Presymptomatic hippocampal atrophy in Alzheimer's disease. A longitudinal MRI study

Jointly constrained group sparse connectivity representation improves early diagnosis of Alzheimer's disease on routinely acquired T1-weighted imaging-based brain network

Background

Radiomics-based morphological brain networks (radMBN) constructed from routinely acquired structural MRI (sMRI) data have gained attention in Alzheimer's disease (AD). However, the radMBN suffers from limited characterization of AD because sMRI only characterizes anatomical changes and is not a direct measure of neuronal pathology or brain activity.

Purpose

To establish a group sparse representation of the radMBN under a joint constraint of group-level white matter fiber connectivity and individual-level sMRI regional similarity (JCGS-radMBN).

Methods

Two publicly available datasets were adopted, including 120 subjects from ADNI with both T1-weighted image (T1WI) and diffusion MRI (dMRI) for JCGS-radMBN construction, 818 subjects from ADNI and 200 subjects solely with T1WI from AIBL for validation in early AD diagnosis. Specifically, the JCGS-radMBN was conducted by jointly estimating non-zero connections among subjects, with the regularization term constrained by group-level white matter fiber connectivity and individual-level sMRI regional similarity. Then, a triplet graph convolutional network was adopted for early AD diagnosis. The discriminative brain connections were identified using a two-sample t-test, and the neurobiological interpretation was validated by correlating the discriminative brain connections with cognitive scores.

Results

The JCGS-radMBN exhibited superior classification performance over five brain network construction methods. For the typical NC vs. AD classification, the JCGS-radMBN increased by 1-30% in accuracy over the alternatives on ADNI and AIBL. The discriminative brain connections exhibited a strong connectivity to hippocampus, parahippocampal gyrus, and basal ganglia, and had significant correlation with MMSE scores.

Conclusion

The proposed JCGS-radMBN facilitated the AD characterization of brain network established on routinely acquired imaging modality of sMRI.

Supplementary Information

The online version of this article (10.1007/s13755-023-00269-0) contains supplementary material, which is available to authorized users.

Relationship between cortical brain atrophy, delirium, and long-term cognitive decline in older surgical patients

In older patients, delirium after surgery is associated with long-term cognitive decline (LTCD). The neural substrates of this association are unclear. Neurodegenerative changes associated with dementia are possible contributors. We investigated the relationship between brain atrophy rates in Alzheimer's disease (AD) and cognitive aging signature regions from magnetic resonance imaging before and one year after surgery, LTCD assessed by the general cognitive performance (GCP) score over 6 years post-operatively, and delirium in 117 elective surgery patients without dementia (mean age = 76). The annual change in cortical thickness was 0.2(1.7) % (AD-signature p = 0.09) and 0.4(1.7) % (aging-signature p = 0.01). Greater atrophy was associated with LTCD (AD-signature: beta(CI) = 0.24(0.06-0.42) points of GCP/mm of cortical thickness; p < 0.01, aging-signature: beta(CI) = 0.55(0.07-1.03); p = 0.03). Atrophy rates were not significantly different between participants with and without delirium. We found an interaction with delirium severity in the association between atrophy and LTCD (AD-signature: beta(CI) = 0.04(0.00-0.08), p = 0.04; aging-signature: beta(CI) = 0.08(0.03-0.12), p < 0.01). The rate of cortical atrophy and severity of delirium are independent, synergistic factors determining postoperative cognitive decline in the elderly.

Cortex-to-caudate volume ratio as a predictor of cognitive decline in Alzheimer's disease and mild cognitive impairment

BACKGROUND

Brain and cortical atrophy play crucial roles in supporting the clinical diagnosis of Alzheimer's disease (AD). This study hypothesized that the ratios of brain or cortical volume to subcortical gray matter structure volumes are potential imaging markers for cognitive alterations in AD dementia and amnestic mild cognitive impairment (aMCI).

METHODS

Seventy-seven subjects diagnosed with AD dementia or aMCI underwent baseline neuropsychological testing, 2-year follow-up cognitive assessments, and high-resolution T1-weighted MRI scans. Total brain/cortical volume and subcortical gray matter structure volumes were automatically segmented and measured. Univariate and multiple linear regression analyses were conducted to determine the associations between volumetric ratios and interval changes in cognitive scores.

RESULTS

The ratio of cortical volume to caudate volume showed the most significant association with changes in MoCA (B = 0.132, SE = 0.042, p = 0.002), MMSE (B = 0.140, SE = 0.040, p = 0.001), and CDR-SOB (B = -0.013, SE = 0.005, p = 0.007) scores over the 2-year follow-up period. These associations remained significant after adjusting for various covariates. Similar associations were observed for the ratios of cortical volume to putamen and globus pallidum volumes.

CONCLUSIONS

The cortex-to-caudate volume ratio is significantly associated with cognitive decline in AD dementia and aMCI. This ratio may serve as a useful biomarker for monitoring disease progression and predicting cognitive outcomes. Our findings highlight the importance of considering the relative atrophy of cortical and subcortical structures in understanding AD pathology.

Beyond the hippocampus: Amygdala and memory functioning in older adults

BACKGROUND

Medial temporal lobe atrophy has been linked to decline in neuropsychological measures of explicit memory function. While the hippocampus has long been identified as a critical structure in learning and memory processes, less is known about contributions of the amygdala to these functions. We sought to investigate the relationship between amygdala volume and memory functioning in a clinical sample of older adults with and without cognitive impairment.

METHODS

A serial clinical sample of older adults that underwent neuropsychological assessment at an outpatient neurology clinic was selected for retrospective chart review. Patients were included in the study if they completed a comprehensive neuropsychological assessment within six months of a structural magnetic resonance imaging scan. Regional brain volumes were quantified using Neuroreader® software. Associations between bilateral hippocampal and amygdala volumes and memory scores, derived from immediate and delayed recall conditions of a verbal story learning task and a visual design reconstruction task, were examined using mixed-effects general linear models, controlling for total intracranial volume, scanner model, age, sex and education. Partial correlation coefficients, adjusted for these covariates, were calculated to estimate the strength of the association between volumes and memory scores.

RESULTS

A total of 68 (39 F, 29 M) participants were included in the analyses, with a mean (SD) adjusted age of 80.1 (6.0) and educational level of 15.9 (2.5) years. Controlling for age, sex, education, and total intracranial volume, greater amygdala volumes were associated with better verbal and visual memory performance, with effect sizes comparable to hippocampal volume. No significant lateralized effects were observed. Partial correlation coefficients ranged from 0.47 to 0.33 (p<.001).

CONCLUSION

These findings contribute to a growing body of knowledge identifying the amygdala as a target for further research in memory functioning. This highlights the importance of considering the broader functioning of the limbic system in which multiple subcortical structures contribute to memory processes and decline in older adults.

Basal forebrain volume and metabolism in carriers of the Colombian mutation for autosomal dominant Alzheimer's disease

We aimed to study atrophy and glucose metabolism of the cholinergic basal forebrain in non-demented mutation carriers for autosomal dominant Alzheimer's disease (ADAD). We determined the level of evidence for or against atrophy and impaired metabolism of the basal forebrain in 167 non-demented carriers of the Colombian PSEN1 E280A mutation and 75 age- and sex-matched non-mutation carriers of the same kindred using a Bayesian analysis framework. We analyzed baseline MRI, amyloid PET, and FDG-PET scans of the Alzheimer's Prevention Initiative ADAD Colombia Trial. We found moderate evidence against an association of carrier status with basal forebrain volume (Bayes factor (BF10) = 0.182). We found moderate evidence against a difference of basal forebrain metabolism (BF10 = 0.167). There was only inconclusive evidence for an association between basal forebrain volume and delayed memory and attention (BF10 = 0.884 and 0.184, respectively), and between basal forebrain volume and global amyloid load (BF10 = 2.1). Our results distinguish PSEN1 E280A mutation carriers from sporadic AD cases in which cholinergic involvement of the basal forebrain is already detectable in the preclinical and prodromal stages. This indicates an important difference between ADAD and sporadic AD in terms of pathogenesis and potential treatment targets.

