Biology subjects

Jahanshad, N.

Publications and source records attributed to Jahanshad, N..

At least 19 recordsLinked to original sources

bioRxiv2026

Distinct spatial organization of human hippocampal perfusion and microvascular pulsatility across age

Arterial pulsation can modulate neuronal activity through mechanosensitive pathways in mice, but whether vascular pulsatility is similarly related to neural organization in humans remains unclear. Here, using 7T MRI in 35 young and older adults, we mapped cerebral perfusion and microvascular volumetric pulsatility index (mvPI) across the unfolded hippocampus and integrated these measures with histological, electrophysiological, and transcriptomic reference maps. Perfusion and mvPI exhibited distinct spatial organization and age-related differences. In young adults, perfusion preferentially corresponded to parvalbumin-interneuron density and beta/gamma distribution, whereas mvPI was associated with theta-band distribution. A number of associations detected in young adults were not detected in the older adults maps. Spectral simulations showed qualitative similarity to heartbeat-related phase resetting as one possible explanation for the mvPI-theta relationship, while spatial transcriptomics revealed perivascular enrichment of PIEZO2, a candidate mediator of vascular mechanotransduction. Together, these findings establish perfusion and tissue-level pulsatility as distinct dimensions of human hippocampal vascular organization and provide a framework for investigating their relationships with neural dynamics across aging.

neuroscience↗

bioRxiv2026

Regional choroid plexus calcifications and their associations with aging, brain structure, and disease

The choroid plexus (CP) maintains brain homeostasis through cerebrospinal fluid production and formation of the blood-cerebrospinal fluid barrier, and its dysfunction has been linked with aging and neurological disease. CP dysfunction may involve both enlargement and calcific tissue change, which may reflect distinct processes; yet population neuroimaging has focused on lateral ventricle CP volume, which cannot directly identify calcified tissue. Using quantitative susceptibility mapping (QSM) in 30,012 UK Biobank participants, we conducted, to our knowledge, the first large scale multi-ventricular study of CP calcification (CPcal), quantifying the lateral (LV), third (3rdV) and fourth (4thV) ventricles. We examined associations with age and sex, endocrine, nutritional and metabolic, mental and behavioral, nervous system, and circulatory diagnoses; family history of Alzheimer's disease and related dementias (ADRD); cardiometabolic traits; and brain macrostructure, white matter microstructure, and subcortical susceptibility. CPcal increased with age (largest association: LV, r = 0.191) and was greater in males in the LV and 3rdV (d = 0.249-0.359) and more prevalent in the 4thV (OR = 1.274). Greater CPcal was associated with endocrine and metabolic, psychiatric, nervous system, and circulatory disorders, with the strongest and most consistent associations observed in the 3rdV (d = 0.097 - 0.125) and persisted after adjustment for ventricular volume, particularly for diabetes (d = 0.303) and tobacco use disorder (d = 0.314). Maternal ADRD family history was associated with greater LV CPcal (d = 0.063). CPcal was also associated with cortical and subcortical volumes (r = 0.033 - 0.046), white matter microstructure (r = 0.017 - 0.042), and subcortical susceptibility (r = -0.064 - 0.131). QSM-derived CPcal is a scalable, radiation-free imaging phenotype that complements volumetry by characterizing calcific CP tissue, which is associated with cardiometabolic health, brain aging, and neurodegenerative risk, particularly in the third ventricle.

neuroscience↗

bioRxiv2026

FastEBM: Fast, Scalable, and Uncertainty-Aware Event-Based Disease Progression Modeling

