Influences of amyloid-ß and tau on white matter neurite alterations in dementia with Lewy bodies.

Dementia with Lewy bodies (DLB) is a neurodegenerative condition often co-occurring with Alzheimer's disease (AD) pathology. Characterizing white matter tissue microstructure using Neurite Orientation Dispersion and Density Imaging (NODDI) may help elucidate the biological underpinnings of white matter injury in individuals with DLB. In this study, diffusion tensor imaging (DTI) and NODDI metrics were compared in 45 patients within the dementia with Lewy bodies spectrum (mild cognitive impairment with Lewy bodies (n = 13) and probable dementia with Lewy bodies (n = 32)) against 45 matched controls using conditional logistic models. We evaluated the associations of tau and amyloid-ß with DTI and NODDI parameters and examined the correlations of AD-related white matter injury with Clinical Dementia Rating (CDR). Structural equation models (SEM) explored relationships among age, APOE ε4, amyloid-ß, tau, and white matter injury. The DLB spectrum group exhibited widespread white matter abnormalities, including reduced fractional anisotropy, increased mean diffusivity, and decreased neurite density index. Tau was significantly associated with limbic and temporal white matter injury, which was, in turn, associated with worse CDR. SEM revealed that amyloid-ß exerted indirect effects on white matter injury through tau. We observed widespread disruptions in white matter tracts in DLB that were not attributed to AD pathologies, likely due to α-synuclein-related injury. However, a fraction of the white matter injury could be attributed to AD pathology. Our findings underscore the impact of AD pathology on white matter integrity in DLB and highlight the utility of NODDI in elucidating the biological basis of white matter injury in DLB.

Can white matter hyperintensities based Fazekas visual assessment scales inform about Alzheimer's disease pathology in the population?

Background: White matter hyperintensities (WMH) are considered hallmark features of cerebral small vessel disease and have recently been linked to Alzheimer's disease pathology. Their distinct spatial distributions, namely periventricular versus deep WMH, may differ by underlying age-related and pathobiological processes contributing to cognitive decline. We aimed to identify the spatial patterns of WMH using the 4-scale Fazekas visual assessment and explore their differential association with age, vascular health, Alzheimer's imaging markers, namely amyloid and tau burden, and cognition. Because our study consisted of scans from GE and Siemens scanners with different resolutions, we also investigated inter-scanner reproducibility and combinability of WMH measurements on imaging. Methods: We identified 1144 participants from the Mayo Clinic Study of Aging consisting of older adults from Olmsted County, Minnesota with available structural magnetic resonance imaging (MRI), amyloid, and tau positron emission tomography (PET). WMH distribution patterns were assessed on FLAIR-MRI, both 2D axial and 3D, using Fazekas ratings of periventricular and deep WMH severity. We compared the association of periventricular and deep WMH scales with vascular risk factors, amyloid-PET and tau-PET standardized uptake value ratio, WMH volume, and cognition using Pearson partial correlation after adjusting for age. We also evaluated vendor compatibility and reproducibility of the Fazekas scales using intraclass correlations (ICC). Results: Periventricular and deep WMH measurements showed similar correlations with age, cardiometabolic conditions score (vascular risk), and cognition, (p < 0.001). Both periventricular WMH and deep WMH showed weak associations with amyloidosis (R = 0.07, p = < 0.001), and none with tau burden. We found substantial agreement between data from the two scanners for Fazekas measurements (ICC = 0.78). The automated WMH volume had high discriminating power for identifying participants with Fazekas ≥ 2 (area under curve = 0.97). Conclusion: Our study investigates risk factors underlying WMH spatial patterns and their impact on global cognition, with no discernible differences between periventricular and deep WMH. We observed minimal impact of amyloidosis on WMH severity. These findings, coupled with enhanced inter-scanner reproducibility of WMH data, suggest the combinability of inter-scanner data assessed by harmonized protocols in the context of vascular contributions to cognitive impairment and dementia biomarker research.

