Correlations between Gray Matter and White Matter Degeneration in Pure Alzheimer's Disease, Pure Subcortical Vascular Dementia, and Mixed Dementia

The hippocampus as a structural and functional network epicentre for distant cortical thinning in neurocognitive aging

Alterations in grey matter (GM) and white matter (WM) are associated with memory impairment across the neurocognitive aging spectrum and theorised to spread throughout brain networks. Functional and structural connectivity (FC,SC) may explain widespread atrophy. We tested the effect of SC and FC to the hippocampus on cortical thickness (CT) of connected areas. In 419 (223 F) participants (agemean=73 ± 8) from the Alzheimer's Disease Neuroimaging Initiative, cortical regions associated with memory (Rey Auditory Verbal Learning Test) were identified using Lasso regression. Two structural equation models (SEM), for SC and resting-state FC, were fitted including CT areas, and SC and FC to the left and right hippocampus (LHIP,RHIP). LHIP (β=-0.150,p=<.001) and RHIP (β=-0.139,p=<.001) SC predicted left temporopolar/rhinal CT; RHIP SC predicted right temporopolar/rhinal CT (β=-0.191,p=<.001). LHIP SC predicted right fusiform/parahippocampal (β=-0.104,p=.011) and intraparietal sulcus/superior parietal CT (β=0.101,p=.028). Increased RHIP FC predicted higher left inferior parietal CT (β=0.132,p=.042) while increased LHIP FC predicted lower right fusiform/parahippocampal CT (β=-0.97; p=.023). The hippocampi may be epicentres for cortical thinning through disrupted connectivity.

Decoding the best automated segmentation tools for vascular white matter hyperintensities in the aging brain: a clinician's guide to precision and purpose

White matter hyperintensities of vascular origin (WMH) are commonly found in individuals over 60 and increase in prevalence with age. The significance of WMH is well-documented, with strong associations with cognitive impairment, risk of stroke, mental health, and brain structure deterioration. Consequently, careful monitoring is crucial for the early identification and management of individuals at risk. Luckily, WMH are detectable and quantifiable on standard MRI through visual assessment scales, but it is time-consuming and has high rater variability. Addressing this issue, the main aim of our study is to decipher the utility of quantitative measures of WMH, assessed with automatic tools, in establishing risk profiles for cerebrovascular deterioration. For this purpose, first, we work to determine the most precise WMH segmentation open access tool compared to clinician manual segmentations (LST-LPA, LST-LGA, SAMSEG, and BIANCA), offering insights into methodology and usability to balance clinical precision with practical application. The results indicated that supervised algorithms (LST-LPA and BIANCA) were superior, particularly in detecting small WMH, and can improve their consistency when used in parallel with unsupervised tools (LST-LGA and SAMSEG). Additionally, to investigate the behavior and real clinical utility of these tools, we tested them in a real-world scenario (N = 300; age > 50 y.o. and MMSE > 26), proposing an imaging biomarker for moderate vascular damage. The results confirmed its capacity to effectively identify individuals at risk comparing the cognitive and brain structural profiles of cognitively healthy adults above and below the resulted threshold.

Motoric Cognitive Risk Syndrome as a Predictor of Adverse Health Outcomes: A Systematic Review and Meta-Analysis

INTRODUCTION

Motoric cognitive risk syndrome (MCR) is a newly proposed pre-dementia syndrome characterized by subjective cognitive complaints (SCCs) and slow gait (SG). Increasing evidence links MCR to several adverse health outcomes, but the specific relationship between MCR and the risk of frailty, Alzheimer's disease (AD), and vascular dementia (VaD) remains unclear. Additionally, literature lacks analysis of MCR's components and associated health outcomes, complicating risk identification. This systematic review and meta-analysis aimed to provide a comprehensive overview of MCR's predictive value for adverse health outcomes.

METHODS

Relevant cross-sectional, cohort, and longitudinal studies examining the association between MCR and adverse health outcomes were extracted from ten electronic databases. The Newcastle-Ottawa Scale (NOS) and modified NOS were used to assess the risk of bias in studies included in the analysis. Relative ratios (RRs) and 95% confidence intervals (CIs) were pooled for outcomes associated with MCR.

RESULTS

Twenty-eight longitudinal or cohort studies and four cross-sectional studies with 1,224,569 participants were included in the final analysis. The risk of bias in all included studies was rated as low or moderate. Pooled analysis of RR indicated that MCR had a greater probability of increased the risk of dementia (adjusted RR = 2.02; 95% CI = 1.94-2.11), cognitive impairment (adjusted RR = 1.72; 95% CI = 1.49-1.99), falls (adjusted RR = 1.32; 95% CI = 1.17-1.50), mortality (adjusted RR = 1.66; 95% CI = 1.32-2.10), and hospitalization (adjusted RR = 1.46; 95% CI = 1.16-1.84); MCR had more prominent predictive efficacy for AD (adjusted RR = 2.23; 95% CI = 1.81-2.76) compared to VaD (adjusted RR = 3.78; 95% CI = 0.49-28.95), while excluding analyses from the study that utilized the timed-up-and-go test and one-leg-standing to evaluate gait speed. One study examined the association between MCR and disability (hazard ratios [HR] = 1.69; 95% CI = 1.08-2.02) and frailty (OR = 5.53; 95% CI = 1.46-20.89). SG was a stronger predictor of the risk for dementia and falls than SCC (adjusted RR = 1.22; 95% CI = 1.11-1.34 vs. adjusted RR = 1.19; 95% CI = 1.03-1.38).

CONCLUSION

MCR increases the risk of developing any discussed adverse health outcomes, and the predictive value for AD is superior to VaD. Additionally, SG is a stronger predictor of dementia and falls than SCC. Therefore, MCR should be routinely assessed among adults to prevent poor prognosis and provide evidence to support future targeted interventions.

