Mikhail Votinov
Biographic Data
| ID | 7532536 |
|---|---|
| NAME | Mikhail Votinov |
| GIVEN NAMES | Mikhail |
| FAMILY NAME | Votinov |
| SIGNATURE | VOTINOV M |
| AFFILIATIONS | Universitätsklinikum Aachen |
| ORCID | 0000-0001-6970-6093 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2023 |
| LATEST PUBLICATION YEAR | 2023 |
| H-INDEX | 0 |
Hormonal abnormalities in alexithymia
Alexithymia is a personality trait characterized by difficulties in emotion recognition and regulation that is associated with deficits in social cognition. High alexithymia levels are considered a transdiagnostic risk factor for a range of psychiatric and medical conditions, including depression, anxiety, and autism. Hormones are known to affect social-emotional cognition and behavior in humans, including the neuropeptides oxytocin and vasopress…
Ensemble graph neural network model for classification of major depressive disorder using whole-brain functional connectivity
Major depressive disorder (MDD) is characterized by impairments in mood and cognitive functioning, and it is a prominent source of global disability and stress. A functional magnetic resonance imaging (fMRI) can aid clinicians in their assessments of individuals for the identification of MDD. Herein, we employ a deep learning approach to the issue of MDD classification. Resting-state fMRI data from 821 individuals with MDD and 765 healthy control…
No prominent works on this page.
Hormonal abnormalities in alexithymia
Alexithymia is a personality trait characterized by difficulties in emotion recognition and regulation that is associated with deficits in social cognition. High alexithymia levels are considered a transdiagnostic risk factor for a range of psychiatric and medical conditions, including depression, anxiety, and autism. Hormones are known to affect social-emotional cognition and behavior in humans, including the neuropeptides oxytocin and vasopress…
Ensemble graph neural network model for classification of major depressive disorder using whole-brain functional connectivity
Major depressive disorder (MDD) is characterized by impairments in mood and cognitive functioning, and it is a prominent source of global disability and stress. A functional magnetic resonance imaging (fMRI) can aid clinicians in their assessments of individuals for the identification of MDD. Herein, we employ a deep learning approach to the issue of MDD classification. Resting-state fMRI data from 821 individuals with MDD and 765 healthy control…
Neuroscience (2 works) · Psychiatry (2 works) · Psychology (2 works) · Alexithymia (1 work) · Artificial Intelligence (1 work) · Clinical Psychology (1 work) · Clinical Psychology (1 work) · Cognition (1 work) · Computer Science (1 work) · Convolutional neural network (1 work)