Biology subjects

Liou, K.

Publications and source records attributed to Liou, K..

5 recordsLinked to original sources

bioRxiv2026

Comparing Harmonization Approaches for Protocol-Related Variability in Multisite Diffusion MRI Data

Diffusion MRI (dMRI) enables assessment of white matter microstructural abnormalities in Alzheimers disease (AD), and multisite datasets enable more robust modeling of non-biological variation that can confound analyses. The Alzheimers Disease Neuroimaging Initiative (ADNI) includes over 10 dMRI protocols, necessitating robust methods to model protocol-related variability when pooling data. Here, we compared three harmonization approaches: (1) mixed-effects models, (2) ComBat-GAM, and (3) eHarmonize, a reference-based lifespan method. We assessed their ability to reduce protocol-related variability in diffusion tensor imaging fractional anisotropy (FA) and mean diffusivity (MD) while preserving associations with cognitive impairment (CI), and amyloid-beta (A{beta}) and tau PET burden in 1,086 ADNI3/4 participants. All approaches yielded more closely aligned FA/MD distributions across protocols. Associations with clinical indicators of CI were highly consistent across approaches, whereas PET associations were less widespread and more variable. Overall, multiple strategies effectively modeled protocol-related variability while preserving AD-related associations.

neuroscience↗

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

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

DTI versus NODDI White Matter Microstructural Biomarkers of Alzheimer's Disease

Diffusion MRI (dMRI) is a powerful tool to assess white matter (WM) microstructural abnormalities in Alzheimers disease (AD). The fourth phase of the Alzheimers Disease Neuroimaging Initiative (ADNI) now includes multiple multishell dMRI protocols, enabling both traditional and advanced dMRI model analyses. There is a need to evaluate whether multishell data offer deeper insights into WM pathology in AD than more widely available single-shell data by overcoming single-shell model limitations. Here, we fit single-shell DTI and multishell NODDI to dMRI data from 533 ADNI3/4 participants to assess their sensitivity to key clinical indicators of AD such as cognitive impairment, amyloid-beta and tau PET burden. Overall, we found that NODDI offered no major advantages in detecting cognitive impairment and tau pathology, but NODDI was marginally more sensitive to amyloid pathology.

neuroscience↗

bioRxiv2024

Integrating genetic and gene expression data in network-based stratification analysis of cancers

Cancers are complex diseases that have heterogeneous genetic drivers and varying clinical outcomes. A critical area of cancer research is organizing patient cohorts into subtypes and associating subtypes with clinical and biological outcomes for more effective prognosis and treatment. Large-scale studies have collected a plethora of omics data across multiple tumor types. These studies provide an extensive dataset for stratifying patient cohorts. Network-based stratification (NBS) approaches have been presented to classify cancer tumors using somatic mutation data. A challenge in cancer stratification is integrating omics data to yield clinically meaningful subtypes. In this study, we integrate somatic mutation data with RNA sequencing data within the NBS framework and investigate the effectiveness of integrated NBS on three cancers: ovarian, bladder, and uterine cancer. We show that integrated NBS subtypes are more significantly associated with overall survival or histology. Integrated NBS networks also reveal highly influential genes that drive cancer initiation and progression. This comprehensive approach underscores the significance of integrating genomic data types in cancer subtyping, offering profound implications for personalized prognosis and treatment strategies.

systems biology↗