Search

The promise of machine learning has fueled the hope for developing diagnostic tools for psychiatry. Initial studies showed high accuracy for the identification of major depressive disorder (MDD) with resting-state connectivity, but progress has been hampered by the absence of large datasets. Here we used regular machine learning and advanced deep learning algorithms to differentiate patients with MDD from healthy controls and identify neurophysiological signatures of depression in two of the largest resting-state datasets for MDD. We obtained resting-state functional magnetic resonance imaging data from the REST-meta-MDD (N = 2338) and PsyMRI (N = 1039) consortia. Classification of functional connectivity matrices was done using support vector machines (SVM) and graph convolutional neural networks (GCN), and performance was evaluated using 5-fold cross-validation. Features were visualized using GCN-Explainer, an ablation study and univariate t-testing. The results showed a mean classification accuracy of 61% for MDD versus controls. Mean accuracy for classifying (non-)medicated subgroups was 62%. Sex classification accuracy was substantially better across datasets (73-81%). Visualization of the results showed that classifications were driven by stronger thalamic connections in both datasets, while nearly all other connections were weaker with small univariate effect sizes. These results suggest that whole brain resting-state connectivity is a reliable though poor biomarker for MDD, presumably due to disease heterogeneity as further supported by the higher accuracy for sex classification using the same methods. Deep learning revealed thalamic hyperconnectivity as a prominent neurophysiological signature of depression in both multicenter studies, which may guide the development of biomarkers in future studies., Funding Agencies|Netherlands Organization for Scientific Research (NWO) [628.011.023]; Philips Research; ZonMW (Vidi) [016.156.318]; Region Ostergoetland; Phyllis and Jerome Lyle Rappaport Foundation; BIAL Foundation; Brain and Behavior Research Foundation; Center for Depression, Anxiety, and Stress Research at McLean Hospital; Ad Astra Chandaria Foundation [DA1151/5-2, SFB-TRR58, Dan3/012/17]; German Research Foundation (DFG); Interdisciplinary Center for Clinical Research (IZKF) of the medical faculty of Munster [1024570]; European Commission [KLI 597-827]; Australian National Health and Medical Research Council of Australia (NHMRC) [F3514-B1, GR 4510/2-1]; Austrian Science Fund (FWF) [WA1539/4-1]; Science Foundation Ireland (SFI); [FOR2107 DA1151/5-1]; [H2020-634541]; [1064643]; [KLI-148-B00]

The promise of machine learning has fueled the hope for developing diagnostic tools for psychiatry. Initial studies showed high accuracy for the identification of major depressive disorder (MDD) with resting-state connectivity, but progress has been hampered by the absence of large datasets. Here we used regular machine learning and advanced deep learning algorithms to differentiate patients with MDD from healthy controls and identify neurophysiological signatures of depression in two of the largest resting-state datasets for MDD. We obtained resting-state functional magnetic resonance imaging data from the REST-meta-MDD (N = 2338) and PsyMRI (N = 1039) consortia. Classification of functional connectivity matrices was done using support vector machines (SVM) and graph convolutional neural networks (GCN), and performance was evaluated using 5-fold cross-validation. Features were visualized using GCN-Explainer, an ablation study and univariate t-testing. The results showed a mean classification accuracy of 61% for MDD versus controls. Mean accuracy for classifying (non-)medicated subgroups was 62%. Sex classification accuracy was substantially better across datasets (73-81%). Visualization of the results showed that classifications were driven by stronger thalamic connections in both datasets, while nearly all other connections were weaker with small univariate effect sizes. These results suggest that whole brain resting-state connectivity is a reliable though poor biomarker for MDD, presumably due to disease heterogeneity as further supported by the higher accuracy for sex classification using the same methods. Deep learning revealed thalamic hyperconnectivity as a prominent neurophysiological signature of depression in both multicenter studies, which may guide the development of biomarkers in future studies., Funding Agencies|Netherlands Organization for Scientific Research (NWO) [628.011.023]; Philips Research; ZonMW (Vidi) [016.156.318]; Region Ostergoetland; Phyllis and Jerome Lyle Rappaport Foundation; BIAL Foundation; Brain and Behavior Research Foundation; Center for Depression, Anxiety, and Stress Research at McLean Hospital; Ad Astra Chandaria Foundation [DA1151/5-2, SFB-TRR58, Dan3/012/17]; German Research Foundation (DFG); Interdisciplinary Center for Clinical Research (IZKF) of the medical faculty of Munster [1024570]; European Commission [KLI 597-827]; Australian National Health and Medical Research Council of Australia (NHMRC) [F3514-B1, GR 4510/2-1]; Austrian Science Fund (FWF) [WA1539/4-1]; Science Foundation Ireland (SFI); [FOR2107 DA1151/5-1]; [H2020-634541]; [1064643]; [KLI-148-B00]

