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5. Brain‐age prediction: Systematic evaluation of site effects, and sample age range and size

Author

Yu, Yuetong, Cui, Hao‐Qi, Haas, Shalaila S, New, Faye, Sanford, Nicole, Yu, Kevin, Zhan, Denghuang, Yang, Guoyuan, Gao, Jia‐Hong, Wei, Dongtao, Qiu, Jiang, Banaj, Nerisa, Boomsma, Dorret I, Breier, Alan, Brodaty, Henry, Buckner, Randy L, Buitelaar, Jan K, Cannon, Dara M, Caseras, Xavier, Clark, Vincent P, Conrod, Patricia J, Crivello, Fabrice, Crone, Eveline A, Dannlowski, Udo, Davey, Christopher G, de Haan, Lieuwe, de Zubicaray, Greig I, Di Giorgio, Annabella, Fisch, Lukas, Fisher, Simon E, Franke, Barbara, Glahn, David C, Grotegerd, Dominik, Gruber, Oliver, Gur, Raquel E, Gur, Ruben C, Hahn, Tim, Harrison, Ben J, Hatton, Sean, Hickie, Ian B, Pol, Hilleke E Hulshoff, Jamieson, Alec J, Jernigan, Terry L, Jiang, Jiyang, Kalnin, Andrew J, Kang, Sim, Kochan, Nicole A, Kraus, Anna, Lagopoulos, Jim, Lazaro, Luisa, McDonald, Brenna C, McDonald, Colm, McMahon, Katie L, Mwangi, Benson, Piras, Fabrizio, Rodriguez‐Cruces, Raul, Royer, Jessica, Sachdev, Perminder S, Satterthwaite, Theodore D, Saykin, Andrew J, Schumann, Gunter, Sevaggi, Pierluigi, Smoller, Jordan W, Soares, Jair C, Spalletta, Gianfranco, Tamnes, Christian K, Trollor, Julian N, Ent, Dennis Van't, Vecchio, Daniela, Walter, Henrik, Wang, Yang, Weber, Bernd, Wen, Wei, Wierenga, Lara M, Williams, Steven CR, Wu, Mon‐Ju, Zunta‐Soares, Giovana B, Bernhardt, Boris, Thompson, Paul, Frangou, Sophia, Ge, Ruiyang, and Group, ENIGMA‐Lifespan Working

Subjects
Biological Psychology, Psychology, Neurosciences, Clinical Research, Aging, Neurological, Mental health, Humans, Adolescent, Female, Aged, Adult, Child, Young Adult, Male, Brain, Aged, 80 and over, Child, Preschool, Middle Aged, Magnetic Resonance Imaging, Neuroimaging, Sample Size, benchmarking, brain aging, brainAGE, ENIGMA‐Lifespan Working Group, Cognitive Sciences, Experimental Psychology, Biological psychology, Cognitive and computational psychology
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.

Published
2024

6. Connectome architecture shapes large-scale cortical alterations in schizophrenia: a worldwide ENIGMA study.

Author

Georgiadis, Foivos, Larivière, Sara, Glahn, David, Hong, L, Kochunov, Peter, Mowry, Bryan, Loughland, Carmel, Pantelis, Christos, Henskens, Frans, Green, Melissa, Cairns, Murray, Michie, Patricia, Rasser, Paul, Catts, Stanley, Tooney, Paul, Scott, Rodney, Schall, Ulrich, Carr, Vaughan, Quidé, Yann, Krug, Axel, Stein, Frederike, Nenadić, Igor, Brosch, Katharina, Kircher, Tilo, Gur, Raquel, Gur, Ruben, Satterthwaite, Theodore, Karuk, Andriana, Pomarol-Clotet, Edith, Radua, Joaquim, Fuentes-Claramonte, Paola, Salvador, Raymond, Spalletta, Gianfranco, Voineskos, Aristotle, Sim, Kang, Crespo-Facorro, Benedicto, Tordesillas Gutiérrez, Diana, Ehrlich, Stefan, Crossley, Nicolas, Grotegerd, Dominik, Repple, Jonathan, Lencer, Rebekka, Dannlowski, Udo, Calhoun, Vince, Rootes-Murdy, Kelly, Demro, Caroline, Ramsay, Ian, Sponheim, Scott, Schmidt, Andre, Borgwardt, Stefan, Tomyshev, Alexander, Lebedeva, Irina, Höschl, Cyril, Spaniel, Filip, Preda, Adrian, Nguyen, Dana, Uhlmann, Anne, Stein, Dan, Howells, Fleur, Temmingh, Henk, Diaz Zuluaga, Ana, López Jaramillo, Carlos, Iasevoli, Felice, Ji, Ellen, Homan, Stephanie, Omlor, Wolfgang, Homan, Philipp, Kaiser, Stefan, Seifritz, Erich, Misic, Bratislav, Valk, Sofie, Thompson, Paul, Van Erp, Theodorus, Turner, Jessica, Bernhardt, Boris, and Kirschner, Matthias

Subjects
Humans, Schizophrenia, Connectome, Adult, Female, Male, Magnetic Resonance Imaging, Cerebral Cortex, Nerve Net, Brain, Middle Aged, Neural Pathways, Young Adult
Abstract

Schizophrenia is a prototypical network disorder with widespread brain-morphological alterations, yet it remains unclear whether these distributed alterations robustly reflect the underlying network layout. We tested whether large-scale structural alterations in schizophrenia relate to normative structural and functional connectome architecture, and systematically evaluated robustness and generalizability of these network-level alterations. Leveraging anatomical MRI scans from 2439 adults with schizophrenia and 2867 healthy controls from 26 ENIGMA sites and normative data from the Human Connectome Project (n = 207), we evaluated structural alterations of schizophrenia against two network susceptibility models: (i) hub vulnerability, which examines associations between regional network centrality and magnitude of disease-related alterations; (ii) epicenter mapping, which identifies regions whose typical connectivity profile most closely resembles the disease-related morphological alterations. To assess generalizability and specificity, we contextualized the influence of site, disease stages, and individual clinical factors and compared network associations of schizophrenia with that found in affective disorders. Our findings show schizophrenia-related cortical thinning is spatially associated with functional and structural hubs, suggesting that highly interconnected regions are more vulnerable to morphological alterations. Predominantly temporo-paralimbic and frontal regions emerged as epicenters with connectivity profiles linked to schizophrenias alteration patterns. Findings were robust across sites, disease stages, and related to individual symptoms. Moreover, transdiagnostic comparisons revealed overlapping epicenters in schizophrenia and bipolar, but not major depressive disorder, suggestive of a pathophysiological continuity within the schizophrenia-bipolar-spectrum. In sum, cortical alterations over the course of schizophrenia robustly follow brain network architecture, emphasizing marked hub susceptibility and temporo-frontal epicenters at both the level of the group and the individual. Subtle variations of epicenters across disease stages suggest interacting pathological processes, while associations with patient-specific symptoms support additional inter-individual variability of hub vulnerability and epicenters in schizophrenia. Our work outlines potential pathways to better understand macroscale structural alterations, and inter- individual variability in schizophrenia.

Published
2024

7. 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, Cassandra, Nelson, Barnaby, Addington, Jean, Allott, Kelly, Anticevic, Alan, Arango, Celso, Baker, Justin, McGorry, Patrick, Mittal, Vijay, Nordentoft, Merete, Nunez, Angela, Pasternak, Ofer, Pearlson, Godfrey, Perez, Jesus, Perkins, Diana, Powers, Albert, Roalf, David, Sabb, Fred, Schiffman, Jason, Shah, Jai, Smesny, Stefan, Spark, Jessica, Stone, William, Strauss, Gregory, Tamayo, Zailyn, Torous, John, Upthegrove, Rachel, Vangel, Mark, Verma, Swapna, Wang, Jijun, Rossum, Inge, Wolf, Daniel, Wolff, Phillip, Wood, Stephen, Yung, Alison, Agurto, Carla, Alvarez-Jimenez, Mario, Amminger, Paul, Armando, Marco, Asgari-Targhi, Ameneh, Cahill, John, Carrión, Ricardo, Castro, Eduardo, Cetin-Karayumak, Suheyla, Mallar Chakravarty, M, Cho, Youngsun, Cotter, David, DAlfonso, Simon, Ennis, Michaela, Fadnavis, Shreyas, Fonteneau, Clara, Gao, Caroline, Gupta, Tina, Gur, Raquel, Gur, Ruben, Hamilton, Holly, Hoftman, Gil, Jacobs, Grace, Jarcho, Johanna, Ji, Jie, Kohler, Christian, Lalousis, Paris, Lavoie, Suzie, Lepage, Martin, Liebenthal, Einat, Mervis, Josh, Murty, Vishnu, Nicholas, Spero, Ning, Lipeng, Penzel, Nora, Poldrack, Russell, Polosecki, Pablo, Pratt, Danielle, Rabin, Rachel, Rahimi Eichi, Habiballah, Rathi, Yogesh, Reichenberg, Avraham, Reinen, Jenna, Rogers, Jack, Ruiz-Yu, Bernalyn, Scott, Isabelle, Seitz-Holland, Johanna, Srihari, Vinod, Srivastava, Agrima, Thompson, Andrew, Turetsky, Bruce, Walsh, Barbara, Whitford, Thomas, Wigman, Johanna, Yao, Beier, Yuen, Hok, Ahmed, Uzair, Byun, Andrew, Chung, Yoonho, Do, Kim, Hendricks, Larry, Huynh, Kevin, Jeffries, Clark, Lane, Erlend, and Langholm, Carsten

Subjects
clinical high risk, consortium, early detection, prediction, prevention, psychosis, Humans, Psychotic Disorders, Schizophrenia, Prospective Studies, Adult, Prodromal Symptoms, Young Adult, International Cooperation, Adolescent, Research Design, Male, Female
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

8. Tbx1 haploinsufficiency leads to local skull deformity, paraflocculus and flocculus dysplasia, and motor-learning deficit in 22q11.2 deletion syndrome

Author

Eom, Tae-Yeon, Schmitt, J. Eric, Li, Yiran, Davenport, Christopher M., Steinberg, Jeffrey, Bonnan, Audrey, Alam, Shahinur, Ryu, Young Sang, Paul, Leena, Hansen, Baranda S., Khairy, Khaled, Pelletier, Stephane, Pruett-Miller, Shondra M., Roalf, David R., Gur, Raquel E., Emanuel, Beverly S., McDonald-McGinn, Donna M., Smith, Jesse N., Li, Cai, Christie, Jason M., Northcott, Paul A., and Zakharenko, Stanislav S.