Constructing hierarchical attentive functional brain networks for early AD diagnosis

Analyzing functional brain networks (FBN) with deep learning has demonstrated great potential for brain disorder diagnosis. The conventional construction of FBN is typically conducted at a single scale with a predefined brain region atlas. However, numerous studies have identified that the structure and function of the brain are hierarchically organized in nature. This urges the need of representing FBN in a hierarchical manner for more effective analysis of the complementary diagnostic insights at different scales. To this end, this paper proposes to build hierarchical FBNs adaptively within the Transformer framework. Specifically, a sparse attention-based node-merging module is designed to work alongside the conventional network feature extraction modules in each layer. The proposed module generates coarser nodes for further FBN construction and analysis by combining fine-grained nodes. By stacking multiple such layers, a hierarchical representation of FBN can be adaptively learned in an end-to-end manner. The hierarchical structure can not only integrate the complementary information from multiscale FBN for joint analysis, but also reduce the model complexity due to decreasing node sizes. Moreover, this paper argues that the nodes defined by the existing atlases are not necessarily the optimal starting level to build FBN hierarchy and exploring finer nodes may further enrich the FBN representation. In this regard, each predefined node in an atlas is split into multiple sub-nodes, overcoming the scale limitation of the existing atlases. Extensive experiments conducted on various data sets consistently demonstrate the superior performance of the proposed method over the competing methods.

Gray and White Matter Voxel-Based Morphometry of Alzheimer's Disease With and Without Significant Cerebrovascular Pathologies

Alzheimer's disease (AD) is the most common type of dementia, and AD individuals often present significant cerebrovascular disease (CVD) symptomology. AD with significant levels of CVD is frequently labeled mixed dementia (or sometimes AD-CVD), and the differentiation of these two neuropathologies (AD, AD-CVD) from each other is challenging, especially at early stages. In this study, we compared the gray matter (GM) and white matter (WM) volumes in AD (n = 83) and AD-CVD (n = 37) individuals compared with those of cognitively healthy controls (n = 85) using voxel-based morphometry (VBM) of their MRI scans. The control individuals, matched for age and sex with our two dementia groups, were taken from the ADNI. The VBM analysis showed widespread patterns of significantly lower GM and WM volume in both dementia groups compared to the control group (P < .05, family-wise error corrected). While comparing with AD-CVD, the AD group mainly demonstrated a trend of lower volumes in the GM of the left putamen and right hippocampus and WM of the right thalamus (uncorrected P < .005 with cluster threshold, K = 10). The AD-CVD group relative to AD tended to present lower GM and WM volumes, mainly in the cerebellar lobules and right brainstem regions, respectively (uncorrected P < .005 with cluster threshold, K = 10). Although finding a discriminatory feature in structural MRI data between AD and AD-CVD neuropathologies is challenging, these results provide preliminary evidence that demands further investigation in a larger sample size.

Self-supervised multimodal learning for group inferences from MRI data: Discovering disorder-relevant brain regions and multimodal links

In recent years, deep learning approaches have gained significant attention in predicting brain disorders using neuroimaging data. However, conventional methods often rely on single-modality data and supervised models, which provide only a limited perspective of the intricacies of the highly complex brain. Moreover, the scarcity of accurate diagnostic labels in clinical settings hinders the applicability of the supervised models. To address these limitations, we propose a novel self-supervised framework for extracting multiple representations from multimodal neuroimaging data to enhance group inferences and enable analysis without resorting to labeled data during pre-training. Our approach leverages Deep InfoMax (DIM), a self-supervised methodology renowned for its efficacy in learning representations by estimating mutual information without the need for explicit labels. While DIM has shown promise in predicting brain disorders from single-modality MRI data, its potential for multimodal data remains untapped. This work extends DIM to multimodal neuroimaging data, allowing us to identify disorder-relevant brain regions and explore multimodal links. We present compelling evidence of the efficacy of our multimodal DIM analysis in uncovering disorder-relevant brain regions, including the hippocampus, caudate, insula, - and multimodal links with the thalamus, precuneus, and subthalamus hypothalamus. Our self-supervised representations demonstrate promising capabilities in predicting the presence of brain disorders across a spectrum of Alzheimer's phenotypes. Comparative evaluations against state-of-the-art unsupervised methods based on autoencoders, canonical correlation analysis, and supervised models highlight the superiority of our proposed method in achieving improved classification performance, capturing joint information, and interpretability capabilities. The computational efficiency of the decoder-free strategy enhances its practical utility, as it saves compute resources without compromising performance. This work offers a significant step forward in addressing the challenge of understanding multimodal links in complex brain disorders, with potential applications in neuroimaging research and clinical diagnosis.

Regional contribution of vascular dysfunction in white matter dementia: clinical and neuropathological insights

The maintenance of adequate blood supply and vascular integrity is fundamental to ensure cerebral function. A wide range of studies report vascular dysfunction in white matter dementias, a group of cerebral disorders characterized by substantial white matter damage in the brain leading to cognitive impairment. Despite recent advances in imaging, the contribution of vascular-specific regional alterations in white matter dementia has been not extensively reviewed. First, we present an overview of the main components of the vascular system involved in the maintenance of brain function, modulation of cerebral blood flow and integrity of the blood-brain barrier in the healthy brain and during aging. Second, we review the regional contribution of cerebral blood flow and blood-brain barrier disturbances in the pathogenesis of three distinct conditions: the archetypal white matter predominant neurocognitive dementia that is vascular dementia, a neuroinflammatory predominant disease (multiple sclerosis) and a neurodegenerative predominant disease (Alzheimer's). Finally, we then examine the shared landscape of vascular dysfunction in white matter dementia. By emphasizing the involvement of vascular dysfunction in the white matter, we put forward a hypothetical map of vascular dysfunction during disease-specific progression to guide future research aimed to improve diagnostics and facilitate the development of tailored therapies.

