Brain atrophy in Alzheimer's Disease and aging

Brain Gray Matter Volume Mediated the Correlation Between Plasma P-Tau and Cognitive Function of Early Alzheimer's Disease in China: A Cross-Sectional Observational Study

BACKGROUND

The primary manifestations of Alzheimer's disease (AD) include cognitive decline and brain gray matter volume (GMV) atrophy. Recent studies have found that plasma phosphorylated-tau (p-tau) concentrations perform better in diagnosing, differentiating, and monitoring the progression of AD. However, the correlation between plasma p-tau, GMV, and cognition remains unclear.

OBJECTIVE

To investigate whether GMV plays a mediating role in the association between plasma p-tau concentrations and cognition.

METHODS

In total, 99 participants (47 patients with AD and 52 cognitively unimpaired [CU] individuals) were included. All participants underwent neuropsychological assessments, laboratory examinations, and magnetic resonance imaging scans. Plasma p-tau217 and p-tau181 concentrations were measured using an enzyme-linked immunosorbent assay kit. Voxel-based morphometry was performed to assess participants' brain GMV. Partial correlation and mediation analyses were conducted in AD group.

RESULTS

Plasma p-tau concentrations were significantly higher in the AD group than in the CU group. Patients with AD had significant brain GMV atrophy in the right hippocampus, bilateral middle temporal gyrus, and right inferior temporal gyrus. In the AD group, there were significant correlations between plasma p-tau217 concentrations, GMV, and Mini-Mental State Examination (MMSE) scores. Brain GMV of the right hippocampus mediated the association between plasma p-tau217 concentrations and MMSE scores. A significant correlation between plasma p-tau181 and MMSE scores was not identified.

CONCLUSION

The findings indicate that p-tau217 is a promising biomarker for central processes affecting brain GMV and cognitive function. This may provide potential targets for future intervention and treatment of tau-targeting therapies in the early stages of AD.

Association between gray matter atrophy, cerebral hypoperfusion, and cognitive impairment in Alzheimer's disease

Background

Alzheimer's disease (AD) is one of the most severe neurodegenerative diseases leading to dementia in the elderly. Cerebral atrophy and hypoperfusion are two important pathophysiological characteristics. However, it is still unknown about the area-specific causal pathways between regional gray matter atrophy, cerebral hypoperfusion, and cognitive impairment in AD patients.

Method

Forty-two qualified AD patients and 49 healthy controls (HC) were recruited in this study. First, we explored voxel-wise inter-group differences in gray matter volume (GMV) and arterial spin labeling (ASL) -derived cerebral blood flow (CBF). Then we explored the voxel-wise associations between GMV and Mini-Mental State Examination (MMSE) score, GMV and CBF, and CBF and MMSE to identify brain targets contributing to cognitive impairment in AD patients. Finally, a mediation analysis was applied to test the causal pathways among atrophied GMV, hypoperfusion, and cognitive impairment in AD.

Results

Voxel-wise permutation test identified that the left middle temporal gyrus (MTG) had both decreased GMV and CBF in the AD. Moreover, the GMV of this region was positively correlated with MMSE and its CBF, and CBF of this region was also positively correlated with MMSE in AD (p < 0.05, corrected). Finally, mediation analysis revealed that gray matter atrophy of left MTG drives cognitive impairment of AD via the mediation of CBF (proportion of mediation = 55.82%, β = 0.242, 95% confidence interval by bias-corrected and accelerated bootstrap: 0.082 to 0.530).

Conclusion

Our findings indicated suggested that left MTG is an important hub linking gray matter atrophy, hypoperfusion, and cognitive impairment for AD, and might be a potential treatment target for AD.

Central Stimulatory Effect of Kynurenic Acid on BDNF-TrkB Signaling and BER Enzymatic Activity in the Hippocampal CA1 Field in Sheep

Deficiency of neurotrophic factors and oxidative DNA damage are common causes of many neurodegenerative diseases. Recently, the importance of kynurenic acid (KYNA), an active metabolite of tryptophan, has increased as a neuroprotective molecule in the brain. Therefore, the present study tested the hypothesis that centrally acting KYNA would positively affect: (1) brain-derived neurotrophic factor (BDNF)-tyrosine receptor kinase B (TrkB) signaling and (2) selected base excision repair (BER) pathway enzymes activities in the hippocampal CA1 field in sheep. Both lower (20 μg in total) and higher (100 μg in total) doses of KYNA infused into the third brain ventricle differentially increased the abundance of BDNF and TrkB mRNA in the CA1 field; additionally, the higher dose increased BDNF tissue concentration. The lower dose of KYNA increased mRNA expression for 8-oxoguanine glycosylase (OGG1), N-methylpurine DNA glycosylase (MPG), and thymine DNA glycosylase and stimulated the repair of 1,N6-ethenodeoxyadenosine and 3,N4-ethenodeoxy-cytosine as determined by the excision efficiency of lesioned nucleobases. The higher dose increased the abundance of OGG1 and MPG transcripts, however, its stimulatory effect on repair activity was less pronounced in all cases compared to the lower dose. The increased level of AP-endonuclease mRNA expression was dose-dependent. In conclusion, the potential neurotrophic and neuroprotective effects of KYNA in brain cells may involve stimulation of the BDNF-TrkB and BER pathways.

Identifying the regional substrates predictive of Alzheimer's disease progression through a convolutional neural network model and occlusion

Progressive brain atrophy is a key neuropathological hallmark of Alzheimer's disease (AD) dementia. However, atrophy patterns along the progression of AD dementia are diffuse and variable and are often missed by univariate methods. Consequently, identifying the major regional atrophy patterns underlying AD dementia progression is challenging. In the current study, we propose a method that evaluates the degree to which specific regional atrophy patterns are predictive of AD dementia progression, while holding all other atrophy changes constant using a total sample of 334 subjects. We first trained a dense convolutional neural network model to differentiate individuals with mild cognitive impairment (MCI) who progress to AD dementia versus those with a stable MCI diagnosis. Then, we retested the model multiple times, each time occluding different regions of interest (ROIs) from the model's testing set's input. We also validated this approach by occluding ROIs based on Braak's staging scheme. We found that the hippocampus, fusiform, and inferior temporal gyri were the strongest predictors of AD dementia progression, in agreement with established staging models. We also found that occlusion of limbic ROIs defined according to Braak stage III had the largest impact on the performance of the model. Our predictive model reveals the major regional patterns of atrophy predictive of AD dementia progression. These results highlight the potential for early diagnosis and stratification of individuals with prodromal AD dementia based on patterns of cortical atrophy, prior to interventional clinical trials.

Multimodal fusion analysis of functional, cerebrovascular and structural neuroimaging in healthy aging subjects

Brain aging is a complex process that requires a multimodal approach. Neuroimaging can provide insights into brain morphology, functional organization, and vascular dynamics. However, most neuroimaging studies of aging have focused on each imaging modality separately, limiting the understanding of interrelations between processes identified by different modalities and their relevance to cognitive decline in aging. Here, we used a data-driven multimodal approach, linked independent component analysis (ICA), to jointly analyze magnetic resonance imaging (MRI) of grey matter volume, cerebrovascular, and functional network topographies in relation to measures of fluid intelligence. Neuroimaging and cognitive data from the Cambridge Centre for Ageing and Neuroscience study were used, with healthy participants aged 18-88 years (main dataset n = 215 and secondary dataset n = 433). Using linked ICA, functional network activities were characterized in independent components but not captured in the same component as structural and cerebrovascular patterns. Split-sample (n = 108/107) and out-of-sample (n = 433) validation analyses using linked ICA were also performed. Global grey matter volume with regional cerebrovascular changes and the right frontoparietal network activity were correlated with age-related and individual differences in fluid intelligence. This study presents the insights from linked ICA to bring together measurements from multiple imaging modalities, with independent and additive information. We propose that integrating multiple neuroimaging modalities allows better characterization of brain pattern variability and changes associated with healthy aging.

An update on the use of gamma (multi)sensory stimulation for Alzheimer's disease treatment

Alzheimer's disease (AD) is characterized by reduced fast brain oscillations in the gamma band (γ, > 30 Hz). Several animal studies show that inducing gamma oscillations through (multi)sensory stimulation at 40 Hz has the potential to impact AD-related cognitive decline and neuropathological processes, including amyloid plaques deposition, neurofibrillary tangles formation, and neuronal and synaptic loss. Therefore Gamma Entrainment Using Sensory stimulation (GENUS) is among the most promising approaches for AD patients' treatment. This review summarizes the evidence on GENUS effectiveness, from animal models to AD patients. Despite the application on human is in its infancy, the available findings suggest its feasibility for the treatment of AD. We discuss such results in light of parameter improvement and possible underlying mechanisms. We finally emphasize the need for further research for its development as a disease-modifying non-pharmacological intervention.

