Xinyuan Liang, Lianglong Sun, Xuhong Liao, Tianyuan Lei, Mingrui Xia, Dingna Duan, Zilong Zeng, Qiongling Li, Zhilei Xu, Weiwei Men, Yanpei Wang, Shuping Tan, Jia-Hong Gao, Shaozheng Qin, Sha Tao, Qi Dong, Tengda Zhao, Yong He <<<

AbstractCortical thinning is an important hallmark of the maturation of brain morphology during childhood and adolescence. However, the connectome-based wiring mechanism that underlies cortical maturation remains unclear. Here, we show cortical thinning patterns primarily located in the lateral frontal and parietal heteromodal nodes during childhood and adolescence, which are structurally constrained by white matter network architecture and are particularly represented using a network-based diffusion model. Furthermore, connectome-based constraints are regionally heterogeneous, with the largest constraints residing in frontoparietal nodes, and are associated with gene expression signatures of microstructural neurodevelopmental events. These results are highly reproducible in another independent dataset. These findings advance our understanding of network-level mechanisms and the associated genetic basis that underlies the maturational process of cortical morphology during childhood and adolescence. <<<

Nga Yan Tse, Aswin Ratheesh, Ye Ella Tian, Colm G. Connolly, Christopher G. Davey, Saampras Ganesan, Ian H. Gotlib, Ben J. Harrison, Laura K. M. Han, Tiffany C. Ho, Alec J. Jamieson, Jaclyn S. Kirshenbaum, Yong Liu, Xiaohong Ma, Amar Ojha, Jiang Qiu, Matthew D. Sacchet, Lianne Schmaal, Alan N. Simmons, John Suckling, Dongtao Wei, Xiao Yang, Tony T. Yang, Robin F. H. Cash, Andrew Zalesky <<<

Nga Yan Tse, Aswin Ratheesh, Ye Ella Tian, Colm G. Connolly, Christopher G. Davey, Saampras Ganesan, Ian H. Gotlib, Ben J. Harrison, Laura K. M. Han, Tiffany C. Ho, Alec J. Jamieson, Jaclyn S. Kirshenbaum, Yong Liu, Xiaohong Ma, Amar Ojha, Jiang Qiu, Matthew D. Sacchet, Lianne Schmaal, Alan N. Simmons, John Suckling, Dongtao Wei, Xiao Yang, Tony T. Yang, Robin F. H. Cash, Andrew Zalesky <<<

AbstractDepression and anxiety are common and often comorbid mental health disorders that represent risk factors for aging-related conditions. Brain aging has shown to be more advanced in patients with major depressive disorder (MDD). Here, we extend prior work by investigating multivariate brain aging in patients with MDD, anxiety disorders, or both, and examine which factors contribute to older-appearing brains. Adults aged 18–57 years from the Netherlands Study of Depression and Anxiety underwent structural MRI. A pretrained brain-age prediction model based on >2000 samples from the ENIGMA consortium was applied to obtain brain-predicted age differences (brain PAD, predicted brain age minus chronological age) in 65 controls and 220 patients with current MDD and/or anxiety. Brain-PAD estimates were associated with clinical, somatic, lifestyle, and biological factors. After correcting for antidepressant use, brain PAD was significantly higher in MDD (+2.78 years, Cohen’sd = 0.25, 95% CI −0.10-0.60) and anxiety patients (+2.91 years, Cohen’sd = 0.27, 95% CI −0.08-0.61), compared with controls. There were no significant associations with lifestyle or biological stress systems. A multivariable model indicated unique contributions of higher severity of somatic depression symptoms (b = 4.21 years per unit increase on average sum score) and antidepressant use (−2.53 years) to brain PAD. Advanced brain aging in patients with MDD and anxiety was most strongly associated with somatic depressive symptomatology. We also present clinically relevant evidence for a potential neuroprotective antidepressant effect on the brain-PAD metric that requires follow-up in future research. <<<

