Tian, Jianqiao

Abstract Background White matter (WM) plays a dual role in interacting with tau pathology. There is an intricate dynamic relationship with WM acting as highways for tau spreading, while the increasing tau burden ultimately compromises WM health. We aimed at studying the associations between tau accumulation and WM integrity using longitudinal data with focus on sex differences. Method We included 374 participants who had longitudinal multishell diffusion MRI and flortaucipir PET (baseline mean age = 70.9 years, 52% male, 17% MCI/AD dementia) scans from the Mayo ADRC and Mayo Clinic Study of Aging. Rate of tau accumulation was calculated from the entorhinal cortex (ERC) and inferior temporal cortex (IT) SUVRs, with IT‐WM serving as the WM highway between the two regions. IT‐WM was defined by JHU “Eve” WM atlas, and its integrity was assessed using neurite density index (NDI), which reflects the packing density of neurites. Structural equation modeling (SEM) evaluated relationships between tau and WM metrics. Result At baseline, males and females showed no significant differences in tau SUVR or IT‐WM NDI ( p >0.05), though males had higher vascular risk and averaged two years older than females (Table 1). Figure 1a showed that baseline ERC‐tau influenced baseline IT‐tau, as well as both rate of ERC‐tau and baseline IT‐tau influenced rate of IT‐tau. After incorporating IT‐WM NDI as a mediator (Figure 1b), we discovered 1) baseline ERC‐tau influencing rate of IT‐tau accumulation through baseline IT‐WM NDI, suggesting the role of WM health in the rate of tau accumulation; 2) Rate of IT‐tau accumulation negatively affected follow‐up IT‐WM NDI suggesting the deterioration of WM health as tau accumulation progresses. These relationships were stronger in males compared to females. We also observed greater impact of vascular risk on WM health in males and greater rates of IT‐tau accumulation in females. Conclusion Our work sheds light on the mechanistic role of intact WM in tau accumulation (through dense microtubules and synaptic connections) and the deterioration of WM that is associated with higher rates of tau accumulation. We found interesting sex differences that warrant further investigation.

Abstract Background Aging and dementia are generally associated with lower metabolism in gray matter on FDG‐PET and have been widely studied. However, white matter (WM) metabolic changes remain understudied. We investigated the metabolic changes in WM associated with aging and dementia and their relationships with established Alzheimer's Disease (AD) biomarkers and cognitive outcomes. Method We analyzed data from 3,169 participants (72 ± 9 years; 53% male; 65% cognitively unimpaired) enrolled in the Mayo Clinic Study of Aging (MCSA) and the Alzheimer's Disease Neuroimaging Initiative (ADNI). All participants underwent baseline MRI, amyloid‐PET, and FDG‐PET scans, as well as neuropsychological evaluations, with at least one clinical follow‐up. Voxel‐wise multiple regression analysis identified WM metabolic changes associated with global cognition in cognitively unimpaired MCSA participants ( n = 1,754). Univariate analyses and structural equation models were applied to elucidate the driving factors underlying the WM metabolic changes in MCSA data. Multiple regression and linear mixed‐effects models evaluated the explanatory utility of WM metabolism for baseline global cognition and cognitive decline in both cohorts. In a subset of MCSA participants, we evaluated neurite density index from diffusion MRI as a surrogate of WM health and glial fibrillary acidic protein (GFAP) as a surrogate of neuroinflammation to understand underlying mechanisms. Result We identified an atypical WM signature (AWM‐Signature) in the anterior and superior corona radiata, where glucose metabolism increased as cognitive performance worsened (Figure 1). Hypermetabolism in AWM‐Signature was related to increased age, amyloidosis, vascular disease, and neuroinflammation, as well as decreasing WM neurite density in the same regions (Figure 2). Hypermetabolism in AWM‐Signature regions predicted faster future cognitive decline, even after accounting for demographics, amyloid, and vascular disease metrics. The results observed in the AWM‐Signature regions were consistently in the opposite direction of the associations seen with gray matter and typical WM regions. Conclusion We found unexpected metabolism increases in corona radiata, a hub that carries significant portion of the brain's neural traffic, with aging and disease which likely indicates a compensatory response. These changes may be useful cognitive resilience measures and require further investigation to understand their role in maintaining cognitive function.