Published
2024
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9. Computer-vision analysis of craniofacial dysmorphology in 22q11.2 deletion syndrome and psychosis spectrum disorders

Author

Roalf, David R., McDonald-McGinn, Donna M., Jee, Joelle, Krall, Mckenna, Crowley, T. Blaine, Moberg, Paul J., Kohler, Christian, Calkins, Monica E., Crow, Andrew J.D., Fleischer, Nicole, Gallagher, R. Sean, Gonzenbach, Virgilio, Clark, Kelly, Gur, Ruben C., McClellan, Emily, McGinn, Daniel E., Mordy, Arianna, Ruparel, Kosha, Turetsky, Bruce I., Shinohara, Russell T., White, Lauren, Zackai, Elaine, and Gur, Raquel E.

Published
2024
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10. Functional connectivity development along the sensorimotor-association axis enhances the cortical hierarchy

Author

Luo, Audrey C., Sydnor, Valerie J., Pines, Adam, Larsen, Bart, Alexander-Bloch, Aaron F., Cieslak, Matthew, Covitz, Sydney, Chen, Andrew A., Esper, Nathalia Bianchini, Feczko, Eric, Franco, Alexandre R., Gur, Raquel E., Gur, Ruben C., Houghton, Audrey, Hu, Fengling, Keller, Arielle S., Kiar, Gregory, Mehta, Kahini, Salum, Giovanni A., Tapera, Tinashe, Xu, Ting, Zhao, Chenying, Salo, Taylor, Fair, Damien A., Shinohara, Russell T., Milham, Michael P., and Satterthwaite, Theodore D.

Published
2024
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11. Source-based morphometry reveals structural brain pattern abnormalities in 22q11.2 deletion syndrome.

Author

Ge, Ruiyang, Ching, Christopher, Bassett, Anne, Kushan, Leila, Antshel, Kevin, van Amelsvoort, Therese, Bakker, Geor, Butcher, Nancy, Campbell, Linda, Chow, Eva, Craig, Michael, Crossley, Nicolas, Cunningham, Adam, Daly, Eileen, Doherty, Joanne, Durdle, Courtney, Emanuel, Beverly, Fiksinski, Ania, Forsyth, Jennifer, Fremont, Wanda, Goodrich-Hunsaker, Naomi, Gudbrandsen, Maria, Gur, Raquel, Jalbrzikowski, Maria, Kates, Wendy, Lin, Amy, Linden, David, McCabe, Kathryn, McDonald-McGinn, Donna, Moss, Hayley, Murphy, Declan, Murphy, Kieran, Owen, Michael, Villalon-Reina, Julio, Repetto, Gabriela, Roalf, David, Ruparel, Kosha, Schmitt, J, Schuite-Koops, Sanne, Angkustsiri, Kathleen, Sun, Daqiang, Vajdi, Ariana, van den Bree, Marianne, Vorstman, Jacob, Thompson, Paul, Vila-Rodriguez, Fidel, and Bearden, Carrie

Subjects
22q11 deletion syndrome, gray matter volume, magnetic resonnance imaging, source-based morphometry, Female, Humans, Adolescent, Male, DiGeorge Syndrome, Magnetic Resonance Imaging, Brain, Psychotic Disorders, Gray Matter
Abstract

22q11.2 deletion syndrome (22q11DS) is the most frequently occurring microdeletion in humans. It is associated with a significant impact on brain structure, including prominent reductions in gray matter volume (GMV), and neuropsychiatric manifestations, including cognitive impairment and psychosis. It is unclear whether GMV alterations in 22q11DS occur according to distinct structural patterns. Then, 783 participants (470 with 22q11DS: 51% females, mean age [SD] 18.2 [9.2]; and 313 typically developing [TD] controls: 46% females, mean age 18.0 [8.6]) from 13 datasets were included in the present study. We segmented structural T1-weighted brain MRI scans and extracted GMV images, which were then utilized in a novel source-based morphometry (SBM) pipeline (SS-Detect) to generate structural brain patterns (SBPs) that capture co-varying GMV. We investigated the impact of the 22q11.2 deletion, deletion size, intelligence quotient, and psychosis on the SBPs. Seventeen GMV-SBPs were derived, which provided spatial patterns of GMV covariance associated with a quantitative metric (i.e., loading score) for analysis. Patterns of topographically widespread differences in GMV covariance, including the cerebellum, discriminated individuals with 22q11DS from healthy controls. The spatial extents of the SBPs that revealed disparities between individuals with 22q11DS and controls were consistent with the findings of the univariate voxel-based morphometry analysis. Larger deletion size was associated with significantly lower GMV in frontal and occipital SBPs; however, history of psychosis did not show a strong relationship with these covariance patterns. 22q11DS is associated with distinct structural abnormalities captured by topographical GMV covariance patterns that include the cerebellum. Findings indicate that structural anomalies in 22q11DS manifest in a nonrandom manner and in distinct covarying anatomical patterns, rather than a diffuse global process. These SBP abnormalities converge with previously reported cortical surface area abnormalities, suggesting disturbances of early neurodevelopment as the most likely underlying mechanism.

Published
2024

12. Distinct neurocognitive profiles and clinical phenotypes associated with copy number variation at the 22q11.2 locus

Author

O'Hora, Kathleen P, Kushan‐Wells, Leila, Schleifer, Charles H, Cruz, Shayne, Hoftman, Gil D, Jalbrzikowski, Maria, Gur, Raquel E, Gur, Ruben C, and Bearden, Carrie E

Subjects
Biological Psychology, Biomedical and Clinical Sciences, Psychology, Applied and Developmental Psychology, Brain Disorders, Mental Health, Neurodegenerative, Basic Behavioral and Social Science, Human Genome, Acquired Cognitive Impairment, Pediatric, Rare Diseases, Behavioral and Social Science, Autism, Intellectual and Developmental Disabilities (IDD), Neurosciences, Clinical Research, Genetics, Mental Illness, 2.1 Biological and endogenous factors, Mental health, Humans, Male, Young Adult, Adult, Adolescent, Female, DNA Copy Number Variations, DiGeorge Syndrome, Autism Spectrum Disorder, Psychotic Disorders, Phenotype, 22q.11.2 deletion syndrome, 22q11.2 duplication, cognition, copy number variation, intellectual ability, memory, psychopathology, psychosis, social function, Velocardiofacial syndrome, Clinical Sciences, Developmental & Child Psychology, Applied and developmental psychology, Clinical and health psychology
Abstract

Rare genetic variants that confer large effects on neurodevelopment and behavioral phenotypes can reveal novel gene-brain-behavior relationships relevant to autism. Copy number variation at the 22q11.2 locus offer one compelling example, as both the 22q11.2 deletion (22qDel) and duplication (22qDup) confer increased likelihood of autism spectrum disorders (ASD) and cognitive deficits, but only 22qDel confers increased psychosis risk. Here, we used the Penn Computerized Neurocognitive Battery (Penn-CNB) to characterized neurocognitive profiles of 126 individuals: 55 22qDel carriers (MAge  = 19.2 years, 49.1% male), 30 22qDup carriers (MAge  = 17.3 years, 53.3% male), and 41 typically developing (TD) subjects (MAge  = 17.3 years, 39.0% male). We performed linear mixed models to assess group differences in overall neurocognitive profiles, domain scores, and individual test scores. We found all three groups exhibited distinct overall neurocognitive profiles. 22qDel and 22qDup carriers showed significant accuracy deficits across all domains relative to controls (episodic memory, executive function, complex cognition, social cognition, and sensorimotor speed), with 22qDel carriers exhibiting more severe accuracy deficits, particularly in episodic memory. However, 22qDup carriers generally showed greater slowing than 22qDel carriers. Notably, slower social cognition speed was uniquely associated with increased global psychopathology and poorer psychosocial functioning in 22qDup. Compared to TD, 22q11.2 copy number variants (CNV) carriers failed to show age-associated improvements in multiple cognitive domains. Exploratory analyses revealed 22q11.2 CNV carriers with ASD exhibited differential neurocognitive profiles, based on 22q11.2 copy number. These results suggest that there are distinct neurocognitive profiles associated with either a loss or gain of genomic material at the 22q11.2 locus.

Published
2023

14. Alprazolam modulates persistence energy during emotion processing in first-degree relatives of individuals with schizophrenia: a network control study.

Author

Parkes, Linden, Larsen, Bart, Adebimpe, Azeez, Kahn, Ari, Gur, Ruben, Gur, Raquel, Satterthwaite, Theodore, Wolf, Daniel, Bassett, Dani, Mahadevan, Arun, Cornblath, Eli, Lydon-Staley, David, and Zhou, Dale

Subjects
Humans, Schizophrenia, Alprazolam, Emotions, Brain, Amygdala, Brain Mapping, Magnetic Resonance Imaging
Abstract

Schizophrenia is marked by deficits in facial affect processing associated with abnormalities in GABAergic circuitry, deficits also found in first-degree relatives. Facial affect processing involves a distributed network of brain regions including limbic regions like amygdala and visual processing areas like fusiform cortex. Pharmacological modulation of GABAergic circuitry using benzodiazepines like alprazolam can be useful for studying this facial affect processing network and associated GABAergic abnormalities in schizophrenia. Here, we use pharmacological modulation and computational modeling to study the contribution of GABAergic abnormalities toward emotion processing deficits in schizophrenia. Specifically, we apply principles from network control theory to model persistence energy - the control energy required to maintain brain activation states - during emotion identification and recall tasks, with and without administration of alprazolam, in a sample of first-degree relatives and healthy controls. Here, persistence energy quantifies the magnitude of theoretical external inputs during the task. We find that alprazolam increases persistence energy in relatives but not in controls during threatening face processing, suggesting a compensatory mechanism given the relative absence of behavioral abnormalities in this sample of unaffected relatives. Further, we demonstrate that regions in the fusiform and occipital cortices are important for facilitating state transitions during facial affect processing. Finally, we uncover spatial relationships (i) between regional variation in differential control energy (alprazolam versus placebo) and (ii) both serotonin and dopamine neurotransmitter systems, indicating that alprazolam may exert its effects by altering neuromodulatory systems. Together, these findings provide a new perspective on the distributed emotion processing network and the effect of GABAergic modulation on this network, in addition to identifying an association between schizophrenia risk and abnormal GABAergic effects on persistence energy during threat processing.