Weak Social Networks in Late Life Predict Incident Alzheimer's Disease: The Kuakini Honolulu-Asia Aging Study

BACKGROUND

We assessed 10-year longitudinal associations between late-life social networks and incidence of all-cause dementia (ACD), Alzheimer's disease (AD), and vascular dementia (VaD) in Japanese-American men.

METHODS

We prospectively analyzed, from baseline (1991-1993) through 1999-2000, 2636 initially nondemented Kuakini Honolulu-Asia Aging Study participants who remained dementia-free during the first 3 years of follow-up. Global cognition was evaluated by the Cognitive Abilities Screening Instrument (CASI); depressive symptoms by the 11-item Center for Epidemiologic Studies Depression (CES-D) Scale; and social networks by the Lubben Social Network Scale (LSNS). Median split of LSNS scores defined weak/strong social network groups. A panel of neurologists and geriatricians diagnosed and classified dementia; AD and VaD diagnoses comprised cases in which AD or VaD, respectively, were considered the primary cause of dementia.

RESULTS

Median (range) baseline age was 77 (71-93) years. Participants with weak (LSNS score ≤29) versus strong (>29) social networks had higher age-adjusted incidence (in person-years) of ACD (12.6 vs. 8.7; p = .014) and AD (6.7 vs. 4.0; p = .007) but not VaD (2.4 vs. 1.4; p = .15). Kaplan-Meier curves showed a lower likelihood of survival free of ACD (log-rank p < .0001) and AD (p = .0006) for men with weak networks. In Cox proportional hazards models adjusting for age, education, APOE ɛ4, prevalent stroke, depressive symptoms, and CASI score (all at baseline), weak networks predicted increased incidence of ACD (hazard ratio [HR] = 1.52, p = .009) and AD (HR = 1.67, p = .014) but not VaD (p > .2).

CONCLUSION

Weak social networks may heighten the risk of dementia and AD, underscoring the need to promote social connectedness in older adults.

Role of Citicoline in Patients With Mild Cognitive Impairment

The term mild cognitive impairment (MCI) defines an intermediate state between normal aging and dementia. Vascular cognitive impairment refers to a decline in cognitive function that is caused by or associated with vascular disease and comprises all the spectrum of cognitive impairments, from MCI of vascular origin to vascular dementia. One of the available treatments for cognitive impairment is cytidine diphosphate-choline (CDP-Choline), or citicoline. The objective of the present manuscript is to provide complete evidence about the efficacy of citicoline for MCI, especially of vascular origin, but also due to other neurodegenerative disorders. Citicoline is a pharmaceutical product constituted by the combination of 2 natural molecules (cytidine and choline) and is marketed as a food supplement. It has been proposed to provide neuroprotective effects through diverse mechanisms of action. Taking into account the available literature, citicoline has shown a consistent improvement in cognitive function in patients with MCI, especially of vascular origin. Moreover, it provides beneficial effects on vascular, Alzheimer, and mixed dementias, stroke sequelae, intracerebral hemorrhages, traumatic brain injuries, and neurodegenerative diseases. Long-term treatment with citicoline has also been demonstrated to be well-tolerated and has not been associated with severe adverse events. Citicoline is a safe, well-tolerated, and promising agent with evidenced neuroprotective properties.

The effects of white matter hyperintensities on MEG power spectra in population with mild cognitive impairment

Cerebrovascular disease is responsible for up to 20% of cases of dementia worldwide, but also it is a major comorbid contributor to the progression of other neurodegenerative diseases, like Alzheimer's disease. White matter hyperintensities (WMH) are the most prevalent imaging marker in cerebrovascular disease. The presence and progression of WMH in the brain have been associated with general cognitive impairment and the risk to develop all types of dementia. The aim of this piece of work is the assessment of brain functional differences in an MCI population based on the WMH volume. One-hundred and twenty-nine individuals with mild cognitive impairment (MCI) underwent a neuropsychological evaluation, MRI assessment (T1 and Flair), and MEG recordings (5 min of eyes closed resting state). Those participants were further classified into vascular MCI (vMCI; n = 61, mean age 75 ± 4 years, 35 females) or non-vascular MCI (nvMCI; n = 56, mean age 72 ± 5 years, 36 females) according to their WMH total volume, assessed with an automatic detection toolbox, LST (SPM12). We used a completely data-driven approach to evaluate the differences in the power spectra between the groups. Interestingly, three clusters emerged: One cluster with widespread larger theta power and two clusters located in both temporal regions with smaller beta power for vMCI compared to nvMCI. Those power signatures were also associated with cognitive performance and hippocampal volume. Early identification and classification of dementia pathogenesis is a crucially important goal for the search for more effective management approaches. These findings could help to understand and try to palliate the contribution of WMH to particular symptoms in mixed dementia progress.

Multi-tasking deep network for tinnitus classification and severity prediction from multimodal structural MR images

Objective:Subjective tinnitus is an auditory phantom perceptual disorder without an objective biomarker. Fast and efficient diagnostic tools will advance clinical practice by detecting or confirming the condition, tracking change in severity, and monitoring treatment response. Motivated by evidence of subtle anatomical, morphological, or functional information in magnetic resonance images of the brain, we examine data-driven machine learning methods for joint tinnitus classification (tinnitus or no tinnitus) and tinnitus severity prediction.Approach:We propose a deep multi-task multimodal framework for tinnitus classification and severity prediction using structural MRI (sMRI) data. To leverage complementary information multimodal neuroimaging data, we integrate two modalities of three-dimensional sMRI-T1 weighted (T1w) and T2 weighted (T2w) images. To explore the key components in the MR images that drove task performance, we segment both T1w and T2w images into three different components-cerebrospinal fluid, grey matter and white matter, and evaluate performance of each segmented image.Main results:Results demonstrate that our multimodal framework capitalizes on the information across both modalities (T1w and T2w) for the joint task of tinnitus classification and severity prediction.Significance:Our model outperforms existing learning-based and conventional methods in terms of accuracy, sensitivity, specificity, and negative predictive value.

