Differential Regional Distribution of Juxtacortical White Matter Signal Abnormalities in Aging and Alzheimer's Disease

Regional White Matter Hyperintensities and Alzheimer's Disease Biomarkers Among Older Adults with Normal Cognition and Mild Cognitive Impairment

BACKGROUND

Alzheimer's disease (AD) frequently co-occurs with other brain pathologies. Recent studies suggest there may be a mechanistic link between AD and small vessel cerebrovascular disease (CVD), as opposed to simply the overlap of two disorders.

OBJECTIVE

We investigated the cross-sectional relationship between white matter hyperintensity (WMH) volumes (markers of CVD) and cerebrospinal fluid (CSF) biomarkers of AD.

METHODS

WMH volumes were assessed globally and regionally (i.e., frontal, parietal, temporal, occipital, and limbic). CSF AD biomarkers (i.e., Aβ 40, Aβ 42, Aβ 42/Aβ 40 ratio, phosphorylated tau-181 [p-tau181], and total tau [t-tau]) were measured among 152 non-demented individuals (134 cognitively unimpaired and 18 with mild cognitive impairment (MCI)).

RESULTS

Linear regression models showed that among all subjects, higher temporal WHM volumes were associated with AD biomarkers (higher levels of p-tau181, t-tau, and Aβ 40), particularly among APOE ɛ 4 carriers (independent of Aβ 42 levels). Higher vascular risk scores were associated with greater parietal and frontal WMH volumes (independent of CSF AD biomarker levels). Among subjects with MCI only, parietal WMH volumes were associated with a lower level of Aβ 42/Aβ 40. In addition, there was an association between higher global WMH volumes and higher CSF t-tau levels among younger participants versus older ones (∼<65 versus 65+ years), independent of Aβ 42/Aβ 40 and p-tau181.

CONCLUSION

These findings suggest that although WMH are primarily related to systemic vascular risk and neurodegeneration (i.e., t-tau), AD-specific pathways may contribute to the formation of WMH in a regionally-specific manner, with neurofibrillary tangles (i.e., p-tau) playing a role in temporal WMHs and amyloid (i.e., Aβ 42/Aβ 40) in parietal WMHs.

The canonical pattern of Alzheimer's disease atrophy is linked to white matter hyperintensities in normal controls, differently in normal controls compared to in AD

White matter signal abnormalities (WMSA), either hypo- or hyperintensities in MRI imaging, are considered a proxy of cerebrovascular pathology and contribute to, and modulate, the clinical presentation of Alzheimer's disease (AD), with cognitive dysfunction being apparent at lower levels of amyloid and/or tau pathology when lesions are present. To what extent the topography of cortical thinning associated with AD may be explained by WMSA remains unclear. Cortical thickness group difference maps and subgroup analyses show that the effect of WMSA on cortical thickness in cognitively normal participants has a higher overlap with the canonical pattern of AD, compared to AD participants. (Age and sex-matched group of 119 NC (AV45 PET negative, CDR = 0) versus 119 participants with AD (AV45 PET-positive, CDR > 0.5). The canonical patterns of cortical atrophy thought to be specific to Alzheimer's disease are strongly linked to cerebrovascular pathology supporting a reinterpretation of the classical models of AD suggesting that a part of the typical AD pattern is due to co-localized cortical loss before the onset of AD.

Conductance-Based Structural Brain Connectivity in Aging and Dementia

Background: Structural brain connectivity has been shown to be sensitive to the changes that the brain undergoes during Alzheimer's disease (AD) progression. Methods: In this work, we used our recently proposed structural connectivity quantification measure derived from diffusion magnetic resonance imaging, which accounts for both direct and indirect pathways, to quantify brain connectivity in dementia. We analyzed data from the second phase of Alzheimer's Disease Neuroimaging Initiative and third release in the Open Access Series of Imaging Studies data sets to derive relevant information for the study of the changes that the brain undergoes in AD. We also compared these data sets to the Human Connectome Project data set, as a reference, and eventually validated externally on two cohorts of the European DTI Study in Dementia database. Results: Our analysis shows expected trends of mean conductance with respect to age and cognitive scores, significant age prediction values in aging data, and regional effects centered among subcortical regions, and cingulate and temporal cortices. Discussion: Results indicate that the conductance measure has prediction potential, especially for age, that age and cognitive scores largely overlap, and that this measure could be used to study effects such as anticorrelation in structural connections. Impact statement This work presents a methodology and a set of analyses that open new possibilities in the study of healthy and pathological aging. The methodology used here is sensitive to direct and indirect pathways in deriving brain connectivity measures from diffusion-weighted magnetic resonance imaging, and therefore provides information that many state-of-the-art methods do not account for. As a result, this technique may provide the research community with ways to detect subtle effects of healthy aging and Alzheimer's disease.