Leonardo Tozzi, Xue Zhang, Adam Pines, Alisa M Olmsted, Emily S Zhai, Esther T Anene, Megan Chesnut, Bailey Holt-Gosselin, Sarah Chang, Patrick C Stetz, Carolina A Ramirez, Laura M Hack, Mayuresh S Korgaonkar, Max Wintermark, Ian H Gotlib, Jun Ma, Leanne M Williams <<<

There is an urgent need to derive quantitative measures based on coherent neurobiological dysfunctions or 'biotypes' to enable stratification of patients with depression and anxiety. We used task-free and task-evoked data from a standardized functional magnetic resonance imaging protocol conducted across multiple studies in patients with depression and anxiety when treatment free (n = 801) and after randomization to pharmacotherapy or behavioral therapy (n = 250). From these patients, we derived personalized and interpretable scores of brain circuit dysfunction grounded in a theoretical taxonomy. Participants were subdivided into six biotypes defined by distinct profiles of intrinsic task-free functional connectivity within the default mode, salience and frontoparietal attention circuits, and of activation and connectivity within frontal and subcortical regions elicited by emotional and cognitive tasks. The six biotypes showed consistency with our theoretical taxonomy and were distinguished by symptoms, behavioral performance on general and emotional cognitive computerized tests, and response to pharmacotherapy as well as behavioral therapy. Our results provide a new, theory-driven, clinically validated and interpretable quantitative method to parse the biological heterogeneity of depression and anxiety. Thus, they represent a promising approach to advance precision clinical care in psychiatry. <<<

The mechanisms underlying phenotypic heterogeneity in autism spectrum disorder (ASD) are not well understood. Using a large neuroimaging dataset, we identified three latent dimensions of functional brain network connectivity that predicted individual differences in ASD behaviors and were stable in cross-validation. Clustering along these three dimensions revealed four reproducible ASD subgroups with distinct functional connectivity alterations in ASD-related networks and clinical symptom profiles that were reproducible in an independent sample. By integrating neuroimaging data with normative gene expression data from two independent transcriptomic atlases, we found that within each subgroup, ASD-related functional connectivity was explained by regional differences in the expression of distinct ASD-related gene sets. These gene sets were differentially associated with distinct molecular signaling pathways involving immune and synapse function, G-protein-coupled receptor signaling, protein synthesis and other processes. Collectively, our findings delineate atypical connectivity patterns underlying different forms of ASD that implicate distinct molecular signaling mechanisms. <<<

Biological aging of human organ systems reflects the interplay of age, chronic disease, lifestyle and genetic risk. Using longitudinal brain imaging and physiological phenotypes from the UK Biobank, we establish normative models of biological age for three brain and seven body systems. Here we find that an organ's biological age selectively influences the aging of other organ systems, revealing a multiorgan aging network. We report organ age profiles for 16 chronic diseases, where advanced biological aging extends from the organ of primary disease to multiple systems. Advanced body age associates with several lifestyle and environmental factors, leukocyte telomere lengths and mortality risk, and predicts survival time (area under the curve of 0.77) and premature death (area under the curve of 0.86). Our work reveals the multisystem nature of human aging in health and chronic disease. It may enable early identification of individuals at increased risk of aging-related morbidity and inform new strategies to potentially limit organ-specific aging in such individuals. <<<

AIMS: Major depression disorder (MDD) is the single greatest cause of disability and morbidity, and affects about 10% of the population worldwide. Currently, there are no clinically useful diagnostic biomarkers that are able to confirm a diagnosis of MDD from bipolar disorder (BD) in the early depressive episode. Therefore, exploring translational biomarkers of mood disorders based on machine learning is in pressing need, though it is challenging, but with great potential to improve our understanding of these disorders.DISCUSSIONS: In this study, we review popular machine-learning methods used for brain imaging classification and predictions, and provide an overview of studies, specifically for MDD, that have used magnetic resonance imaging data to either (a) classify MDDs from controls or other mood disorders or (b) investigate treatment outcome predictors for individual patients. Finally, challenges, future directions, and potential limitations related to MDD biomarker identification are also discussed, with a goal of offering a comprehensive overview that may help readers to better understand the applications of neuroimaging data mining in depression.CONCLUSIONS: We hope such efforts may highlight the need for an urgently needed paradigm shift in treatment, to guide personalized optimal clinical care. <<<

