Biology subjects

Allali, G.

Publications and source records attributed to Allali, G..

3 recordsLinked to original sources

bioRxiv2026

Altered Hippocampal Structure-Function Coupling and Brain-Cognition Aging Profile in Mild Cognitive Impairment

Hippocampal atrophy and altered functional connectivity are prominent features of mild cognitive impairment (MCI), yet their relationships and joint contribution to cognitive impairment remain unclear. We evaluated multidomain cognitive-sensorimotor performance, whole-brain gray matter atrophy patterns, associated functional connectivity, and their coupling in MCI and HC. We conducted this cross-sectional study in 25 clinically diagnosed amnestic and non-amnestic MCI patients and 15 age- and education-matched healthy controls (HC). Participants (57-81 years old) completed a battery assessing general cognition, executive functions, attention, speech-in-noise perception, manual dexterity, and balance, combined with structural and resting-state functional magnetic resonance imaging. Among the 25 MCI patients, 8 (32%) presented with amnestic MCI, 15 (60%) with non-amnestic MCI, and 2 (8%) with subjective cognitive decline. Linear mixed models revealed that semantic fluency (g = 0.74) was the largest discriminator of MCI, followed by attention (g = 0.61), phonemic fluency (g = 0.59), and speech-in-noise perception (g = 0.56). Voxel-based morphometry showed bilateral hippocampal and anteromedial cerebellar gray matter atrophy in MCI. Seed-based functional connectivity analyses indicated that atrophy was not uniformly associated with reduced connectivity. Cortico-subcortical hypoconnectivity emerged only in networks associated with left hippocampal atrophy in MCI compared with HC. Network-based analyses showed disrupted brain-behavior coupling and a stronger detrimental influence of age in MCI than in HC. These findings confirm the critical role of hippocampal structure-function relationships in multidomain MCI symptoms. A decoupled brain-cognition aging profile suggests that multimodal indices integrating hippocampal structure, connectivity, and behavioral performance may strengthen MCI diagnosis.

neuroscience↗

bioRxiv2024

Residual Partial Least Squares Learning: Brain Cortical Thickness Simultaneously Predicts Eight Non-pairwise-correlated Behavioural and Disease Outcomes in Alzheimer's Disease

Alzheimers Disease (AD) is the leading cause of dementia, affecting brain structure, function, cognition, and behaviour. While previous studies have linked brain regions to univariate outcomes (e.g., disease status), the relationship between brain-wide changes and multiple disease and behavioural outcomes of AD is still not well understood. Here, we propose Residual Partial Least Squares (re-PLS) Learning, an explainable and generalisable framework that models high-dimensional brain features and multivariate outcomes, accounting for confounders. Using re-PLS, we map the many-to-many pathways between cortical thickness and multivariate AD outcomes; identify neural biomarkers that simultaneously predict multiple outcomes; control for confounding variables; conduct longitudinal AD prediction; and perform cross-cohort AD prediction. To evaluate its efficacy, we first carry out within-cohort cross-subject validation using ADNI data, and further examine its reproducibility via between-cohort cross-validation using ADNI and OASIS data. Together, our results unveil brain regions jointly but differentially predictive of distinctive cognitive-behavioural scores in AD.

neuroscience↗

bioRxiv2022

The evolution of information transmission in mammalian brain networks

Brain communication, defined as information transmission through white-matter connections, is at the foundation of the brains computational capacities that subtend almost all aspects of behavior: from sensory perception shared across mammalian species, to complex cognitive functions in humans. How did communication strategies in macroscale brain networks adapted across evolution to accomplish increasingly complex functions? By applying a graph- and information-theory approach to assess information-related pathways in mouse, macaque and human brains, we show a brain communication gap between selective information transmission in non-human mammals, where brain regions share information through single polysynaptic pathways, and parallel information transmission in humans, where regions share information through multiple parallel pathways. In humans, parallel transmission acts as a major connector between unimodal and transmodal systems. The layout of information-related pathways is unique to individuals across different mammalian species, pointing at the individual-level specificity of information routing architecture. Our work provides evidence that different communication patterns are tied to the evolution of mammalian brain networks.

neuroscience↗