Event-based models (EBMs) are used to infer ordering of biomarker alteration patterns with respect to disease progression. However, EBM approaches rely on computationally expensive permutation-based inference, assumptions of feature independence, and likelihood optimization that can limit scalability and stability in high-dimensional settings. Here, we introduce Fast Event-Based Model (FastEBM), a scalable, uncertainty aware, Markov- chain-based framework that reformulates disease progression inference as a subject-ordering problem on a data-driven diffusion manifold. The progression uncertainty, used to derive positional variance diagrams, is quantified using first-passage-time variability derived directly from the inferred Markov process. Using synthetic experiments varying feature dimensionality, cohort size, noise level, and feature-correlation structure, we compared FastEBM with established methods, including Gaussian mixture model EBM (GMM-EBM), kernel density estimation EBM (KDE-EBM), and discriminative EBM (DEBM). FastEBM achieved the best accuracy and runtime. In low-subject/high-dimensional stress tests, FastEBM retained event-order recovery. FastEBM remained robust in simulations containing correlated and redundant features after decorrelation and feature-group handling. We applied FastEBM to real-world data to characterize biomarker progression in Alzheimers disease. First, we evaluated a low-dimensional multi- modal dataset from The Alzheimers Disease Prediction Of Longitudinal Evolution (TAD- POLE) challenge. Second, to demonstrate high-dimensional disease progression mapping, we applied FastEBM to regional cortical tau-PET data from the Alzheimers Disease Neuroimaging Initiative (ADNI). In both cases, FastEBM recovered progression patterns broadly consistent with the literature, also revealing lateralized progression trends. These results show that diffusion-based Markov geometry provides a scalable and robust alternative to conventional event-based modeling. FastEBM is available at: https://github.com/sjusc07/FastEBM.

bioinformatics↗

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

The ENIGMA-PD-WML Pipeline: A Containerized, User-Friendly Approach for Accurate, Standardized Segmentation of White Matter Lesions in Multi-Site MRI Data

Understanding vascular contributions to disease is a major unmet need. White matter lesions (WML) are an accepted imaging marker of cerebral small vessel disease, giving insights into its related pathologies. A unified approach for WML analyses in large multi-site data is lacking despite the need for pooling of data to overcome the limitations of often small heterogenous MRI studies which make subtyping and identifying patterns within disease groups difficult. Our ENIGMA-PD-WML pipeline is an open-source containerized pipeline containing all the code and packages required for pre-processing, processing and post-processing of T1-weighted and FLAIR data, outputting accurate and reproducible binary WML maps using a UNet approach. The pipeline provides a standardized image analysis approach for WML and outputs data in both native and MNI space to allow for sharing and pooling of data from multiple sites for large-data analysis. In addition to a reliable standardized approach for WML segmentation, key priorities when developing the pipeline included: usability, i.e., requiring minimal manual input and technical expertise to use, and suitability to run on various MRI scanners and acquisition parameters as is common in multi-site data. This paper describes the pipeline in detail, with rationale for each step, providing transparency and facilitating its usage to overcome reproducibility issues in large-scale WML analyses.

neuroscience↗

bioRxiv2026

Comparison of Automated White Matter Lesion Segmentation Approaches for Use in Large, Multi-Site Data Analyses in Parkinson's Disease