The promise of machine learning has fueled the hope for developing diagnostic tools for psychiatry. Initial studies showed high accuracy for the identification of major depressive disorder (MDD) with resting-state connectivity, but progress has been hampered by the absence of large datasets. Here we used regular machine learning and advanced deep learning algorithms to differentiate patients with MDD from healthy controls and identify neurophysiological signatures of depression in two of the largest resting-state datasets for MDD. We obtained resting-state functional magnetic resonance imaging data from the REST-meta-MDD (N = 2338) and PsyMRI (N = 1039) consortia. Classification of functional connectivity matrices was done using support vector machines (SVM) and graph convolutional neural networks (GCN), and performance was evaluated using 5-fold cross-validation. Features were visualized using GCN-Explainer, an ablation study and univariate t-testing. The results showed a mean classification accuracy of 61% for MDD versus controls. Mean accuracy for classifying (non-)medicated subgroups was 62%. Sex classification accuracy was substantially better across datasets (73-81%). Visualization of the results showed that classifications were driven by stronger thalamic connections in both datasets, while nearly all other connections were weaker with small univariate effect sizes. These results suggest that whole brain resting-state connectivity is a reliable though poor biomarker for MDD, presumably due to disease heterogeneity as further supported by the higher accuracy for sex classification using the same methods. Deep learning revealed thalamic hyperconnectivity as a prominent neurophysiological signature of depression in both multicenter studies, which may guide the development of biomarkers in future studies., Funding Agencies|Netherlands Organization for Scientific Research (NWO) [628.011.023]; Philips Research; ZonMW (Vidi) [016.156.318]; Region Ostergoetland; Phyllis and Jerome Lyle Rappaport Foundation; BIAL Foundation; Brain and Behavior Research Foundation; Center for Depression, Anxiety, and Stress Research at McLean Hospital; Ad Astra Chandaria Foundation [DA1151/5-2, SFB-TRR58, Dan3/012/17]; German Research Foundation (DFG); Interdisciplinary Center for Clinical Research (IZKF) of the medical faculty of Munster [1024570]; European Commission [KLI 597-827]; Australian National Health and Medical Research Council of Australia (NHMRC) [F3514-B1, GR 4510/2-1]; Austrian Science Fund (FWF) [WA1539/4-1]; Science Foundation Ireland (SFI); [FOR2107 DA1151/5-1]; [H2020-634541]; [1064643]; [KLI-148-B00]

The promise of machine learning has fueled the hope for developing diagnostic tools for psychiatry. Initial studies showed high accuracy for the identification of major depressive disorder (MDD) with resting-state connectivity, but progress has been hampered by the absence of large datasets. Here we used regular machine learning and advanced deep learning algorithms to differentiate patients with MDD from healthy controls and identify neurophysiological signatures of depression in two of the largest resting-state datasets for MDD. We obtained resting-state functional magnetic resonance imaging data from the REST-meta-MDD (N = 2338) and PsyMRI (N = 1039) consortia. Classification of functional connectivity matrices was done using support vector machines (SVM) and graph convolutional neural networks (GCN), and performance was evaluated using 5-fold cross-validation. Features were visualized using GCN-Explainer, an ablation study and univariate t-testing. The results showed a mean classification accuracy of 61% for MDD versus controls. Mean accuracy for classifying (non-)medicated subgroups was 62%. Sex classification accuracy was substantially better across datasets (73-81%). Visualization of the results showed that classifications were driven by stronger thalamic connections in both datasets, while nearly all other connections were weaker with small univariate effect sizes. These results suggest that whole brain resting-state connectivity is a reliable though poor biomarker for MDD, presumably due to disease heterogeneity as further supported by the higher accuracy for sex classification using the same methods. Deep learning revealed thalamic hyperconnectivity as a prominent neurophysiological signature of depression in both multicenter studies, which may guide the development of biomarkers in future studies., Funding Agencies|Netherlands Organization for Scientific Research (NWO) [628.011.023]; Philips Research; ZonMW (Vidi) [016.156.318]; Region Ostergoetland; Phyllis and Jerome Lyle Rappaport Foundation; BIAL Foundation; Brain and Behavior Research Foundation; Center for Depression, Anxiety, and Stress Research at McLean Hospital; Ad Astra Chandaria Foundation [DA1151/5-2, SFB-TRR58, Dan3/012/17]; German Research Foundation (DFG); Interdisciplinary Center for Clinical Research (IZKF) of the medical faculty of Munster [1024570]; European Commission [KLI 597-827]; Australian National Health and Medical Research Council of Australia (NHMRC) [F3514-B1, GR 4510/2-1]; Austrian Science Fund (FWF) [WA1539/4-1]; Science Foundation Ireland (SFI); [FOR2107 DA1151/5-1]; [H2020-634541]; [1064643]; [KLI-148-B00]

