Tractometry analysis represents a significant advancement in neuroimaging, offering a detailed examination of the brain's white matter at a micro level. Unlike traditional ROI or voxel-based methods, tractometry precisely reconstructs and characterizes white matter tracts. Using advanced diffusion MRI and tractography algorithms, it maps the trajectory, shape, and connectivity patterns of individual white matter bundles. Accurate alignment of these tracts across different groups is crucial for reliable and reproducible results. Nonlinear registration techniques are essential for achieving this alignment, harmonizing bundle shapes, and improving sensitivity to disease-related changes. However, nonlinear registration is complex, especially with tractography data, which digitally represents the brain's white matter anatomy. Potential structural changes in the bundle's shape during registration can lead to artifacts that obscure critical anatomical details needed for disease identification. We introduce BundleWarp, a streamline-based nonlinear deformable registration method designed specifically for white matter tracts. BundleWarp employs a sophisticated approach to align two white matter bundles while preserving their topological and anatomical features. It is formulated as a probability density estimation problem with motion coherence penalties, ensuring coherent movement of points along streamlines and maintaining the anatomical integrity of tracts through displacement field regularization. Additionally, we introduce a tract morphometry framework utilizing the displacement field generated by BundleWarp to analyze white matter tract shape differences. Our results show that BundleWarp effectively quantifies bundle shape differences and enhances structural harmonization in tractometry analysis for diverse subjects, including those with Alzheimer's and Parkinson's disease. Test-retest experiments further demonstrate that BundleWarp substantially improves subject fingerprinting by increasing within-subject reproducibility of both bundle shape and microstructural profiles (FA, MD, RD, AD). It precisely maps the brain's neuronal pathways, offering a robust tractometry framework with enhanced sensitivity for detecting disease-related structural and microstructural changes in white matter tracts associated with Mild Cognitive Impairment (MCI), dementia, and early-stage Alzheimer's biomarkers, including amyloid-beta plaques and tau neurofibrillary tangles.

Tractometry enables detailed mapping of white matter microstructure along individual tracts and is widely used to study disease effects such as those seen in Alzheimer's disease (AD). However, how different tractography algorithms influence tractometry outcomes remains unclear. Here, we compared whole-brain deterministic and probabilistic tractography using the BUndle ANalytics (BUAN) framework in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, including 118 AD and 728 cognitively normal (CN) participants. Both approaches revealed the expected pattern of lower fractional anisotropy (FA) and higher mean, radial, and axial diffusivity (MD, RD, AxD) in AD, consistent with white matter degeneration. Despite broadly similar global trends, substantial bundle-level differences emerged between the two tractography methods. Probabilistic tracking produced stronger and more spatially extended effects in the fornix, a small and highly curved limbic pathway vulnerable to AD-related degeneration, whereas deterministic tracking showed greater sensitivity in the posterior segments of the right superior longitudinal fasciculus (SLF_R). These discrepancies highlight that the choice of tractography algorithm can alter detecting disease effects, emphasizing the need for cross-method validation to ensure the robustness and interpretability of along-tract measures.

Chronic anemia is observed in individuals with sickle cell disease (SCD) and thalassemia syndromes. It has been associated with a range of neurological complications, particularly progressive silent cerebral infarcts in brain regions with high oxygen extraction-specifically in vascular watershed areas-suggesting that regional tissue hypoxia may play a causal role. However, recent work in sickle cell mice reveals widespread white matter demyelination and chronic neuroinflammation superimposed upon regional ischemia. In light of these findings, we utilized high-fidelity diffusion imaging and modeling techniques to identify predictors of white matter damage in human subjects diagnosed with SCD (n = 76) and thalassemia (n = 20) compared to healthy individuals (n = 32). Our results demonstrate that white matter damage extended beyond vascular watershed areas in chronically anemic subjects and had MRI changes characteristic of demyelination. These findings were proportional to hemoglobin levels and largely disappeared after controlling for anemia severity. However, patients with SCD exhibited small but significant residual white matter derangements not seen in those with thalassemia. These residual abnormalities disappeared after LDH or reticulocyte count were included as markers of hemolytic rate. From a functional perspective, neuropsychological processing speed was correlated with white matter integrity in chronic anemia subjects, with stronger associations seen in patients with SCD. Taken together, these results demonstrate that chronic anemia is associated with widespread white matter demyelination that cannot be explained by regional blood flow variation and is proportional to anemia severity. Patients with SCD may have more severe disease and functional consequences than patients with thalassemia.

