Comparison of different MRI brain atrophy rate measures with clinical disease progression in AD

MRI-Derived AD Signature of Cortical Thinning and Plasma P-Tau217 for Predicting Alzheimer Dementia Among Community-Dwelling Older Adults

Background and Objectives

Structural brain MRI and blood-based phosphorylated tau (p-tau) measures are among the least invasive and least expensive Alzheimer's disease (AD) biomarkers to date. The extent to which these biomarkers may outperform one another in predicting future Alzheimer dementia diagnosis is poorly understood, however. This study investigated 2 specific AD biomarkers, i.e., a cortical thickness signature of AD (AD-CT) and plasma p-tau217, for predicting Alzheimer dementia.

Methods

Data came from community-dwelling older participants of the Religious Orders Study or the Rush Memory and Aging Project. AD-CT was obtained from 3T MRI scans using a magnetization-prepared rapid acquisition gradient echo sequence and by averaging thickness from previously identified cortical regions implicated in AD. Plasma p-tau217 was quantified using an immunoassay developed by Lilly Research Laboratories on the MSD platform. Both MRI scans and blood specimens were collected at the same visits, and subsequent diagnoses of Alzheimer dementia were determined through annual detailed clinical evaluations. Cox proportional hazards models examined the associations of the 2 biomarkers with incident Alzheimer dementia, and prediction accuracy was assessed using c-statistics.

Results

A total of 198 older adults, on average 84 years of age, were included. Over a mean follow-up of 4 years, 60 (30%) individuals developed Alzheimer dementia. AD-CT (hazard ratio: 1.71, 95% CI 1.26-2.31) and separately plasma p-tau217 (hazard ratio: 2.57, 95% CI 1.83-3.61) were associated with incident Alzheimer dementia. The c-statistic for prediction accuracy was consistently higher for plasma p-tau217 (between 0.74 and 0.81) than AD-CT (between 0.70 and 0.75) across a range of time horizons. Furthermore, with both biomarkers included in the same model, there was only modest improvement in the c-statistic due to AD-CT.

Discussion

Plasma p-tau217 outperforms an imaging-based cortical thickness signature of AD in predicting future Alzheimer dementia diagnosis. Furthermore, the AD cortical thickness signature adds little to the prediction accuracy above and beyond plasma p-tau217.

Exploration of neuroanatomical characteristics to differentiate prodromal Alzheimer's disease from cognitively unimpaired amyloid-positive individuals

Differentiating clinical stages based solely on positive findings from amyloid PET is challenging. We aimed to investigate the neuroanatomical characteristics at the whole-brain level that differentiate prodromal Alzheimer's disease (AD) from cognitively unimpaired amyloid-positive individuals (CU A+) in relation to amyloid deposition and regional atrophy. We included 45 CU A+ participants and 135 participants with amyloid-positive prodromal AD matched 1:3 by age, sex, and education. All participants underwent 18F-florbetaben positron emission tomography and 3D structural T1-weighted magnetic resonance imaging. We compared the standardized uptake value ratios (SUVRs) and volumes in 80 regions of interest (ROIs) between CU A+ and prodromal AD groups using independent t-tests, and employed the least absolute selection and shrinkage operator (LASSO) logistic regression model to identify ROIs associated with prodromal AD in relation to amyloid deposition, regional atrophy, and their interaction. After applying False Discovery Rate correction at < 0.1, there were no differences in global and regional SUVR between CU A+ and prodromal AD groups. Regional volume differences between the two groups were observed in the amygdala, hippocampus, entorhinal cortex, insula, parahippocampal gyrus, and inferior temporal and parietal cortices. LASSO logistic regression model showed significant associations between prodromal AD and atrophy in the entorhinal cortex, inferior parietal cortex, both amygdalae, and left hippocampus. The mean SUVR in the right superior parietal cortex (beta coefficient = 0.0172) and its interaction with the regional volume (0.0672) were also selected in the LASSO model. The mean SUVR in the right superior parietal cortex was associated with an increased likelihood of prodromal AD (Odds ratio [OR] 1.602, p = 0.014), particularly in participants with lower regional volume (OR 3.389, p < 0.001). Only regional volume differences, not amyloid deposition, were observed between CU A+ and prodromal AD. The reduced volume in the superior parietal cortex may play a significant role in the progression to prodromal AD through its interaction with amyloid deposition in that region.

Exploring morphological similarity and randomness in Alzheimer's disease using adjacent grey matter voxel-based structural analysis

BACKGROUND

Alzheimer's disease is characterized by large-scale structural changes in a specific pattern. Recent studies developed morphological similarity networks constructed by brain regions similar in structural features to represent brain structural organization. However, few studies have used local morphological properties to explore inter-regional structural similarity in Alzheimer's disease.

METHODS

Here, we sourced T1-weighted MRI images of 342 cognitively normal participants and 276 individuals with Alzheimer's disease from the Alzheimer's Disease Neuroimaging Initiative database. The relationships of grey matter intensity between adjacent voxels were defined and converted to the structural pattern indices. We conducted the information-based similarity method to evaluate the structural similarity of structural pattern organization between brain regions. Besides, we examined the structural randomness on brain regions. Finally, the relationship between the structural randomness and cognitive performance of individuals with Alzheimer's disease was assessed by stepwise regression.

RESULTS

Compared to cognitively normal participants, individuals with Alzheimer's disease showed significant structural pattern changes in the bilateral posterior cingulate gyrus, hippocampus, and olfactory cortex. Additionally, individuals with Alzheimer's disease showed that the bilateral insula had decreased inter-regional structural similarity with frontal regions, while the bilateral hippocampus had increased inter-regional structural similarity with temporal and subcortical regions. For the structural randomness, we found significant decreases in the temporal and subcortical areas and significant increases in the occipital and frontal regions. The regression analysis showed that the structural randomness of five brain regions was correlated with the Mini-Mental State Examination scores of individuals with Alzheimer's disease.

CONCLUSIONS

Our study suggested that individuals with Alzheimer's disease alter micro-structural patterns and morphological similarity with the insula and hippocampus. Structural randomness of individuals with Alzheimer's disease changed in temporal, frontal, and occipital brain regions. Morphological similarity and randomness provide valuable insight into brain structural organization in Alzheimer's disease.

Assessment of Efficacy and Safety of Zagotenemab: Results From PERISCOPE-ALZ, a Phase 2 Study in Early Symptomatic Alzheimer Disease

BACKGROUND AND OBJECTIVES

Zagotenemab (LY3303560), a monoclonal antibody that preferentially targets misfolded, extracellular, aggregated tau, was assessed in the PERISCOPE-ALZ phase 2 study to determine its ability to slow cognitive and functional decline relative to placebo in early symptomatic Alzheimer disease (AD).

METHODS

Participants were enrolled across 56 sites in North America and Japan. Key eligibility criteria included age of 60-85 years, Mini-Mental State Examination score of 20-28, and intermediate levels of brain tau on PET imaging. In this double-blind study, participants were equally randomized to 1,400 mg or 5,600 mg of zagotenemab, or placebo (IV infusion every 4 weeks for 100 weeks). The primary outcome was change on the Integrated AD Rating Scale (iADRS) assessed by a Bayesian Disease Progression model. Secondary measures include mixed model repeated measures analysis of additional cognitive and functional endpoints as well as biomarkers of AD pathology.

RESULTS

A total of 360 participants (mean age = 75.4 years; female = 52.8%) were randomized, and 218 completed the treatment period. Demographics and baseline characteristics were reasonably balanced among arms. The mean disease progression ratio (proportional decline in the treated vs placebo group) with 95% credible intervals for the iADRS was 1.10 (0.959-1.265) for the zagotenemab low-dose group and 1.05 (0.907-1.209) for the high-dose, where a ratio less than 1 favors the treatment group. Secondary clinical endpoint measures failed to show a drug-placebo difference in favor of zagotenemab. No treatment effect was demonstrated by flortaucipir PET, volumetric MRI, or neurofilament light chain (NfL) analyses. A dose-related increase in plasma phosphorylated tau181 and total tau was demonstrated. Zagotenemab treatment groups reported a higher incidence of adverse events (AEs) (85.1%) compared with the placebo group (74.6%). This difference was not attributable to any specific AE or category of AEs.

DISCUSSION

In participants with early symptomatic AD, zagotenemab failed to achieve significant slowing of clinical disease progression compared with placebo. Imaging biomarker and plasma NfL findings did not show evidence of pharmacodynamic activity or disease modification.

TRIAL REGISTRATION INFORMATION

ClinicalTrials.gov: NCT03518073.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that for patients with early symptomatic AD, zagotenemab does not slow clinical disease progression.

Single-session reproducibility of MR spectroscopy measures of glutathione in the mesial temporal lobe with MEGA-PRESS

BACKGROUND AND PURPOSE

Magnetic resonance spectroscopy (MRS) measures neurochemicals in vivo. Glutathione (GSH) is a neuroprotective chemical shown to vary significantly in patients with Alzheimer's disease (AD). This work investigates the reproducibility of GSH measures in the mesial temporal lobe (MTL) to identify its potential clinical utility.

METHODS

MRS data were acquired from eight healthy volunteers (31.1 ± 5.2 years; 4 male/female) using Mescher-Garwood-Point Resolved Spectroscopy (MEGA-PRESS) from the MTL in the left hemisphere across two scan sessions in the same visit. Total N-acetylaspartate (tNAA), choline (tCho), creatine (tCr), and GSH were quantified. Reproducibility of quantifications of these neurochemicals were tested using coefficient of variance (CV) between scan sessions. Reproducibility of voxel placement on the left MTL was calculated by measuring the tissue overlap and percent of hippocampus within that voxel. CV measured across different scan sessions in each individual, with a CV<15% was accepted as "good" reproducibility. Paired t-tests were carried out to establish the significant differences between the two scans across each individual with p<.05 as significant.

