Biology subjects

Tahmasian, M.

Publications and source records attributed to Tahmasian, M..

7 recordsLinked to original sources

bioRxiv2026

Hormonal Modulation of Subjective Sleep Quality and Perivascular Space Volume Across the Menstrual Cycle: An Observational Dense-Sampling Study

Background Hormonal fluctuations during the menstrual cycle (and their suppression) are accompanied by altered mood, cognition, and sleep, yet their relationship to subjective sleep quality and glymphatic function - a sleep-dependent brain waste-clearance pathway that involves perivascular spaces (PVS) - is unclear. Methods We examined daily oestradiol, progesterone, luteinizing hormone (LH), follicle-stimulating hormone (FSH), subjective sleep quality, and MRI-derived PVS volume (PVSV) in a single healthy woman across two 30-day periods: during her physiological menstrual cycle and, one year later, under oral contraceptive pill (OCP) use. We employed linear regression models to examine whether hormones are associated with sleep quality and PVSV, and whether they moderate the sleep quality-PVSV association during the physiological menstrual cycle. Subsequently, we conducted permutation-based Spearman rank correlations to assess whether day-to-day changes in sleep quality were associated with corresponding changes in PVSV across the physiological menstrual cycle and OCP use. False discovery rate (FDR) correction was used to control for multiple comparisons. Findings Higher LH levels were associated with poorer sleep quality during the physiological cycle (b=2.15, 95% CI 0.64 to 3.67, pFDR=0.028), while FSH moderated the sleep quality-PVSV association (b=-92.98, 95% CI -160.15 to -25.81, pFDR=0.034), suggesting that lower FSH was associated with lower PVSV, whereas this association reversed at elevated FSH levels characteristic of the periovulatory window. During the physiological menstrual cycle, day-to-day decreases in sleep quality were associated with synchronous increases in PVSV, driven by centrum semiovale PVSV ({rho}=0.55, 95% CI 0.30 to 0.67, pperm=0.0095). This association was absent during OCP use and differed between hormonal milieus ({Delta} {rho}=0.72, p=0.040). Interpretation Ovulation is characterized by poor sleep quality and reduced PVSV, potentially reflecting noradrenergic modulation. The association between sleep quality and PVSV day-to-day changes is shaped by the hormonal milieu, being absent under OCP. PVSV increase associated with poor sleep may reflect elevated sleep pressure promoting vasomotor-mediated cerebrospinal fluid inflow.

neuroscience↗

bioRxiv2024

Can we predict sleep health based on brain features? A large-scale machine learning study

BackgroundsSeveral correlational or group comparison evidence highlighted robust associations between sleep health and macro-scale brain organization. However, inter-individual variability is critical in such interplay. Therefore, in this study, we aimed to investigate the role of brain imaging features in predicting diverse sleep health-related characteristics at the individual subject level using the Machine Learning (ML) approach. MethodsA sample of 28,088 participants from the UK Biobank was employed to calculate 4677 structural and functional neuroimaging markers. Then, we employed them to predict self-reported insomnia symptoms, sleep duration, easiness of getting up in the morning, chronotype, daily nap, daytime sleepiness, and snoring. To assess the predictability of brain features, we built seven different linear and nonlinear ML models for each sleep health-related characteristic. ResultsWe performed extensive ML analyses that involved more than 19 years of compute time. We observed relatively low performance in predicting all sleep health-related characteristics from brain images (e.g., balanced accuracy ranging between 0.50-0.59). Across all models, the best performance achieved was 0.59, using a linear ML model to predict the ease of getting up in the morning. In fact, a similar performance was achieved with models trained solely on age and sex, indicating that these demographic factors might be the ones driving the predictions. ConclusionsThe low capability of multimodal neuroimaging markers in predicting sleep health-related characteristics, even under extensive ML optimization in a large population sample, suggests a complex relationship between sleep health and brain organization.

neuroscience↗

bioRxiv2024

Cardiometabolic health, cortical thickness, and neurotransmitter systems: a large-scale multivariate study