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.

Diffusion MRI (dMRI) enables assessment of white matter microstructural abnormalities in Alzheimer's disease (AD), and multisite datasets enable more robust modeling of non-biological variation that can confound analyses. The Alzheimer's 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 (Aβ) 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.

Importance:22q11.2 deletion syndrome (22q11DS) is among the strongest genetic risk factors for neuropsychiatric disorders and has marked effects on brain structure. Yet, it remains unclear which neuroanatomical features reflect uniform effects of the deletion versus inter-individual biological processes relevant to psychiatric outcomes. Identifying these features is critical for developing targeted treatments and interventions. Objective:To identify brain regions where 22q11DS exerts its most consistent and most variable impacts, and to test whether these patterns align with normative neurotransmitter receptor distributions and cortical growth trajectories. Design:Multisite cross-sectional case-control study. Setting:T1-weighted brain MRI data were obtained across 15 scanners. MRI data underwent standardized processing, quality control procedures and statistical site-adjustment using ComBat. Participants:A total of N = 438 individuals with 22q11DS (5-54 years, 48% females) and 380 typically developing controls (6-58 years, 48% females). Main Outcomes and Measures:Primary outcomes were global and regional cortical thickness and surface area. Mean and dispersion estimates were calculated using double generalized linear models, correcting for age, age2, sex (and intracranial volume for surface area). Quantile shift functions characterized fine-scale distributional differences. Sensitivity analyses adjustedt for co-occurring neuropsychiatric disorders, antipsychotic use and deletion subtype. Secondary outcomes included spatial correspondence between regional structural alterations and normative maps of neurotransmitter receptor density and cortical expansion. Results:Compared with controls, individuals with 22q11DS showed widespread mean differences in cortical thickness and surface area. Notably, 22q11DS was associated with greater regional heterogeneity in both measures, except for reduced dispersion in the anterior cingulate. Effects were attenuated after covariate adjustment. Cortical thickness differences spatially overlapped with regions enriched for glutamatergic and GABAergic receptors. There was partial evidence linking surface area dispersion patterns to normative cortical growth trajectories. Conclusions and Relevance:22q11DS exerts broad effects on cortical structure consistent with a global developmental mechanism, reflected in widespread mean shifts. Beyond these, region-specific variability, particularly in cortical thickness, suggests individualized neurobiological processes. The anterior cingulate emerges as a region of consistent structural deviation. Overall, structural variability in 22q11DS aligns with normative patterns of excitatory-inhibitory signaling and cortical development, implicating these pathways as potential targets for intervention.

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 Matérn 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.

22q11.2 deletion (22qDel) and duplication (22qDup) carriers have an increased risk of neurodevelopmental disorders and exhibit altered brain structure, including white matter microstructure. However, the underlying cellular architecture and age-related changes contributing to these white matter alterations remain poorly understood. Neurite orientation dispersion and density imaging (NODDI) was used on mixed cross-sectional and longitudinal data to examine group differences and age-related trajectories in measures of axonal density (i.e., intracellular volume fraction; ICVF), axonal orientation (orientation dispersion index; ODI) and free water diffusion (isotropic volume fraction; ISO) in 50 22qDel (n scans = 69, mean age = 20.7, age range = 7.4-51.1, 64.0% female) and 24 22qDup (n scans = 34, mean age = 21.6, age range = 8.3-49.4, 54.2% female) carriers, and 890 controls (n scans = 901, mean age = 21.9, age range = 7.8-51.1, 54.5% female). The results showed widespread gene dosage effects, with higher ICVF in 22qDel and lower ICVF in 22qDup compared to controls, and region-specific effects of the 22qDel and 22qDup on ODI and ISO measures. However, 22qDel and 22qDup carriers did not exhibit altered age-related trajectories relative to controls. Observed differences in ICVF suggest higher white matter axonal density in 22qDel and lower axonal density in 22qDup compared to controls. Conversely, differences in ODI are highly localized, indicating region-specific effects on axonal dispersion in white matter. We do not find evidence for altered developmental trajectories of axonal density or dispersion among 22q11.2 CNV carriers, suggesting stable disruptions to neurodevelopmental events before childhood.