The effect of vascular health factors on white matter microstructure mediates age-related differences in executive function performance

Even within healthy aging, vascular risk factors can detrimentally influence cognition, with executive functions (EF) particularly vulnerable. Fronto-parietal white matter (WM) connectivity in part, supports EF and may be particularly sensitive to vascular risk. Here, we utilized structural equation modeling in 184 healthy adults (aged 20-94 years of age) to test the hypotheses that: 1) fronto-parietal WM microstructure mediates age effects on EF; 2) higher blood pressure (BP) and white matter hyperintensity (WMH) burden influences this association. All participants underwent comprehensive cognitive and neuropsychological testing including tests of processing speed, executive function (with a focus on tasks that require switching and inhibition) and completed an MRI scanning session that included FLAIR imaging for semi-automated quantification of white matter hyperintensity burden and diffusion-weighted imaging for tractography. Structural equation models were specified with age (as a continuous variable) and blood pressure predicting within-tract WMH burden and fractional anisotropy predicting executive function and processing speed. Results indicated that fronto-parietal white matter of the genu of the corpus collosum, superior longitudinal fasciculus, and the inferior frontal occipital fasciculus (but not cortico-spinal tract) mediated the association between age and EF. Additionally, increased systolic blood pressure and white matter hyperintensity burden within these white matter tracts contribute to worsening white matter health and are important factors underlying age-brain-behavior associations. These findings suggest that aging brings about increases in both BP and WMH burden, which may be involved in the degradation of white matter connectivity and in turn, negatively impact executive functions as we age.

Identifying and characterizing different stages toward Alzheimer's disease using ordered core features and machine learning

Based on the joint HCPMMP parcellation method we developed before, which divides the cortical brain into 360 regions, the concept of ordered core features (OCF) is first proposed to reveal the functional brain connectivity relationship among different cohorts of Alzheimer's disease (AD), late mild cognitive impairment (LMCI), early mild cognitive impairment (EMCI) and healthy controls (HC). A set of core network features that change significantly under the specifically progressive relationship were extracted and used as supervised machine learning classifiers. The network nodes in this set mainly locate in the frontal lobe and insular, forming a narrow band, which are responsible for cognitive impairment as suggested by previous finding. By using these features, the accuracy ranged from 86.0% to 95.5% in binary classification between any pair of cohorts, higher than 70.1%-91.0% when using all network features. In multi-group classification, the average accuracy was 75% or 78% for HC, EMCI, LMCI or EMCI, LMCI, AD against baseline of 33%, and 53.3% for HC, EMCI, LMCI and AD against baseline of 25%. In addition, the recognition rate was lower when combining EMCI and LMCI patients into one group of mild cognitive impairment (MCI) for classification, suggesting that there exists a big difference between early and late MCI patients. This finding supports the EMCI/LMCI inclusion criteria introduced by ADNI based on neuropsychological assessments.

Segmentation of white matter hyperintensities on 18F-FDG PET/CT images with a generative adversarial network

PURPOSE

White matter hyperintensities (WMH) are typically segmented using MRI because WMH are hardly visible on ¹⁸F-FDG PET/CT. This retrospective study was conducted to segment WMH and estimate their volumes from ¹⁸F-FDG PET with a generative adversarial network (W_hyperGAN).

METHODS

We selected patients whose interval between MRI and FDG PET/CT scans was within 3 months, from January 2017 to December 2018, and classified them into mild, moderate, and severe groups by following the semiquantitative rating method of Fazekas. For each group, 50 patients were selected, and of them, we randomly selected 35 patients for training and 15 for testing. WMH were automatically segmented from FLAIR MRI with manual adjustment. Patches of WMH were extracted from ¹⁸F-FDG PET and segmented MRI. W_hyperGAN was compared with H-DenseUnet, a deep learning method widely used for segmentation tasks, for segmentation performance based on the dice similarity coefficient (DSC), recall, and average volume differences (AVD). For volume estimation, the predicted WMH volumes from PET were compared with ground truth volumes.