Characterization of structural and functional network organization after focal prefrontal lesions in humans in proof of principle study

Lesion research classically maps behavioral effects of focal damage to the directly injured brain region. However, such damage can also have distant effects that can be assessed with modern imaging methods. Furthermore, the combination and comparison of imaging methods in a lesion model may shed light on the biological basis of structural and functional networks in the healthy brain. We characterized network organization assessed with multiple MRI imaging modalities in 13 patients with chronic focal damage affecting either superior or inferior frontal gyrus (SFG, IFG) and 18 demographically matched healthy Controls. We first defined structural and functional network parameters in Controls and then investigated grey matter (GM) and white matter (WM) differences between patients and Controls. Finally, we examined the differences in functional coupling to large-scale resting state networks (RSNs). The results suggest lesions are associated with widespread within-network GM loss at distal sites, yet leave WM and RSNs relatively preserved. Lesions to either prefrontal region also had a similar relative level of impact on structural and functional networks. The findings provide initial evidence for causal contributions of specific prefrontal regions to brain networks in humans that will ultimately help to refine models of the human brain.

White matter hyperintensities-related cortical changes and correlation with mild behavioral impairment

PURPOSE

The aim of this study was to analyze cortical thickness and gray matter volume (GMV) changes in white matter hyperintensities (WMH) which were associated brain regions and their association with mild behavioral impairment (MBI) by means of voxel- and surface-based morphology (VBM and SBM).

METHODS

A total of 60 patients underwent 3T MRI scan and MBI checklist (MBI-C) assessment and were divided into two groups: lower WMH (LWMH) and higher WMH (HWMH). After adjusting for confounding factors i.e. age, gender, education, and total intracranial volume, we found a GMV decrease in the left anterior insula (AIns), right middle frontal gyrus, right central operculum, right fusiform gyrus, left cerebellum exterior, and thalamus proper in the HWMH group based VBM, while in the HWMH group based SBM we found cortical thickness decrease in the left lingual, right posterior cingulate cortex (rPCC), right precentral, left superior frontal, right medial orbitofrontal gyrus, and left pars opercularis.

RESULTS

The HWMH group had higher MBI-C scores. The GMV in the left AIns and thalamus proper and the thickness of rPCC negatively correlated with the MBI-C scores. The mediation analysis suggested that WMH may partially mediate MBI-C scores by reducing the GMV and cortical thickness of the mentioned brain regions.

CONCLUSIONS

In WMH patients, the occurrence of MBI is associated with atrophy of gray matter and cortex. The occurrence of MBI may be partially mediated by WMH through gray matter and cortical atrophy. It provides a new insight into the relationship between WMH and dementia.

Understanding brain function in vascular cognitive impairment and dementia with EEG and MEG: A systematic review

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Vascular Cognitive Impairment (VCI) is the second most prevalent dementia worldwide.

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Cerebrovascular disease is a major comorbid contributor to neurodegenerative diseases.

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VCI patients show specific spectral, connectivity and evoked responses patterns.

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Literature suggests that EEG-MEG might provide promising biomarkers for early VCI.

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Further neurophysiological research is needed for VCI subtypes differentiation.

Vascular Cognitive Impairment (VCI) is the second most prevalent dementia after Alzheimer’s Disease (AD), and cerebrovascular disease (CBVD) is a major comorbid contributor to the progression of most neurodegenerative diseases. Early differentiation of cognitive impairment is critical given both the high prevalence of CBVD, and that its risk factors are modifiable. The ability for electroencephalogram (EEG) and magnetoencephalogram (MEG) to detect changes in brain functioning for other dementias suggests that they may also be promising biomarkers for early VCI. The present systematic review aims to summarize the literature regarding electrophysiological patterns of mild and major VCI. Despite considerable heterogeneity in clinical definition and electrophysiological methodology, common patterns exist when comparing patients with VCI to healthy controls (HC) and patients with AD, though there is a low specificity when comparing between VCI subgroups. Similar to other dementias, slowed frequency patterns and disrupted inter- and intra-hemispheric connectivity are repeatedly reported for VCI patients, as well as longer latencies and smaller amplitudes in evoked responses. Further study is needed to fully establish MEG and EEG as clinically useful biomarkers, including a clear definition of VCI and standardized methodology, allowing for comparison across groups and consolidation of multicenter efforts.

Multiple-region grey matter atrophy as a predictor for the development of dementia in a community: the Hisayama Study

OBJECTIVE

To assess the association of regional grey matter atrophy with dementia risk in a general older Japanese population.

METHODS

We followed 1158 dementia-free Japanese residents aged ≥65 years for 5.0 years. Regional grey matter volume (GMV) at baseline was estimated by applying voxel-based morphometry methods. The GMV-to-total brain volume ratio (GMV/TBV) was calculated, and its association with dementia risk was estimated using Cox proportional hazard models. We assessed whether the predictive ability of a model based on known dementia risk factors could be improved by adding the total number of regions with grey matter atrophy among dementia-related brain regions, where the cut-off value for grey matter atrophy in each region was determined by receiver operating characteristic curves.

RESULTS

During the follow-up, 113 participants developed all-cause dementia, including 83 with Alzheimer's disease (AD). Lower GMV/TBV of the medial temporal lobe, insula, hippocampus and amygdala were significantly/marginally associated with higher risk of all-cause dementia and AD (all p for trend ≤0.08). The risks of all-cause dementia and AD increased significantly with increasing total number of brain regions exhibiting grey matter atrophy (both p for trend <0.01). Adding the total number of regions with grey matter atrophy into a model consisting of known risk factors significantly improved the predictive ability for AD (Harrell's c-statistics: 0.765-0.802; p=0.02).

CONCLUSIONS

Our findings suggest that the total number of regions with grey matter atrophy among the medial temporal lobe, insula, hippocampus and amygdala is a significant predictor for developing dementia, especially AD, in the general older population.

