| 1. |
- Gallo, Selene, et al.
(författare)
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Functional connectivity signatures of major depressive disorder: machine learning analysis of two multicenter neuroimaging studies
- 2023
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Ingår i: Molecular Psychiatry. - : SPRINGERNATURE. - 1359-4184 .- 1476-5578. ; 28:7, s. 3013-3022
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 2. |
- Javaheripour, Nooshin, et al.
(författare)
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Altered resting-state functional connectome in major depressive disorder : a mega-analysis from the PsyMRI consortium
- 2021
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Ingår i: Translational Psychiatry. - : Springer Nature. - 2158-3188. ; 11:1
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 3. |
- Oltedal, Leif, et al.
(författare)
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The Global ECT-MRI Research Collaboration (GEMRIC): Establishing a multi-site investigation of the neural mechanisms underlying response to electroconvulsive therapy
- 2017
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Ingår i: NeuroImage. - : ELSEVIER SCI LTD. - 2213-1582. ; 14, s. 422-432
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Tidskriftsartikel (refereegranskat)abstract
- Major depression, currently the worlds primary cause of disability, leads to profound personal suffering and increased risk of suicide. Unfortunately, the success of antidepressant treatment varies amongst individuals and can take weeks to months in those who respond. Electroconvulsive therapy (ECT), generally prescribed for the most severely depressed and when standard treatments fail, produces a more rapid response and remains the most effective intervention for severe depression. Exploring the neurobiological effects of ECT is thus an ideal approach to better understand the mechanisms of successful therapeutic response. Though several recent neuroimaging studies show structural and functional changes associated with ECT, not all brain changes associate with clinical outcome. Larger studies that can address individual differences in clinical and treatment parameters may better target biological factors relating to or predictive of ECT-related therapeutic response. We have thus formed the Global ECT-MRI Research Collaboration (GEMRIC) that aims to combine longitudinal neuroimaging as well as clinical, behavioral and other physiological data across multiple independent sites. Here, we summarize the ECT sample characteristics from currently participating sites, and the common data-repository and standardized image analysis pipeline developed for this initiative. This includes data harmonization across sites and MRI platforms, and a method for obtaining unbiased estimates of structural change based on longitudinal measurements with serial MRI scans. The optimized analysis pipeline, together with the large and heterogeneous combined GEMRIC dataset, will provide new opportunities to elucidate the mechanisms of ECT response and the factors mediating and predictive of clinical outcomes, which may ultimately lead to more effective personalized treatment approaches. (C) 2017 The Author(s). Published by Elsevier Inc.
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| 4. |
- Opel, Nils, et al.
(författare)
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Elevated body weight modulates subcortical volume change and associated clinical response following electroconvulsive therapy
- 2021
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Ingår i: Journal of Psychiatry & Neuroscience. - : CMA-Canadian Medical Association. - 1180-4882 .- 1488-2434. ; 46:4, s. E418-E426
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Tidskriftsartikel (refereegranskat)abstract
- Background: Obesity is a frequent somatic comorbidity of major depression, and it has been associated with worse clinical outcomes and brain structural abnormalities. Converging evidence suggests that electroconvulsive therapy (ECT) induces both clinical improvements and increased subcortical grey matter volume in patients with depression. However, it remains unknown whether increased body weight modulates the clinical response and structural neuroplasticity that occur with ECT. Methods: To address this question, we conducted a longitudinal investigation of structural MRI data from the Global ECT-MRI Research Collaboration (GEMRIC) in 223 patients who were experiencing a major depressive episode (10 scanning sites). Structural MRI data were acquired before and after ECT, and we assessed change in subcortical grey matter volume using FreeSurfer and Quarc. Results: Higher body mass index (BMI) was associated with a significantly lower increase in subcortical grey matter volume following ECT. We observed significant negative associations between BMI and change in subcortical grey matter volume, with pronounced effects in the thalamus and putamen, where obese participants showed increases in grey matter volume that were 43.3% and 49.6%, respectively, of the increases found in participants with normal weight. As well, BMI significantly moderated the association between subcortical grey matter volume change and clinical response to ECT. We observed no significant association between BMI and clinical response to ECT. Limitations: Because only baseline BMI values were available, we were unable to study BMI changes during ECT and their potential association with clinical and grey matter volume change. Conclusion: Future studies should take into account the relevance of body weight as a modulator of structural neuroplasticity during ECT treatment and aim to further explore the functional relevance of this novel finding.
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| 5. |
- Ousdal, Olga Therese, et al.
(författare)
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Brain Changes Induced by Electroconvulsive Therapy Are Broadly Distributed
- 2020
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Ingår i: Biological Psychiatry. - : Elsevier. - 0006-3223 .- 1873-2402. ; 87:5, s. 451-461
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Electroconvulsive therapy (ECT) is associated with volumetric enlargements of corticolimbic brain regions. However, the pattern of whole-brain structural alterations following ECT remains unresolved. Here, we examined the longitudinal effects of ECT on global and local variations in gray matter, white matter, and ventricle volumes in patients with major depressive disorder as well as predictors of ECT-related clinical response. METHODS: Longitudinal magnetic resonance imaging and clinical data from the Global ECT-MRI Research Collaboration (GEMRIC) were used to investigate changes in white matter, gray matter, and ventricle volumes before and after ECT in 328 patients experiencing a major depressive episode. In addition, 95 nondepressed control subjects were scanned twice. We performed a mega-analysis of single subject data from 14 independent GEMRIC sites. RESULTS: Volumetric increases occurred in 79 of 84 gray matter regions of interest. In total, the cortical volume increased by mean +/- SD of 1.04 +/- 1.03% (Cohens d = 1.01, p amp;lt; .001) and the subcortical gray matter volume increased by 1.47 +/- 1.05% (d = 1.40, p amp;lt; .001) in patients. The subcortical gray matter increase was negatively associated with total ventricle volume (Spearmans rank correlation rho = -.44, p amp;lt; .001), while total white matter volume remained unchanged (d = -0.05, p = .41). The changes were modulated by number of ECTs and mode of electrode placements. However, the gray matter volumetric enlargements were not associated with clinical outcome. CONCLUSIONS: The findings suggest that ECT induces gray matter volumetric increases that are broadly distributed. However, gross volumetric increases of specific anatomically defined regions may not serve as feasible biomarkers of clinical response.
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