Biology subjects

Habert, M.-O.

Publications and source records attributed to Habert, M.-O..

3 recordsLinked to original sources

bioRxiv2026

Episodic memory ERPs reflect preclinical Alzheimers disease progression

Structured AbstractO_ST_ABSBackgroundC_ST_ABSAlzheimers disease (AD) neuropathology emerges decades before symptoms, requiring sensitive, non-invasive markers for early diagnosis. We investigated behavioural and EEG markers of episodic memory in cognitively normal older adults at-risk for AD. MethodsForty-five INSIGHT-preAD cohort participants (15 progressors to prodromal AD, 15 stable {beta}-amyloid positive [A{beta}+] participants, 15 controls [A{beta}-]) completed an Old/New word recognition task during high-density EEG. Behavioural performance and memory-related evoked-related potentials (ERPs; P3, FN400, P600, post-retrieval monitoring) were analysed longitudinally over 5 years. ResultsProgressors showed significantly reduced accuracy, sensitivity, and slower responses. They showed attenuated FN400, P600, and monitoring amplitudes over left parieto-occipital regions. Source analysis revealed hypoactivation in the left inferior frontal and right posterior cingulate cortex, across components with additional disruption during P600. Stable A{beta}+ individuals showed milder P600 hypoactivations. DiscussionBehavioural and ERP alterations preceded clinical decline, supporting EEG-based markers of episodic memory decline for preclinical AD risk stratification.

neuroscience↗

bioRxiv2022

Theta band-power shapes amyloid-driven longitudinal EEG changes in pre-clinical Alzheimer's Disease

Alzheimers Disease (AD) includes progressive symptoms spread along a continuum of pre-clinical (pre-AD) and clinical stages. Pre-AD refers to cognitively healthy individuals with presence of positive pathophysiological biomarkers of AD (i.e., markers of amyloidopathy and tauopathy). Although numerous studies uncovered the neuro-cognitive changes of AD, very little is known on the natural history of brain lesions and modifications of brain networks of pre-AD. To address this issue, we analysed resting-state EEG data of 318 cognitively healthy individuals with subjective memory complains from the INSIGHT-preAD cohort at the time of their first visit (M0) and two-years later (M24). Using 18F-florbetapir PET-scanner, subjects were stratified between amyloid positive (A-; n=230) and amyloid negative (A+; n=88) groups. Differences between A+ and A- individuals were estimated at source level in each band of the EEG power spectrum. At M0, we found an increase of theta-band power in the mid-frontal cortex in A+ compared to A-. No significant association was found between mid-frontal theta power and the individuals cognitive performance. While the very same effect was not replicated at M24, theta-band power increased in A+ relative to A- individuals in the posterior cingulate cortex and the pre-cuneus. Furthermore, alpha band revealed a peculiar decremental trend in posterior brain regions in the A+ relative to the A- group only at M24. These results provide the first source-level longitudinal evidence on the impact of brain amyloidosis on the EEG dynamics of a large-scale, monocentric cohort of pre-AD. Theta-band power increase over the mid-frontal and mid-posterior cortices suggests an hypoactivation of the default-mode network in individuals at-risk of AD and a non-linear longitudinal progression of the AD-spectrum.

neuroscience↗

bioRxiv2018

Reproducible evaluation of classification methods in Alzheimer’s disease: framework and application to MRI and PET data

A large number of papers have introduced novel machine learning and feature extraction methods for automatic classification of Alzheimers disease (AD). However, while the vast majority of these works use the public dataset ADNI for evaluation, they are difficult to reproduce because different key components of the validation are often not readily available. These components include selected participants and input data, image preprocessing and cross-validation procedures. The performance of the different approaches is also difficult to compare objectively. In particular, it is often difficult to assess which part of the method (e.g. preprocessing, feature extraction or classification algorithms) provides a real improvement, if any. In the present paper, we propose a framework for reproducible and objective classification experiments in AD using three publicly available datasets (ADNI, AIBL and OASIS). The framework comprises: i) automatic conversion of the three datasets into a standard format (BIDS); ii) a modular set of preprocessing pipelines, feature extraction and classification methods, together with an evaluation framework, that provide a baseline for benchmarking the different components. We demonstrate the use of the framework for a large-scale evaluation on 1960 participants using T1 MRI and FDG PET data. In this evaluation, we assess the influence of different modalities, preprocessing, feature types (regional or voxel-based features), classifiers, training set sizes and datasets. Performances were in line with the state-of-the-art. FDG PET outperformed T1 MRI for all classification tasks. No difference in performance was found for the use of different atlases, image smoothing, partial volume correction of FDG PET images, or feature type. Linear SVM and L2-logistic regression resulted in similar performance and both outperformed random forests. The classification performance increased along with the number of subjects used for training. Classifiers trained on ADNI generalized well to AIBL and OASIS, performing better than the classifiers trained and tested on each of these datasets independently. All the code of the framework and the experiments is publicly available.

neuroscience↗