Dan Benjamini

Brain aging is accompanied by profound cellular and microstructural changes that precede overt tissue loss, yet in vivo MRI studies largely emphasize macroscopic measures or isolated diffusion and relaxation metrics, providing limited insight into how cellular‐scale tissue architecture is altered across the adult lifespan. Here, we apply multidimensional diffusion–relaxation MRI (MD‐MRI) to map voxel‐wise microstructural phenotypes in cognitively unimpaired adults spanning early adulthood to late life (23–77 years). Rather than relying on predefined compartment models, MD‐MRI resolves continuous voxel‐wise distributions in a joint diffusion–relaxation space, enabling an integrated, model‐free description of how cellular shape, size, restriction, and chemical environment vary with age. Using this approach, we reveal age‐related multilateral shifts within a complex microstructural landscape, marked by increasing heterogeneity and disorder across brain tissue. With age, cellular‐scale features showed a systematic transition from small to larger length‐scale structures accompanied by reduced microscopic restriction, indicating a loss of fine cellular barriers and expansion of extracellular space. In parallel, we show tissue‐dependent alterations in fast‐relaxing properties aligned with known gray‐ and white‐matter aging processes, including iron accumulation and myelin loss. Together, these findings indicate that normative brain aging involves progressive reorganization of structure and composition at the cellular level, rather than uniform shifts in bulk tissue properties. By decoupling cellular‐scale shape, size, restriction, and chemical environment in vivo, MD‐MRI identifies increasing cellular heterogeneity and breakdown of microscopic restriction as central features of human brain aging and provides a biologically interpretable framework for linking microstructural reorganization to age‐related functional decline.

Purpose: To demonstrate the feasibility of whole-brain multidimensional T ₁ - T ₂ relaxation imaging on a portable 0.064 T MRI scanner. Methods: A two-dimensional inversion-recovery, fast spin-echo acquisition was used to jointly encode T ₁ and T ₂ relaxation. Joint relaxation distributions were reconstructed voxel-wise using marginal-distribution constrained optimization (MADCO). The approach was evaluated in a quantitative relaxation MRI phantom and subsequently applied in vivo in a healthy volunteer. Joint distributions, marginal distributions, distribution-derived measures, and statistical dependencies between the two relaxation dimensions were examined. Results: Phantom joint relaxation estimates showed good overall agreement with mono-exponential relaxation measurements and previously reported values at 0.064 T. The in vivo acquisition provided whole-brain coverage over a broad range of relaxation weightings and tissue contrasts. Reconstructed joint T ₁ - T ₂ distributions showed spatially organized features across the relaxation space, including broad and overlapping relaxation components that were not fully represented by single-value relaxation maps or by either 1D marginal distribution alone. Selected regions of the joint relaxation space produced component maps with distinct spatial patterns. Statistical analysis further showed a dependence between the two relaxation dimensions, supporting the presence of added information in the joint distribution that is lost when treating T ₁ and T ₂ separately. Conclusion: Whole-brain multidimensional T ₁ - T ₂ relaxation imaging is clinically feasible on a portable 0.064 T MRI scanner. Joint relaxation distributions provide information beyond single-value relaxation mapping and may support further developments of quantitative multidimensional imaging at ultra-low field.

Alzheimer’s disease (AD) pathology involves amyloid deposition, reactive gliosis, and localized tissue alterations that coexist within the same brain regions, creating heterogeneous microstructural environments within individual imaging voxels. Conventional diffusion MRI averages these environments into aggregate measures, potentially obscuring their distinct contributions. Frequency-dependent multidimensional MRI (ωMD-MRI) resolves distributions of water components with different diffusion length scales, anisotropies, and relaxation properties, providing sensitivity to microstructural restriction, heterogeneity, and shape-size correlations within a voxel. Whether these measurements reveal microstructural complexity associated with AD pathology remains unclear. Here, we performed ωMD-MRI on ex vivo brain specimens from approximately 8-month-old 5xFAD and wild-type mice and interpreted the imaging findings alongside complementary histology. ωMD-MRI revealed widespread but spatially nonuniform differences between 5xFAD and wild-type brains. Measurements sensitive to microstructural restriction, heterogeneity, and shape-size correlations consistently indicated greater microstructural heterogeneity in 5xFAD brains, with the most prominent differences in the hippocampal formation and major cerebral white matter tracts. Complementary qualitative histology demonstrated extensive amyloid deposition and glial activation in affected regions, while overall cytoarchitecture and myelin organization remained largely preserved. Thus, the ωMD-MRI abnormalities occurred in tissue characterized by multiple coexisting pathological and relatively preserved microstructural environments rather than widespread structural degeneration. These findings demonstrate that ωMD-MRI can reveal the spatial and microstructural heterogeneity associated with amyloid pathology and provide a more comprehensive characterization of AD-related tissue alterations.

