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Lariviere, S.

Publications and source records attributed to Lariviere, S..

3 recordsLinked to original sources

Functional connectome contractions in temporal lobe epilepsy

AO_SCPLOWBSTRACTC_SCPLOWTemporal lobe epilepsy (TLE) is the most common drug-resistant epilepsy in adults. While commonly related to hippocampal pathology, increasing evidence suggests structural changes beyond the mesiotemporal lobe. Functional anomalies and their link to underlying structural alterations, however, remain incompletely understood. We studied 30 drug-resistant TLE patients and 57 healthy controls using multimodal magnetic resonance imaging analyses. We developed a novel framework that parameterizes functional connectivity distance, consolidating functional and geometric properties of macroscale networks. Compared to controls, TLE showed connectivity distance reductions in temporo-insular and prefrontal networks, suggesting topological segregation of functional networks. Our novel approach furthermore allowed for the testing of morphological and microstructural associations, and revealed that functional connectivity contractions occurred independently from TLE-related cortical atrophy but were mediated by microstructural changes in the underlying white matter. All patients underwent a comparable resective surgery after our study and a regularized supervised machine learning paradigm with 5-fold cross-validation demonstrated that patient-specific functional anomalies predicted post-surgical seizure outcome with 74{+/-}8% accuracy, outperforming classifiers operating on clinical and structural imaging features. Our findings suggest connectivity distance contractions as a clinically relevant pathoconnectomic substrate of TLE. Functional topological isolation may represent a microstructurally mediated network mechanism that tilts the balance towards epileptogenesis.

neuroscience

BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets

Understanding how higher order cognitive function emerges from the underlying brain structure depends on quantifying how the behaviour of discrete regions are integrated within the broader cortical landscape. Recent work has established that this macroscale brain organization and function can be quantified in a compact manner through the use of multivariate machine learning approaches that identify manifolds often described as cortical gradients. By quantifying topographic principles of macroscale organization, cortical gradients lend an analytical framework to study structural and functional brain organization across species, throughout development and aging, and its perturbations in disease. More generally, its macroscale perspective on brain organization offers novel possibilities to investigate the complex relationships between brain structure, function, and cognition in a quantified manner. Here, we present a compact workflow and open-access toolbox that allows for (i) the identification of gradients (from structural or functional imaging data), (ii) their alignment (across subjects or modalities), and (iii) their visualization (in embedding or cortical space). Our toolbox also allows for controlled association studies between gradients with other brain-level features, adjusted with respect to several null models that account for spatial autocorrelation. The toolbox is implemented in both Python and Matlab, programming languages widely used by the neuroimaging and network neuroscience communities. Several use-case examples and validation experiments demonstrate the usage and consistency of our tools for the analysis of functional and microstructural gradients across different spatial scales.

neuroscience

Targeting age-related differences in brain and cognition with multimodal imaging and connectome topography profiling

I.AO_SCPLOWBSTRACTC_SCPLOWAging is characterised by accumulation of structural and metabolic changes in the brain. Recent studies suggest transmodal brain networks are especially sensitive to aging, which, we hypothesise, may be due to their apical position in the cortical hierarchy. Studying an open-access healthy cohort (n=102, age range = 30-89 years) with MRI and A{beta} PET data, we estimated age-related cortical thinning, hippocampal atrophy and A{beta} deposition. In addition to carrying out surface-based morphological and metabolic mapping, we stratified effects along neocortical and hippocampal resting-state functional connectome gradients derived from independent datasets. The cortical gradient depicts an axis of functional differentiation from sensory-motor regions to transmodal regions, whereas the hippocampal gradient recapitulates its long-axis. While age-related thinning and increased A{beta} deposition occurred across the entire cortical topography, increased A{beta} deposition was especially pronounced towards higher-order transmodal regions. Age-related atrophy was greater towards the posterior end of the hippocampal long-axis. No significant effect of age on A{beta} deposition in the hippocampus was observed. Imaging markers correlated with behavioural measures of fluid intelligence and episodic memory in a topography-specific manner. Our results strengthen existing evidence of structural and metabolic change in the aging brain and support the use of connectivity gradients as a compact framework to analyse and conceptualize brain-based biomarkers of aging.

neuroscience