The promise of machine learning has fueled the hope for developing diagnostic tools for psychiatry. Initial studies showed high accuracy for the identification of major depressive disorder (MDD) with resting-state connectivity, but progress has been hampered by the absence of large datasets. Here we used regular machine learning and advanced deep learning algorithms to differentiate patients with MDD from healthy controls and identify neurophysiological signatures of depression in two of the largest resting-state datasets for MDD. We obtained resting-state functional magnetic resonance imaging data from the REST-meta-MDD (N = 2338) and PsyMRI (N = 1039) consortia. Classification of functional connectivity matrices was done using support vector machines (SVM) and graph convolutional neural networks (GCN), and performance was evaluated using 5-fold cross-validation. Features were visualized using GCN-Explainer, an ablation study and univariate t-testing. The results showed a mean classification accuracy of 61% for MDD versus controls. Mean accuracy for classifying (non-)medicated subgroups was 62%. Sex classification accuracy was substantially better across datasets (73-81%). Visualization of the results showed that classifications were driven by stronger thalamic connections in both datasets, while nearly all other connections were weaker with small univariate effect sizes. These results suggest that whole brain resting-state connectivity is a reliable though poor biomarker for MDD, presumably due to disease heterogeneity as further supported by the higher accuracy for sex classification using the same methods. Deep learning revealed thalamic hyperconnectivity as a prominent neurophysiological signature of depression in both multicenter studies, which may guide the development of biomarkers in future studies., Funding Agencies|Netherlands Organization for Scientific Research (NWO) [628.011.023]; Philips Research; ZonMW (Vidi) [016.156.318]; Region Ostergoetland; Phyllis and Jerome Lyle Rappaport Foundation; BIAL Foundation; Brain and Behavior Research Foundation; Center for Depression, Anxiety, and Stress Research at McLean Hospital; Ad Astra Chandaria Foundation [DA1151/5-2, SFB-TRR58, Dan3/012/17]; German Research Foundation (DFG); Interdisciplinary Center for Clinical Research (IZKF) of the medical faculty of Munster [1024570]; European Commission [KLI 597-827]; Australian National Health and Medical Research Council of Australia (NHMRC) [F3514-B1, GR 4510/2-1]; Austrian Science Fund (FWF) [WA1539/4-1]; Science Foundation Ireland (SFI); [FOR2107 DA1151/5-1]; [H2020-634541]; [1064643]; [KLI-148-B00]

Psychotic major depression (PMD) is hypothesized to be a distinct clinical entity from nonpsychotic major depression (NPMD). However, neurobiological evidence supporting this notion is scarce. The aim of this study is to identify gray matter volume (GMV) differences between PMD and NPMD and their longitudinal change following electroconvulsive therapy (ECT). Structural magnetic resonance imaging (MRI) data from 8 independent sites in the Global ECT-MRI Research Collaboration (GEMRIC) database (n = 108; 56 PMD and 52 NPMD; mean age 71.7 in PMD and 70.2 in NPMD) were analyzed. All participants underwent MRI before and after ECT. First, cross-sectional whole-brain voxel-wise GMV comparisons between PMD and NPMD were conducted at both time points. Second, in a flexible factorial model, a main effect of time and a group-by-time interaction were examined to identify longitudinal effects of ECT on GMV and longitudinal differential effects of ECT between PMD and NPMD, respectively. Compared with NPMD, PMD showed lower GMV in the prefrontal, temporal and parietal cortex before ECT; PMD showed lower GMV in the medial prefrontal cortex (MPFC) after ECT. Although there was a significant main effect of time on GMV in several brain regions in both PMD and NPMD, there was no significant group-by-time interaction. Lower GMV in the MPFC was consistently identified in PMD, suggesting this may be a trait-like neural substrate of PMD. Longitudinal effect of ECT on GMV may not explain superior ECT response in PMD, and further investigation is needed., Funding Agencies|Keio University Medical Science Fund [99-095-0007]; AMED [JP20dm0307102h0003]; Research Foundation Flanders (FWO) grantFWO [G0C0319N]; KU Leuven Fund [C24/18/095]; Sequoia Fund for Research on Ageing and Mental Health; National Institute of HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [MH125126, MH111826]; Carlos III Health InstituteInstituto de Salud Carlos III [CD20/00189]; Lundbeck FoundationLundbeckfonden; Western Norway Regional Health Authority [911986, 912238]

