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Altered effective connectivity in sensorimotor cortices is a signature of severity and clinical course in depression. / Ray, Dipanjan; Bezmaternykh, Dmitry; Mel’nikov, Mikhail et al.

In: Proceedings of the National Academy of Sciences of the United States of America, Vol. 118, No. 40, e2105730118, 05.10.2021.

Research output: Contribution to journalArticlepeer-review

Harvard

Ray, D, Bezmaternykh, D, Mel’nikov, M, Friston, KJ & Das, M 2021, 'Altered effective connectivity in sensorimotor cortices is a signature of severity and clinical course in depression', Proceedings of the National Academy of Sciences of the United States of America, vol. 118, no. 40, e2105730118. https://doi.org/10.1073/pnas.2105730118

APA

Ray, D., Bezmaternykh, D., Mel’nikov, M., Friston, K. J., & Das, M. (2021). Altered effective connectivity in sensorimotor cortices is a signature of severity and clinical course in depression. Proceedings of the National Academy of Sciences of the United States of America, 118(40), [e2105730118]. https://doi.org/10.1073/pnas.2105730118

Vancouver

Ray D, Bezmaternykh D, Mel’nikov M, Friston KJ, Das M. Altered effective connectivity in sensorimotor cortices is a signature of severity and clinical course in depression. Proceedings of the National Academy of Sciences of the United States of America. 2021 Oct 5;118(40):e2105730118. doi: 10.1073/pnas.2105730118

Author

Ray, Dipanjan ; Bezmaternykh, Dmitry ; Mel’nikov, Mikhail et al. / Altered effective connectivity in sensorimotor cortices is a signature of severity and clinical course in depression. In: Proceedings of the National Academy of Sciences of the United States of America. 2021 ; Vol. 118, No. 40.

BibTeX

@article{d1d463c1bc214e3082a52c32cd65bc98,
title = "Altered effective connectivity in sensorimotor cortices is a signature of severity and clinical course in depression",
abstract = "Functional neuroimaging research on depression has traditionally targeted neural networks associated with the psychological aspects of depression. In this study, instead, we focus on alterations of sensorimotor function in depression. We used resting-state functional MRI data and dynamic causal modeling (DCM) to assess the hypothesis that depression is associated with aberrant effective connectivity within and between key regions in the sensorimotor hierarchy. Using hierarchical modeling of between-subject effects in DCM with parametric empirical Bayes we first established the architecture of effective connectivity in sensorimotor cortices. We found that in (interoceptive and exteroceptive) sensory cortices across participants, the backward connections are predominantly inhibitory, whereas the forward connections are mainly excitatory in nature. In motor cortices these parities were reversed. With increasing depression severity, these patterns are depreciated in exteroceptive and motor cortices and augmented in the interoceptive cortex, an observation that speaks to depressive symptomatology. We established the robustness of these results in a leave-one-out cross-validation analysis and by reproducing the main results in a follow-up dataset. Interestingly, with (nonpharmacological) treatment, depression-associated changes in backward and forward effective connectivity partially reverted to group mean levels. Overall, altered effective connectivity in sensorimotor cortices emerges as a promising and quantifiable candidate marker of depression severity and treatment response.",
keywords = "Depression, Effective connectivity, Embodiment, Predictive processes, Spectral DCM",
author = "Dipanjan Ray and Dmitry Bezmaternykh and Mikhail Mel{\textquoteright}nikov and Friston, {Karl J.} and Moumita Das",
note = "Funding Information: ACKNOWLEDGMENTS. We thank Prof. Mark Shtark who supervised the data collection and Dr. Andrey Savelov who conducted MRI and fMRI acquisition. This research was supported by the Basque Government through the Basque Excellence Research Centres 2018-2 021 program; the Spanish Ministry of Science, Innovation, and Universities (Basque Center on Cognition, Brain and Language Severo Ochoa excellence accreditation SEV-2015-0 490 and Basque Center of Applied Mathematics (BCAM) Severo Ochoa accreditation SEV-2017-0 718); and project MTM2017-82 379-R (Agencia Estatal de Inves-tigaci{\'o}n/Fondo Europeo de Desarrollo Regional, Uni{\'o}n Europea; principal investigator Dr. Maria Xose Rodriguez, BCAM). Data collection was funded by Russian Science Foundation Grant 16-15-00 183. K.J.F. was funded by a Wellcome Trust Principal Research Fellowship (Reference 088 130/Z/09/Z). For the purpose of Open Access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript (AAM) version arising from this submission. Publisher Copyright: {\textcopyright} 2021 National Academy of Sciences. All rights reserved.",
year = "2021",
month = oct,
day = "5",
doi = "10.1073/pnas.2105730118",
language = "English",
volume = "118",
journal = "Proceedings of the National Academy of Sciences of the United States of America",
issn = "0027-8424",
publisher = "National Academy of Sciences",
number = "40",

}

RIS

TY - JOUR

T1 - Altered effective connectivity in sensorimotor cortices is a signature of severity and clinical course in depression

AU - Ray, Dipanjan

AU - Bezmaternykh, Dmitry

AU - Mel’nikov, Mikhail

AU - Friston, Karl J.