Increasing empathic concern relates to salience network hyperconnectivity in cognitively healthy older adults with elevated amyloid-β burden

Enhanced emotional empathy, the ability to share others' affective experiences, can be a feature of Alzheimer's disease (AD), but whether emotional empathy increases in the preclinical phase of the disease is unknown. We measured emotional empathy over time (range = 0 - 7.3 years, mean = 2.4 years) in 86 older adults during a period in which they were cognitively healthy, functionally normal, and free of dementia symptoms. For each participant, we computed longitudinal trajectories for empathic concern (i.e., an other-oriented form of emotional empathy that promotes prosocial actions) and emotional contagion (i.e., a self-focused form of emotional empathy often accompanied by feelings of distress) from informant ratings of participants' empathy on the Interpersonal Reactivity Index. Amyloid-β (Aβ) positron emission tomography (PET) scans were used to classify participants as either Aβ positive (Aβ+, n = 23) or negative (Aβ-, n = 63) based on Aβ-PET cortical binding. Participants also underwent structural and task-free functional magnetic resonance imaging approximately two years on average after their last empathy assessment, at which time most participants remained cognitively healthy. Results indicated that empathic concern, but not emotional contagion, increased more over time in Aβ+ participants than in Aβ- participants despite no initial group difference at the first measurement. Higher connectivity between certain salience network node-pairs (i.e., pregenual anterior cingulate cortex and periaqueductal gray) predicted longitudinal increases in empathic concern in the Aβ+ group but not in the Aβ- group. The Aβ+ participants also had higher overall salience network connectivity than Aβ- participants despite no differences in gray matter volume. These results suggest gains in empathic concern may be a very early feature of AD pathophysiology that relates to hyperconnectivity in the salience network, a system that supports emotion generation and interoception. A better understanding of emotional empathy trajectories in the early stages of AD pathophysiology will broaden the lens on preclinical AD changes and help clinicians to identify older adults who should be screened for AD biomarkers.

Multi-modality MRI for Alzheimer's disease detection using deep learning

Diffusion tensor imaging (DTI) is a new technology in magnetic resonance imaging, which allows us to observe the insightful structure of the human body in vivo and non-invasively. It identifies the microstructure of white matter (WM) connectivity by estimating the movement of water molecules at each voxel. This makes possible the identification of the damage to WM integrity caused by Alzheimer's disease (AD) at its early stage, called mild cognitive impairment (MCI). Furthermore, the brain's gray matter (GM) atrophy characterizes the main structural changes in AD, which can be sensitively detected by structural MRI (sMRI) modality. In this research, we aimed to classify the Alzheimer's diseases stages by developing a novel multi-modality MRI (DTI and sMRI) fusion strategy to detect WM alterations and GM atrophy in AD patients. The latter is based on a 2-dimensional deep convolutional neural network (CNN) features extractor and a support vector machine (SVM) classifier. The fusion framework consists of merging features extracted from DTI scalar metrics [(fractional anisotropy (FA) and mean diffusivity (MD)], and GM using 2D-CNN and feeding them to SVM to classify AD versus cognitively normal (CN), AD versus MCI, and MCI versus CN. Our novel multimodal AD method demonstrates a superior performance with an accuracy of 99.79%, 99.6%, and 97.00% for AD/CN, AD/MCI, and MCI/CN respectively.

Neurodegeneration and astrogliosis in the entorhinal cortex in Alzheimer's disease: Stereological layer-specific assessment and proteomic analysis

INTRODUCTION

The entorhinal cortex is among the earliest areas involved in Alzheimer's disease. Volume reduction and neural loss in this area have been widely reported. Human entorhinal cortex atrophy is, in part, due to neural loss, but microglial and/or astroglial involvement in the different layers remains unclear. Additionally, -omic approaches in the human entorhinal cortex are scarce.

METHODS

Herein, stereological layer-specific and proteomic analyses were carried out in the human brain.

RESULTS

Neurodegeneration, microglial reduction, and astrogliosis have been demonstrated, and proteomic data have revealed relationships with up- (S100A6, PPP1R1B, BAG3, and PRDX6) and downregulated (GSK3B, SYN1, DLG4, and RAB3A) proteins. Namely, clusters of these proteins were related to synaptic, neuroinflammatory, and oxidative stress processes.

DISCUSSION

Differential layer involvement among neural and glial populations determined by proteinopathies and identified proteins related to neurodegeneration and astrogliosis could explain how the cortical circuitry facilitates pathological spreading within the medial temporal lobe.

Mesenchymal stromal cells for the treatment of Alzheimer’s disease: Strategies and limitations

Alzheimer’s disease (AD) is a major cause of age-related dementia and is characterized by progressive brain damage that gradually destroys memory and the ability to learn, which ultimately leads to the decline of a patient’s ability to perform daily activities. Although some of the pharmacological treatments of AD are available for symptomatic relief, they are not able to limit the progression of AD and have several side effects. Mesenchymal stem/stromal cells (MSCs) could be a potential therapeutic option for treating AD due to their immunomodulatory, anti-inflammatory, regenerative, antioxidant, anti-apoptotic, and neuroprotective effects. MSCs not only secret neuroprotective and anti-inflammatory factors to promote the survival of neurons, but they also transfer functional mitochondria and miRNAs to boost their bioenergetic profile as well as improve microglial clearance of accumulated protein aggregates. This review focuses on different clinical and preclinical studies using MSC as a therapy for treating AD, their outcomes, limitations and the strategies to potentiate their clinical translation.

Associations between blood-based biomarkers of Alzheimer's disease with cognition in motoric cognitive risk syndrome: A pilot study using plasma Aβ42 and total tau

Background

Motoric cognitive risk (MCR) syndrome is a conceptual construct that combines slow gait speed with subjective cognitive complaints and has been shown to be associated with an increased risk of developing dementia. However, the relationships between the pathology of Alzheimer's disease (AD) and MCR syndrome remain uncertain. Therefore, the purpose of this study was to determine the levels of plasma AD biomarkers (Aβ42 and total tau) and their relationships with cognition in individuals with MCR.

Materials and methods

This was a cross-sectional pilot study that enrolled 25 individuals with normal cognition (NC), 27 with MCR, and 16 with AD. Plasma Aβ42 and total tau (t-tau) levels were measured using immunomagnetic reduction (IMR) assays. A comprehensive neuropsychological assessment was also performed.

Results

The levels of plasma t-tau proteins did not differ significantly between the MCR and AD groups, but that of plasma t-tau was significantly increased in the MCR and AD groups, compared to the NC group. Visuospatial performance was significantly lower in the MCR group than in the NC group. The levels of plasma t-tau correlated significantly with the Montreal Cognitive Assessment (MoCA) and Boston naming test scores in the MCR group.

Conclusion

In this pilot study, we found significantly increased plasma t-tau proteins in the MCR and AD groups, compared with the NC group. The plasma t-tau levels were also significantly correlated with the cognitive function of older adults with MCR. These results implied that MCR and AD may share similar pathology. However, these findings need further confirmation in longitudinal studies.

Bidirectional communication between brain and visceral white adipose tissue: Its potential impact on Alzheimer's disease

A variety of axes between brain and abdominal organs have been reported, but the interaction between brain and visceral white adipose tissue (vWAT) remains unclear. In this review, we summarized human studies on the association between brain and vWAT, and generalized their interaction and the underlying mechanisms according to animal and cell experiments. On that basis, we come up with the concept of the brain-vWAT axis (BVA). Furthermore, we analyzed the potential mechanisms of involvement of BVA in the pathogenesis of Alzheimer's disease (AD), including vWAT-derived fatty acids, immunological properties of vWAT, vWAT-derived retinoic acid and vWAT-regulated insulin resistance. The proposal of BVA may expand our understanding to some extent of how the vWAT impacts on brain health and diseases, and provide a novel approach to study the pathogenesis and treatment strategies of neurodegenerative disorders.

Transcriptomic Profiling Identifies CD8+ T Cells in the Brain of Aged and Alzheimer's Disease Transgenic Mice as Tissue-Resident Memory T Cells

Peripheral immune cell infiltration into the brain is a prominent feature in aging and various neurodegenerative diseases such as Alzheimer's disease (AD). As AD progresses, CD8+ T cells infiltrate into the brain parenchyma, where they tightly associate with neurons and microglia. The functional properties of CD8+ T cells in the brain are largely unknown. To gain further insights into the putative functions of CD8+ T cells in the brain, we explored and compared the transcriptomic profile of CD8+ T cells isolated from the brain and blood of transgenic AD (APPswe/PSEN1dE9, line 85 [APP-PS1]) and age-matched wild-type (WT) mice. Brain CD8+ T cells of APP-PS1 and WT animals had similar transcriptomic profiles and substantially differed from blood circulating CD8+ T cells. The gene signature of brain CD8+ T cells identified them as tissue-resident memory (Trm) T cells. Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analysis on the significantly upregulated genes revealed overrepresentation of biological processes involved in IFN-β signaling and the response to viral infections. Furthermore, brain CD8+ T cells of APP-PS1 and aged WT mice showed similar differentially regulated genes as brain Trm CD8+ T cells in mouse models with acute virus infection, chronic parasite infection, and tumor growth. In conclusion, our profiling of brain CD8+ T cells suggests that in AD, these cells exhibit similar adaptive immune responses as in other inflammatory diseases of the CNS, potentially opening the door for immunotherapy in AD.