Nils R Winter, Julian Blanke, Ramona Leenings, Jan Ernsting, Lukas Fisch, Kelvin Sarink, Carlotta Barkhau, Daniel Emden, Katharina Thiel, Kira Flinkenflügel, Alexandra Winter, Janik Goltermann, Susanne Meinert, Katharina Dohm, Jonathan Repple, Marius Gruber, Elisabeth J Leehr, Nils Opel, Dominik Grotegerd, Ronny Redlich, Robert Nitsch, Jochen Bauer, Walter Heindel, Joachim Gross, Benjamin Risse, Till F M Andlauer, Andreas J Forstner, Markus M Nöthen, Marcella Rietschel, Stefan G Hofmann, Julia-Katharina Pfarr, Lea Teutenberg, Paula Usemann, Florian Thomas-Odenthal, Adrian Wroblewski, Katharina Brosch, Frederike Stein, Andreas Jansen, Hamidreza Jamalabadi, Nina Alexander, Benjamin Straube, Igor Nenadic, Tilo Kircher, Udo Dannlowski, Tim Hahn <<<

Importance: Biological psychiatry aims to understand mental disorders in terms of altered neurobiological pathways. However, for one of the most prevalent and disabling mental disorders, major depressive disorder (MDD), no informative biomarkers have been identified.Objective: To evaluate whether machine learning (ML) can identify a multivariate biomarker for MDD.Design, Setting, and Participants: This study used data from the Marburg-Münster Affective Disorders Cohort Study, a case-control clinical neuroimaging study. Patients with acute or lifetime MDD and healthy controls aged 18 to 65 years were recruited from primary care and the general population in Münster and Marburg, Germany, from September 11, 2014, to September 26, 2018. The Münster Neuroimaging Cohort (MNC) was used as an independent partial replication sample. Data were analyzed from April 2022 to June 2023.Exposure: Patients with MDD and healthy controls.Main Outcome and Measure: Diagnostic classification accuracy was quantified on an individual level using an extensive ML-based multivariate approach across a comprehensive range of neuroimaging modalities, including structural and functional magnetic resonance imaging and diffusion tensor imaging as well as a polygenic risk score for depression.Results: Of 1801 included participants, 1162 (64.5%) were female, and the mean (SD) age was 36.1 (13.1) years. There were a total of 856 patients with MDD (47.5%) and 945 healthy controls (52.5%). The MNC replication sample included 1198 individuals (362 with MDD [30.1%] and 836 healthy controls [69.9%]). Training and testing a total of 4 million ML models, mean (SD) accuracies for diagnostic classification ranged between 48.1% (3.6%) and 62.0% (4.8%). Integrating neuroimaging modalities and stratifying individuals based on age, sex, treatment, or remission status does not enhance model performance. Findings were replicated within study sites and also observed in structural magnetic resonance imaging within MNC. Under simulated conditions of perfect reliability, performance did not significantly improve. Analyzing model errors suggests that symptom severity could be a potential focus for identifying MDD subgroups.Conclusion and Relevance: Despite the improved predictive capability of multivariate compared with univariate neuroimaging markers, no informative individual-level MDD biomarker-even under extensive ML optimization in a large sample of diagnosed patients-could be identified. <<<