Quantitative susceptibility mapping from basal ganglia and related structures: correlation with disease severity in progressive supranuclear palsy.

OBJECTIVE: We examined whether mean magnetic susceptibility values from deep gray matter structures in patients with progressive supranuclear palsy (PSP) differed from those in patients with Parkinson's disease (PD) and healthy volunteers, and correlated with the PSP rating scale. METHODS: Head of caudate nucleus, putamen, globus pallidus, substantia nigra and red nucleus were the regions of interest. Mean susceptibility values from these regions in PSP patients were estimated using quantitative susceptibility mapping. Correlations with clinical severity of disease as measured by the PSP rating scale were examined. The mean susceptibility values were also compared with those from healthy volunteers and age- and disease duration-matched patients with PD. RESULTS: Data from 26 healthy volunteers, 26 patients with PD and 27 patients with PSP, were analysed. Patients with PSP had higher mean susceptibility values from all regions of interest when compared to both the other groups. The PSP rating scale scores correlated strongly with mean susceptibility values from the red nucleus and moderately with those from the putamen and substantia nigra. The scores did not correlate with mean susceptibility values from the caudate nucleus or globus pallidus. In patients with PD, the motor deficits correlated moderately with mean susceptibility values from substantia nigra. CONCLUSIONS: In patients with PSP, mean susceptibility values indicating the severity of mineralization of basal ganglia and related structures correlate with disease severity, the correlation of red nucleus being the strongest. Further studies are warranted to explore whether mean susceptibility values could serve as biomarkers for PSP.

Vascular risk, gait, behavioral, and plasma indicators of VCID.

INTRODUCTION: Cost-effective screening tools for vascular contributions to cognitive impairment and dementia (VCID) has significant implications. We evaluated non-imaging indicators of VCID using magnetic resonance imaging (MRI)-measured white matter (WM) damage and hypothesized that these indicators differ based on age. METHODS: In 745 participants from the Mayo Clinic Study of Aging (≥50 years of age) with serial WM assessments from diffusion MRI and fluid-attenuated inversion recovery (FLAIR)-MRI, we examined associations between baseline non-imaging indicators (demographics, vascular risk factors [VRFs], gait, behavioral, plasma glial fibrillary acidic protein [GFAP], and plasma neurofilament light chain [NfL]) and WM damage across three age tertiles. RESULTS: VRFs and gait were associated with diffusion changes even in low age strata. All measures (VRFs, gait, behavioral, plasma GFAP, plasma NfL) were associated with white matter hyperintensities (WMHs) but mainly in intermediate and high age strata. DISCUSSION: Non-imaging indicators of VCID were related to WM damage and may aid in screening participants and assessing outcomes for VCID. HIGHLIGHTS: Non-imaging indicators of VCID can aid in prediction of MRI-measured WM damage but their importance differed by age. Vascular risk and gait measures were associated with early VCID changes measured using diffusion MRI. Plasma markers explained variability in WMH across age strata. Most non-imaging measures explained variability in WMH and vascular WM scores in intermediate and older age groups. The framework developed here can be used to evaluate new non-imaging VCID indicators proposed in the future.

Evaluation of 'Normal' Cognitive Functions and Correlation With MRI Volumetry: Towards a Definition of Vascular Cognitive Impairment.