Sleep, physical activity, sedentary behavior, and risk of incident dementia: a prospective cohort study of 431,924 UK Biobank participants

Although sleep, physical activity and sedentary behavior have been found to be associated with dementia risk, findings are inconsistent and their joint relationship remains unclear. This study aimed to investigate independent and joint associations of these three modifiable behaviors with dementia risks. A total of 431,924 participants (median follow-up 9.0 years) without dementia from UK Biobank were included. Multiple Cox regressions were used to estimate adjusted hazard ratios (HRs) and 95% confidence intervals (CIs). Models fitted with restricted cubic spline were conducted to test for linear and nonlinear shapes of each association. Sleep duration, leisure-time physical activity (LTPA), and screen-based sedentary behavior individually associated with dementia risks in different non-linear patterns. Sleep duration associated with dementia in a U-shape with a nadir at 7 h/day. LTPA revealed a curvilinear relationship with dementia in diminishing tendency, while sedentary behavior revealed a J-shaped relationship. The dementia risk was 17% lower in the high LTPA group (HR[95%CI]: 0.83[0.76-0.91]) and 22% higher in the high sedentary behavior group (1.22[1.10-1.35]) compared to the corresponding low-level group, respectively. A combination of seven-hour/day sleep, moderate-to-high LTPA, and low-to-moderate sedentary behavior showed the lowest dementia risk (0.59[0.50-0.69]) compared to the referent group (longer or shorter sleep/low LTPA/high sedentary behavior). Notably, each behavior was non-linearly associated with brain structures in a pattern similar to its association with dementia, suggesting they may affect dementia risk by affecting brain structures. Our findings highlight the potential to change these three daily behaviors individually and simultaneously to reduce the risk of dementia.

Ultrastructural characterization of dark microglia during aging in a mouse model of Alzheimer's disease pathology and in human post-mortem brain samples

A diverse heterogeneity of microglial cells was previously described in Alzheimer's disease (AD) pathology, including dark microglia, a state characterized by ultrastructural markers of cellular stress. To provide novel insights into the roles of dark microglia during aging in the context of AD pathology, we performed a quantitative density and ultrastructural analysis of these cells using high-throughput scanning electron microscopy in the ventral hippocampus CA1 stratum lacunosum-moleculare of 20-month-old APP-PS1 vs C57BL/6J male mice. The density of dark microglia was significantly higher in APP-PS1 vs C57BL/6J mice, with these cells accounting for nearly half of all microglia observed near amyloid-beta (Aβ) plaques. This dark microglial state interacted more with dystrophic neurites compared to other APP-PS1 microglia and possessed glycogen granules, associated with a metabolic shift toward glycolysis, which provides the first ultrastructural evidence of their presence in microglia. Dark microglia were further observed in aging human post-mortem brain samples showing similar ultrastructural features as in mouse. Overall, our results provide a quantitative ultrastructural characterization of a microglial state associated with cellular stress (i.e., dark microglia) that is primarily restricted near Aβ plaques and dystrophic neurites. The presence of this microglial state in the aging human post-mortem brain is further revealed.

Clinical relevance of biomarkers, new therapeutic approaches, and role of post-translational modifications in the pathogenesis of Alzheimer's disease

Alzheimer's disease (AD) is a neurodegenerative disorder that causes progressive loss of cognitive functions like thinking, memory, reasoning, behavioral abilities, and social skills thus affecting the ability of a person to perform normal daily functions independently. There is no definitive cure for this disease, and treatment options available for the management of the disease are not very effective as well. Based on histopathology, AD is characterized by the accumulation of insoluble deposits of amyloid beta (Aβ) plaques and neurofibrillary tangles (NFTs). Although several molecular events contribute to the formation of these insoluble deposits, the aberrant post-translational modifications (PTMs) of AD-related proteins (like APP, Aβ, tau, and BACE1) are also known to be involved in the onset and progression of this disease. However, early diagnosis of the disease as well as the development of effective therapeutic approaches is impeded by lack of proper clinical biomarkers. In this review, we summarized the current status and clinical relevance of biomarkers from cerebrospinal fluid (CSF), blood and extracellular vesicles involved in onset and progression of AD. Moreover, we highlight the effects of several PTMs on the AD-related proteins, and provide an insight how these modifications impact the structure and function of proteins leading to AD pathology. Finally, for disease-modifying therapeutics, novel approaches, and targets are discussed for the successful treatment and management of AD.

Shared and specific dynamics of brain activity and connectivity in amnestic and nonamnestic mild cognitive impairment

AIMS

The present study aimed to compare temporal variability in the spontaneous fluctuations of activity and connectivity between amnestic MCI (aMCI) and nonamnestic MCI (naMCI), which enhances the understanding of their different pathophysiologies and provides targets for individualized intervention.

METHODS

Sixty-five naMCI and 48 aMCI subjects and 75 healthy controls were recruited. A sliding window analysis was used to evaluate the dynamic amplitude of low-frequency fluctuations (dALFF), dynamic regional homogeneity (dReHo), and dynamic functional connectivity (dFC). The caudal/rostral hippocampus was selected as the seeds for calculating dFC.

RESULTS

Both aMCI and naMCI exhibited abnormal dALFF, dReHo, and hippocampal dFC compared with healthy controls. Compared with individuals with naMCI, those with aMCI exhibited (1) higher dALFF variability in the right putamen, left Rolandic operculum, and right middle cingulum, (2) lower dReHo variability in the right superior parietal lobule, and (3) lower dFC variability between the hippocampus and other regions (left superior occipital gyrus, middle frontal gyrus, inferior cerebellum, precuneus, and right superior frontal gyrus). Additionally, variability in dALFF, dReHo, and hippocampal dFC exhibited different associations with cognitive scores in aMCI and naMCI patients, respectively. Finally, dReHo variability in the right superior parietal lobule and dFC variability between the right caudal hippocampus and left inferior cerebellum exhibited partially mediated effects on the different memory scores between people with aMCI and naMCI.

CONCLUSION

The aMCI and naMCI patients exhibited shared and specific patterns of dynamic brain activity and connectivity. The dReHo of the superior parietal lobule and dFC of the hippocampus-cerebellum contributed to the memory heterogeneity of MCI subtypes. Analyzing the temporal variability in the spontaneous fluctuations of brain activity and connectivity provided a new perspective for exploring the different pathophysiological mechanisms in MCI subtypes.

All roads lead to heterogeneity: The complex involvement of astrocytes and microglia in the pathogenesis of Alzheimer’s disease

In recent years, glial cells have been acknowledged as key players in the pathogenesis of Alzheimer’s disease (AD), a neurodegenerative condition in which an accumulation of intracellular neurofibrillary tangles and extracellular fibrillar amyloid beta is notably observed in the central nervous system. Genome-wide association studies have shown, both in microglia and astrocytes, an increase in gene variants associated with a higher risk of developing late-onset AD. Microglia, the resident innate immune cells of the brain, and astrocytes, glial cells crucial for vascular integrity and neuronal support, both agglomerate near amyloid beta plaques and dystrophic neurites where they participate in the elimination of these harmful parenchymal elements. However, their role in AD pathogenesis has been challenging to resolve due to the highly heterogeneous nature of these cell populations, i.e., their molecular, morphological, and ultrastructural diversity, together with their ever-changing responsiveness and functions throughout the pathological course of AD. With the recent expansions in the field of glial heterogeneity through innovative advances in state-of-the-art microscopy and -omics techniques, novel concepts and questions arose, notably pertaining to how the diverse microglial and astrocytic states interact with each other and with the AD hallmarks, and how their concerted efforts/actions impact the progression of the disease. In this review, we discuss the recent advances and findings on the topic of glial heterogeneity, particularly focusing on the relationships of these cells with AD hallmarks (e.g., amyloid beta plaques, neurofibrillary tangles, synaptic loss, and dystrophic neurites) in murine models of AD pathology and post-mortem brain samples of patients with AD.

Nomograms of human hippocampal volume shifted by polygenic scores

Nomograms are important clinical tools applied widely in both developing and aging populations. They are generally constructed as normative models identifying cases as outliers to a distribution of healthy controls. Currently used normative models do not account for genetic heterogeneity. Hippocampal volume (HV) is a key endophenotype for many brain disorders. Here, we examine the impact of genetic adjustment on HV nomograms and the translational ability to detect dementia patients. Using imaging data from 35,686 healthy subjects aged 44-82 from the UK Biobank (UKB), we built HV nomograms using Gaussian process regression (GPR), which - compared to a previous method - extended the application age by 20 years, including dementia critical age ranges. Using HV polygenic scores (HV-PGS), we built genetically adjusted nomograms from participants stratified into the top and bottom 30% of HV-PGS. This shifted the nomograms in the expected directions by ~100 mm3 (2.3% of the average HV), which equates to 3 years of normal aging for a person aged ~65. Clinical impact of genetically adjusted nomograms was investigated by comparing 818 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database diagnosed as either cognitively normal (CN), having mild cognitive impairment (MCI) or Alzheimer's disease (AD) patients. While no significant change in the survival analysis was found for MCI-to-AD conversion, an average of 68% relative decrease was found in intra-diagnostic-group variance, highlighting the importance of genetic adjustment in untangling phenotypic heterogeneity.