Psychotic major depression (PMD) is hypothesized to be a distinct clinical entity from nonpsychotic major depression (NPMD). However, neurobiological evidence supporting this notion is scarce. The aim of this study is to identify gray matter volume (GMV) differences between PMD and NPMD and their longitudinal change following electroconvulsive therapy (ECT). Structural magnetic resonance imaging (MRI) data from 8 independent sites in the Global ECT-MRI Research Collaboration (GEMRIC) database (n = 108; 56 PMD and 52 NPMD; mean age 71.7 in PMD and 70.2 in NPMD) were analyzed. All participants underwent MRI before and after ECT. First, cross-sectional whole-brain voxel-wise GMV comparisons between PMD and NPMD were conducted at both time points. Second, in a flexible factorial model, a main effect of time and a group-by-time interaction were examined to identify longitudinal effects of ECT on GMV and longitudinal differential effects of ECT between PMD and NPMD, respectively. Compared with NPMD, PMD showed lower GMV in the prefrontal, temporal and parietal cortex before ECT; PMD showed lower GMV in the medial prefrontal cortex (MPFC) after ECT. Although there was a significant main effect of time on GMV in several brain regions in both PMD and NPMD, there was no significant group-by-time interaction. Lower GMV in the MPFC was consistently identified in PMD, suggesting this may be a trait-like neural substrate of PMD. Longitudinal effect of ECT on GMV may not explain superior ECT response in PMD, and further investigation is needed., Funding Agencies|Keio University Medical Science Fund [99-095-0007]; AMED [JP20dm0307102h0003]; Research Foundation Flanders (FWO) grantFWO [G0C0319N]; KU Leuven Fund [C24/18/095]; Sequoia Fund for Research on Ageing and Mental Health; National Institute of HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [MH125126, MH111826]; Carlos III Health InstituteInstituto de Salud Carlos III [CD20/00189]; Lundbeck FoundationLundbeckfonden; Western Norway Regional Health Authority [911986, 912238]

Psychotic major depression (PMD) is hypothesized to be a distinct clinical entity from nonpsychotic major depression (NPMD). However, neurobiological evidence supporting this notion is scarce. The aim of this study is to identify gray matter volume (GMV) differences between PMD and NPMD and their longitudinal change following electroconvulsive therapy (ECT). Structural magnetic resonance imaging (MRI) data from 8 independent sites in the Global ECT-MRI Research Collaboration (GEMRIC) database (n = 108; 56 PMD and 52 NPMD; mean age 71.7 in PMD and 70.2 in NPMD) were analyzed. All participants underwent MRI before and after ECT. First, cross-sectional whole-brain voxel-wise GMV comparisons between PMD and NPMD were conducted at both time points. Second, in a flexible factorial model, a main effect of time and a group-by-time interaction were examined to identify longitudinal effects of ECT on GMV and longitudinal differential effects of ECT between PMD and NPMD, respectively. Compared with NPMD, PMD showed lower GMV in the prefrontal, temporal and parietal cortex before ECT; PMD showed lower GMV in the medial prefrontal cortex (MPFC) after ECT. Although there was a significant main effect of time on GMV in several brain regions in both PMD and NPMD, there was no significant group-by-time interaction. Lower GMV in the MPFC was consistently identified in PMD, suggesting this may be a trait-like neural substrate of PMD. Longitudinal effect of ECT on GMV may not explain superior ECT response in PMD, and further investigation is needed., Funding Agencies|Keio University Medical Science Fund [99-095-0007]; AMED [JP20dm0307102h0003]; Research Foundation Flanders (FWO) grantFWO [G0C0319N]; KU Leuven Fund [C24/18/095]; Sequoia Fund for Research on Ageing and Mental Health; National Institute of HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [MH125126, MH111826]; Carlos III Health InstituteInstituto de Salud Carlos III [CD20/00189]; Lundbeck FoundationLundbeckfonden; Western Norway Regional Health Authority [911986, 912238]