The link between brain health and risk/protective factors for non-communicable diseases (such as high blood pressure, high body mass index, diet, smoking, physical activity, etc.) is increasingly acknowledged. However, the specific effects that these factors have on brain health are still poorly understood, delaying their implementation in precision brain health. Here, we studied the multivariate relationships between risk factors for non-communicable diseases and brain structure, including cortical thickness (CT) and grey matter volume (GMV). Furthermore, we adopted a systems-level perspective to understand such relationships, by characterizing the cortical patterns (yielded in association to risk factors) with regards to brain morphological and functional features, as well as with neurotransmitter systems. Similarly, we related the pattern of risk/protective factors dimensions with a peripheral marker of inflammation. First, we identified latent dimensions linking a broad set of risk factors for non-communicable diseases to parcel-wise CT and GMV across the whole cortex. Data was obtained from the UK Biobank (n=7370, age range=46-81 years). We used regularized canonical correlation analysis (RCCA) embedded in a machine learning framework. This approach allows us to capture inter-individual variability in a multivariate association and to assess the generalizability of the model. The brain patterns (captured in association with risk/protective factors) were characterized from a multi-level perspective, by performing correlations (spin tests) between them and different brain patterns of structure, function, and neurotransmitter systems. The association between the risk/protective factors pattern and C-reactive protein (CRP, a marker of inflammation) was examined using Spearman correlation. We found two significant and partly replicable latent dimensions. One latent dimension linked cardiometabolic health to brain patterns of CT and GMV and was consistent across sexes. The other latent dimension linked physical robustness (including non-fat mass and strength) to patterns of CT and GMV, with the association to GMV being consistent across sexes and the association to CT appearing only in men. The CT and GMV patterns of both latent dimensions were associated to the binding potentials of several neurotransmitter systems. Finally, the cardiometabolic health dimension was correlated to CRP, while physical robustness was only very weakly associated to it. We observed robust, multi-level and multivariate links between both cardiometabolic health and physical robustness with respect to CT, GMV, and neurotransmitter systems. Interestingly, we found that cardiometabolic health and physical robustness are associated with not only increases in CT or GMV, but also with decreases of CT or GMV in some brain regions. Our results also suggested a role for low-grade chronic inflammation in the association between cardiometabolic health and brain structural health. These findings support the relevance of adopting a holistic perspective in health, by integrating neurocognitive and physical health. Moreover, our findings contribute to the challenge to the classical conceptualization of neuropsychiatric and physical illnesses as categorical entities. In this perspective, future studies should further examine the effects of risk/protective factors on different brain regions in order to deepen our understanding of the clinical significance of such increased and decreased CT and GMV.

neuroscience↗

bioRxiv2024

Lower motor performance is linked with poor sleep quality, depressive symptoms, and grey matter volume alterations

Motor performance (MP) is essential for functional independence and well-being, particularly in later life. However, the relationship between behavioural aspects such as sleep quality and depressive symptoms, which contribute to MP, and the underlying structural brain substrates of their interplay remains unclear. This study used three population-based cohorts of younger and older adults (n=1,950) from the Human Connectome Project-Young Adult (HCP-YA), HCP-Aging (HCP-A), and enhanced Nathan Kline Institute-Rockland sample (eNKI-RS). Several canonical correlation analyses were computed within a machine learning framework to assess the associations between each of the three domains (sleep quality, depressive symptoms, grey matter volume (GMV)) and MP. The HCP-YA analyses showed progressively stronger associations between MP and each domain: depressive symptoms (unexpectedly positive, r=0.13, SD=0.06), sleep quality (r=0.17, SD=0.05), and GMV (r=0.19, SD=0.06). Combining sleep and depressive symptoms significantly improved the canonical correlations (r=0.25, SD=0.05), while the addition of GMV exhibited no further increase (r=0.23, SD=0.06). In young adults, better sleep quality, mild depressive symptoms, and GMV of several brain regions were associated with better MP. This was conceptually replicated in young adults from the eNKI-RS cohort. In HCP-Aging, better sleep quality, fewer depressive symptoms, and increased GMV were associated with MP. Robust multivariate associations were observed between sleep quality, depressive symptoms and GMV with MP, as well as age-related variations in these factors. Future studies should further explore these associations and consider interventions targeting sleep and mental health to test the potential effects on MP across the lifespan.

neuroscience↗

bioRxiv2019

Gray matter volume and estimated brain age gap are not associated with sleep-disordered breathing in subjects from the ADNI cohort

