Resting-State Functional Connectivity Impairment in Patients with Major Depressive Episode

Stoyanov, Drozdstoy; Khorev, Vladimir; Paunova, Rositsa; Kandilarova, Sevdalina; Simeonova, Denitsa; Badarin, Artem; Hramov, Alexander E.; Kurkin, Semen A.

doi:10.3390/ijerph192114045

Cited by 22 publications

(6 citation statements)

References 73 publications

(75 reference statements)

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“…The region is essential for the selective attention, working memory and inhibitory control, which are impaired during a major depressive episode. In addition, lingual and fusiform gyri appear to be a hub of disturbed connectivity in our most recent studies of functional connectivity (Stoyanov, 2022b). That may be interpreted in the sense of an over-arousal or vulnerability of the same region at rest, which is highlighted by the diagnostic task in patients with depression as hypo-activation.…”

Section: Discussionmentioning

confidence: 85%

Group Independent Components Underpin Responses to Items from a Depression Scale

Stoyanov¹,

Khorev²,

Paunova³

et al. 2023

Preprint

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The aim of the presents study is to further expand the model of translational cross-validation by investigating the brain circuits or networks which underpin the same tasks by means of group independent component analysis for FMRI toolbox (GIFT). We hypothesized that there will be neural network patterns of activation and deactivation, which correspond to real time performance on clinical self-evaluation scales. We recruited 42 subjects: 20 healthy controls and 22 patients with major depressive episode. All subjects underwent functional MRI scanning using paradigm comprised of diagnostic clinical self-assessment depression scale contrasted to neutral scale. The data were processed with group independent component analysis for functional MRI toolbox and statistical parametric mapping. The results have demonstrated that there exist positively or negatively modulated brain networks during processing of diagnostic specific task questions for major depressive disorder. There have also been confirmed differences in the networks processing diagnostic versus off blocks between patients and controls in anterior cingulate cortex and middle frontal gyrus. Diagnostic conditions (depression scale) when contrasted to neutral conditions demonstrate differential activity of right superior frontal gyrus and right middle cingulate cortex in the comparison of patients with healthy controls. It is for the first time when potential neuroimaging state dependent biomarker has been directly linked with clinical assessment self-evaluation scale, administered as stimuli simultaneously with the fMRI acquisition. It may be regarded as further evidence in support of the ability of both methods to concordantly distinguish groups by means of incremental translational cross-validation.

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Section: Discussionmentioning

confidence: 85%

Group Independent Components Underpin Responses to Items from a Depression Scale

Stoyanov¹,

Khorev²,

Paunova³

et al. 2023

Preprint

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“…Other literature are dedicated to the prediction of Caesarean section risk factors [ 68 , 69 , 70 , 71 , 72 ]. Supervised learning in combination with electronic health records demonstrated a high effectiveness in prediction of postpartum depression [ 73 , 74 , 75 , 76 ], and in combination with resting state fMRI-based functional connectivity demonstrated a high performance in the classification of major depressive disorder [ 77 , 78 ].…”

Section: Resultsmentioning

confidence: 99%

Analysis of Publication Activity and Research Trends in the Field of AI Medical Applications: Network Approach

Карпов

Pitsik

Kurkin

et al. 2023

IJERPH

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Artificial intelligence (AI) has revolutionized numerous industries, including medicine. In recent years, the integration of AI into medical practices has shown great promise in enhancing the accuracy and efficiency of diagnosing diseases, predicting patient outcomes, and personalizing treatment plans. This paper aims at the exploration of the AI-based medicine research using network approach and analysis of existing trends based on PubMed. Our findings are based on the results of PubMed search queries and analysis of the number of papers obtained by the different search queries. Our goal is to explore how are the AI-based methods used in healthcare research, which approaches and techniques are the most popular, and to discuss the potential reasoning behind the obtained results. Using analysis of the co-occurrence network constructed using VOSviewer software, we detected the main clusters of interest in AI-based healthcare research. Then, we proceeded with the thorough analysis of publication activity in various categories of medical AI research, including research on different AI-based methods applied to different types of medical data. We analyzed the results of query processing in the PubMed database over the past 5 years obtained via a specifically designed strategy for generating search queries based on the thorough selection of keywords from different categories of interest. We provide a comprehensive analysis of existing applications of AI-based methods to medical data of different modalities, including the context of various medical fields and specific diseases that carry the greatest danger to the human population.