Psychotic major depression (PMD) is hypothesized to be a distinct clinical entity from nonpsychotic major depression (NPMD). However, neurobiological evidence supporting this notion is scarce. The aim of this study is to identify gray matter volume (GMV) differences between PMD and NPMD and their longitudinal change following electroconvulsive therapy (ECT). Structural magnetic resonance imaging (MRI) data from 8 independent sites in the Global ECT-MRI Research Collaboration (GEMRIC) database (n = 108; 56 PMD and 52 NPMD; mean age 71.7 in PMD and 70.2 in NPMD) were analyzed. All participants underwent MRI before and after ECT. First, cross-sectional whole-brain voxel-wise GMV comparisons between PMD and NPMD were conducted at both time points. Second, in a flexible factorial model, a main effect of time and a group-by-time interaction were examined to identify longitudinal effects of ECT on GMV and longitudinal differential effects of ECT between PMD and NPMD, respectively. Compared with NPMD, PMD showed lower GMV in the prefrontal, temporal and parietal cortex before ECT; PMD showed lower GMV in the medial prefrontal cortex (MPFC) after ECT. Although there was a significant main effect of time on GMV in several brain regions in both PMD and NPMD, there was no significant group-by-time interaction. Lower GMV in the MPFC was consistently identified in PMD, suggesting this may be a trait-like neural substrate of PMD. Longitudinal effect of ECT on GMV may not explain superior ECT response in PMD, and further investigation is needed., Funding Agencies|Keio University Medical Science Fund [99-095-0007]; AMED [JP20dm0307102h0003]; Research Foundation Flanders (FWO) grantFWO [G0C0319N]; KU Leuven Fund [C24/18/095]; Sequoia Fund for Research on Ageing and Mental Health; National Institute of HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [MH125126, MH111826]; Carlos III Health InstituteInstituto de Salud Carlos III [CD20/00189]; Lundbeck FoundationLundbeckfonden; Western Norway Regional Health Authority [911986, 912238]

Psychotic major depression (PMD) is hypothesized to be a distinct clinical entity from nonpsychotic major depression (NPMD). However, neurobiological evidence supporting this notion is scarce. The aim of this study is to identify gray matter volume (GMV) differences between PMD and NPMD and their longitudinal change following electroconvulsive therapy (ECT). Structural magnetic resonance imaging (MRI) data from 8 independent sites in the Global ECT-MRI Research Collaboration (GEMRIC) database (n = 108; 56 PMD and 52 NPMD; mean age 71.7 in PMD and 70.2 in NPMD) were analyzed. All participants underwent MRI before and after ECT. First, cross-sectional whole-brain voxel-wise GMV comparisons between PMD and NPMD were conducted at both time points. Second, in a flexible factorial model, a main effect of time and a group-by-time interaction were examined to identify longitudinal effects of ECT on GMV and longitudinal differential effects of ECT between PMD and NPMD, respectively. Compared with NPMD, PMD showed lower GMV in the prefrontal, temporal and parietal cortex before ECT; PMD showed lower GMV in the medial prefrontal cortex (MPFC) after ECT. Although there was a significant main effect of time on GMV in several brain regions in both PMD and NPMD, there was no significant group-by-time interaction. Lower GMV in the MPFC was consistently identified in PMD, suggesting this may be a trait-like neural substrate of PMD. Longitudinal effect of ECT on GMV may not explain superior ECT response in PMD, and further investigation is needed., Funding Agencies|Keio University Medical Science Fund [99-095-0007]; AMED [JP20dm0307102h0003]; Research Foundation Flanders (FWO) grantFWO [G0C0319N]; KU Leuven Fund [C24/18/095]; Sequoia Fund for Research on Ageing and Mental Health; National Institute of HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [MH125126, MH111826]; Carlos III Health InstituteInstituto de Salud Carlos III [CD20/00189]; Lundbeck FoundationLundbeckfonden; Western Norway Regional Health Authority [911986, 912238]