Body size interacts with the structure of the central nervous system: A multi-center in vivo neuroimaging study

Clinical research emphasizes the implementation of rigorous and reproducible study designs that rely on between-group matching or controlling for sources of biological variation such as subject's sex and age. However, corrections for body size (i.e. height and weight) are mostly lacking in clinical neuroimaging designs. This study investigates the importance of body size parameters in their relationship with spinal cord (SC) and brain magnetic resonance imaging (MRI) metrics. Data were derived from a cosmopolitan population of 267 healthy human adults (age 30.1±6.6 years old, 125 females). We show that body height correlated strongly or moderately with brain gray matter (GM) volume, cortical GM volume, total cerebellar volume, brainstem volume, and cross-sectional area (CSA) of cervical SC white matter (CSA-WM; 0.44≤r≤0.62). In comparison, age correlated weakly with cortical GM volume, precentral GM volume, and cortical thickness (-0.21≥r≥-0.27). Body weight correlated weakly with magnetization transfer ratio in the SC WM, dorsal columns, and lateral corticospinal tracts (-0.20≥r≥-0.23). Body weight further correlated weakly with the mean diffusivity derived from diffusion tensor imaging (DTI) in SC WM (r=-0.20) and dorsal columns (-0.21), but only in males. CSA-WM correlated strongly or moderately with brain volumes (0.39≤r≤0.64), and weakly with precentral gyrus thickness and DTI-based fractional anisotropy in SC dorsal columns and SC lateral corticospinal tracts (-0.22≥r≥-0.25). Linear mixture of sex and age explained 26±10% of data variance in brain volumetry and SC CSA. The amount of explained variance increased at 33±11% when body height was added into the mixture model. Age itself explained only 2±2% of such variance. In conclusion, body size is a significant biological variable. Along with sex and age, body size should therefore be included as a mandatory variable in the design of clinical neuroimaging studies examining SC and brain structure.

Novel insights into brain lipid metabolism in Alzheimer's disease: Oligodendrocytes and white matter abnormalities

Alzheimer's disease (AD) is the most common cause of dementia. A genome-wide association study has shown that several AD risk genes are involved in lipid metabolism. Additionally, epidemiological studies have indicated that the levels of several lipid species are altered in the AD brain. Therefore, lipid metabolism is likely changed in the AD brain, and these alterations might be associated with an exacerbation of AD pathology. Oligodendrocytes are glial cells that produce the myelin sheath, which is a lipid-rich insulator. Dysfunctions of the myelin sheath have been linked to white matter abnormalities observed in the AD brain. Here, we review the lipid composition and metabolism in the brain and myelin and the association between lipidic alterations and AD pathology. We also present the abnormalities in oligodendrocyte lineage cells and white matter observed in AD. Additionally, we discuss metabolic disorders, including obesity, as AD risk factors and the effects of obesity and dietary intake of lipids on the brain.

Depressive symptoms and hippocampal volume in autosomal dominant Alzheimer's disease

INTRODUCTION

Depressive symptoms are among early behavioral changes in Alzheimer's disease (AD); however, the relationship between neurodegeneration and depressive symptoms remains inconclusive. To better understand this relationship in preclinical AD, we examined hippocampal volume and depressive symptoms in cognitively unimpaired carriers of the presenilin-1 (PSEN1) E280A mutation for autosomal dominant AD.

METHODS

A total of 27 PSEN1 mutation carriers and 26 non-carrier family members were included. Linear regression was used to test the relationship between hippocampal volume and 15-item Geriatric Depression Scale.

RESULTS

Carriers and non-carriers did not differ in depressive symptoms or hippocampal volume. Within carriers, lower hippocampal volume was associated with greater depressive symptoms, which remained significant after adjusting for age and cognition. This relationship was not significant in non-carriers.

DISCUSSION

Hippocampal neurodegeneration may underlie depressive symptoms in preclinical autosomal dominant AD. These findings provide support for the utility of targeting depressive symptoms in AD prevention.

HIGHLIGHTS

We compared unimpaired autosomal dominant Alzheimer's disease (AD) mutation carriers and non-carriers. Carriers and non-carriers did not differ in severity of depressive symptoms. In carriers, hippocampal volume was inversely associated with depressive symptoms. Depressive symptoms may be a useful target in AD prevention.

Temporal Alterations in White Matter in An App Knock-In Mouse Model of Alzheimer's Disease

Alzheimer's disease (AD) is the most common form of dementia and results in neurodegeneration and cognitive impairment. White matter (WM) is affected in AD and has implications for neural circuitry and cognitive function. The trajectory of these changes across age, however, is still not well understood, especially at earlier stages in life. To address this, we used the AppNL-G-F/NL-G-F knock-in (APPKI) mouse model that harbors a single copy knock-in of the human amyloid precursor protein (APP) gene with three familial AD mutations. We performed in vivo diffusion tensor imaging (DTI) to study how the structural properties of the brain change across age in the context of AD. In late age APPKI mice, we observed reduced fractional anisotropy (FA), a proxy of WM integrity, in multiple brain regions, including the hippocampus, anterior commissure (AC), neocortex, and hypothalamus. At the cellular level, we observed greater numbers of oligodendrocytes in middle age (prior to observations in DTI) in both the AC, a major interhemispheric WM tract, and the hippocampus, which is involved in memory and heavily affected in AD, prior to observations in DTI. Proteomics analysis of the hippocampus also revealed altered expression of oligodendrocyte-related proteins with age and in APPKI mice. Together, these results help to improve our understanding of the development of AD pathology with age, and imply that middle age may be an important temporal window for potential therapeutic intervention.

Baseline Frontoparietal Gray Matter Volume Predicts Executive Function Performance in Aging and Mild Cognitive Impairment at 24-Month Follow-Up

Background

Executive dysfunction in mild cognitive impairment (MCI) has been associated with gray matter atrophy. Prior studies have yielded limited insight into associations between gray matter volume and executive function in early and late amnestic MCI (aMCI).

Objective

To examine the relative importance of predictors of executive function at 24 months and relationships between baseline regional gray matter volume and executive function performance at 24-month follow-up in non-demented older adults.

Methods

147 participants from the Alzheimer's Disease Neuroimaging Initiative (mean age = 70.6 years) completed brain magnetic resonance imaging and neuropsychological testing and were classified as cognitively normal (n = 49), early aMCI (n = 60), or late aMCI (n = 38). Analyses explored the importance of demographic, APOEɛ4, biomarker (p-tau/Aβ42, t-tau/Aβ42), and gray matter regions-of-interest (ROI) variables to 24-month executive function, whether ROIs predicted executive function, and whether relationships varied by baseline diagnostic status.

Results

Across all participants, baseline anterior cingulate cortex and superior parietal lobule volumes were the strongest predictors of 24-month executive function performance. In early aMCI, anterior cingulate cortex volume was the strongest predictor and demonstrated a significant interaction such that lower volume related to worse 24-month executive function in early aMCI. Educational attainment and inferior frontal gyrus volume were the strongest predictors of 24-month executive function performance for cognitively normal and late aMCI groups, respectively.

