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The Neurobiology of Chronotype: Insights from a Comprehensive Multimodal Population Study

Poster No:

2581

Submission Type:

Abstract Submission

Authors:

LE ZHOU¹^,2, Karin Saltoun¹^,2, Danilo Bzdok¹^,2^,3^,4

Institutions:

¹McConnell Brain Imaging Centre, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada, ²Mila - Quebec Artificial Intelligence Institute, Montreal, Canada, ³Department of Biomedical Engineering, Faculty of Medicine, McGill University, Montreal, Canada, ⁴School of Computer Science, McGill University, Montreal, Canada

First Author:

LE ZHOU
McConnell Brain Imaging Centre, Montreal Neurological Institute (MNI), McGill University|Mila - Quebec Artificial Intelligence Institute
Montreal, Canada|Montreal, Canada

Co-Author(s):

Karin Saltoun
McConnell Brain Imaging Centre, Montreal Neurological Institute (MNI), McGill University|Mila - Quebec Artificial Intelligence Institute
Montreal, Canada|Montreal, Canada

Danilo Bzdok
McConnell Brain Imaging Centre, Montreal Neurological Institute (MNI), McGill University|Mila - Quebec Artificial Intelligence Institute|Department of Biomedical Engineering, Faculty of Medicine, McGill University|School of Computer Science, McGill University
Montreal, Canada|Montreal, Canada|Montreal, Canada|Montreal, Canada

Introduction:

The advent of the industrial age has precipitated noteworthy alterations in daily routines and lifestyles. The widespread use of electronic devices has been particularly transformative, significantly impacting people's sleep patterns and social interactions[1]. These shifts have sparked interest in understanding the impact of chronotype on brain and behavior, a clear manifestation of circadian rhythms that can be effectively assessed through self-reporting[2]. However, the brain basis of chronotype differences in humans are rarely examined, let alone on a population scale. In our population neuroscience study, we combine the data field of chronotype and various phenotypes and three brain image modalities (region volume, white-matter fiber tracts and functional connectivity) in 27030 UK-biobank participants to investigate the association between chronotype and a rich portfolio of ~1,000 health and lifestyle markers.

Methods:

Using self-report chronotype data from the UK-biobank, we categorized subjects as 0 for morningness and 1 for eveningness. Moreover, we excluded subjects with inconsistent assessments between the initial assessment and imaging visit, as well as those with shift work experiences. Finally, we selected 27030 subjects who had complete data for structure MRI, diffusion MRI, and resting-state fMRI. We first conducted Phenome-Wide Association Study(PheWAS) between chronotype and 976 other phenotypes[3] in our population dataset. Then, in our main analysis, we applied the linear discriminant model to investigate the multivariate brain patterns linked to chronotype across three neuroimaging modalities separately. For each neuroimaging feature, we regressed out the effects of head motion, head size, head position, body mass index (BMI), position of scanner table, data acquisition site, sleep duration, insomnia, snoring, dozing, and nap. To identify the most important brain features, we implemented 100 bootstraps to extract the relevant coefficients.

Results:

In our PheWAS analysis, 161 phenotypes exceeded the threshold at p<0.05 after Bonferroni's family-wise error correction (Figure 1). Lifestyle factors, including leisure activity, exercise, smoking and alcohol consumption, were highly associated with chronotype. Additionally, BMI, vitamin D, and 56 self-reported mental health phenotypes were also highlighted. We further examined the association between chronotype and three neuroimaging modalities. The significant coefficients of regional gray matter volume and white matter tracts are shown in Figure 2a, while resting-state functional connectivity is presented in Figure 2b.

Supporting Image: Figure2_brain_features.png

Conclusions:

Our study confirmed the evident disparities in lifestyles between morningness and eveningness. Aligning with prior research, the eveningness tends to be more vulnerability to mood disorder and unfavorable habits[4,5]. Complementary to behavioral observations, our neurobiological findings emphasize the involvement of key brain regions, including the basal ganglia, orbital frontal cortex, hippocampus, precentral cortex and cerebellum, crucial in the establishment of new habits[6]. These regions also exert influence over the hypothalamic-pituitary-adrenal (HPA) axis[7] and play a pivotal role in the reward system[8]. Furthermore, the subcallosal cortex, paracingulate gyrus, and parahippocampal areas were also significantly associated with chronotype. These regions, integral to the limbic system, are strongly linked to emotional regulations[9,10]. The observed white matter tracts of the fornix and the confirmed functional connectivity patterns involving the basal ganglia, cerebellum, primary somatosensory cortex, temporal medial cortex, and frontal-parietal cortex provide additional support for our neurobiological insights. In summary, our population study delved into the neurobiological associations of chronotypes, shedding light on their associations with brain regions pivotal in habit formation, HPA axis, and emotional regulation.

Emotion, Motivation and Social Neuroscience:

Social Interaction

Emotion and Motivation Other

Modeling and Analysis Methods:

Classification and Predictive Modeling

Multivariate Approaches ²

Perception, Attention and Motor Behavior:

Sleep and Wakefulness ¹

Keywords:

Machine Learning

MRI

Neurological

Sleep

Other - Chronotype; PheWAS; Multimodal

^1|2Indicates the priority used for review

Provide references using author date format

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2 Levandovski, R., et al., (2013) Chronotype: A Review of the Advances, Limits and Applicability of the Main Instruments Used in the Literature to Assess Human Phenotype. Trends in Psychiatry and Psychotherapy, 35, 3–11. https://doi.org/10.1590/s2237-60892013000100002.
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