Laura K M Han, Richard Dinga, Tim Hahn, Christopher R K Ching, Lisa T Eyler, Lyubomir Aftanas, Moji Aghajani, André Aleman, Bernhard T Baune, Klaus Berger, Ivan Brak, Geraldo Busatto Filho, Angela Carballedo, Colm G Connolly, Baptiste Couvy-Duchesne, Kathryn R Cullen, Udo Dannlowski, Christopher G Davey, Danai Dima, Fabio L S Duran, Verena Enneking, Elena Filimonova, Stefan Frenzel, Thomas Frodl, Cynthia H Y Fu, Beata R Godlewska, Ian H Gotlib, Hans J Grabe, Nynke A Groenewold, Dominik Grotegerd, Oliver Gruber, Geoffrey B Hall, Ben J Harrison, Sean N Hatton, Marco Hermesdorf, Ian B Hickie, Tiffany C Ho, Norbert Hosten, Andreas Jansen, Claas Kähler, Tilo Kircher, Bonnie Klimes-Dougan, Bernd Krämer, Axel Krug, Jim Lagopoulos, Ramona Leenings, Frank P MacMaster, Glenda MacQueen, Andrew McIntosh, Quinn McLellan, Katie L McMahon, Sarah E Medland, Bryon A Mueller, Benson Mwangi, Evgeny Osipov, Maria J Portella, Elena Pozzi, Liesbeth Reneman, Jonathan Repple, Pedro G P Rosa, Matthew D Sacchet, Philipp G Sämann, Knut Schnell, Anouk Schrantee, Egle Simulionyte, Jair C Soares, Jens Sommer, Dan J Stein, Olaf Steinsträter, Lachlan T Strike, Sophia I Thomopoulos, Marie-José van Tol, Ilya M Veer, Robert R J M Vermeiren, Henrik Walter, Nic J A van der Wee, Steven J A van der Werff, Heather Whalley, Nils R Winter, Katharina Wittfeld, Margaret J Wright, Mon-Ju Wu, Henry Völzke, Tony T Yang, Vasileios Zannias, Greig I de Zubicaray, Giovana B Zunta-Soares, Christoph Abé, Martin Alda, Ole A Andreassen, Erlend Bøen, Caterina M Bonnin, Erick J Canales-Rodriguez, Dara Cannon, Xavier Caseras, Tiffany M Chaim-Avancini, Torbjørn Elvsåshagen, Pauline Favre, Sonya F Foley, Janice M Fullerton, Jose M Goikolea, Bartholomeus C M Haarman, Tomas Hajek, Chantal Henry, Josselin Houenou, Fleur M Howells, Martin Ingvar, Rayus Kuplicki, Beny Lafer, Mikael Landén, Rodrigo Machado-Vieira, Ulrik F Malt, Colm McDonald, Philip B Mitchell, Leila Nabulsi, Maria Concepcion Garcia Otaduy, Bronwyn J Overs, Mircea Polosan, Edith Pomarol-Clotet, Joaquim Radua, Maria M Rive, Gloria Roberts, Henricus G Ruhe, Raymond Salvador, Salvador Sarró, Theodore D Satterthwaite, Jonathan Savitz, Aart H Schene, Peter R Schofield, Mauricio H Serpa, Kang Sim, Marcio Gerhardt Soeiro-de-Souza, Ashley N Sutherland, Henk S Temmingh, Garrett M Timmons, Anne Uhlmann, Eduard Vieta, Daniel H Wolf, Marcus V Zanetti, Neda Jahanshad, Paul M Thompson, Dick J Veltman, Brenda W J H Penninx, Andre F Marquand, James H Cole, Lianne Schmaal <<<

