Topic: structural MRI

Develop pretrained models of functional, diffusion and structural MRI that predict individual cognition with calibrated uncertainty. The ANR-funded MIND–GIN Bordeaux collaboration combines preprocessing and harmonization of large datasets, generative and self-supervised models, and transfer to smaller cognitive-neuroscience studies. The doctorate will release open pipelines and benchmarks. Python and PyTorch or JAX, probability, statistics and representation learning are required; neuroimaging and large-scale computing experience are desirable. Appointment: 3 years; advertised start 2027-01-01

Identifying a structural brain lesion on MRI is the most important factor that correlates with seizure freedom after surgery in patients suffering from drug-resistant focal epilepsy. By providing better image contrast and higher spatial resolution, structural MRI at 7 Tesla (7T) can lead to lesion detection in about 25% of patients presenting with negative MRI at lower fields. In addition to a better detection/delineation/phenotyping of epileptogenic lesions, higher signal at ultra-high field also facilitates more detailed analyses of several functional and molecular alterations of tissues, su

Late-life cognitive impairment and dementia are heterogeneous and multifactorial conditions driven by a combination of genetic, vascular, and lifestyle-related factors. More than 75% of patients with dementia have evidence of cerebrovascular pathology at autopsy. Cerebrovascular disease lesions can be detected on structural MRI and used as biomarkers to determine the extent of cerebrovascular pathology. These biomarkers are associated with cognitive difficulties and increase the risk of dementia for the same level of neurodegenerative pathology. Given that some of the risk factors for cerebrov

Accurate localization of EEG scalp electrodes is crucial for precisely estimating the underlying neuronal activity from observed electrical potential changes on the scalp. Various techniques for electrode localization have been proposed, categorized into direct measurements on the subject's head or indirect methods utilizing structural MRI scans. Most direct measurement methods are expensive as they rely on complex stereo vision systems or are magnetic-field-driven, making them especially inconvenient for experiments involving simultaneous EEG-fMRI acquisition and cannot be used in the vicinit

There is currently a paucity of neuroimaging data from the African continent, limiting the diversity of data from a significant proportion of the global population. This in turn diminishes global health research and innovation. To address this issue, we present and describe the first Magnetic Resonance Imaging (MRI) dataset from individuals in the African nation of Nigeria. This dataset contains pseudonymized structural MRI (T1w, T2w, FLAIR) data of clinical quality, with 33 images from healthy control subjects, 32 images from individuals diagnosed with age-related dementia and 21 from individ