Conclusions

Baseline frontoparietal gray matter regions were significant predictors of executive function performance in the context of aMCI and may identify those at risk of Alzheimer's disease. Anterior cingulate cortex volume may predict executive function performance in early aMCI.

A Case-Control Clinical Trial on a Deep Learning-Based Classification System for Diagnosis of Amyloid-Positive Alzheimer's Disease

OBJECTIVE

A deep learning-based classification system (DLCS) which uses structural brain magnetic resonance imaging (MRI) to diagnose Alzheimer's disease (AD) was developed in a previous recent study. Here, we evaluate its performance by conducting a single-center, case-control clinical trial.

METHODS

We retrospectively collected T1-weighted brain MRI scans of subjects who had an accompanying measure of amyloid-beta (Aβ) positivity based on a 18F-florbetaben positron emission tomography scan. The dataset included 188 Aβ-positive patients with mild cognitive impairment or dementia due to AD, and 162 Aβ-negative controls with normal cognition. We calculated the sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC) of the DLCS in the classification of Aβ-positive AD patients from Aβ-negative controls.

RESULTS

The DLCS showed excellent performance, with sensitivity, specificity, positive predictive value, negative predictive value, and AUC of 85.6% (95% confidence interval [CI], 79.8-90.0), 90.1% (95% CI, 84.5-94.2), 91.0% (95% CI, 86.3-94.1), 84.4% (95% CI, 79.2-88.5), and 0.937 (95% CI, 0.911-0.963), respectively.

CONCLUSION

The DLCS shows promise in clinical settings where it could be routinely applied to MRI scans regardless of original scan purpose to improve the early detection of AD.

Morphometric analysis of medial temporal lobe subregions in Alzheimer's disease using high-resolution MRI

The spread pattern of progressive degeneration seen in Alzheimer's disease (AD) to small-scale medial temporal lobe subregions is critical for early diagnosis. In this context, it was aimed to examine the morphometric changes of the hippocampal subfields, amygdala nuclei, entorhinal cortex (ERC), and parahippocampal cortex (PHC) using MRI. MRI data of patients diagnosed with 20 Alzheimer's disease dementia (ADD), 30 amnestic mild cognitive impairment (aMCI), and 30 subjective cognitive impairment (SCI) without demographic differences were used. Segmentation and parcellation were performed using FreeSurfer. The segmentation process obtained volume values of 12 hippocampal subfields and 9 amygdala nuclei. Thickness values of ERC and PHC were calculated with the parcellation process. ANCOVA was performed using age, education and gender as covariates to evaluate the intergroup differences. Linear discriminant analysis was used to investigate whether atrophy predicted groups at an early stage. ERC and PHC thickness decreased significantly throughout the disease continuum, while only ERC was affected in the early stage. When the hippocampal and amygdala subfields were compared volumetrically, significant differences were found in the amygdala between the SCI and aMCI groups. In the early period, only volume reduction in the anterior amygdaloid area of the amygdala nuclei exceeded the significance threshold. Research on AD primarily focuses on original hippocampocentric structures and their main function which is episodic memory. Our results emphasized the significance of so far relatively neglected olfactocentric structures and their functions, such as smell and social cognition in the pre-dementia stages of the AD process.

Regional and interhemispheric differences of neuronal representations in dentate gyrus and CA3 inferred from expression of zif268

The hippocampal formation is one of the best studied brain regions for spatial and mnemonic representations. These representations have been reported to differ in their properties for individual hippocampal subregions. One approach that allows the detection of neuronal representations is immediate early gene imaging, which relies on the visualization of genomic responses of activated neuronal populations, so called engrams. This method permits the within-animal comparison of neuronal representations across different subregions. In this work, we have used compartmental analysis of temporal activity by fluorescence in-situ hybridisation (catFISH) of the immediate early gene zif268/erg1 to compare neuronal representations between subdivisions of the dentate gyrus and CA3 upon exploration of different contexts. Our findings give an account of subregion-specific ensemble sizes. We confirm previous results regarding disambiguation abilities in dentate gyrus and CA3 but in addition report novel findings: Although ensemble sizes in the lower blade of the dentate gyrus are significantly smaller than in the upper blade both blades are responsive to environmental change. Beyond this, we show significant differences in the representation of familiar and novel environments along the longitudinal axis of dorsal CA3 and most interestingly between CA3 regions of both hemispheres.

Pathway-specific polygenic scores for Alzheimer's disease are associated with changes in brain structure in younger and older adults

Genome-wide association studies have identified multiple Alzheimer's disease risk loci with small effect sizes. Polygenic risk scores, which aggregate these variants, are associated with grey matter structural changes. However, genome-wide scores do not allow mechanistic interpretations. The present study explored associations between disease pathway-specific scores and grey matter structure in younger and older adults. Data from two separate population cohorts were used as follows: the Avon Longitudinal Study of Parents and Children, mean age 19.8, and UK Biobank, mean age 64.4 (combined n = 18 689). Alzheimer's polygenic risk scores were computed using the largest genome-wide association study of clinically assessed Alzheimer's to date. Relationships between subcortical volumes and cortical thickness, pathway-specific scores and genome-wide scores were examined. Increased pathway-specific scores were associated with reduced cortical thickness in both the younger and older cohorts. For example, the reverse cholesterol transport pathway score showed evidence of association with lower left middle temporal cortex thickness in the younger Avon participants (P = 0.034; beta = -0.013, CI -0.025, -0.001) and in the older UK Biobank participants (P = 0.019; beta = -0.003, CI -0.005, -4.56 × 10-4). Pathway scores were associated with smaller subcortical volumes, such as smaller hippocampal volume, in UK Biobank older adults. There was also evidence of positive association between subcortical volumes in Avon younger adults. For example, the tau protein-binding pathway score was negatively associated with left hippocampal volume in UK Biobank (P = 8.35 × 10-05; beta = -11.392, CI -17.066, -5.718) and positively associated with hippocampal volume in the Avon study (P = 0.040; beta = 51.952, CI 2.445, 101.460). The immune response score had a distinct pattern of association, being only associated with reduced thickness in the right posterior cingulate in older and younger adults (P = 0.011; beta = -0.003, CI -0.005, -0.001 in UK Biobank; P = 0.034; beta = -0.016, CI -0.031, -0.001 in the Avon study). The immune response score was associated with smaller subcortical volumes in the older adults, but not younger adults. The disease pathway scores showed greater evidence of association with imaging phenotypes than the genome-wide score. This suggests that pathway-specific polygenic methods may allow progress towards a mechanistic understanding of structural changes linked to polygenic risk in pre-clinical Alzheimer's disease. Pathway-specific profiling could further define pathophysiology in individuals, moving towards precision medicine in Alzheimer's disease.

A Brief Review on the Potential of Psychedelics for Treating Alzheimer's Disease and Related Depression

Alzheimer's disease (AD), the most common form of senile dementia, is poised to place an even greater societal and healthcare burden as the population ages. With few treatment options for the symptomatic relief of the disease and its unknown etiopathology, more research into AD is urgently needed. Psychedelic drugs target AD-related psychological pathology and symptoms such as depression. Using microdosing, psychedelic drugs may prove to help combat this devastating disease by eliciting psychiatric benefits via acting through various mechanisms of action such as serotonin and dopamine pathways. Herein, we review the studied benefits of a few psychedelic compounds that may show promise in treating AD and attenuating its related depressive symptoms. We used the listed keywords to search through PubMed for relevant preclinical, clinical research, and review articles. The putative mechanism of action (MOA) for psychedelics is that they act mainly as serotonin receptor agonists and induce potential beneficial effects for treating AD and related depression.

