Abstract Background Vascular contributions to cognitive impairment and dementia (VCID) and Alzheimer’s disease (AD) are the two most common forms of dementia, with overlapping risk factors including cardiovascular risk factors such as hypertension and dyslipidemia. The etiology of both VCID and AD shows sex‐based differences, as well as sex‐based differences in cardiovascular risk factors. However, how sex differences influence AD and angiogenic biomarkers in older adults who have high cardiovascular risk factors is not known. Method AD and angiogenic biomarkers for VCID were measured from the plasma of a subgroup (n=96) of participants from the ‘risk reduction for Alzheimer’s disease’ (rrAD) two‐year clinical trial (NCT02913664; completed Jan. 2022; n=513 total participants). rrAD participants had a family history of dementia or subjective memory complaints, hypertension, and dyslipidemia. The subgroup in the study included 31 males and 65 females (aged: 71‐85). Baseline values for AD biomarkers: Total tau, pTau181, Aβ40, and Aβ42, angiogenic biomarkers: Tie‐2, VEGF‐A, VEGF‐C, VEGFR1, and VCID biomarkers: bFGF, VEGF‐D, PlGF were analyzed using Meso Scale Discovery (MSD). Nonparametric analysis evaluated the sex differences in biomarkers, linear regression evaluated the relationship between AD biomarkers and angiogenic biomarkers in both sexes. Result We found sex‐based differences in AD biomarkers such that females had a higher expression in Total tau and Total tau/Aβ42 (p=0.0021, p=0.0003, respectively) while pTau181 was higher in males (p=0.0216). pTau181/ Aβ42 and Aβ42/40 ratios showed no sex differences, nor did baseline angiogenic biomarkers. There was, however, a selective sex difference in the association between angiogenic and AD biomarkers. In females, Total tau is associated with VEGF‐D (R 2 = 0.0746, p=0.0277), and Tau/Aβ42 is associated with VEGF‐A and VEGFR‐1(R 2 = 0.0747, p=0.0275; R 2 = 0.0973, p=0.0114, respectively). In males, pTau181 and pTau181/Aβ42 are associated with VEGF‐D (R 2 = 0.2637, p=0.0031; R 2 = 0.1799, p=0.0174, respectively), Aβ42/Aβ40 is associated with VEGF‐C (R 2 = 0.1491, p=0.0319), and Tau/Aβ42 is associated with bFGF (R 2 = 0.1458, p=0.0340). Conclusion There is a selective sex‐based difference in plasma AD biomarkers and their association with angiogenic biomarkers in this preliminary cohort of older adults with high risks of developing Alzheimer’s disease and related dementias.
Abstract Background White matter (WM) free water (FW) is likely associated with cerebral small vessel disease (CSVD). FW is the fraction of unconstrained water within an image voxel, which can be estimated from diffusion‐weighted images. T2‐weighted Fluid‐Attenuated Inversion Recovery (FLAIR) white matter hyperintensity (WMH) is a widely used index to assess the damages caused by CSVD. It is critical to characterize how FW content is altered in WMH lesions. In this work, we proposed a data processing framework to assess FW distributions in WMH and normal‐appearing WM as well as in different WM fiber tracts. Method Single‐shell diffusion‐weighted image (SS‐DWI) and T2 FLAIR image data of 133 cognitively normal (CN) adults (Table 1) were obtained from the ADNI dataset (http://adni.loni.usc.edu). FW maps were generated from SS‐DWI images using the DIPY software package (https://dipy.org/). WMH lesions were identified with the lesion segmentation toolbox implemented in SPM. We co‐registered FW and WMH maps of individual subjects in the MNI space. Then we derived group WM fiber tracts using the tract‐based spatial statistics (TBSS). Finally, we examined the localization of WMH and FW in the major WM tracts. Result Figure 1 shows the FW map, FLAIR image, and WMH lesion from a representative subject. Figure 2 shows group average and individual FW distributions in WMH and normal‐appearing WM. Two sample T‐Test found that FW content in WMH is significantly higher than that in normal‐appearing WM (mean FW and standard deviation FW in WMH are 0.430 and 0.112, mean FW and standard deviation FW in normal‐appearing WM are 0.203 and 0.06, P < 1×10 ‐30 ). TBSS based analysis showed that: 1) WMH lesions are mainly located in the corona radiata, posterior thalamic radiation, tapetum, superior fronto‐occipital fasciculus, superior longitudinal fasciculus, corpus callosum, and sagittal stratum; 2) Compared with mean FW in normal‐appearing WM, elevated FW content is mainly at the fornix, tapetum, cingulum hippocampus, superior fronto‐occipital fasciculus, corpus callosum, sagittal stratum, and posterior thalamic radiation. Conclusion We developed an image processing pipeline to estimate FW changes in WMH lesions and normal‐appearing WM. Free water content is elevated in WMH lesions, which is likely driven by CSVD.