Major depressive disorder (MDD) is associated with an increased risk of brain atrophy, aging-related diseases, and mortality. We examined potential advanced brain aging in adult MDD patients, and whether this process is associated with clinical characteristics in a large multicenter international dataset. We performed a mega-analysis by pooling brain measures derived from T1-weighted MRI scans from 19 samples worldwide. Healthy brain aging was estimated by predicting chronological age (18-75 years) from 7 subcortical volumes, 34 cortical thickness and 34 surface area, lateral ventricles and total intracranial volume measures separately in 952 male and 1236 female controls from the ENIGMA MDD working group. The learned model coefficients were applied to 927 male controls and 986 depressed males, and 1199 female controls and 1689 depressed females to obtain independent unbiased brain-based age predictions. The difference between predicted "brain age" and chronological age was calculated to indicate brain-predicted age difference (brain-PAD). On average, MDD patients showed a higher brain-PAD of +1.08 (SE 0.22) years (Cohen's d = 0.14, 95% CI: 0.08-0.20) compared with controls. However, this difference did not seem to be driven by specific clinical characteristics (recurrent status, remission status, antidepressant medication use, age of onset, or symptom severity). This highly powered collaborative effort showed subtle patterns of age-related structural brain abnormalities in MDD. Substantial within-group variance and overlap between groups were observed. Longitudinal studies of MDD and somatic health outcomes are needed to further assess the clinical value of these brain-PAD estimates. <<<

Mingrui Xia, Tianmei Si, Xiaoyi Sun, Qing Ma, Bangshan Liu, Li Wang, Jie Meng, Miao Chang, Xiaoqi Huang, Ziqi Chen, Yanqing Tang, Ke Xu, Qiyong Gong, Fei Wang, Jiang Qiu, Peng Xie, Lingjiang Li, Yong He, DIDA-Major Depressive Disorder Working Group <<<

Resting-state functional MRI (R-fMRI) studies have demonstrated widespread alterations in brain function in patients with major depressive disorder (MDD). However, a clear and consistent conclusion regarding a repeatable pattern of MDD-relevant alterations is still limited due to the scarcity of large-sample, multisite datasets. Here, we address this issue by including a large R-fMRI dataset with 1434 participants (709 patients with MDD and 725 healthy controls) from five centers in China. Individual functional activity maps that represent very local to long-range connections are computed using the amplitude of low-frequency fluctuations, regional homogeneity and distance-related functional connectivity strength. The reproducibility analyses involve different statistical strategies, global signal regression, across-center consistency, clinical variables, and sample size. We observed significant hypoactivity in the orbitofrontal, sensorimotor, and visual cortices and hyperactivity in the frontoparietal cortices in MDD patients compared to the controls. These alterations are not affected by different statistical analysis strategies, global signal regression and medication status and are generally reproducible across centers. However, these between-group differences are partially influenced by the episode status and the age of disease onset in patients, and the brain-clinical variable relationship exhibits poor cross-center reproducibility. Bootstrap analyses reveal that at least 400 subjects in each group are required to replicate significant alterations (an extent threshold of P < .05 and a height threshold of P < .001) at 50% reproducibility. Together, these results highlight reproducible patterns of functional alterations in MDD and relevant influencing factors, which provides crucial guidance for future neuroimaging studies of this disorder. <<<

Xiaoyi Sun, Jin Liu, Qing Ma, Jia Duan, Xindi Wang, Yuehua Xu, Zhilei Xu, Ke Xu, Fei Wang, Yanqing Tang, Yong He, Mingrui Xia <<<

Schizophrenia (SCZ) is a highly heterogeneous disorder with remarkable intersubject variability in clinical presentations. Previous neuroimaging studies in SCZ have primarily focused on identifying group-averaged differences in the brain connectome between patients and healthy controls (HCs), largely neglecting the intersubject differences among patients. We acquired whole-brain resting-state functional MRI data from 121 SCZ patients and 183 HCs and examined the intersubject variability of the functional connectome (IVFC) in SCZ patients and HCs. Between-group differences were determined using permutation analysis. Then, we evaluated the relationship between IVFC and clinical variables in SCZ. Finally, we used datasets of patients with bipolar disorder (BD) and major depressive disorder (MDD) to assess the specificity of IVFC alteration in SCZ. The whole-brain IVFC pattern in the SCZ group was generally similar to that in HCs. Compared with the HC group, the SCZ group exhibited higher IVFC in the bilateral sensorimotor, visual, auditory, and subcortical regions. Moreover, altered IVFC was negatively correlated with age of onset, illness duration, and Brief Psychiatric Rating Scale scores and positively correlated with clinical heterogeneity. Although the SCZ shared altered IVFC in the visual cortex with BD and MDD, the alterations of IVFC in the sensorimotor, auditory, and subcortical cortices were specific to SCZ. The alterations of whole-brain IVFC in SCZ have potential implications for the understanding of the high clinical heterogeneity of SCZ and the future individualized clinical diagnosis and treatment of this disease. <<<

