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Functional connecti...
Functional connectivity signatures of major depressive disorder: machine learning analysis of two multicenter neuroimaging studies
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- Gallo, Selene (författare)
- Amsterdam UMC, Netherlands; Amsterdam Neurosci, Netherlands
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- El-Gazzar, Ahmed (författare)
- Amsterdam UMC, Netherlands; Amsterdam Neurosci, Netherlands
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- Zhutovsky, Paul (författare)
- Amsterdam UMC, Netherlands; Amsterdam Neurosci, Netherlands
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visa fler...
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- Thomas, Rajat M. (författare)
- Amsterdam UMC, Netherlands; Amsterdam Neurosci, Netherlands
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- Javaheripour, Nooshin (författare)
- Jena Univ Hosp, Germany
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- Li, Meng (författare)
- Jena Univ Hosp, Germany
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- Bartova, Lucie (författare)
- Med Univ Vienna, Austria
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- Bathula, Deepti (författare)
- Indian Inst Technol IIT, India
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- Dannlowski, Udo (författare)
- Univ Munster, Germany
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- Davey, Christopher (författare)
- Univ Melbourne, Australia
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- Frodl, Thomas (författare)
- Otto von Guericke Univ, Germany; German Ctr Mental Hlth, Germany
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- Gotlib, Ian (författare)
- Stanford Univ, CA 94305 USA
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- Grimm, Simone (författare)
- Charite Univ Med Berlin, Germany
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- Grotegerd, Dominik (författare)
- Univ Munster, Germany
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- Hahn, Tim (författare)
- Univ Munster, Germany
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- Hamilton, Paul J., 1970- (författare)
- Linköpings universitet,Centrum för social och affektiv neurovetenskap,Medicinska fakulteten,Centrum för medicinsk bildvetenskap och visualisering, CMIV
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- Harrison, Ben J. (författare)
- Univ Melbourne, Australia
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- Jansen, Andreas (författare)
- Univ Marburg, Germany
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Kircher, Tilo (författare)
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- Meyer, Bernhard (författare)
- Med Univ Vienna, Austria
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- Nenadic, Igor (författare)
- Univ Marburg, Germany
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- Olbrich, Sebastian (författare)
- Univ Hosp Zurich, Switzerland
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- Paul, Elisabeth, 1991- (författare)
- Linköpings universitet,Centrum för social och affektiv neurovetenskap,Medicinska fakulteten
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- Pezawas, Lukas (författare)
- Med Univ Vienna, Austria
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- Sacchet, Matthew D. (författare)
- Harvard Med Sch, MA USA
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- Saemann, Philipp (författare)
- Max Planck Inst Psychiat, Germany
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- Wagner, Gerd (författare)
- Jena Univ Hosp, Germany
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- Walter, Henrik (författare)
- Charite Univ Med Berlin, Germany
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- Walter, Martin (författare)
- Otto von Guericke Univ, Germany; German Ctr Mental Hlth, Germany
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PsyMRI, Guido (författare)
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- van Wingen, Guido (författare)
- Amsterdam UMC, Netherlands; Amsterdam Neurosci, Netherlands
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(creator_code:org_t)
- 2023-02-15
- 2023
- Engelska.
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Ingår i: Molecular Psychiatry. - : SPRINGERNATURE. - 1359-4184 .- 1476-5578. ; 28:7, s. 3013-3022
- Relaterad länk:
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https://liu.diva-por... (primary) (Raw object)
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Psykiatri (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Psychiatry (hsv//eng)
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Gallo, Selene
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El-Gazzar, Ahmed
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Zhutovsky, Paul
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Thomas, Rajat M.
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Javaheripour, No ...
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Li, Meng
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visa fler...
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Bartova, Lucie
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Bathula, Deepti
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Dannlowski, Udo
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Davey, Christoph ...
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Frodl, Thomas
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Gotlib, Ian
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Grimm, Simone
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Grotegerd, Domin ...
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Hahn, Tim
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Hamilton, Paul J ...
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Harrison, Ben J.
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Jansen, Andreas
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Kircher, Tilo
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Meyer, Bernhard
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Nenadic, Igor
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Olbrich, Sebasti ...
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Paul, Elisabeth, ...
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Pezawas, Lukas
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Sacchet, Matthew ...
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Saemann, Philipp
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Wagner, Gerd
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Walter, Henrik
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Walter, Martin
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PsyMRI, Guido
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van Wingen, Guid ...
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- Om ämnet
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- MEDICIN OCH HÄLSOVETENSKAP
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MEDICIN OCH HÄLS ...
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och Klinisk medicin
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och Psykiatri
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Molecular Psychi ...
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Linköpings universitet