Major depressive disorder (MDD) is associated with abnormal neural circuitry. It can be measured by assessing functional connectivity (FC) at resting-state functional MRI, that may help identifying neural markers of MDD and provide further efficient diagnosis and monitor treatment outcomes. The main aim of the present study is to investigate, in an unbiased way, functional alterations in patients with MDD using a large multi-center dataset from the PsyMRI consortium including 1546 participants from 19 centers (). After applying strict exclusion criteria, the final sample consisted of 606 MDD patients (age: 35.8 +/- 11.9 y.o.; females: 60.7%) and 476 healthy participants (age: 33.3 +/- 11.0 y.o.; females: 56.7%). We found significant relative hypoconnectivity within somatosensory motor (SMN), salience (SN) networks and between SMN, SN, dorsal attention (DAN), and visual (VN) networks in MDD patients. No significant differences were detected within the default mode (DMN) and frontoparietal networks (FPN). In addition, alterations in network organization were observed in terms of significantly lower network segregation of SMN in MDD patients. Although medicated patients showed significantly lower FC within DMN, FPN, and SN than unmedicated patients, there were no differences between medicated and unmedicated groups in terms of network organization in SMN. We conclude that the network organization of cortical networks, involved in processing of sensory information, might be a more stable neuroimaging marker for MDD than previously assumed alterations in higher-order neural networks like DMN and FPN., Funding Agencies|German Research Foundation (DFG)German Research Foundation (DFG) [KR 3822/7-1, KR 3822/7-2, FOR2107 KI588/14-1, FOR2107 KI588/14-2, FOR2107 NE2254/2-1, FOR2107 NE2254/3-1, FOR2107 DA1151/5-1, DA1151/5-2, HA7070/2-2, HA7070/3, HA7070/4]; German Research Foundation (Deutsche Forschungsgemeinschaft, DFG)German Research Foundation (DFG) [SFB779-A06]; Austrian Science Fund (FWF)Austrian Science Fund (FWF) [KLI 597-B27, KLI-148-B00, F3514-B11]; Health Research Board (HRB) Ireland; Science Foundation Ireland (SFI)Science Foundation Ireland; National Health and Medical Research CouncilNational Health and Medical Research Council of Australia [GNT1176689]; National Health and Medical Research Council of Australia (NHMRC)National Health and Medical Research Council of Australia [510135, 1037196]; German Research FoundationGerman Research Foundation (DFG) [GR 4510/2-1]; European CommissionEuropean CommissionEuropean Commission Joint Research Centre [H2020-634541]; Australian National Health and Medical Research Council of Australia (NHMRC)National Health and Medical Research Council of Australia [1064643, 1024570]; NHMRC Career Development FellowshipsNational Health and Medical Research Council of Australia [1061757, 1124472]; Interdisciplinary Center for Clinical Research (IZKF) of the medical faculty of Munster [Dan3/012/17, MzH 3/020/20]; AOP Orphan; Medizin Medien Austria; Vertretungsnetz; Schwabe Austria; JanssenJohnson & JohnsonJohnson & Johnson USAJanssen Biotech Inc; Angelini

Major depressive disorder (MDD) is associated with abnormal neural circuitry. It can be measured by assessing functional connectivity (FC) at resting-state functional MRI, that may help identifying neural markers of MDD and provide further efficient diagnosis and monitor treatment outcomes. The main aim of the present study is to investigate, in an unbiased way, functional alterations in patients with MDD using a large multi-center dataset from the PsyMRI consortium including 1546 participants from 19 centers (). After applying strict exclusion criteria, the final sample consisted of 606 MDD patients (age: 35.8 +/- 11.9 y.o.; females: 60.7%) and 476 healthy participants (age: 33.3 +/- 11.0 y.o.; females: 56.7%). We found significant relative hypoconnectivity within somatosensory motor (SMN), salience (SN) networks and between SMN, SN, dorsal attention (DAN), and visual (VN) networks in MDD patients. No significant differences were detected within the default mode (DMN) and frontoparietal networks (FPN). In addition, alterations in network organization were observed in terms of significantly lower network segregation of SMN in MDD patients. Although medicated patients showed significantly lower FC within DMN, FPN, and SN than unmedicated patients, there were no differences between medicated and unmedicated groups in terms of network organization in SMN. We conclude that the network organization of cortical networks, involved in processing of sensory information, might be a more stable neuroimaging marker for MDD than previously assumed alterations in higher-order neural networks like DMN and FPN., Funding Agencies|German Research Foundation (DFG)German Research Foundation (DFG) [KR 3822/7-1, KR 3822/7-2, FOR2107 KI588/14-1, FOR2107 KI588/14-2, FOR2107 NE2254/2-1, FOR2107 NE2254/3-1, FOR2107 DA1151/5-1, DA1151/5-2, HA7070/2-2, HA7070/3, HA7070/4]; German Research Foundation (Deutsche Forschungsgemeinschaft, DFG)German Research Foundation (DFG) [SFB779-A06]; Austrian Science Fund (FWF)Austrian Science Fund (FWF) [KLI 597-B27, KLI-148-B00, F3514-B11]; Health Research Board (HRB) Ireland; Science Foundation Ireland (SFI)Science Foundation Ireland; National Health and Medical Research CouncilNational Health and Medical Research Council of Australia [GNT1176689]; National Health and Medical Research Council of Australia (NHMRC)National Health and Medical Research Council of Australia [510135, 1037196]; German Research FoundationGerman Research Foundation (DFG) [GR 4510/2-1]; European CommissionEuropean CommissionEuropean Commission Joint Research Centre [H2020-634541]; Australian National Health and Medical Research Council of Australia (NHMRC)National Health and Medical Research Council of Australia [1064643, 1024570]; NHMRC Career Development FellowshipsNational Health and Medical Research Council of Australia [1061757, 1124472]; Interdisciplinary Center for Clinical Research (IZKF) of the medical faculty of Munster [Dan3/012/17, MzH 3/020/20]; AOP Orphan; Medizin Medien Austria; Vertretungsnetz; Schwabe Austria; JanssenJohnson & JohnsonJohnson & Johnson USAJanssen Biotech Inc; Angelini