AbstractBackgroundWhite matter (WM) damage is seen with neurodegenerative and cerebrovascular pathologies and contributes to cognitive dysfunction. We hypothesized that there are multiple disease progression patterns in WM microstructural changes related to aging and dementia, and these can be identified using SuStaIn (data‐driven clustering algorithm for disease subtype and stage discovery) on multishell diffusion MRI (NODDI) data. NODDI provides information on cellular tissue architecture – neurite density index (NDI) measures packing density of axons and dendrites, and free water fraction (FWF) measures unrestricted water, e.g., interstitial and cerebrospinal fluid.MethodWe identified 1136 participants (mean age=70 years, 51% male, 40% Aβ‐positive, 19% MCI/AD dementia) from the Mayo ADRC and Mayo Clinic Study of Aging. We analyzed NDI and FWF in 55 WM regions. We selected a subset of WM features that monotonically changed with clinical disease progression and then applied the Z‐score SuStaIn algorithm.ResultNDI in seven regions and FWF in six regions survived selection criteria (Figure 1). Noteworthy, the NDI regions were predominantly located in the temporal lobe, while FWF regions were primarily distributed in the frontal and subcortical regions. The SuStaIn algorithm discovered two stable clusters: the NDI‐First (Temporal) and FWF‐First (Frontal) groups (Figure 2). For the NDI‐First group, temporal lobe NDI changed early followed by frontal FWF. The FWF‐First group had the reverse order. There was clear phase separation based on which measure changed first. Stages of the NDI‐First group correlated better with meta‐ROI Tau‐PET SUVR and with MMSE than the FWF‐First group (p<0.01), suggesting that NDI‐First may be indicative of a primarily AD pathway.ConclusionGiven NDI changes are likely degenerative and FWF are suggested to be other processes such as neurodegeneration or inflammatory, the observed changes in the temporal and frontal regions may be due to distinct etiologies, i.e., neurodegenerative in the temporal lobes vs. vascular dysfunction often observed in the frontal lobes. The SuStaIn algorithm showed promise in discovering disease subtypes of WM microstructural changes. Further work is needed to understand the mechanistic basis of these observed changes and integrate WM changes for differential etiology identification and disease staging.

AbstractBackgroundWhite matter (WM) damage is seen with neurodegenerative and cerebrovascular pathologies and contributes to cognitive dysfunction. We hypothesized that there are multiple disease progression patterns in WM microstructural changes related to aging and dementia, and these can be identified using SuStaIn (data‐driven clustering algorithm for disease subtype and stage discovery) on multishell diffusion MRI (NODDI) data. NODDI provides information on cellular tissue architecture – neurite density index (NDI) measures packing density of axons and dendrites, and free water fraction (FWF) measures unrestricted water, e.g., interstitial and cerebrospinal fluid.MethodWe identified 1136 participants (mean age=70 years, 51% male, 40% Aß‐positive, 19% MCI/AD dementia) from the Mayo ADRC and Mayo Clinic Study of Aging. We analyzed NDI and FWF in 55 WM regions. We selected a subset of WM features that monotonically changed with clinical disease progression and then applied the Z‐score SuStaIn algorithm.ResultNDI in seven regions and FWF in six regions survived selection criteria (Figure 1). Noteworthy, the NDI regions were predominantly located in the temporal lobe, while FWF regions were primarily distributed in the frontal and subcortical regions. The SuStaIn algorithm discovered two stable clusters: the NDI‐First (Temporal) and FWF‐First (Frontal) groups (Figure 2). For the NDI‐First group, temporal lobe NDI changed early followed by frontal FWF. The FWF‐First group had the reverse order. There was clear phase separation based on which measure changed first. Stages of the NDI‐First group correlated better with meta‐ROI Tau‐PET SUVR and with MMSE than the FWF‐First group (p<0.01), suggesting that NDI‐First may be indicative of a primarily AD pathway.ConclusionGiven NDI changes are likely degenerative and FWF are suggested to be other processes such as neurodegeneration or inflammatory, the observed changes in the temporal and frontal regions may be due to distinct etiologies, i.e., neurodegenerative in the temporal lobes vs. vascular dysfunction often observed in the frontal lobes. The SuStaIn algorithm showed promise in discovering disease subtypes of WM microstructural changes. Further work is needed to understand the mechanistic basis of these observed changes and integrate WM changes for differential etiology identification and disease staging.