RESULTS

TNAA (%CV = 7.2; p = .5), tCr (%CV = 7.8; p = .6) and tCho (%CV = 9.3; p = .4), and GSH (%CV = 22; p = .1). The dice coefficient that reflects the level of overlap of hippocampal tissue in the voxel was shown to be 0.8 ± 0.1. Voxel tissue composition were: Scan 1 (cerebrospinal fluid [CSF]: 5 ± 1%, white matter [WM]: 52 ± 3%, gray matter [GM]: 43 ± 3%); Scan 2 (CSF: 5 ± 1%, WM: 52 ± 4%, GM: 44 ± 4%).

CONCLUSION

The data suggest measures of abundant metabolites in the MTL using the MEGA-PRESS sequence has a high reproducibility. Reproducibility of GSH in this area was poorer requiring care when interpreting measures of GSH in the MTL for clinical translational purposes.

Understanding ADC variation by fat content effect using a dual-function MRI phantom

BACKGROUND

A dual-function phantom designed to quantify the apparent diffusion coefficient (ADC) in different fat contents (FCs) and glass bead densities (GBDs) to simulate the human tissues has not been documented yet. We propose a dual-function phantom to quantify the FC and to measure the ADC at different FCs and different GBDs.

METHODS

A fat-containing diffusion phantom comprised by 30 glass-bead-containing fat-water emulsions consisting of six different FCs (0, 10, 20, 30, 40, and 50%) multiplied by five different GBDs (0, 0.1, 0.25, 0.5, and 1.0 g/50 mL). The FC and ADC were measured by the "iterative decomposition of water and fat with echo asymmetry and least squares estimation-IQ," IDEAL-IQ, and single-shot echo-planar diffusion-weighted imaging, SS-EP-DWI, sequences, respectively. Linear regression analysis was used to evaluate the relationship among the fat fraction (FF) measured by IDEAL-IQ, GBD, and ADC.

RESULTS

The ADC was significantly, negatively, and linearly associated with the FF (the linear slope ranged from -0.005 to -0.017, R2 = 0.925 to 0.986, all p < 0.001). The slope of the linear relationship between the ADC and the FF, however, varied among different GBDs (the higher the GBD, the lower the slope). ADCs among emulsions across different GBDs and FFs were overlapped. Emulsions with low GBDs plus high FFs shared a same lower ADC range with those with median or high GBDs plus median or lower FFs.

CONCLUSIONS

A novel dual-function phantom simulating the human tissues allowed to quantify the influence of FC and GBD on ADC.

RELEVANCE STATEMENT

The study developed an innovative dual-function MRI phantom to explore the impact of FC on ADC variation that can affect clinical results. The results revealed the superimposed effect on FF and GBD density on ADC measurements.

KEY POINTS

• A dual-function phantom made of glass bead density (GBD) and fat fraction (FF) emulsion has been developed. • Apparent diffusion coefficient (ADC) values are determined by GBD and FF. • The dual-function phantom showed the mutual ADC addition between FF and GBD.

Path integration deficits are associated with phosphorylated tau accumulation in the entorhinal cortex

Alzheimer's disease is a devastating disease that is accompanied by dementia, and its incidence increases with age. However, no interventions have exhibited clear therapeutic effects. We aimed to develop and characterize behavioural tasks that allow the earlier identification of signs preceding dementia that would facilitate the development of preventative and therapeutic interventions for Alzheimer's disease. To this end, we developed a 3D virtual reality task sensitive to the activity of grid cells in the entorhinal cortex, which is the region that first exhibits neurofibrillary tangles in Alzheimer's disease. We investigated path integration (assessed by error distance) in a spatial navigation task sensitive to grid cells in the entorhinal cortex in 177 volunteers, aged 20-89 years, who did not have self-reported dementia. While place memory was intact even in old age, path integration deteriorated with increasing age. To investigate the relationship between neurofibrillary tangles in the entorhinal cortex and path integration deficit, we examined a mouse model of tauopathy (P301S mutant tau-overexpressing mice; PS19 mice). At 6 months of age, PS19 mice showed a significant accumulation of phosphorylated tau only in the entorhinal cortex, associated with impaired path integration without impairments in spatial cognition. These data are consistent with the idea that path integration deficit is caused by the accumulation of phosphorylated tau in the entorhinal cortex. This method may allow the early identification of individuals likely to develop Alzheimer's disease.

Pathology steered stratification network for subtype identification in Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) is a heterogeneous, multifactorial neurodegenerative disorder characterized by three neurobiological factors beta-amyloid, pathologic tau, and neurodegeneration. There are no effective treatments for AD at a late stage, urging for early detection and prevention. However, existing statistical inference approaches in neuroimaging studies of AD subtype identification do not take into account the pathological domain knowledge, which could lead to ill-posed results that are sometimes inconsistent with the essential neurological principles.

PURPOSE

Integrating systems biology modeling with machine learning, the study aims to assist clinical AD prognosis by providing a subpopulation classification in accordance with essential biological principles, neurological patterns, and cognitive symptoms.

METHODS

We propose a novel pathology steered stratification network (PSSN) that incorporates established domain knowledge in AD pathology through a reaction-diffusion model, where we consider non-linear interactions between major biomarkers and diffusion along the brain structural network. Trained on longitudinal multimodal neuroimaging data, the biological model predicts long-term evolution trajectories that capture individual characteristic progression pattern, filling in the gaps between sparse imaging data available. A deep predictive neural network is then built to exploit spatiotemporal dynamics, link neurological examinations with clinical profiles, and generate subtype assignment probability on an individual basis. We further identify an evolutionary disease graph to quantify subtype transition probabilities through extensive simulations.

RESULTS

Our stratification achieves superior performance in both inter-cluster heterogeneity and intra-cluster homogeneity of various clinical scores. Applying our approach to enriched samples of aging populations, we identify six subtypes spanning AD spectrum, where each subtype exhibits a distinctive biomarker pattern that is consistent with its clinical outcome.

CONCLUSIONS

The proposed PSSN (i) reduces neuroimage data to low-dimensional feature vectors, (ii) combines AT[N]-Net based on real pathological pathways, (iii) predicts long-term biomarker trajectories, and (iv) stratifies subjects into fine-grained subtypes with distinct neurological underpinnings. PSSN provides insights into pre-symptomatic diagnosis and practical guidance on clinical treatments, which may be further generalized to other neurodegenerative diseases.

Performance reserves in brain-imaging-based phenotype prediction

This study examines the impact of sample size on predicting cognitive and mental health phenotypes from brain imaging via machine learning. Our analysis shows a 3- to 9-fold improvement in prediction performance when sample size increases from 1,000 to 1 M participants. However, despite this increase, the data suggest that prediction accuracy remains worryingly low and far from fully exploiting the predictive potential of brain imaging data. Additionally, we find that integrating multiple imaging modalities boosts prediction accuracy, often equivalent to doubling the sample size. Interestingly, the most informative imaging modality often varied with increasing sample size, emphasizing the need to consider multiple modalities. Despite significant performance reserves for phenotype prediction, achieving substantial improvements may necessitate prohibitively large sample sizes, thus casting doubt on the practical or clinical utility of machine learning in some areas of neuroimaging.

Associations between afternoon napping, left amygdala volume and cognitive performance in elderly with normal cognitive function

BACKGROUND

The relationship between afternoon napping and cognitive function in the elderly is very complex and the mechanism is unknown.

METHODS

In the current study, 194 community elders with normal cognitive functions were included. All subjects completed baseline clinical assessment, baseline neuropsychological test as well as baseline structural MRI. Based on their napping status, these 194 participants were divided into the napping group (n = 88) and the non-napping group (n = 106). We then compared the differences in cognitive performance and structural magnetic resonance between the two groups.

RESULTS

In the intergroup analysis, we found that the nappers showed poorer cognitive performance on both overall cognitive function and domain specific cognitive function; while on the whole sample, we found a significant negative association (F = 20.27, p<0.001) between afternoon napping and left amygdala volume. However, we did not find any effect of night sleep length or napping frequency on cognitive performance or left amygdala volume.

CONCLUSIONS

In community elders with normal cognitive functions, afternoon napping is associated with cognitive performance, and left amygdala may play an important role in this process.

Hippocampus substructure segmentation using morphological vision transformer learning

The hippocampus plays a crucial role in memory and cognition. Because of the associated toxicity from whole brain radiotherapy, more advanced treatment planning techniques prioritize hippocampal avoidance, which depends on an accurate segmentation of the small and complexly shaped hippocampus. To achieve accurate segmentation of the anterior and posterior regions of the hippocampus from T1 weighted (T1w) MR images, we developed a novel model, Hippo-Net, which uses a cascaded model strategy. The proposed model consists of two major parts: (1) a localization model is used to detect the volume-of-interest (VOI) of hippocampus. (2) An end-to-end morphological vision transformer network (Franchietal2020Pattern Recognit.102107246, Ranemetal2022 IEEE/CVF Conf. on Computer Vision and Pattern Recognition Workshops (CVPRW) pp 3710-3719) is used to perform substructures segmentation within the hippocampus VOI. The substructures include the anterior and posterior regions of the hippocampus, which are defined as the hippocampus proper and parts of the subiculum. The vision transformer incorporates the dominant features extracted from MR images, which are further improved by learning-based morphological operators. The integration of these morphological operators into the vision transformer increases the accuracy and ability to separate hippocampus structure into its two distinct substructures. A total of 260 T1w MRI datasets from medical segmentation decathlon dataset were used in this study. We conducted a five-fold cross-validation on the first 200 T1w MR images and then performed a hold-out test on the remaining 60 T1w MR images with the model trained on the first 200 images. In five-fold cross-validation, the Dice similarity coefficients were 0.900 ± 0.029 and 0.886 ± 0.031 for the hippocampus proper and parts of the subiculum, respectively. The mean surface distances (MSDs) were 0.426 ± 0.115 mm and 0.401 ± 0.100 mm for the hippocampus proper and parts of the subiculum, respectively. The proposed method showed great promise in automatically delineating hippocampus substructures on T1w MR images. It may facilitate the current clinical workflow and reduce the physicians' effort.