RESULTS

The DSC values were associated with WMH volumes on MRI. For volumes >60 mL, the DSC values were 0.751 for W_hyperGAN and 0.564 for H-DenseUnet. For volumes ≤60 mL, the DSC values rapidly decreased as the volume decreased (0.362 for W_hyperGAN vs. 0.237 for H-DenseUnet). For recall, W_hyperGAN achieved the highest value in the severe group (0.579 for W_hyperGAN vs. 0.509 for H-DenseUnet). For AVD, W_hyperGAN achieved the lowest score in the severe group (0.494 for W_hyperGAN vs. 0.941 for H-DenseUnet). For the WMH volume estimation, W_hyperGAN performed better than H-DenseUnet and yielded excellent correlation coefficients (r = 0.998, 0.983, and 0.908 in the severe, moderate, and mild group).

CONCLUSIONS

Although limited by visual analysis, the W_hyperGAN based can be used to automatically segment and estimate volumes of WMH from ¹⁸F-FDG PET/CT. This would increase the usefulness of ¹⁸F-FDG PET/CT for the evaluation of WMH in patients with cognitive impairment.

Common Brain Structural Alterations Associated with Cardiovascular Disease Risk Factors and Alzheimer's Dementia: Future Directions and Implications

Recent reports suggest declines in the age-specific risk of Alzheimer's dementia in higher income Western countries. At the same time, investigators believe that worldwide trends of increasing mid-life modifiable risk factors [e.g., cardiovascular disease (CVD) risk factors] coupled with the growth of the world's oldest age groups may nonetheless lead to an increase in Alzheimer's dementia. Thus, understanding the overlap in neuroanatomical profiles associated with CVD risk factors and AD may offer more relevant targets for investigating ways to reduce the growing dementia epidemic than current targets specific to isolated AD-related neuropathology. We hypothesized that a core group of common brain structural alterations exist between CVD risk factors and Alzheimer's dementia. Two co-authors conducted independent literature reviews in PubMed using search terms for CVD risk factor burden (separate searches for 'cardiovascular disease risk factors', 'hypertension', and 'Type 2 diabetes') and 'aging' or 'Alzheimer's dementia' with either 'grey matter volumes' or 'white matter'. Of studies that reported regionally localized results, we found support for our hypothesis, determining 23 regions commonly associated with both CVD risk factors and Alzheimer's dementia. Within this context, we outline future directions for research as well as larger cerebrovascular implications for these commonalities. Overall, this review supports previous as well as more recent calls for the consideration that both vascular and neurodegenerative factors contribute to the pathogenesis of dementia.

Prediction of clinical and biomarker conformed Alzheimer's disease and mild cognitive impairment from multi-feature brain structural MRI using age-correction from a large independent lifespan sample

Structural neuroimaging has been applied to the identification of individuals with Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, these methods are greatly impacted by age limiting their utility for detection of preclinical pathology. We built linear models for age based on multiple combined structural features using a large independent lifespan sample of 272 healthy adults across a wide age range from the Human Connectome Project Aging study. These models were then used to create a new support vector machine (SVM) training model in 136 AD and 268 control participants based on residues of fit from the expected age-effects relationship. Subsequent validation assessed the accuracy of the SVM model in new datasets. Finally, we applied the classifier to 276 individuals with MCI to evaluate prediction for early impairment and longitudinal cognitive change. The optimal 10-fold cross-validation accuracy was 93.07%, compared to 91.83% without age detrending. In the validation dataset, the classifier for AD obtained an accuracy of 84.85% (56/66), sensitivity of 85.36% (35/41) and specificity of 84% (21/25). Classification accuracy was improved when using the lifespan sample as opposed to the classification sample. Importantly, we observed cross-sectional greater AD specific biomarkers, as well as faster cognitive decline in MCI who were classified as more 'AD-like' (MCI-AD), and these effects were pronounced in individuals who were late MCI. The top five contributive features were volumes of left hippocampus, right hippocampus, left amygdala, the thickness of left and right middle temporal & parahippocampus gyrus. Linear detrending for age in SVM for combined structural features resulted in good performance for recognition of AD and AD-specific biomarkers, as well as prediction of MCI progression. Such procedures may be used in future work to enhance prediction in samples with atypical age distributions.