Introduction It is important to establish criteria to define vascular cognitive impairment (VCI) in India as VCI is an image-based diagnosis and magnetic resonance imaging (MRI) changes resulting from age with prevalent vascular risk factors may confound MRI interpretation. The objective of this study was to establish normative community data for MRI volumetry including white matter hyperintensity volume (WMHV), correlated with age-stratified cognitive scores and vascular risk factors (VRFs), in adults aged 40 years and above.  Methods We screened 2651 individuals without known neurological morbidity, living in Mumbai and nearby rural areas, using validated Marathi translations of Kolkata Cognitive Battery (KCB) and geriatric depression score (GDS). We stratified 1961 persons with GDS ≤9 by age and cognitive score, and randomly selected 10% from each subgroup for MRI brain volumetry. Crude volumes were standardized to reflect percentage of intracranial volume.  Results MRI volumetry studies were done in 199 individuals (F/M = 90/109; 73 with body mass index (BMI) ≥25; 44 hypertensives; 29 diabetics; mean cognitive score 76.3). Both grey and white matter volumes decreased with increasing age. WMHV increased with age and hypertension. Grey matter volume (GMV) decreased with increasing WMHV. Positive predictors of cognition included standardized hippocampal volume (HCV), urban living, education, and BMI, while WMHV and age were negative predictors. Urban dwellers had higher cognitive scores than rural, and, paradoxically, smaller HCV.  Conclusion In this study of MRI volumetry correlated with age, cognitive scores and VRFs, increasing age and WMHV predicted lower cognitive scores, whereas urban living and hippocampal volume predicted higher scores. Age and WMHV also correlated with decreasing GMV. Further study is warranted into sociodemographic and biological factors that mutually influence cognition and brain volumes, including nutritional and endocrine factors, especially at lower cognitive score bands. In this study, at the lower KCB score bins, the lack of laboratory data pertaining to nutritional and endocrine deficiencies is a drawback that reflects the logistical limitations of screening large populations at the community level. Our volumetric data which is age and cognition stratified, and takes into account the vascular risk factors associated, nevertheless constitutes important baseline data for the Indian population. Our findings could possibly contribute to the formulation of baseline criteria for defining VCI in India and could help in early diagnosis and control of cognitive decline and its key risk factors.

Advancing Tau-PET quantification in Alzheimer's disease with machine learning: introducing THETA, a novel tau summary measure.

Alzheimer's disease (AD) exhibits spatially heterogeneous 3R/4R tau pathology distributions across participants, making it a challenge to quantify extent of tau deposition. Utilizing Tau-PET from three independent cohorts, we trained and validated a machine learning model to identify visually positive Tau-PET scans from regional SUVR values and developed a novel summary measure, THETA, that accounts for heterogeneity in tau deposition. The model for identification of tau positivity achieved a balanced test accuracy of 95% and accuracy of ≥87% on the validation datasets. THETA captured heterogeneity of tau deposition, had better association with clinical measures, and corresponded better with visual assessments in comparison with the temporal meta-region-of-interest Tau-PET quantification methods. Our novel approach aids in identification of positive Tau-PET scans and provides a quantitative summary measure, THETA, that effectively captures the heterogeneous tau deposition seen in AD. The application of THETA for quantifying Tau-PET in AD exhibits great potential.

White Matter Degeneration Pathways Associated With Tau Deposition in Alzheimer Disease.

BACKGROUND AND OBJECTIVES: The dynamics of white matter (WM) changes are understudied in Alzheimer disease (AD). Our goal was to study the association between flortaucipir PET and WM health using neurite orientation dispersion and density imaging (NODDI) and evaluate its association with cognitive performance. Specifically, we focused on NODDI's Neurite Density Index (NDI), which aids in capturing axonal degeneration in WM and has greater specificity than single-shell diffusion MRI methods. METHOD: We estimated regional flortaucipir PET standard uptake value ratios (SUVRs) from 3 regions corresponding to Braak stage I, III/IV, and V/VI to capture the spatial distribution pattern of the 3R/4R tau in AD. Then, we evaluated the associations between these measurements and NDIs in 29 candidate WM tracts using Pearson correlation and multiple regression models. RESULTS: Based on 223 participants who were amyloid positive (mean age of 78 years and 57.0% male, 119 cognitively unimpaired, 56 mild cognitive impairment, and 48 dementia), the results showed that WM tracts NDI decreased with increasing regional Braak tau SUVRs. Of all the significant WM tracts, the uncinate fasciculus (r = -0.274 for Braak I, -0.311 for Braak III/IV, and -0.292 for Braak V/VI, p < 0.05) and cingulum adjoining hippocampus (r = -0.274, -0.288, -0.233, p < 0.05), both tracts anatomically connected to areas of early tau deposition, were consistently found to be within the top 5 distinguishing WM tracts associated with flortaucipir SUVRs. The increase in tau deposition measurable outside the medial temporal lobes in Braak III-VI was associated with a decrease in NDI in the middle and inferior temporal WM tracts. For cognitive performance, WM NDI had similar coefficients of determination (r 2 = 31%) as regional Braak flortaucipir SUVRs (29%), and together WM NDI and regional Braak flortaucipir SUVRs explained 46% of the variance in cognitive performance. DISCUSSION: We found spatially dependent WM degeneration associated with regional flortaucipir SUVRs in Braak stages, suggesting a spatial pattern in WM damage. NDI, a specific marker of axonal density, provides complementary information about disease staging and progression in addition to tau deposition. Measurements of WM changes are important for the mechanistic understanding of multifactorial pathways through which AD causes cognitive dysfunction.