Published
2023

15. Chromatin regulators in the TBX1 network confer risk for conotruncal heart defects in 22q11.2DS.

Author

Zhao, Yingjie, Wang, Yujue, Shi, Lijie, McDonald-McGinn, Donna, Crowley, T, McGinn, Daniel, Tran, Oanh, Miller, Daniella, Lin, Jhih-Rong, Zackai, Elaine, Johnston, H, Chow, Eva, Vorstman, Jacob, Vingerhoets, Claudia, van Amelsvoort, Therese, Gothelf, Doron, Swillen, Ann, Breckpot, Jeroen, Vermeesch, Joris, Eliez, Stephan, Schneider, Maude, van den Bree, Marianne, Owen, Michael, Kates, Wendy, Repetto, Gabriela, Shashi, Vandana, Schoch, Kelly, Digilio, M, Unolt, Marta, Putotto, Carolina, Marino, Bruno, Pontillo, Maria, Armando, Marco, Vicari, Stefano, Angkustsiri, Kathleen, Campbell, Linda, Busa, Tiffany, Heine-Suñer, Damian, Murphy, Kieran, Murphy, Declan, García-Miñaúr, Sixto, Fernández, Luis, Zhang, Zhengdong, Goldmuntz, Elizabeth, Gur, Raquel, Emanuel, Beverly, Zheng, Deyou, Marshall, Christian, Bassett, Anne, Wang, Tao, Morrow, Bernice, and Bearden, Carrie

Abstract

Congenital heart disease (CHD) affecting the conotruncal region of the heart, occurs in 40-50% of patients with 22q11.2 deletion syndrome (22q11.2DS). This syndrome is a rare disorder with relative genetic homogeneity that can facilitate identification of genetic modifiers. Haploinsufficiency of TBX1, encoding a T-box transcription factor, is one of the main genes responsible for the etiology of the syndrome. We suggest that genetic modifiers of conotruncal defects in patients with 22q11.2DS may be in the TBX1 gene network. To identify genetic modifiers, we analyzed rare, predicted damaging variants in whole genome sequence of 456 cases with conotruncal defects and 537 controls, with 22q11.2DS. We then performed gene set approaches and identified chromatin regulatory genes as modifiers. Chromatin genes with recurrent damaging variants include EP400, KAT6A, KMT2C, KMT2D, NSD1, CHD7 and PHF21A. In total, we identified 37 chromatin regulatory genes, that may increase risk for conotruncal heart defects in 8.5% of 22q11.2DS cases. Many of these genes were identified as risk factors for sporadic CHD in the general population. These genes are co-expressed in cardiac progenitor cells with TBX1, suggesting that they may be in the same genetic network. The genes KAT6A, KMT2C, CHD7 and EZH2, have been previously shown to genetically interact with TBX1 in mouse models. Our findings indicate that disturbance of chromatin regulatory genes impact the TBX1 gene network serving as genetic modifiers of 22q11.2DS and sporadic CHD, suggesting that there are some shared mechanisms involving the TBX1 gene network in the etiology of CHD.

Published
2023

16. Compression supports low-dimensional representations of behavior across neural circuits

Author

Zhou, Dale, Kim, Jason Z., Pines, Adam R., Sydnor, Valerie J., Roalf, David R., Detre, John A., Gur, Ruben C., Gur, Raquel E., Satterthwaite, Theodore D., and Bassett, Dani S.

Subjects
Quantitative Biology - Neurons and Cognition
Abstract

Dimensionality reduction, a form of compression, can simplify representations of information to increase efficiency and reveal general patterns. Yet, this simplification also forfeits information, thereby reducing representational capacity. Hence, the brain may benefit from generating both compressed and uncompressed activity, and may do so in a heterogeneous manner across diverse neural circuits that represent low-level (sensory) or high-level (cognitive) stimuli. However, precisely how compression and representational capacity differ across the cortex remains unknown. Here we predict different levels of compression across regional circuits by using random walks on networks to model activity flow and to formulate rate-distortion functions, which are the basis of lossy compression. Using a large sample of youth ($n=1,040$), we test predictions in two ways: by measuring the dimensionality of spontaneous activity from sensorimotor to association cortex, and by assessing the representational capacity for 24 behaviors in neural circuits and 20 cognitive variables in recurrent neural networks. Our network theory of compression predicts the dimensionality of activity ($t=12.13, p<0.001$) and the representational capacity of biological ($r=0.53, p=0.016$) and artificial ($r=0.61, p<0.001$) networks. The model suggests how a basic form of compression is an emergent property of activity flow between distributed circuits that communicate with the rest of the network., Comment: arXiv admin note: text overlap with arXiv:2001.05078

Published
2022

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

Author

Groenewold, Nynke, Bas-Hoogendam, Janna, Amod, Alyssa, Laansma, Max, Van Velzen, Laura, Aghajani, Moji, Hilbert, Kevin, Oh, Hyuntaek, Salas, Ramiro, Jackowski, Andrea, Pan, Pedro, Salum, Giovanni, Blair, James, Blair, Karina, Hirsch, Joy, Pantazatos, Spiro, Schneier, Franklin, Talati, Ardesheer, Roelofs, Karin, Volman, Inge, Blanco-Hinojo, Laura, Cardoner, Narcís, Pujol, Jesus, Beesdo-Baum, Katja, Ching, Christopher, Thomopoulos, Sophia, Jansen, Andreas, Kircher, Tilo, Krug, Axel, Nenadić, Igor, Stein, Frederike, Dannlowski, Udo, Grotegerd, Dominik, Lemke, Hannah, Meinert, Susanne, Winter, Alexandra, Erb, Michael, Kreifelts, Benjamin, Gong, Qiyong, Lui, Su, Zhu, Fei, Mwangi, Benson, Soares, Jair, Wu, Mon-Ju, Bayram, Ali, Canli, Mesut, Tükel, Raşit, Westenberg, P, Heeren, Alexandre, Cremers, Henk, Hofmann, David, Straube, Thomas, Doruyter, Alexander, Lochner, Christine, Peterburs, Jutta, Van Tol, Marie-José, Gur, Raquel, Kaczkurkin, Antonia, Larsen, Bart, Satterthwaite, Theodore, Filippi, Courtney, Gold, Andrea, Harrewijn, Anita, Zugman, André, Bülow, Robin, Grabe, Hans, Völzke, Henry, Wittfeld, Katharina, Böhnlein, Joscha, Dohm, Katharina, Kugel, Harald, Schrammen, Elisabeth, Zwanzger, Peter, Leehr, Elisabeth, Sindermann, Lisa, Ball, Tali, Fonzo, Gregory, Paulus, Martin, Stein, Murray, Klumpp, Heide, Phan, K, Furmark, Tomas, Månsson, Kristoffer, Manzouri, Amirhossein, Avery, Suzanne, Blackford, Jennifer, Clauss, Jacqueline, Feola, Brandee, Harper, Jennifer, Sylvester, Chad, Lueken, Ulrike, Veltman, Dick, Winkler, Anderson, Jahanshad, Neda, Pine, Daniel, Thompson, Paul, Stein, Dan, Van der Wee, Nic, and Simmons, Alan

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

There is limited convergence in neuroimaging investigations into volumes of subcortical brain regions in social anxiety disorder (SAD). The inconsistent findings may arise from variations in methodological approaches across studies, including sample selection based on age and clinical characteristics. The ENIGMA-Anxiety Working Group initiated a global mega-analysis to determine whether differences in subcortical volumes can be detected in adults and adolescents with SAD relative to healthy controls. Volumetric data from 37 international samples with 1115 SAD patients and 2775 controls were obtained from ENIGMA-standardized protocols for image segmentation and quality assurance. Linear mixed-effects analyses were adjusted for comparisons across seven subcortical regions in each hemisphere using family-wise error (FWE)-correction. Mixed-effects d effect sizes were calculated. In the full sample, SAD patients showed smaller bilateral putamen volume than controls (left: d = -0.077, pFWE = 0.037; right: d = -0.104, pFWE = 0.001), and a significant interaction between SAD and age was found for the left putamen (r = -0.034, pFWE = 0.045). Smaller bilateral putamen volumes (left: d = -0.141, pFWE

Published
2023

20. Brain-based classification of youth with anxiety disorders: transdiagnostic examinations within the ENIGMA-Anxiety database using machine learning

Author

Bruin, Willem B., Zhutovsky, Paul, van Wingen, Guido A., Bas-Hoogendam, Janna Marie, Groenewold, Nynke A., Hilbert, Kevin, Winkler, Anderson M., Zugman, Andre, Agosta, Federica, Åhs, Fredrik, Andreescu, Carmen, Antonacci, Chase, Asami, Takeshi, Assaf, Michal, Barber, Jacques P., Bauer, Jochen, Bavdekar, Shreya Y., Beesdo-Baum, Katja, Benedetti, Francesco, Bernstein, Rachel, Björkstrand, Johannes, Blair, Robert J., Blair, Karina S., Blanco-Hinojo, Laura, Böhnlein, Joscha, Brambilla, Paolo, Bressan, Rodrigo A., Breuer, Fabian, Cano, Marta, Canu, Elisa, Cardinale, Elise M., Cardoner, Narcís, Cividini, Camilla, Cremers, Henk, Dannlowski, Udo, Diefenbach, Gretchen J., Domschke, Katharina, Doruyter, Alexander G. G., Dresler, Thomas, Erhardt, Angelika, Filippi, Massimo, Fonzo, Gregory A., Freitag, Gabrielle F., Furmark, Tomas, Ge, Tian, Gerber, Andrew J., Gosnell, Savannah N., Grabe, Hans J., Grotegerd, Dominik, Gur, Ruben C., Gur, Raquel E., Hamm, Alfons O., Han, Laura K. M., Harper, Jennifer C., Harrewijn, Anita, Heeren, Alexandre, Hofmann, David, Jackowski, Andrea P., Jahanshad, Neda, Jett, Laura, Kaczkurkin, Antonia N., Khosravi, Parmis, Kingsley, Ellen N., Kircher, Tilo, Kostic, Milutin, Larsen, Bart, Lee, Sang-Hyuk, Leehr, Elisabeth J., Leibenluft, Ellen, Lochner, Christine, Lui, Su, Maggioni, Eleonora, Manfro, Gisele G., Månsson, Kristoffer N. T., Marino, Claire E., Meeten, Frances, Milrod, Barbara, Jovanovic, Ana Munjiza, Mwangi, Benson, Myers, Michael J., Neufang, Susanne, Nielsen, Jared A., Ohrmann, Patricia A., Ottaviani, Cristina, Paulus, Martin P., Perino, Michael T., Phan, K. Luan, Poletti, Sara, Porta-Casteràs, Daniel, Pujol, Jesus, Reinecke, Andrea, Ringlein, Grace V., Rjabtsenkov, Pavel, Roelofs, Karin, Salas, Ramiro, Salum, Giovanni A., Satterthwaite, Theodore D., Schrammen, Elisabeth, Sindermann, Lisa, Smoller, Jordan W., Soares, Jair C., Stark, Rudolf, Stein, Frederike, Straube, Thomas, Straube, Benjamin, Strawn, Jeffrey R., Suarez-Jimenez, Benjamin, Sylvester, Chad M., Talati, Ardesheer, Thomopoulos, Sophia I., Tükel, Raşit, van Nieuwenhuizen, Helena, Werwath, Kathryn, Wittfeld, Katharina, Wright, Barry, Wu, Mon-Ju, Yang, Yunbo, Zilverstand, Anna, Zwanzger, Peter, Blackford, Jennifer U., Avery, Suzanne N., Clauss, Jacqueline A., Lueken, Ulrike, Thompson, Paul M., Pine, Daniel S., Stein, Dan J., van der Wee, Nic J. A., Veltman, Dick J., and Aghajani, Moji

