Kubicki, M. | bioRxiv Science

bioRxiv2018

Individual variations of the human corticospinal tract and its hand-related motor fibers using diffusion MRI tractography

The corticospinal tract (CST) is one of the most well-studied tracts in human neuroanatomy. Its clinical significance can be demonstrated in many notable traumatic conditions and diseases such as stroke, spinal cord injury (SCI) or amyotrophic lateral sclerosis (ALS). With the advent of diffusion MRI and tractography the computational representation of the human CST in a 3D model became available. However, the representation of the entire CST and, specifically, the hand motor area has remained elusive. In this paper we proposed a novel method, using manually-drawn ROIs based on robustly identifiable neuroanatomic structures to delineate the entire CST and isolate its hand motor representation as well as to estimate their variability and generate a database of their volume, length and biophysical parameters. Using 37 healthy human subjects we performed a qualitative and quantitative analysis of the CST and the hand-related motor fiber tracts (HMFTs). Finally, we have created variability heatmaps from 37 subjects for both the aforementioned tracts, which could be utilized as reference for clinicians to explore neuropathology in both trauma and disease states.

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↗