Identifying healthy individuals with Alzheimer's disease neuroimaging phenotypes in the UK Biobank

BACKGROUND

Identifying prediagnostic neurodegenerative disease is a critical issue in neurodegenerative disease research, and Alzheimer's disease (AD) in particular, to identify populations suitable for preventive and early disease-modifying trials. Evidence from genetic and other studies suggests the neurodegeneration of Alzheimer's disease measured by brain atrophy starts many years before diagnosis, but it is unclear whether these changes can be used to reliably detect prediagnostic sporadic disease.

METHODS

We trained a Bayesian machine learning neural network model to generate a neuroimaging phenotype and AD score representing the probability of AD using structural MRI data in the Alzheimer's Disease Neuroimaging Initiative (ADNI) Cohort (cut-off 0.5, AUC 0.92, PPV 0.90, NPV 0.93). We go on to validate the model in an independent real-world dataset of the National Alzheimer's Coordinating Centre (AUC 0.74, PPV 0.65, NPV 0.80) and demonstrate the correlation of the AD-score with cognitive scores in those with an AD-score above 0.5. We then apply the model to a healthy population in the UK Biobank study to identify a cohort at risk for Alzheimer's disease.

RESULTS

We show that the cohort with a neuroimaging Alzheimer's phenotype has a cognitive profile in keeping with Alzheimer's disease, with strong evidence for poorer fluid intelligence, and some evidence of poorer numeric memory, reaction time, working memory, and prospective memory. We found some evidence in the AD-score positive cohort for modifiable risk factors of hypertension and smoking.

CONCLUSIONS

This approach demonstrates the feasibility of using AI methods to identify a potentially prediagnostic population at high risk for developing sporadic Alzheimer's disease.

Hippocampal avoidance in whole brain radiotherapy and prophylactic cranial irradiation: a systematic review and meta-analysis

PURPOSE

We systematically reviewed the current landscape of hippocampal-avoidance radiotherapy, focusing specifically on rates of hippocampal tumor recurrence and changes in neurocognitive function.

METHODS

PubMed was queried for studies involving hippocampal-avoidance radiation therapy and results were screened using PRISMA guidelines. Results were analyzed for median overall survival, progression-free survival, hippocampal relapse rates, and neurocognitive function testing.

RESULTS

Of 3709 search results, 19 articles were included and a total of 1611 patients analyzed. Of these studies, 7 were randomized controlled trials, 4 prospective cohort studies, and 8 retrospective cohort studies. All studies evaluated hippocampal-avoidance whole brain radiation treatment (WBRT) and/or prophylactic cranial irradiation (PCI) in patients with brain metastases. Hippocampal relapse rates were low (overall effect size = 0.04; 95% confidence interval [0.03, 0.05]) and there was no significant difference in risk of relapse between the five studies that compared HA-WBRT/HA-PCI and WBRT/PCI groups (risk difference = 0.01; 95% confidence interval [- 0.02, 0.03]; p = 0.63). 11 out of 19 studies included neurocognitive function testing. Significant differences were reported in overall cognitive function and memory and verbal learning 3-24 months post-RT. Differences in executive function were reported by one study, Brown et al., at 4 months. No studies reported differences in verbal fluency, visual learning, concentration, processing speed, and psychomotor speed at any timepoint.

CONCLUSION

Current studies in HA-WBRT/HA-PCI showed low hippocampal relapse or metastasis rates. Significant differences in neurocognitive testing were most prominent in overall cognitive function, memory, and verbal learning. Studies were hampered by loss to follow-up.

Impact of social isolation on grey matter structure and cognitive functions: A population-based longitudinal neuroimaging study

Background

Social isolation has been suggested to increase the risk to develop cognitive decline. However, our knowledge on causality and neurobiological underpinnings is still limited.

Methods

In this preregistered analysis, we tested the impact of social isolation on central features of brain and cognitive ageing using a longitudinal population-based magnetic resonance imaging (MRI) study. We assayed 1992 cognitively healthy participants (50-82years old, 921women) at baseline and 1409 participants after~6y follow-up.

Results

We found baseline social isolation and change in social isolation to be associated with smaller volumes of the hippocampus and clusters of reduced cortical thickness. Furthermore, poorer cognitive functions (memory, processing speed, executive functions) were linked to greater social isolation, too.

Conclusions

Combining advanced neuroimaging outcomes with prevalent lifestyle characteristics from a well-characterized population of middle- to older aged adults, we provide evidence that social isolation contributes to human brain atrophy and cognitive decline. Within-subject effects of social isolation were similar to between-subject effects, indicating an opportunity to reduce dementia risk by promoting social networks.

Funding

European Union, European Regional Development Fund, Free State of Saxony, LIFE-Leipzig Research Center for Civilization Diseases, University of Leipzig, German Research Foundation.

An open-source tool for longitudinal whole-brain and white matter lesion segmentation

In this paper we describe and validate a longitudinal method for whole-brain segmentation of longitudinal MRI scans. It builds upon an existing whole-brain segmentation method that can handle multi-contrast data and robustly analyze images with white matter lesions. This method is here extended with subject-specific latent variables that encourage temporal consistency between its segmentation results, enabling it to better track subtle morphological changes in dozens of neuroanatomical structures and white matter lesions. We validate the proposed method on multiple datasets of control subjects and patients suffering from Alzheimer's disease and multiple sclerosis, and compare its results against those obtained with its original cross-sectional formulation and two benchmark longitudinal methods. The results indicate that the method attains a higher test-retest reliability, while being more sensitive to longitudinal disease effect differences between patient groups. An implementation is publicly available as part of the open-source neuroimaging package FreeSurfer.

The impact of depression on language function in individuals with Alzheimer's disease: a pre/post-treatment design

BACKGROUND

It is uncertain whether depression might affect cognitive function in Alzheimer's disease (AD). Most of studies on the effect of depression treatment on cognitive function in AD were briefly evaluated by Mini-Mental State Examination (MMSE). MMSE is poor sensitive to detect cognitive change. This study examined the cognitive response to depression treatment in AD via multi-domain assessment. In addition, we explored whether effect of depression treatment in AD is different those of late-life depression (LLD).

METHODS

This study include AD patients with depression (AD + D) and without depression (AD - D), LLD patients (LLD), and healthy controls (HC). The patients were treated according to their diagnosis for 16 weeks: acetylcholinesterase inhibitors (AChEIs) and selective serotonin reuptake inhibitors (SSRIs) for AD + D, AChEIs for AD - D, and SSRIs for LLD. The cognitive changes from pre- to post-treatment were compared between AD + D and AD - D or LLD and HC. An independent sample t test was performed to compare the degree of change between the groups. Paired t tests were used to determine cognitive function changes in each depression treatment responder group.

RESULTS

At baseline, AD + D had more impairment in language function compared to AD - D, and LLD had greater deficit in executive function than HC. After depression treatment, more impaired cognitive domains at baseline were improved in AD + D and LLD, respectively. Moreover, AD + D showed an improvement in the global cognitive function (MMSE).

CONCLUSIONS

Results indicated that language function was influenced by depression in AD, which is first evidence for specific cognitive domain related to depression in AD. Our finding indicates that depression could negatively impact cognitive function, and depression treatment may have beneficial cognitive effect in both AD and LLD. This study suggests the importance of early detection and treatment of depression in AD and LLD. Trial registration Clinical Research Information Service, CRIS, ID#: KCT0004041, Registered 5 June 2019, retrospectively registered after first patient enrollment date (4 March 2014) https://cris.nih.go.kr/cris/search/detailSearch.do?seq=14140&status=5&seq_group=14140&search_page=M .