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“…Our comprehension of MDDs effect on large-scale brain networks and alterations in functional connectivity is a continuously advancing area of study. Several functional neuroimaging studies in MDD report network dysfunction across a variety of networks at both intra-and inter-hemispheric levels of functional connectivity (5)(6)(7)(8)(9)(10)(11)(12).…”

Section: Background and Rationale {6a}mentioning

confidence: 99%

Engaging Mood Brain Circuits with Psilocybin (EMBRACE): a study protocol for a randomized, proof-of-principle, placebo-controlled and crossover, neuroimaging trial in depression

Poulin,

Bigford,

Lanctot

et al. 2023

Preprint

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Background: Major Depressive Disorder (MDD) is a leading cause of disability worldwide across domains of health and cognition, affecting overall quality of life. Approximately one third of individuals with depression do not fully respond to treatments (e.g., conventional antidepressants, psychotherapy) and alternative strategies are needed. Recent early phase trials suggest psilocybin may be a safe and efficacious intervention with rapid-acting antidepressant properties. Psilocybin is thought to exert therapeutic benefits by altering brain network connectivity and inducing neuroplastic changes that endure for weeks post-treatment. Although early clinical results are encouraging, psilocybin’s acute neurobiological effects on neuroplasticity have not been fully investigated. We aim to examine for the first time how psilocybin acutely (intraday) and subacutely (weeks) alters functional brain networks implicated in depression. Methods: Thirty-six participants diagnosed with MDD or Persistent Depressive Disorder (PDD) will be recruited from a tertiary mood disorders clinic and undergo 1:1 randomization into either an experimental or control arm. Participants will be given either 25 mg psilocybin or active placebo (100 mg niacin) for the first treatment. Three weeks later, those in the control arm will cross over and all participants will receive 25 mg psilocybin. We will investigate whether treatments are associated with changes in arterial spin labelling and blood oxygenation level dependent contrast neuroimaging assessments at acute and subacute timepoints. Primary outcomes include testing whether psilocybin demonstrates acute changes in 1) cerebral blood flow and 2) functional brain activity in networks associated with mood regulation and depression when compared to placebo. Secondary outcomes include changes in MADRS score over time compared to placebo, and changes across complementary clinical psychiatric, cognitive, and functional scales from baseline to final follow-up. Serum peripheral neurotrophic and inflammatory biomarkers will be collected at baseline to examine relationships with clinical response, and neuroimaging measures. Discussion: This study will investigate the acute and additive subacute neuroplastic effects of psilocybin on brain networks affected by depression using advanced serial neuroimaging methods. Results will improve our understanding of psilocybin’s antidepressant mechanisms versus placebo response and whether biological measures of brain function can provide early predictors of treatment response. Trial registration: ClinicalTrials.gov Identifier: NCT06072898. Registered on 6 October 2023.

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Resting-State Functional Connectivity Impairment in Patients with Major Depressive Episode

Cited by 22 publications

References 73 publications

Group Independent Components Underpin Responses to Items from a Depression Scale

Group Independent Components Underpin Responses to Items from a Depression Scale

Analysis of Publication Activity and Research Trends in the Field of AI Medical Applications: Network Approach

Engaging Mood Brain Circuits with Psilocybin (EMBRACE): a study protocol for a randomized, proof-of-principle, placebo-controlled and crossover, neuroimaging trial in depression

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