Textural features reflecting local activity of the hippocampus improve the diagnosis of Alzheimer’s disease and amnestic mild cognitive impairment: A radiomics study based on functional magnetic resonance imaging

Background

Textural features of the hippocampus in structural magnetic resonance imaging (sMRI) images can serve as potential diagnostic biomarkers for Alzheimer’s disease (AD), while exhibiting a relatively poor discriminant performance in detecting early AD, such as amnestic mild cognitive impairment (aMCI). In contrast to sMRI, functional magnetic resonance imaging (fMRI) can identify brain functional abnormalities in the early stages of cerebral disorders. However, whether the textural features reflecting local functional activity in the hippocampus can improve the diagnostic performance for AD and aMCI remains unclear. In this study, we combined the textural features of the amplitude of low frequency fluctuation (ALFF) in the slow-5 frequency band and structural images in the hippocampus to investigate their diagnostic performance for AD and aMCI using multimodal radiomics technique.

Methods

Totally, 84 AD, 50 aMCI, and 44 normal controls (NCs) were included in the current study. After feature extraction and feature selection, the radiomics models incorporating sMRI images, ALFF values and their combinations in the bilateral hippocampus were established for the diagnosis of AD and aMCI. The effectiveness of these models was evaluated by receiver operating characteristic (ROC) analysis. The radiomics models were further validated using the external data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database.

Results

The results of ROC analysis showed that the radiomics models based on structural images in the hippocampus had a better diagnostic performance for AD compared with the models using ALFF, while the ALFF-based model exhibited better discriminant performance for aMCI than the models with structural images. The radiomics models based on the combinations of structural images and ALFF were found to exhibit the highest accuracy for distinguishing AD from NCs and aMCI from NCs.

Conclusion

In this study, we found that the textural features reflecting local functional activity could improve the diagnostic performance of traditional structural models for both AD and aMCI. These findings may deepen our understanding of the pathogenesis of AD, contributing to the early diagnosis of AD.

Obesity-Related Neuroinflammation: Magnetic Resonance and Microscopy Imaging of the Brain

Obesity is a major risk factor of Alzheimer’s disease and related dementias. The principal feature of dementia is a loss of neurons and brain atrophy. The mechanistic links between obesity and the neurodegenerative processes of dementias are not fully understood, but recent research suggests that obesity-related systemic inflammation and subsequent neuroinflammation may be involved. Adipose tissues release multiple proinflammatory molecules (fatty acids and cytokines) that impact blood and vessel cells, inducing low-grade systemic inflammation that can transition to tissues, including the brain. Inflammation in the brain—neuroinflammation—is one of key elements of the pathobiology of neurodegenerative disorders; it is characterized by the activation of microglia, the resident immune cells in the brain, and by the structural and functional changes of other cells forming the brain parenchyma, including neurons. Such cellular changes have been shown in animal models with direct methods, such as confocal microscopy. In humans, cellular changes are less tangible, as only indirect methods such as magnetic resonance (MR) imaging are usually used. In these studies, obesity and low-grade systemic inflammation have been associated with lower volumes of the cerebral gray matter, cortex, and hippocampus, as well as altered tissue MR properties (suggesting microstructural variations in cellular and molecular composition). How these structural variations in the human brain observed using MR imaging relate to the cellular variations in the animal brain seen with microscopy is not well understood. This review describes the current understanding of neuroinflammation in the context of obesity-induced systemic inflammation, and it highlights need for the bridge between animal microscopy and human MR imaging studies.

Validation of the Alzheimer's disease-resemblance atrophy index in classifying and predicting progression in Alzheimer's disease

Background

Automated tools for characterising dementia risk have the potential to aid in the diagnosis, prognosis, and treatment of Alzheimer's disease (AD). Here, we examined a novel machine learning-based brain atrophy marker, the AD-resemblance atrophy index (AD-RAI), to assess its test-retest reliability and further validate its use in disease classification and prediction.

Methods

Age- and sex-matched 44 probable AD (Age: 69.13 ± 7.13; MMSE: 27-30) and 22 non-demented control (Age: 69.38 ± 7.21; MMSE: 27-30) participants were obtained from the Minimal Interval Resonance Imaging in Alzheimer's Disease (MIRIAD) dataset. Serial T1-weighted images (n = 678) from up to nine time points over a 2-year period, including 179 pairs of back-to-back scans acquired on same participants on the same day and 40 pairs of scans acquired at 2-week intervals were included. All images were automatically processed with AccuBrain® to calculate the AD-RAI. Its same-day repeatability and 2-week reproducibility were first assessed. The discriminative performance of AD-RAI was evaluated using the receiver operating characteristic curve, where DeLong's test was used to evaluate its performance against quantitative medial temporal lobe atrophy (QMTA) and hippocampal volume adjusted by intracranial volume (ICV)-proportions and ICV-residuals methods, respectively (HVR and HRV). Linear mixed-effects modelling was used to investigate longitudinal trajectories of AD-RAI and baseline AD-RAI prediction of cognitive decline. Finally, the longitudinal associations between AD-RAI and MMSE scores were assessed.

Results

AD-RAI had excellent same-day repeatability and excellent 2-week reproducibility. AD-RAI's AUC (99.8%; 95%CI = [99.3%, 100%]) was equivalent to that of QMTA (96.8%; 95%CI = [92.9%, 100%]), and better than that of HVR (86.8%; 95%CI = [78.2%, 95.4%]) or HRV (90.3%; 95%CI = [83.0%, 97.6%]). While baseline AD-RAI was significantly higher in the AD group, it did not show detectable changes over 2 years. Baseline AD-RAI was negatively associated with MMSE scores and the rate of the change in MMSE scores over time. A negative longitudinal association was also found between AD-RAI values and the MMSE scores among AD patients.

Conclusions

The AD-RAI represents a potential biomarker that may support AD diagnosis and be used to predict the rate of future cognitive decline in AD patients.

Endophenotypic effects of the SORL1 variant rs2298813 on regional brain volume in patients with late-onset Alzheimer’s disease

Introduction: Two common variants of sortilin-related receptor 1 gene (SORL1), rs2298813 and rs1784933, have been associated with late-onset Alzheimer’s disease (AD) in the Han Chinese population in Taiwan. However, neuroimaging correlates of these two SORL1 variants remain unknown. We aimed to determine whether the two SORL1 polymorphisms were associated with any volumetric differences in brain regions in late-onset AD patients.

Methods: We recruited 200 patients with late-onset AD from Taipei Veterans General Hospital. All patients received a structural magnetic resonance (MR) imaging brain scan and completed a battery of neurocognitive tests at enrollment. We followed up to assess changes in Mini-Mental State Examination (MMSE) scores in 155 patients (77.5%) at an interval of 2 years. Volumetric measures and cortical thickness of various brain regions were performed using FreeSurfer. Regression analysis controlled for apolipoprotein E status. Multiple comparisons were corrected for using the false discovery rate.

Results: The homozygous major allele of rs2298813 was associated with larger volumes in the right putamen (p = 0.0442) and right pallidum (p = 0.0346). There was no link between the rs1784933 genotypes with any regional volume or thickness of the brain. In the rs2298813 homozygous major allele carriers, the right putaminal volume was associated with verbal fluency (p = 0.008), and both the right putaminal and pallidal volumes were predictive of clinical progression at follow-up (p = 0.020). In the minor allele carriers, neither of the nuclei was related to cognitive test performance or clinical progression.

Conclusion: The major and minor alleles of rs2298813 had differential effects on the right lentiform nucleus volume and distinctively modulated the association between the regional volume and cognitive function in patients with AD.

Early diagnosis of Alzheimer's disease using machine learning: a multi-diagnostic, generalizable approach

BACKGROUND

Early and accurate diagnosis of Alzheimer's disease (AD) is essential for disease management and therapeutic choices that can delay disease progression. Machine learning (ML) approaches have been extensively used in attempts to develop algorithms for reliable early diagnosis of AD, although clinical usefulness, interpretability, and generalizability of the classifiers across datasets and MRI protocols remain limited.

METHODS

We report a multi-diagnostic and generalizable approach for mild cognitive impairment (MCI) and AD diagnosis using structural MRI and ML. Classifiers were trained and tested using subjects from the AD Neuroimaging Initiative (ADNI) database (n = 570) and the Open Access Series of Imaging Studies (OASIS) project database (n = 531). Several classifiers are compared and combined using voting for a decision. Additionally, we report tests of generalizability across datasets and protocols (IR-SPGR and MPRAGE), the impact of using graph theory measures on diagnostic classification performance, the relative importance of different brain regions on classification for better interpretability, and an evaluation of the potential for clinical applicability of the classifier.