Depression is a common mental disorder characterized by heterogeneous cognitive and behavioral symptoms. The emerging research paradigm of functional connectomics has provided a quantitative theoretical framework and analytic tools for parsing variations in the organization and function of brain networks in depression. In this review, we first discuss recent progress in depression-associated functional connectome variations. We then discuss treatment-specific brain network outcomes in depression and propose a hypothetical model highlighting the advantages and uniqueness of each treatment in relation to the modulation of specific brain network connectivity and symptoms of depression. Finally, we look to the future promise of combining multiple treatment types in clinical practice, using multisite datasets and multimodal neuroimaging approaches, and identifying biological depression subtypes. <<<

Hong Yang , Xiao Chen , Zuo-Bing Chen , Le Li , Xue-Ying Li , Francisco Xavier Castellanos , Tong-Jian Bai , Qi-Jing Bo , Jun Cao , Zhi-Kai Chang , Guan-Mao Chen , Ning-Xuan Chen , Wei Chen , Chang Cheng , Yu-Qi Cheng , Xi-Long Cui , Jia Duan , Yiru Fang , Qi-Yong Gong , Wen-Bin Guo , Zheng-Hua Hou , Lan Hu , Li Kuang , Feng Li , Hui-Xian Li , Kai-Ming Li , Tao Li , Yan-Song Liu , Zhe-Ning Liu , Yi-Cheng Long , Bin Lu , Qing-Hua Luo , Hua-Qing Meng , Daihui Peng , Hai-Tang Qiu , Jiang Qiu , Yue-Di Shen , Yu-Shu Shi , Tian-Mei Si , Yan-Qing Tang , Chuan-Yue Wang , Fei Wang , Kai Wang , Li Wang , Xiang Wang , Ying Wang , Yu-Wei Wang , Xiao-Ping Wu , Xin-Ran Wu , Chun-Ming Xie , Guang-Rong Xie , Hai-Yan Xie , Peng Xie , Xiu-Feng Xu , Jian Yang , Jia-Shu Yao , Shu-Qiao Yao , Ying-Ying Yin , Yong-Gui Yuan , Yu-Feng Zang , Ai-Xia Zhang , Hong Zhang , Ke-Rang Zhang , Lei Zhang , Zhi-Jun Zhang , Jing-Ping Zhao , Rubai Zhou , Yi-Ting Zhou , Jun-Juan Zhu , Zhi-Chen Zhu , Chao-Jie Zou , Xi-Nian Zuo , Chao-Gan Yan <<<

AbstractAberrant topological organization of whole-brain networks has been inconsistently reported in studies of patients with major depressive disorder (MDD), reflecting limited sample sizes. To address this issue, we utilized a big data sample of MDD patients from the REST-meta-MDD Project, including 821 MDD patients and 765 normal controls (NCs) from 16 sites. Using the Dosenbach 160 node atlas, we examined whole-brain functional networks and extracted topological features (e.g., global and local efficiency, nodal efficiency, and degree) using graph theory-based methods. Linear mixed-effect models were used for group comparisons to control for site variability; robustness of results was confirmed (e.g., multiple topological parameters, different node definitions, and several head motion control strategies were applied). We found decreased global and local efficiency in patients with MDD compared to NCs. At the nodal level, patients with MDD were characterized by decreased nodal degrees in the somatomotor network (SMN), dorsal attention network (DAN) and visual network (VN) and decreased nodal efficiency in the default mode network (DMN), SMN, DAN, and VN. These topological differences were mostly driven by recurrent MDD patients, rather than first-episode drug naive (FEDN) patients with MDD. In this highly powered multisite study, we observed disrupted topological architecture of functional brain networks in MDD, suggesting both locally and globally decreased efficiency in brain networks. <<<

