. The current study tests the hypothesis that omega-3 supplementation reduces schizotypy in children., Study Design: In this intention-to-treat, randomized, single-blind, stratified, factorial trial, a community sample of 290 children aged 11-12 years were randomized into Omega-3 Only, Cognitive Behavioral Therapy (CBT) Only, Omega-3 + CBT, and Control groups. Schizotypy was assessed using the SPQ-C (Schizotypal Personality Questionnaire for Children) at 0 months (baseline), 3 months (end of treatment), 6 months (3 months post-treatment), and 12 months (9 months post-treatment)., Study Results: A significant group × time interaction (P = .013) indicated that, compared with Controls, total schizotypy scores were reduced in both Omega-3 Only and Omega-3 + CBT groups immediately post-treatment (d = 0.56 and 0.47, respectively), and also 3 months after supplementation terminated (d = 0.49, d = 0.70). Stronger findings were observed for the interpersonal schizotypy factor, with both omega-3 groups showing reductions 9 months post-treatment compared with the CBT Only group. Schizotypy reductions were significantly stronger for those with higher dietary intake of omega-3 at intake. Sensitivity analyses confirmed findings., Conclusions: Results are unique in the field and suggest that omega-3 can help reduce schizotypal personality in community-residing children. From an epidemiological standpoint, if replicated and extended, these findings could have implications for early prevention of more significant schizotypal features developing later in adolescence., Clinical Trial Registration: "Healthy Brains & Behavior: Understanding and Treating Youth Aggression (HBB)." ClinicalTrials.gov Identifier: NCT00842439, https://clinicaltrials.gov/ct2/show/NCT00842439., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)
Published: 2024
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43. Meta-analysis of variance in tDCS effects on response inhibition.

Author: Lasogga L, Gramegna C, Müller D, Habel U, Mehler DMA, Gur RC, and Weidler C
Subjects: Humans, Reaction Time physiology, Transcranial Direct Current Stimulation methods, Inhibition, Psychological
Abstract: Deficiencies in response inhibition are associated with numerous mental health conditions, warranting innovative treatments. Transcranial direct current stimulation (tDCS), a non-invasive brain stimulation technique, modulates cortical excitability and has shown promise in improving response inhibition. However, tDCS effects on response inhibition often yield contradictory findings. Previous research emphasized the importance of inter-individual factors that are mostly ignored in conventional meta-analyses of mean effects. We aimed to fill this gap and promote the complementary use of the coefficient of variation ratio and standardized mean effects. The systematic literature search included single-session and sham-controlled tDCS studies utilizing stop-signal task or Go-NoGo tasks, analyzing 88 effect sizes from 53 studies. Considering the impact of inter-individual factors, we hypothesized that variances increase in the active versus sham tDCS. However, the results showed that variances between both groups did not differ. Additionally, analyzing standardized mean effects supported previous research showing an improvement in the stop-signal task but not in the Go-NoGo task following active tDCS. These findings suggest that inter-individual differences do not increase variances in response inhibition, implying that the heterogeneity cannot be attributed to higher variance in response inhibition during and after active tDCS. Furthermore, methodological considerations are crucial for tDCS efficacy., (© 2024. The Author(s).)
Published: 2024
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44. Regression and alignment for functional data and network topology.

Author: Tu D, Wrobel J, Satterthwaite TD, Goldsmith J, Gur RC, Gur RE, Gertheiss J, Bassett DS, and Shinohara RT
Abstract: In the brain, functional connections form a network whose topological organization can be described by graph-theoretic network diagnostics. These include characterizations of the community structure, such as modularity and participation coefficient, which have been shown to change over the course of childhood and adolescence. To investigate if such changes in the functional network are associated with changes in cognitive performance during development, network studies often rely on an arbitrary choice of preprocessing parameters, in particular the proportional threshold of network edges. Because the choice of parameter can impact the value of the network diagnostic, and therefore downstream conclusions, we propose to circumvent that choice by conceptualizing the network diagnostic as a function of the parameter. As opposed to a single value, a network diagnostic curve describes the connectome topology at multiple scales-from the sparsest group of the strongest edges to the entire edge set. To relate these curves to executive function and other covariates, we use scalar-on-function regression, which is more flexible than previous functional data-based models used in network neuroscience. We then consider how systematic differences between networks can manifest in misalignment of diagnostic curves, and consequently propose a supervised curve alignment method that incorporates auxiliary information from other variables. Our algorithm performs both functional regression and alignment via an iterative, penalized, and nonlinear likelihood optimization. The illustrated method has the potential to improve the interpretability and generalizability of neuroscience studies where the goal is to study heterogeneity among a mixture of function- and scalar-valued measures., (© The Author(s) 2024. Published by Oxford University Press. All rights reserved. [br]For permissions, please e-mail: journals.permissions@oup.com.)
Published: 2024
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45. Large-scale analysis of structural brain asymmetries during neurodevelopment: Associations with age and sex in 4265 children and adolescents.