Published
2024
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21. Atypical Brain Aging and Its Association With Working Memory Performance in Major Depressive Disorder

Author

Adamson, Chris, Adler, Sophie, Alexander-Bloch, Aaron F., Anagnostou, Evdokia, Anderson, Kevin M., Areces-Gonzalez, Ariosky, Astle, Duncan E., Auyeung, Bonnie, Ayub, Muhammad, Bae, Jong Bin, Ball, Gareth, Baron-Cohen, Simon, Beare, Richard, Bedford, Saashi A., Benegal, Vivek, Bethlehem, Richard A.I., Beyer, Frauke, Blangero, John, Cábez, Manuel Blesa, Boardman, James P., Borzage, Matthew, Bosch-Bayard, Jorge F., Bourke, Niall, Bullmore, Edward T., Calhoun, Vince D., Chakravarty, Mallar M., Chen, Christina, Chertavian, Casey, Chetelat, Gaël, Chong, Yap S., Corvin, Aiden, Costantino, Manuela, Courchesne, Eric, Crivello, Fabrice, Cropley, Vanessa L., Crosbie, Jennifer, Crossley, Nicolas, Delarue, Marion, Delorme, Richard, Desrivieres, Sylvane, Devenyi, Gabriel, Di Biase, Maria A., Dolan, Ray, Donald, Kirsten A., Donohoe, Gary, Dorfschmidt, Lena, Dunlop, Katharine, Edwards, Anthony D., Elison, Jed T., Ellis, Cameron T., Elman, Jeremy A., Eyler, Lisa, Fair, Damien A., Fletcher, Paul C., Fonagy, Peter, Franz, Carol E., Galan-Garcia, Lidice, Gholipour, Ali, Giedd, Jay, Gilmore, John H., Glahn, David C., Goodyer, Ian M., Grant, P.E., Groenewold, Nynke A., Gudapati, Shreya, Gunning, Faith M., Gur, Raquel E., Gur, Ruben C., Hammill, Christopher F., Hansson, Oskar, Hedden, Trey, Heinz, Andreas, Henson, Richard N., Heuer, Katja, Hoare, Jacqueline, Holla, Bharath, Holmes, Avram J., Huang, Hao, Ipser, Jonathan, Jack, Clifford R., Jr., Jackowski, Andrea P., Jia, Tianye, Jones, David T., Jones, Peter B., Kahn, Rene S., Karlsson, Hasse, Karlsson, Linnea, Kawashima, Ryuta, Kelley, Elizabeth A., Kern, Silke, Kim, Ki-Woong, Kitzbichler, Manfred G., Kremen, William S., Lalonde, François, Landeau, Brigitte, Lerch, Jason, Lewis, John D., Li, Jiao, Liao, Wei, Liston, Conor, Lombardo, Michael V., Lv, Jinglei, Mallard, Travis T., Marcelis, Machteld, Mathias, Samuel R., Mazoyer, Bernard, McGuire, Philip, Meaney, Michael J., Mechelli, Andrea, Misic, Bratislav, Morgan, Sarah E., Mothersill, David, Ortinau, Cynthia, Ossenkoppele, Rik, Ouyang, Minhui, Palaniyappan, Lena, Paly, Leo, Pan, Pedro M., Pantelis, Christos, Park, Min Tae M., Paus, Tomas, Pausova, Zdenka, Paz-Linares, Deirel, Binette, Alexa Pichet, Pierce, Karen, Qian, Xing, Qiu, Anqi, Raznahan, Armin, Rittman, Timothy, Rodrigue, Amanda, Rollins, Caitlin K., Romero-Garcia, Rafael, Ronan, Lisa, Rosenberg, Monica D., Rowitch, David H., Salum, Giovanni A., Satterthwaite, Theodore D., Schaare, H. Lina, Schabdach, Jenna, Schachar, Russell J., Schöll, Michael, Schultz, Aaron P., Seidlitz, Jakob, Sharp, David, Shinohara, Russell T., Skoog, Ingmar, Smyser, Christopher D., Sperling, Reisa A., Stein, Dan J., Stolicyn, Aleks, Suckling, John, Sullivan, Gemma, Thyreau, Benjamin, Toro, Roberto, Traut, Nicolas, Tsvetanov, Kamen A., Turk-Browne, Nicholas B., Tuulari, Jetro J., Tzourio, Christophe, Vachon-Presseau, Étienne, Valdes-Sosa, Mitchell J., Valdes-Sosa, Pedro A., Valk, Sofie L., van Amelsvoort, Therese, Vandekar, Simon N., Vasung, Lana, Vértes, Petra E., Victoria, Lindsay W., Villeneuve, Sylvia, Villringer, Arno, Vogel, Jacob W., Wagstyl, Konrad, Wang, Yin-Shan S., Warfield, Simon K., Warrier, Varun, Westman, Eric, Westwater, Margaret L., Whalley, Heather C., White, Simon R., Witte, A. Veronica, Yang, Ning, Yeo, B.T. Thomas, Yun, Hyuk Jin, Zalesky, Andrew, Zar, Heather J., Zettergren, Anna, Zhou, Juan H., Ziauddeen, Hisham, Zimmerman, Dabriel, Zugman, Andre, Zuo, Xi-Nian N., Ho, Natalie C.W., Nogovitsyn, Nikita, Metzak, Paul, Ballester, Pedro L., Hassel, Stefanie, Rotzinger, Susan, Poppenk, Jordan, Lam, Raymond W., Taylor, Valerie H., Milev, Roumen, Frey, Benicio N., Harkness, Kate L., Addington, Jean, and Kennedy, Sidney H.

Published
2024
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22. ASLPrep: a platform for processing of arterial spin labeled MRI and quantification of regional brain perfusion

Author

Adebimpe, Azeez, Bertolero, Maxwell, Dolui, Sudipto, Cieslak, Matthew, Murtha, Kristin, Baller, Erica B, Boeve, Bradley, Boxer, Adam, Butler, Ellyn R, Cook, Phil, Colcombe, Stan, Covitz, Sydney, Davatzikos, Christos, Davila, Diego G, Elliott, Mark A, Flounders, Matthew W, Franco, Alexandre R, Gur, Raquel E, Gur, Ruben C, Jaber, Basma, McMillian, Corey, Milham, Michael, Mutsaerts, Henk JMM, Oathes, Desmond J, Olm, Christopher A, Phillips, Jeffrey S, Tackett, Will, Roalf, David R, Rosen, Howard, Tapera, Tinashe M, Tisdall, M Dylan, Zhou, Dale, Esteban, Oscar, Poldrack, Russell A, Detre, John A, and Satterthwaite, Theodore D

Subjects
Biological Sciences, Bioengineering, Clinical Research, Neurosciences, Brain Disorders, Biomedical Imaging, Brain, Cerebrovascular Circulation, Humans, Magnetic Resonance Imaging, Perfusion, Spin Labels, ALLFTD Consortium, Technology, Medical and Health Sciences, Developmental Biology, Biological sciences
Abstract

Arterial spin labeled (ASL) magnetic resonance imaging (MRI) is the primary method for noninvasively measuring regional brain perfusion in humans. We introduce ASLPrep, a suite of software pipelines that ensure the reproducible and generalizable processing of ASL MRI data.

Published
2022

23. A normative chart for cognitive development in a genetically selected population

Author

Fiksinski, Ania M, Bearden, Carrie E, Bassett, Anne S, Kahn, René S, Zinkstok, Janneke R, Hooper, Stephen R, Tempelaar, Wanda, McDonald-McGinn, Donna, Swillen, Ann, Emanuel, Beverly, Morrow, Bernice, Gur, Raquel, Chow, Eva, van den Bree, Marianne, Vermeesch, Joris, Warren, Stephen, Owen, Michael, van Amelsvoort, Therese, Eliez, Stephan, Gothelf, Doron, Arango, Celso, Kates, Wendy, Simon, Tony, Murphy, Kieran, Repetto, Gabriela, Suner, Damian Heine, Vicari, Stefano, Cubells, Joseph, Armando, Marco, Philip, Nicole, Campbell, Linda, Garcia-Minaur, Sixto, Schneider, Maude, Shashi, Vandana, Vorstman, Jacob, and Breetvelt, Elemi J

Subjects
Brain Disorders, Serious Mental Illness, Pediatric, Mental Health, Schizophrenia, Clinical Research, Neurosciences, 2.1 Biological and endogenous factors, Aetiology, Mental health, Adult, Cognition, DiGeorge Syndrome, Humans, Intelligence Tests, 22q11DS International Consortium on Brain and Behavior, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract

Certain pathogenic genetic variants impact neurodevelopment and cause deviations from typical cognitive trajectories. Understanding variant-specific cognitive trajectories is clinically important for informed monitoring and identifying patients at risk for comorbid conditions. Here, we demonstrate a variant-specific normative chart for cognitive development for individuals with 22q11.2 deletion syndrome (22q11DS). We used IQ data from 1365 individuals with 22q11DS to construct variant-specific normative charts for cognitive development (Full Scale, Verbal, and Performance IQ). This allowed us to calculate Z-scores for each IQ datapoint. Then, we calculated the change between first and last available IQ assessments (delta Z-IQ-scores) for each individual with longitudinal IQ data (n = 708). We subsequently investigated whether using the variant-specific IQ-Z-scores would decrease required sample size to detect an effect with schizophrenia risk, as compared to standard IQ-scores. The mean Z-IQ-scores for FSIQ, VIQ, and PIQ were close to 0, indicating that participants had IQ-scores as predicted by the normative chart. The mean delta-Z-IQ-scores were equally close to 0, demonstrating a good fit of the normative chart and indicating that, as a group, individuals with 22q11DS show a decline in IQ-scores as they grow into adulthood. Using variant-specific IQ-Z-scores resulted in 30% decrease of required sample size, as compared to the standard IQ-based approach, to detect the association between IQ-decline and schizophrenia (p

Published
2022

27. Validation of the structured interview section of the penn computerized adaptive test for neurocognitive and clinical psychopathology assessment (CAT GOASSESS)

Author

Zoupou, Eirini, Moore, Tyler M., Kennedy, Kelly P., Calkins, Monica E., Gorgone, Alesandra, Sandro, Akira Di, Rush, Sage, Lopez, Katherine C., Ruparel, Kosha, Daryoush, Tarlan, Okoyeh, Paul, Savino, Andrew, Troyan, Scott, Wolf, Daniel H., Scott, J. Cobb, Gur, Raquel E., and Gur, Ruben C.