White matter microglia heterogeneity in the CNS

Microglia, the resident myeloid cells in the central nervous system (CNS) play critical roles in shaping the brain during development, responding to invading pathogens, and clearing tissue debris or aberrant protein aggregations during ageing and neurodegeneration. The original concept that like macrophages, microglia are either damaging (pro-inflammatory) or regenerative (anti-inflammatory) has been updated to a kaleidoscope view of microglia phenotypes reflecting their wide-ranging roles in maintaining homeostasis in the CNS and, their contribution to CNS diseases, as well as aiding repair. The use of new technologies including single cell/nucleus RNA sequencing has led to the identification of many novel microglia states, allowing for a better understanding of their complexity and distinguishing regional variations in the CNS. This has also revealed differences between species and diseases, and between microglia and other myeloid cells in the CNS. However, most of the data on microglia heterogeneity have been generated on cells isolated from the cortex or whole brain, whereas white matter changes and differences between white and grey matter have been relatively understudied. Considering the importance of microglia in regulating white matter health, we provide a brief update on the current knowledge of microglia heterogeneity in the white matter, how microglia are important for the development of the CNS, and how microglial ageing affects CNS white matter homeostasis. We discuss how microglia are intricately linked to the classical white matter diseases such as multiple sclerosis and genetic white matter diseases, and their putative roles in neurodegenerative diseases in which white matter is also affected. Understanding the wide variety of microglial functions in the white matter may provide the basis for microglial targeted therapies for CNS diseases.

Alzheimer's disease alters oligodendrocytic glycolytic and ketolytic gene expression

INTRODUCTION

Sporadic Alzheimer's disease (AD) is strongly correlated with impaired brain glucose metabolism, which may affect AD onset and progression. Ketolysis has been suggested as an alternative pathway to fuel the brain.

METHODS

RNA-seq profiles of post mortem AD brains were used to determine whether dysfunctional AD brain metabolism can be determined by impairments in glycolytic and ketolytic gene expression. Data were obtained from the Knight Alzheimer's Disease Research Center (62 cases; 13 controls), Mount Sinai Brain Bank (110 cases; 44 controls), and the Mayo Clinic Brain Bank (80 cases; 76 controls), and were normalized to cell type: astrocytes, microglia, neurons, oligodendrocytes.

RESULTS

In oligodendrocytes, both glycolytic and ketolytic pathways were significantly impaired in AD brains. Ketolytic gene expression was not significantly altered in neurons, astrocytes, and microglia.

DISCUSSION

Oligodendrocytes may contribute to brain hypometabolism observed in AD. These results are suggestive of a potential link between hypometabolism and dysmyelination in disease physiology. Additionally, ketones may be therapeutic in AD due to their ability to fuel neurons despite impaired glycolytic metabolism.

Risk Stratification Tool for Ischemic Stroke: A Risk Assessment Model Based on Traditional Risk Factors Combined With White Matter Lesions and Retinal Vascular Caliber

Objective: This study aims to establish a risk assessment model based on traditional risk factors combined with the Fazekas classification of white matter lesions and retinal vascular caliber for screening the patients at high risk of ischemic stroke.

Methods: This study included 296 patients (128 cases of ischemic stroke and 168 cases in the normal control group). The basic data of the patients were collected. Color fundus photography was performed after pupil dilation, and the retinal vascular caliber was measured using semiautomated vascular measurement software (IVAN Software, Sydney, Australia). The severity of white matter lesions (WML) on cranial nuclear magnetic fluid-attenuated inversion recovery images were assessed using the Fazekas scale. Moreover, logistic regression analysis was used to establish different risk assessment models for ischemic stroke. The effects of models were evaluated through the receiver operating characteristic (ROC) curve and the Delong test compared area under the curve.

Results: The sensitivity and specificity of models 1 (the traditional risk factor model), 2 (the retinal vascular caliber model), 3 (the WML model), and 4 (the combined the traditional risk factor, WML and central retinal artery equivalent (CRAE) model) were 71 and 55%, 48 and 71%, 49 and 67%, and 68 and 68.5% with areas under the curve of 0.658, 0.586, 0.601, and 0.708, respectively. The area under the receiver operating characteristic curve in models 1, 2, 3, and 4 showed statistically significant differences. Moreover, no statistical significance exists in the pairwise comparison of other models.

Conclusion: The risk assessment model of ischemic stroke combined with Fazekas grade of WML and CRAE is superior to the traditional risk factor and the single-index model. This model is helpful for risk stratification of high-risk stroke patients.

Grey and white matter network disruption is associated with sensory deficits after stroke

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Somatosensory deficits occur in about 60% of patients after ischaemic stroke.

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Clinical and imaging data of 101 ischaemic stroke patients were analysed.

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Stroke lesions may disrupt grey (GM) and/or white matter (WM) network.

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Lesion volume explains 23% of sensory deficit variance; GM / WM disruption adds 14%

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Subnetwork of postcentral, supramarginal, transverse temporal gyri involved.

Somatosensory deficits after ischaemic stroke are common and can occur in patients with lesions in the anterior parietal cortex and subcortical nuclei. It is less clear to what extent damage to white matter tracts within the somatosensory system may contribute to somatosensory deficits after stroke. We compared the roles of cortical damage and disruption of subcortical white matter tracts as correlates of somatosensory deficit after ischaemic stroke.

Clinical and imaging data were assessed in incident stroke patients. Somatosensory deficits were measured using a standardized somatosensory test. Remote effects were quantified by projecting the MRI-based segmented stroke lesions onto a predefined atlas of white matter connectivity. Direct ischaemic damage to grey matter was computed by lesion overlap with grey matter areas. The association between lesion impact scores and sensory deficit was assessed statistically.

In 101 patients, median sensory score was 188/193 (97.4%). Lesion volume was associated with somatosensory deficit, explaining 23.3% of variance. Beyond this, the stroke-induced grey and white matter disruption within a subnetwork of the postcentral, supramarginal, and transverse temporal gyri explained an additional 14% of the somatosensory outcome variability. On mutual comparison, white matter network disruption was a stronger predictor than grey matter damage.