AU - Das, Moumita

N1 - Funding Information: ACKNOWLEDGMENTS. We thank Prof. Mark Shtark who supervised the data collection and Dr. Andrey Savelov who conducted MRI and fMRI acquisition. This research was supported by the Basque Government through the Basque Excellence Research Centres 2018-2 021 program; the Spanish Ministry of Science, Innovation, and Universities (Basque Center on Cognition, Brain and Language Severo Ochoa excellence accreditation SEV-2015-0 490 and Basque Center of Applied Mathematics (BCAM) Severo Ochoa accreditation SEV-2017-0 718); and project MTM2017-82 379-R (Agencia Estatal de Inves-tigación/Fondo Europeo de Desarrollo Regional, Unión Europea; principal investigator Dr. Maria Xose Rodriguez, BCAM). Data collection was funded by Russian Science Foundation Grant 16-15-00 183. K.J.F. was funded by a Wellcome Trust Principal Research Fellowship (Reference 088 130/Z/09/Z). For the purpose of Open Access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript (AAM) version arising from this submission. Publisher Copyright: © 2021 National Academy of Sciences. All rights reserved.

PY - 2021/10/5

Y1 - 2021/10/5

N2 - Functional neuroimaging research on depression has traditionally targeted neural networks associated with the psychological aspects of depression. In this study, instead, we focus on alterations of sensorimotor function in depression. We used resting-state functional MRI data and dynamic causal modeling (DCM) to assess the hypothesis that depression is associated with aberrant effective connectivity within and between key regions in the sensorimotor hierarchy. Using hierarchical modeling of between-subject effects in DCM with parametric empirical Bayes we first established the architecture of effective connectivity in sensorimotor cortices. We found that in (interoceptive and exteroceptive) sensory cortices across participants, the backward connections are predominantly inhibitory, whereas the forward connections are mainly excitatory in nature. In motor cortices these parities were reversed. With increasing depression severity, these patterns are depreciated in exteroceptive and motor cortices and augmented in the interoceptive cortex, an observation that speaks to depressive symptomatology. We established the robustness of these results in a leave-one-out cross-validation analysis and by reproducing the main results in a follow-up dataset. Interestingly, with (nonpharmacological) treatment, depression-associated changes in backward and forward effective connectivity partially reverted to group mean levels. Overall, altered effective connectivity in sensorimotor cortices emerges as a promising and quantifiable candidate marker of depression severity and treatment response.

AB - Functional neuroimaging research on depression has traditionally targeted neural networks associated with the psychological aspects of depression. In this study, instead, we focus on alterations of sensorimotor function in depression. We used resting-state functional MRI data and dynamic causal modeling (DCM) to assess the hypothesis that depression is associated with aberrant effective connectivity within and between key regions in the sensorimotor hierarchy. Using hierarchical modeling of between-subject effects in DCM with parametric empirical Bayes we first established the architecture of effective connectivity in sensorimotor cortices. We found that in (interoceptive and exteroceptive) sensory cortices across participants, the backward connections are predominantly inhibitory, whereas the forward connections are mainly excitatory in nature. In motor cortices these parities were reversed. With increasing depression severity, these patterns are depreciated in exteroceptive and motor cortices and augmented in the interoceptive cortex, an observation that speaks to depressive symptomatology. We established the robustness of these results in a leave-one-out cross-validation analysis and by reproducing the main results in a follow-up dataset. Interestingly, with (nonpharmacological) treatment, depression-associated changes in backward and forward effective connectivity partially reverted to group mean levels. Overall, altered effective connectivity in sensorimotor cortices emerges as a promising and quantifiable candidate marker of depression severity and treatment response.

KW - Depression

KW - Effective connectivity

KW - Embodiment

KW - Predictive processes

KW - Spectral DCM

UR - http://www.scopus.com/inward/record.url?scp=85116340639&partnerID=8YFLogxK

U2 - 10.1073/pnas.2105730118

DO - 10.1073/pnas.2105730118

M3 - Article

C2 - 34593640

AN - SCOPUS:85116340639

VL - 118

JO - Proceedings of the National Academy of Sciences of the United States of America

JF - Proceedings of the National Academy of Sciences of the United States of America

SN - 0027-8424

IS - 40

M1 - e2105730118

ER -

ID: 34401350