Published
2024
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28. Genes To Mental Health (G2MH): A Framework to Map the Combined Effects of Rare and Common Variants on Dimensions of Cognition and Psychopathology

Author

Jacquemont, Sébastien, Huguet, Guillaume, Klein, Marieke, Chawner, Samuel JRA, Donald, Kirsten A, van den Bree, Marianne BM, Sebat, Jonathan, Ledbetter, David H, Constantino, John N, Earl, Rachel K, McDonald-McGinn, Donna M, van Amelsvoort, Therese, Swillen, Ann, O’Donnell-Luria, Anne H, Glahn, David C, Almasy, Laura, Eichler, Evan E, Scherer, Stephen W, Robinson, Elise, Bassett, Anne S, Martin, Christa Lese, Finucane, Brenda, Vorstman, Jacob AS, Bearden, Carrie E, and Gur, Raquel E

Subjects
Clinical and Health Psychology, Biomedical and Clinical Sciences, Clinical Sciences, Psychology, Behavioral and Social Science, Biotechnology, Human Genome, Brain Disorders, Mental Health, Genetics, Basic Behavioral and Social Science, Mental health, Good Health and Well Being, Cognition, Humans, Mental Disorders, Psychiatry, Psychopathology, Genes to Mental Health Network, Autism Spectrum Disorder, Diagnosis and Classification, Genetics/Genomics, Intellectual Disabilities, Neurodevelopmental Disorders, Schizophrenia Spectrum and Other Psychotic Disorders, Medical and Health Sciences, Psychology and Cognitive Sciences, Clinical sciences, Clinical and health psychology
Abstract

Rare genomic disorders (RGDs) confer elevated risk for neurodevelopmental psychiatric disorders. In this era of intense genomics discoveries, the landscape of RGDs is rapidly evolving. However, there has not been comparable progress to date in scalable, harmonized phenotyping methods. As a result, beyond associations with categorical diagnoses, the effects on dimensional traits remain unclear for many RGDs. The nature and specificity of RGD effects on cognitive and behavioral traits is an area of intense investigation: RGDs are frequently associated with more than one psychiatric condition, and those studied to date affect, to varying degrees, a broad range of developmental and cognitive functions. Although many RGDs have large effects, phenotypic expression is typically influenced by additional genomic and environmental factors. There is emerging evidence that using polygenic risk scores in individuals with RGDs offers opportunities to refine prediction, thus allowing for the identification of those at greatest risk of psychiatric illness. However, translation into the clinic is hindered by roadblocks, which include limited genetic testing in clinical psychiatry, and the lack of guidelines for following individuals with RGDs, who are at high risk of developing psychiatric symptoms. The Genes to Mental Health Network (G2MH) is a newly funded National Institute of Mental Health initiative that will collect, share, and analyze large-scale data sets combining genomics and dimensional measures of psychopathology spanning diverse populations and geography. The authors present here the most recent understanding of the effects of RGDs on dimensional behavioral traits and risk for psychiatric conditions and discuss strategies that will be pursued within the G2MH network, as well as how expected results can be translated into clinical practice to improve patient outcomes.

Published
2022

29. Efficient coding in the economics of human brain connectomics.

Author

Lynn, Christopher, Cui, Zaixu, Ciric, Rastko, Baum, Graham, Moore, Tyler, Roalf, David, Detre, John, Gur, Ruben, Gur, Raquel, Satterthwaite, Theodore, Bassett, Dani, and Zhou, Dale

Subjects
Hierarchical organization, Integration, Lossy compression, Metabolic resources, Network communication dynamics, Network hubs, Rate-distortion theory
Abstract

In systems neuroscience, most models posit that brain regions communicate information under constraints of efficiency. Yet, evidence for efficient communication in structural brain networks characterized by hierarchical organization and highly connected hubs remains sparse. The principle of efficient coding proposes that the brain transmits maximal information in a metabolically economical or compressed form to improve future behavior. To determine how structural connectivity supports efficient coding, we develop a theory specifying minimum rates of message transmission between brain regions to achieve an expected fidelity, and we test five predictions from the theory based on random walk communication dynamics. In doing so, we introduce the metric of compression efficiency, which quantifies the trade-off between lossy compression and transmission fidelity in structural networks. In a large sample of youth (n = 1,042; age 8-23 years), we analyze structural networks derived from diffusion-weighted imaging and metabolic expenditure operationalized using cerebral blood flow. We show that structural networks strike compression efficiency trade-offs consistent with theoretical predictions. We find that compression efficiency prioritizes fidelity with development, heightens when metabolic resources and myelination guide communication, explains advantages of hierarchical organization, links higher input fidelity to disproportionate areal expansion, and shows that hubs integrate information by lossy compression. Lastly, compression efficiency is predictive of behavior-beyond the conventional network efficiency metric-for cognitive domains including executive function, memory, complex reasoning, and social cognition. Our findings elucidate how macroscale connectivity supports efficient coding and serve to foreground communication processes that utilize random walk dynamics constrained by network connectivity.

Published
2022

30. Effects of copy number variations on brain structure and risk for psychiatric illness: Large‐scale studies from the ENIGMA working groups on CNVs

Author

Sønderby, Ida E, Ching, Christopher RK, Thomopoulos, Sophia I, van der Meer, Dennis, Sun, Daqiang, Villalon‐Reina, Julio E, Agartz, Ingrid, Amunts, Katrin, Arango, Celso, Armstrong, Nicola J, Ayesa‐Arriola, Rosa, Bakker, Geor, Bassett, Anne S, Boomsma, Dorret I, Bülow, Robin, Butcher, Nancy J, Calhoun, Vince D, Caspers, Svenja, Chow, Eva WC, Cichon, Sven, Ciufolini, Simone, Craig, Michael C, Crespo‐Facorro, Benedicto, Cunningham, Adam C, Dale, Anders M, Dazzan, Paola, de Zubicaray, Greig I, Djurovic, Srdjan, Doherty, Joanne L, Donohoe, Gary, Draganski, Bogdan, Durdle, Courtney A, Ehrlich, Stefan, Emanuel, Beverly S, Espeseth, Thomas, Fisher, Simon E, Ge, Tian, Glahn, David C, Grabe, Hans J, Gur, Raquel E, Gutman, Boris A, Haavik, Jan, Håberg, Asta K, Hansen, Laura A, Hashimoto, Ryota, Hibar, Derrek P, Holmes, Avram J, Hottenga, Jouke‐Jan, Pol, Hilleke E Hulshoff, Jalbrzikowski, Maria, Knowles, Emma EM, Kushan, Leila, Linden, David EJ, Liu, Jingyu, Lundervold, Astri J, Martin‐Brevet, Sandra, Martínez, Kenia, Mather, Karen A, Mathias, Samuel R, McDonald‐McGinn, Donna M, McRae, Allan F, Medland, Sarah E, Moberget, Torgeir, Modenato, Claudia, Sánchez, Jennifer Monereo, Moreau, Clara A, Mühleisen, Thomas W, Paus, Tomas, Pausova, Zdenka, Prieto, Carlos, Ragothaman, Anjanibhargavi, Reinbold, Céline S, Marques, Tiago Reis, Repetto, Gabriela M, Reymond, Alexandre, Roalf, David R, Rodriguez‐Herreros, Borja, Rucker, James J, Sachdev, Perminder S, Schmitt, James E, Schofield, Peter R, Silva, Ana I, Stefansson, Hreinn, Stein, Dan J, Tamnes, Christian K, Tordesillas‐Gutiérrez, Diana, Ulfarsson, Magnus O, Vajdi, Ariana, van 't Ent, Dennis, van den Bree, Marianne BM, Vassos, Evangelos, Vázquez‐Bourgon, Javier, Vila‐Rodriguez, Fidel, Walters, G Bragi, Wen, Wei, Westlye, Lars T, Wittfeld, Katharina, Zackai, Elaine H, Stefánsson, Kári, and Jacquemont, Sebastien

Subjects
Biological Psychology, Biomedical and Clinical Sciences, Psychology, Mental Health, Mental Illness, Brain Disorders, Human Genome, Genetics, Prevention, Pediatric, Basic Behavioral and Social Science, Clinical Research, Biomedical Imaging, Behavioral and Social Science, Neurosciences, 2.1 Biological and endogenous factors, Mental health, Neurological, Brain, DNA Copy Number Variations, Humans, Magnetic Resonance Imaging, Mental Disorders, Multicenter Studies as Topic, Neurodevelopmental Disorders, Neuroimaging, brain structural imaging, copy number variant, diffusion tensor imaging, evolution, genetics-first approach, neurodevelopmental disorders, psychiatric disorders, ENIGMA-CNV Working Group, ENIGMA 22q11.2 Deletion Syndrome Working Group, Cognitive Sciences, Experimental Psychology, Biological psychology, Cognitive and computational psychology
Abstract

The Enhancing NeuroImaging Genetics through Meta-Analysis copy number variant (ENIGMA-CNV) and 22q11.2 Deletion Syndrome Working Groups (22q-ENIGMA WGs) were created to gain insight into the involvement of genetic factors in human brain development and related cognitive, psychiatric and behavioral manifestations. To that end, the ENIGMA-CNV WG has collated CNV and magnetic resonance imaging (MRI) data from ~49,000 individuals across 38 global research sites, yielding one of the largest studies to date on the effects of CNVs on brain structures in the general population. The 22q-ENIGMA WG includes 12 international research centers that assessed over 533 individuals with a confirmed 22q11.2 deletion syndrome, 40 with 22q11.2 duplications, and 333 typically developing controls, creating the largest-ever 22q11.2 CNV neuroimaging data set. In this review, we outline the ENIGMA infrastructure and procedures for multi-site analysis of CNVs and MRI data. So far, ENIGMA has identified effects of the 22q11.2, 16p11.2 distal, 15q11.2, and 1q21.1 distal CNVs on subcortical and cortical brain structures. Each CNV is associated with differences in cognitive, neurodevelopmental and neuropsychiatric traits, with characteristic patterns of brain structural abnormalities. Evidence of gene-dosage effects on distinct brain regions also emerged, providing further insight into genotype-phenotype relationships. Taken together, these results offer a more comprehensive picture of molecular mechanisms involved in typical and atypical brain development. This "genotype-first" approach also contributes to our understanding of the etiopathogenesis of brain disorders. Finally, we outline future directions to better understand effects of CNVs on brain structure and behavior.

