Biology subjects

Bouix, S.

Publications and source records attributed to Bouix, S..

3 recordsLinked to original sources

bioRxiv2026

Nipoppy: A framework for standardizing neuroimaging studies to facilitate international derived-data sharing

Neuroimaging data management and processing are tedious and error-prone, prompting reproducibility concerns. Globally, studies with heterogeneous infrastructure and governance policies lead to eclectic data processing and sharing, necessitating standardization of data workflows to ensure reusability and comparability of multi-centric datasets. The Nipoppy neuroinformatics framework facilitates such standardization by combining specification, protocol, and software to manage study-level data workflows. With its adoption, researchers can share standardized, derived datasets enabling efficient, reproducible, and inclusive research.

bioinformatics↗

bioRxiv2019

MRI-based Parcellation and Morphometry of the Individual Rhesus Monkey Brain: a translational system referencing a standardized ontology

The rhesus macaque is the closest animal model to the human, and investigations into the brain of the rhesus monkey has shed light on the function and organization of the primate brain at a scale and resolution not yet possible in studies of the human brain A cornerstone of the linkage between non-human primate and human studies of the brain is magnetic resonance imaging, which allows for an association to be made between the detailed structural and physiological analysis of the non-human primate and that of the human brain. To further this end, we present a novel parcellation system for the rhesus monkey brain, referred to as the monkey Harvard Oxford Atlas (mHOA) which is based on the human Harvard-Oxford Atlas (HOA) and grounded in an ontological and taxonomic framework. Consistent anatomical features were used to delimit and parcellate brain regions in the macaque, which were then categorized according to functional systems. This system of parcellation will be expanded with advances in technology and like the HOA, will provide a framework upon which the results from other experimental studies (e.g., functional magnetic resonance imaging (fMRI), physiology, connection, graph theory) can be interpreted.

neuroscience↗

bioRxiv2018

Retrospective harmonization of multi-site diffusion MRI data acquired with different acquisition parameters

A joint and integrated analysis of multi-site diffusion MRI (dMRI) datasets can dramatically increase the statistical power of neuroimaging studies and enable comparative studies pertaining to several brain disorders. However, dMRI data sets acquired on multiple scanners cannot be naively pooled for joint analysis due to scanner specific nonlinear effects as well as differences in acquisition parameters. Consequently, for joint analysis, the dMRI data has to be harmonized, which involves removing scanner-specific differences from the raw dMRI signal. In this work, we present a dMRI harmonization method that, when applied to multi-site data, is capable of removing scanner-specific effects, while accounting for minor differences in acquisition parameters such as b-value, spatial resolution and number of gradient directions in the dMRI data (typical for multi-site clinical research scans). We validate our algorithm on dMRI data acquired from two sites: Philadelphia Neurodevelopmental Cohort (PNC) with 800 healthy adolescents (ages 8 to 22 years) and Brigham and Womens Hospital (BWH) with 70 healthy subjects (ages 14 to 54 years). In particular, we show that gender differences and maturation in different age groups are preserved after harmonization, as measured using effect sizes (small, medium and large), irrespective of the test sample size. Further, because we use matched control subjects from different scanners to estimate scanner-specific effects, we tested how many subjects are needed from each site to achieve best harmonization results. Our results indicate that at-least 16 to 18 well-matched healthy controls from each site are needed to reliably capture scanner related differences. The proposed method can thus be used for retrospective harmonization of raw dMRI data across sites despite differences in acquisition parameters, while preserving inter-subject anatomical variability.

neuroscience↗