Association between late-life air pollution exposure and medial temporal lobe atrophy in older women

Background

Ambient air pollution exposures increase risk for Alzheimer's disease (AD) and related dementias, possibly due to structural changes in the medial temporal lobe (MTL). However, existing MRI studies examining exposure effects on the MTL were cross-sectional and focused on the hippocampus, yielding mixed results.

Method

To determine whether air pollution exposures were associated with MTL atrophy over time, we conducted a longitudinal study including 653 cognitively unimpaired community-dwelling older women from the Women's Health Initiative Memory Study with two MRI brain scans (MRI-1: 2005-6; MRI-2: 2009-10; Mage at MRI-1=77.3±3.5years). Using regionalized universal kriging models, exposures at residential locations were estimated as 3-year annual averages of fine particulate matter (PM2.5) and nitrogen dioxide (NO2) prior to MRI-1. Bilateral gray matter volumes of the hippocampus, amygdala, parahippocampal gyrus (PHG), and entorhinal cortex (ERC) were summed to operationalize the MTL. We used linear regressions to estimate exposure effects on 5-year volume changes in the MTL and its subregions, adjusting for intracranial volume, sociodemographic, lifestyle, and clinical characteristics.

Results

On average, MTL volume decreased by 0.53±1.00cm3 over 5 years. For each interquartile increase of PM2.5 (3.26μg/m3) and NO2 (6.77ppb), adjusted MTL volume had greater shrinkage by 0.32cm3 (95%CI=[-0.43, -0.21]) and 0.12cm3 (95%CI=[-0.22, -0.01]), respectively. The exposure effects did not differ by APOE ε4 genotype, sociodemographic, and cardiovascular risk factors, and remained among women with low-level PM2.5 exposure. Greater PHG atrophy was associated with higher PM2.5 (b=-0.24, 95%CI=[-0.29, -0.19]) and NO2 exposures (b=-0.09, 95%CI=[-0.14, -0.04]). Higher exposure to PM2.5 but not NO2 was also associated with greater ERC atrophy. Exposures were not associated with amygdala or hippocampal atrophy.

Conclusion

In summary, higher late-life PM2.5 and NO2 exposures were associated with greater MTL atrophy over time in cognitively unimpaired older women. The PHG and ERC - the MTL cortical subregions where AD neuropathologies likely begin, may be preferentially vulnerable to air pollution neurotoxicity.

Alzheimer's Related Neurodegeneration Mediates Air Pollution Effects on Medial Temporal Lobe Atrophy

Exposure to ambient air pollution, especially particulate matter with aerodynamic diameter <2.5 μm (PM2.5) and nitrogen dioxide (NO2), are environmental risk factors for Alzheimer's disease and related dementia. The medial temporal lobe (MTL) is an important brain region subserving episodic memory that atrophies with age, during the Alzheimer's disease continuum, and is vulnerable to the effects of cerebrovascular disease. Despite the importance of air pollution it is unclear whether exposure leads to atrophy of the MTL and by what pathways. Here we conducted a longitudinal study examining associations between ambient air pollution exposure and MTL atrophy and whether putative air pollution exposure effects resembled Alzheimer's disease-related neurodegeneration or cerebrovascular disease-related neurodegeneration. Participants included older women (n = 627; aged 71-87) who underwent two structural brain MRI scans (MRI-1: 2005-6; MRI-2: 2009-10) as part of the Women's Health Initiative Memory Study of Magnetic Resonance Imaging. Regionalized universal kriging was used to estimate annual concentrations of PM2.5 and NO2 at residential locations aggregated to 3-year averages prior to MRI-1. The outcome was 5-year standardized change in MTL volumes. Mediators included voxel-based MRI measures of the spatial pattern of neurodegeneration of Alzheimer's disease (Alzheimer's disease pattern similarity scores [AD-PS]) and whole-brain white matter small-vessel ischemic disease (WM-SVID) volume as a proxy of global cerebrovascular damage. Structural equation models were constructed to examine whether the associations between exposures with MTL atrophy were mediated by the initial level or concurrent change in AD-PS score or WM-SVID while adjusting for sociodemographic, lifestyle, clinical characteristics, and intracranial volume. Living in locations with higher PM2.5 (per interquartile range [IQR]=3.17μg/m3) or NO2 (per IQR=6.63ppb) was associated with greater MTL atrophy (βPM2.5 = -0.29, 95% confidence interval [CI]=[-0.41,-0.18]; βNO2 =-0.12, 95%CI=[-0.23,-0.02]). Greater PM2.5 was associated with larger increases in AD-PS (βPM2.5 = 0.23, 95%CI=[0.12,0.33]) over time, which partially mediated associations with MTL atrophy (indirect effect= -0.10; 95%CI=[-0.15, -0.05]), explaining approximately 32% of the total effect. NO2 was positively associated with AD-PS at MRI-1 (βNO2=0.13, 95%CI=[0.03,0.24]), which partially mediated the association with MTL atrophy (indirect effect= -0.01, 95% CI=[-0.03,-0.001]). Global WM-SVID at MRI-1 or concurrent change were not significant mediators between exposures and MTL atrophy. Findings support the mediating role of Alzheimer's disease-related neurodegeneration contributing to MTL atrophy associated with late-life exposures to air pollutants. Alzheimer's disease-related neurodegeneration only partially explained associations between exposure and MTL atrophy suggesting the role of multiple neuropathological processes underlying air pollution neurotoxicity on brain aging.

Comparing machine learning-derived MRI-based and blood-based neurodegeneration biomarkers in predicting syndromal conversion in early AD

INTRODUCTION

We compared the machine learning-derived, MRI-based Alzheimer's disease (AD) resemblance atrophy index (AD-RAI) with plasma neurofilament light chain (NfL) level in predicting conversion of early AD among cognitively unimpaired (CU) and mild cognitive impairment (MCI) subjects.

METHODS

We recruited participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) who had the following data: clinical features (age, gender, education, Montreal Cognitive Assessment [MoCA]), structural MRI, plasma biomarkers (p-tau181 , NfL), cerebrospinal fluid biomarkers (CSF) (Aβ42, p-tau181 ), and apolipoprotein E (APOE) ε4 genotype. We defined AD using CSF Aβ42 (A+) and p-tau181 (T+). We defined conversion (C+) if a subject progressed to the next syndromal stage within 4 years.

RESULTS

Of 589 participants, 96 (16.3%) were A+T+C+. AD-RAI performed better than plasma NfL when added on top of clinical features, plasma p-tau181 , and APOE ε4 genotype (area under the curve [AUC] = 0.832 vs. AUC = 0.650 among CU, AUC = 0.853 vs. AUC = 0.805 among MCI) in predicting A+T+C+.

DISCUSSION

AD-RAI outperformed plasma NfL in predicting syndromal conversion of early AD.

HIGHLIGHTS

AD-RAI outperformed plasma NfL in predicting syndromal conversion among early AD. AD-RAI showed better metrics than volumetric hippocampal measures in predicting syndromal conversion. Combining clinical features, plasma p-tau181 and apolipoprotein E (APOE) with AD-RAI is the best model for predicting syndromal conversion.

Beyond the amyloid cascade: An update of Alzheimer's disease pathophysiology

Alzheimer's disease (AD) is a multi-etiology disease. The biological system of AD is associated with multidomain genetic, molecular, cellular, and network brain dysfunctions, interacting with central and peripheral immunity. These dysfunctions have been primarily conceptualized according to the assumption that amyloid deposition in the brain, whether from a stochastic or a genetic accident, is the upstream pathological change. However, the arborescence of AD pathological changes suggests that a single amyloid pathway might be too restrictive or inconsistent with a cascading effect. In this review, we discuss the recent human studies of late-onset AD pathophysiology in an attempt to establish a general updated view focusing on the early stages. Several factors highlight heterogenous multi-cellular pathological changes in AD, which seem to work in a self-amplifying manner with amyloid and tau pathologies. Neuroinflammation has an increasing importance as a major pathological driver, and perhaps as a convergent biological basis of aging, genetic, lifestyle and environmental risk factors.

Age-, sex-, and pathology-related variability in brain structure and cognition

This work aimed to investigate potential pathways linking age and imaging measures to early age- and pathology-related changes in cognition. We used [18F]-Flutemetamol (amyloid) and [18F]-Flortaucipir (tau) positron emission tomography (PET), structural MRI, and neuropsychological assessment from 232 elderly individuals aged 50-89 years (46.1% women, 23% APOE-ε4 carrier, 23.3% MCI). Tau-PET was available for a subsample of 93 individuals. Structural equation models were used to evaluate cross-sectional pathways between age, amyloid and tau burden, grey matter thickness and volumes, white matter hyperintensity volume, lateral ventricle volume, and cognition. Our results show that age is associated with worse outcomes in most of the measures examined and had similar negative effects on episodic memory and executive functions. While increased lateral ventricle volume was consistently associated with executive function dysfunction, participants with mild cognitive impairment drove associations between structural measures and episodic memory. Both age and amyloid-PET could be associated with medial temporal lobe tau, depending on whether we used a continuous or a dichotomous amyloid variable. Tau burden in entorhinal cortex was related to worse episodic memory in individuals with increased amyloid burden (Centiloid >12) independently of medial temporal lobe atrophy. Testing models for sex differences revealed that amyloid burden was more strongly associated with regional atrophy in women compared with men. These associations were likely mediated by higher tau burden in women. These results indicate that influences of pathological pathways on cognition and sex-specific vulnerabilities are dissociable already in early stages of neuropathology and cognitive impairment.

The association between post-traumatic stress disorder (PTSD) and cognitive impairment: A systematic review of neuroimaging findings

BACKGROUND

Accumulating evidence suggests that post-traumatic stress disorder (PTSD) may increase the risk of various types of dementia. Despite the large number of studies linking these critical conditions, the underlying mechanisms remain unclear. The past decade has witnessed an exponential increase in interest on brain imaging research to assess the neuroanatomical underpinnings of PTSD. This systematic review provides a critical assessment of available evidence of neuroimaging correlates linking PTSD to a higher risk of dementia.