Author: Kurth F, Schijven D, van den Heuvel OA, Hoogman M, van Rooij D, Stein DJ, Buitelaar JK, Bölte S, Auzias G, Kushki A, Venkatasubramanian G, Rubia K, Bollmann S, Isaksson J, Jaspers-Fayer F, Marsh R, Batistuzzo MC, Arnold PD, Bressan RA, Stewart SE, Gruner P, Sorensen L, Pan PM, Silk TJ, Gur RC, Cubillo AI, Haavik J, O'Gorman Tuura RL, Hartman CA, Calvo R, McGrath J, Calderoni S, Jackowski A, Chantiluke KC, Satterthwaite TD, Busatto GF, Nigg JT, Gur RE, Retico A, Tosetti M, Gallagher L, Szeszko PR, Neufeld J, Ortiz AE, Ghisleni C, Lazaro L, Hoekstra PJ, Anagnostou E, Hoekstra L, Simpson B, Plessen JK, Deruelle C, Soreni N, James A, Narayanaswamy J, Reddy JY, Fitzgerald J, Bellgrove MA, Salum GA, Janssen J, Muratori F, Vila M, Giral MG, Ameis SH, Bosco P, Remnélius KL, Huyser C, Pariente JC, Jalbrzikowski M, Rosa PG, O'Hearn KM, Ehrlich S, Mollon J, Zugman A, Christakou A, Arango C, Fisher SE, Kong X, Franke B, Medland SE, Thomopoulos SI, Jahanshad N, Glahn DC, Thompson PM, Francks C, and Luders E
Subjects: Humans, Adolescent, Male, Child, Female, Child, Preschool, Infant, Age Factors, Child Development physiology, Functional Laterality physiology, Adolescent Development physiology, Sex Characteristics, Magnetic Resonance Imaging, Brain diagnostic imaging, Brain growth & development, Brain anatomy & histology
Abstract: Only a small number of studies have assessed structural differences between the two hemispheres during childhood and adolescence. However, the existing findings lack consistency or are restricted to a particular brain region, a specific brain feature, or a relatively narrow age range. Here, we investigated associations between brain asymmetry and age as well as sex in one of the largest pediatric samples to date (n = 4265), aged 1-18 years, scanned at 69 sites participating in the ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) consortium. Our study revealed that significant brain asymmetries already exist in childhood, but their magnitude and direction depend on the brain region examined and the morphometric measurement used (cortical volume or thickness, regional surface area, or subcortical volume). With respect to effects of age, some asymmetries became weaker over time while others became stronger; sometimes they even reversed direction. With respect to sex differences, the total number of regions exhibiting significant asymmetries was larger in females than in males, while the total number of measurements indicating significant asymmetries was larger in males (as we obtained more than one measurement per cortical region). The magnitude of the significant asymmetries was also greater in males. However, effect sizes for both age effects and sex differences were small. Taken together, these findings suggest that cerebral asymmetries are an inherent organizational pattern of the brain that manifests early in life. Overall, brain asymmetry appears to be relatively stable throughout childhood and adolescence, with some differential effects in males and females., (© 2024 The Author(s). Human Brain Mapping published by Wiley Periodicals LLC.)
Published: 2024
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46. A network control theory pipeline for studying the dynamics of the structural connectome.

Author: Parkes L, Kim JZ, Stiso J, Brynildsen JK, Cieslak M, Covitz S, Gur RE, Gur RC, Pasqualetti F, Shinohara RT, Zhou D, Satterthwaite TD, and Bassett DS
Abstract: Network control theory (NCT) is a simple and powerful tool for studying how network topology informs and constrains the dynamics of a system. Compared to other structure-function coupling approaches, the strength of NCT lies in its capacity to predict the patterns of external control signals that may alter the dynamics of a system in a desired way. An interesting development for NCT in the neuroscience field is its application to study behavior and mental health symptoms. To date, NCT has been validated to study different aspects of the human structural connectome. NCT outputs can be monitored throughout developmental stages to study the effects of connectome topology on neural dynamics and, separately, to test the coherence of empirical datasets with brain function and stimulation. Here, we provide a comprehensive pipeline for applying NCT to structural connectomes by following two procedures. The main procedure focuses on computing the control energy associated with the transitions between specific neural activity states. The second procedure focuses on computing average controllability, which indexes nodes' general capacity to control the dynamics of the system. We provide recommendations for comparing NCT outputs against null network models, and we further support this approach with a Python-based software package called 'network control theory for python'. The procedures in this protocol are appropriate for users with a background in network neuroscience and experience in dynamical systems theory., (© 2024. Springer Nature Limited.)
Published: 2024
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47. Neurocognitive profiles of 22q11.2 and 16p11.2 deletions and duplications.