Ischaemic damage to both grey and white matter are structural correlates of acute somatosensory disturbance after ischaemic stroke. Our data suggest that white matter integrity of a somatosensory network of primary and secondary cortex is a prerequisite for normal processing of somatosensory inputs and might be considered as an additional parameter for stroke outcome prediction in the future.

CD8+ T Cell-Mediated Mechanisms Contribute to the Progression of Neurocognitive Impairment in Both Multiple Sclerosis and Alzheimer's Disease?

Neurocognitive impairment (NCI) is one of the most relevant clinical manifestations of multiple sclerosis (MS). The profile of NCI and the structural and functional changes in the brain structures relevant for cognition in MS share some similarities to those in Alzheimer's disease (AD), the most common cause of neurocognitive disorders. Additionally, despite clear etiopathological differences between MS and AD, an accumulation of effector/memory CD8+ T cells and CD8+ tissue-resident memory T (Trm) cells in cognitively relevant brain structures of MS/AD patients, and higher frequency of effector/memory CD8+ T cells re-expressing CD45RA (TEMRA) with high capacity to secrete cytotoxic molecules and proinflammatory cytokines in their blood, were found. Thus, an active pathogenetic role of CD8+ T cells in the progression of MS and AD may be assumed. In this mini-review, findings supporting the putative role of CD8+ T cells in the pathogenesis of MS and AD are displayed, and putative mechanisms underlying their pathogenetic action are discussed. A special effort was made to identify the gaps in the current knowledge about the role of CD8+ T cells in the development of NCI to "catalyze" translational research leading to new feasible therapeutic interventions.

Alzheimer's diagnosis using deep learning in segmenting and classifying 3D brain MR images

BACKGROUND AND OBJECTIVES

Dementia is one of the brain diseases with serious symptoms such as memory loss, and thinking problems. According to the World Alzheimer Report 2016, in the world, there are 47 million people having dementia and it can be 131 million by 2050. There is no standard method to diagnose dementia, and consequently unable to access the treatment effectively. Hence, the computational diagnosis of the disease from brain Magnetic Resonance Image (MRI) scans plays an important role in supporting the early diagnosis. Alzheimer's Disease (AD), a common type of Dementia, includes problems related to disorientation, mood swings, not managing self-care, and behavioral issues. In this article, we present a new computational method to diagnosis Alzheimer's disease from 3D brain MR images.

METHODS

An efficient approach to diagnosis Alzheimer's disease from brain MRI scans is proposed comprising two phases: I) segmentation and II) classification, both based on deep learning. After the brain tissues are segmented by a model that combines Gaussian Mixture Model (GMM) and Convolutional Neural Network (CNN), a new model combining Extreme Gradient Boosting (XGBoost) and Support Vector Machine (SVM) is used to classify Alzheimer's disease based on the segmented tissues.

RESULTS

We present two evaluations for segmentation and classification. For comparison, the new method was evaluated using the AD-86 and AD-126 datasets leading to Dice 0.96 for segmentation in both datasets and accuracies 0.88, and 0.80 for classification, respectively.

CONCLUSION

Deep learning gives prominent results for segmentation and feature extraction in medical image processing. The combination of XGboost and SVM improves the results obtained.

A technical review of canonical correlation analysis for neuroscience applications

Collecting comprehensive data sets of the same subject has become a standard in neuroscience research and uncovering multivariate relationships among collected data sets have gained significant attentions in recent years. Canonical correlation analysis (CCA) is one of the powerful multivariate tools to jointly investigate relationships among multiple data sets, which can uncover disease or environmental effects in various modalities simultaneously and characterize changes during development, aging, and disease progressions comprehensively. In the past 10 years, despite an increasing number of studies have utilized CCA in multivariate analysis, simple conventional CCA dominates these applications. Multiple CCA-variant techniques have been proposed to improve the model performance; however, the complicated multivariate formulations and not well-known capabilities have delayed their wide applications. Therefore, in this study, a comprehensive review of CCA and its variant techniques is provided. Detailed technical formulation with analytical and numerical solutions, current applications in neuroscience research, and advantages and limitations of each CCA-related technique are discussed. Finally, a general guideline in how to select the most appropriate CCA-related technique based on the properties of available data sets and particularly targeted neuroscience questions is provided.

The Effects of Longitudinal White Matter Hyperintensity Change on Cognitive Decline and Cortical Thinning over Three Years

White matter hyperintensity (WMH) has been recognised as a surrogate marker of small vessel disease and is associated with cognitive impairment. We investigated the dynamic change in WMH in patients with severe WMH at baseline, and the effects of longitudinal change of WMH volume on cognitive decline and cortical thinning. Eighty-seven patients with subcortical vascular mild cognitive impairment were prospectively recruited from a single referral centre. All of the patients were followed up with annual neuropsychological tests and 3T brain magnetic resonance imaging. The WMH volume was quantified using an automated method and the cortical thickness was measured using surface-based methods. Participants were classified into WMH progression and WMH regression groups based on the delta WMH volume between the baseline and the last follow-up. To investigate the effects of longitudinal change in WMH volume on cognitive decline and cortical thinning, a linear mixed effects model was used. Seventy patients showed WMH progression and 17 showed WMH regression over a three-year period. The WMH progression group showed more rapid cortical thinning in widespread regions compared with the WMH regression group. However, the rate of cognitive decline in language, visuospatial function, memory and executive function, and general cognitive function was not different between the two groups. The results of this study indicated that WMH volume changes are dynamic and WMH progression is associated with more rapid cortical thinning.