Hong Yang, Xiao Chen, Zuo-Bing Chen, Le Li, Xue-Ying Li, Francisco Xavier Castellanos, Tong-Jian Bai, Qi-Jing Bo, Jun Cao, Zhi-Kai Chang, Guan-Mao Chen, Ning-Xuan Chen, Wei Chen, Chang Cheng, Yu-Qi Cheng, Xi-Long Cui, Jia Duan, Yiru Fang, Qi-Yong Gong, Wen-Bin Guo, Zheng-Hua Hou, Lan Hu, Li Kuang, Feng Li, Hui-Xian Li, Kai-Ming Li, Tao Li, Yan-Song Liu, Zhe-Ning Liu, Yi-Cheng Long, Bin Lu, Qing-Hua Luo, Hua-Qing Meng, Daihui Peng, Hai-Tang Qiu, Jiang Qiu, Yue-Di Shen, Yu-Shu Shi, Tian-Mei Si, Yan-Qing Tang, Chuan-Yue Wang, Fei Wang, Kai Wang, Li Wang, Xiang Wang, Ying Wang, Yu-Wei Wang, Xiao-Ping Wu, Xin-Ran Wu, Chun-Ming Xie, Guang-Rong Xie, Hai-Yan Xie, Peng Xie, Xiu-Feng Xu, Jian Yang, Jia-Shu Yao, Shu-Qiao Yao, Ying-Ying Yin, Yong-Gui Yuan, Yu-Feng Zang, Ai-Xia Zhang, Hong Zhang, Ke-Rang Zhang, Lei Zhang, Zhi-Jun Zhang, Jing-Ping Zhao, Rubai Zhou, Yi-Ting Zhou, Jun-Juan Zhu, Zhi-Chen Zhu, Chao-Jie Zou, Xi-Nian Zuo, Chao-Gan Yan <<<

Aberrant topological organization of whole-brain networks has been inconsistently reported in studies of patients with major depressive disorder (MDD), reflecting limited sample sizes. To address this issue, we utilized a big data sample of MDD patients from the REST-meta-MDD Project, including 821 MDD patients and 765 normal controls (NCs) from 16 sites. Using the Dosenbach 160 node atlas, we examined whole-brain functional networks and extracted topological features (e.g., global and local efficiency, nodal efficiency, and degree) using graph theory-based methods. Linear mixed-effect models were used for group comparisons to control for site variability; robustness of results was confirmed (e.g., multiple topological parameters, different node definitions, and several head motion control strategies were applied). We found decreased global and local efficiency in patients with MDD compared to NCs. At the nodal level, patients with MDD were characterized by decreased nodal degrees in the somatomotor network (SMN), dorsal attention network (DAN) and visual network (VN) and decreased nodal efficiency in the default mode network (DMN), SMN, DAN, and VN. These topological differences were mostly driven by recurrent MDD patients, rather than first-episode drug naive (FEDN) patients with MDD. In this highly powered multisite study, we observed disrupted topological architecture of functional brain networks in MDD, suggesting both locally and globally decreased efficiency in brain networks. <<<