Medial Temporal Lobe Tau Aggregation Relates to Divergent Cognitive and Emotional Empathy Abilities in Alzheimer's Disease

BACKGROUND

In Alzheimer's disease (AD), the gradual accumulation of amyloid-β (Aβ) and tau proteins may underlie alterations in empathy.

OBJECTIVE

To assess whether tau aggregation in the medial temporal lobes related to differences in cognitive empathy (the ability to take others' perspectives) and emotional empathy (the ability to experience others' feelings) in AD.

METHODS

Older adults (n = 105) completed molecular Aβ positron emission tomography (PET) scans. Sixty-eight of the participants (35 women) were Aβ positive and symptomatic with diagnoses of mild cognitive impairment, dementia of the Alzheimer's type, logopenic variant primary progressive aphasia, or posterior cortical atrophy. The remaining 37 (22 women) were asymptomatic Aβ negative healthy older controls. Using the Interpersonal Reactivity Index, we compared current levels of informant-rated cognitive empathy (Perspective-Taking subscale) and emotional empathy (Empathic Concern subscale) in the Aβ positive and negative participants. The Aβ positive participants also underwent molecular tau-PET scans, which were used to investigate whether regional tau burden in the bilateral medial temporal lobes related to empathy.

RESULTS

Aβ positive participants had lower perspective-taking and higher empathic concern than Aβ negative healthy controls. Medial temporal tau aggregation in the Aβ positive participants had divergent associations with cognitive and emotional empathy. Whereas greater tau burden in the amygdala predicted lower perspective-taking, greater tau burden in the entorhinal cortex predicted greater empathic concern. Tau burden in the parahippocampal cortex did not predict either form of empathy.

CONCLUSIONS

Across AD clinical syndromes, medial temporal lobe tau aggregation is associated with lower perspective-taking yet higher empathic concern.

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

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

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

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

Generalizable deep learning model for early Alzheimer's disease detection from structural MRIs

Early diagnosis of Alzheimer's disease plays a pivotal role in patient care and clinical trials. In this study, we have developed a new approach based on 3D deep convolutional neural networks to accurately differentiate mild Alzheimer's disease dementia from mild cognitive impairment and cognitively normal individuals using structural MRIs. For comparison, we have built a reference model based on the volumes and thickness of previously reported brain regions that are known to be implicated in disease progression. We validate both models on an internal held-out cohort from The Alzheimer's Disease Neuroimaging Initiative (ADNI) and on an external independent cohort from The National Alzheimer's Coordinating Center (NACC). The deep-learning model is accurate, achieved an area-under-the-curve (AUC) of 85.12 when distinguishing between cognitive normal subjects and subjects with either MCI or mild Alzheimer's dementia. In the more challenging task of detecting MCI, it achieves an AUC of 62.45. It is also significantly faster than the volume/thickness model in which the volumes and thickness need to be extracted beforehand. The model can also be used to forecast progression: subjects with mild cognitive impairment misclassified as having mild Alzheimer's disease dementia by the model were faster to progress to dementia over time. An analysis of the features learned by the proposed model shows that it relies on a wide range of regions associated with Alzheimer's disease. These findings suggest that deep neural networks can automatically learn to identify imaging biomarkers that are predictive of Alzheimer's disease, and leverage them to achieve accurate early detection of the disease.

Geometric deep learning reveals a structuro-temporal understanding of healthy and pathologic brain aging

Background

Brain age has historically been investigated primarily at the whole brain level. The ability to deconstruct the brain into its composite parts and explore brain age at the sub-structure level offers unique advantages. These include the exploration of dynamic and interconnected relationships between different brain structures in healthy and pathologic aging. To achieve this, individual brain structures can be rendered as surface representations on which morphologic analysis is carried out. Combining the advantages of deep learning with the strengths of surface analysis, we investigate the aging process at the individual structure level with the hypothesis being that pathologic aging does not uniformly affect the aging process of individual structures.

Methods

MRI data, age at scan time and diagnosis of dementia were collected from seven publicly available data repositories. The data from 17,440 unique subjects were collected, representing a total of 26,276 T1-weighted MRI accounting for longitudinal acquisitions. Surfaces were extracted for the cortex and seven subcortical structures. Deep learning networks were trained to estimate a subject's age either using several structures together or a single structure. We conducted a cross-sectional analysis to assess the difference between the predicted and actual ages for all structures between healthy subjects, individuals with mild cognitive impairment (MCI) or Alzheimer's disease dementia (ADD). We then performed a longitudinal analysis to assess the difference in the aging pace for each structure between stable healthy controls and healthy controls converting to either MCI or ADD.

Findings

Using an independent cohort of healthy subjects, age was well estimated for all structures. Cross-sectional analysis identified significantly larger predicted age for all structures in patients with either MCI and ADD compared to healthy subjects. Longitudinal analysis revealed varying degrees of involvement of individual subcortical structures for both age difference across groups and aging pace across time. These findings were most notable in the whole brain, cortex, hippocampus and amygdala.

Conclusion

Although similar patterns of abnormal aging were found related to MCI and ADD, the involvement of individual subcortical structures varied greatly and was consistently more pronounced in ADD patients compared to MCI patients.

Mapping the Genetic-Imaging-Clinical Pathway with Applications to Alzheimer’s Disease

Alzheimer's disease is a progressive form of dementia that results in problems with memory, thinking, and behavior. It often starts with abnormal aggregation and deposition of β amyloid and tau, followed by neuronal damage such as atrophy of the hippocampi, leading to Alzheimers Disease (AD). The aim of this paper is to map the genetic-imaging-clinical pathway for AD in order to delineate the genetically-regulated brain changes that drive disease progression based on the Alzheimers Disease Neuroimaging Initiative (ADNI) dataset. We develop a novel two-step approach to delineate the association between high-dimensional 2D hippocampal surface exposures and the Alzheimers Disease Assessment Scale (ADAS) cognitive score, while taking into account the ultra-high dimensional clinical and genetic covariates at baseline. Analysis results suggest that the radial distance of each pixel of both hippocampi is negatively associated with the severity of behavioral deficits conditional on observed clinical and genetic covariates. These associations are stronger in Cornu Ammonis region 1 (CA1) and subiculum subregions compared to Cornu Ammonis region 2 (CA2) and Cornu Ammonis region 3 (CA3) subregions. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

Multi-task multi-level feature adversarial network for joint Alzheimer’s disease diagnosis and atrophy localization using sMRI

Capitalizing on structural magnetic resonance imaging (sMRI), existing deep learning methods (especially convolutional neural networks, CNNs) have been widely and successfully applied to computer-aided diagnosis of Alzheimer’s disease (AD) and its prodromal stage (i.e. mild cognitive impairment, MCI). But considering the generalization capability of the obtained model trained on limited number of samples, we construct a multi-task multi-level feature adversarial network (M2FAN) for joint diagnosis and atrophy localization using baseline sMRI. Specifically, the linear-aligned T1 MR images were first processed by a lightweight CNN backbone to capture the shared intermediate feature representations, which were then branched into a global subnet for preliminary dementia diagnosis and a multi instance learning network for brain atrophy localization in multi-task learning manner. As the global discriminative information captured by the global subnet might be unstable for disease diagnosis, we further designed a module of multi-level feature adversarial learning that accounts for regularization to make global features robust against the adversarial perturbation synthesized by the local/instance features to improve the diagnostic performance. Our proposed method was evaluated on three public datasets (i.e. ADNI-1, ADNI-2, and AIBL), demonstrating competitive performance compared with several state-of-the-art methods in both tasks of AD diagnosis and MCI conversion prediction.