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. ### Competing Interest Statement The authors have declared no competing interest. NIH Common Fund, https://ror.org/001d55x84, RF1AG057892, RO1AG060610, R01MH129858, T32AG058507, K01MH135160 Alzheimer's Association, https://ror.org/0375f4d26, AARG-23-1149996

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 Alzheimer's 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.

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 subject's 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 Alzheimer's 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.

Normative models of brain metrics based on large populations could be extremely valuable for detecting brain abnormalities in patients with a variety of disorders, including degenerative, psychiatric and neurodevelopmental conditions, but no such models exist for the brain's white matter (WM) microstructure. Here we present a large-scale normative model of brain WM microstructure - based on 19 international diffusion MRI datasets covering almost the entire lifespan (totaling N = 54,583 individuals; age: 4-91 years). We extracted regional diffusion tensor imaging (DTI) metrics using a standardized analysis and quality control protocol and used hierarchical Bayesian regression (HBR) to model the statistical distribution of derived WM metrics as a function of age and sex. We extracted the average lifespan trajectories and corresponding centile curves for each WM region. We illustrate the utility of the method by applying it to detect and visualize profiles of WM microstructural deviations in a variety of contexts: in mild cognitive impairment, Alzheimer's disease, and 22q11.2 deletion syndrome - a neurogenetic condition that markedly increases risk for schizophrenia. The resulting large-scale model provides a common reference to identify disease effects on the brain's microstructure in individuals or groups, and to compare disorders, and discover factors affecting WM abnormalities. The derived normative models are a valuable resource publicly available to the community, adaptable and extendable to future datasets as the available data expands.

Diffusion MRI (dMRI) is sensitive to microstructural brain abnormalities, which may precede standard MRI macrostructural changes in the progression of Alzheimer's disease (AD) and related dementias. dMRI may allow for earlier amyloid beta (Aβ) detection and intervention before significant cognitive decline and help differentiate between Aβ+ and Aβ- dementias. Event-based modeling (EBM) is a probabilistic data-driven method applicable to cross-sectional data that can order multimodal biomarkers as they become abnormal in the course of disease progression. We used EBM to examine whether changes in cortical microstructure precede T1-weighted (T1w) cortical thickness (CTh) and hippocampal volume changes with respect to Aβ status. T1w, dMRI, and Aβ-PET data were analyzed for 1,033 participants from ADNI3, HABS-HD, OASIS3, and PREVENT-AD (age range: 46-92; Figure 1A). DTI and two more advanced models were fitted to the dMRI data, yielding 10 diffusion metrics extracted from the cortical gray matter (Figure 1B). CTh and hippocampal volumes were also extracted. All MRI measures were harmonized for cross-scanner differences using ComBat. First, we identified whether full cortex dMRI measures, total CTh, hippocampal volume, and MMSE differed between Aβ- and Aβ+ individuals. Measures that differed significantly were then ordered based on the Aβ- to Aβ+ continuum using EBM. To validate the resulting order, we tested for associations between EBM-derived subject-wise Aβ stage estimates and 1) clinical diagnosis and 2) Aβ positivity within the dementia group indicative of AD. All biomarkers differed significantly between Aβ+ and Aβ- participants and were sequenced with EBM (Figure 2A). Full cortex changes in all dMRI measures preceded hippocampal volume, MMSE, and total CTh. Subject-level Aβ stage estimates showed significant differences between all diagnostic groups (Figure 3A). In regional analyses, 73 biomarkers were associated with Aβ status (Figure 1B). Regional EBM revealed earliest abnormalities in pericalcarine CTh and temporal dMRI measures (Figure 2C). Subject-level stages showed significant differences between diagnostic groups (Figure 3B), and between Aβ- and Aβ+ dementia participants (Figure 3C). Aβ-related abnormalities in dMRI cortical microstructure may precede abnormalities in traditional biomarkers of brain atrophy in AD. Future work will determine if EBM-derived Aβ stage estimates may also help to differentiate AD from other dementia subtypes.