METHODS

The EMBASE, PubMed/MEDLINE, and SCOPUS electronic databases were systematically searched from 1980 to May 22, 2021 for original references on neuroimaging correlates of PTSD and risk of dementia. Literature search, screening of references, methodological quality appraisal of included articles as well as data extractions were independently conducted by at least two investigators. Eligibility criteria included: 1) a clear PTSD definition; 2) a subset of included participants must have developed dementia or cognitive impairment at any time point after the diagnosis of PTSD through any diagnostic criteria; and 3) brain imaging protocols [structural, molecular or functional], including whole-brain morphologic and functional MRI, and PET imaging studies linking PTSD to a higher risk of cognitive impairment/dementia.

RESULTS

Overall, seven articles met eligibility criteria, comprising findings from 366 participants with PTSD. Spatially convergent structural abnormalities in individuals with PTSD and co-occurring cognitive dysfunction involved primarily the bilateral frontal (e.g., prefrontal, orbitofrontal, cingulate cortices), temporal (particularly in those with damage to the hippocampi), and parietal (e.g., superior and precuneus) regions.

LIMITATIONS

A meta-analysis could not be performed due to heterogeneity and paucity of measurable data in the eligible studies.

CONCLUSIONS

Our systematic review provides putative neuroimaging correlates associated with PTSD and co-occurring dementia/cognitive impairment particularly involving the hippocampi. Further research examining neuroimaging features linking PTSD to dementia are clearly an unmet need of the field. Future imaging studies should provide a better control for relevant confounders, such as the selection of more homogeneous samples (e.g., age, race, education), a proper control for co-occurring disorders (e.g., co-occurring major depressive and anxiety disorders) as well as the putative effects of psychotropic medication use. Furthermore, prospective studies examining imaging biomarkers associated with a higher rate of conversion from PTSD to dementia could aid in the stratification of people with PTSD at higher risk for developing dementia for whom putative preventative interventions could be especially beneficial.

A cross-sectional study of explainable machine learning in Alzheimer's disease: diagnostic classification using MR radiomic features

Introduction

Alzheimer's disease (AD) even nowadays remains a complex neurodegenerative disease and its diagnosis relies mainly on cognitive tests which have many limitations. On the other hand, qualitative imaging will not provide an early diagnosis because the radiologist will perceive brain atrophy on a late disease stage. Therefore, the main objective of this study is to investigate the necessity of quantitative imaging in the assessment of AD by using machine learning (ML) methods. Nowadays, ML methods are used to address high dimensional data, integrate data from different sources, model the etiological and clinical heterogeneity, and discover new biomarkers in the assessment of AD.

Methods

In this study radiomic features from both entorhinal cortex and hippocampus were extracted from 194 normal controls (NC), 284 mild cognitive impairment (MCI) and 130 AD subjects. Texture analysis evaluates statistical properties of the image intensities which might represent changes in MRI image pixel intensity due to the pathophysiology of a disease. Therefore, this quantitative method could detect smaller-scale changes of neurodegeneration. Then the radiomics signatures extracted by texture analysis and baseline neuropsychological scales, were used to build an XGBoost integrated model which has been trained and integrated.

Results

The model was explained by using the Shapley values produced by the SHAP (SHapley Additive exPlanations) method. XGBoost produced a f1-score of 0.949, 0.818, and 0.810 between NC vs. AD, MC vs. MCI, and MCI vs. AD, respectively.

Discussion

These directions have the potential to help to the earlier diagnosis and to a better manage of the disease progression and therefore, develop novel treatment strategies. This study clearly showed the importance of explainable ML approach in the assessment of AD.

Uncovering heterogeneous cognitive trajectories in mild cognitive impairment: a data-driven approach

BACKGROUND

Given the complex and progressive nature of mild cognitive impairment (MCI), the ability to delineate and understand the heterogeneous cognitive trajectories is crucial for developing personalized medicine and informing trial design. The primary goals of this study were to examine whether different cognitive trajectories can be identified within subjects with MCI and, if present, to characterize each trajectory in relation to changes in all major Alzheimer's disease (AD) biomarkers over time.

METHODS

Individuals with a diagnosis of MCI at the first visit and ≥ 1 follow-up cognitive assessment were selected from the Alzheimer's Disease Neuroimaging Initiative database (n = 936; age 73 ± 8; 40% female; 16 ± 3 years of education; 50% APOE4 carriers). Based on the Alzheimer's Disease Assessment Scale-Cognitive Subscale-13 (ADAS-Cog-13) total scores from baseline up to 5 years follow-up, a non-parametric k-means longitudinal clustering method was performed to obtain clusters of individuals with similar patterns of cognitive decline. We further conducted a series of linear mixed-effects models to study the associations of cluster membership with longitudinal changes in other cognitive measures, neurodegeneration, and in vivo AD pathologies.

RESULTS

Four distinct cognitive trajectories emerged. Cluster 1 consisted of 255 individuals (27%) with a nearly non-existent rate of change in the ADAS-Cog-13 over 5 years of follow-up and a healthy-looking biomarker profile. Individuals in the cluster 2 (n = 336, 35%) and 3 (n = 240, 26%) groups showed relatively mild and moderate cognitive decline trajectories, respectively. Cluster 4, comprising about 11% of our study sample (n = 105), exhibited an aggressive cognitive decline trajectory and was characterized by a pronouncedly abnormal biomarker profile.

CONCLUSIONS

Individuals with MCI show substantial heterogeneity in cognitive decline. Our findings may potentially contribute to improved trial design and patient stratification.

Challenges and opportunities of diagnostic markers of Alzheimer's disease based on structural magnetic resonance imaging

OBJECTIVES

This article aimed to carry out a narrative literature review of early diagnostic markers of Alzheimer's disease (AD) based on both micro and macro levels of pathology, indicating the shortcomings of current biomarkers and proposing a novel biomarker of structural integrity that associates the hippocampus and adjacent ventricle together. This could help to reduce the influence of individual variety and improve the accuracy and validity of structural biomarker.

METHODS

This review was based on presenting comprehensive background of early diagnostic markers of AD. We have compiled those markers into micro level and macro level, and discussed the advantages and disadvantages of them. Eventually the ratio of gray matter volume to ventricle volume was put forward.

RESULTS

The costly methodologies and related high patient burden of "micro" biomarkers (cerebrospinal fluid biomarkers) hinder the implementation in routine clinical examination. In terms of "macro" biomarkers- hippocampal volume (HV), there is a large variation of it among population, which undermines its validity Considering the gray matter atrophies while the adjacent ventricular volume enlarges, we assume the hippocampal to ventricle ratio (HVR) is a more reliable marker than HV alone the emerging evidence showed hippocampal to ventricle ratio predicts memory functions better than HV alone in elderly sample.

CONCLUSIONS

The ratio between gray matter structures and adjacent ventricular volumes counts as a promising superior diagnostic marker of early neurodegeneration.

Hippocampus shape characterization with 3D Zernike transformation in clinical Alzheimer's disease progression

Alzheimer's disease (AD) is a neurodegenerative disease and the most common cause of dementia among older adults. Mild cognitive impairment (MCI) is considered a transitional phase between healthy cognitive aging and dementia. Progressive brain volume reduction/atrophy, particularly of the hippocampus, is associated with the transition from normal to MCI, and then to AD. We aimed to develop methods to characterize the shape of hippocampus and explore its potential as an imaging marker to monitor clinical AD progression. We implemented a 3D Zernike transformation to characterize the shape changes of hippocampus in 428 older subjects with high-quality T1 -weighted volumetric brain scans from the Alzheimer's Disease Neuroimaging Initiative data set (151 normal, 258 MCI, and 19 AD). Over 2 years, 15 cognitively normal subjects converted to MCI, and 42 subjects with MCI converted to AD. We found a significant correlation between hippocampal volume changes and Zernike shape metrics. Before a clinical diagnosis of AD, the shapes of the left and right hippocampi changed slowly. After AD diagnosis, both volume and shape changed rapidly but were uncorrelated to each other. During the transition from a clinical diagnosis of MCI to AD, the shape of the left and right hippocampi changed in a correlated manner but became uncorrelated after AD diagnosis. Finally, the pace of hippocampus shape change was associated with its shape and the subject's age and disease condition. In conclusion, the hippocampus shape features characterized with 3D Zernike transformation, in complement to volume measures, may serve as a novel imaging marker to monitor clinical AD progression.

Race, sex, and mid-life changes in brain health: Cardia MRI substudy

OBJECTIVE

To examine longitudinal race and sex differences in mid-life brain health and to evaluate whether cardiovascular health (CVH) or apolipoprotein E (APOE) ε4 explain differences.

METHODS

The study included 478 Black and White participants (mean age: 50 years). Total (TBV), gray (GMV), white (WMV), and white matter hyperintensity (WMH) volumes and GM-cerebral blood flow (CBF) were acquired with 3T-magnetic resonance imaging at baseline and 5-year follow-up. Analyses were based on general linear models.

RESULTS

There were race x sex interactions for GMV (P-interaction = .004) and CBF (P-interaction = .01) such that men showed more decline than women, and this was most evident in Blacks. Blacks compared to Whites had a significantly greater increase in WMH (P = .002). All sex-race differences in change were marginally attenuated by CVH and APOE ε4.

CONCLUSION

Race-sex differences in brain health emerge by mid-life. Identifying new environmental factors beyond CVH is needed to develop early interventions to maintain brain health.