Model-based stratification of progression along the Alzheimer disease continuum highlights the centrality of biomarker synergies

BACKGROUND

The progression rates of Alzheimer's disease (AD) are variable and dynamic, yet the mechanisms that contribute to heterogeneity in progression rates remain ill-understood. Particularly, the role of synergies in pathological processes reflected by biomarkers for amyloid-beta ('A'), tau ('T'), and neurodegeneration ('N') in progression along the AD continuum is not fully understood.

METHODS

Here, we used a combination of model and data-driven approaches to address this question. Working with a large dataset (N = 321 across the training and testing cohorts), we first applied unsupervised clustering on longitudinal cognitive assessments to divide individuals on the AD continuum into those showing fast vs. moderate decline. Next, we developed a deep learning model that differentiated fast vs. moderate decline using baseline AT(N) biomarkers.

RESULTS

Training the model with AT(N) biomarker combination revealed more prognostic utility than any individual biomarkers alone. We additionally found little overlap between the model-driven progression phenotypes and established atrophy-based AD subtypes. Our model showed that the combination of all AT(N) biomarkers had the most prognostic utility in predicting progression along the AD continuum. A comprehensive AT(N) model showed better predictive performance than biomarker pairs (A(N) and T(N)) and individual biomarkers (A, T, or N).

CONCLUSIONS

This study combined data and model-driven methods to uncover the role of AT(N) biomarker synergies in the progression of cognitive decline along the AD continuum. The results suggest a synergistic relationship between AT(N) biomarkers in determining this progression, extending previous evidence of A-T synergistic mechanisms.

What can 7T sodium MRI tell us about cellular energy depletion and neurotransmission in Alzheimer's disease?

The pathophysiological processes underlying the development and progression of Alzheimer's disease (AD) on the neuronal level are still unclear. Previous research has hinted at metabolic energy deficits and altered sodium homeostasis with impaired neuronal function as a potential metabolic marker relevant for neurotransmission in AD. Using sodium (²³ Na) magnetic resonance (MR) imaging on an ultra-high-field 7 Tesla MR scanner, we found increased cerebral tissue sodium concentration (TSC) in 17 biomarker-defined AD patients compared to 22 age-matched control subjects in vivo. TSC was highly discriminative between controls and early AD stages and was predictive for cognitive state, and associated with regional tau load assessed with flortaucipir-positron emission tomography as a possible mediator of TSC-associated neurodegeneration. TSC could therefore serve as a non-invasive, stage-dependent, metabolic imaging marker. Setting a focus on cellular metabolism and potentially disturbed interneuronal communication due to energy-dependent altered cell homeostasis could hamper progressive cognitive decline by targeting these processes in future interventions.

Transcranial Sonography Findings in Alzheimer's Disease: A New Imaging Biomarker

OBJECTIVE

 To validate transcranial sonography (TCS) as a novel imaging tool for the assessment of medial temporal lobe (MTL) atrophy (MTA).

MATERIALS AND METHODS

 Participants with Alzheimer's disease (AD, n = 30) and age-sex-matched controls (n = 30) underwent TCS and MRI. On TCS, MTL structures (choroidal fissure (CF) and temporal horn (TH)) were measured and combined to create an MTA score in sonography (MTA-S). Furthermore, both THs and the third ventricle were combined to form the ventricle enlargement score in sonography (VES-S). On MRI, the MTL was evaluated by linear measurements, MTA scale and hippocampal volumetry. Validation was performed by comparing imaging methods and the patient group.

RESULTS

 Intraclass correlations for CF and TH showed substantial intra/inter-rater reliability (> 0.80). TCS and MRI showed strong to moderate correlation regarding TH (right = 0.88, left = 0.89) and CF (right = 0.70, left = 0.47). MTA-S correlated significantly with the hippocampal volume (right = -0.51, left = -0.47), predicted group membership in logistic regression (Exp(B) right = 3.0, left = 2.7), and could separate AD patients from controls (AUC = 0.93). An MTA-S of 6 mm and 10 mm discriminated MRI MTA scores 0-1 (from 2-4) and MTA score 4 (from 0-3) with 100 % specificity, respectively. VES-S also showed a moderate correlation with the hippocampal volume (r = -0.66) and could differentiate AD patients from controls (AUC = 0.93).

CONCLUSION

 Our results suggest that TCS may be an alternative imaging tool for the assessment of MTL atrophy and ventricular enlargement for patients in whom MRI scanning is not possible. Additionally, TCS offers a practical, patient-friendly and inexpensive option for the screening and follow-up of individuals with AD.

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.

Loss and dispersion of superficial white matter in Alzheimer's disease: a diffusion MRI study

Pathological cerebral white matter changes in Alzheimer's disease have been shown using diffusion tensor imaging. Superficial white matter changes are relatively understudied despite their importance in cortico-cortical connections. Measuring superficial white matter degeneration using diffusion tensor imaging is challenging due to its complex organizational structure and proximity to the cortex. To overcome this, we investigated diffusion MRI changes in young-onset Alzheimer's disease using standard diffusion tensor imaging and Neurite Orientation Dispersion and Density Imaging to distinguish between disease-related changes that are degenerative (e.g. loss of myelinated fibres) and organizational (e.g. increased fibre dispersion). Twenty-nine young-onset Alzheimer's disease patients and 22 healthy controls had both single-shell and multi-shell diffusion MRI. We calculated fractional anisotropy, mean diffusivity, neurite density index, orientation dispersion index and tissue fraction (1-free water fraction). Diffusion metrics were sampled in 15 a priori regions of interest at four points along the cortical profile: cortical grey matter, grey/white boundary, superficial white matter (1 mm below grey/white boundary) and superficial/deeper white matter (2 mm below grey/white boundary). To estimate cross-sectional group differences, we used average marginal effects from linear mixed effect models of participants' diffusion metrics along the cortical profile. The superficial white matter of young-onset Alzheimer's disease individuals had lower neurite density index compared to controls in five regions (superior and inferior parietal, precuneus, entorhinal and parahippocampus) (all P < 0.05), and higher orientation dispersion index in three regions (fusiform, entorhinal and parahippocampus) (all P < 0.05). Young-onset Alzheimer's disease individuals had lower fractional anisotropy in the entorhinal and parahippocampus regions (both P < 0.05) and higher fractional anisotropy within the postcentral region (P < 0.05). Mean diffusivity was higher in the young-onset Alzheimer's disease group in the parahippocampal region (P < 0.05) and lower in the postcentral, precentral and superior temporal regions (all P < 0.05). In the overlying grey matter, disease-related changes were largely consistent with superficial white matter findings when using neurite density index and fractional anisotropy, but appeared at odds with orientation dispersion and mean diffusivity. Tissue fraction was significantly lower across all grey matter regions in young-onset Alzheimer's disease individuals (all P < 0.001) but group differences reduced in magnitude and coverage when moving towards the superficial white matter. These results show that microstructural changes occur within superficial white matter and along the cortical profile in individuals with young-onset Alzheimer's disease. Lower neurite density and higher orientation dispersion suggests underlying fibres undergo neurodegeneration and organizational changes, two effects previously indiscernible using standard diffusion tensor metrics in superficial white matter.

