Contribution of physiological dynamics in predicting major depressive disorder severity
García Pagès, Esther (Universitat Autònoma de Barcelona. Departament de Microelectrònica i Sistemes Electrònics)
Kontaxis, Spyridon (Universidad de Zaragoza)
Siddi, Sara (Universitat Autònoma de Barcelona. Departament de Matemàtiques)
Posadas de Miguel, Mar de (Universidad de Zaragoza)
de la Cámara, Concepción (Universidad de Zaragoza)
Bernal, Maria Luisa (Universidad de Zaragoza)
Castro Ribeiro, Thaís (Universitat Autònoma de Barcelona. Departament de Microelectrònica i Sistemes Electrònics)
Laguna, Pablo (Universidad de Zaragoza)
Badiella Busquets, Llorenç (Universitat Autònoma de Barcelona. Departament de Matemàtiques)
Bailón, Raquel (Universidad de Zaragoza)
Haro Abad, Josep Maria (Institut de Recerca Sant Joan de Déu)
Aguiló Llobet, Jordi (Universitat Autònoma de Barcelona. Departament de Microelectrònica i Sistemes Electrònics)

Data:	2025
Resum:	This study aimed to explore the physiological dynamics of cognitive stress in patients with Major Depressive Disorder (MDD) and design a multiparametric model for objectively measuring severity of depression. Physiological signal recordings from 40 MDD patients and 40 healthy controls were collected in a baseline stage, in a stress-inducing stage using two cognitive tests, and in the recovery period. Several features were extracted from electrocardiography, photoplethysmography, electrodermal activity, respiration, and temperature. Differences between values of these features under different conditions were used as indexes of autonomic reactivity and recovery. Finally, a linear model was designed to assess MDD severity, using the Beck Depression Inventory scores as the outcome variable. The performance of this model was assessed using the MDD condition as the response variable. General physiological hyporeactivity and poor recovery from stress predict depression severity across all physiological signals except for respiration. The model to predict depression severity included gender, body mass index, cognitive scores, and mean heart rate recovery, and achieved an accuracy of 78%, a sensitivity of 97% and a specificity of 59%. There is an observed correlation between the behavior of the autonomic nervous system, assessed through physiological signals analysis, and depression severity. Our findings demonstrated that decreased autonomic reactivity and recovery are linked with an increased level of depression. Quantifying the stress response together with a cognitive evaluation and personalization variables may facilitate a more precise diagnosis and monitoring of depression, enabling the tailoring of therapeutic interventions to individual patient needs.
Ajuts:	Ministerio de Sanidad y Consumo CB06/01/0049 European Commission 115902 Ministerio de Ciencia e Innovación TED2021-131106B-I00 Agencia Estatal de Investigación RTI2018-096072-B-I00
Nota:	Altres ajuts: acords transformatius de la UAB
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Cognitive test ; Generalized linear models ; Major depressive disorder ; Physiological signals ; Stress reactivity
Publicat a:	Psychophysiology, Vol. 62, Núm. 2 (February 2025) , art. e14729, ISSN 1469-8986

DOI: 10.1111/psyp.14729
PMID: 39552159

22 p, 1.0 MB

El registre apareix a les col·leccions:
Articles > Articles de recerca
Articles > Articles publicats