Numerous machine learning (ML) approaches have been proposed for automatic classification
of Alzheimer's disease (AD) from brain imaging data. In particular, over 30 papers have …

Background and Objective: As deep learning faces a reproducibility crisis and studies on
deep learning applied to neuroimaging are contaminated by methodological flaws, there is an …

Algorithms for computer-aided diagnosis of dementia based on structural MRI have
demonstrated high performance in the literature, but are difficult to compare as different data sets …

A large number of papers have introduced novel machine learning and feature extraction
methods for automatic classification of Alzheimer's disease (AD). However, while the vast …

Importance Presymptomatic carriers of chromosome 9 open reading frame 72 (C9orf72)
mutation, the most frequent genetic cause of frontotemporal lobar degeneration and …

We present Clinica ( www.clinica.run ), an open-source software platform designed to make
clinical neuroscience studies easier and more reproducible. Clinica aims for researchers to (i…

Objective To assess the added value of neurite orientation dispersion and density imaging (NODDI)
compared with conventional diffusion tensor imaging (DTI) and anatomical MRI to …

The Brain Imaging Data Structure (BIDS) is a standard for organizing and describing
neuroimaging datasets, serving not only to facilitate the process of data sharing and aggregation, …

The preclinical stage of frontotemporal lobar degeneration (FTLD) is not well characterized.
We conducted a brain metabolism (FDG-PET) and structural (cortical thickness) study to …

Repeated failures in clinical trials for Alzheimer’s disease (AD) have raised a strong interest
for the prodromal phase of the disease. A better understanding of the brain alterations …