BackgroundParkinsons disease (PD) is the second most common neurodegenerative disorder. PD currently lacks effective disease-modifying treatments, likely due to its diverse clinical features and underlying neuropathology. The vascular role in PD is emerging, with vascular mechanisms increasingly implicated, yet the literature remains conflicted, motivating large-data analyses with greater statistical power. White matter lesions (WML) are an accepted imaging marker of small vessel disease. Accurate automated WML segmentation techniques are crucial for large-scale studies in PD due to the impracticality of manual segmentation for extensive datasets and to ensure consistency. Evaluation of the optimum approach in PD for large-scale analysis is lacking. This study aimed to evaluate various automated WML segmentation algorithms to determine the most accurate and reliable method, among those selected, for assessing WML for multi-site large data analysis in PD. MethodsWe assessed whole-brain volumetric T1-weighted and FLAIR images from 201 PD patients (mean age, 66.6 {+/-} 7.86 years) and 64 healthy controls (HC; mean age, 66.3 {+/-} 8.67) across three datasets: the Parkinsons Progression Markers Initiative (PPMI), the University of Pennsylvania (UPenn) and the Montreal Neurological Institute Biobank: Clinical Biological Imaging and Genetic Repository (C-BIG). The sample included different scanners, imaging parameters and lesion loads, as would be expected for multi-site data. WML were manually segmented to provide the gold standard, and four freely available automated algorithms were evaluated: FSLs BIANCA, FreeSurfer, SPMs LST-LPA and U-Net-pgs using the performance metrics: Dice score, Hausdorff distance, recall, precision, F1 score, log absolute volume difference (LOGAVD) and intraclass correlation coefficient (ICC). Subgroup analyses were performed based on lesion load and lobar regions. The associations of data from these automated approaches with age, and with Fazekas and Wahlund visual rating scales, were assessed through partial correlation analysis. ResultsU-Net-pgs performed best overall, with the highest Dice score (PD: 0.46 {+/-} 0.21; HC: 0.39 {+/-} 0.21), recall (PD: 0.76 {+/-} 0.25; HC: 0.62 {+/-} 0.31), precision (PD: 0.49 {+/-} 0.25; HC: 0.63 {+/-} 0.27), F1 score (PD: 0.54 {+/-} 0.22; HC: 0.56 {+/-} 0.22) and ICC (PD: 0.965; HC: 0.967) and lowest Hausdorff distance (PD: 8.89 {+/-} 3.96; HC: 6.33 {+/-} 2.91). U-Net-pgs achieved the lowest LOGAVD in the PD group (0.31 {+/-} 0.31) whereas BIANCA-LOO with a threshold of 0.9 was lowest in HC (0.27 {+/-} 0.30). U-Net also showed superior performances in all lesion loads for PD and overall across various brain regions in both PD and HC. ConclusionOverall, U-Net-pgs emerged as the best performing automated method, of those we evaluated, for WML segmentation in PD and HC within a dataset collected with various scanner and image acquisition parameters. U-Net-pgs consistently outperformed other automated approaches across lesion loads and brain regions, for most metrics. The accuracy and reliability of U-Net-pgs make it a promising tool for large-scale analyses, facilitating future research investigating WML in PD.

neuroscience↗

bioRxiv2026

The Regional Vulnerability Index (RVI) as a Neuroimaging-Based Biomarker for Autism: Associations with Likelihood, Cognition, and Longitudinal Social Outcomes

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition with symptoms spanning cognitive, social and behavioral domains and leading to lifelong challenges. Autism is heritable but specific genetic and environmental factors that shape its early neurodevelopmental trajectory remain unknown. Despite the clinical variability, neuroimaging findings from Enhancing Neuro Imaging Genetics through Meta Analysis (ENIGMA)-ASD consortium identified stable and replicable patterns of cortical and subcortical differences that were consistent with those reported by an independent consortium, the Cognitive Genetics Collaborative Research Organization (COCORO) in Japan. Here we developed and evaluated a regional vulnerability index (RVI), a similarity metric that quantifies how closely a persons brain resembles the specific pattern of an individual with autism. RVI-ASD is based on combining the regional effect sizes for regional brain measurements published by the ENIGMA-ASD group with microstructural white matter integrity differences reported by COCORO. RVI-ASD showed significantly higher effect size for case-control differences relative to any individual brain measure (d=0.30 vs. d=0.01-0.21) in individuals with autism, particularly in the adolescent-to-adult sample (N=2,577; Mean age = 16.1; SD=5.0). We next calculated RVI-ASD in the baseline and follow-up (ages 10 and 12) data from normally developing participants of the ABCD study (N=4,201). We tested the longitudinal stability, heritability, genotype-by-age interaction and sensitivity of RVI-ASD to known factors and cognitive measurements. RVI-ASD were stable on the 2-year follow up (ICC=0.76-0.92); showed significant heritability (h{superscript 2}=0.55-0.83, p<10-16) but was not affected by gene-by-age interaction. RVI-ASD showed significant positive correlation with paternal age, while correlation with maternal age was non-significant. Baseline and follow-up RVI-ASD were negatively correlated with cognitive measures including total, fluid and crystallized intelligence (r=-0.09 to -0.11, p<10-6). RVI-ASD scores tracked with core autism phenotypes including poor eye contact and rigid routines (p < .01). In a sub-sample of children with symptoms of autism (N=20), we found that earlier age of symptom onset was strongly correlated with higher White Matter RVI (r = -0.61), linking early behavioral emergence to the neuroanatomical signature. Longitudinal changes in subcortical RVI-ASD are significantly correlated with change in social functioning. The RVI-ASD is a neuroimaging-based biomarker linked to stable and reproducible brain patterns in autism. RVI-ASD allows researchers to study associations with factors associated with the likelihood for autism and cognition across large and inclusive non-clinical samples, thus moving beyond simple case-control models to understand the biological pathways of autism.