RESULTS

Our "healthy controls (HC) vs. AD" classifier trained and tested on the combination of ADNI and OASIS datasets obtained a balanced accuracy (BAC) of 90.6% and a Matthew's correlation coefficient (MCC) of 0.811. Our "HC vs. MCI vs. AD" classifier trained and tested on the ADNI dataset obtained a 62.1% BAC (33.3% being the by-chance cut-off) and 0.438 MCC. Hippocampal features were the strongest contributors to the classification decisions (approx. 25-45%), followed by temporal (approx. 13%), cingulate, and frontal regions (approx. 8-13% each), which is consistent with our current understanding of AD and its progression. Classifiers generalized well across both datasets and protocols. Finally, using graph theory measures did not improve classification performance.

CONCLUSIONS

In sum, we present a diagnostic tool for MCI and AD trained using baseline scans and a follow-up diagnosis regardless of progression, which is multi-diagnostic, generalizable across independent data sources and acquisition protocols, and with transparently reported performance. Rated as potentially clinically applicable, our tool may be clinically useful to inform diagnostic decisions in dementia, if successful in real-world prospective clinical trials.

Deep neural networks learn general and clinically relevant representations of the ageing brain

The discrepancy between chronological age and the apparent age of the brain based on neuroimaging data - the brain age delta - has emerged as a reliable marker of brain health. With an increasing wealth of data, approaches to tackle heterogeneity in data acquisition are vital. To this end, we compiled raw structural magnetic resonance images into one of the largest and most diverse datasets assembled (n=53542), and trained convolutional neural networks (CNNs) to predict age. We achieved state-of-the-art performance on unseen data from unknown scanners (n=2553), and showed that higher brain age delta is associated with diabetes, alcohol intake and smoking. Using transfer learning, the intermediate representations learned by our model complemented and partly outperformed brain age delta in predicting common brain disorders. Our work shows we can achieve generalizable and biologically plausible brain age predictions using CNNs trained on heterogeneous datasets, and transfer them to clinical use cases.

SVR-based Multimodal Active Subspace Analysis for the Brain using Neuroimaging Data.. [DOI: 10.1101/2022.07.28.501879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Understanding the patterns of changes in brain function and structure due to various disorders and diseases is of utmost importance. There have been numerous efforts toward successful biomarker discovery for complex brain disorders by evaluating neuroimaging datasets with novel analytical frameworks. However, due to the multi-faceted nature of the disorders involving a wide and overlapping range of symptoms as well as complex changes in structural and functional brain networks, it is increasingly important to devise computational frameworks that can consider the underlying patterns of heterogeneous changes with specific target assessments, at the same time producing a summarizing output from the high-dimensional neuroimaging data. While various machine learning approaches focus on diagnostic prediction, many learning frameworks analyze important features at the level of brain regions involved in prediction using supervised methods. Unsupervised learning methods have also been utilized to break down the neuroimaging features into lower dimensional components. However, most learning frameworks either do not consider the target assessment information while extracting brain subspaces, or can extract only higher dimensional importance associations as an ordered list of involved features, making manual interpretation at the level of subspaces difficult. We present a novel multimodal active subspace learning framework to understand various subspaces within the brain that are associated with changes in particular biological and cognitive traits. For a given cognitive or biological trait, our framework performs a decomposition of the feature importances to extract robust multimodal subspaces that define the most significant change in the given trait. Through a rigorous cross-validation procedure on an Alzheimer’s disease (AD) dataset, we show that our framework can extract subspaces covering both functional and structural modalities, which are specific to a given clinical assessment (like memory and other cognitive skills) and also retain predictive performance in standard machine learning algorithms. We show that our framework not only uncovers AD-related brain regions (e.g., hippocampus, entorhinal cortex) in the associated brain subspaces, but also enables an automated identification of multiple underlying structural and functional sub-systems of the brain that collectively characterize changes in memory and cognitive skill proficiency related to brain disorders like AD.

Atrophy of the hippocampal CA1 subfield relates to long-term forgetting in focal epilepsy

OBJECTIVE

The mechanisms underlying accelerated long-term forgetting (ALF) in patients with epilepsy are still under investigation. We examined the contribution of hippocampal subfields and their morphology to long-term memory performance in patients with focal epilepsy.

METHODS

We prospectively assessed long-term memory and performed magnetic resonance imaging in 80 patients with focal epilepsy (61 with temporal lobe epilepsy and 19 with extratemporal lobe epilepsy) and 30 healthy controls. The patients also underwent electroencephalography recording. Verbal and visuospatial memory was tested 30 seconds, 10 minutes, and 1 week after learning. We assessed the volumes of the whole hippocampus and seven subfields and deformation of the hippocampal shape. The contributions of the hippocampal volumes and shape deformation to long-term forgetting, controlling for confounding factors, including the presence of interictal epileptiform discharges, were assessed by multiple regression analyses.

RESULTS

Patients with focal epilepsy had lower intelligence quotients and route recall scores at 10 minutes than controls. The focal epilepsy group had smaller volumes of both the right and left hippocampal tails than the control group, but there were no significant group differences for the volumes of the whole hippocampus or other hippocampal subfields. Multiple regression analyses showed a significant association between the left CA1 volume and the 1-week story retention (β = 7.76; Bonferroni-corrected P = 0.044), but this was not found for the whole hippocampus or other subfield volumes. Hippocampal shape analyses revealed that atrophy of the superior-lateral, superior-central, and inferior-medial regions of the left hippocampus, corresponding to CA1 and CA2/3, was associated with the verbal retention rate.

SIGNIFICANCE

Our results suggest that atrophy of the hippocampal CA1 region and its associated structures disrupts long-term memory consolidation in focal epilepsy. Neuronal cell loss in specific hippocampal subfields could be a key underlying cause of ALF in patients with epilepsy.

Structural Connectivity in Down Syndrome and Alzheimer's Disease

Down syndrome (DS) arises from the triplication of chromosome 21, which leads to an atypical neurodevelopment and the overproduction of the amyloid precursor protein, predisposing to early Alzheimer's disease (AD). Not surprisingly, trisomy 21 is widely considered a model to study predementia stages of AD. After decades, in which neural loss was the main focus, research in AD is now moving toward understanding the neurodegenerative aspects affecting white matter. Motivated by the development of magnetic resonance imaging (MRI)-based diffusion techniques, this shift in focus has led to several exploratory studies on both young and older individuals with DS. In this review, we synthesise the initial efforts made by researchers in characterising in-vivo structural connectivity in DS, together with the AD footprint on top of such pre-existing connectivity related to atypical brain development. The white matter structures found to be affected in DS are the corpus callosum and all the main long-association fibres, namely the inferior fronto-occipital fasciculus, the inferior and superior longitudinal fasciculus, the uncinate fasciculus and the cingulum bundle. Furthermore, the cingulum bundle and the corpus callosum appear to be particularly sensitive to early AD changes in this population. Findings are discussed in terms of their functional significance, alongside methodological considerations and implications for future research.

Tea Polyphenols as Prospective Natural Attenuators of Brain Aging

No organism can avoid the process of aging, which is often accompanied by chronic disease. The process of biological aging is driven by a series of interrelated mechanisms through different signal pathways, including oxidative stress, inflammatory states, autophagy and others. In addition, the intestinal microbiota play a key role in regulating oxidative stress of microglia, maintaining homeostasis of microglia and alleviating age-related diseases. Tea polyphenols can effectively regulate the composition of the intestinal microbiota. In recent years, the potential anti-aging benefits of tea polyphenols have attracted increasing attention because they can inhibit neuroinflammation and prevent degenerative effects in the brain. The interaction between human neurological function and the gut microbiota suggests that intervention with tea polyphenols is a possible way to alleviate brain-aging. Studies have been undertaken into the possible mechanisms underpinning the preventative effect of tea polyphenols on brain-aging mediated by the intestinal microbiota. Tea polyphenols may be regarded as potential neuroprotective substances which can act with high efficiency and low toxicity.

Comparison of Tau and Amyloid-β Targeted Immunotherapy Nanoparticles for Alzheimer’s Disease

Alzheimer’s disease (AD) is a rapidly growing global concern associated with the accumulation of amyloid-β plaques and intracellular neurofibrillary tangles in the brain combined with a high acetylcholinesterase activity. AD diagnosis is usually made too late, when patients have an extensive neuronal death, and brain damage is irreversible. Several therapeutic targets have been defined mainly related to two hypotheses of AD: the tau hypothesis and the amyloid-β hypothesis. Here, we intend to investigate and to compare different therapeutic approaches for AD, mainly based on nanoparticles (NPs) targeted at the brain and at the pathological hallmarks of the disease. We analyzed preclinical trials that have successfully improved drug bioavailability in the brain by using targeted nanocarriers towards either tau, amyloid-β, or both. We then compared these trials to find out which protein is more efficient in therapeutic targeting. We found that the search for a cure was mostly based on the amyloid-β hypothesis, with Aβ dysplasia emerging as the most confirmed and convincing therapeutic target. Targeted NPs have proven useful to enhance both the bioavailability and the performance of therapies against AD in animal models. A better understanding of AD mechanisms will help the successful application of targeted NPs for combined therapies.

Does Loss of Integrity of the Cingulum Bundle Link Amyloid-β Accumulation and Neurodegeneration in Alzheimer’s Disease?