Nutritional intervention for diabetes mellitus with Alzheimer's disease

The combined disease burden of diabetes mellitus (DM) and Alzheimer's disease (AD) is increasing, and the two diseases share some common pathological changes. However, the pharmacotherapeutic approach to this clinical complexity is limited to symptomatic rather than disease-arresting, with the possible exception of metformin. Whether nutritional intervention might extend or synergize with these effects of metformin is of interest. In particular, dietary patterns with an emphasis on dietary diversity shown to affect cognitive function are of growing interest in a range of food cultural settings. This paper presents the association between diabetes and AD. In addition, the cross-cultural nutritional intervention programs with the potential to mitigate both insulin resistance (IR) and hyperglycemia, together with cognitive impairment are also reviewed. Both dietary patterns and nutritional supplementation showed the effects of improving glycemic control and reducing cognitive decline in diabetes associated with AD, but the intervention specificity remained controversial. Multi-nutrient supplements combined with diverse diets may have preventive and therapeutic potential for DM combined with AD, at least as related to the B vitamin group and folate-dependent homocysteine (Hcy). The nutritional intervention has promise in the prevention and management of DM and AD comorbidities, and more clinical studies would be of nutritional scientific merit.

The emerging neuroprotective roles of exerkines in Alzheimer’s disease

Despite the extensive knowledge of the beneficial effects of physical exercise, a sedentary lifestyle is still a predominant harm in our society. Sedentarism is one of the major modifiable risk factors for metabolic diseases such as diabetes mellitus, obesity and neurological disorders, including Alzheimer’s disease (AD)–characterized by synaptic failure, amyloid protein deposition and memory loss. Physical exercise promotes neuroprotective effects through molecules released in circulation and mediates the physiological crosstalk between the periphery and the brain. This literature review summarizes the current understanding of the roles of exerkines, molecules released during physical exercise, as systemic and central factors that mediate the beneficial effects of physical exercise on cognition. We highlight the neuroprotective role of irisin—a myokine released from the proteolytic cleavage of fibronectin type III domain-containing protein 5 (FNDC5) transmembrane protein. Lastly, we review evidence pointing to physical exercise as a potential preventative and interventional strategy against cognitive decline in AD.

Machine learning-based automatic estimation of cortical atrophy using brain computed tomography images

Cortical atrophy is measured clinically according to established visual rating scales based on magnetic resonance imaging (MRI). Although brain MRI is the primary imaging marker for neurodegeneration, computed tomography (CT) is also widely used for the early detection and diagnosis of dementia. However, they are seldom investigated. Therefore, we developed a machine learning algorithm for the automatic estimation of cortical atrophy on brain CT. Brain CT images (259 Alzheimer’s dementia and 55 cognitively normal subjects) were visually rated by three neurologists and used for training. We constructed an algorithm by combining the convolutional neural network and regularized logistic regression (RLR). Model performance was then compared with that of neurologists, and feature importance was measured. RLR provided fast and reliable automatic estimations of frontal atrophy (75.2% accuracy, 93.6% sensitivity, 67.2% specificity, and 0.87 area under the curve [AUC]), posterior atrophy (79.6% accuracy, 87.2% sensitivity, 75.9% specificity, and 0.88 AUC), right medial temporal atrophy (81.2% accuracy, 84.7% sensitivity, 79.6% specificity, and 0.88 AUC), and left medial temporal atrophy (77.7% accuracy, 91.1% sensitivity, 72.3% specificity, and 0.90 AUC). We concluded that RLR-based automatic estimation of brain CT provided a comprehensive rating of atrophy that can potentially support physicians in real clinical settings.

TR-GAN: Multi-Session Future MRI Prediction With Temporal Recurrent Generative Adversarial Network

Magnetic Resonance Imaging (MRI) has been proven to be an efficient way to diagnose Alzheimer's disease (AD). Recent dramatic progress on deep learning greatly promotes the MRI analysis based on data-driven CNN methods using a large-scale longitudinal MRI dataset. However, most of the existing MRI datasets are fragmented due to unexpected quits of volunteers. To tackle this problem, we propose a novel Temporal Recurrent Generative Adversarial Network (TR-GAN) to complete missing sessions of MRI datasets. Unlike existing GAN-based methods, which either fail to generate future sessions or only generate fixed-length sessions, TR-GAN takes all past sessions to recurrently and smoothly generate future ones with variant length. Specifically, TR-GAN adopts recurrent connection to deal with variant input sequence length and flexibly generate future variant sessions. Besides, we also design a multiple scale & location (MSL) module and a SWAP module to encourage the model to better focus on detailed information, which helps to generate high-quality MRI data. Compared with other popular GAN architectures, TR-GAN achieved the best performance in all evaluation metrics of two datasets. After expanding the Whole MRI dataset, the balanced accuracy of AD vs. cognitively normal (CN) vs. mild cognitive impairment (MCI) and stable MCI vs. progressive MCI classification can be increased by 3.61% and 4.00%, respectively.

Brain ventricles, CSF and cognition: a narrative review

The brain ventricles are structures that have been related to cognition since antiquity. They are essential components in the development and maintenance of brain functions. The aging process runs with the enlargement of ventricles and is related to a less selective blood-cerebrospinal fluid barrier and then a more toxic cerebrospinal fluid environment. The study of brain ventricles as a biological marker of aging is promissing because they are structures easily identified in neuroimaging studies, present good inter-rater reliability, and measures of them can identify brain atrophy earlier than cortical structures. The ventricular system also plays roles in the development of dementia, since dysfunction in the clearance of beta-amyloid protein is a key mechanism in sporadic Alzheimer's disease. The morphometric and volumetric studies of the brain ventricles can help to distinguish between healthy elderly and persons with mild cognitive impairment (MCI) and dementia. Brain ventricle data may contribute to the appropriate allocation of individuals in groups at higher risk for MCI-dementia progression in clinical trials and to measuring therapeutic responses in these studies, as well as providing differential diagnosis, such as normal pressure hydrocephalus. Here, we reviewed the pathophysiology of healthy aging and cognitive decline, focusing on the role of the choroid plexus and brain ventricles in this process.

A fuzzy membership based comparison of the grey matter (GM) in cognitively normal (CN), mild cognitive impairment (MCI), and Alzheimer’s disease (AD) using brain images

Grey matter (GM) in human brain contains most of the important cells covering the regions involved in neurophysiological operations such as memory, emotions, decision making, etc. Alzheimer’s disease (AD) is a neurological disease that kills the brain cells in regions which are mostly involved in the neurophysiological operations. Mild Cognitive Impairment (MCI) is a stage between Cognitively Normal (CN) and AD, where a significant cognitive declination can be observed. The destruction of brain cells causes a reduction in the size of GM. Evaluation of changes in GM, may help in studying the overall brain transformations and accurate classification of different stages of AD. In this work, firstly skull of brain images is stripped for 5 different slices, then segmentation of GM is performed. Finally, the average number of pixels in grey region and the average atrophy in grey pixels per year is calculated and compared amongst CN, MCI, and AD patients of various ages and genders. It is observed that, for some subjects (in some particular ages) from different dementia stages, pattern of GM changes is almost identical. To solve this issue, we have used the concept of fuzzy membership functions to classify the dementia stages more accurately. It is observed from the comparison that average difference in the number of pixels between CN and MCI= 10.01%, CN and AD= 19.63%, MCI and AD= 10.72%. It can be also observed from the comparison that, the average atrophy in grey matter per year in CN= 1.92%, MCI= 3.13%, and AD= 4.33%.

The Value of Neuroimaging in Dementia Diagnosis

PURPOSE OF REVIEW

This article discusses neuroimaging in dementia diagnosis, with a focus on new applications of MRI and positron emission tomography (PET).

RECENT FINDINGS

Although the historical use of MRI in dementia diagnosis has been supportive to exclude structural etiologies, recent innovations allow for quantification of atrophy patterns that improve sensitivity for supporting the diagnosis of dementia causes. Neuronuclear approaches allow for localization of specific amyloid and tau neuropathology on PET and are available for clinical use, in addition to dopamine transporter scans in dementia with Lewy bodies and metabolic studies with fludeoxyglucose PET (FDG-PET).

SUMMARY

Using computerized software programs for MRI analysis and cross-sectional and longitudinal evaluations of hippocampal, ventricular, and lobar volumes improves sensitivity in support of the diagnosis of Alzheimer disease and frontotemporal dementia. MRI protocol requirements for such quantification are three-dimensional T1-weighted volumetric imaging protocols, which may need to be specifically requested. Fluid-attenuated inversion recovery (FLAIR) and 3.0T susceptibility-weighted imaging (SWI) sequences are useful for the detection of white matter hyperintensities as well as microhemorrhages in vascular dementia and cerebral amyloid angiopathy. PET studies for amyloid and/or tau pathology can add additional specificity to the diagnosis but currently remain largely inaccessible outside of research settings because of prohibitive cost constraints in most of the world. Dopamine transporter PET scans can help identify Lewy body dementia and are thus of potential clinical value.

Neuroimaging Modalities in Alzheimer’s Disease: Diagnosis and Clinical Features

Alzheimer’s disease (AD) is a neurodegenerative disease causing progressive cognitive decline until eventual death. AD affects millions of individuals worldwide in the absence of effective treatment options, and its clinical causes are still uncertain. The onset of dementia symptoms indicates severe neurodegeneration has already taken place. Therefore, AD diagnosis at an early stage is essential as it results in more effective therapy to slow its progression. The current clinical diagnosis of AD relies on mental examinations and brain imaging to determine whether patients meet diagnostic criteria, and biomedical research focuses on finding associated biomarkers by using neuroimaging techniques. Multiple clinical brain imaging modalities emerged as potential techniques to study AD, showing a range of capacity in their preciseness to identify the disease. This review presents the advantages and limitations of brain imaging modalities for AD diagnosis and discusses their clinical value.

Associations Between Dietary Patterns and Neuroimaging Markers: A Systematic Review

Dementia is a complex, growing challenge for population health worldwide. Dietary patterns (DPs) may offer an opportunity to beneficially influence cognitive ageing and potentially reduce an individuals’ risk of dementia through diet-related mechanisms. However, previous studies within this area have shown mixed results, which may be partly explained by the lack of sensitivity and accuracy within cognitive testing methods. Novel neuroimaging techniques provide a sensitive method to analyse brain changes preceding cognitive impairment which may have previously remained undetected. The purpose of this systematic review was to elucidate the role of DPs in relation to brain ageing processes, by summarising current prospective and intervention studies. Nine prospective studies met the inclusion criteria for the review, seven evaluated the Mediterranean diet (MeDi), one evaluated the Alternative Healthy Eating Index-2010, and one evaluated a posteriori derived DPs. No intervention studies were eligible for inclusion in this review. There was some evidence of an association between healthy DPs and neuroimaging markers including changes within these markers over time. Consequently, it is plausible that better adherence to such DPs may positively influence brain ageing and neurodegeneration. Future studies may benefit from the use of multi-modal neuroimaging techniques, to further investigate how adherence to a DP influences brain health. The review also highlights the crucial need for further intervention studies within this research area.