Cross-scanner harmonization methods for structural MRI may need further work: A comparison study.

The clinical usefulness MRI biomarkers for aging and dementia studies relies on precise brain morphological measurements; however, scanner and/or protocol variations may introduce noise or bias. One approach to address this is post-acquisition scan harmonization. In this work, we evaluate deep learning (neural style transfer, CycleGAN and CGAN), histogram matching, and statistical (ComBat and LongComBat) methods. Participants who had been scanned on both GE and Siemens scanners (cross-sectional participants, known as Crossover (n = 113), and longitudinally scanned participants on both scanners (n = 454)) were used. The goal was to match GE MPRAGE (T1-weighted) scans to Siemens improved resolution MPRAGE scans. Harmonization was performed on raw native and preprocessed (resampled, affine transformed to template space) scans. Cortical thicknesses were measured using FreeSurfer (v.7.1.1). Distributions were checked using Kolmogorov-Smirnov tests. Intra-class correlation (ICC) was used to assess the degree of agreement in the Crossover datasets and annualized percent change in cortical thickness was calculated to evaluate the Longitudinal datasets. Prior to harmonization, the least agreement was found at the frontal pole (ICC = 0.72) for the raw native scans, and at caudal anterior cingulate (0.76) and frontal pole (0.54) for the preprocessed scans. Harmonization with NST, CycleGAN, and HM improved the ICCs of the preprocessed scans at the caudal anterior cingulate (>0.81) and frontal poles (>0.67). In the Longitudinal raw native scans, over- and under-estimations of cortical thickness were observed due to the changing of the scanners. ComBat matched the cortical thickness distributions throughout but was not able to increase the ICCs or remove the effects of scanner changeover in the Longitudinal datasets. CycleGAN and NST performed slightly better to address the cortical thickness variations between scanner change. However, none of the methods succeeded in harmonizing the Longitudinal dataset. CGAN was the worst performer for both datasets. In conclusion, the performance of the methods was overall similar and region dependent. Future research is needed to improve the existing approaches since none of them outperformed each other in terms of harmonizing the datasets at all ROIs. The findings of this study establish framework for future research into the scan harmonization problem.

Imaging biomarkers for early multiple system atrophy.

OBJECTIVE: To systematically evaluate structural MRI and diffusion MRI features for cross-sectional discrimination and tracking of longitudinal disease progression in early multiple system atrophy (MSA). METHODS: In a prospective, longitudinal study of synucleinopathies with imaging on 14 controls and 29 MSA patients recruited at an early disease stage (15 predominant cerebellar ataxia subtype or MSA-C and 14 predominant parkinsonism subtype or MSA-P), we computed regional morphometric and diffusion MRI features. We identified morphometric features by ranking them based on their ability to distinguish MSA-C from controls and MSA-P from controls and evaluated diffusion changes in these regions. For the top performing regions, we evaluated their utility for tracking longitudinal disease progression using imaging from 12-month follow-up and computed sample size estimates for a hypothetical clinical trial in MSA. We also computed these selected morphometric features in an independent validation dataset. RESULTS: We found that morphometric changes in the cerebellar white matter, brainstem, and pons can separate early MSA-C patients from controls both cross-sectionally and longitudinally (p < 0.01). The putamen and striatum, though useful for separating early MSA-P patients from control subjects at baseline, were not useful for tracking MSA disease progression. Cerebellum white matter diffusion changes aided in capturing early disease related degeneration in MSA. INTERPRETATION: Regardless of clinically predominant features at the time of MSA assessment, brainstem and cerebellar pathways progressively deteriorate with disease progression. Quantitative measurements of these regions are promising biomarkers for MSA diagnosis in early disease stage and potential surrogate markers for future MSA clinical trials.