Normative models (NM) of brain metrics based on large, diverse populations offer novel strategies to detect individual brain abnormalities. To create an age-dependent statistical model of brain microstructure over the human lifespan, we built the largest multi-site NM of white matter (WM) diffusion tensor imaging (DTI) metrics based on 54,591 subjects. We used state-of-the-art tools to adjust for site-dependent effects. We used Hierarchical Bayesian Regression (HBR) to determine the age trajectory and lifespan centile curves (ages 3-100 years) by merging data from different sections of the lifespan. We analyzed 19 international public datasets with the ENIGMA-DTI protocol. Mean fractional anisotropy (FA), mean, axial, and radial diffusivity (MD, AxD, RD) were extracted for 21 bilateral ROIs from the JHU-WM atlas and the whole WM skeleton. Regressions were run with each metric per ROI as a function of age and sex, and the DTI protocol as the batch effect (37 protocols). Z-scores were derived for 81 patients with Alzheimer’s disease (AD; mean age: 77y±8.4, 46M/35F) and 225 MCI participants (mean age: 75.1y±8.1, 127M/98F) and extreme deviations (Z>|2|) were quantified for each condition. ROI-wise areas under the ROC curve were calculated to determine the classification accuracy of the Z-scores. The average FA for the overall WM skeleton reached its peak value at 27 years, with minima for MD, RD and AxD at 47, 42 and 54 years, respectively. We found extreme FA deviations (Z>2) for MCI (AUC=0.67) and AD (AUC=0.66) in the genu of the corpus callosum, extreme RD deviations (Z<2) in the tapetum (AUC=0.73) and Extreme AxD deviations (Z>2) for MCI in the s uperior longitudinal fasciculus (AUC=0.65). See Figures 1 & 2. NM of brain microstructure with HBR yielded lifespan trajectories for widely-used DTI metrics. By pooling large scale multi-site dMRI to create a statistical reference, single subject deviations are detectable across the lifespan for AD and MCI. These NM will be publicly available to the research community.

BACKGROUND: Copy number variants (CNVs) may increase the risk for neurodevelopmental conditions. The neurobiological mechanisms that link these high-risk genetic variants to clinical phenotypes are largely unknown. An important question is whether brain abnormalities in individuals who carry CNVs are associated with their degree of penetrance. METHODS: We investigated whether increased CNV penetrance for schizophrenia and other developmental disorders was associated with variations in cortical and subcortical morphology. We pooled T1-weighted brain magnetic resonance imaging and genetic data from 22 cohorts from the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis)-CNV consortium. In the main analyses, we included 9268 individuals (aged 7-90 years, 54% female), from which we identified 398 carriers of 36 neurodevelopmental CNVs at 20 distinct loci. A secondary analysis was performed including additional neuroimaging data from the ENIGMA-22q consortium, including 274 carriers of the 22q11.2 deletion and 291 noncarriers. CNV penetrance was estimated through penetrance scores that were previously generated from large cohorts of patients and controls. These scores represent the probability risk of developing either schizophrenia or other developmental disorders (including developmental delay, autism spectrum disorder, and congenital malformations). RESULTS: For both schizophrenia and developmental disorders, increased penetrance scores were associated with lower surface area in the cerebral cortex and lower intracranial volume. For both conditions, associations between CNV-penetrance scores and cortical surface area were strongest in regions of the occipital lobes, specifically in the cuneus and lingual gyrus. CONCLUSIONS: Our findings link global and regional cortical morphometric features with CNV penetrance, providing new insights into neurobiological mechanisms of genetic risk for schizophrenia and other developmental disorders.