neuroscience↗

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↗

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

Structural Brain Network Alterations in Relation to Treatment and Illness Severity in Bipolar Disorder

BackgroundLarge-scale T1-weighted MRI studies have established grey-matter abnormalities in bipolar disorder (BD), with our group contributing to consensus findings. However, structural connectivity, particularly within emotion- and reward-related circuits, remains poorly understood. Diffusion-weighted MRI (dMRI) enables investigation of white-matter pathways, yet prior work is constrained by small samples, methodological heterogeneity, and unclear medication effects. We conducted the largest dMRI network analysis in BD, relating symptom burden and polypharmacy to tractography-derived connectivity and graph-theoretic metrics. MethodsCross-sectional structural and diffusion MRI scans from 449 individuals with BD (35.7{+/-}12.6 years) and 510 controls (33.3{+/-}12.6 years), aged 18-65, were analyzed across 16 ENIGMA-BD sites. Standardized segmentation/parcellation and constrained spherical deconvolution tractography generated individual structural connectivity matrices. Graph-theoretic metrics of global and subnetwork organization were related to symptom severity and medications. ResultsBD showed widespread network alterations (lower density and efficiency, longer path length, and higher betweenness centrality), altered microstructural organization in a limbic-basal ganglia circuit, and abnormal streamline counts in a default-mode/salience/fronto-limbic-basal ganglia network. Longer illness duration, later onset, and psychosis history were associated with greater abnormalities in network architecture, whereas more manic episodes were associated with greater fronto-limbic connectivity. Antidepressant (particularly SSRI), anticonvulsant, and antipsychotic use related to poorer global and fronto-limbic connectivity; no clear lithium effects emerged. ConclusionsAs the largest structural connectivity study in BD, we reveal widespread disruption in reward and emotion-regulation networks influenced by illness severity and medication use. Results show that multisite harmonization is feasible and highlight ENIGMA-BD as a scalable framework for identifying reproducible neurobiological markers.

neuroscience↗

bioRxiv2026

Distributional and Centile Calibration of Diffusion Tensor Imaging Normative Models as Training Sample Sizes Increase to 40,000 Subjects

Normative modeling (NM) is a powerful framework for quantifying individual deviations in brain structure and function relative to a population reference. However, its clinical utility depends on well-calibrated models trained on heterogeneous datasets such as those found in neuroimaging. Here, we systematically examine the effect of training sample size on the distributional and centile calibration of hierarchical Bayesian regression (HBR)-based NMs. Using multisite 3D diffusion MRI scans of the brain from 54,583 subjects, spanning almost the entire lifespan (age: 4-91 years), we trained NMs of white matter fractional anisotropy, a key microstructural metric, on subsamples ranging from 5,000 to 40,000 subjects. HBR was modeled with a Sinh-Arcsinh likelihood. Model calibration was evaluated using Kernelized Stein Discrepancy (KSD) to assess distributional agreement of Z-scores with the standard normal distribution; we also used Mean Absolute Centile Error (MACE) to quantify centile accuracy. Both metrics showed consistent and substantial improvements as the training sample size increased, indicating reduced posterior uncertainty and improved estimation of distributional parameters, particularly at the centile extremes. These results demonstrate that large training cohorts are essential for well-calibrated NMs derived from heterogeneous neuroimaging data and highlight the importance of large-scale data aggregation for reliable individual-level inference.

bioinformatics↗

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

Assessing molecular, cellular and transcriptomic bases of laminar perfusion and cytoarchitecture coupling in the human cortex