Blood pressure changes impact corticospinal integrity and downstream gait and balance control.

Blood pressure (BP) plays an important role in white matter integrity. We sought to determine the role of intra-individual BP changes on white matter and evaluate the impact on gait speed and imbalance by sex. We identified 990 eligible participants in the population-based Mayo Clinic Study of Aging and analyzed fractional anisotropy (FA) in white matter regions. Using structural equation models (SEM), we assessed the effect of BP slope on corticospinal tract (CST) FA and downstream effects on gait speed and imbalance after age and sex effects. Of 990 participants, 438 (44%) were female with mean age of 76 years. In linear models predicting CST FA, a greater change in BP slope (0.0004; p = 0.026) and female sex (0.017; p < 0.001) were significant predictors of lower CST FA. SEMs showed that older age, female sex, and higher BP slope predicted lower CST FA, and lower CST FA predicted worse downstream motor control. Therefore, intra-individual BP slope and variability impact corticospinal tract microstructural properties of white matter with females having increased susceptibility to damage.

Neuropathologic scales of cerebrovascular disease associated with diffusion changes on MRI.

Summarizing the multiplicity and heterogeneity of cerebrovascular disease (CVD) features into a single measure has been difficult in both neuropathology and imaging studies. The objective of this work was to evaluate the association between neuroimaging surrogates of CVD and two available neuropathologic CVD scales in those with both antemortem imaging CVD measures and postmortem CVD evaluation. Individuals in the Mayo Clinic Study of Aging with MRI scans within 5 years of death (N = 51) were included. Antemortem CVD measures were computed from diffusion MRI (dMRI), FLAIR, and T2* GRE imaging modalities and compared with postmortem neuropathologic findings using Kalaria and Strozyk Scales. Of all the neuroimaging measures, both regional and global dMRI measures were associated with Kalaria and Strozyk Scales (p < 0.05) and modestly correlated with global cognitive performance. The major conclusions from this study were: (i) microstructural white matter injury measurements using dMRI may be meaningful surrogates of neuropathologic CVD scales, because they aid in capturing diffuse (and early) changes to white matter and secondary neurodegeneration due to lesions; (ii) vacuolation in the corpus callosum may be associated with white matter changes measured on antemortem dMRI imaging; (iii) Alzheimer's disease neuropathologic change did not associate with neuropathologic CVD scales; and (iv) future work should be focused on developing better quantitative measures utilizing dMRI to optimally assess CVD-related neuropathologic changes.

Causal structure discovery identifies risk factors and early brain markers related to evolution of white matter hyperintensities.

Our goal was to understand the complex relationship between age, sex, midlife risk factors, and early white matter changes measured by diffusion tensor imaging (DTI) and their role in the evolution of longitudinal white matter hyperintensities (WMH). We identified 1564 participants (1396 cognitively unimpaired, 151 mild cognitive impairment and 17 dementia participants) with age ranges of 30-90 years from the population-based sample of Mayo Clinic Study of Aging. We used computational causal structure discovery and regression analyses to evaluate the predictors of WMH and DTI, and to ascertain the mediating effect of DTI on WMH. We further derived causal graphs to understand the complex interrelationships between midlife protective factors, vascular risk factors, diffusion changes, and WMH. Older age, female sex, and hypertension were associated with higher baseline and progression of WMH as well as DTI measures (P ≤ 0.003). The effects of hypertension and sex on WMH were partially mediated by microstructural changes measured on DTI. Higher midlife physical activity was predictive of lower WMH through a direct impact on better white matter tract integrity as well as an indirect effect through reducing the risk of hypertension by lowering BMI. This study identified key risks factors, early brain changes, and pathways that may lead to the evolution of WMH.

