Discriminating stress from rest based on resting-state connectivity of the human brain: A supervised machine learning study.
Adult
Amygdala
/ diagnostic imaging
Connectome
Default Mode Network
/ diagnostic imaging
Female
Gyrus Cinguli
/ diagnostic imaging
Humans
Magnetic Resonance Imaging
Male
Nerve Net
/ diagnostic imaging
Parietal Lobe
/ diagnostic imaging
Stress, Psychological
/ diagnostic imaging
Supervised Machine Learning
acute stress
default mode network
functional connectivity
machine learning
resting-state functional magnetic resonance imaging
salience network
stress effects
Journal
Human brain mapping
ISSN: 1097-0193
Titre abrégé: Hum Brain Mapp
Pays: United States
ID NLM: 9419065
Informations de publication
Date de publication:
01 08 2020
01 08 2020
Historique:
received:
10
02
2020
revised:
11
03
2020
accepted:
19
03
2020
pubmed:
16
4
2020
medline:
11
11
2021
entrez:
16
4
2020
Statut:
ppublish
Résumé
Acute stress induces large-scale neural reorganization with relevance to stress-related psychopathology. Here, we applied a novel supervised machine learning method, combining the strengths of a priori theoretical insights with a data-driven approach, to identify which connectivity changes are most prominently associated with a state of acute stress and individual differences therein. Resting-state functional magnetic resonance imaging scans were taken from 334 healthy participants (79 females) before and after a formal stress induction. For each individual scan, mean time-series were extracted from 46 functional parcels of three major brain networks previously shown to be potentially sensitive to stress effects (default mode network (DMN), salience network (SN), and executive control networks). A data-driven approach was then used to obtain discriminative spatial linear filters that classified the pre- and post-stress scans. To assess potential relevance for understanding individual differences, probability of classification using the most discriminative filters was linked to individual cortisol stress responses. Our model correctly classified pre- versus post-stress states with highly significant accuracy (above 75%; leave-one-out validation relative to chance performance). Discrimination between pre- and post-stress states was mainly based on connectivity changes in regions from the SN and DMN, including the dorsal anterior cingulate cortex, amygdala, posterior cingulate cortex, and precuneus. Interestingly, the probability of classification using these connectivity changes were associated with individual cortisol increases. Our results confirm the involvement of DMN and SN using a data-driven approach, and specifically single out key regions that might receive additional attention in future studies for their relevance also for individual differences.
Identifiants
pubmed: 32293072
doi: 10.1002/hbm.25000
pmc: PMC7336146
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
3089-3099Informations de copyright
© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals, Inc.
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