Biology subjects

Jack, C. R.

Publications and source records attributed to Jack, C. R..

11 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

Multi-Contrast MRI Inputs Enable Self-Consistent Tissue Segmentation & Robust Perivascular Space Identification

Different MRI image contrasts are designed to highlight various tissue properties and combining them allows extension of probabilistic segmentation beyond the commonly used "gray-white-CSF" models. This work describes a fully automated method that combines T1-weighted, T2-FLAIR, and conventional T2-weighted images to provide internal consistency across prediction of tissue segmentations including segmentation of superficial and deep gray matter, white matter hyperintensities, and MR-visible perivascular spaces. Results from 773 imaging datasets from 403 participants in the Mayo Clinic Study of Aging and Mayo Clinic Alzheimers Disease Research Center (ADRC) are presented.

neuroscience↗

bioRxiv2026

Diffusion MRI based biomarkers reveal a Prolonged Pre Lesional Phase of Cerebral Small Vessel Disease

BackgroundWhite matter hyperintensities (WMH) are widely used to assess cerebral small vessel disease (SVD) but reflect late-stage injury. Diffusion MRI based biomarkers have been proposed to capture earlier SVD-related microstructural damage but their temporal progression relative to WMH and the risk factors associated with this progression have not been explored. MethodsWe analyzed longitudinal neuroimaging data from 2,047 participants from a population-based cohort study (aged 49-101 years, 47% female). Using multi-output nonlinear mixed-effects models, we characterized the temporal progression of WMH and four diffusion MRI based biomarkers: fractional anisotropy of the genu of the corpus callosum (Genu-FA), peak width of skeletonized mean diffusivity (PSMD), free water (FW), and Arteriolosclerosis-score (ARTS). Models incorporated participant-specific time shifts, correlations between biomarkers, and effects of risk factors (sex, education, APOE {varepsilon}4 status, and cardiometabolic conditions). ResultsARTS, Genu-FA, FW, and PSMD became abnormal in 50% of the study population 16, 12, 10, and 7 years before WMH, respectively. Global markers (ARTS, FW, PSMD, WMH) were correlated, indicating shared substrates of widespread white matter injury. Genu-FA, a vascular risk microstructural injury biomarker, was weakly coupled with WMH and had an earlier but more linear worsening across adulthood. Cardiometabolic conditions predicted earlier worsening of all biomarkers. Females showed earlier WMH, Genu-FA, and ARTS abnormalities whereas males exhibited earlier PSMD and FW abnormalities. ConclusionsDiffusion MRI based biomarkers capture microstructural injury at least a decade before appearance of WMH, revealing a prolonged phase of early SVD and highlighting their potential for SVD prevention.

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

White Matter Tract Vulnerability to Amyloid Pathology on the Alzheimer's Disease Continuum

Alzheimers disease (AD) is marked by progressive cognitive decline and memory loss, due to the abnormal accumulation of amyloid-beta (A{beta}) plaques, which facilitate the spread of tau pathology, and a gradually spreading pattern of neuronal loss. Understanding how amyloid positivity affects the brains neural pathways is critical for understanding how the brain changes with AD pathology. Tractometry offers a powerful approach for the in vivo, 3D quantitative assessment of white matter tracts, enabling the localization of microstructural abnormalities in diseased populations and those at risk. In this study, we applied BUAN (Bundle Analytics) tractometry to multi-cohort diffusion MRI data from a total of 1,908 participants: 606 participants in ADNI3 (Alzheimers Disease Neuroimaging Initiative Phase 3) and 1,302 participants from the HABS-HD (Health and Aging Brain Study-Health Disparities). Using BUAN and along-tract statistical analysis, we assessed the localized effects of amyloid positivity on white-matter pathways, which may be further influenced by downstream tau accumulation. Amyloid positivity was quantified via amyloid-sensitive positron emission tomography (PET). BUAN enables tract-specific quantification of white matter microstructure and supports statistical testing along the full length of fiber bundles to detect subtle, spatially localized associations. We present 3D visualizations of tractwise amyloid associations, highlighting distinct patterns of white matter degeneration in AD.

neuroscience↗

bioRxiv2024

Microstructural Mapping of Neural Pathways in Alzheimer's Disease using Macrostructure-Informed Normative Tractometry