Diffusion MRI (dMRI) is sensitive to small changes in brain microstructure and may offer sensitivity to early Alzheimer's disease (AD) neuropathology that precedes macrostructural brain changes. Subtle Aβ effects may be better captured by dMRI measures in cortical gray matter (GM), where early AD histopathological changes occur, compared to more conventional cortical thickness (CTh) measures. Here, we evaluated relationships between Aβ-PET and single-shell cortical dMRI measures in cognitively normal (CN) individuals from four AD studies. For comparison, CTh was also evaluated. T1w, dMRI, and Aβ-PET data were analyzed in 769 CN participants from ADNI3, HABS-HD, OASIS3, and PREVENT-AD (Figure 1a); 425 participants had tau-PET data. In addition to DTI, advanced single-shell NODDI-DTI and MAP-AMURA models were fit to dMRI data. CTh and 10 mean dMRI measures (defined in Figure 1b) were extracted from 34 cortical regions parcellated from T1w images with FreeSurfer, and the full cortex. Random-effects linear regressions were used to test for associations between regional cortical MRI measures and Aβ-PET centiloids (CL), adjusting for age, sex, education, ethnicity/race, total CTh (excluded from CTh analyses), intracranial volume, and grouping by study dMRI protocol. We also tested the interactive effects of Aβ-CL and tau-PET positivity on significant cortical measures; tau positivity was defined for each respective study (Figure 1a). Aβ-CL was associated with three dMRI measures and CTh (P FDR <0.05; Figure 2a). Limited regional associations were found between greater Aβ and higher DTI FA and CTh. More widespread associations were detected with advanced dMRI models; greater Aβ was associated with higher APA and lower ODI in the full cortex. ODI was moderated by tau; tau+ individuals showed steeper negative ODI slopes with respect to Aβ burden (Figure 2b). Compared to CTh, advanced dMRI measures showed more widespread associations with Aβ load. Greater hindered diffusion (i.e., higher APA, FA) associations with greater Aβ burden may reflect cellular hypertrophy or inflammatory microglia infiltration increasing the number of diffusion barriers in early stages, irrespective of tau status. In contrast, lower neurite dispersion (ODI) could be driven by neurite loss and neurodegeneration; these effects were greater in tau+ participants. dMRI measures may offer insight into early pathological disease processes.

Diffusion tensor imaging (DTI) is a key neuroimaging modality for assessing brain tissue microstructure, yet high-quality acquisitions are costly, time-intensive, and prone to artifacts. To address data scarcity and privacy concerns - and to augment the available data for training deep learning methods - synthetic DTI generation has gained interest. Specifically, denoising diffusion probabilistic models (DDPMs) have emerged as a promising approach due to their superior fidelity, diversity, controllability, and stability compared to generative adversarial networks (GANs) and variational autoencoders (VAEs). In this work, we evaluate the quality, fidelity and added value for downstream applications of synthetic DTI mean diffusivity (MD) maps generated by 2D slice-wise and 3D volume-wise DDPMs. We evaluate their computational efficiency and utility for data augmentation in two downstream tasks: sex classification and dementia classification using 2D and 3D convolutional neural networks (CNNs). Our findings show that 3D synthesis outperforms 2D slice-wise generation in downstream tasks. We present a benchmark analysis of synthetic diffusion-weighted imaging approaches, highlighting key trade-offs in image quality, diversity, efficiency, and downstream performance.