Background: Alzheimer’s disease is characterized by the accumulation of amyloid-β (Aβ) into plaques, aggregation of tau into neurofibrillary tangles, and neurodegenerative processes including atrophy. However, there is a poorly understood spatial discordance between initial Aβ deposition and local neurodegeneration. Objective: Here, we test the hypothesis that the cingulum bundle links Aβ deposition in the cingulate cortex to medial temporal lobe (MTL) atrophy. Methods: 21 participants with mild cognitive impairment (MCI) from the UMC Utrecht memory clinic (UMCU, discovery sample) and 37 participants with MCI from Alzheimer’s Disease Neuroimaging Initiative (ADNI, replication sample) with available Aβ-PET scan, T1-weighted and diffusion-weighted MRI were included. Aβ load of the cingulate cortex was measured by the standardized uptake value ratio (SUVR), white matter integrity of the cingulum bundle was assessed by mean diffusivity and atrophy of the MTL by normalized MTL volume. Relationships were tested with linear mixed models, to accommodate multiple measures for each participant. Results: We found at most a weak association between cingulate Aβ and MTL volume (added R2 <0.06), primarily for the posterior hippocampus. In neither sample, white matter integrity of the cingulum bundle was associated with cingulate Aβ or MTL volume (added R2 <0.01). Various sensitivity analyses (Aβ-positive individuals only, posterior cingulate SUVR, MTL sub region volume) provided similar results. Conclusion: These findings, consistent in two independent cohorts, do not support our hypothesis that loss of white matter integrity of the cingulum is a connecting factor between cingulate gyrus Aβ deposition and MTL atrophy.

Through Thick and Thin: Baseline Cortical Volume and Thickness Predict Performance and Response to Transcranial Direct Current Stimulation in Primary Progressive Aphasia

Objectives

We hypothesized that measures of cortical thickness and volume in language areas would correlate with response to treatment with high-definition transcranial direct current stimulation (HD-tDCS) in persons with primary progressive aphasia (PPA).

Materials and Methods

In a blinded, within-group crossover study, PPA patients (N = 12) underwent a 2-week intervention HD-tDCS paired with constraint-induced language therapy (CILT). Multi-level linear regression (backward-fitted models) were performed to assess cortical measures as predictors of tDCS-induced naming improvements, measured by the Western Aphasia Battery-naming subtest, from baseline to immediately after and 6 weeks post-intervention.

Results

Greater baseline thickness of the pars opercularis significantly predicted naming gains (p = 0.03) immediately following intervention, while greater thickness of the middle temporal gyrus (MTG) and lower thickness of the superior temporal gyrus (STG) significantly predicted 6-week naming gains (p's < 0.02). Thickness did not predict naming gains in sham. Volume did not predict immediate gains for active stimulation. Greater volume of the pars triangularis and MTG, but lower STG volume significantly predicted 6-week naming gains in active stimulation. Greater pars orbitalis and MTG volume, and lower STG volume predicted immediate naming gains in sham (p's < 0.05). Volume did not predict 6-week naming gains in sham.

Conclusion

Cortical thickness and volume were predictive of tDCS-induced naming improvement in PPA patients. The finding that frontal thickness predicted immediate active tDCS-induced naming gains while temporal areas predicted naming changes at 6-week suggests that a broader network of regions may be important for long-term maintenance of treatment gains. The finding that volume predicted immediate naming performance in the sham condition may reflect the benefits of behavioral speech language therapy and neural correlates of its short-lived treatment gains. Collectively, thickness and volume were predictive of treatment gains in the active condition but not sham, suggesting that pairing HD-tDCS with CILT may be important for maintaining treatment effects.

A link between age, affect, and predictions?

The prevalence of depressive symptoms decreases from late adolescence to middle age adulthood. Furthermore, despite significant losses in motor and cognitive functioning, overall emotional well-being tends to increase with age, and a bias to positive information has been observed multiple times. Several causes have been discussed for this age-related development, such as improvement in emotion regulation, less regret, and higher socioeconomic status. Here, we explore a further explanation. Our minds host mental models that generate predictions about forthcoming events to successfully interact with our physical and social environment. To keep these models faithful, the difference between the predicted and the actual event, that is, the prediction error, is computed. We argue that prediction errors are attenuated in the middle age and older mind, which, in turn, may translate to less negative affect, lower susceptibility to affective disorders, and possibly, to a bias to positive information. Our proposal is primarily linked to perceptual inferences, but may hold as well for higher-level, cognitive, and emotional forms of error processing.

Hippocampal calcification and its effects on cognitive function and symptoms in dementia

BACKGROUND

Hippocampal calcification (HC), highly prevalent in older people, has not attracted attention until recently. Despite its potential effects on cognition and behaviour, and its possible impact on the diagnosis and severity of dementia, it has not been investigated. This study aimed to evaluate the prevalence of HC and its influence on cognition and behavioural symptoms in patients with dementia.

METHODS

Data from consecutive patients who visited a medical centre for dementia, for the first time between April 2016 and September 2018, were extracted and analysed. These data included the patients' demographics, the presence of HC and hippocampal thickness as measured on computed tomography, the diagnosis of dementia and its type, cognitive function measured using the Mini-Mental State Examination and the Clock Drawing Test, and the chief complaints or symptoms prompting the visit.

RESULTS

A high incidence of HC (85/267 patients) was observed. There was no significant difference in the ages of patients with and without HC. Patients with HC had higher cognitive function than those without HC at their first visit. This result was contrary to our expectations as it was not explained by the chief complaints recorded at the first visit.

CONCLUSIONS

Our study showed a high prevalence of HC in older patients with dementia. Patients with HC had better cognitive function than did those without HC during their first hospital visit. This study suggests that HC may not affect the cognitive functions related to dementia. However, further research is needed to evaluate the long-term consequences of dementia with HC.

Brain Morphometric Correlates of Depressive Symptoms among Patients with and without Dementia

Introduction

Findings regarding brain morphometry among patients with dementia and concomitant depressive symptoms have been inconsistent. Thus, the aim of the present study was to test the hypothesis that dementia and concomitant depressive symptoms are associated with structural brain changes in the temporal lobe measured with structural magnetic resonance imaging (MRI).

Methods

A sample of 492 patients from Norwegian memory clinics (n = 363) and Old Age Psychiatry services (n = 129) was studied. The assessment included the Cornell Scale for Depression in Dementia (CSDD), Instrumental Activities of Daily Living Scale, Mini Mental State Examination, and MRI of the brain, processed with FreeSurfer to derive ROI measures of cortical thickness, volume, and area using the Desikan-Killiany parcellation, as well as subcortical volumes. Dementia was diagnosed according to ICD-10 research criteria. Correlates of brain morphometry using multiple linear regression were examined.

Results

Higher scores on the CSDD were associated with larger cortical volume (β = 0.125; p value = 0.003) and area of the left isthmus of the cingulate gyrus (β = 0.151; p value = <0.001) across all patients. Inclusion of an interaction term (dementia × CSDD) revealed a smaller area in the left temporal pole (β = −0.345; p value = 0.001) and right-transverse temporal cortex (β = −0.321; p value = 0.001) in patients with dementia and depressive symptoms.

Discussion/Conclusion

We confirm the previous findings of structural brain changes in temporal regions among patients with dementia and concomitant depressive symptoms. This may contribute to a better understanding of the mechanisms underlying depression in dementia. To the best of our knowledge, this is the largest study conducted on this topic to date.

Classification of early-MCI patients from healthy controls using evolutionary optimization of graph measures of resting-state fMRI, for the Alzheimer's disease neuroimaging initiative

Identifying individuals with early mild cognitive impairment (EMCI) can be an effective strategy for early diagnosis and delay the progression of Alzheimer's disease (AD). Many approaches have been devised to discriminate those with EMCI from healthy control (HC) individuals. Selection of the most effective parameters has been one of the challenging aspects of these approaches. In this study we suggest an optimization method based on five evolutionary algorithms that can be used in optimization of neuroimaging data with a large number of parameters. Resting-state functional magnetic resonance imaging (rs-fMRI) measures, which measure functional connectivity, have been shown to be useful in prediction of cognitive decline. Analysis of functional connectivity data using graph measures is a common practice that results in a great number of parameters. Using graph measures we calculated 1155 parameters from the functional connectivity data of HC (n = 72) and EMCI (n = 68) extracted from the publicly available database of the Alzheimer's disease neuroimaging initiative database (ADNI). These parameters were fed into the evolutionary algorithms to select a subset of parameters for classification of the data into two categories of EMCI and HC using a two-layer artificial neural network. All algorithms achieved classification accuracy of 94.55%, which is extremely high considering single-modality input and low number of data participants. These results highlight potential application of rs-fMRI and efficiency of such optimization methods in classification of images into HC and EMCI. This is of particular importance considering that MRI images of EMCI individuals cannot be easily identified by experts.