Published
2022

31. Inter-rater reliability of subthreshold psychotic symptoms in individuals with 22q11.2 deletion syndrome

Author

Moore, Tyler M, Salzer, Deby, Bearden, Carrie E, Calkins, Monica E, Kates, Wendy R, Kushan, Leila, Gallagher, Robert Sean, Frumer, Dafna Sofrin, Weinberger, Ronnie, McDonald-McGinn, Donna M, Gur, Raquel E, and Gothelf, Doron

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Pediatric, Clinical Research, Mental health, Adolescent, Adult, Autism Spectrum Disorder, Child, DiGeorge Syndrome, Female, Humans, Male, Marfan Syndrome, Psychotic Disorders, Reproducibility of Results, Young Adult, Velocardiofacial syndrome, DiGeorge, Subthreshold psychotic symptoms, Structured Interview for Prodromal Syndromes, Scale of Prodromal Symptoms, Inter-rater reliability, Psychosis risk syndrome, Neurosciences, Psychology
Abstract

BackgroundPathways leading to psychosis in 22q11.2 deletion syndrome (22q11.2DS) have been the focus of intensive research during the last two decades. One of the common clinical risk factors for the evolution of psychosis in 22q11.2DS is the presence of positive and negative subthreshold psychotic symptoms. The gold standard for measuring subthreshold symptoms is the Structured Interview for Prodromal Syndromes (SIPS) and its accompanying Scale of Prodromal Symptoms (SOPS) ratings. Although the scale has been used by many centers studying 22q11.2DS, the inter-site reliability of the scale in this population has never been established.MethodsIn the present study, experienced clinical assessors from three large international centers studying 22q11.2DS independently rated video recordings of 18 adolescents and young adults with 22q11.2DS.ResultsThe intraclass correlations coefficients (ICCs) among three raters for the SOPS total scores, as well as for the positive, negative, and disorganization subscale scores, were good-to-excellent (ICCs range 0.73-0.93). The raters were also able to reliably determine the subjects' subthreshold syndrome status (ICC = 0.71). The reliability of individual items was good-to-excellent for all items, ranging from 0.61 for motor disturbances [G3] to 0.95 for bizarre thinking.ConclusionsOur results show that trained clinicians can reliably screen for subthreshold psychotic symptoms in individuals with 22q11.2DS. To increase assessment reliability, we suggest specific clarifications and simplifications to the standard SIPS interview for future studies.

Published
2021

32. Toward Generalizable and Transdiagnostic Tools for Psychosis Prediction: An Independent Validation and Improvement of the NAPLS-2 Risk Calculator in the Multisite PRONIA Cohort.

Author

Koutsouleris, Nikolaos, Worthington, Michelle, Dwyer, Dominic B, Kambeitz-Ilankovic, Lana, Sanfelici, Rachele, Fusar-Poli, Paolo, Rosen, Marlene, Ruhrmann, Stephan, Anticevic, Alan, Addington, Jean, Perkins, Diana O, Bearden, Carrie E, Cornblatt, Barbara A, Cadenhead, Kristin S, Mathalon, Daniel H, McGlashan, Thomas, Seidman, Larry, Tsuang, Ming, Walker, Elaine F, Woods, Scott W, Falkai, Peter, Lencer, Rebekka, Bertolino, Alessandro, Kambeitz, Joseph, Schultze-Lutter, Frauke, Meisenzahl, Eva, Salokangas, Raimo KR, Hietala, Jarmo, Brambilla, Paolo, Upthegrove, Rachel, Borgwardt, Stefan, Wood, Stephen, Gur, Raquel E, McGuire, Philip, and Cannon, Tyrone D

Subjects
Humans, Prognosis, Risk Factors, Longitudinal Studies, Psychotic Disorders, Prodromal Symptoms, Clinical high-risk states, First-episode depression, Machine learning, Psychosis prediction, Reciprocal external validation, Risk calculators, Prevention, Mental Health, Brain Disorders, Patient Safety, Mental health, Good Health and Well Being, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract

BackgroundTransition to psychosis is among the most adverse outcomes of clinical high-risk (CHR) syndromes encompassing ultra-high risk (UHR) and basic symptom states. Clinical risk calculators may facilitate an early and individualized interception of psychosis, but their real-world implementation requires thorough validation across diverse risk populations, including young patients with depressive syndromes.MethodsWe validated the previously described NAPLS-2 (North American Prodrome Longitudinal Study 2) calculator in 334 patients (26 with transition to psychosis) with CHR or recent-onset depression (ROD) drawn from the multisite European PRONIA (Personalised Prognostic Tools for Early Psychosis Management) study. Patients were categorized into three risk enrichment levels, ranging from UHR, over CHR, to a broad-risk population comprising patients with CHR or ROD (CHR|ROD). We assessed how risk enrichment and different predictive algorithms influenced prognostic performance using reciprocal external validation.ResultsAfter calibration, the NAPLS-2 model predicted psychosis with a balanced accuracy (BAC) (sensitivity, specificity) of 68% (73%, 63%) in the PRONIA-UHR cohort, 67% (74%, 60%) in the CHR cohort, and 70% (73%, 66%) in patients with CHR|ROD. Multiple model derivation in PRONIA-CHR|ROD and validation in NAPLS-2-UHR patients confirmed that broader risk definitions produced more accurate risk calculators (CHR|ROD-based vs. UHR-based performance: 67% [68%, 66%] vs. 58% [61%, 56%]). Support vector machines were superior in CHR|ROD (BAC = 71%), while ridge logistic regression and support vector machines performed similarly in CHR (BAC = 67%) and UHR cohorts (BAC = 65%). Attenuated psychotic symptoms predicted psychosis across risk levels, while younger age and reduced processing speed became increasingly relevant for broader risk cohorts.ConclusionsClinical-neurocognitive machine learning models operating in young patients with affective and CHR syndromes facilitate a more precise and generalizable prediction of psychosis. Future studies should investigate their therapeutic utility in large-scale clinical trials.

Published
2021

33. Normative modelling of brain morphometry across the lifespan with CentileBrain: algorithm benchmarking and model optimisation

Author

Agartz, Ingrid, Asherson, Philip, Ayesa-Arriola, Rosa, Banaj, Nerisa, Banaschewski, Tobias, Baumeister, Sarah, Bertolino, Alessandro, Borgwardt, Stefan, Bourque, Josiane, Brandeis, Daniel, Breier, Alan, Buitelaar, Jan K, Cannon, Dara M, Cervenka, Simon, Conrod, Patricia J, Crespo-Facorro, Benedicto, Davey, Christopher G, de Haan, Lieuwe, de Zubicaray, Greig I, Di Giorgio, Annabella, Frodl, Thomas, Gruner, Patricia, Gur, Raquel E, Gur, Ruben C, Harrison, Ben J, Hatton, Sean N, Hickie, Ian, Howells, Fleur M, Huyser, Chaim, Jernigan, Terry L, Jiang, Jiyang, Joska, John A, Kahn, René S, Kalnin, Andrew J, Kochan, Nicole A, Koops, Sanne, Kuntsi, Jonna, Lagopoulos, Jim, Lazaro, Luisa, Lebedeva, Irina S, Lochner, Christine, Martin, Nicholas G, Mazoyer, Bernard, McDonald, Brenna C, McDonald, Colm, McMahon, Katie L, Medland, Sarah, Modabbernia, Amirhossein, Mwangi, Benson, Nakao, Tomohiro, Nyberg, Lars, Piras, Fabrizio, Portella, Maria J, Qiu, Jiang, Roffman, Joshua L, Sachdev, Perminder S, Sanford, Nicole, Satterthwaite, Theodore D, Saykin, Andrew J, Sellgren, Carl M, Sim, Kang, Smoller, Jordan W, Soares, Jair C, Sommer, Iris E, Spalletta, Gianfranco, Stein, Dan J, Thomopoulos, Sophia I, Tomyshev, Alexander S, Tordesillas-Gutiérrez, Diana, Trollor, Julian N, van 't Ent, Dennis, van den Heuvel, Odile A, van Erp, Theo GM, van Haren, Neeltje EM, Vecchio, Daniela, Veltman, Dick J, Wang, Yang, Weber, Bernd, Wei, Dongtao, Wen, Wei, Westlye, Lars T, Williams, Steven CR, Wright, Margaret J, Wu, Mon-Ju, Yu, Kevin, Ge, Ruiyang, Yu, Yuetong, Qi, Yi Xuan, Fan, Yu-nan, Chen, Shiyu, Gao, Chuntong, Haas, Shalaila S, New, Faye, Boomsma, Dorret I, Brodaty, Henry, Brouwer, Rachel M, Buckner, Randy, Caseras, Xavier, Crivello, Fabrice, Crone, Eveline A, Erk, Susanne, Fisher, Simon E, Franke, Barbara, Glahn, David C, Dannlowski, Udo, Grotegerd, Dominik, Gruber, Oliver, Hulshoff Pol, Hilleke E, Schumann, Gunter, Tamnes, Christian K, Walter, Henrik, Wierenga, Lara M, Jahanshad, Neda, Thompson, Paul M, and Frangou, Sophia

Published
2024
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36. Validation of the cognitive section of the Penn computerized adaptive test for neurocognitive and clinical psychopathology assessment (CAT-CCNB)

Author

Di Sandro, Akira, Moore, Tyler M., Zoupou, Eirini, Kennedy, Kelly P., Lopez, Katherine C., Ruparel, Kosha, Njokweni, Lucky J., Rush, Sage, Daryoush, Tarlan, Franco, Olivia, Gorgone, Alesandra, Savino, Andrew, Didier, Paige, Wolf, Daniel H., Calkins, Monica E., Cobb Scott, J., Gur, Raquel E., and Gur, Ruben C.