Effect of Probiotic Bifidobacterium breve in Improving Cognitive Function and Preventing Brain Atrophy in Older Patients with Suspected Mild Cognitive Impairment: Results of a 24-Week Randomized, Double-Blind, Placebo-Controlled Trial

Background:

Probiotics have been reported to ameliorate cognitive impairment.

Objective:

We investigated the effect of the probiotic strain Bifidobacterium breve MCC1274 (A1) in enhancing cognition and preventing brain atrophy of older patients with mild cognitive impairment (MCI).

Methods:

In this RCT, 130 patients aged from 65 to 88 years old with suspected MCI received once daily either probiotic (B. breve MCC1274, 2×10¹⁰ CFU) or placebo for 24 weeks. Cognitive functions were assessed by ADAS-Jcog and MMSE tests. Participants underwent MRI to determine brain atrophy changes using Voxel-based Specific Regional Analysis System for Alzheimer’s disease (VSRAD). Fecal samples were collected for the analysis of gut microbiota composition.

Results:

Analysis was performed on 115 participants as the full analysis set (probiotic 55, placebo 60). ADAS-Jcog subscale “orientation” was significantly improved compared to placebo at 24 weeks. MMSE subscales “orientation in time” and “writing” were significantly improved compared to placebo in the lower baseline MMSE (< 25) subgroup at 24 weeks. VSRAD scores worsened in the placebo group; probiotic supplementation tended to suppress the progression, in particular among those subjects with progressed brain atrophy (VOI Z-score ≥1.0). There were no marked changes in the overall composition of the gut microbiota by the probiotic supplementation.

Conclusion:

Improvement of cognitive function was observed on some subscales scores only likely due to the lower sensitiveness of these tests for MCI subjects. Probiotics consumption for 24 weeks suppressed brain atrophy progression, suggesting that B. breve MCC1274 helps prevent cognitive impairment of MCI subjects.

Brain oxygen extraction and neural tissue susceptibility are associated with cognitive impairment in older individuals

BACKGROUND AND PURPOSE

We investigated the effects of aging, white matter hyperintensities (WMH), and cognitive impairment on brain iron levels and cerebral oxygen metabolism, known to be altered in Alzheimer's disease (AD), using quantitative susceptibility mapping and MR-based cerebral oxygen extraction fraction (OEF).

METHODS

In 100 individuals over the age of 50 (68/32 cognitively impaired/intact), OEF and neural tissue susceptibility (χ_n ) were computed retrospectively from MRI multi-echo gradient echo data, obtained on a 3 Tesla MRI scanner. The effects of age and WMH on OEF and χ_n were assessed within groups, and OEF and χ_n were assessed between groups, using multivariate regression analyses.

RESULTS

Cognitively impaired subjects were found to have 19% higher OEF and 34% higher χ_n than cognitively intact subjects in the cortical gray matter and several frontal, temporal, and parietal regions (p < .05). Increased WMH burden was significantly associated with decreased OEF in the cognitively impaired, but not in the cognitively intact. Older age had a stronger association with decreased OEF in the cognitively intact group. Both older age and increased WMH burden were significantly associated with increased χ_n in temporoparietal regions in the cognitively impaired.

CONCLUSIONS

Higher brain OEF and χ_n in cognitively impaired older individuals may reflect altered oxygen metabolism and iron in areas with underlying AD pathology. Both age and WMH have associations with OEF and χ_n but are modified by the presence of cognitive impairment.

Subtyping of mild cognitive impairment using a deep learning model based on brain atrophy patterns

Trajectories of cognitive decline vary considerably among individuals with mild cognitive impairment (MCI). To address this heterogeneity, subtyping approaches have been developed, with the objective of identifying more homogeneous subgroups. To date, subtyping of MCI has been based primarily on cognitive measures, often resulting in indistinct boundaries between subgroups and limited validity. Here, we introduce a subtyping method for MCI based solely upon brain atrophy. We train a deep learning model to differentiate between Alzheimer’s disease (AD) and cognitively normal (CN) subjects based on whole-brain MRI features. We then deploy the trained model to classify MCI subjects based on whole-brain gray matter resemblance to AD-like or CN-like patterns. We subsequently validate the subtyping approach using cognitive, clinical, fluid biomarker, and molecular imaging data. Overall, the results suggest that atrophy patterns in MCI are sufficiently heterogeneous and can thus be used to subtype individuals into biologically and clinically meaningful subgroups.

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Individuals with mild cognitive impairment (MCI) are subtyped using a deep learning model

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The model is able to subtype MCI based solely on structural brain atrophy patterns

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The model-based subtypes differ in amyloid burden, brain metabolism, and cognition

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The model-based subtyping approach captures marked differences in cognitive decline

Brain aging mechanisms with mechanical manifestations

Brain aging is a complex process that affects everything from the subcellular to the organ level, begins early in life, and accelerates with age. Morphologically, brain aging is primarily characterized by brain volume loss, cortical thinning, white matter degradation, loss of gyrification, and ventricular enlargement. Pathophysiologically, brain aging is associated with neuron cell shrinking, dendritic degeneration, demyelination, small vessel disease, metabolic slowing, microglial activation, and the formation of white matter lesions. In recent years, the mechanics community has demonstrated increasing interest in modeling the brain's (bio)mechanical behavior and uses constitutive modeling to predict shape changes of anatomically accurate finite element brain models in health and disease. Here, we pursue two objectives. First, we review existing imaging-based data on white and gray matter atrophy rates and organ-level aging patterns. This data is required to calibrate and validate constitutive brain models. Second, we review the most critical cell- and tissue-level aging mechanisms that drive white and gray matter changes. We focuse on aging mechanisms that ultimately manifest as organ-level shape changes based on the idea that the integration of imaging and mechanical modeling may help identify the tipping point when normal aging ends and pathological neurodegeneration begins.

Prediction of Alzheimer's Disease Progression Based on Magnetic Resonance Imaging

The neuroimaging method of multimodal magnetic resonance imaging (MRI) can identify the changes in brain structure and function caused by Alzheimer's disease (AD) at different stages, and it is a practical method to study the mechanism of AD progression. This paper reviews the studies of methods and biomarkers for predicting AD progression based on multimodal MRI. First, different approaches for predicting AD progression are analyzed and summarized, including machine learning, deep learning, regression, and other MRI analysis methods. Then, the effective biomarkers of AD progression under structural magnetic resonance imaging, diffusion tensor imaging, functional magnetic resonance imaging, and arterial spin labeling modes of MRI are summarized. It is believed that the brain changes shown on MRI may be related to the cognitive decline in different prodrome stages of AD, which is conducive to the further realization of early intervention and prevention of AD. Finally, the deficiencies of the existing studies are analyzed in terms of data set size, data heterogeneity, processing methods, and research depth. More importantly, future research directions are proposed, including enriching data sets, simplifying biomarkers, utilizing multimodal magnetic resonance, etc. In the future, the study of AD progression by multimodal MRI will still be a challenge but also a significant research hotspot.

Neurodegeneration Over 3 Years Following Ischaemic Stroke: Findings From the Cognition and Neocortical Volume After Stroke Study

Background: Stroke survivors are at high risk of dementia, associated with increasing age and vascular burden and with pre-existing cognitive impairment, older age. Brain atrophy patterns are recognised as signatures of neurodegenerative conditions, but the natural history of brain atrophy after stroke remains poorly described. We sought to determine whether stroke survivors who were cognitively normal at time of stroke had greater total brain (TBV) and hippocampal volume (HV) loss over 3 years than controls. We examined whether stroke survivors who were cognitively impaired (CI) at 3 months following their stroke had greater brain volume loss than cognitively normal (CN) stroke participants over the next 3 years.

Methods: Cognition And Neocortical Volume After Stroke (CANVAS) study is a multi-centre cohort study of first-ever or recurrent adult ischaemic stroke participants compared to age- and sex-matched community controls. Participants were followed with MRI and cognitive assessments over 3 years and were free of a history of cognitive impairment or decline at inclusion. Our primary outcome measure was TBV change between 3 months and 3 years; secondary outcomes were TBV and HV change comparing CI and CN participants. We investigated associations between group status and brain volume change using a baseline-volume adjusted linear regression model with robust standard error.

Results: Ninety-three stroke (26 women, 66.7 ± 12 years) and 39 control participants (15 women, 68.7 ± 7 years) were available at 3 years. TBV loss in stroke patients was greater than controls: stroke mean (M) = 20.3 cm³ ± SD 14.8 cm³; controls M = 14.2 cm³ ± SD 13.2 cm³; [adjusted mean difference 7.88 95%CI (2.84, 12.91) p-value = 0.002]. TBV decline was greater in those stroke participants who were cognitively impaired (M = 30.7 cm³; SD = 14.2 cm³) at 3 months (M = 19.6 cm³; SD = 13.8 cm³); [adjusted mean difference 10.42; 95%CI (3.04, 17.80), p-value = 0.006]. No statistically significant differences in HV change were observed.

Conclusions: Ischaemic stroke survivors exhibit greater neurodegeneration compared to stroke-free controls. Brain atrophy is greater in stroke participants who were cognitively impaired early after their stroke. Early cognitive impairment was associated greater subsequent atrophy, reflecting the combined impacts of stroke and vascular brain burden. Atrophy rates could serve as a useful biomarker for trials testing interventions to reduce post-stroke secondary neurodegeneration.

