Chronotype Subtypes Explain Unique Behavioral Profiles and Health Associations
Presented During:
Saturday, June 28, 2025: 11:30 AM - 12:45 PM
Brisbane Convention & Exhibition Centre
Room:
M1 & M2 (Mezzanine Level)
Poster No:
2088
Submission Type:
Abstract Submission
Authors:
Le Zhou1, Karin Saltoun1, Danilo Bzdok1
Institutions:
1McConnell Brain Imaging Centre, Montreal Neurological Institute (MNI), McGill University, Montreal, Quebec
First Author:
Le Zhou
McConnell Brain Imaging Centre, Montreal Neurological Institute (MNI), McGill University
Montreal, Quebec
McConnell Brain Imaging Centre, Montreal Neurological Institute (MNI), McGill University
Montreal, Quebec
Co-Author(s):
Karin Saltoun
McConnell Brain Imaging Centre, Montreal Neurological Institute (MNI), McGill University
Montreal, Quebec
McConnell Brain Imaging Centre, Montreal Neurological Institute (MNI), McGill University
Montreal, Quebec
Danilo Bzdok
McConnell Brain Imaging Centre, Montreal Neurological Institute (MNI), McGill University
Montreal, Quebec
McConnell Brain Imaging Centre, Montreal Neurological Institute (MNI), McGill University
Montreal, Quebec
Introduction:
Chronotype is a multifaceted construct linked to a broad spectrum of behaviors and health outcomes (Hasler, 2023; Montaruli et al., 2021). Individuals with similar chronotypes may exhibit distinct brain and behavioral patterns. To investigate this relationship, we developed a holistic pattern-learning algorithm. Our approach integrates three brain imaging modalities-regional gray matter volume, white matter microstructure, and functional connectivity-with 977 phenotypes, 1,447 diagnoses, and 133 medications from 27,030 UK Biobank participants. By disentangling chronotype subtypes, we move beyond simple dichotomies to uncover individual differences in chronotype expression and distinct brain-behavior patterns with potential health implications.
Methods:
Participants from UK Biobank with inconsistent chronotype assessments or shift work experience were excluded, resulting in 27,030 participants with complete data for structural MRI, diffusion MRI, and resting-state fMRI. To identify brain patterns linked to chronotype, we used partial least squares correlation analysis to capture the most explanatory projections of brain features in relation to chronotype (Kopal et al., 2024). Statistical robustness was assessed via 1,000 non-parametric permutation tests. Significant latent brain patterns were further explored through phenome-wide association studies to examine links with phenotypes, diagnoses, and medications ( Saltoun et al., 2023).
Results:
Despite our starting point with a binary chronotype phenotype, we identified five distinct subtypes associated with chronotype. Participants' brain features were projected into a five-dimensional space, with X scores representing alignment with the five components. Each participant was classified into one of five subtypes based on their highest X score.
The first mode exhibited a night owl pattern, implicating the basal ganglia, limbic cortex, and amygdala in GMV variations. This subtype was predominantly associated with mania, suggesting diminished emotional regulation ability. The second most explanatory mode, also a night owl pattern, was linked to depression and anxiety and highlighted the basal ganglia, thalamus, medial frontal gyrus, and lower white matter integrity in all tracts. This group was strongly associated with smoking and hypertension, as confirmed across phenotypes, diagnoses, and medications. The third mode was a morningness pattern with variations in the limbic cortex, amygdala, and fornix integrity opposite to those observed in mode 1. This subtype was associated with lower alcohol consumption, non-smoking behavior, and better cardiovascular health, despite being linked to higher TV usage and lower physical activity.
The fourth mode was a female-dominant morningness pattern, characterized by the insula, limbic cortex, caudate, and putamen. This subtype was associated with depressive symptoms, hormonal shifts, and menstrual disorders. Finally, the fifth mode represented an eveningness male-dominant pattern, featuring the medial frontal cortex, inferior frontal gyrus, ventral striatum, putamen, and visual cortex. This group was associated with alcohol and smoking phenotypes, cardiovascular risk, risk-taking, and cannabis use.
The first mode exhibited a night owl pattern, implicating the basal ganglia, limbic cortex, and amygdala in GMV variations. This subtype was predominantly associated with mania, suggesting diminished emotional regulation ability. The second most explanatory mode, also a night owl pattern, was linked to depression and anxiety and highlighted the basal ganglia, thalamus, medial frontal gyrus, and lower white matter integrity in all tracts. This group was strongly associated with smoking and hypertension, as confirmed across phenotypes, diagnoses, and medications. The third mode was a morningness pattern with variations in the limbic cortex, amygdala, and fornix integrity opposite to those observed in mode 1. This subtype was associated with lower alcohol consumption, non-smoking behavior, and better cardiovascular health, despite being linked to higher TV usage and lower physical activity.
The fourth mode was a female-dominant morningness pattern, characterized by the insula, limbic cortex, caudate, and putamen. This subtype was associated with depressive symptoms, hormonal shifts, and menstrual disorders. Finally, the fifth mode represented an eveningness male-dominant pattern, featuring the medial frontal cortex, inferior frontal gyrus, ventral striatum, putamen, and visual cortex. This group was associated with alcohol and smoking phenotypes, cardiovascular risk, risk-taking, and cannabis use.
·Fig 1 | Mode 1 is a night owl pattern associated with emotional regulation.
·Fig 2 | Mode 2 is a night owl pattern associated with smoking, cardiovascular risks, and depressive symptoms.
Conclusions:
Through the application of this supervised pattern-learning model, our findings underscore the pivotal roles of the basal ganglia and limbic cortex in chronotype-related processes and reveal five distinct patterns of chronotype manifestation. The first and third modes corresponded to eveningness and morningness, respectively, while the second mode was notably associated with smoking, depressive symptoms, and cardiovascular risks. The final two modes reflected sex-specific patterns, with one predominantly female and the other predominantly male. These insights offer a refined understanding of individual differences in chronotype, revealing distinct brain patterns and health implications, including links to emotional regulation, substance use, and cardiovascular health.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia)
Emotion, Motivation and Social Neuroscience:
Emotion and Motivation Other
Modeling and Analysis Methods:
Multivariate Approaches 2
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Anatomy and Functional Systems
Perception, Attention and Motor Behavior:
Sleep and Wakefulness 1
Keywords:
Affective Disorders
Multivariate
Other - Chronotype; Emotional Regulation; Subtypes; PheWAS
1|2Indicates the priority used for review
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2. Kopal, J., et al. (2024). High-effect gene-coding variants impact cognition, mental well-being, and neighborhood safety substrates in brain morphology. medRxiv. doi:10.1101/2024.05.21.24307729
3. Montaruli, A., et al. (2021). Biological Rhythm and Chronotype: New Perspectives in Health. Biomolecules, 11(4), 487. doi:10.3390/biom11040487
4. Saltoun, K., et al. (2023). Dissociable brain structural asymmetry patterns reveal unique phenome-wide profiles. Nature Human Behaviour, 7(2), 251-268. doi:10.1038/s41562-022-01461-0