White matter damage due to vascular, tau, and TDP-43 pathologies and its relevance to cognition.

Multi-compartment modelling of white matter microstructure using Neurite Orientation Dispersion and Density Imaging (NODDI) can provide information on white matter health through neurite density index and free water measures. We hypothesized that cerebrovascular disease, Alzheimer's disease, and TDP-43 proteinopathy would be associated with distinct NODDI readouts of white matter damage which would be informative for identifying the substrate for cognitive impairment. We identified two independent cohorts with multi-shell diffusion MRI, amyloid and tau PET, and cognitive assessments: specifically, a population-based cohort of 347 elderly randomly sampled from the Olmsted county, Minnesota, population and a clinical research-based cohort of 61 amyloid positive Alzheimer's dementia participants. We observed an increase in free water and decrease in neurite density using NODDI measures in the genu of the corpus callosum associated with vascular risk factors, which we refer to as the vascular white matter component. Tau PET signal reflective of 3R/4R tau deposition was associated with worsening neurite density index in the temporal white matter where we measured parahippocampal cingulum and inferior temporal white matter bundles. Worsening temporal white matter neurite density was associated with (antemortem confirmed) FDG TDP-43 signature. Post-mortem neuropathologic data on a small subset of this sample lend support to our findings. In the community-dwelling cohort where vascular disease was more prevalent, the NODDI vascular white matter component explained variability in global cognition (partial R2 of free water and neurite density = 8.3%) and MMSE performance (8.2%) which was comparable to amyloid PET (7.4% for global cognition and 6.6% for memory). In the AD dementia cohort, tau deposition was the greatest contributor to cognitive performance (9.6%), but there was also a non-trivial contribution of the temporal white matter component (8.5%) to cognitive performance. The differences observed between the two cohorts were reflective of their distinct clinical composition. White matter microstructural damage assessed using advanced diffusion models may add significant value for distinguishing the underlying substrate (whether cerebrovascular disease versus neurodegenerative disease caused by tau deposition or TDP-43 pathology) for cognitive impairment in older adults.

Relationship between Cerebral Perfusion on Arterial Spin Labeling (ASL) MRI with Brain Volumetry and Cognitive Performance in Mild Cognitive Impairment and Dementia due to Alzheimer's Disease.

CONTEXT: Cerebral blood flow (CBF) measurement using arterial spin labelling (ASL) MRI sequences has recently emerged as a prominent tool in dementia research. AIMS: To establish association between quantified regional cerebral perfusion and gray matter (GM) volumes with cognitive measures in mild cognitive impairment (MCI) and early Alzheimer's Dementia (AD), using three dimensional fast spin echo pseudo-continuous ASL MRI sequences. SETTINGS AND DESIGN: Hospital-based cross-sectional study. METHODS AND MATERIAL: Three age-matched groups, i.e., 21 cognitively normal healthy controls (HC), 20 MCI and 19 early AD patients diagnosed using neuropsychological tests and who consented for multimodality 3T MRI were recruited for the study. STATISTICAL ANALYSIS USED: Statistical parametric mapping and regions of interest (ROI) multivariate analysis of variance was used to ascertain differences between patients and controls on MRI-volumetry and ASL. Linear regression was used to assess relationship between CBF with GM atrophy and neuropsychological test measures. RESULTS: Compared to HC, patients with MCI and AD had significantly lower quantified perfusion in posterior cingulate and lingual gyri, over hippocampus in MCI, with no differences noted between MCI and AD. Atrophy over the middle temporal gyrus and hippocampus differentiated AD from MCI. No significant positive correlations were noted between perfusion and GM volumes in ROI with the exception of temporal neocortex. Significantly positive coefficient b-value (p < 0.01) were apparent between global cognition with CBF in precuneus, temporal neocortex and precuneus volume, with negative b-values noted between medial temporal CBF for global cognition and recall scores. CONCLUSIONS: ROI-based CBF measurements differentiated MCI and AD from HC; volumetry of medial and neocortical temporal GM separates AD from MCI. Correlations between CBF and neuropsychology are variable and require further longitudinal studies to gauge its predictive utility on cognitive trajectory in MCI.