Brain aging is accompanied by profound cellular and microstructural changes that precede overt tissue loss, yet in vivo MRI studies largely emphasize macroscopic measures or isolated diffusion and relaxation metrics, providing limited insight into how cellular-scale tissue architecture is altered across the adult lifespan. Here, we apply multidimensional diffusion–relaxation MRI (MD-MRI) to map voxel-wise microstructural phenotypes in cognitively unimpaired adults spanning early adulthood to late life (23–77 years). Rather than relying on predefined compartment models, MD-MRI resolves continuous voxel-wise distributions in a joint diffusion–relaxation space, enabling an integrated, model-free description of how cellular shape, size, restriction, and chemical environment vary with age. Using this approach, we reveal age-related multilateral shifts within a complex microstructural landscape, marked by increasing heterogeneity and disorder across brain tissue. With age, cellular-scale features showed a systematic transition from small to larger length-scale structures accompanied by reduced microscopic restriction, indicating a loss of fine cellular barriers and expansion of extracellular space. In parallel, we show tissue-dependent alterations in fast-relaxing properties aligned with known gray- and white-matter aging processes, including iron accumulation and myelin loss. Together, these findings indicate that normative brain aging involves progressive reorganization of structure and composition at the cellular level, rather than uniform shifts in bulk tissue properties. By decoupling cellular-scale shape, size, restriction, and chemical environment in vivo, MD-MRI identifies increasing cellular heterogeneity and breakdown of microscopic restriction as central features of human brain aging and provides a biologically interpretable framework for linking microstructural reorganization to age-related functional decline.

Purpose: To develop a robust and efficient multidimensional MRI (MD‐MRI) data processing framework for accurately estimating joint frequency‐dependent diffusion‐relaxation distributions, while overcoming computational limitations and noise instability inherent to Monte Carlo (MC) inversion. Methods: We introduced an Informed Dictionary‐guided Monte Carlo (ID‐MC) strategy that incorporates data‐driven dictionary matching into the inversion process, followed by targeted local mutation refinement to enhance flexibility and reduce overfitting. This hybrid approach aims to improve the stability, accuracy, and reproducibility of MD‐MRI parameter estimation. We evaluated ID‐MC through in silico simulations across a range of signal‐to‐noise ratios and in vivo test–retest experiments in the human brain. Reproducibility was assessed using intraclass correlation coefficients (ICC) and within‐subject variability, allowing rigorous comparison with MC. Results: In simulations, the ID‐MC approach consistently achieved lower fitting errors and higher estimation accuracy across a wide range of noise levels, demonstrating its ability to balance local flexibility and global biological plausibility. Compared to MC inversion, ID‐MC also reduced computation time by approximately 69%, highlighting its potential for time‐efficient large‐scale applications. In in vivo test–retest analyses, ID‐MC substantially improved reproducibility, doubling the number of MD‐MRI parameters with ICC greater than 0.75 relative to MC. Notably, diffusion frequency‐dependent parameters, previously poorly reproducible with MC, showed up to 146% higher ICC with ID‐MC. Conclusion: By integrating data‐driven dictionary matching with targeted mutation refinement, ID‐MC improves the robustness, reproducibility, and computational efficiency of MD‐MRI inversion, supporting studies that require highly sensitive detection of subtle brain microstructural changes.