Published
2024
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37. Psychosis brain subtypes validated in first-episode cohorts and related to illness remission: results from the PHENOM consortium

Author

Dwyer, Dominic B., Chand, Ganesh B., Pigoni, Alessandro, Khuntia, Adyasha, Wen, Junhao, Antoniades, Mathilde, Hwang, Gyujoon, Erus, Guray, Doshi, Jimit, Srinivasan, Dhivya, Varol, Erdem, Kahn, Rene S., Schnack, Hugo G., Meisenzahl, Eva, Wood, Stephen J., Zhuo, Chuanjun, Sotiras, Aristeidis, Shinohara, Russell T., Shou, Haochang, Fan, Yong, Schaulfelberger, Maristela, Rosa, Pedro, Lalousis, Paris A., Upthegrove, Rachel, Kaczkurkin, Antonia N., Moore, Tyler M., Nelson, Barnaby, Gur, Raquel E., Gur, Ruben C., Ritchie, Marylyn D., Satterthwaite, Theodore D., Murray, Robin M., Di Forti, Marta, Ciufolini, Simone, Zanetti, Marcus V., Wolf, Daniel H., Pantelis, Christos, Crespo-Facorro, Benedicto, Busatto, Geraldo F., Davatzikos, Christos, Koutsouleris, Nikolaos, and Dazzan, Paola

Published
2023
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38. Rare coding variants as risk modifiers of the 22q11.2 deletion implicate postnatal cortical development in syndromic schizophrenia

Author

Lin, Jhih-Rong, Zhao, Yingjie, Jabalameli, M. Reza, Nguyen, Nha, Mitra, Joydeep, Swillen, Ann, Vorstman, Jacob A. S., Chow, Eva W. C., van den Bree, Marianne, Emanuel, Beverly S., Vermeesch, Joris R., Owen, Michael J., Williams, Nigel M., Bassett, Anne S., McDonald-McGinn, Donna M., Gur, Raquel E., Bearden, Carrie E., Morrow, Bernice E., Lachman, Herbert M., and Zhang, Zhengdong D.

Published
2023
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39. Medical Image Harmonization Using Deep Learning Based Canonical Mapping: Toward Robust and Generalizable Learning in Imaging

Author

Bashyam, Vishnu M., Doshi, Jimit, Erus, Guray, Srinivasan, Dhivya, Abdulkadir, Ahmed, Habes, Mohamad, Fan, Yong, Masters, Colin L., Maruff, Paul, Zhuo, Chuanjun, Völzke, Henry, Johnson, Sterling C., Fripp, Jurgen, Koutsouleris, Nikolaos, Satterthwaite, Theodore D., Wolf, Daniel H., Gur, Raquel E., Gur, Ruben C., Morris, John C., Albert, Marilyn S., Grabe, Hans J., Resnick, Susan M., Bryan, R. Nick, Wolk, David A., Shou, Haochang, Nasrallah, Ilya M., and Davatzikos, Christos

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Conventional and deep learning-based methods have shown great potential in the medical imaging domain, as means for deriving diagnostic, prognostic, and predictive biomarkers, and by contributing to precision medicine. However, these methods have yet to see widespread clinical adoption, in part due to limited generalization performance across various imaging devices, acquisition protocols, and patient populations. In this work, we propose a new paradigm in which data from a diverse range of acquisition conditions are "harmonized" to a common reference domain, where accurate model learning and prediction can take place. By learning an unsupervised image to image canonical mapping from diverse datasets to a reference domain using generative deep learning models, we aim to reduce confounding data variation while preserving semantic information, thereby rendering the learning task easier in the reference domain. We test this approach on two example problems, namely MRI-based brain age prediction and classification of schizophrenia, leveraging pooled cohorts of neuroimaging MRI data spanning 9 sites and 9701 subjects. Our results indicate a substantial improvement in these tasks in out-of-sample data, even when training is restricted to a single site.

Published
2020

40. Network controllability in transmodal cortex predicts psychosis spectrum symptoms

Author

Parkes, Linden, Moore, Tyler M., Calkins, Monica E., Cieslak, Matthew, Roalf, David R., Wolf, Daniel H., Gur, Ruben C., Gur, Raquel E., Satterthwaite, Theodore D., and Bassett, Danielle S.

Subjects
Quantitative Biology - Neurons and Cognition
Abstract

The psychosis spectrum is associated with structural dysconnectivity concentrated in transmodal association cortex. However, understanding of this pathophysiology has been limited by an exclusive focus on the direct connections to a region. Using Network Control Theory, we measured variation in both direct and indirect structural connections to a region to gain new insights into the pathophysiology of the psychosis spectrum. We used psychosis symptom data and structural connectivity in 1,068 youths aged 8 to 22 years from the Philadelphia Neurodevelopmental Cohort. Applying a Network Control Theory metric called average controllability, we estimated each brain region's capacity to leverage its direct and indirect structural connections to control linear brain dynamics. Next, using non-linear regression, we determined the accuracy with which average controllability could predict negative and positive psychosis spectrum symptoms in out-of-sample testing. We also compared prediction performance for average controllability versus strength, which indexes only direct connections to a region. Finally, we assessed how the prediction performance for psychosis spectrum symptoms varied over the functional hierarchy spanning unimodal to transmodal cortex. Average controllability outperformed strength at predicting positive psychosis spectrum symptoms, demonstrating that indexing indirect structural connections to a region improved prediction performance. Critically, improved prediction was concentrated in association cortex for average controllability, whereas prediction performance for strength was uniform across the cortex, suggesting that indexing indirect connections is crucial in association cortex. Examining inter-individual variation in direct and indirect structural connections to association cortex is crucial for accurate prediction of positive psychosis spectrum symptoms.

Published
2020

41. Anticholinergic Medication Burden–Associated Cognitive Impairment in Schizophrenia

Author

Joshi, Yash B, Thomas, Michael L, Braff, David L, Green, Michael F, Gur, Ruben C, Gur, Raquel E, Nuechterlein, Keith H, Stone, William S, Greenwood, Tiffany A, Lazzeroni, Laura C, MacDonald, Laura R, Molina, Juan L, Nungaray, John A, Radant, Allen D, Silverman, Jeremy M, Sprock, Joyce, Sugar, Catherine A, Tsuang, Debby W, Tsuang, Ming T, Turetsky, Bruce I, Swerdlow, Neal R, and Light, Gregory A

Subjects
Biological Psychology, Pharmacology and Pharmaceutical Sciences, Biomedical and Clinical Sciences, Psychology, Clinical Research, Behavioral and Social Science, Schizophrenia, Serious Mental Illness, Neurosciences, Brain Disorders, Mental Health, 6.1 Pharmaceuticals, Evaluation of treatments and therapeutic interventions, Mental health, Adolescent, Adult, Aged, Cholinergic Antagonists, Cognition, Cognitive Dysfunction, Cohort Studies, Cross-Sectional Studies, Humans, Middle Aged, Neuropsychological Tests, Young Adult, Anticholinergics, Cognition/Learning/Memory, Psychopharmacology, Schizophrenia Spectrum and Other Psychotic Disorders, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Clinical sciences, Clinical and health psychology
Abstract

ObjectiveMany psychotropic medications used to treat schizophrenia have significant anticholinergic properties, which are linked to cognitive impairment and dementia risk in healthy subjects. Clarifying the impact of cognitive impairment attributable to anticholinergic medication burden may help optimize cognitive outcomes in schizophrenia. The aim of this study was to comprehensively characterize how this burden affects functioning across multiple cognitive domains in schizophrenia outpatients.MethodsCross-sectional data were analyzed using inferential statistics and exploratory structural equation modeling to determine the relationship between anticholinergic medication burden and cognition. Patients with a diagnosis of schizophrenia or schizoaffective disorder (N=1,120) were recruited from the community at five U.S. universities as part of the Consortium on the Genetics of Schizophrenia-2. For each participant, prescribed medications were rated and summed according to a modified Anticholinergic Cognitive Burden (ACB) scale. Cognitive functioning was assessed by performance on domains of the Penn Computerized Neurocognitive Battery (PCNB).ResultsACB score was significantly associated with cognitive performance, with higher ACB groups scoring worse than lower ACB groups on all domains tested on the PCNB. Similar effects were seen on other cognitive tests. Effects remained significant after controlling for demographic characteristics and potential proxies of illness severity, including clinical symptoms and chlorpromazine-equivalent antipsychotic dosage.ConclusionsAnticholinergic medication burden in schizophrenia is substantial, common, conferred by multiple medication classes, and associated with cognitive impairments across all cognitive domains. Anticholinergic medication burden from all medication classes-including psychotropics used in usual care-should be considered in treatment decisions and accounted for in studies of cognitive functioning in schizophrenia.