Heterogeneous Disease Progression in a Mouse Model of Vascular Cognitive Impairment

Recently, an asymmetric vascular compromise approach that replicates many aspects of human vascular cognitive impairment (VCI) has been reported. The present study aimed to first investigate on the reproducibility in the disease progression of this newly reported VCI model using wild-type C57BL6/J mice. The second aim was to assess how this approach will affect the disease progression of transgenic Alzheimer’s disease (AD) 5XFAD mice subjected to VCI. C57BL6/J and 5XFAD mice were subjected to VCI by placing an ameroid constrictor on the right CCA and a microcoil on the left CCA. Infarcts and hippocampal neuronal loss did not appear predominantly in the right (ameroid side) as expected but randomly in both hemispheres. The mortality rate of C57BL6/J mice was unexpectedly high. Inducing VCI reduced amyloid burden in the hippocampi of 5XFAD mice. Since VCI is known to be complex and complicated, the heterogeneous disease progression observed from this current study shares close resemblance to the clinical manifestation of VCI. This heterogeneity, however, makes it challenging to test novel treatment options using this model. Further study is warranted to tackle the heterogeneous nature of VCI.

Alzheimer's disease diagnosis from diffusion tensor images using convolutional neural networks

Machine learning algorithms are currently being implemented in an escalating manner to classify and/or predict the onset of some neurodegenerative diseases; including Alzheimer's Disease (AD); this could be attributed to the fact of the abundance of data and powerful computers. The objective of this work was to deliver a robust classification system for AD and Mild Cognitive Impairment (MCI) against healthy controls (HC) in a low-cost network in terms of shallow architecture and processing. In this study, the dataset included was downloaded from the Alzheimer's disease neuroimaging initiative (ADNI). The classification methodology implemented was the convolutional neural network (CNN), where the diffusion maps, and gray-matter (GM) volumes were the input images. The number of scans included was 185, 106, and 115 for HC, MCI and AD respectively. Ten-fold cross-validation scheme was adopted and the stacked mean diffusivity (MD) and GM volume produced an AUC of 0.94 and 0.84, an accuracy of 93.5% and 79.6%, a sensitivity of 92.5% and 62.7%, and a specificity of 93.9% and 89% for AD/HC and MCI/HC classification respectively. This work elucidates the impact of incorporating data from different imaging modalities; i.e. structural Magnetic Resonance Imaging (MRI) and Diffusion Tensor Imaging (DTI), where deep learning was employed for the aim of classification. To the best of our knowledge, this is the first study assessing the impact of having more than one scan per subject and propose the proper maneuver to confirm the robustness of the system. The results were competitive among the existing literature, which paves the way for improving medications that could slow down the progress of the AD or prevent it.

Multivariate analysis reveals anatomical correlates of naming errors in primary progressive aphasia

Primary progressive aphasia (PPA) is an overarching term for a heterogeneous group of neurodegenerative diseases which affect language processing. Impaired picture naming has been linked to atrophy of the anterior temporal lobe in the semantic variant of PPA. Although atrophy of the anterior temporal lobe proposedly impairs picture naming by undermining access to semantic knowledge, picture naming also entails object recognition and lexical retrieval. Using multivariate analysis, we investigated whether cortical atrophy relates to different types of naming errors generated during picture naming in 43 PPA patients (13 semantic, 9 logopenic, 11 nonfluent, and 10 mixed variant). Omissions were associated with atrophy of the anterior temporal lobes. Semantic errors, for example, mistaking a rhinoceros for a hippopotamus, were associated with atrophy of the left mid and posterior fusiform cortex and the posterior middle and inferior temporal gyrus. Semantic errors and atrophy in these regions occurred in each PPA subtype, without major between-subtype differences. We propose that pathological changes to neural mechanisms associated with semantic errors occur across the PPA spectrum.

Heterogeneity of microglia and their differential roles in white matter pathology

Microglia are resident immune cells that play multiple roles in central nervous system (CNS) development and disease. Although the classical concept of microglia/macrophage activation is based on a biphasic beneficial‐versus‐deleterious polarization, growing evidence now suggests a much more heterogenous profile of microglial activation that underlie their complex roles in the CNS. To date, the majority of data are focused on microglia in gray matter. However, demyelination is a prominent pathologic finding in a wide range of diseases including multiple sclerosis, Alzheimer's disease, and vascular cognitive impairment and dementia. In this mini‐review, we discuss newly discovered functional subsets of microglia that contribute to white matter response in CNS disease onset and progression. Microglia show different molecular patterns and morphologies depending on disease type and brain region, especially in white matter. Moreover, in later stages of disease, microglia demonstrate unconventional immuno‐regulatory activities such as increased phagocytosis of myelin debris and secretion of trophic factors that stimulate oligodendrocyte lineage cells to facilitate remyelination and disease resolution. Further investigations of these multiple microglia subsets may lead to novel therapeutic approaches to treat white matter pathology in CNS injury and disease.

The new insights into human brain imaging after stroke

Over the last two decades, developments of human brain stroke imaging have raised several questions about the place of new MRI biomarkers in the acute management of stroke and the prediction of poststroke outcome. Recent studies have demonstrated the main role of perfusion-weighted imaging in the identification of the best cerebral perfusion profile for a better response after reperfusion therapies in acute ischemic stroke. A major issue remains the early prediction of stroke outcome. While voxel-based lesion-symptom mapping emphasized the influence of stroke location, the analysis of the brain parenchyma underpinning the stroke lesion showed the relevance of prestroke cerebral status, including cortical atrophy, white matter integrity, or presence of chronic cortical cerebral microinfarcts. Moreover, besides the evaluation of the visually abnormal brain tissue, the analysis of normal-appearing brain parenchyma using diffusion tensor imaging and magnetization transfer imaging or spectroscopy offered new biomarkers to improve the prediction of the prognosis and new targets to follow in therapeutic trials. The aim of this review was to depict the main new radiological biomarkers reported in the last two decades that will provide a more thorough prediction of functional, motor, and neuropsychological outcome following the stroke. These new developments in neuroimaging might be a cornerstone in the emerging personalized medicine for stroke patients.