IntroductionDiffusion MRI is sensitive to the microstructural properties of brain tissues, and shows great promise in detecting the effects of degenerative diseases. However, many approaches analyze single measures averaged over regions of interest, without considering the underlying fiber geometry. MethodsHere, we propose a novel Macrostructure-Informed Normative Tractometry (MINT) framework, to investigate how white matter microstructure and macrostructure are jointly altered in mild cognitive impairment (MCI) and dementia. We compare MINT-derived metrics with univariate metrics from diffusion tensor imaging (DTI), to examine how fiber geometry may impact interpretation of microstructure. ResultsIn two multi-site cohorts from North America and India, we find consistent patterns of microstructural and macrostructural anomalies implicated in MCI and dementia; we also rank diffusion metrics sensitivity to dementia. DiscussionWe show that MINT, by jointly modeling tract shape and microstructure, has potential to disentangle and better interpret the effects of degenerative disease on the brains neural pathways.

neuroscience↗

bioRxiv2023

Increasing hub disruption parallels dementia severity in autosomal dominant Alzheimer disease

Hub regions in the brain, recognized for their roles in ensuring efficient information transfer, are vulnerable to pathological alterations in neurodegenerative conditions, including Alzheimer Disease (AD). Given their essential role in neural communication, disruptions to these hubs have profound implications for overall brain network integrity and functionality. Hub disruption, or targeted impairment of functional connectivity at the hubs, is recognized in AD patients. Computational models paired with evidence from animal experiments hint at a mechanistic explanation, suggesting that these hubs may be preferentially targeted in neurodegeneration, due to their high neuronal activity levels--a phenomenon termed "activity-dependent degeneration". Yet, two critical issues were unresolved. First, past research hasnt definitively shown whether hub regions face a higher likelihood of impairment (targeted attack) compared to other regions or if impairment likelihood is uniformly distributed (random attack). Second, human studies offering support for activity-dependent explanations remain scarce. We applied a refined hub disruption index to determine the presence of targeted attacks in AD. Furthermore, we explored potential evidence for activity-dependent degeneration by evaluating if hub vulnerability is better explained by global connectivity or connectivity variations across functional systems, as well as comparing its timing relative to amyloid beta deposition in the brain. Our unique cohort of participants with autosomal dominant Alzheimer Disease (ADAD) allowed us to probe into the preclinical stages of AD to determine the hub disruption timeline in relation to expected symptom emergence. Our findings reveal a hub disruption pattern in ADAD aligned with targeted attacks, detectable even in pre-clinical stages. Notably, the disruptions severity amplified alongside symptomatic progression. Moreover, since excessive local neuronal activity has been shown to increase amyloid deposition and high connectivity regions show high level of neuronal activity, our observation that hub disruption was primarily tied to regional differences in global connectivity and sequentially followed changes observed in A{beta} PET cortical markers is consistent with the activity-dependent degeneration model. Intriguingly, these disruptions were discernible 8 years before the expected age of symptom onset. Taken together, our findings not only align with the targeted attack on hubs model but also suggest that activity-dependent degeneration might be the cause of hub vulnerability. This deepened understanding could be instrumental in refining diagnostic techniques and developing targeted therapeutic strategies for AD in the future.

neuroscience↗

bioRxiv2022

Synthesizing Images of Tau Pathology from Cross-modal Neuroimaging using Deep Learning

Given the prevalence of dementia and the development of pathology-specific disease modifying therapies, high-value biomarker strategies to inform medical decision making are critical. In-vivo tau positron emission tomography (PET) is an ideal target as a biomarker for Alzheimers disease diagnosis and treatment outcome measure. However, tau PET is not currently widely accessible to patients compared to other neuroimaging methods. In this study, we present a convolutional neural network (CNN) model that impute tau PET images from more widely-available cross-modality imaging inputs. Participants (n=1,192) with brain MRI, fluorodeoxyglucose (FDG) PET, amyloid PET, and tau PET were included. We found that a CNN model can impute tau PET images with high accuracy, the highest being for the FDG-based model followed by amyloid PET and MRI. In testing implications of AI-imputed tau PET, only the FDG-based model showed a significant improvement of performance in classifying tau positivity and diagnostic groups compared to the original input data, suggesting that application of the model could enhance the utility of the metabolic images. The interpretability experiment revealed that the FDG- and MRI-based models utilized the non-local input from physically remote ROIs to estimate the tau PET, but this was not the case for the PiB-based model. This implies that the model can learn the distinct biological relationship between FDG PET, MRI, and tau PET from the relationship between amyloid PET and tau PET. Our study suggests that extending neuroimagings use with artificial intelligence to predict protein specific pathologies has great potential to inform emerging care models.

bioengineering↗

bioRxiv2022

Neuroimaging within the Dominantly Inherited Alzheimer's Network (DIAN): PET and MRI