Systemic inflammatory markers in relation to cognitive function and measures of brain atrophy: a Mendelian randomization study

Observational studies have implied associations between multiple cytokines and cognitive decline, anti-inflammatory drugs however did not yield any protective effects on cognitive decline. We aimed to assess the associations of systemic inflammation, as measured by multiple cytokine and growth factor, with cognitive performance and brain atrophy using two-sample Mendelian randomization (MR). Independent genetic instruments (p < 5e − 8 and p < 5e − 6) for 41 systemic inflammatory markers were retrieved from a genome-wide association study conducted in 8293 Finnish participants. Summary statistics for gene-outcome associations were obtained for cognitive performance (N = 257,841) and for brain atrophy measures of cerebral cortical surface area and thickness (N = 51,665) and hippocampal volume (N = 33,536). To rule out the heterogeneity in the cognitive performance, we additionally included three domains: the fluid intelligence score (N = 108,818), prospective memory result (N = 111,099), and reaction time (N = 330,069). Main results were computed by inverse-variance weighting; sensitivity analyses taking pleiotropy and invalid instruments into account were performed by using weighted-median estimator, MR-Egger, and MR PRESSO. After correcting for multiple testing using false discovery rate, only genetically predicted (with p < 5e − 6 threshold) per-SD (standard deviation) higher IL-8 was associated with − 0.103 (− 0.155, − 0.051, p_adjusted = 0.004) mm³ smaller hippocampal volume and higher intelligence fluid score [β: 0.103 SD (95% CI: 0.042, 0.165), p_adjusted = 0.041]. Sensitivity analyses generally showed similar results, and no pleiotropic effect, heterogeneity, or possible reverse causation was detected. Our results suggested a possible causal association of high IL-8 levels with better cognitive performance but smaller hippocampal volume among the general healthy population, highlighting the complex role of inflammation in dementia-related phenotypes. Further research is needed to elucidate mechanisms underlying these associations.

Sirt1 protects against hippocampal atrophy and its induced cognitive impairment in middle-aged mice

BACKGROUND

Sirtuin 1 (Sirt1) is a recognized longevity gene and has been shown to be associated with aging and its related diseases. Hippocampal volume is considered to be the most sensitive brain imaging phenotype for cognition, but the effect of Sirt1 on hippocampal morphology during aging has not been reported.

RESULTS

Herein, we investigated the effect of conditional Sirt1 knockdown on hippocampal volume in middle-aged mice, as well as its cognitive function and the underlying molecular mechanisms. Brain structural magnetic resonance imaging (MRI) showed that adeno-associated virus (AAV) mediated hippocampal Sirt1 knockdown caused hippocampal atrophy in 8-month-old mice. Open field test (OFT) and Morris Water Maze (MWM) test revealed that hippocampal Sirt1 knockdown significantly weakened spatial learning and memory of mice without effect on anxiety and exploratory behavior. Western blotting analysis showed that P-tau levels at serine 396 epitope were significantly increased with slightly decreased T-tau levels, while PSD95 and NMDAR2B levels were obviously reduced, indicating that hippocampal Sirt1 knockdown could activate tau hyperphosphorylation and synaptic damage.

CONCLUSIONS

This work revealed that Sirt1 is an important protective gene against hippocampal atrophy and its induced cognitive impairment during aging, providing potential therapeutic targets for the prevention and intervention of aging-related neuropsychic diseases.

Training with Odors Impacts Hippocampal Thickness in Patients with Mild Cognitive Impairment

Background: The olfactory system is affected early in Alzheimer’s disease and olfactory loss can already be observed in patients with mild cognitive impairment (MCI). Olfactory training is effective for improving olfactory and cognitive function by stimulating the olfactory pathway, but its effect on patients with MCI remains unclear. Objective: The aim of this randomized, prospective, controlled, blinded study was to assess whether a 4-month period of olfactory training (frequent short-term sniffing various odors) may have an effect on olfactory function, cognitive function, and morphology of medial temporal lobe (MTL) subregions and olfactory bulb in MCI patients. Methods: A total of thirty-seven MCI patients were randomly assigned to the training group or a placebo group, which were performed twice a day for 4 months. Olfactory assessments, cognitive tests and magnetic resonance imaging were performed at the baseline and follow-up period. Results: After the training, there was an increase in odor discrimination, and increased cortical thickness of bilateral hippocampus (CA23DG and CA1) and mean MTL. Additionally, the change of olfactory score was positively associated with change of volume of olfactory bulb and hippocampus; the change of global cognition was positively associated with change of cortical thickness of hippocampus, entorhinal cortex and mean MTL; the change of cortical thickness of entorhinal cortex was positively associated with change of executive function. Conclusion: Olfactory training was associated with an increase in cortical thickness of the hippocampus but not olfactory bulb volume in patients with MCI. Olfactory training may serve as an early intervention of preventing hippocampal atrophy.

Functional Imaging for Neurodegenerative Diseases

Diagnosis and monitoring of neurodegenerative diseases has changed profoundly over the past twenty years. Biomarkers are now included in most diagnostic procedures as well as in clinical trials. Neuroimaging biomarkers provide access to brain structure and function over the course of neurodegenerative diseases. They have brought new insights into a wide range of neurodegenerative diseases and have made it possible to describe some of the imaging challenges in clinical populations. MRI mainly explores brain structure while molecular imaging, functional MRI and electro- and magnetoencephalography examine brain function. In this paper, we describe and analyse the current and potential contribution of MRI and molecular imaging in the field of neurodegenerative diseases.

Lower Posterior Cingulate N-Acetylaspartate to Creatine Level in Early Detection of Biologically Defined Alzheimer's Disease

Alzheimer’s disease (AD) was recently defined as a biological construct to reflect neuropathologic status, and both abnormal amyloid and tau are required for a diagnosis of AD. We aimed to determine the proton MR spectroscopic (1H-MRS) patterns of the posterior cingulate in biologically defined AD. A total of 68 participants were included in this study, comprising 37 controls, 16 early AD, and 15 late AD, who were classified according to their amyloid and tau status and presence of hippocampal atrophy. Compared with controls, early AD showed lower N-acetylaspartate (NAA)/creatine (Cr) (p = 0.003), whereas late AD showed lower NAA/Cr and higher myoInositol (mI)/Cr (all with p < 0.05). Lower NAA/Cr correlated with a greater global amyloid load (r = −0.47, p < 0.001) and tau load (r = −0.51, p < 0.001) and allowed a discrimination of early AD from controls (p < 0.001). Subgroup analysis showed that NAA/Cr also allowed a differentiation of early AD from controls in the cognitively unimpaired subjects, with an area under the receiver operating characteristics curve, sensitivity, and specificity of 0.96, 100%, and 83.8%, respectively. Lower posterior cingulate NAA levels may help to inform underlying neuropathologic changes in the early stage of AD.

PET Imaging in Animal Models of Alzheimer’s Disease

The successful development and translation of PET imaging agents targeting β-amyloid plaques and hyperphosphorylated tau tangles have allowed for in vivo detection of these hallmarks of Alzheimer’s disease (AD) antemortem. Amyloid and tau PET have been incorporated into the A/T/N scheme for AD characterization and have become an integral part of ongoing clinical trials to screen patients for enrollment, prove drug action mechanisms, and monitor therapeutic effects. Meanwhile, preclinical PET imaging in animal models of AD can provide supportive information for mechanistic studies. With the recent advancement of gene editing technologies and AD animal model development, preclinical PET imaging in AD models will further facilitate our understanding of AD pathogenesis/progression and the development of novel treatments. In this study, we review the current state-of-the-art in preclinical PET imaging using animal models of AD and suggest future research directions.

FLAIR MRI biomarkers of the normal appearing brain matter are related to cognition

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Normal appearing brain matter (NABM) biomarkers in FLAIR MRI are related to cognition.

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NABM texture in FLAIR MRI is correlated to mean diffusivity (MD) in dMRI.

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Analysis conducted on large multicentre FLAIR MRI dataset: 1400 subjects, 87 centers.

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NABM biomarkers vary differently across age and MoCA categories.

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Biomarkers showed differences in patients with AD dementia and vascular disease.

A novel biomarker panel was proposed to quantify macro and microstructural biomarkers from the normal-appearing brain matter (NABM) in multicentre fluid-attenuation inversion recovery (FLAIR) MRI. The NABM is composed of the white and gray matter regions of the brain, with the lesions and cerebrospinal fluid removed. The primary hypothesis was that NABM biomarkers from FLAIR MRI are related to cognitive outcome as determined by MoCA score. There were three groups of features designed for this task based on 1) texture: microstructural integrity (MII), macrostructural damage (MAD), microstructural damage (MID), 2) intensity: median, skewness, kurtosis and 3) volume: NABM to ICV volume ratio. Biomarkers were extracted from over 1400 imaging volumes from more than 87 centres and unadjusted ANOVA analysis revealed significant differences in means of the MII, MAD, and NABM volume biomarkers across all cognitive groups. In an adjusted ANCOVA model, a significant relationship between MoCA categories was found that was dependent on subject age for MII, MAD, intensity, kurtosis and NABM volume biomarkers. These results demonstrate that structural brain changes in the NABM are related to cognitive outcome (with different relationships depending on the age of the subjects). Therefore these biomarkers have high potential for clinical translation. As a secondary hypothesis, we investigated whether texture features from FLAIR MRI can quantify microstructural changes related to how “structured” or “damaged” the tissue is. Based on correlation analysis with diffusion weighted MRI (dMRI), it was shown that FLAIR MRI texture biomarkers (MII and MAD) had strong correlations to mean diffusivity (MD) which is related to tissue degeneration in the GM and WM regions. As FLAIR MRI is routinely collected for clinical neurological examinations, novel biomarkers from FLAIR MRI could be used to supplement current clinical biomarkers and for monitoring disease progression. Biomarkers could also be used to stratify patients into homogeneous disease subgroups for clinical trials, or to learn more about mechanistic development of dementia disease.

