Dr. Mohamed Abdelhack, Centre for Addiction and Mental Health
Scientific publication
Abdelhack M, Zhukovsky P, Milic M, Harita S, Wainberg M, Tripathy SJ, Griffiths JD, Hill SL, Felsky D. Opposing brain signatures of sleep in task-based and resting-state conditions. Nat Commun. 2023 Dec 1;14(1):7927.
Large-scale analysis reveals opposing brain signatures of sleep in task-based and resting-state conditions
Sleep and depression have a complex birectional relationship. For example, while most people suffering from depression also suffer from insomnia, others report hypersomnia. Contradictory results are also seen in scientific studies of this relationship. A new study by Mohamed Abdelhack and colleagues analyzed how brain signals change with differences in sleep habits, depression symptoms, and cognitive abilities in over 30,000 people. By performing such large scale and comprehensive analyses, they reveal opposing relationships between change in brain signals when a person is doing a task and when they are not (resting state). These results provide important insights into the relationship between depressive symptoms and sleep in the general population.
By analysis data from over 30,000 participants from UK-Biobank and Human Connectome Project, the researchers found contradictions in brain-wide associations of sleep and depression depending on participant’s state. The researchers found brain regions were hyperconnected under resting conditions with insomnia and depression. These results indicate that, in insomnia and depression, resting-state dynamics are resembling those of rested-wakefulness. The brain is signalling a lack of need for sleep which could signal hyperarousal.
When the researcher analysed data from people while they were performing a task, they instead observed a drop in connection between brain regions, which could be signifying a “local sleep” phase which decreases the cognitive performance as the brain is unable to recruit its resources to perform the task.
This publication shows counterintuitive results where neural signatures of sleep and depression when the participant was doing a task contradicted those when the participant was not doing any task (resting state). It highlights the importance of probing the effect of mental health in different conditions. These results could also guide advances in clinical practice to investigate more details of sleep habits to optimize care plans while also tracking the cognitive load of patients to assess treatment efficacy.
About Dr. Mohamed Abdelhack
Mohamed is a Postdoctoral Fellow at the Whole Person and Population Modelling laboratory at the Krembil Centre for Neuroinformatics working on using statistical analysis of big data and machine/deep learning to model mental health and psychiatric disorders. He mainly uses fMRI imaging, statistical data analysis, and computational modelling.
He previously worked as a Postdoctoral Researcher in Washington University in St. Louis where he was building machine learning models to predict post-surgical medical complications. He also worked as a researcher in Kyoto University Hospital studying neural activity markers of Schizophrenia using brain decoding and deep learning techniques. His doctoral work in Kyoto University focused on using deep learning models to understand robustness of human brain in recognizing degraded visual input.
He also founded the Arabs in Neuroscience (AiN) not-for-profit, which is a grassroots organization that aims to enhance education and research among Arabic-speaking scientists and students all over the world. Through AiN, he runs an online introductory course in computational neuroscience. He is also a teaching assistant at the Computational Neuroscience Imbizo summer school and a member of The Africa I Know non-profit.
Website: https://mabdelhack.github.io/
Twitter handle: @mabdelhack
Sources of funding
This study was funded by grants for Daniel Felsky from The Koerner Family Foundation New Scientist Program, The Krembil Foundation, the Canadian Institutes of Health Research, the Canadian Foundation for Innovation, and the CAMH Discovery Fund. Author Mohamed Abdelhack was further supported by the CAMH womenmind postdoctoral fellowship.