Alzheimers disease (AD) and sleep-disordered breathing (SDB) are prevalent conditions with rising burden. It is suggested that SDB may contribute to cognitive decline and advanced aging. Here, we assessed the link between self-reported SDB and gray matter volume in patients with AD, mild cognitive impairment (MCI) and healthy controls (HC). We further investigated whether SDB was associated with advanced brain aging. We included a total of 330 participants, divided based on self-reported history of SDB, and matched across diagnoses for age, sex and presence of the ApoE4 allele, from the Alzheimers Disease Neuroimaging Initiative. Gray-matter volume was measured using voxel-wise morphometry and differences reflecting SDB, cognitive status, and their interaction were evaluated. Further, using an age-prediction model fitted on gray-matter data of external datasets, we predicted study participants age from their structural scans. Cognitive decline (MCI/AD diagnosis) and advanced age were associated with lower gray matter volume in various regions, particularly in the bilateral temporal lobes. BrainAGE was well predicted from the morphological data in HC and, as expected, elevated in MCI and particularly in AD. However, there was neither a significant difference between regional gray matter volume in any diagnostic group related to the SDB status nor an SDB-by-cognitive status interaction. Also, we found neither a significant difference in BrainAGE gap (estimated - chronological age) related to SDB nor an SDB-by-cognitive status interaction. In summary, contrary to our expectations, we were not able to find a general nor a diagnostic specific effect on either gray-matter volumetric or brain aging.\n\nStatement of significanceDementia syndromes including Alzheimers disease (AD), are a major global concern, and unraveling modifiable predisposing risk factors is indispensable. Sleep-disordered breathing (SDB) and its most prevalent form, obstructive sleep apnea, are suggested as modifiable risk factors of AD, but their contribution to AD hallmarks, like brain atrophy and advanced brain aging, is not clear to this day. While self-reported SDB is suggested to propagate aging process and cognitive decline to AD in clinical studies, here, we demonstrated that, SDB might not necessarily associate to brain atrophy and the advanced brain aging assessed by morphological data, in AD progession. However, multimodal longitudinal studies with polysomnographic assessment of SDB are needed to confirm such fundings.

neuroscience↗

bioRxiv2019

PHENOTYPIC AND GENETIC CORRELATION BETWEEN SLEEP, BEHAVIOR, AND MACROSCALE CORTICAL GREY MATTER

Humans need about 7 to 9 hours of sleep per night. Sleep habits are heritable, associated with brain function and structure, and intrinsically related to well-being, mental and physical health. This raises the question whether associations between sleep, mental and physical health can be attributed to a shared macroscale neurobiology.\n\nCombining neuroimaging and behavioral genetic approaches in two independent large-scale datasets (n=1887) we demonstrate phenotypic and genetic correspondence between sleep, intelligence, and BMI. Sleep was associated with local thickness variation in frontal, temporal, and occipital cortices. Using a comprehensive multivariate approach, we identified two robust latent components highlighting the interdigitation of sleep, intelligence, BMI, and depression and their shared relation to regions in unimodal and heteromodal association cortices. Latent relationships were heritable and driven by shared additive genetic factors. These observations provide a system-level perspective on the interrelation of sleep, mental, and physical conditions, anchored in grey-matter neuroanatomy.

neuroscience↗

bioRxiv2019

Decoupling of inter-regional functional connectivity and regional neural activity in Alzheimer Disease

Alzheimers disease (AD) and mild cognitive impairment (MCI) are characterized by aberrant regional neural activity and disrupted inter-regional functional connectivity (FC). It is, however, poorly understood how changes in regional neural activity and inter-regional FC interact in AD and MCI. Here, we investigated the link between regional neural activity and nodal topological measures of FC through simultaneous PET/MR measurement in 20 patients with MCI, 33 patients with AD, and 26 healthy individuals. First, we assessed regional glucose metabolism identified through FDG-PET (rFDG) (as a proxy of regional neural activity), and regional FC topology through clustering coefficient (CC) and degree centrality (DC) (as surrogates of local segregation and global connectivity, respectively). Next, we examined the potential moderating effect of disease status (AD or MCI) on the link between rFDG and FC topology using hierarchical moderated multiple regression analysis. Alterations in rFDG, CC, and DC were widespread in patients, and AD alters physiological coupling between regional metabolism and functional connectivity particularly in the inferior temporal gyus and supplementary motor areas. While rFDG correlated with CC in healthy subjects, this correlation was lost in AD patients. We suggest that AD pathology decouples the normal association between regional neural activity and functional segregation.

neuroscience↗