Antroquinonol administration in animal preclinical studies for Alzheimer's disease (AD): A new avenue for modifying progression of AD pathophysiology

Despite the rise of Alzheimer's disease (AD) in an ageing population, no cure is currently available for this disorder. This study assessed the role of a natural compound, Antroquinonol, in modifying the progression of AD when administered at the start and/or before appearance of symptoms and when the disease was well established, in a transgenic animal model. Antroquinonol was administered daily for 8 weeks, in 11 week (early stage) and 9 month (late stage) male transgenic mice (3 times Transgenic mice PS1_M146V, APP_Swe, and tau_P301L, 3 ​Tg XAD) and their respective aged controls. Behavioural testing (including Elevated Plus Maze Watermaze, Recognition object testing and Y maze) was performed at the end of the drug administration. In addition AD biomarkers (Amyloid beta 42 (Aβ42), tau and phospho-tau levels), oxidative stress and inflammatory markers, were assessed in tested mice brains after their sacrifice at the end of the treatment. When administered before the start of symptoms at 11 weeks, Antroquinonol treatment at 34 ​mg/kg (D2) and more consistently at 75 ​mg/kg (D3), had a significant effect on reducing systemic inflammatory markers (Interleukin 1, IL-1β and TNF-α) and AD biomarker (Amyloid Beta 42, Aβ42 and tau) levels in the brain. The reduction of behavioural impairment reported for 3TgXAD mice was observed significantly for the D3 drug dose only and for all behavioural tests, when administered at 11 weeks.

Similarly, beneficial effects of Antroquinonol (at higher dose D3) were noted in the transgenic mice in terms of AD biomarkers (tau and phosphorylated-tau), systemic inflammatory (IL-1β), brain anti-inflammatory (Nrf2) and oxidative (3-Nitrotyrosine, 3NT) markers. Improvement of memory impairment was also reported when Antroquinonol (D3) was administered at late stage (9 months).

Since Antroquinonol has been used without adverse effects in previous successful clinical trials, this drug may offer a new avenue of treatment to modify AD development and progression.

Validation of an automatic tool for the rapid measurement of brain atrophy and white matter hyperintensity: QyScore®

OBJECTIVES

QyScore® is an imaging analysis tool certified in Europe (CE marked) and the US (FDA cleared) for the automatic volumetry of grey and white matter (GM and WM respectively), hippocampus (HP), amygdala (AM), and white matter hyperintensity (WMH). Here we compare QyScore® performances with the consensus of expert neuroradiologists.

METHODS

Dice similarity coefficient (DSC) and the relative volume difference (RVD) for GM, WM volumes were calculated on 50 3DT1 images. DSC and the F1 metrics were calculated for WMH on 130 3DT1 and FLAIR images. For each index, we identified thresholds of reliability based on current literature review results. We hypothesized that DSC/F1 scores obtained using QyScore® markers would be higher than the threshold. In contrast, RVD scores would be lower. Regression analysis and Bland-Altman plots were obtained to evaluate QyScore® performance in comparison to the consensus of three expert neuroradiologists.

RESULTS

The lower bound of the DSC/F1 confidence intervals was higher than the threshold for the GM, WM, HP, AM, and WMH, and the higher bounds of the RVD confidence interval were below the threshold for the WM, GM, HP, and AM. QyScore®, compared with the consensus of three expert neuroradiologists, provides reliable performance for the automatic segmentation of the GM and WM volumes, and HP and AM volumes, as well as WMH volumes.

CONCLUSIONS

QyScore® represents a reliable medical device in comparison with the consensus of expert neuroradiologists. Therefore, QyScore® could be implemented in clinical trials and clinical routine to support the diagnosis and longitudinal monitoring of neurological diseases.

KEY POINTS

• QyScore® provides reliable automatic segmentation of brain structures in comparison with the consensus of three expert neuroradiologists. • QyScore® automatic segmentation could be performed on MRI images using different vendors and protocols of acquisition. In addition, the fast segmentation process saves time over manual and semi-automatic methods. • QyScore® could be implemented in clinical trials and clinical routine to support the diagnosis and longitudinal monitoring of neurological diseases.

Artificial intelligence in brain MRI analysis of Alzheimer's disease over the past 12 years: A systematic review

INTRODUCTION

Multiple structural brain changes in Alzheimer's disease (AD) and mild cognitive impairment (MCI) have been revealed on magnetic resonance imaging (MRI). There is a fast-growing effort in applying artificial intelligence (AI) to analyze these data. Here, we review and evaluate the AI studies in brain MRI analysis with synthesis.

METHODS

A systematic review of the literature, spanning the years from 2009 to 2020, was completed using the PubMed database. AI studies using MRI imaging to investigate normal aging, mild cognitive impairment, and AD-dementia were retrieved for review. Bias assessment was completed using the PROBAST criteria.

RESULTS

97 relevant studies were included in the review. The studies were typically focused on the classification of AD, MCI, and normal aging (71% of the reported studies) and the prediction of MCI conversion to AD (25%). The best performance was achieved by using the deep learning-based convolution neural network algorithms (weighted average accuracy 89%), in contrast to 76-86% using Logistic Regression, Support Vector Machines, and other AI methods.

DISCUSSION

The synthesized evidence is paramount to developing sophisticated AI approaches to reliably capture and quantify multiple subtle MRI changes in the whole brain that exemplify the complexity and heterogeneity of AD and brain aging.

High-Resolution Digital Panorama of Multiple Structures in Whole Brain of Alzheimer's Disease Mice

Simultaneously visualizing Amyloid-β (Aβ) plaque with its surrounding brain structures at the subcellular level in the intact brain is essential for understanding the complex pathology of Alzheimer's disease, but is still rarely achieved due to the technical limitations. Combining the micro-optical sectioning tomography (MOST) system, whole-brain Nissl staining, and customized image processing workflow, we generated a whole-brain panorama of Alzheimer's disease mice without specific labeling. The workflow employed the steps that include virtual channel splitting, feature enhancement, iso-surface rendering, direct volume rendering, and feature fusion to extract and reconstruct the different signals with distinct gray values and morphologies. Taking advantage of this workflow, we found that the denser-distribution areas of Aβ plaques appeared with relatively more somata and smaller vessels, but show a dissimilar distributing pattern with nerve tracts. In addition, the entorhinal cortex and adjacent subiculum regions present the highest density and biggest diameter of plaques. The neuronal processes in the vicinity of these Aβ plaques showed significant structural alternation such as bending or abrupt branch ending. The capillaries inside or adjacent to the plaques were observed with abundant distorted micro-vessels and abrupt ending. Depicting Aβ plaques, somata, nerve processes and tracts, and blood vessels simultaneously, this panorama enables us for the first time, to analyze how the Aβ plaques interact with capillaries, somata, and processes at a submicron resolution of 3D whole-brain scale, which reveals potential pathological effects of Aβ plaques from a new cross-scale view. Our approach opens a door to routine systematic studies of complex interactions among brain components in mouse models of Alzheimer's disease.

Alzheimer's Disease: A Silent Pandemic - A Systematic Review on the Situation and Patent Landscape of the Diagnosis

BACKGROUND

Alzheimer's disease (AD) is characterized by cognitive impairment, tau protein deposits, and amyloid beta plaques. AD impacted 44 million people in 2016, and it is estimated to affect 100 million people by 2050. AD is disregarded as a pandemic compared with other diseases. To date, there is no effective treatment or diagnosis.

OBJECTIVE

We aimed to discuss the current tools used to diagnose COVID-19, to point out their potential to be adapted for AD diagnosis, and to review the landscape of existing patents in the AD field and future perspectives for AD diagnosis.

METHOD

We carried out a scientific screening following a research strategy in PubMed; Web of Science; the Derwent Innovation Index; the KCI-Korean Journal Database; SciELO; the Russian Science Citation index; and the CDerwent, EDerwent, and MDerwent index databases.

RESULTS

A total of 326 from 6,446 articles about AD and 376 from 4,595 articles about COVID-19 were analyzed. Of these, AD patents were focused on biomarkers and neuroimaging with no accurate, validated diagnostic methods, and only 7% of kit development patents were found. In comparison, COVID-19 patents were 60% about kit development for diagnosis; they are highly accurate and are now commercialized.

CONCLUSION

AD is still neglected and not recognized as a pandemic that affects the people and economies of all nations. There is a gap in the development of AD diagnostic tools that could be filled if the interest and effort that has been invested to tackle the COVID-19 emergency could also be applied for innovation.