Major depressive disorder (MDD) is associated with abnormal neural circuitry. It can be measured by assessing functional connectivity (FC) at resting-state functional MRI, that may help identifying neural markers of MDD and provide further efficient diagnosis and monitor treatment outcomes. The main aim of the present study is to investigate, in an unbiased way, functional alterations in patients with MDD using a large multi-center dataset from the PsyMRI consortium including 1546 participants from 19 centers (). After applying strict exclusion criteria, the final sample consisted of 606 MDD patients (age: 35.8 +/- 11.9 y.o.; females: 60.7%) and 476 healthy participants (age: 33.3 +/- 11.0 y.o.; females: 56.7%). We found significant relative hypoconnectivity within somatosensory motor (SMN), salience (SN) networks and between SMN, SN, dorsal attention (DAN), and visual (VN) networks in MDD patients. No significant differences were detected within the default mode (DMN) and frontoparietal networks (FPN). In addition, alterations in network organization were observed in terms of significantly lower network segregation of SMN in MDD patients. Although medicated patients showed significantly lower FC within DMN, FPN, and SN than unmedicated patients, there were no differences between medicated and unmedicated groups in terms of network organization in SMN. We conclude that the network organization of cortical networks, involved in processing of sensory information, might be a more stable neuroimaging marker for MDD than previously assumed alterations in higher-order neural networks like DMN and FPN., Funding Agencies|German Research Foundation (DFG)German Research Foundation (DFG) [KR 3822/7-1, KR 3822/7-2, FOR2107 KI588/14-1, FOR2107 KI588/14-2, FOR2107 NE2254/2-1, FOR2107 NE2254/3-1, FOR2107 DA1151/5-1, DA1151/5-2, HA7070/2-2, HA7070/3, HA7070/4]; German Research Foundation (Deutsche Forschungsgemeinschaft, DFG)German Research Foundation (DFG) [SFB779-A06]; Austrian Science Fund (FWF)Austrian Science Fund (FWF) [KLI 597-B27, KLI-148-B00, F3514-B11]; Health Research Board (HRB) Ireland; Science Foundation Ireland (SFI)Science Foundation Ireland; National Health and Medical Research CouncilNational Health and Medical Research Council of Australia [GNT1176689]; National Health and Medical Research Council of Australia (NHMRC)National Health and Medical Research Council of Australia [510135, 1037196]; German Research FoundationGerman Research Foundation (DFG) [GR 4510/2-1]; European CommissionEuropean CommissionEuropean Commission Joint Research Centre [H2020-634541]; Australian National Health and Medical Research Council of Australia (NHMRC)National Health and Medical Research Council of Australia [1064643, 1024570]; NHMRC Career Development FellowshipsNational Health and Medical Research Council of Australia [1061757, 1124472]; Interdisciplinary Center for Clinical Research (IZKF) of the medical faculty of Munster [Dan3/012/17, MzH 3/020/20]; AOP Orphan; Medizin Medien Austria; Vertretungsnetz; Schwabe Austria; JanssenJohnson & JohnsonJohnson & Johnson USAJanssen Biotech Inc; Angelini