Visual short-term memory impairments in presymptomatic familial Alzheimer's disease: A longitudinal observational study

Visual short-term memory (VSTM) deficits including VSTM binding have been associated with Alzheimer's disease (AD) from preclinical to dementia stages, cross-sectionally. Yet, longitudinal investigations are lacking. The objective of this study was to evaluate VSTM function longitudinally and in relation to expected symptom onset in a cohort of familial Alzheimer's disease. Ninety-nine individuals (23 presymptomatic; 9 symptomatic and 67 controls) were included in an extension cross-sectional study and a sub-sample of 48 (23 presymptomatic carriers, 6 symptomatic and 19 controls), attending two to five visits with a median interval of 1.3 years, included in the longitudinal study. Participants completed the “What was where?” relational binding task (which measures memory for object identification, localisation and object-location binding under different conditions of memory load and delay), neuropsychology assessments and genetic testing. Compared to controls, presymptomatic carriers within 8.5 years of estimated symptom onset showed a faster rate of decline in localisation performance in long-delay conditions (4s) and in traditional neuropsychology measures of verbal episodic memory. This study represents the first longitudinal VSTM investigation and shows that changes in memory resolution may be sensitive to tracking cognitive decline in preclinical AD at least as early as changes in the more traditional verbal episodic memory tasks.

•

VSTM function was investigated in presymptomatic and symptomatic FAD carriers.

•

PMCs showed faster decline in VSTM function (target localisation) than controls.

•

Target localisation accuracy decreased with proximity to expected symptom onset.

•

“What was where?” may be sensitive to tracking preclinical cognitive decline.

Early Spatial Memory Impairment in a Double Transgenic Model of Alzheimer's Disease TgF-344 AD

Before the course of Alzheimer’s disease fully manifests itself and largely impairs a patient’s cognitive abilities, its progression has already lasted for a considerable time without being noticed. In this project, we mapped the development of spatial orientation impairment in an active place avoidance task—a highly sensitive test for mild hippocampal damage. We tested vision, anxiety and spatial orientation performance at four age levels of 4, 6, 9, and 12 months across male and female TgF-344 AD rats carrying human genes for presenilin-1 and amyloid precursor protein. We found a progressive deterioration of spatial navigation in transgenic animals, beginning already at the age of 4 months, that fully developed at 6 months of age across both male and female groups, compared to their age-matched controls. In addition, we described the gradual vision impairment that was accentuated in females at the age of 12 months. These results indicate a rather early onset of cognitive impairment in the TgF-344 AD Alzheimer’s disease model, starting earlier than shown to date, and preceding the reported development of amyloid plaques.

Ameliorating Effect on Aβ-Induced Alzheimer's Mice by Litsea cubeba Persoon Powder

Alzheimer’s disease (AD) is caused by excessive oxidative damage and aging. The objective of this study was to investigate the anti-dementia effect of LCP fruit powder on amyloid β (Aβ)-induced Alzheimer’s mice. The composition of LCP essential oil was determined by gas chromatography/mass spectrometry. In addition, the water maze was used to evaluate the learning and memorizing abilities of the mice. The concentrations of malondialdehyde (MDA), protein carbonyl, phosphorylated τ-protein, and the deposition of Aβ plaques in mouse brains were also assessed. The results showed that the main components of essential oils in LCP and d-limonene, neral, and geranial contents were 14.15%, 30.94%, and 31.74%, respectively. Furthermore, oral administration with different dosages of LCP significantly decreased the escape time (21.25~33.62 s) and distance (3.23~5.07 m) in the reference memory test, and increased the duration time (26.14~28.90 s) and crossing frequency (7.00~7.88 times) in the target zone of probe test (p < 0.05). LCP also inhibited the contents of MDA and the phosphor-τ-protein from oxidative stress, reduced the brain atrophy by about 3~8%, and decreased the percentage of Aβ plaques from 0.44 to 0.05%. Finally, it was observed that the minimum dosage of LCP fruit powder (LLCP, 30.2 mg/day) could prevent oxidative stress induced by Aβ and subsequently facilitate memory and learning deficits in Aβ-induced neurotoxicity and cognitively impaired mice.

Alzheimer's disease alters oligodendrocytic glycolytic and ketolytic gene expression

INTRODUCTION

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

METHODS

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

RESULTS

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

DISCUSSION

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

Multi-Resemblance Multi-Target Low-Rank Coding for Prediction of Cognitive Decline With Longitudinal Brain Images

An effective presymptomatic diagnosis and treatment of Alzheimer's disease (AD) would have enormous public health benefits. Sparse coding (SC) has shown strong potential for longitudinal brain image analysis in preclinical AD research. However, the traditional SC computation is time-consuming and does not explore the feature correlations that are consistent over the time. In addition, longitudinal brain image cohorts usually contain incomplete image data and clinical labels. To address these challenges, we propose a novel two-stage Multi-Resemblance Multi-Target Low-Rank Coding (MMLC) method, which encourages that sparse codes of neighboring longitudinal time points are resemblant to each other, favors sparse code low-rankness to reduce the computational cost and is resilient to both source and target data incompleteness. In stage one, we propose an online multi-resemblant low-rank SC method to utilize the common and task-specific dictionaries in different time points to immune to incomplete source data and capture the longitudinal correlation. In stage two, supported by a rigorous theoretical analysis, we develop a multi-target learning method to address the missing clinical label issue. To solve such a multi-task low-rank sparse optimization problem, we propose multi-task stochastic coordinate coding with a sequence of closed-form update steps which reduces the computational costs guaranteed by a theoretical convergence proof. We apply MMLC on a publicly available neuroimaging cohort to predict two clinical measures and compare it with six other methods. Our experimental results show our proposed method achieves superior results on both computational efficiency and predictive accuracy and has great potential to assist the AD prevention.

Differential Role for Hippocampal Subfields in Alzheimer's Disease Progression Revealed with Deep Learning

Mild cognitive impairment (MCI) is often considered the precursor of Alzheimer's disease. However, MCI is associated with substantially variable progression rates, which are not well understood. Attempts to identify the mechanisms that underlie MCI progression have often focused on the hippocampus but have mostly overlooked its intricate structure and subdivisions. Here, we utilized deep learning to delineate the contribution of hippocampal subfields to MCI progression. We propose a dense convolutional neural network architecture that differentiates stable and progressive MCI based on hippocampal morphometry with an accuracy of 75.85%. A novel implementation of occlusion analysis revealed marked differences in the contribution of hippocampal subfields to the performance of the model, with presubiculum, CA1, subiculum, and molecular layer showing the most central role. Moreover, the analysis reveals that 10.5% of the volume of the hippocampus was redundant in the differentiation between stable and progressive MCI.

Associations of increased interstitial fluid with vascular and neurodegenerative abnormalities in a memory clinic sample

The vascular and neurodegenerative processes related to clinical dementia cause cell loss which induces, amongst others, an increase in interstitial fluid (ISF). We assessed microvascular, parenchymal integrity, and a proxy of ISF volume alterations with intravoxel incoherent motion imaging in 21 healthy controls and 53 memory clinic patients - mainly affected by neurodegeneration (mild cognitive impairment, Alzheimer's disease dementia), vascular pathology (vascular cognitive impairment), and presumed to be without significant pathology (subjective cognitive decline). The microstructural components were quantified with spectral analysis using a non-negative least squares method. Linear regression was employed to investigate associations of these components with hippocampal and white matter hyperintensity (WMH) volumes. In the normal appearing white matter, a large f_int (a proxy of ISF volume) was associated with a large WMH volume and low hippocampal volume. Likewise, a large f_int value was associated with a lower hippocampal volume in the hippocampi. Large ISF volume (f_int) was shown to be a prominent factor associated with both WMHs and neurodegenerative abnormalities in memory clinic patients and is argued to play a potential role in impaired glymphatic functioning.