Diffusion models reveal white matter microstructural changes with ageing, pathology and cognition.

White matter microstructure undergoes progressive changes during the lifespan, but the neurobiological underpinnings related to ageing and disease remains unclear. We used an advanced diffusion MRI, Neurite Orientation Dispersion and Density Imaging, to investigate the microstructural alterations due to demographics, common age-related pathological processes (amyloid, tau and white matter hyperintensities) and cognition. We also compared Neurite Orientation Dispersion and Density Imaging findings to the older Diffusion Tensor Imaging model-based findings. Three hundred and twenty-eight participants (264 cognitively unimpaired, 57 mild cognitive impairment and 7 dementia with a mean age of 68.3 ± 13.1 years) from the Mayo Clinic Study of Aging with multi-shell diffusion imaging, fluid attenuated inversion recovery MRI as well as amyloid and tau PET scans were included in this study. White matter tract level diffusion measures were calculated from Diffusion Tensor Imaging and Neurite Orientation Dispersion and Density Imaging. Pearson correlation and multiple linear regression analyses were performed with diffusion measures as the outcome and age, sex, education/occupation, white matter hyperintensities, amyloid and tau as predictors. Analyses were also performed with each diffusion MRI measure as a predictor of cognitive outcomes. Age and white matter hyperintensities were the strongest predictors of all white matter diffusion measures with low associations with amyloid and tau. However, neurite density decrease from Neurite Orientation Dispersion and Density Imaging was observed with amyloidosis specifically in the temporal lobes. White matter integrity (mean diffusivity and free water) in the corpus callosum showed the greatest associations with cognitive measures. All diffusion measures provided information about white matter ageing and white matter changes due to age-related pathological processes and were associated with cognition. Neurite orientation dispersion and density imaging and diffusion tensor imaging are two different diffusion models that provide distinct information about variation in white matter microstructural integrity. Neurite Orientation Dispersion and Density Imaging provides additional information about synaptic density, organization and free water content which may aid in providing mechanistic insights into disease progression.

Study of Symptomatic vs. Silent Brain Infarctions on MRI in Elderly Subjects.

Brain infarctions are closely associated with future risk of stroke and dementia. Our goal was to report (i) frequency and characteristics that differentiate symptomatic vs. silent brain infarctions (SBI) on MRI and (ii) frequency and location by vascular distribution (location of stroke by major vascular territories) in a population based sample. From Mayo Clinic Study of Aging, 347 participants (≥50 years) with infarcts detected on their first MRI were included. Infarct information was identified visually on a FLAIR MRI image and a vascular territory atlas was registered to the FLAIR image data in order to identify the arterial territory of infarction. We identified the subset with a clinical history of stroke based on medical chart review and used a logistic regression to evaluate the risk factors associated with greater probability of a symptomatic stroke vs. SBI. We found that 14% of all individuals with infarctions had a history of symptomatic stroke (Silent: n = 300, symptomatic: n = 47). Factors associated with a symptomatic vs. SBI were size which had an odds ratio of 3.07 (p < 0.001), greater frequency of hypertension (odds ratio of 4.12, p = 0.025) and alcohol history (odds ratio of 4.58, p = 0.012). The frequency of infarcts was greater in right hemisphere compared to the left for SBI. This was primarily driven by middle cerebral artery (MCA) infarcts (right = 60%, left = 40%, p = 0.005). While left hemisphere strokes are more common for symptomatic carotid disease and in clinical trials, right hemispheric infarcts may be more frequent in the SBI group.

White matter changes in empirically derived incident MCI subtypes in the Mayo Clinic Study of Aging.