The effect of white matter signal abnormalities on default mode network connectivity in mild cognitive impairment

Regions within the default mode network (DMN) are particularly vulnerable to Alzheimer's disease pathology and mechanisms of DMN disruption in mild cognitive impairment (MCI) are still unclear. White matter lesions are presumed to be mechanistically linked to vascular dysfunction whereas cortical atrophy may be related to neurodegeneration. We examined associations between DMN seed‐based connectivity, white matter lesion load, and cortical atrophy in MCI and cognitively healthy controls. MCI showed decreased functional connectivity (FC) between the precuneus‐seed and bilateral lateral temporal cortex (LTC), medial prefrontal cortex (mPFC), posterior cingulate cortex, and inferior parietal lobe compared to those with controls. When controlling for white matter lesion volume, DMN connectivity differences between groups were diminished within bilateral LTC, although were significantly increased in the mPFC explained by significant regional associations between white matter lesion volume and DMN connectivity only in the MCI group. When controlling for cortical thickness, DMN FC was similarly decreased across both groups. These findings suggest that white matter lesions and cortical atrophy are differentially associated with alterations in FC patterns in MCI. Associations between white matter lesions and DMN connectivity in MCI further support at least a partial but important vascular contribution to age‐associated neural and cognitive impairment.

Associations between cerebral blood flow and structural and functional brain imaging measures in individuals with neuropsychologically defined mild cognitive impairment

Reduced cerebral blood flow (CBF), an indicator of neurovascular processes and metabolic demands, is a common finding in Alzheimer's disease. However, little is known about what contributes to CBF deficits in individuals with mild cognitive impairment (MCI). We examine regional CBF differences in 17 MCI compared with 21 age-matched cognitively healthy older adults. Next, we examined associations between CBF, white matter lesion (WML) volume, amplitude of low-frequency fluctuations, and cortical thickness to better understand whether altered CBF was detectable before other markers and the potential mechanistic underpinnings of CBF deficits in MCI. MCI had significantly reduced CBF, whereas cortical thickness and amplitude of low-frequency fluctuation were not affected. Reduced CBF was associated with the WML volume but not associated with other measures. Given the presumed vascular etiology of WML and relative worsening of vascular health in MCI, it may suggest CBF deficits result from early vascular as opposed to metabolic deficits in MCI. These findings may support vascular mechanisms as an underlying component of cognitive impairment.

The relationship between white matter hyperintensities and cognitive reference abilities across the life span

We examined the relationship between white matter hyperintensities (WMH) burden and performance on 4 reference abilities: episodic memory, perceptual speed, fluid reasoning, and vocabulary. Cross-sectional data of 486 healthy adults from 20 to 80 years old enrolled in an ongoing longitudinal study were analyzed. A piecewise regression across age identified an inflection point at 43 years old, where WMH total volume began to increase with age. Subsequent analyses focused on participants above that age (N = 351). WMH total volume had significant inverse correlations with perceptual speed and memory. Regional measures of WMH showed inverse correlations with all reference abilities. We performed principal component analysis of the regional WMH data to create a model of principal components regression. Parietal WMH regional volume burden mediated the relationship between age and perceptual speed in simple and multiple mediation models. The principal components regression pattern associated with perceptual speed also mediated the relationship between age and perceptual speed performance. These results across the extended adult life span help clarify the influence of WMH on cognitive aging.

Independent effects of white matter hyperintensities on cognitive, neuropsychiatric, and functional decline: a longitudinal investigation using the National Alzheimer's Coordinating Center Uniform Data Set

BACKGROUND

Longitudinal investigations are needed to improve understanding of the contributions of cerebral small vessel disease to the clinical manifestation of Alzheimer's disease, particularly in the early disease stages. This study leveraged the National Alzheimer's Coordinating Center Uniform Data Set to longitudinally examine the association between white matter hyperintensities and neuropsychological, neuropsychiatric, and functional decline among participants with normal cognition.