Clinical Trail Registration:http://www.clinicaltrials.gov, identifier: NCT02205424.

Grey-matter brain healthcare quotient and cognitive function: A large cohort study of an MRI brain screening system in Japan

There is sometimes a divergence between brain atrophy and impairments in cognitive function. The present study aimed to assess the relationship between cognitive function and the grey-matter brain healthcare quotient (GM-BHQ), which represents brain volume as a deviation value. In addition, we aimed to investigate lifestyle factors that can help maintain cognitive function despite brain atrophy. A total of 1,757 adults included in a Japanese MRI brain screening cohort underwent MRI. We classified the participants into two age groups: under 65 years old (young adult/middle age group) and over 64 years old (elder group). The GM-BHQ was more strongly correlated with cognitive function in the young adult/middle age group than in the elder group (p < .01). Regression analysis revealed that years of education was associated with the maintenance of cognitive function despite brain atrophy (p < .05). In conclusion, our findings suggest that the relationship between brain volume and cognitive function becomes more obscure with age.

Hippocampo-Horn Percentage and Parietal Atrophy Score for Easy Visual Assessment of Brain Atrophy on Magnetic Resonance Imaging in Early- and Late-Onset Alzheimer's Disease

BACKGROUND

Magnetic resonance imaging (MRI) visual scales of brain atrophy are important for differential diagnosis of dementias in routine clinical practice. Atrophy patterns in early- and late-onset Alzheimer's disease (AD) can be different according to some studies.

OBJECTIVE

Our goal was to assess brain atrophy patterns in early- and late-onset AD using our recently developed simple MRI visual scales and evaluate their reliability.

METHODS

We used Hippocampo-horn percentage (Hip-hop) and Parietal Atrophy Score (PAS) to compare mediotemporal and parietal atrophy on brain MRI among 4 groups: 26 patients with early-onset AD, 21 younger cognitively normal persons, 32 patients with late-onset AD, and 36 older cognitively normal persons. Two raters scored all brain MRI to assess reliability of the Hip-hop and PAS. Brain MRIs were obtained from Alzheimer's Disease Neuroimaging Initiative (ADNI) database.

RESULTS

The patients with early-onset AD had significantly more pronounced mediotemporal and also parietal atrophy bilaterally compared to the controls (both p < 0.01). The patients with late-onset AD had significantly more pronounced only mediotemporal atrophy bilaterally compared to the controls (p < 0.000001), but parietal lobes were the same. Intra-rater and inter-rater reliability of both visual scales Hip-hop and PAS were almost perfect in all cases (weighted-kappa value ranged from 0.90 to 0.99).

CONCLUSION

While mediotemporal atrophy detected using Hip-hop is universal across the whole AD age spectrum, parietal atrophy detected using PAS is worth rating only in early-onset AD. Hip-hop and PAS are very reliable MRI visual scales.

Characterization of B1 + field variation in brain at 3 T using 385 healthy individuals across the lifespan

PURPOSE

The transmit field B 1 + at 3 T in brain affects the spatial uniformity and contrast of most image acquisitions. Here, B 1 + spatial variation in brain at 3 T is characterized in a large healthy population.

METHODS

Bloch-Siegert B 1 + maps were acquired at 3 T from 385 healthy subjects aged 5-90 years on a single MRI system. After transforming all B 1 + maps to a standard brain atlas space, region-of-interest analysis was performed, and intersubject voxel-wise coefficient of variation was calculated across the whole brain. The B 1 + variability due to age and brain size was studied separately in males and females, along with B 1 + variability due to nonideal transmit calibration.

RESULTS

The voxel-based mean coefficient of variation was 4.0% across all subjects, and the difference in B 1 + between central (left thalamus) and outer regions (left frontal gray matter) was 24.2% ± 2.3%. The least intersubject variability occurred in central regions, whereas regions toward brain edges increased markedly in variation. The B 1 + variability with age was mostly attributed to lifespan changes in CSF volume (which alters brain conductivity) and head orientation. Larger brain size correlated with more B 1 + inhomogeneity (p < .001). Varying head position and anatomy resulted in an inaccurate transmit calibration.

CONCLUSION

In standard atlas space, intersubject B 1 + variability at 3 T was relatively small in a large population aged 5-90 years. The B 1 + varied with age-related changes of CSF volume and head orientation, as well as differences in brain size and transmit calibration.

Data analysis with Shapley values for automatic subject selection in Alzheimer's disease data sets using interpretable machine learning

BACKGROUND

For the recruitment and monitoring of subjects for therapy studies, it is important to predict whether mild cognitive impaired (MCI) subjects will prospectively develop Alzheimer's disease (AD). Machine learning (ML) is suitable to improve early AD prediction. The etiology of AD is heterogeneous, which leads to high variability in disease patterns. Further variability originates from multicentric study designs, varying acquisition protocols, and errors in the preprocessing of magnetic resonance imaging (MRI) scans. The high variability makes the differentiation between signal and noise difficult and may lead to overfitting. This article examines whether an automatic and fair data valuation method based on Shapley values can identify the most informative subjects to improve ML classification.

METHODS

An ML workflow was developed and trained for a subset of the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. The validation was executed for an independent ADNI test set and for the Australian Imaging, Biomarker and Lifestyle Flagship Study of Ageing (AIBL) cohort. The workflow included volumetric MRI feature extraction, feature selection, sample selection using Data Shapley, random forest (RF), and eXtreme Gradient Boosting (XGBoost) for model training as well as Kernel SHapley Additive exPlanations (SHAP) values for model interpretation.

RESULTS

The RF models, which excluded 134 of the 467 training subjects based on their RF Data Shapley values, outperformed the base models that reached a mean accuracy of 62.64% by 5.76% (3.61 percentage points) for the independent ADNI test set. The XGBoost base models reached a mean accuracy of 60.00% for the AIBL data set. The exclusion of those 133 subjects with the smallest RF Data Shapley values could improve the classification accuracy by 2.98% (1.79 percentage points). The cutoff values were calculated using an independent validation set.

CONCLUSION

The Data Shapley method was able to improve the mean accuracies for the test sets. The most informative subjects were associated with the number of ApolipoproteinE ε4 (ApoE ε4) alleles, cognitive test results, and volumetric MRI measurements.

Associations of Vascular Risk with Cognition, Brain Glucose Metabolism, and Clinical Progression in Cognitively Intact Elders

BACKGROUND

Increasing evidence supports an important role of vascular risk in cognitive decline and dementia.

OBJECTIVE

This study aimed to examine whether vascular risk was associated with cognitive decline, cerebral hypometabolism, and clinical progression in cognitively intact elders.

METHODS

Vascular risk was assessed by the Framingham Heart Study general Cardiovascular disease (FHS-CVD) risk score. The cross-sectional and longitudinal associations of FHS-CVD risk score with cognition and brain glucose metabolism were explored using multivariate linear regression and linear mixed effects models, respectively. The risk of clinical progression conversion was assessed using Kaplan-Meier survival curves and multivariate Cox proportional hazard models.

RESULTS

A total of 491 cognitively intact elders were included from Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Participants with high FHS-CVD risk scores had lower baseline Mini-Mental State Examination (MMSE) (p = 0.009), executive function (EF) (p < 0.001), memory function (MEM) (p < 0.001) scores, and F18-fluorodeoxyglucose positron emission tomography (FDG-PET) uptake (p < 0.001) than those with low FHS-CVD risk scores. In longitudinal analyses, individuals with higher FHS-CVD risk scores had greater longitudinal declines in MMSE (p = 0.043), EF (p = 0.029) scores, and FDG-PET uptake (p = 0.035). Besides, individuals with a higher vascular risk had an increased risk of clinical progression (p = 0.004).

CONCLUSION

These findings indicated effects of vascular risk on cognitive decline, cerebral hypometabolism, and clinical progression. Early detection and management of vascular risk factors might be useful in the prevention of dementia.

Cognitive Function Assessment and Prediction for Subjective Cognitive Decline and Mild Cognitive Impairment

Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative dementia. Recent studies found that subjective cognitive decline (SCD) may be the early clinical precursor that precedes mild cognitive impairment (MCI) for AD. SCD subjects with normal cognition may already have some medial temporal lobe atrophy. Although brain changes by AD have been widely studied in the literature, it is still challenging to investigate the anatomical subtle changes in SCD. This paper proposes a machine learning framework by combination of sparse coding and random forest (RF) to identify the informative imaging biomarkers for assessment and prediction of cognitive functions and their changes in individuals with MCI, SCD and normal control (NC) using magnetic resonance imaging (MRI). First, we compute the volumes from both the regions of interest from whole brain and the subregions of hippocampus and amygdala as the features of structural MRIs. Then, sparse coding is applied to identify the relevant features. Finally, the proximity-based RF is used to combine three sets of volumetric features and establish a regression model for predicting clinical scores. Our method has double feature selections to better explore the relevant features for prediction and is evaluated with the T1-weighted structural MR images from 36 MCI, 112 SCD, 78 NC subjects. The results demonstrate the effectiveness of proposed method. In addition to hippocampus and amygdala, we also found that the fimbria, basal nucleus and cortical nucleus subregions are more important than other regions for prediction of Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) scores and their changes.

Effects of Supplementation with Folic Acid and Its Combinations with Other Nutrients on Cognitive Impairment and Alzheimer's Disease: A Narrative Review

Cognitive impairment and Alzheimer's Disease, among other cognitive dysfunctions, has been recognized as a major public health problem. Folic acid is a well-known essential nutrient whose deficiency has been linked to neurocognitive dysfunctions, owing to hyperhomocysteinemia, an independent risk factor for cardio- and cerebrovascular diseases, including cognitive impairment, Alzheimer's Disease, and vascular dementia. However, to date, there is certain controversy about the efficacy of vitamin supplementation in patients with these pathologies. Therefore, we have reviewed the available dietary intervention studies based on folic acid, either alone or in combination with different vitamins or nutrients into the progression of Alzheimer's Disease and Cognitive impairment, highlighting the cognition and biochemical markers employed for the evaluation of the disease progression. Undeniably, the compiled information supports the potential benefits of vitamin supplementation in these pathologies, especially relevant to the aging process and quality of life, although more research is urgently needed to confirm these positive findings.