INTRODUCTION: The aim of this study was to examine white matter hyperintensities (WMH) and fractional anisotropy (FA) in empirically derived incident mild cognitive impairment (MCI) subtypes. METHODS: We evaluated 188 participants with incident MCI in the Mayo Clinic Study of Aging (MCSA) identified as having one of four cluster-derived subtypes: subtle cognitive impairment, amnestic, dysnomic, and dysexecutive. We used linear regression models to evaluate whole brain and regional WMH volumes. We examined fractional anisotropy (FA) on a subset of 63 participants with diffusion tensor imaging. RESULTS: Amnestic and dysexecutive subtypes had higher WMH volumes in differing patterns than cognitively unimpaired; the dysexecutive subtype had higher WMH than subtle cognitive impairment. There was widespread WM degeneration in long association and commissural fibers in the amnestic, dysnomic, and dysexecutive subtypes, and corpus callosum FA accounted for significant variability in global cognition. DISCUSSION: White matter changes likely contribute to cognitive symptoms in incident MCI.

Reduced fractional anisotropy of the genu of the corpus callosum as a cerebrovascular disease marker and predictor of longitudinal cognition in MCI.

Our goal was to evaluate the utility of diffusion tensor imaging (DTI) for predicting future cognitive decline in mild cognitive impairment (MCI) in conjunction with Alzheimer's disease (AD) biomarkers (amyloid positron emission tomography and AD signature neurodegeneration) in 132 MCI individuals ≥60 year old with structural magnetic resonance imaging, DTI, amyloid positron emission tomography, and at least one clinical follow-up. We used mixed-effect models to evaluate the prognostic ability of fractional anisotropy of the genu of the corpus callosum (FA-Genu), as a cerebrovascular disease marker, for predicting cognitive decline along with AD biomarkers. We contrasted the value of white matter hyperintensities, a traditional cerebrovascular disease marker as well as FA in the hippocampal cingulum bundle with the FA-Genu models. FA-Genu significantly predicted cognitive decline even after accounting for AD biomarkers. WMH was not associated with cognitive decline in the model with both WMH and FA-Genu. DTI specifically FA-Genu provides unique complementary information to AD biomarkers and has significant utility for prediction of cognitive decline in MCI.

Does resting state functional connectivity differ between mild cognitive impairment and early Alzheimer's dementia?

PURPOSE: This study aimed to investigate differences in functional connectivity (FC) among different resting state networks (RSN) in clinically non-progressive mild cognitive impairment (MCI) and Alzheimer's disease (AD). METHODS: Using 3T MRI acquired resting-state functional MRI (rs-fMRI), we attempted identification of different RSN using independent component analysis (ICA) in amnestic-MCI, convertors to early AD and age-matched cognitively normal healthy controls. Regions of interest (ROI) that showed significant differences in connectivity on group ICA were selected as seeds for seed-voxel analysis. Group differences in FC for each network-connectivity map were entered into a general linear model with age, gender and total intra-cranial volume (TIV) as covariates. RESULTS: In this cross-sectional design 31 HC, 30 MCI and 30 MCI-convertors to early AD were evaluated. Seed-based analysis between AD and controls revealed reduced posterior connectivity within the default mode (DMN), dorsal attention (DAN) and antero-posterior connectivity with sensori-motor (SMN) networks. Reduced cerebellar connectivity of DMN and posterior connectivity within the frontal parietal network (FPN) separated AD from MCI. MCI-control comparisons revealed differences only on ICA. Positive correlation was observed between FC in DMN network clusters with verbal list-learning (r = 0.50) and recall scores (r = 0.51) in AD, the latter additionally demonstrating correlation with SMN clusters (r = 0.50). CONCLUSIONS: Widespread network hypo-connectivity is apparent in AD as opposed to MCI non-convertors in comparison to controls, along with positive correlation with memory scores. Reduced connectivity involving DMN and FPN helps to differentiate between AD and MCI-nonconvertors to dementia. Longitudinal studies are required into utility of these measures.