Author: Gur RC, Bearden CE, Jacquemont S, Swillen A, van Amelsvoort T, van den Bree M, Vorstman J, Sebat J, Ruparel K, Gallagher RS, McClellan E, White L, Crowley TB, Giunta V, Kushan L, O'Hora K, Verbesselt J, Vandensande A, Vingerhoets C, van Haelst M, Hall J, Harwood J, Chawner SJRA, Patel N, Palad K, Hong O, Guevara J, Martin CO, Jizi K, Bélanger AM, Scherer SW, Bassett AS, McDonald-McGinn DM, and Gur RE
Abstract: Rare recurrent copy number variants (CNVs) at chromosomal loci 22q11.2 and 16p11.2 are genetic disorders with lifespan risk for neuropsychiatric disorders. Microdeletions and duplications are associated with neurocognitive deficits, yet few studies compared these groups using the same measures to address confounding measurement differences. We report a prospective international collaboration applying the same computerized neurocognitive assessment, the Penn Computerized Neurocognitive Battery (CNB), administered in a multi-site study on rare genomic disorders: 22q11.2 deletions (n = 492); 22q11.2 duplications (n = 106); 16p11.2 deletion (n = 117); and 16p11.2 duplications (n = 46). Domains examined include executive functions, episodic memory, complex cognition, social cognition, and psychomotor speed. Accuracy and speed for each domain were included as dependent measures in a mixed-model repeated measures analysis. Locus (22q11.2, 16p11.2) and Copy number (deletion/duplication) were grouping factors and Measure (accuracy, speed) and neurocognitive domain were repeated measures factors, with Sex and Site as covariates. We also examined correlation with IQ. We found a significant Locus × Copy number × Domain × Measure interaction (p = 0.0004). 22q11.2 deletions were associated with greater performance accuracy deficits than 22q11.2 duplications, while 16p11.2 duplications were associated with greater specific deficits than 16p11.2 deletions. Duplications at both loci were associated with reduced speed compared to deletions. Performance profiles differed among the groups with particularly poor memory performance of the 22q11.2 deletion group while the 16p11.2 duplication group had greatest deficits in complex cognition. Average accuracy on the CNB was moderately correlated with Full Scale IQ. Deletions and duplications of 22q11.2 and 16p11.2 have differential effects on accuracy and speed of neurocognition indicating locus specificity of performance profiles. These profile differences can help inform mechanistic substrates to heterogeneity in presentation and outcome, and can only be established in large-scale international consortia using the same neurocognitive assessment. Future studies could aim to link performance profiles to clinical features and brain function., (© 2024. The Author(s).)
Published: 2024
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48. Brain-age prediction: Systematic evaluation of site effects, and sample age range and size.