METHODS

The sample included 465 participants from the National Alzheimer's Coordinating Center Uniform Data Set who had quantitated volume of white matter hyperintensities from fluid-attenuated inversion recovery MRI, had normal cognition at the time of their MRI, and were administered the National Alzheimer's Coordinating Center Uniform Data Set neuropsychological test battery within 1 year of study evaluation and had at least two post-MRI time points of clinical data. Neuropsychiatric status was assessed by the Geriatric Depression Scale-15 and Neuropsychiatric Inventory-Questionnaire. Clinical Dementia Rating Sum of Boxes defined functional status. For participants subsequently diagnosed with mild cognitive impairment (MCI) or dementia, their impairment must have been attributed to Alzheimer's disease (AD) to evaluate the relationships between WMH and the clinical presentation of AD.

RESULTS

Of the 465 participants, 56 converted to MCI or AD dementia (average follow-up = 5 years). Among the 465 participants, generalized estimating equations controlling for age, sex, race, education, APOE ε4, and total brain and hippocampal volume showed that higher baseline log-white matter hyperintensities predicted accelerated decline on the following neuropsychological tests in rank order of effect size: Trails B (p < 0.01), Digit Symbol Coding (p < 0.01), Logical Memory Immediate Recall (p = 0.02), Trail Making A (p < 0.01), and Semantic Fluency (p < 0.01). White matter hyperintensities predicted increases in Clinical Dementia Rating Sum of Boxes (p < 0.01) and Geriatric Depression Scale-15 scores (p = 0.01). Effect sizes were comparable to total brain and hippocampal volume. White matter hyperintensities did not predict diagnostic conversion. All effects also remained after including individuals with non-AD suspected etiologies for those who converted to MCI or dementia.

CONCLUSIONS

In this baseline cognitively normal sample, greater white matter hyperintensities were associated with accelerated cognitive, neuropsychiatric, and functional decline independent of traditional risk factors and MRI biomarkers for Alzheimer's disease.

A Clinicopathological Investigation of White Matter Hyperintensities and Alzheimer's Disease Neuropathology

BACKGROUND

White matter hyperintensities (WMH) on magnetic resonance imaging (MRI) have been postulated to be a core feature of Alzheimer's disease. Clinicopathological studies are needed to elucidate and confirm this possibility.

OBJECTIVE

This study examined: 1) the association between antemortem WMH and autopsy-confirmed Alzheimer's disease neuropathology (ADNP), 2) the relationship between WMH and dementia in participants with ADNP, and 3) the relationships among cerebrovascular disease, WMH, and ADNP.

METHODS

The sample included 82 participants from the National Alzheimer's Coordinating Center's Data Sets who had quantitated volume of WMH from antemortem FLAIR MRI and available neuropathological data. The Clinical Dementia Rating (CDR) scale (from MRI visit) operationalized dementia status. ADNP+ was defined by moderate to frequent neuritic plaques and Braak stage III-VI at autopsy. Cerebrovascular disease neuropathology included infarcts or lacunes, microinfarcts, arteriolosclerosis, atherosclerosis, and cerebral amyloid angiopathy.

RESULTS

60/82 participants were ADNP+. Greater volume of WMH predicted increased odds for ADNP (p = 0.037). In ADNP+ participants, greater WMH corresponded with increased odds for dementia (CDR≥1; p = 0.038). WMH predicted cerebral amyloid angiopathy, microinfarcts, infarcts, and lacunes (ps < 0.04). ADNP+ participants were more likely to have moderate-severe arteriolosclerosis and cerebral amyloid angiopathy compared to ADNP-participants (ps < 0.04).

CONCLUSIONS

This study found a direct association between total volume of WMH and increased odds for having ADNP. In patients with Alzheimer's disease, FLAIR MRI WMH may be able to provide key insight into disease severity and progression. The association between WMH and ADNP may be explained by underlying cerebrovascular disease.