Case Study: A Precision Medicine Approach to Multifactorial Dementia and Alzheimer's Disease

We report a case of a patient with mixed dementia successfully treated with a personalized multimodal therapy. Monotherapeutics are inadequate for the treatment of Alzheimer's disease (AD) and mixed dementia; therefore, we approach treatment through an adaptive personalized multimodal program. Many multimodal programs are pre-determined, and thus may not address the underlying contributors to cognitive decline in each particular individual. The combination of a targeted, personalized, precision medicine approach using a multimodal program promises advantages over monotherapies and untargeted multimodal therapies for multifactorial dementia. In this case study, we describe successful treatment for a patient diagnosed with AD, using a multimodal, programmatic, precision medicine intervention encompassing therapies targeting multiple dementia diastheses. We describe specific interventions used in this case that are derived from a comprehensive protocol for AD precision medicine. After treatment, our patient demonstrated improvements in quantitative neuropsychological testing, volumetric neuroimaging, PET scans, and serum chemistries, accompanied by symptomatic improvement over a 3.5-year period. This case outcome supports the need for rigorous trials of comprehensive, targeted combination therapies to stabilize, restore, and prevent cognitive decline in individuals with potentially many underlying causes of such decline and dementia. Our multimodal therapy included personalized treatments to address each potential perturbation to neuroplasticity. In particular, neuroinflammation and metabolic subsystems influence cognitive function and hippocampal volume. In this patient with a primary biliary cholangitis (PBC) multimorbidity component, we introduced a personalized diet that helped reduce liver inflammation. Together, all these components of multimodal therapy showed a sustained functional and cognitive benefit. Multimodal therapies may have systemwide benefits on all dementias, particularly in the context of multimorbidity. Furthermore, these therapies provide generalized health benefits, as many of the factors - such as inflammation - that impact cognitive function also impact other systems.

Magnetic Resonance Imaging Findings Among Individuals With Olfactory and Cognitive Impairment

OBJECTIVES

The underlying mechanism of the association between olfactory impairment and dementia may be explained by neurodegenerative changes detected on magnetic resonance imaging (MRI). The purpose of this systematic review is to describe neurodegenerative changes on MRI in patients with olfactory impairment and mild cognitive impairment (MCI) or dementia.

STUDY DESIGN

Systematic review.

METHODS

A literature search encompassing PubMed, Embase, Cochrane Library, Web of Science, Scopus, and Google Scholar for studies with MRI and olfactory testing among participants diagnosed with MCI or dementia was performed. Sample size, study design, cognitive impairment type, olfactory testing, and MRI findings were abstracted. Two investigators independently reviewed all articles.

RESULTS

The search yielded 556 nonduplicate abstracts, from which 86 articles were reviewed and 24 were included. Seventeen (71%) of 24 studies reported hippocampal volume findings, with 14 studies reporting a relationship between hippocampal volume and olfactory performance. Two (50%) of four prospective studies reported the potential utility of baseline hippocampal volume as a marker of dementia conversion from MCI. Five (21%) of 24 studies reporting olfactory functional MRI (fMRI) findings highlighted the utility of olfactory fMRI to identify individuals in the early stages of cognitive decline.

CONCLUSION

Current evidence suggests hippocampal volume correlates with olfactory performance in individuals with cognitive impairment, and that olfactory fMRI may improve early detection of AD. However, the predictive utility of these imaging markers is limited in prospective studies. MRI may be a useful modality for selecting patients at high risk of future cognitive decline for enrollment in early treatment trials. Laryngoscope, 2021.

Association of Osteoarthritis With Changes in Structural Neuroimaging Markers Over Time Among Non-demented Older Adults

Objective: Although emerging evidence suggests that both osteoarthritis (OA) and brain atrophy (as assessed by structural neuroimaging markers) are associated with the risk of dementia, little is known about the association between OA and structural neuroimaging markers. This study aimed to examine the association of OA with changes in structural neuroimaging markers among non-demented older people. Methods: We examined the cross-sectional and longitudinal associations between OA and structural neuroimaging markers (hippocampal volume, entorhinal volume, ventricular volume, and volume of gray matter of the whole brain) among non-demented older people. We categorized our participants as those without OA (OA-) and those with OA (OA+). At baseline, we included 1,281 non-demented older adults, including 1,050 without OA and 231 with OA. Results: In the cross-sectional analysis, we did not observe any significant difference in structural neuroimaging markers between the two OA groups. In the longitudinal analysis, we found that compared to participants without OA, those with OA showed a steeper decline in volumes of the gray matter of the whole brain among non-demented older adults. Conclusions: OA was associated with a steeper decline in volumes of the gray matter of the whole brain over time among non-demented older people.

Ocimum sanctum Linn. Extract Improves Cognitive Deficits in Olfactory Bulbectomized Mice via the Enhancement of Central Cholinergic Systems and VEGF Expression

This study aimed to clarify the antidementia effects of ethanolic extract of Ocimum sanctum Linn. (OS) and its underlying mechanisms using olfactory bulbectomized (OBX) mice. OBX mice were treated daily with OS or a reference drug, donepezil (DNP). Spatial and nonspatial working memory performance was measured using a modified Y maze test and a novel object recognition test, respectively. Brain tissues of the animals were subjected to histochemical and neurochemical analysis. OS treatment attenuated OBX-induced impairment of spatial and nonspatial working memories. OBX induced degeneration of septal cholinergic neurons, enlargement of the lateral ventricles, and suppression of hippocampal neurogenesis. OS and DNP treatment also depressed these histological damages. OS administration reduced ex vivo activity of acetylcholinesterase in the brain. OBX diminished the expression levels of genes coding vascular endothelial growth factor (VEGF) and VEGF receptor type 2 (VEGFR2). Treatment with OS and DNP reversed OBX-induced decrease in VEGF gene and protein expression levels without affecting the expression of the VEGFR2 gene. These results demonstrate that the administration of OS can lessen the cognitive deficits and neurohistological damages of OBX and that these actions are, at least in part, mediated by the enhancement of central cholinergic systems and VEGF expression.

White matter hyperintensities in autopsy-confirmed frontotemporal lobar degeneration and Alzheimer's disease

Background

We aimed to systematically describe the burden and distribution of white matter hyperintensities (WMH) and investigate correlations with neuropsychiatric symptoms in pathologically proven Alzheimer’s disease (AD) and frontotemporal lobar degeneration (FTLD).

Methods

Autopsy-confirmed cases were identified from the Sunnybrook Dementia Study, including 15 cases of AD and 58 cases of FTLD (22 FTLD-TDP cases; 10 FTLD-Tau [Pick’s] cases; 11 FTLD-Tau Corticobasal Degeneration cases; and 15 FTLD-Tau Progressive Supranuclear Palsy cases). Healthy matched controls (n = 35) were included for comparison purposes. Data analyses included ANCOVA to compare the burden of WMH on antemortem brain MRI between groups, adjusted linear regression models to identify associations between WMH burden and neuropsychiatric symptoms, and image-guided pathology review of selected areas of WMH from each pathologic group.

Results

Burden and regional distribution of WMH differed significantly between neuropathological groups (F_5,77 = 2.67, P’ = 0.029), with the FTLD-TDP group having the highest mean volume globally (8032 ± 8889 mm³) and in frontal regions (4897 ± 6163 mm³). The AD group had the highest mean volume in occipital regions (468 ± 420 mm³). Total score on the Neuropsychiatric Inventory correlated with bilateral frontal WMH volume (β = 0.330, P = 0.006), depression correlated with bilateral occipital WMH volume (β = 0.401, P < 0.001), and apathy correlated with bilateral frontal WMH volume (β = 0.311, P = 0.009), all corrected for the false discovery rate. Image-guided neuropathological assessment of selected cases with the highest burden of WMH in each pathologic group revealed presence of severe gliosis, myelin pallor, and axonal loss, but with no distinguishing features indicative of the underlying proteinopathy.

Conclusions

These findings suggest that WMH are associated with neuropsychiatric manifestations in AD and FTLD and that WMH burden and regional distribution in neurodegenerative disorders differ according to the underlying neuropathological processes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13195-021-00869-6.

Bioinformatics analysis of differentially expressed genes and identification of an miRNA-mRNA network associated with entorhinal cortex and hippocampus in Alzheimer's disease

Background

Alzheimer’s disease (AD) is a fatal neurodegenerative disorder, and the lesions originate in the entorhinal cortex (EC) and hippocampus (HIP) at the early stage of AD progression. Gaining insight into the molecular mechanisms underlying AD is critical for the diagnosis and treatment of this disorder. Recent discoveries have uncovered the essential roles of microRNAs (miRNAs) in aging and have identified the potential of miRNAs serving as biomarkers in AD diagnosis.

Methods

We sought to apply bioinformatics tools to investigate microarray profiles and characterize differentially expressed genes (DEGs) in both EC and HIP and identify specific candidate genes and pathways that might be implicated in AD for further analysis. Furthermore, we considered that DEGs might be dysregulated by miRNAs. Therefore, we investigated patients with AD and healthy controls by studying the gene profiling of their brain and blood samples to identify AD-related DEGs, differentially expressed miRNAs (DEmiRNAs), along with gene ontology (GO) analysis, KEGG pathway analysis, and construction of an AD-specific miRNA–mRNA interaction network.

Results

Our analysis identified 10 key hub genes in the EC and HIP of patients with AD, and these hub genes were focused on energy metabolism, suggesting that metabolic dyshomeostasis contributed to the progression of the early AD pathology. Moreover, after the construction of an miRNA–mRNA network, we identified 9 blood-related DEmiRNAs, which regulated 10 target genes in the KEGG pathway.

Conclusions

Our findings indicated these DEmiRNAs having the potential to act as diagnostic biomarkers at an early stage of AD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41065-021-00190-0.