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Wastling, S.

Publications and source records attributed to Wastling, S..

2 recordsLinked to original sources

Tractography dissection variability: what happens when 42 groups dissect 14 white matter bundles on the same dataset?

White matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white matter fiber pathways in vivo in human brains. However, like other analyses of complex data, there is considerable variability in segmentation protocols and techniques. This can result in different reconstructions of the same intended white matter pathways, which directly affects tractography results, quantification, and interpretation. In this study, we aim to evaluate and quantify the variability that arises from different protocols for bundle segmentation. Through an open call to users of fiber tractography, including anatomists, clinicians, and algorithm developers, 42 independent teams were given processed sets of human whole-brain streamlines and asked to segment 14 white matter fascicles on six subjects. In total, we received 57 different bundle segmentation protocols, which enabled detailed volume-based and streamline-based analyses of agreement and disagreement among protocols for each fiber pathway. Results show that even when given the exact same sets of underlying streamlines, the variability across protocols for bundle segmentation is greater than all other sources of variability in the virtual dissection process, including variability within protocols and variability across subjects. In order to foster the use of tractography bundle dissection in routine clinical settings, and as a fundamental analytical tool, future endeavors must aim to resolve and reduce this heterogeneity. Although external validation is needed to verify the anatomical accuracy of bundle dissections, reducing heterogeneity is a step towards reproducible research and may be achieved through the use of standard nomenclature and definitions of white matter bundles and well-chosen constraints and decisions in the dissection process.

neuroscience↗

Skeletal muscle MRI differentiates SBMA and ALS and correlates with disease severity

ObjectiveTo investigate the use of muscle MRI for the differential diagnosis and as a disease progression biomarker for two major forms of motor neuron disorders, spinal bulbar muscular atrophy (SBMA) and amyotrophic lateral sclerosis (ALS).\n\nMethodsWe applied quantitative 3-point Dixon and semi-quantitative T1-weighted and STIR imaging to bulbar and lower limb muscles and performed clinical and functional assessments in ALS (n=21) and SBMA (n=21) patients, alongside healthy controls (n=16). Acquired images were analyzed for the presence of fat infiltration or edema as well as specific patterns of muscle involvement. Quantitative MRI measurements were correlated with clinical parameters of disease severity in ALS and SBMA.\n\nResultsQuantitative imaging revealed significant fat infiltration in bulbar (p<0.001) and limb muscles in SBMA compared to controls (thigh: p<0.001; calf: p=0.001), identifying a characteristic pattern of muscle involvement. In ALS, semi-quantitative STIR imaging detected marked hyperintensities in lower limb muscles, distinguishing ALS from SBMA and controls. Lastly, MRI measurements correlated significantly with clinical scales of disease severity in both ALS and SBMA.\n\nConclusionsOur findings show that muscle MRI differentiates between SBMA and ALS and correlates with disease severity, supporting its use as a diagnostic tool and biomarker for disease progression. This highlights the clinical utility of muscle MRI in motor neuron disorders and contributes to establish objective outcome measures, which is crucial for the development of new drugs.

neuroscience↗