Biology subjects

Alibrando, J. D.

Publications and source records attributed to Alibrando, J. D..

5 recordsLinked to original sources

bioRxiv2026

SPECTRA: Spatial Inference for Tractometry Toward Precision Mapping of White Matter Microstructure

Diffusion MRI tractometry characterizes white matter microstructure along fiber bundles, but standard along-tract profiling collapses measurements across the bundle cross-section, obscuring radial heterogeneity and producing spatially inconsistent units of inference. We present SPECTRA (Spatial Inference for Tractometry), a framework designed to address these limitations through a unified design of parameterization and statistical inference. First, we propose a 2D bundle parameterization that extends along-tract profiling to include a radial dimension defined on the atlas bundle. Second, we develop a two-stage hierarchical false discovery rate (hFDR) procedure for multi-bundle inference, which aggregates evidence at a coarser spatial scale before proceeding to finer-grained inference, with spatial scales derived from a Matern kernel. Across extensive simulation conditions, we found that hFDR improves statistical power and reduces the sample size required to detect effects compared to global FDR correction, while maintaining appropriate error control. We further characterized how sensitivity-specificity tradeoffs depend on sample size, the magnitude, spatial extent, and configurations of effects, thereby providing practical guidance for tractometry study design. In an empirical analysis of mild cognitive impairment and dementia in more than 4,000 subjects across 63 bundles, SPECTRA revealed spatially localized patterns that were absent in 1D profiles. Together, these results demonstrate that spatially resolved parameterization and adaptive error control jointly enable precise mapping of white matter microstructure in large-scale tractometry studies. SPECTRA is openly available as a Python package.

neuroscience↗

bioRxiv2026

Along-Tract Microstructural Alterations Associated with Stimulant Misuse Localized using Diffusion MRI Tractometry

Diffusion brain MRI (dMRI) studies of substance use disorders have reported widespread but modest white matter (WM) microstructural alterations with limited anatomical specificity. Here, we applied segment-wise along-tract 3D tractometry to brain dMRI scans to localize fine-scale WM alterations associated with stimulant misuse using two complementary analytical frameworks: Bundle Analytics (BUAN) and Medial Tractography Analysis (MeTA). We analyzed 3D profiles of widely-used diffusion metrics across 33 major WM bundles in independent cohorts of cocaine (74 cases;58 controls) and amphetamine (22 cases;18 controls) users, testing the statistical associations with brain microstructure of pooled stimulant effects, substance-specific effects, and direct comparisons between stimulant classes. Segment-wise analyses revealed focal differences localized to specific tract segments rather than uniform differences along entire bundles. In pooled stimulant misuse, convergent findings across analysis pipelines were localized to hippocampal pathways and were consistent with altered microstructural organization. Amphetamines misuse showed a broader pattern of segment-wise differences across commissural, projection, and association pathways, involving altered axonal organization. No robust segment-wise differences were detected for cocaine misuse or between stimulant classes. These results show that WM alterations are spatially localized and reproducible across tractometry frameworks, highlighting the value of along-tract 3D mapping for improving anatomical specificity in addiction neuroimaging.

neuroscience↗

bioRxiv2026

Tractometry-Based Quantification of Along-Tract White-Matter Hemispheric Asymmetry in Alzheimer's Disease

White-matter hemispheric asymmetry is a fundamental property of human brain organization and is known to change in aging, neurodevelopment, and neurodegenerative disorders. Tractometry analyzes diffusion-derived microstructural measures along the full length of tracts, localizing changes to specific tract-segments rather than collapsing tracts into a single value. Yet, existing frameworks lack a principled way to quantify left-right hemispheric asymmetries along homologous tracts. Here, we introduce an asymmetry-aware tractometry framework that integrates a symmetric white-matter atlas with BUAN (Bundle Analytics) to enable anatomically consistent, along-tract comparison of homologous pathways. By defining homologous bundles with a shared template and consistent orientation, each left-hemisphere segment is directly matched to its right-hemisphere counterpart, enabling principled, segment-wise comparison and revealing spatially localized asymmetries along-tract. Applying this framework to diffusion MRI data from the Alzheimers Disease Neuroimaging Initiative (ADNI) comprising 1,215 subjects, we demonstrate how this approach reveals systematic left-right asymmetries across major white-matter pathways and show how these patterns differentiate cognitively normal (CN) individuals from those with mild cognitive impairment (MCI) and dementia. This method provides a sensitive and anatomically grounded tool for studying hemispheric specialization and its disruption in aging and disease, and establishes a general approach for asymmetry-aware tractometry in population neuroimaging studies.

neuroscience↗

bioRxiv2025

Evaluating Sample-Size Efficiency and Sensitivity of Tractometry in Alzheimer's Disease

Tractometry allows quantitative analysis of white matter microstructure along the brains fiber tracts, but the impact of study design parameters--such as sample size and along-tract resolution--on sensitivity and specificity is not well understood. In this study, we conducted tractometry bootstrap analysis using linear-mixed models across four diffusion tensor imaging (DTI) metrics to systematically evaluate how these factors affect the detection of dementia- and amyloidrelated effects. While coarser along-tract segments yield greater sensitivity and higher mean effect sizes, finer segments tend to produce higher peak effect sizes, revealing more spatially localized effects. Dementia-related effects were more widespread and detectable with fewer subjects, whereas amyloid-related effects were more subtle and localized, requiring larger cohorts to detect them. These findings highlight that tractometry offers improved spatial specificity and can reliably detect small, fine-scale effects, but study design should be tailored to specific research questions, considering the expected spatial extent and magnitude of effects, to optimize sample size efficiency and interpretability.

neuroscience↗

bioRxiv2025

Deterministic versus Probabilistic Tractography: Impact on White Matter Bundle Shape

In diffusion MRI-based tractography, deterministic and probabilistic algorithms reconstruct white matter using distinct strategies, yet their impact on bundle morphology remains uncertain. Using bundle shape similarity analysis, we compared both methods for the left arcuate fasciculus (AF_L) (The left arcuate fasciculus is a critical white matter tract that connects language comprehension and production areas in the human brain, enabling fluent language processing) across four datasets: Alzheimers Disease Neuroimaging Initiative (ADNI), Human Connectome Project-Aging (HCP-A), National Institute of Mental Health and Neurosciences (NIMHANS), and Pediatric Imaging, Neurocognition, and Genetics (PING). Probabilistic tractography consistently produced higher inter-subject shape similarity, by capturing broader anatomical trajectories and enhancing reproducibility. However, this extensive coverage may obscure subtle pathological variations critical for clinical detection. Bundle shape similarity analysis with atlas corroborated these findings, showing stronger alignment for probabilistic tracking and highlighting its utility in quantitative quality control. These results emphasize the need to balance morphological consistency with sensitivity to neuroanatomical variation when selecting tractography methods for research and clinical applications.

neuroscience↗