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Hyperdimensional (HD) computing is a brain-inspired form of computing based on the manipulation of high-dimensional vectors. Offering robust data representation and relatively fast learning, HD computing is a promising candidate for energy-efficient classification of biological signals. This paper describes the application of HD computing-based machine learning to the classification of biological gender from resting-state and task functional magnetic resonance imaging (fMRI) from the publicly available Human Connectome Project (HCP). The developed HD algorithm derives predictive features through mean dynamic functional connectivity (dFC) analysis. Record encoding is employed to map features onto hyperdimensional space. Utilizing adaptive retraining techniques, the HD computing-based classifier achieves an average biological gender classification accuracy of 87%, as compared to 84% achieved by edge entropy measure.
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Functional brain connectivity predicts sleep duration in youth and adults
Abstract Sleep is critical to a variety of cognitive functions and insufficient sleep can have negative consequences for mood and behavior across the lifespan. An important open question is how sleep duration is related to functional brain organization which may in turn impact cognition. To characterize the functional brain networks related to sleep across youth and young adulthood, we analyzed data from the publicly available Human Connectome Project (HCP) dataset, which includesn‐back task‐based and resting‐state fMRI data from adults aged 22–35 years (taskn = 896; restn = 898). We applied connectome‐based predictive modeling (CPM) to predict participants' mean sleep duration from their functional connectivity patterns. Models trained and tested using 10‐fold cross‐validation predicted self‐reported average sleep duration for the past month fromn‐back task and resting‐state connectivity patterns. We replicated this finding in data from the 2‐year follow‐up study session of the Adolescent Brain Cognitive Development (ABCD) Study, which also includesn‐back task and resting‐state fMRI for adolescents aged 11–12 years (taskn = 786; restn = 1274) as well as Fitbit data reflecting average sleep duration per night over an average duration of 23.97 days. CPMs trained and tested with 10‐fold cross‐validation again predicted sleep duration fromn‐back task and resting‐state functional connectivity patterns. Furthermore, demonstrating that predictive models are robust across independent datasets, CPMs trained on rest data from the HCP sample successfully generalized to predict sleep duration in the ABCD Study sample and vice versa. Thus, common resting‐state functional brain connectivity patterns reflect sleep duration in youth and young adults.
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- Award ID(s):
- 2043740
- PAR ID:
- 10508009
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Human Brain Mapping
- Volume:
- 44
- Issue:
- 18
- ISSN:
- 1065-9471
- Page Range / eLocation ID:
- 6293 to 6307
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1002/hbm.26488