Connectome-derived diffusion characteristics of the fornix in Alzheimer's disease

The fornix bundle is a major white matter pathway of the hippocampus. While volume of the hippocampus has been a primary imaging biomarker of Alzheimer's disease progression, recent research has suggested that the volume and microstructural characteristics of the fornix bundle connecting the hippocampus could add relevant information for diagnosing and staging Alzheimer's disease. Using a robust fornix bundle isolation technique in native diffusion space, this study investigated whether diffusion measurements of the fornix differed between normal older adults and Alzheimer's disease patients when controlling for volume measurements. Data were collected using high gradient multi-shell diffusion-weighted MRI from a Siemens CONNECTOM scanner in 23 Alzheimer's disease and 23 age- and sex-matched control older adults (age range = 53–92). These data were used to reconstruct a continuous fornix bundle in every participant's native diffusion space, from which tract-derived volumetric and diffusion metrics were extracted and compared between groups. Diffusion metrics included those from a tensor model and from a generalized q-sampling imaging model. Results showed no significant differences in tract-derived fornix volumes but did show altered diffusion metrics within tissue classified as the fornix in the Alzheimer's disease group. Comparisons to a manual tracing method indicated the same pattern of results and high correlations between the methods. These results suggest that in Alzheimer's disease, diffusion characteristics may provide more sensitive measures of fornix degeneration than do volume measures and may be a potential early marker for loss of medial temporal lobe connectivity.

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An enhanced method for measurement of continuous fornix bundles is described.

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Diffusion characteristics of the fornix were degraded in Alzheimer's disease.

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Alzheimer's disease primarily affected the crus and body of the fornix.

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Diffusion differences were observed controlling for fornix volume differences.

White matter abnormalities and cognition in patients with conflicting diagnoses and CSF profiles

OBJECTIVE

To determine whether white matter changes influence progression of cognitive decline in individuals with clinically diagnosed Alzheimer disease (AD) and differing biomarker profiles.

METHODS

Two hundred thirty-six individuals from the Alzheimer's Disease Neuroimaging Initiative database with clinical diagnoses of cognitively normal older adult (older controls [OCs]), mild cognitive impairment, and AD were studied. Support vector machine experiments were first performed to determine the utility of various biomarkers for classifying individuals by clinical diagnosis. General linear models were implemented to assess the relationships between CSF measures of β-amyloid 1-42, phosphorylated tau_181p, and MRI-based white matter signal abnormality (WMSA) volumes and cognitive decline. Analyses were performed across all patients as well as within subgroups of individuals that were defined by clinical cutoff points for both CSF measures.

RESULTS

CSF biomarkers alone classified individuals with AD vs OCs with 82% accuracy, and the addition of WMSA did not enhance this. Both CSF biomarkers as well as WMSA volume significantly contributed to predicting cognitive decline in executive and memory domains when assessed across all 236 individuals. In individuals with pathologic levels of both CSF biomarkers, WMSA only significantly contributed to models of future executive function decline. In individuals with subpathologic CSF biomarker levels (levels similar to those in OC individuals), WMSA significantly contributed to prediction of memory decline and were the sole significant predictor of executive function decline.

CONCLUSIONS

WMSA hold additional predictive power regarding cognitive progression in older individuals and are most effective as biomarkers in individuals who are cognitively impaired but do not fit the expected CSF biomarker profile of AD.

White matter signal abnormalities in former National Football League players

Introduction

Later-life brain alterations in former tackle football players are poorly understood, particularly regarding their relationship with repetitive head impacts (RHIs) and clinical function. We examined white matter signal abnormalities (WMSAs) and their association with RHIs and clinical function in former National Football League (NFL) players.

Methods

Eighty-six clinically symptomatic former NFL players and 23 same-age reportedly asymptomatic controls without head trauma exposure underwent magnetic resonance imaging and neuropsychological testing. FreeSurfer calculated WMSAs. A cumulative head impact index quantified RHIs.

Results

In former NFL players, increased volume of WMSAs was associated with higher cumulative head impact index scores (P = .043) and worse psychomotor speed and executive function (P = .015). Although former NFL players had greater WMSA volume than controls (P = .046), these findings are inconclusive due to recruitment of controls based on lack of clinical symptoms and head trauma exposure.

Discussion

In former NFL players, WMSAs may reflect long-term microvascular and nonmicrovascular pathologies from RHIs that negatively impact cognition.

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Repetitive head impact exposure was positively associated with WMSAs in former NFL players.

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In former NFL players, greater WMSAs was associated with worse psychomotor speed and executive function.

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The pathologies of WMSAs may contribute to the clinical presentation of chronic traumatic encephalopathy.