Frequency‐dependent diffusion–relaxation distribution MRI provides information beyond the traditional voxel‐averaged metric that may better characterize the microstructural features of biological tissue. Frequency‐dependent multidimensional (MD) MRI reproducibility has been established in clinical settings, but has yet to be thoroughly evaluated under preclinical conditions, where superior hardware and modulated gradient waveforms enhance its performance. In this study, we investigate the reproducibility of MD‐MRI using a micro‐imaging system to investigate ex vivo mouse brains. Notably, the estimated signal fractions of intra‐voxel spectral components in the MD‐MRI distribution, corresponding to white and gray matter, along with the frequency‐dependent parameters, demonstrated high reproducibility. We identified bias between scan and rescan in some of the metrics, which we attribute to the time gap between repeated scans pointing to a long‐time progressive fixation effect. We compare our results with in vivo results from clinical scanners and show the reproducibility of diffusion frequency‐dependent metrics to benefit from the improved gradient hardware on our preclinical setup. Our results inform future micro‐imaging ex vivo studies of the reproducibility of MD‐MRI metrics and their dependence on fixation time.

Background: 40% of individuals aged over 65 years suffer memory loss with increasing evidence showing early or concomitant motor decline which might be warning sign for neurodegenerative diseases like Alzheimer's disease (AD). Gray matter (GM) temporal‐meta regions (ROIs) are affected early in aging/AD population, but little work has been done in conjunction with motor‐related regions. Current work (a) characterized cognitive and motor impairment in aging cohort; (b) assessed GM and WM microstructural changes using the mean apparent propagator (MAP) diffusion MRI model along with DTI; and (c) validated findings with standard MoCA screening. Method: 707 subjects (36 to 90 years) imaging dataset from Human Connectome Project‐Aging (HCP‐A) was used. NIH‐Fluid and walk‐endurance measures classified cognitive‐motor normal (CMN), cognitive impairment (CI), motor impairment (MI) and cognitive‐motor impairment (CMI) age‐matched groups using k‐means clustering. Five MAP‐MRI metrics (return to origin/axis/plane probability‐RTOP, RTAP, RTPP, non‐Gaussianity‐NG, and propagator anisotropy‐PA) and 4 DTI metrics (fractional anisotropy‐FA, axial diffusivity‐AD, radial diffusivity‐RD, and trace‐TR) were examined. Anatomical images were used to segment GM and WM tract regions that are related to temporal‐meta ROIs and motor function (Table 2). Linear regression model assessed GM and WM differences between the 4 groups: 𝑃𝑖=𝛽0+𝛽 _group *group+𝛽 _sex *𝑠𝑒𝑥+𝛽 _YOE *𝑌𝑂𝐸+𝛽 _site *𝑠𝑖𝑡𝑒+𝛽 _BMI *BMI, (𝑃𝑖 ‐ mean MRI ROI value for the i th ROI). Sex, years of education (YOE), body mass index (BMI) and site were co‐variates. False discovery rate corrected p ‐value < 0.05 was used. Result: Table 1 shows participant demographics and neurobehavioral test results. Examples of axial RTOP maps averaged across participants from the 4 groups at different age ranges are shown in Figure 1A. These maps indicate that the CMI group exhibits greater regional RTOP values, regardless of age. Regression results, shown in Figure 1B, reveal significantly higher RTAP/RTOP/RTPP and lower RD/TR values in the CMI group, compared with the CMN group in regions with overlapping cognitive and motor functions and motor specific regions. CMN compared to CI showed significant inverse results in caudate, pallidum and nigrostriatal labels. CMN compared to MI showed no significant difference. Conclusion: Concomitant motor and cognitive impairment lead to enhanced brain GM and WM changes in aging population.

INTRODUCTION: Dual cognitive–motor deficit in aging is a strong predictor of dementia, yet its effects on vulnerable gray matter region microstructure remain unexplored. METHODS: This study classified 582 individuals aged 36 to 90 into cognitive–motor deficit, isolated cognitive or motor deficit, and control groups. Microstructural differences in 27 temporal and motor‐related gray matter (GM) regions and white matter (WM) tracts were assessed using diffusion tensor imaging and mean apparent propagator, a technique well suited for GM analysis. RESULTS: We found widespread microstructural alterations among individuals with dual cognitive–motor deficit. These changes were not observed in isolated cognitive or motor deficits after multiple comparisons correction. DISCUSSION: Dual cognitive–motor deficit is associated with distinct microstructural features that are hypothesized to indicate reduced cellular density in temporal GM, decreased fiber coherence, and potential demyelination in WM tracts. These findings could help understand brain aging and facilitate interventions to slow neurodegeneration and delay dementia onset. Highlights: Dual cognitive–motor deficit strongly predicts dementia in older adults. Five hundred eighty‐two individuals were classified by cognitive and motor deficiency. Mean apparent propagator magnetic resonance imaging (MRI) and diffusion tensor imaging identified widespread microstructural brain alterations. Only the dual deficit showed significant gray matter and white matter differences after correction. Results support early detection of dementia via diffusion MRI microstructure metrics.