Published
2021

42. Genetic contributors to risk of schizophrenia in the presence of a 22q11.2 deletion.

Author

Cleynen, Isabelle, Engchuan, Worrawat, Hestand, Matthew S, Heung, Tracy, Holleman, Aaron M, Johnston, H Richard, Monfeuga, Thomas, McDonald-McGinn, Donna M, Gur, Raquel E, Morrow, Bernice E, Swillen, Ann, Vorstman, Jacob AS, Bearden, Carrie E, Chow, Eva WC, van den Bree, Marianne, Emanuel, Beverly S, Vermeesch, Joris R, Warren, Stephen T, Owen, Michael J, Chopra, Pankaj, Cutler, David J, Duncan, Richard, Kotlar, Alex V, Mulle, Jennifer G, Voss, Anna J, Zwick, Michael E, Diacou, Alexander, Golden, Aaron, Guo, Tingwei, Lin, Jhih-Rong, Wang, Tao, Zhang, Zhengdong, Zhao, Yingjie, Marshall, Christian, Merico, Daniele, Jin, Andrea, Lilley, Brenna, Salmons, Harold I, Tran, Oanh, Holmans, Peter, Pardinas, Antonio, Walters, James TR, Demaerel, Wolfram, Boot, Erik, Butcher, Nancy J, Costain, Gregory A, Lowther, Chelsea, Evers, Rens, van Amelsvoort, Therese AMJ, van Duin, Esther, Vingerhoets, Claudia, Breckpot, Jeroen, Devriendt, Koen, Vergaelen, Elfi, Vogels, Annick, Crowley, T Blaine, McGinn, Daniel E, Moss, Edward M, Sharkus, Robert J, Unolt, Marta, Zackai, Elaine H, Calkins, Monica E, Gallagher, Robert S, Gur, Ruben C, Tang, Sunny X, Fritsch, Rosemarie, Ornstein, Claudia, Repetto, Gabriela M, Breetvelt, Elemi, Duijff, Sasja N, Fiksinski, Ania, Moss, Hayley, Niarchou, Maria, Murphy, Kieran C, Prasad, Sarah E, Daly, Eileen M, Gudbrandsen, Maria, Murphy, Clodagh M, Murphy, Declan G, Buzzanca, Antonio, Fabio, Fabio Di, Digilio, Maria C, Pontillo, Maria, Marino, Bruno, Vicari, Stefano, Coleman, Karlene, Cubells, Joseph F, Ousley, Opal Y, Carmel, Miri, Gothelf, Doron, Mekori-Domachevsky, Ehud, Michaelovsky, Elena, Weinberger, Ronnie, Weizman, Abraham, Kushan, Leila, Jalbrzikowski, Maria, Armando, Marco, Eliez, Stéphan, Sandini, Corrado, and Schneider, Maude

Subjects
International 22q11.2DS Brain and Behavior Consortium, Prevention, Serious Mental Illness, Genetics, Human Genome, Schizophrenia, Neurosciences, Mental Health, Clinical Research, Pediatric, Brain Disorders, 2.1 Biological and endogenous factors, Mental health, Psychiatry, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences
Abstract

Schizophrenia occurs in about one in four individuals with 22q11.2 deletion syndrome (22q11.2DS). The aim of this International Brain and Behavior 22q11.2DS Consortium (IBBC) study was to identify genetic factors that contribute to schizophrenia, in addition to the ~20-fold increased risk conveyed by the 22q11.2 deletion. Using whole-genome sequencing data from 519 unrelated individuals with 22q11.2DS, we conducted genome-wide comparisons of common and rare variants between those with schizophrenia and those with no psychotic disorder at age ≥25 years. Available microarray data enabled direct comparison of polygenic risk for schizophrenia between 22q11.2DS and independent population samples with no 22q11.2 deletion, with and without schizophrenia (total n = 35,182). Polygenic risk for schizophrenia within 22q11.2DS was significantly greater for those with schizophrenia (padj = 6.73 × 10-6). Novel reciprocal case-control comparisons between the 22q11.2DS and population-based cohorts showed that polygenic risk score was significantly greater in individuals with psychotic illness, regardless of the presence of the 22q11.2 deletion. Within the 22q11.2DS cohort, results of gene-set analyses showed some support for rare variants affecting synaptic genes. No common or rare variants within the 22q11.2 deletion region were significantly associated with schizophrenia. These findings suggest that in addition to the deletion conferring a greatly increased risk to schizophrenia, the risk is higher when the 22q11.2 deletion and common polygenic risk factors that contribute to schizophrenia in the general population are both present.

Published
2021

43. Sex differences in the functional topography of association networks in youth

Author

Shanmugan, Sheila, Seidlitz, Jakob, Cui, Zaixu, Adebimpe, Azeez, Bassett, Danielle S., Bertolero, Maxwell A., Davatzikos, Christos, Fair, Damien A., Gur, Raquel E., Gur, Ruben C., Larsen, Bart, Li, Hongming, Pines, Adam, Raznahan, Armin, Roalf, David R., Shinohara, Russell T., Vogel, Jacob, Wolf, Daniel H., Fan, Yong, Alexander-Bloch, Aaron, and Satterthwaite, Theodore D.

Published
2022

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

Author

Cieslak, Matthew, Cook, Philip A, He, Xiaosong, Yeh, Fang-Cheng, Dhollander, Thijs, Adebimpe, Azeez, Aguirre, Geoffrey K, Bassett, Danielle S, Betzel, Richard F, Bourque, Josiane, Cabral, Laura M, Davatzikos, Christos, Detre, John A, Earl, Eric, Elliott, Mark A, Fadnavis, Shreyas, Fair, Damien A, Foran, Will, Fotiadis, Panagiotis, Garyfallidis, Eleftherios, Giesbrecht, Barry, Gur, Ruben C, Gur, Raquel E, Kelz, Max B, Keshavan, Anisha, Larsen, Bart S, Luna, Beatriz, Mackey, Allyson P, Milham, Michael P, Oathes, Desmond J, Perrone, Anders, Pines, Adam R, Roalf, David R, Richie-Halford, Adam, Rokem, Ariel, Sydnor, Valerie J, Tapera, Tinashe M, Tooley, Ursula A, Vettel, Jean M, Yeatman, Jason D, Grafton, Scott T, and Satterthwaite, Theodore D

Subjects
Biological Sciences, Bioengineering, Neurosciences, Clinical Research, Biomedical Imaging, Cardiovascular, Brain, Diffusion Magnetic Resonance Imaging, Humans, Image Processing, Computer-Assisted, Programming Languages, Software, Workflow, Technology, Medical and Health Sciences, Developmental Biology, Biological sciences
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

46. Prioritizing Genetic Contributors to Cortical Alterations in 22q11.2 Deletion Syndrome Using Imaging Transcriptomics

Author

Forsyth, Jennifer K, Mennigen, Eva, Lin, Amy, Sun, Daqiang, Vajdi, Ariana, Kushan-Wells, Leila, Ching, Christopher RK, Villalon-Reina, Julio E, Thompson, Paul M, Jonas, Rachel K, Pacheco-Hansen, Laura, Bakker, Geor, van Amelsvoort, Therese, Antshel, Kevin M, Fremont, Wanda, Kates, Wendy R, Campbell, Linda E, McCabe, Kathryn L, Craig, Michael C, Daly, Eileen, Gudbrandsen, Maria, Murphy, Clodagh M, Murphy, Declan G, Murphy, Kieran C, Fiksinski, Ania, Koops, Sanne, Vorstman, Jacob, Crowley, T Blaine, Emanuel, Beverly S, Gur, Raquel E, McDonald-McGinn, Donna M, Roalf, David R, Ruparel, Kosha, Schmitt, J Eric, Zackai, Elaine H, Durdle, Courtney A, Goodrich-Hunsaker, Naomi J, Simon, Tony J, Bassett, Anne S, Butcher, Nancy J, Chow, Eva WC, Vila-Rodriguez, Fidel, Cunningham, Adam, Doherty, Joanne L, Linden, David E, Moss, Hayley, Owen, Michael J, van den Bree, Marianne, Crossley, Nicolas A, Repetto, Gabriela M, and Bearden, Carrie E

Subjects
Biomedical and Clinical Sciences, Neurosciences, Pediatric, Human Genome, Congenital Structural Anomalies, Genetics, Rare Diseases, Biotechnology, Clinical Research, 2.1 Biological and endogenous factors, 22q11 Deletion Syndrome, Brain Cortical Thickness, Case-Control Studies, Cerebral Cortex, DNA Copy Number Variations, Gene Expression Profiling, Gene Expression Regulation, Developmental, Haploinsufficiency, Humans, Magnetic Resonance Imaging, MicroRNAs, Mitochondrial Proteins, RNA-Binding Proteins, Receptors, Purinergic P2, copy number variant, cortical thickness, DGCR8, gene expression, surface area, 22q11.2 ENIGMA Consortium, Psychology, Cognitive Sciences, Experimental Psychology, Biological psychology, Cognitive and computational psychology
Abstract

22q11.2 deletion syndrome (22q11DS) results from a hemizygous deletion that typically spans 46 protein-coding genes and is associated with widespread alterations in brain morphology. The specific genetic mechanisms underlying these alterations remain unclear. In the 22q11.2 ENIGMA Working Group, we characterized cortical alterations in individuals with 22q11DS (n = 232) versus healthy individuals (n = 290) and conducted spatial convergence analyses using gene expression data from the Allen Human Brain Atlas to prioritize individual genes that may contribute to altered surface area (SA) and cortical thickness (CT) in 22q11DS. Total SA was reduced in 22q11DS (Z-score deviance = -1.04), with prominent reductions in midline posterior and lateral association regions. Mean CT was thicker in 22q11DS (Z-score deviance = +0.64), with focal thinning in a subset of regions. Regional expression of DGCR8 was robustly associated with regional severity of SA deviance in 22q11DS; AIFM3 was also associated with SA deviance. Conversely, P2RX6 was associated with CT deviance. Exploratory analysis of gene targets of microRNAs previously identified as down-regulated due to DGCR8 deficiency suggested that DGCR8 haploinsufficiency may contribute to altered corticogenesis in 22q11DS by disrupting cell cycle modulation. These findings demonstrate the utility of combining neuroanatomic and transcriptomic datasets to derive molecular insights into complex, multigene copy number variants.

Published
2021

47. Development of white matter fiber covariance networks supports executive function in youth

Author

Bagautdinova, Joëlle, Bourque, Josiane, Sydnor, Valerie J., Cieslak, Matthew, Alexander-Bloch, Aaron F., Bertolero, Maxwell A., Cook, Philip A., Gur, Raquel E., Gur, Ruben C., Hu, Fengling, Larsen, Bart, Moore, Tyler M., Radhakrishnan, Hamsanandini, Roalf, David R., Shinohara, Russel T., Tapera, Tinashe M., Zhao, Chenying, Sotiras, Aristeidis, Davatzikos, Christos, and Satterthwaite, Theodore D.

Published
2023
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50. The molecular genetic landscape of human brain size variation

Author

Seidlitz, Jakob, Mallard, Travis T., Vogel, Jacob W., Lee, Younga H., Warrier, Varun, Ball, Gareth, Hansson, Oskar, Hernandez, Leanna M., Mandal, Ayan S., Wagstyl, Konrad, Lombardo, Michael V., Courchesne, Eric, Glessner, Joseph T., Satterthwaite, Theodore D., Bethlehem, Richard A.I., Bernstock, Joshua D., Tasaki, Shinya, Ng, Bernard, Gaiteri, Chris, Smoller, Jordan W., Ge, Tian, Gur, Raquel E., Gandal, Michael J., and Alexander-Bloch, Aaron F.

Published
2023
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