Biology subjects

Toussaint, P.-J.

Publications and source records attributed to Toussaint, P.-J..

5 recordsLinked to original sources

bioRxiv2026

FUNCTIONAL FRAGMENTATION AND STRUCTURAL DRIVERS OF THALAMO-CORTICAL CIRCUITS IN TEMPORAL LOBE EPILEPSY

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSObjectiveC_ST_ABSIn temporal lobe epilepsy (TLE), the thalamus acts as a nexus in a pathophysiological network that implicates mesiotemporal, subcortical, and neocortical regions. Studying a large multimodal and multicentre dataset, we profiled thalamic, hippocampal, and neocortical functional connectivity (FC), assessed structural mediators, and examined clinical associations. MethodsWe studied resting-state FC alongside structural and diffusion MRI data in 250 unilateral TLE patients and 259 healthy controls, with measures aggregated across four independent datasets. Data were processed using open-access neuroinformatics workflows and analyzed at a subregional level to maximize anatomical precision. Statistical analysis and mediation models assessed between-group FC changes, structural contributors, and clinical correlations. ResultsCompared to controls, TLE patients presented with reduced thalamo-cortical FC, which was most marked in mesiotemporal, fronto-central, and occipital regions. Thalamo-hippocampal FC was also reduced, with effects seen in all CA subfields. In the thalamus, FC reductions peaked in the ventral posterior nucleus when considering neocortical target regions and in the mediodorsal nucleus when considering hippocampal target regions. While ipsilateral hippocampal volume and diffusion changes mediated thalamo-hippocampal FC, thalamo-cortical FC appeared decoupled from structural alterations. Findings were consistent in left and right TLE patients, in patients with short and long disease duration, and across imaging sites, suggesting that thalamo-cortical FC imbalances are a consistent signature of TLE. Conversely, thalamo-hippocampal FC was elevated in patients with focal-to-bilateral-tonic-clonic seizures and FC alterations were more marked in the subgroup of operated patients that became seizure-free after surgery. ConclusionOur multi-site findings demonstrate marked thalamic circuit fragmentation in TLE. Ipsilateral findings robustly showed subdivision-specific effects, which point to both mesiotemporal co-lateralization as well as broader system-level involvement. Mediation analyses furthermore confirmed a key role of hippocampal pathology in disrupted thalamo-hippocampal connectivity in TLE, while broader thalamo-cortical fragmentation becomes increasingly independent of mesiotemporal compromise. Critically, thalamic FC represents a network substrate for seizure generalization and can serve as a prognostic indicator for surgical outcome. These results underscore the contribution of the thalamus as a hub in macroscale dysfunction in TLE.

neuroscience↗

bioRxiv2023

Exploring the Brain Characteristics of Structure-informed Functional Connectivity through Graph Attention Network

Independent brain regions in neuroanatomy achieve a specific function through connections. As one of the significant morphological features of the cerebral cortex, previous studies have found significant differences in the structure and function of the cerebral gyri and sulci, which provides a basis for us to study the functional connectivity differences between these two anatomic parts. Previous studies using fully connected functional connectivity (FC) and structural connectivity (SC) matrices found significant differences in the perspective of region or connection in gyri and sulci. However, a clear issue is that previous studies have only analyzed the differences through either FC or SC, without effectively integrating both. Meanwhile, another nonnegligible issue is that the subcortical areas, involved in various tasks, have not been systematically explored with cortical regions. Due to the strong coupling between FC and SC, we use SC-informed FC to systematically explore the functional characteristics of gyri/sulci and subcortical regions by combining deep learning method with magnetic resonance imaging (MRI) technology. Specifically, we use graph attention network (GAT) to explore the important connections in the SC-informed FC through the Human Connectome Project (HCP) dataset. With high classification results of above 99%, we have successfully discovered important connections under different tasks. We have successfully explored the importance of different types of connections. In low threshold, gyri-gyri are the most important connections. With the threshold increasing, sub-sub become the most important. Gyri have a higher importance in functional connectivity than sulci. In the seven task states, these connections are mainly distributed among the front, subcortical, and occipital. This study provides a novel way to explore the characteristics of functional connectivity at the whole brain scale.

bioinformatics↗

bioRxiv2023

Multi-task machine learning reveals the neuroanatomy fingerprint of mental processing

Mental processing delineates the functions of human mind encompassing a wide range of motor, sensory, emotional, and cognitive processes, each of which is underlain by the neuroanatomical substrates. Identifying accurate representation of functional neuroanatomy substrates of mental processing could inform understanding of its neural mechanism. The challenge is that it is unclear whether a specific mental process possesses a functional neuroanatomy fingerprint, i.e., a unique and reliable pattern of functional neuroanatomy that underlies the mental process. To address this question, we utilized a multi-task deep learning model to disentangle the functional neuroanatomy fingerprint of seven different and representative mental processes including Emotion, Gambling, Language, Motor, Relational, Social, and Working Memory. Results based on the functional magnetic resonance imaging data of two independent cohorts of 1235 subjects from the US and China consistently show that each of the seven mental processes possessed a functional neuroanatomy fingerprint, which is represented by a unique set of functional activity weights of whole-brain regions characterizing the degree of each region involved in the mental process. The functional neuroanatomy fingerprint of a specific mental process exhibits high discrimination ability (93% classification accuracy and AUC of 0.99) with those of the other mental processes, and is robust across different datasets and using different brain atlases. This study provides a solid functional neuroanatomy foundation for investigating the neural mechanism of mental processing. One-Sentence SummaryThere exists a functional neuroanatomy fingerprint to underlie the mental process.

bioinformatics↗

bioRxiv2021

BigBrainWarp: Toolbox for integration of BigBrain 3D histology with mutlimodal neuroimaging

AO_SCPLOWBSTRACTC_SCPLOWNeuroimaging stands to benefit from emerging ultrahigh-resolution histological atlases of the human brain; the first of which is "BigBrain". Ongoing research aims to characterise regional differentiation of cytoarchitecture with BigBrain and to optimise registration of BigBrain with standard neuroimaging templates. Together, this work paves the way for multi-scale investigations of brain organisation. However, working with BigBrain can present new challenges for neuroimagers, including dealing with cellular resolution neuroanatomy and complex transformation procedures. To simplify workflows and support adoption of best practices, we developed BigBrainWarp, a toolbox for integration of BigBrain with multimodal neuroimaging. The primary BigBrainWarp function wraps multiple state-of-the-art deformation matrices into one line of code, allowing users to easily map data between BigBrain and standard MRI spaces. Additionally, the toolbox contains ready-to-use cytoarchitectural features to improve accessibility of histological information. The present article discusses recent contributions to BigBrain-MRI integration and demonstrates the utility of BigBrainWarp for further investigations.

neuroscience↗

bioRxiv2019

Amplifying the Neural Power Spectrum

We introduce a novel method that employs a parametric model of human electroen-cephalographic (EEG) brain signal power spectra to evaluate cognitive science experiments and test scientific hypotheses. We develop the Neural Power Amplifier (NPA), a data-driven approach to EEG pre-processing that can replace current filtering strategies with a principled method based on combining filters with log-arithmic and Gaussian magnitude responses. Presenting the first time domain evidence to validate an increasingly popular model for neural power spectra [1], we show that filtering out the 1/f background signal and selecting peaks improves a time-domain decoding experiment for visual stimulus of human faces versus random noise.

neuroscience↗