Classification of MDD patients with using network measures DOI
Andrey Andreev

Published: Sept. 18, 2023

Major depressive disorder (MDD) is a common and debilitating psychiatric illness that affects millions of people worldwide. Despite advancements in the understanding its underlying mechanisms, diagnosis treatment MDD remain significant challenge. In this paper, we present an approach for classification patients with based on their functional network measures. Our results demonstrate simple Linear Discriminant Analysis achieves high accuracy (83 %) two cases: when use all network's couplings or only strongest ones.

Language: Английский

Neural Correlates of Social Touch Processing: An fMRI Study on Brain Functional Connectivity DOI Creative Commons
Vladimir Khorev, Semen Kurkin, L. A. Mayorova

et al.

Journal of Integrative Neuroscience, Journal Year: 2025, Volume and Issue: 24(1)

Published: Jan. 20, 2025

Background: The significance of tactile stimulation in human social development and personal interaction is well documented; however, the underlying cerebral processes remain under-researched. This study employed functional magnetic resonance imaging (fMRI) to investigate neural correlates touch processing, with a particular focus on connectivity associated aftereffects touch. Methods: A total 27 experimental subjects were recruited for study, all whom underwent 5-minute calf foot massage prior undergoing resting-state fMRI. Additionally, 11 healthy controls participated solely fMRI recording. network analysis was conducted examine alterations connections between different brain regions following massage. Results: findings indicated involvement discrete networks processing touch, notable discrepancies observed control groups. revealed that group exhibited higher degree within subnetwork comprising 25 23 nodes than intervention. showed hypoactivation this left anterior pulvinar thalamus right pregenual cingulate cortex, which serve as key hubs subnetwork, clustering increased node strength group. Relatively small unequal sample sizes are limitations may affect generalizability results. Conclusions: These elucidate underpinnings experiences their potential impact behavior emotional state. Gaining insight into these mechanisms could inform therapeutic approaches utilize mitigate stress enhance mental health. From practical standpoint, our results have significant implications sensory strategies patients prolonged disorders consciousness, loss, autism spectrum disorders, or limited access upper extremities.

Language: Английский

Citations

0
Disruptions in segregation mechanisms in fMRI-based brain functional network predict the major depressive disorder condition DOI
Vladimir Khorev, Semen Kurkin,

Gabriella Zlateva

et al.

Chaos Solitons & Fractals, Journal Year: 2024, Volume and Issue: 188, P. 115566 - 115566

Published: Oct. 1, 2024

Language: Английский

Citations

3
Abnormal changes of dynamic topological characteristics in patients with major depressive disorder DOI
Yue Zhou,

Yihui Zhu,

Hongting Ye

et al.

Journal of Affective Disorders, Journal Year: 2023, Volume and Issue: 345, P. 349 - 357

Published: Oct. 25, 2023

Language: Английский

Citations

8
Characteristics of brain functional networks specific for different types of tactile perception DOI
Semen Kurkin, Vladimir Khorev, Ivan V. Skorokhodov

et al.

The European Physical Journal Special Topics, Journal Year: 2023, Volume and Issue: 233(3), P. 499 - 504

Published: Dec. 1, 2023

Language: Английский

Citations

5
Methodology of collection, recording and markup of biophysical multimodal data in the study of human psychoemotional states DOI Creative Commons
Natalia Shusharina

Izvestiya of Saratov University Physics, Journal Year: 2024, Volume and Issue: 24(3), P. 239 - 249

Published: Aug. 22, 2024

Аннотация.Цель настоящей работы -проанализировать требования к методике сбора биофизических данных на основе открытых наборов определения психоэмоционального состояния, аппаратному и программному обеспечению для их первичной обработки.Сформулировать методику формирования мультимодальных данных, пригодную исследования психических состояний изменений, в том числе с использованием алгоритмов машинного обучения.Описать возможный метод реализации этих требований аппаратно-программных комплексах.Методы.Для анализа основных особенностей характеризующих психические были выбраны открытые наборы пациентов депрессивными расстройствами.Основные сформулированы изучения публикаций об особенностях применения диагностики депрессивных расстройств.Результатом являются набор мультимодальным данным биопотенциалов психоэмоциональных состояний, методика функциональная концепция аппаратно-программного комплекса регистрации, синхронизации записи аннотированном виде.Заключение.На примере депрессивного расстройства показана целесообразность возможность регистрации мультимодальных, синхронизированных между собой аннотированных о психоэмоциональном состоянии испытуемого исследовательских, диагностических целей качестве обучающей выборки алгоритмах обучения.Предложенная программно-аппаратного позволяют

Language: Русский

Citations

0
Recurrency time entropy of brain wave rhythms as an indicator of performance on visual search tasks in schoolchildren DOI
Artem Badarin,

Nikita Brusinskii,

Vadim Grubov

et al.

The European Physical Journal Special Topics, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 2, 2024

Language: Английский

Citations

0
Intermediary-guided windowed attention Aggregation network for fine-grained characterization of Major Depressive Disorder fMRI DOI
Xue Ming Yuan,

Maozhou Chen,

Peng Ding

et al.

Biomedical Signal Processing and Control, Journal Year: 2024, Volume and Issue: 100, P. 107166 - 107166

Published: Nov. 6, 2024

Language: Английский

Citations

0
Regime switching in coupled nonlinear systems: Sources, prediction, and control—Minireview and perspective on the Focus Issue DOI
Igor Franović, Sebastian Eydam, Deniz Eroglu

et al.

Chaos An Interdisciplinary Journal of Nonlinear Science, Journal Year: 2024, Volume and Issue: 34(12)

Published: Dec. 1, 2024

Regime switching, the process where complex systems undergo transitions between qualitatively different dynamical states due to changes in their conditions, is a widespread phenomenon, from climate and ocean circulation, ecosystems, power grids, brain. Capturing mechanisms that give rise isolated or sequential switching dynamics, as well developing generic robust methods for forecasting, detecting, controlling them essential maintaining optimal performance preventing dysfunctions even collapses systems. This Focus Issue provides new insights into regime covering recent advances theoretical analysis harnessing reduction approaches, data-driven detection non-feedback control strategies. Some of key challenges addressed include development techniques coupled stochastic adaptive systems, influence multiple timescale dynamics on chaotic structures cyclic patterns forced role saddles heteroclinic cycles pattern oscillators. The contributions further highlight deep learning applications predicting grid failures, use blinking networks enhance synchronization, creating strategies epidemic spreading, suppress epileptic seizures. These developments are intended catalyze dialog branches complexity.

Language: Английский

Citations

0
Classification of MDD patients with using network measures DOI
Andrey Andreev

Published: Sept. 18, 2023

Major depressive disorder (MDD) is a common and debilitating psychiatric illness that affects millions of people worldwide. Despite advancements in the understanding its underlying mechanisms, diagnosis treatment MDD remain significant challenge. In this paper, we present an approach for classification patients with based on their functional network measures. Our results demonstrate simple Linear Discriminant Analysis achieves high accuracy (83 %) two cases: when use all network's couplings or only strongest ones.

Language: Английский

Citations

0