Functional MRI (fMRI) studies have traditionally relied on intersubject normalization based on global brain morphology, which cannot establish proper functional correspondence between subjects due to substantial intersubject variability in functional organization. Here, we reliably identified a set of discrete, homologous functional regions in individuals to improve intersubject alignment of fMRI data. These functional regions demonstrated marked intersubject variability in size, position, and connectivity. We found that previously reported intersubject variability in functional connectivity maps could be partially explained by variability in size and position of the functional regions. Importantly, individual differences in network topography are associated with individual differences in task-evoked activations, suggesting that these individually specified regions may serve as the "localizer" to improve the alignment of task-fMRI data. We demonstrated that aligning task-fMRI data using the regions derived from resting state fMRI may lead to increased statistical power of task-fMRI analyses. In addition, resting state functional connectivity among these homologous regions is able to capture the idiosyncrasies of subjects and better predict fluid intelligence (gF) than connectivity measures derived from group-level brain atlases. Critically, we showed that not only the connectivity but also the size and position of functional regions are related to human behavior. Collectively, these findings suggest that identifying homologous functional regions across individuals can benefit a wide range of studies in the investigation of connectivity, task activation, and brain-behavior associations. <<<

Chao Xie, Shitong Xiang, Chun Shen, Xuerui Peng, Jujiao Kang, Yuzhu Li, Wei Cheng, Shiqi He, Marina Bobou, M John Broulidakis, Betteke Maria van Noort, Zuo Zhang, Lauren Robinson, Nilakshi Vaidya, Jeanne Winterer, Yuning Zhang, Sinead King, Tobias Banaschewski, Gareth J Barker, Arun L W Bokde, Uli Bromberg, Christian Büchel, Herta Flor, Antoine Grigis, Hugh Garavan, Penny Gowland, Andreas Heinz, Bernd Ittermann, Hervé Lemaître, Jean-Luc Martinot, Marie-Laure Paillère Martinot, Frauke Nees, Dimitri Papadopoulos Orfanos, Tomáš Paus, Luise Poustka, Juliane H Fröhner, Ulrike Schmidt, Julia Sinclair, Michael N Smolka, Argyris Stringaris, Henrik Walter, Robert Whelan, Sylvane Desrivières, Barbara J Sahakian, Trevor W Robbins, Gunter Schumann, Tianye Jia, Jianfeng Feng, IMAGEN Consortium, STRATIFY/ESTRA Consortium, ZIB Consortium <<<

Recent studies proposed a general psychopathology factor underlying common comorbidities among psychiatric disorders. However, its neurobiological mechanisms and generalizability remain elusive. In this study, we used a large longitudinal neuroimaging cohort from adolescence to young adulthood (IMAGEN) to define a neuropsychopathological (NP) factor across externalizing and internalizing symptoms using multitask connectomes. We demonstrate that this NP factor might represent a unified, genetically determined, delayed development of the prefrontal cortex that further leads to poor executive function. We also show this NP factor to be reproducible in multiple developmental periods, from preadolescence to early adulthood, and generalizable to the resting-state connectome and clinical samples (the ADHD-200 Sample and the Stratify Project). In conclusion, we identify a reproducible and general neural basis underlying symptoms of multiple mental health disorders, bridging multidimensional evidence from behavioral, neuroimaging and genetic substrates. These findings may help to develop new therapeutic interventions for psychiatric comorbidities. <<<

Childhood maltreatment (CM) has a long impact on physical and mental health of children. However, the neural underpinnings of CM are still unclear. In this study, we aimed to establish the associations between functional connectome of large-scale brain networks and influences of CM evaluated through Childhood Trauma Questionnaire (CTQ) at the individual level based on resting-state functional magnetic resonance imaging data of 215 adults. A novel individual functional mapping approach was employed to identify subject-specific functional networks and functional network connectivities (FNCs). A connectome-based predictive modeling (CPM) was used to estimate CM total and subscale scores using individual FNCs. The CPM established with FNCs can well predict CM total scores and subscale scores including emotion abuse, emotion neglect, physical abuse, physical neglect, and sexual abuse. These FNCs primarily involve default mode network, fronto-parietal network, visual network, limbic network, motor network, dorsal and ventral attention networks, and different networks have distinct contributions to predicting CM and subtypes. Moreover, we found that CM showed age and sex effects on individual functional connections. Taken together, the present findings revealed that different types of CM are associated with different atypical neural networks which provide new clues to understand the neurobiological consequences of childhood adversity. <<<