Tractometry enables detailed mapping of white matter microstructure along individual tracts and is widely used to study disease effects such as those seen in Alzheimer's disease (AD). However, how different tractography algorithms influence tractometry outcomes remains unclear. Here, we compared whole-brain deterministic and probabilistic tractography using the BUndle ANalytics (BUAN) framework in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, including 118 AD and 728 cognitively normal (CN) participants. Both approaches revealed the expected pattern of lower fractional anisotropy (FA) and higher mean, radial, and axial diffusivity (MD, RD, AxD) in AD, consistent with white matter degeneration. Despite broadly similar global trends, substantial bundle-level differences emerged between the two tractography methods. Probabilistic tracking produced stronger and more spatially extended effects in the fornix, a small and highly curved limbic pathway vulnerable to AD-related degeneration, whereas deterministic tracking showed greater sensitivity in the posterior segments of the right superior longitudinal fasciculus (SLF_R). These discrepancies highlight that the choice of tractography algorithm can alter detecting disease effects, emphasizing the need for cross-method validation to ensure the robustness and interpretability of along-tract measures.

Diffusion MRI (dMRI) metrics of brain microstructure offer valuable insight into Alzheimer’s disease (AD) pathology; recent reports have identified dMRI metrics that (1) tightly link with CSF or PET measures of amyloid and tau burden; and (2) mediate the relationship between CSF markers of AD and delayed logical memory performance, commonly impaired in early AD [1,2]. To better localize white matter tract disruption in AD, our BUndle ANalytic (BUAN) [3] tractometry pipeline allows principled use of statistical methods to map factors affecting microstructural metrics along the 3D length of the brain’s fiber tracts. Here, we extended BUAN to pool data from multiple scanning protocols/sites - using a new harmonized tractometry approach, based on ComBat [4,5], a widely-used harmonization method modeling variations in multi-site datasets due to site- and scanner-specific effects. We illustrate the effects of integrating ComBat into our BUAN tractometry pipeline. Analyzing Alzheimer’s Disease Neuroimaging Initiative (ADNI3) [6] data, we examined the impact of mild cognitive impairment (MCI) and AD on 38 white matter tracts, comparing results with and without harmonization. We analyzed data from 730 ADNI3 participants, scanned with seven dMRI protocols. After data preprocessing with the ADNI3 protocol [7,8], BUAN extracted 38 bundles and created along-tract bundle profiles for microstructural metrics: FA, MD, AD, and RD (see Figure 1 for full names). ComBat was applied to each point in bundle profiles to correct for scanner protocol effects and linear mixed models (LMM) were used to study the group differences in MCI and AD versus cognitively healthy controls (CN). Harmonized BUAN maps reveal AD and MCI effects throughout specific tracts (e.g., the left cingulum and right arcuate fasciculus; Figure 1, bottom panel ). ComBat harmonization enhanced the sensitivity to detect group differences compared to LMM without scanner correction. Adding data - even from different scanner protocols - boosted power (Figure 2f-g). In this application of harmonization in neurodegenerative tractometry analysis, we integrated ComBat into BUAN tractometry to merge dMRI data from diverse scanning protocols and sites. Future research will examine various ComBat versions and deep-learning approaches to track AD pathology effects on the brain’s neural circuitry.

Generation of high-quality synthetic brain MRI data could be beneficial for advancing neuroimaging research, particularly when access to large-scale, labeled datasets is limited. In this work, we leverage a pretrained Diffusion Transformer (DiT) architecture to synthesize 3D mean diffusivity (MD) scalar maps from the Cam-Can dataset. To adapt the DiT model—originally trained on 2D natural images—for 3D neuroimaging data, we implemented a preprocessing strategy that tiles 2D slices from 3D volumes into composite 2D images, enabling effective finetuning. The quality of the generated synthetic images was evaluated using Multi-Scale Structural Similarity (MS-SSIM) and Maximum Mean Discrepancy (MMD) metrics, demonstrating high fidelity and anatomical coherence. To assess the utility of synthetic data in downstream tasks, we conducted transfer learning experiments for dementia classification on the ADNI dataset. A sex classification model, trained on both real and synthetic Cam-Can data, was repurposed for this task, showing that synthetic samples can enhance model performance. These results highlight the potential of diffusion-based generative models for augmenting neuroimaging datasets and supporting clinical applications.