Extending the “resting state hypothesis of depression” – dynamics and topography of abnormal rest-task modulation

Gupta, Anvita; Wolff, Annemarie; Northoff, Georg

doi:10.1016/j.pscychresns.2021.111367

Cited by 11 publications

(4 citation statements)

References 51 publications

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“…Another complementary perspective of the findings, which show the correlation between 5-HT 2a and 5-HT 1a serotonergic receptors and regions with difference in the intervention between the groups, is that of the overactivation of DMN in depression both in terms of its connectivity profile and temporal characterization. 22-24 , 63 It has been shown that serotonergic raphe nuclei have direct anatomical projections to the regions of DMN and 5HT 2 receptors expressed in DMN regions, 64 and it is thus possible that the psychedelic action enables modulation of the DMN via serotonergic pathways in responders but not non-responders.…”

Section: Discussionmentioning

confidence: 99%

“…Such behaviour has been linked to the abnormal spatio-temporal organization of resting state activity. 22 Converging findings have found the overactivation of default mode network (DMN) and hypoconnectivity in the control executive network in MDD. 23-25 This is consistent with the triple-network model whereby depressive states promote the inward and self-referential experience at the expense of the interactions and attention towards the external environment.…”

Section: Introductionmentioning

confidence: 99%

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Brain dynamics predictive of response to psilocybin for treatment-resistant depression

Vohryzek,

Cabral,

Lord

et al. 2024

Brain Communications

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Psilocybin therapy for depression has started to show promise, yet the underlying causal mechanisms are not currently known. Here we leveraged the differential outcome in responders and non-responders to psilocybin (10mg and 25mg, 7 days apart) therapy for depression - to gain new insights into regions and networks implicated in the restoration of healthy brain dynamics. We used large-scale brain modelling to fit the spatiotemporal brain dynamics at rest in both responders and non-responders before treatment. Dynamic sensitivity analysis of systematic perturbation of these models enabled us to identify specific brain regions implicated in a transition from a depressive brain state to a heathy one. Binarizing the sample into treatment responders (>50% reduction in depressive symptoms) versus non-responders enabled us to identify a subset of regions implicated in this change. Interestingly, these regions correlate with in vivo density maps of serotonin receptors 5-Hydroxytryptamine 2a and 5-Hydroxytryptamine 1a, which psilocin, the active metabolite of psilocybin, has an appreciable affinity for, and where it acts as a full-to-partial agonist. Serotonergic transmission has long been associated with depression and our findings provide causal mechanistic evidence for the role of brain regions in the recovery from depression via psilocybin.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Brain dynamics predictive of response to psilocybin for treatment-resistant depression

Vohryzek,

Cabral,

Lord

et al. 2024

Brain Communications

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“…According to the resting-state hypothesis of depression proposed by Northoff et al 34 and expanded by Gupta et al, 35 the abnormality in the resting-state brain activity is the core feature of MDD and one of the most consistent findings among the variety of different and in part contradictory findings in MDD. In this study, DMN, AN, VN, SMN, and cerebellar networks contributed the most to distinguishing RD and NRD.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Early Antidepressant Efficacy in Patients with Major Depressive Disorder Based on Multidimensional Features of rs-fMRI and P11 Gene DNA Methylation: Prédiction de l’efficacité précoce d’un antidépresseur chez des patients souffrant du trouble dépressif majeur d’après les caractéristiques multidimensionnelles de la méthylation de l’ADN du gène P11 et de la IRMf-rs

Wang,

Gao,

et al. 2023

Can J Psychiatry

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Objective This study established a machine learning model based on the multidimensional data of resting-state functional activity of the brain and P11 gene DNA methylation to predict the early efficacy of antidepressant treatment in patients with major depressive disorder (MDD). Methods A total of 98 Han Chinese MDD were analysed in this study. Patients were divided into 51 responders and 47 nonresponders according to whether the Hamilton Depression Rating Scale-17 items (HAMD-17) reduction rate was ≥50% after 2 weeks of antidepressant treatment. At baseline, the Illumina HiSeq Platform was used to detect the methylation of 74 CpG sites of the P11 gene in peripheral blood samples. Resting-state functional magnetic resonance imaging (rs-fMRI) scan detected the amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), and functional connectivity (FC) in 116 brain regions. The least absolute shrinkage and selection operator analysis method was used to perform feature reduction and feature selection. Four typical machine learning methods were used to establish support vector machine (SVM), random forest (RF), Naïve Bayes (NB), and logistic regression (LR) prediction models based on different combinations of functional activity of the brain, P11 gene DNA methylation and clinical/demographic features after screening. Results The SVM model based on ALFF, ReHo, FC, P11 methylation, and clinical/demographic features showed the best performance, with 95.92% predictive accuracy and 0.9967 area under the receiver operating characteristic curve, which was better than RF, NB, and LR models. Conclusion The multidimensional data features combining rs-fMRI, DNA methylation, and clinical/demographic features can predict the early antidepressant efficacy in MDD.

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“…These changes in spontaneous brain activity occur synchronously in certain brain regions and can fluctuate across networks dynamically. The fluctuations among these functional intrinsic connectivity networks can be associated with certain mental states and physiological states (Chen et al, 2020 ) during rest or they can influence mental processing, cognitive performance or brain activity during task-related activity following the resting state in healthy or vulnerbale individuals (e.g., Northoff et al, 2010 ; Gupta et al, 2021 ; Wolff et al, 2022 ). Several resting state brain networks have been identified that might be hierachically organized and whose intrinsic activity is distinct from or partially overlapping with task-related or task-positive brain networks that change activity when processing or performing a certain task (for an overview e.g., Seitzman et al, 2019 ).…”

Section: Summary Of the Research Contributions Of The Research Topicmentioning

confidence: 99%

Editorial: Analyzing and computing humans - the role of language, culture, brain and health

Herbert,

Northoff

2024

Front. Hum. Neurosci.

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Extending the “resting state hypothesis of depression” – dynamics and topography of abnormal rest-task modulation

Cited by 11 publications

References 51 publications

Brain dynamics predictive of response to psilocybin for treatment-resistant depression

Brain dynamics predictive of response to psilocybin for treatment-resistant depression

Editorial: Analyzing and computing humans - the role of language, culture, brain and health

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