The Vascular Hypothesis of Alzheimer's Disease: A Key to Preclinical Prediction of Dementia Using Neuroimaging

The vascular hypothesis of Alzheimer's disease (VHAD) was proposed 24 years ago from observations made in our laboratory using aging rats subjected to chronic brain hypoperfusion. In recent years, VHAD has become a mother-lode to numerous neuroimaging studies targeting cerebral hemodynamic changes, particularly brain hypoperfusion in elderly patients at risk of developing Alzheimer's disease (AD). There is a growing consensus among neuroradiologists that brain hypoperfusion is likely involved in the pathogenesis of AD and that disturbed cerebral blood flow (CBF) can serve as a key biomarker for predicting conversion of mild cognitive impairment to AD. The use of cerebral hypoperfusion as a preclinical predictor of AD is becoming decisive in stratifying low and high risk patients that may develop cognitive decline and for assessing the effectiveness of therapeutic interventions. There is currently an international research drive from neuroimaging groups to seek new perspectives that can broaden our understanding of AD and improve lifestyle. Diverse neuroimaging methods are currently being used to monitor normal and dyscognitive brain activity. Some techniques are very powerful and can detect, diagnose, quantify, prognose, and predict cognitive decline before AD onset, even from a healthy cognitive state. Multimodal imaging offers new insights in the treatment and prevention of cognitive decline during advanced aging and better understanding of the functional and structural organization of the human brain. This review discusses the impact the VHAD and CBF are having on the neuroimaging technology that can usher practical strategies to help prevent AD.

Precentral degeneration and cerebellar compensation in amyotrophic lateral sclerosis: A multimodal MRI analysis

Amyotrophic lateral sclerosis (ALS) is a progressive and intractable neurodegenerative disease of human motor system characterized by progressive muscular weakness and atrophy. A considerable body of research has demonstrated significant structural and functional abnormalities of the primary motor cortex in patients with ALS. In contrast, much less attention has been paid to the abnormalities of cerebellum in this disease. Using multimodal magnetic resonance imagining data of 60 patients with ALS and 60 healthy controls, we examined changes in gray matter volume (GMV), white matter (WM) fractional anisotropy (FA), and functional connectivity (FC) in patients with ALS. Compared with healthy controls, patients with ALS showed decreased GMV in the left precentral gyrus and increased GMV in bilateral cerebellum, decreased FA in the left corticospinal tract and body of corpus callosum, and decreased FC in multiple brain regions, involving bilateral postcentral gyrus, precentral gyrus and cerebellum anterior lobe, among others. Meanwhile, we found significant intermodal correlations among GMV of left precentral gyrus, FA of altered WM tracts, and FC of left precentral gyrus, and that WM microstructural alterations seem to play important roles in mediating the relationship between GMV and FC of the precentral gyrus, as well as the relationship between GMVs of the precentral gyrus and cerebellum. These findings provided evidence for the precentral degeneration and cerebellar compensation in ALS, and the involvement of WM alterations in mediating the relationship between pathologies of the primary motor cortex and cerebellum, which may contribute to a better understanding of the pathophysiology of ALS.

Multimodal imaging analyses in patients with genetic and sporadic forms of small vessel disease

Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is thought to be a pure genetic form of subcortical vascular cognitive impairment (SVCI). The aim of this study was to compare white matter integrity and cortical thickness between typical CADASIL, a genetic form, and two sporadic forms of SVCI (with NOTCH3 and without NOTCH3 variants). We enrolled typical CADASIL patients (N = 11) and SVCI patients [with NOTCH3 variants (N = 15), without NOTCH3 variants (N = 101)]. To adjust the age difference, which reflects the known difference in clinical and radiologic courses between typical CADASIL patients and SVCI patients, we constructed a W-score of measurement for diffusion tensor image and cortical thickness. Typical CADASIL patients showed more frequent white matter hyperintensities in the bilateral posterior temporal region compared to SVCI patients (p < 0.001, uncorrected). We found that SVCI patients, regardless of the presence of NOTCH3 variants, showed significantly greater microstructural alterations (W-score, p < 0.05, FWE-corrected) and cortical thinning (W-score, p < 0.05, FDR-corrected) than typical CADASIL patients. In this study, typical CADASIL and SVCI showed distinct anatomic vulnerabilities in the cortical and subcortical structures. However, there was no difference between SVCI with NOTCH3 variants and SVCI without NOTCH3 variants.

Lateral Ventricle Enlargement and Cortical Thinning in Idiopathic Normal-pressure Hydrocephalus Patients

We utilized three-dimensional, surface-based, morphometric analysis to investigate ventricle shape between 2 groups: (1) idiopathic normal-pressure hydrocephalus (INPH) patients who had a positive response to the cerebrospinal fluid tap test (CSFTT) and (2) healthy controls. The aims were (1) to evaluate the location of INPH-related structural abnormalities of the lateral ventricles and (2) to investigate relationships between lateral ventricular enlargement and cortical thinning in INPH patients. Thirty-three INPH patients and 23 healthy controls were included in this study. We used sparse canonical correlation analysis to show correlated regions of ventricular surface expansion and cortical thinning. Significant surface expansion in the INPH group was observed mainly in clusters bilaterally located in the superior portion of the lateral ventricles, adjacent to the high convexity of the frontal and parietal regions. INPH patients showed a significant bilateral expansion of both the temporal horns of the lateral ventricles and the medial aspects of the frontal horns of the lateral ventricles to surrounding brain regions, including the medial frontal lobe. Ventricular surface expansion was associated with cortical thinning in the bilateral orbitofrontal cortex, bilateral rostral anterior cingulate cortex, left parahippocampal cortex, left temporal pole, right insula, right inferior temporal cortex, and right fusiform gyrus. These results suggest that patients with INPH have unique patterns of ventricular surface expansion. Our findings encourage future studies to elucidate the underlying mechanism of lateral ventricular morphometric abnormalities in INPH patients.