Abstract Background Existing work suggests that Alzheimer’s disease (AD) pathology can affect the direction and intensity of information signaling in functional brain regions. This study aims to explore how mild cognitive impairment (MCI) can affect the brain effective connectivity. Method We used an event‐related functional magnetic resonance imaging (fMRI) paradigm to compare patients with aMCI and healthy controls with normal cognition (NC) as they encoded 90 ecologically‐relevant object‐location associations (OLAs). Two additional OLAs, repeated a total of 45 times, served as control stimuli. Memory for these OLAs was assessed following a 1‐hour delay. The groups were well matched on demographics and brain volumetrics. A total of 44 right‐handed participants (19 NC, 25 aMCI, age mean: 71.5) completed this study. We evaluated the effective connectivity between all the possible functional brain region pairs using causalized convergent cross mapping (cCCM), which measures the intensity of directional information transfer between the brain regions. Result The region pairs where the effective connectivity (in terms of cCCM values) of NC and MCI exhibit the largest, statistically significant differences (p‐value < 0.05) are shown in Figure 1, and the result for the visuospatial network alone is shown in Figure 2. Our analysis shows that during the encoding of ecologically relevant object‐location associations, MCI demonstrated statistically significant reduction in effective connectivity (p‐value < 0.05) across some regions, but also showed increased effective connectivity across other regions. Conclusion The increased effective connectivity of MCI in some regions indicates the presence of detours in information transfer due to the weakening of connectivity in other regions and may reflect the compensatory mechanism of the brain.
Abstract Background Risk Reduction for Alzheimer’s Disease (rrAD) is a recently completed randomized controlled trial assessing the effects of aerobic exercise training and pharmacological cardio‐vascular interventions on neurocognitive function in hypertensive older adults with a family history of dementia or subjective cognitive decline. The comprehensive neuroimaging protocol included anatomical, functional, and physiological MRI scans, obtained at baseline and after two years of intervention. 420 older subjects (68.8±5.9 years) were scanned on five different 3T MRI scanners (two Siemens, two GE, and one Philips). Brain atrophy and ventricular enlargement are common in this elderly study population, leading to substantial deviations from the standard MNI152 template (25.02±4.9 years). Severe artifacts might occur when transforming non‐normative brains into MNI standard space. Using the high‐quality baseline data of this study, we aim to integrate images from multiple modalities to create a reference that is better suited to investigate older populations. Methods For each subject, we acquired T1 MPRAGE, T2 FLAIR, ASL, DTI, and resting‐state fMRI. Each scan underwent rigorous manual quality control and linear sequence‐specific high precision alignment to the T1 image. All T1 images then underwent non‐linear DARTEL registration into a common space to create cohort‐specific transformations. We then transformed all images into the new common space, namely rrAD420 ( Figure 1 ). Averages across subjects make up cohort‐ and modality‐specific templates ( Figure 2 ). Seed‐based and data‐driven decompositions of resting‐state fMRI and DTI data created functional and structural connectivity atlases ( Figure 3 ). Anatomical dual regression transforms common brain atlases from MNI into rrAD420 space, facilitating cross‐referencing. Results The rrAD420 templates and atlases integrate multimodal neuroimaging data of older populations into a common space, accounting for cohort‐specific distinctions, such as cortical atrophy and enlarged ventricles. It provides references to commonly used brain parcellations which are integrated with functional and structural atlases created from the rrAD cohort. Thus, rrAD420 provides a reference framework for comprehensive multimodal MRI analyses of elderly cohorts. Conclusions Through rrAD420, longitudinal multi‐modal image analyses can now be carried out using templates unique to this population. Furthermore, rrAD420 should also apply to older populations in general, facilitating data integration, and biomarker detection.