Author: Yu Y, Cui HQ, Haas SS, New F, Sanford N, Yu K, Zhan D, Yang G, Gao JH, Wei D, Qiu J, Banaj N, Boomsma DI, Breier A, Brodaty H, Buckner RL, Buitelaar JK, Cannon DM, Caseras X, Clark VP, Conrod PJ, Crivello F, Crone EA, Dannlowski U, Davey CG, de Haan L, de Zubicaray GI, Di Giorgio A, Fisch L, Fisher SE, Franke B, Glahn DC, Grotegerd D, Gruber O, Gur RE, Gur RC, Hahn T, Harrison BJ, Hatton S, Hickie IB, Hulshoff Pol HE, Jamieson AJ, Jernigan TL, Jiang J, Kalnin AJ, Kang S, Kochan NA, Kraus A, Lagopoulos J, Lazaro L, McDonald BC, McDonald C, McMahon KL, Mwangi B, Piras F, Rodriguez-Cruces R, Royer J, Sachdev PS, Satterthwaite TD, Saykin AJ, Schumann G, Sevaggi P, Smoller JW, Soares JC, Spalletta G, Tamnes CK, Trollor JN, Van't Ent D, Vecchio D, Walter H, Wang Y, Weber B, Wen W, Wierenga LM, Williams SCR, Wu MJ, Zunta-Soares GB, Bernhardt B, Thompson P, Frangou S, and Ge R
Subjects: Humans, Adolescent, Female, Aged, Adult, Child, Young Adult, Male, Aged, 80 and over, Child, Preschool, Middle Aged, Neuroimaging methods, Neuroimaging standards, Sample Size, Brain diagnostic imaging, Brain anatomy & histology, Brain growth & development, Aging physiology, Magnetic Resonance Imaging methods
Abstract: Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model. Here we expand this work to develop, empirically validate, and disseminate a pre-trained brain-age model to cover most of the human lifespan. To achieve this, we selected the best-performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5-90 years; 53.59% female). The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8-80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9-25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age-bins (5-40 and 40-90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain-age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open-science, web-based platform for individualized neuroimaging metrics., (© 2024 The Author(s). Human Brain Mapping published by Wiley Periodicals LLC.)
Published: 2024
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49. Mapping Individual Differences in Intermodal Coupling in Neurodevelopment.

Author: Weinstein SM, Tu D, Hu F, Pan R, Zhang R, Vandekar SN, Baller EB, Gur RC, Gur RE, Alexander-Bloch AF, Satterthwaite TD, and Park JY
Abstract: Within-individual coupling between measures of brain structure and function evolves in development and may underlie differential risk for neuropsychiatric disorders. Despite increasing interest in the development of structure-function relationships, rigorous methods to quantify and test individual differences in coupling remain nascent. In this article, we explore and address gaps in approaches for testing and spatially localizing individual differences in intermodal coupling. We propose a new method, called CIDeR, which is designed to simultaneously perform hypothesis testing in a way that limits false positive results and improve detection of true positive results. Through a comparison across different approaches to testing individual differences in intermodal coupling, we delineate subtle differences in the hypotheses they test, which may ultimately lead researchers to arrive at different results. Finally, we illustrate the utility of CIDeR in two applications to brain development using data from the Philadelphia Neurodevelopmental Cohort., Competing Interests: Declaration of competing interest AAB receives consulting income from Octave Bioscience and holds equity in Centile Bioscience.
Published: 2024
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50. Computer-vision analysis of craniofacial dysmorphology in 22q11.2 deletion syndrome and psychosis spectrum disorders.

Author: Roalf DR, McDonald-McGinn DM, Jee J, Krall M, Crowley TB, Moberg PJ, Kohler C, Calkins ME, Crow AJD, Fleischer N, Gallagher RS, Gonzenbach V, Clark K, Gur RC, McClellan E, McGinn DE, Mordy A, Ruparel K, Turetsky BI, Shinohara RT, White L, Zackai E, and Gur RE
Subjects: Humans, Female, Male, Adolescent, Child, Young Adult, Adult, Machine Learning, Image Processing, Computer-Assisted, DiGeorge Syndrome genetics, DiGeorge Syndrome physiopathology, Psychotic Disorders genetics, Craniofacial Abnormalities genetics
Abstract: Background: Minor physical anomalies (MPAs) are congenital morphological abnormalities linked to disruptions of fetal development. MPAs are common in 22q11.2 deletion syndrome (22q11DS) and psychosis spectrum disorders (PS) and likely represent a disruption of early embryologic development that may help identify overlapping mechanisms linked to psychosis in these disorders., Methods: Here, 2D digital photographs were collected from 22q11DS (n = 150), PS (n = 55), and typically developing (TD; n = 93) individuals. Photographs were analyzed using two computer-vision techniques: (1) DeepGestalt algorithm (Face2Gene (F2G)) technology to identify the presence of genetically mediated facial disorders, and (2) Emotrics-a semi-automated machine learning technique that localizes and measures facial features., Results: F2G reliably identified patients with 22q11DS; faces of PS patients were matched to several genetic conditions including FragileX and 22q11DS. PCA-derived factor loadings of all F2G scores indicated unique and overlapping facial patterns that were related to both 22q11DS and PS. Regional facial measurements of the eyes and nose were smaller in 22q11DS as compared to TD, while PS showed intermediate measurements., Conclusions: The extent to which craniofacial dysmorphology 22q11DS and PS overlapping and evident before the impairment or distress of sub-psychotic symptoms may allow us to identify at-risk youths more reliably and at an earlier stage of development., (© 2024. The Author(s).)
Published: 2024
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