Despite the presence of significant Alzheimer's disease pathology, characterized by amyloid β (Aβ) plaques and phosphorylated tau (pTau) tangles, some cognitively unimpaired elderly individuals do not inevitably develop dementia. Cortical astroglial inflammation, a ubiquitous feature of symptomatic Alzheimer's disease, shows a strong correlation with cognitive impairment severity, highlighting the influence of factors beyond classical pathology. However, non-invasively imaging neuroinflammation, particularly astrogliosis, using MRI remains a significant challenge. Here we sought to address this challenge and to leverage multidimensional (MD) MRI, a powerful approach that combines relaxation with diffusion MR contrasts, to map cortical astrogliosis in the human brain by accessing sub-voxel information. Our goal was to investigate whether MD-MRI can map astroglial pathology in the cerebral cortex, and if so, whether it can distinguish cognitively normal state from dementia in the presence of hallmark Alzheimer's disease neuropathological changes. We adopted a multimodal approach by integrating histological and MRI analyses using human postmortem brain samples from two independent discovery and replication cohorts. Ex vivo cerebral cortical tissue specimens were derived from two groups—non-demented individuals with varying levels of postmortem Alzheimer's disease pathology and individuals with both Alzheimer's disease pathology and dementia—and scanned using 7 T MRI. We acquired and processed MD-MRI, diffusion tensor, and quantitative T1 and T2 MRI data, followed by histopathology on the same tissue. By co-registering MRI and microscopy data, we performed quantitative multimodal analyses, leveraging targeted immunostaining to assess MD-MRI sensitivity and specificity towards Aβ, pTau, and glial fibrillary acidic protein (GFAP), a marker for astrogliosis. Our discovery analysis reveals a distinct MD-MRI signature of cortical astrogliosis, enabling the creation of predictive maps for cognitive state amid Alzheimer's disease neuropathological changes. Multiple linear regression analysis linked histological values to MRI changes, revealing that the MD-MRI cortical astrogliosis biomarker was significantly associated with GFAP burden (standardized β = 0.658/0.709, pFDR < 0.0001), but not with Aβ (standardized β = 0.009/0.120, pFDR = 0.913/0.274) or pTau (standardized β = −0.196/0.158, pFDR = 0.051/0.251), for the discovery/replication groups, respectively. Conversely, none of the conventional MRI parameters showed significant associations with GFAP burden in the cortex. Finally, we showed that the MD-MRI-derived astrogliosis biomarker is the only MRI measure capable of predicting cognitive state. While the extent to which pathological glial activation contributes to neuronal damage and cognitive impairment in Alzheimer's disease is uncertain, developing a non-invasive imaging method to see its effects holds promise from a mechanistic perspective and as a potential predictor of cognitive outcomes.

INTRODUCTION: Dual cognitive-motor impairment in aging is a strong predictor of dementia, yet its effects on vulnerable gray matter regions microstructure remain unexplored. METHODS: This study classified 582 individuals aged 36–90 into cognitive-motor impairment, isolated cognitive or motor impairment, and control groups. Microstructural differences in 27 temporal and motor-related gray matter regions and white matter tracts were assessed using DTI and mean apparent propagator (MAP-MRI), a technique well-suited for gray matter analysis. RESULTS: We found widespread microstructural alterations in gray and white matter among individuals with dual cognitive-motor impairment. These changes were not observed in isolated cognitive or motor impairment after multiple comparisons correction. DISCUSSION: Dual cognitive-motor impairment is associated with reduced cellular density in temporal gray matter, decreased fiber coherence, and potential demyelination in white matter tracts, suggesting widespread microstructural disruption. These findings could help understand brain aging and facilitate interventions to slow neurodegeneration and delay dementia onset.