The Dominantly Inherited Alzheimer Network (DIAN) Observational Study is an international collaboration studying autosomal dominant Alzheimer disease (ADAD). This rare form of Alzheimer disease (AD) is caused by mutations in the presenilin 1 (PSEN1), presenilin 2 (PSEN2), or amyloid precursor protein (APP) genes. As individuals from these families have a 50% chance of inheriting the familial mutation, this provides researchers with a well-matched cohort of carriers vs non-carriers for case-control studies. An important trait of ADAD is that the age at symptom onset is highly predictable and consistent for each specific mutation, allowing researchers to estimate an individuals point in their disease time course prior to symptom onset. Although ADAD represents only a small proportion (approximately 0.1%) of all AD cases, studying this form of AD allows researchers to investigate preclinical AD and the progression of changes that occur within the brain prior to AD symptom onset. Furthermore, the young age at symptom onset (typically 30-60 years) means age-related comorbidities are much less prevalent than in sporadic AD, thereby allowing AD pathophysiology to be studied independent of these confounds. A major goal of the DIAN Observational Study is to create a global resource for AD researchers. To that end, the current manuscript provides an overview of the DIAN magnetic resonance imaging (MRI) and positron emission tomography (PET) protocols and highlights the key imaging results of this study to date.

neuroscience↗

bioRxiv2019

Common genetic variation indicates separate etiologies for periventricular and deep white matter hyperintensities

We conducted a genome-wide association meta-analysis of two ischemic white matter disease subtypes in the brain, periventricular and deep white matter hyperintensities (PVWMH and DWMH). In 26,654 participants, we found 10 independent genome-wide significant loci only associated with PVWMH, four of which have not been described previously for total WMH burden (16q24.2, 17q21.31, 10q23.1, 7q36.1). Additionally, in both PVWMH and DWMH we observed the previous association of the 17q25.1 locus with total WMH. We found that both phenotypes have shared but also distinct genetic architectures, consistent with both different underlying and related pathophysiology. PVWMH had more extensive genetic overlap with small vessel ischemic stroke, and unique associations with several loci implicated in ischemic stroke. DWMH were characterized by associations with loci previously implicated in vascular as well as astrocytic and neuronal function. Our study confirms the utility of these phenotypes and identifies new candidate genes associated only with PVWMH.

neuroscience↗

bioRxiv2018

Diffusion MRI Indices and their Relation to Cognitive Impairment in Brain Aging: The updated multi-protocol approach in ADNI3

Brain imaging with diffusion-weighted MRI (dMRI) is sensitive to microstructural white matter changes associated with brain aging and neurodegeneration. In its third phase, the Alzheimers Disease Neuroimaging Initiative (ADNI3) is collecting data across multiple sites and scanners using different dMRI acquisition protocols, to better understand disease effects. It is vital to understand when data can be pooled across scanners, and how the choice of dMRI protocol affects the sensitivity of extracted measures to differences in clinical impairment. Here, we analyzed ADNI3 data from 317 participants (mean age: 75.4{+/-}7.9 years; 143 men/174 women), who were each scanned at one of 47 sites with one of six dMRI protocols using scanners from three different manufacturers. We computed four standard diffusion tensor imaging (DTI) indices including fractional anisotropy (FADTI) and mean, radial, and axial diffusivity, and one FA index based on the tensor distribution function (FATDF), in 24 bilaterally averaged white matter regions of interest. We found that protocol differences significantly affected dMRI indices, in particular FADTI. We ranked the diffusion indices for their strength of association with four clinical assessments. In addition to diagnosis, we evaluated cognitive impairment as indexed by three commonly used screening tools for detecting dementia and Alzheimers disease: the Alzheimers Disease Assessment Scale (ADAS-cog), the Mini-Mental State Examination (MMSE), and the Clinical Dementia Rating scale sum-of-boxes (CDR-sob). Using a nested random-effects model to account for protocol and site, we found that across all dMRI indices and clinical measures, the hippocampal-cingulum and fornix (crus) / stria terminalis regions most consistently showed strong associations with clinical impairment. Overall, the greatest effect sizes were detected in the hippocampal-cingulum and uncinate fasciculus for associations between axial or mean diffusivity and CDR-sob. FATDF detected robust widespread associations with clinical measures, while FADTI was the weakest of the five indices for detecting associations. Ultimately, we were able to successfully pool dMRI data from multiple acquisition protocols from ADNI3 and detect consistent and robust associations with clinical impairment and age.

neuroscience↗