AbstractBackgroundWhite matter (WM) plays an important role in cognitive function and undergoes substantial changes due to aging and increase in AD and AD related dementia (AD/ADRD) pathologies. While recent work proposed measurement of global WM changes from diffusion MRI as biomarkers for Cerebrovascular disease (CVD) such as PSMD and free water, there needs to be a better understanding of WM changes due to AD/ADRD pathologies. The goal of our recent work was to understand white matter changes with CVD and AD – common pathologies in AD/ADRD ‐ utilizing data from Mayo Clinic Study of Aging and Mayo ADRC.MethodUsing diffusion MRI (dMRI) in 718 participants from Mayo Clinic Study of Aging (mean age = 71.1 years, 56% male), we investigated relationships between commonly used dMRI measures and vascular risk as well as global amyloid PET. In a subset of 233 participants who were amyloid‐PET positive and also competed Tau‐PET (flortaucipir) from Mayo Clinic Study of Aging and Mayo ADRC, we investigated associations between regional flortaucipir SUVRs in Braak stages and regional WM changes.ResultsWe found greater frontal lobe WM damage with increasing vascular risk across all dMRI measures. We previously validated the strong relationship between the genu of the corpus callosum (anterior interhemispheric connections) dMRI measurements made on antemortem scans and cerebrovascular pathology observed on postmortem tissue (Nguyen et. al. Acta Neuropathologica 2022) supporting the utility of genu of the corpus callosum dMRI measures as a potential CVD biomarker. Also, we found greater posterior WM damage with increasing amyloid burden. Among the 233 amyloid positive participants, we found spatially dependent WM degeneration associated with regional flortaucipir SUVRs in Braak stages with greater extent of posterior WM damage. Further, dMRI measurements of the WM damage provided complementary information about disease staging and progression of AD in addition to flortaucipir SUVR measurements.ConclusionsAnterior WM measurements may be more specific to CVD and posterior WM changes are seen with increasing AD pathological burden. Our results suggest that WM changes observed with CVD and typical AD may be different and have clinical utility for differential diagnosis and disease staging.

AbstractBackgroundThe dynamics of white matter (WM) changes are understudied in Alzheimer’s disease (AD). Leveraging tau deposition measured from Tau‐PET, our goal was to study the association between tau and white matter health using NODDI from multi‐shell diffusion MRI (dMRI). Specifically, we focused on the Neurite Density Index (NDI) from NODDI, which in WM captures the signal from axons.MethodWe identified 251 participants (126 cognitively normal, 58 MCI, 67 dementia) from the Mayo ADRC and Mayo Clinic Study of Aging with Aβ‐PET, Tau‐PET, and multi‐shell dMRI who were Aβ‐positive (mean age of 77 years and 55% male). We limited to Aβ‐positive participants because Aβ deposition accelerates tau deposition. For Tau‐PET, we estimated SUVRs from three regions corresponding to Braak I/II, III/IV, and V/VI (Figure 1.a). To understand the associations between Tau‐PET SUVRs at three Braak regions and NDIs at 30 candidate WM tracts, we implemented three analyses methods ‐ Pearson correlation, linear regression, and multilayer perceptron machine learning regression.ResultHigher tau burden in all three Braak regions was associated with lower neurite density in 10 significant WM tracts (Table 1). Interestingly, uncinate fasciculus (UNC) was found significantly associated with all three Braak regions. All three analysis methods consistently showed differential involvement of WM pathways for Braak staging such as ‐ Cingulum adjoining Hippocampus (CGH) for Braak I/II regions, Medial Temporal WM (MTWM) for Braak III/IV regions, and Inferior Occipital WM (IOWM) for Braak V/VI regions (Figure 1.b). The linear regression and machine learning regression models also suggested that WM measurements reflected 18‐35% of variance due to tau depositionConclusionWe found that CGH and UNC WM were the most impacted ROIs associated with Braak I/II regions as expected. UNC, directly connecting anterior temporal lobes to the orbitofrontal cortex, was a key WM tract identified as important for all Braak I‐VI regions and warrants further investigation in the study of AD progression. This study highlights the significant impact tau deposition may have on WM. This work also demonstrates the feasibility of using NODDI as an informative supplementary imaging modality for monitoring AD progression.