Understanding how cellular architecture organizes cortical function requires mesoscopic approaches that resolve structure-function coupling in vivo. Here we introduce cerebral blood flow (CBF) and cell-body staining intensity (CSI) similarity index (CCSI), a localized similarity index between the CBF estimates from ultra-high-field 7T arterial spin labeling images and CSI from the BigBrain histology images to serve as a quantitative marker of laminar perfusion-cytoarchitecture coupling. CCSI revealed a reproducible, region- specific alignment between laminar vascular and cellular profiles across the cortex. Going beyond CBF, CCSI selectively tracked mitochondrial respiratory capacity and colocalized with capillary endothelial and mature non-myelination oligodendrocyte populations forming neurovascular interfaces. Transcriptomic enrichment highlighted pathways related to vascular remodeling, oxidative metabolism, and lipid-myelin homeostasis, indicating that CCSI reflects integrated metabolic-structural specialization. At the systems level, CCSI strengthened structure-function gradient correspondence in transmodal cortices, such as the default mode network. Together, these findings establish CCSI as a physiologically grounded, non-invasive marker of perfusion-cytoarchitecture alignment, providing a cross-scale framework linking cortical microstructure, metabolism, and functional organization.

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↗

bioRxiv2025

Streamline Density Normalization: A Robust Approach to Mitigate Bundle Variability in Multi-Site Diffusion MRI

Tractometry enables quantitative analysis of tissue microstructure is sensitive to variability introduced during tractography and bundle segmentation. Differences in processing parameters and bundle geometry can lead to inconsistent streamline reconstructions and sampling, ultimately affecting the reproducibility of tractometry analysis. In this study, we introduce Streamline Density Normalization (SDNorm), a supervised two-step method designed to reduce variability in bundle reconstructions. SDNorm first computes streamline weights using linear regression to match a subjects bundle to a template streamline density map, then iteratively prunes streamlines to achieve a target density using a novel metric called effective Streamline Point Density (eSPD). We evaluate SDNorm across multiple bundles and acquisition protocols in dMRI data from a subset of subjects from Alzheimers Disease Neuroimaging Initiative and demonstrate that it can significantly reduce variability in streamline density, improve consistency in along-tract microstructure profiles, and provide useful metrics for automated bundle quality control. These results suggest that SDNorm can help enhance the reproducibility and robustness of bundle reconstruction across heterogeneous image acquisition protocols and tractography settings, making it well-suited for large-scale and multi-site neuroimaging studies.

neuroscience↗

bioRxiv2025

White matter microstructure changes across the lifespan: a meta-analysis of longitudinal diffusion MRI studies

BackgroundWhite matter in the human brain is known to play a critical role in facilitating communication between different brain regions. White matter microstructure is often quantified using fractional anisotropy (FA) derived from diffusion-weighted MRI and is often considered a key measure of neural efficiency that is positively associated with motor and cognitive functioning. While lifespan trajectories of FA have been well studied in cross-sectional designs, it remains less clear how FA changes longitudinally with age across the lifespan, and whether the rates of change are influenced by genetic variation. MethodsWe systematically reviewed the evidence of white matter changes, as measured by fractional anisotropy (FA) with diffusion magnetic resonance imaging longitudinally across the lifespan, and the genetic influences on this change. Searches were conducted in Medline, PsycInfo, and EMBASE up to August 2023 with terms related to DTI/FA and longitudinal/change. Following this, genetic-related search terms were applied to the results, and the search was broadened to include other measures of white matter change. Our systematic search resulted in 29 studies that met our criteria. In addition, 14 studies investigated genetic influences on FA change rates across the lifespan. A meta-regression using a thin-plate spline model was conducted to examine annual whole-brain FA change as a function of age. ResultsAcross childhood and adolescence, FA increased, and the rate of increase slowed into early adulthood. Between ages 20 and 35, changes in FA were not statistically significant. This was followed by a significant decline in FA between ages 36 and 50. The decreases plateaued between ages 51 and 61 and then continued at a slightly slower rate towards the upper end of the age range assessed (77 years). Average FA change per year relative to baseline assessment reached a maximum of +1.1% during development, and-0.6% per year, during ageing. Significant heritability was found for change in local but not global FA during development. During ageing, common variants in genes that have been related to increased risk for neuropsychiatric disorders (APOE, HTT, MAPT) were associated in some studies with accelerated local FA decreases over time. ConclusionsIn conclusion, there are changes in white matter microstructure within individuals across the lifespan, with increases during childhood, adolescence and early adulthood, followed by a period of relative stability during early to mid-adulthood, and subsequent gradual declines from midlife onwards. Evidence is emerging for genetic influences on white matter changes over time, shaping individual trajectories.

neuroscience↗