Abstract Background Diffusion magnetic resonance imaging (dMRI) permits characterizing differences in white matter microstructure associated with amnestic mild cognitive impairment (aMCI) and Alzheimer's dementia (AD). However, most dMRI measures aggregate signals across multiple axonal fiber populations with varying spatial orientations, which limits the sensitivity and specificity of clinical diagnosis. To overcome this shortcoming, we estimated fiber density (FD) measures, independently from crossing fiber populations, and extracellular cerebral spinal fluid (CSF). We hypothesized that aMCI and AD diagnoses are associated with differential patterns of FD changes in larger and smaller diameter fiber populations. Method We evaluated cross‐sectional dMRI data from 179 clinically characterized participants enrolled in the University of Michigan Memory and Aging Project. Image processing leveraged the MRtrix3 multi‐shell multi‐tissue fixel‐based analysis framework to estimate FD, separately for three fiber orientations, and CSF. Data analysis used multi‐block partial least squares correlation (PLS‐C) to estimate factors from multidirectional FD and CSF images correlated with differences between cognitively unimpaired (CU; n=98) and those diagnosed with aMCI (n=52) or AD (n=29). Result The PLS‐C model yielded three significant latent variables (LVs; Figure 1), reflecting patterns of both significant positive and negative associations between diagnosis and FD in crossing fibers. LV1 explained 80% of the differences from CU to aMCI to AD, demonstrating a stepwise reduction of FD and increased CSF with greater disease severity. However, LV2 and LV3 showed FD differences in smaller crossing fibers, distinguishing clinical diagnoses. Intriguingly, participants in the aMCI and AD groups showed different regions with increased or decreased FD in smaller crossing fibers, relative to CU participants. Pairwise PLS‐C models showed aMCI and AD diagnoses were associated with similar patterns of FD changes in smaller crossing fibers in overlapping regions. Conclusion The present study found distinctive patterns of white matter alterations that systematically differ across diagnostic severity in MCI and Alzheimer’s dementia. These results highlight the value of decomposing signals from crossing fibers as a sensitive neuroimaging correlate to clinical diagnosis. These findings challenge the common perspective that MCI and Alzheimer’s dementia are associated with monotonic declines in white matter integrity.
Abstract Background Diffusion magnetic resonance imaging (dMRI) permits characterizing differences in white matter microstructure associated with amnestic mild cognitive impairment (aMCI) and Alzheimer’s dementia (AD). However, most dMRI measures aggregate signals across multiple axonal fiber populations with varying spatial orientations, which limits the sensitivity and specificity of clinical diagnosis. To overcome this shortcoming, we estimated fiber density (FD) measures, independently from crossing fiber populations, and extracellular cerebral spinal fluid (CSF). We hypothesized that aMCI and AD diagnoses are associated with differential patterns of FD changes in larger and smaller diameter fiber populations. Method We evaluated cross‐sectional dMRI data from 179 clinically characterized participants enrolled in the University of Michigan Memory and Aging Project. Image processing leveraged the MRtrix3 multi‐shell multi‐tissue fixel‐based analysis framework to estimate FD, separately for three fiber orientations, and CSF. Data analysis used multi‐block partial least squares correlation (PLS‐C) to estimate factors from multidirectional FD and CSF images correlated with differences between cognitively unimpaired (CU; n = 98) and those diagnosed with aMCI (n = 52) or AD (n = 29). Result The PLS‐C model yielded three significant latent variables (LVs; Fig. 1), reflecting patterns of both significant positive and negative associations between diagnosis and FD in crossing fibers. LV1 explained 80% of the differences from CU to aMCI to AD, demonstrating a stepwise reduction of FD and increased CSF with greater disease severity. However, LV2 and LV3 showed FD differences in smaller crossing fibers, distinguishing clinical diagnoses. Intriguingly, participants in the aMCI and AD groups showed different regions with increased or decreased FD in smaller crossing fibers, relative to CU participants. Pairwise PLS‐C models showed aMCI and AD diagnoses were associated with similar patterns of FD changes in smaller crossing fibers in overlapping regions. Conclusion The present study found distinctive patterns of white matter alterations that systematically differ across diagnostic severity in MCI and Alzheimer’s dementia. These results highlight the value of decomposing signals from crossing fibers as a sensitive neuroimaging correlate to clinical diagnosis. These findings challenge the common perspective that MCI and Alzheimer’s dementia are associated with monotonic declines in white matter integrity.
Spontaneous fluctuations of resting-state functional connectivity have been studied in many ways, but grasping the complexity of brain activity has been difficult. Dimensional complexity measures, which are based on Eigenvalue (EV) spectrum analyses (e.g., Ω entropy) have been successfully applied to EEG data, but have not been fully evaluated on functional MRI recordings, because only through the recent introduction of fast multiband fMRI sequences, feasable temporal resolutions are reached. Combining the Eigenspectrum normalization of Ω entropy and the scalable architecture of the so called Multivariate Principal Subspace Entropy (MPSE) leads to a new complexity measure, namely normalized MPSE (nMPSE). It allows functional brain complexity analyses at varying levels of EV energy, independent from global shifts in data variance. Especially the restriction of the EV spectrum to the first dimensions, carrying the most prominent data variance, can act as a filter to reveal the most discriminant factors of dependent variables. Here we look at the effects of healthy aging on the dimensional complexity of brain activity. We employ a large open access dataset, providing a great number of high quality fast multiband recordings. Using nMPSE on whole brain, regional, network and searchlight approaches, we were able to find many age related changes, i.e., in sensorimotoric and right inferior frontal brain regions. Our results implicate that research on dimensional complexity of functional MRI recordings promises to be a unique resource for understanding brain function and for the extraction of biomarkers.