Major depressive disorder (MDD) is associated with abnormal neural circuitry. It can be measured by assessing functional connectivity (FC) at resting-state functional MRI, that may help identifying neural markers of MDD and provide further efficient diagnosis and monitor treatment outcomes. The main aim of the present study is to investigate, in an unbiased way, functional alterations in patients with MDD using a large multi-center dataset from the PsyMRI consortium including 1546 participants from 19 centers (). After applying strict exclusion criteria, the final sample consisted of 606 MDD patients (age: 35.8 +/- 11.9 y.o.; females: 60.7%) and 476 healthy participants (age: 33.3 +/- 11.0 y.o.; females: 56.7%). We found significant relative hypoconnectivity within somatosensory motor (SMN), salience (SN) networks and between SMN, SN, dorsal attention (DAN), and visual (VN) networks in MDD patients. No significant differences were detected within the default mode (DMN) and frontoparietal networks (FPN). In addition, alterations in network organization were observed in terms of significantly lower network segregation of SMN in MDD patients. Although medicated patients showed significantly lower FC within DMN, FPN, and SN than unmedicated patients, there were no differences between medicated and unmedicated groups in terms of network organization in SMN. We conclude that the network organization of cortical networks, involved in processing of sensory information, might be a more stable neuroimaging marker for MDD than previously assumed alterations in higher-order neural networks like DMN and FPN., Funding Agencies|German Research Foundation (DFG)German Research Foundation (DFG) [KR 3822/7-1, KR 3822/7-2, FOR2107 KI588/14-1, FOR2107 KI588/14-2, FOR2107 NE2254/2-1, FOR2107 NE2254/3-1, FOR2107 DA1151/5-1, DA1151/5-2, HA7070/2-2, HA7070/3, HA7070/4]; German Research Foundation (Deutsche Forschungsgemeinschaft, DFG)German Research Foundation (DFG) [SFB779-A06]; Austrian Science Fund (FWF)Austrian Science Fund (FWF) [KLI 597-B27, KLI-148-B00, F3514-B11]; Health Research Board (HRB) Ireland; Science Foundation Ireland (SFI)Science Foundation Ireland; National Health and Medical Research CouncilNational Health and Medical Research Council of Australia [GNT1176689]; National Health and Medical Research Council of Australia (NHMRC)National Health and Medical Research Council of Australia [510135, 1037196]; German Research FoundationGerman Research Foundation (DFG) [GR 4510/2-1]; European CommissionEuropean CommissionEuropean Commission Joint Research Centre [H2020-634541]; Australian National Health and Medical Research Council of Australia (NHMRC)National Health and Medical Research Council of Australia [1064643, 1024570]; NHMRC Career Development FellowshipsNational Health and Medical Research Council of Australia [1061757, 1124472]; Interdisciplinary Center for Clinical Research (IZKF) of the medical faculty of Munster [Dan3/012/17, MzH 3/020/20]; AOP Orphan; Medizin Medien Austria; Vertretungsnetz; Schwabe Austria; JanssenJohnson & JohnsonJohnson & Johnson USAJanssen Biotech Inc; Angelini

Major depressive disorder (MDD) is associated with abnormal neural circuitry. It can be measured by assessing functional connectivity (FC) at resting-state functional MRI, that may help identifying neural markers of MDD and provide further efficient diagnosis and monitor treatment outcomes. The main aim of the present study is to investigate, in an unbiased way, functional alterations in patients with MDD using a large multi-center dataset from the PsyMRI consortium including 1546 participants from 19 centers (). After applying strict exclusion criteria, the final sample consisted of 606 MDD patients (age: 35.8 +/- 11.9 y.o.; females: 60.7%) and 476 healthy participants (age: 33.3 +/- 11.0 y.o.; females: 56.7%). We found significant relative hypoconnectivity within somatosensory motor (SMN), salience (SN) networks and between SMN, SN, dorsal attention (DAN), and visual (VN) networks in MDD patients. No significant differences were detected within the default mode (DMN) and frontoparietal networks (FPN). In addition, alterations in network organization were observed in terms of significantly lower network segregation of SMN in MDD patients. Although medicated patients showed significantly lower FC within DMN, FPN, and SN than unmedicated patients, there were no differences between medicated and unmedicated groups in terms of network organization in SMN. We conclude that the network organization of cortical networks, involved in processing of sensory information, might be a more stable neuroimaging marker for MDD than previously assumed alterations in higher-order neural networks like DMN and FPN., Funding Agencies|German Research Foundation (DFG)German Research Foundation (DFG) [KR 3822/7-1, KR 3822/7-2, FOR2107 KI588/14-1, FOR2107 KI588/14-2, FOR2107 NE2254/2-1, FOR2107 NE2254/3-1, FOR2107 DA1151/5-1, DA1151/5-2, HA7070/2-2, HA7070/3, HA7070/4]; German Research Foundation (Deutsche Forschungsgemeinschaft, DFG)German Research Foundation (DFG) [SFB779-A06]; Austrian Science Fund (FWF)Austrian Science Fund (FWF) [KLI 597-B27, KLI-148-B00, F3514-B11]; Health Research Board (HRB) Ireland; Science Foundation Ireland (SFI)Science Foundation Ireland; National Health and Medical Research CouncilNational Health and Medical Research Council of Australia [GNT1176689]; National Health and Medical Research Council of Australia (NHMRC)National Health and Medical Research Council of Australia [510135, 1037196]; German Research FoundationGerman Research Foundation (DFG) [GR 4510/2-1]; European CommissionEuropean CommissionEuropean Commission Joint Research Centre [H2020-634541]; Australian National Health and Medical Research Council of Australia (NHMRC)National Health and Medical Research Council of Australia [1064643, 1024570]; NHMRC Career Development FellowshipsNational Health and Medical Research Council of Australia [1061757, 1124472]; Interdisciplinary Center for Clinical Research (IZKF) of the medical faculty of Munster [Dan3/012/17, MzH 3/020/20]; AOP Orphan; Medizin Medien Austria; Vertretungsnetz; Schwabe Austria; JanssenJohnson & JohnsonJohnson & Johnson USAJanssen Biotech Inc; Angelini