Early Detection of Alzheimer's Disease Based on Clinical Trials, Three-Dimensional Imaging Data, and Personal Information Using Autoencoders

Background:

A timely diagnosis of Alzheimer's disease (AD) is crucial to obtain more practical treatments. In this article, a novel approach using Auto-Encoder Neural Networks (AENN) for early detection of AD was proposed.

Method:

The proposed method mainly deals with the classification of multimodal data and the imputation of missing data. The data under study involve the MiniMental State Examination, magnetic resonance imaging, positron emission tomography, cerebrospinal fluid data, and personal information. Natural logarithm was used for normalizing the data. The Auto-Encoder Neural Networks was used for imputing missing data. Principal component analysis algorithm was used for reducing dimensionality of data. Support Vector Machine (SVM) was used as classifier. The proposed method was evaluated using Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Then, 10fold crossvalidation was used to audit the detection accuracy of the method.

Results:

The effectiveness of the proposed approach was studied under several scenarios considering 705 cases of ADNI database. In three binary classification problems, that is AD vs. normal controls (NCs), mild cognitive impairment (MCI) vs. NC, and MCI vs. AD, we obtained the accuracies of 95.57%, 83.01%, and 78.67%, respectively.

Conclusion:

Experimental results revealed that the proposed method significantly outperformed most of the stateoftheart methods.

Accuracy of MRI Classification Algorithms in a Tertiary Memory Center Clinical Routine Cohort

BACKGROUND

Automated volumetry software (AVS) has recently become widely available to neuroradiologists. MRI volumetry with AVS may support the diagnosis of dementias by identifying regional atrophy. Moreover, automatic classifiers using machine learning techniques have recently emerged as promising approaches to assist diagnosis. However, the performance of both AVS and automatic classifiers have been evaluated mostly in the artificial setting of research datasets.

OBJECTIVE

Our aim was to evaluate the performance of two AVS and an automatic classifier in the clinical routine condition of a memory clinic.

METHODS

We studied 239 patients with cognitive troubles from a single memory center cohort. Using clinical routine T1-weighted MRI, we evaluated the classification performance of: 1) univariate volumetry using two AVS (volBrain and Neuroreader™); 2) Support Vector Machine (SVM) automatic classifier, using either the AVS volumes (SVM-AVS), or whole gray matter (SVM-WGM); 3) reading by two neuroradiologists. The performance measure was the balanced diagnostic accuracy. The reference standard was consensus diagnosis by three neurologists using clinical, biological (cerebrospinal fluid) and imaging data and following international criteria.

RESULTS

Univariate AVS volumetry provided only moderate accuracies (46% to 71% with hippocampal volume). The accuracy improved when using SVM-AVS classifier (52% to 85%), becoming close to that of SVM-WGM (52 to 90%). Visual classification by neuroradiologists ranged between SVM-AVS and SVM-WGM.

CONCLUSION

In the routine practice of a memory clinic, the use of volumetric measures provided by AVS yields only moderate accuracy. Automatic classifiers can improve accuracy and could be a useful tool to assist diagnosis.

Structural Brain Magnetic Resonance Imaging to Rule Out Comorbid Pathology in the Assessment of Alzheimer's Disease Dementia: Findings from the Ontario Neurodegenerative Disease Research Initiative (ONDRI) Study and Clinical Trials Over the Past 10 Years

BACKGROUND/OBJECTIVE

Structural brain magnetic resonance imaging (MRI) is not mandatory in Alzheimer's disease (AD) research or clinical guidelines. We aimed to explore the use of structural brain MRI in AD/mild cognitive impairment (MCI) trials over the past 10 years and determine the frequency with which inclusion of standardized structural MRI acquisitions detects comorbid vascular and non-vascular pathologies.

METHODS

We systematically searched ClinicalTrials.gov for AD clinical trials to determine their neuroimaging criteria and then used data from an AD/MCI cohort who underwent standardized MRI protocols, to determine type and incidence of clinically relevant comorbid pathologies.

RESULTS

Of 210 AD clinical trials, 105 (50%) included structural brain imaging in their eligibility criteria. Only 58 (27.6%) required MRI. 16,479 of 53,755 (30.7%) AD participants were in trials requiring MRI. In the observational AD/MCI cohort, 141 patients met clinical criteria; 22 (15.6%) had relevant MRI findings, of which 15 (10.6%) were exclusionary for the study.

DISCUSSION

In AD clinical trials over the last 10 years, over two-thirds of participants could have been enrolled without brain MRI and half without even a brain CT. In a study sample, relevant comorbid pathology was found in 15% of participants, despite careful screening. Standardized structural MRI should be incorporated into NIA-AA diagnostic guidelines (when available) and research frameworks routinely to reduce diagnostic heterogeneity.

Light-weight cross-view hierarchical fusion network for joint localization and identification in Alzheimer's disease with adaptive instance-declined pruning

Magnetic resonance imaging (MRI) has been widely used in assessing development of Alzheimer's disease (AD) by providing structural information of disease-associated regions (e.g. atrophic regions). In this paper, we propose a light-weight cross-view hierarchical fusion network (CvHF-net), consisting of local patch and global subject subnets, for joint localization and identification of the discriminative local patches and regions in the whole brain MRI, upon which feature representations are then jointly learned and fused to construct hierarchical classification models for AD diagnosis. Firstly, based on the extracted class-discriminative 3D patches, we employ the local patch subnets to utilize multiple 2D views to represent 3D patches by using an attention-aware hierarchical fusion structure in a divide-and-conquer manner. Since different local patches are with various abilities in AD identification, the global subject subnet is developed to bias the allocation of available resources towards the most informative parts among these local patches to obtain global information for AD identification. Besides, an instance declined pruning algorithm is embedded in the CvHF-net for adaptively selecting most discriminant patches in a task-driven manner. The proposed method was evaluated on the AD Neuroimaging Initiative dataset and the experimental results show that our proposed method can achieve good performance on AD diagnosis.

Hippocampal transcriptome-wide association study and neurobiological pathway analysis for Alzheimer's disease

Genome-wide association studies (GWASs) have identified multiple susceptibility loci for Alzheimer’s disease (AD), which is characterized by early and progressive damage to the hippocampus. However, the association of hippocampal gene expression with AD and the underlying neurobiological pathways remain largely unknown. Based on the genomic and transcriptomic data of 111 hippocampal samples and the summary data of two large-scale meta-analyses of GWASs, a transcriptome-wide association study (TWAS) was performed to identify genes with significant associations between hippocampal expression and AD. We identified 54 significantly associated genes using an AD-GWAS meta-analysis of 455,258 individuals; 36 of the genes were confirmed in another AD-GWAS meta-analysis of 63,926 individuals. Fine-mapping models further prioritized 24 AD-related genes whose effects on AD were mediated by hippocampal expression, including APOE and two novel genes (PTPN9 and PCDHA4). These genes are functionally related to amyloid-beta formation, phosphorylation/dephosphorylation, neuronal apoptosis, neurogenesis and telomerase-related processes. By integrating the predicted hippocampal expression and neuroimaging data, we found that the hippocampal expression of QPCTL and ERCC2 showed significant difference between AD patients and cognitively normal elderly individuals as well as correlated with hippocampal volume. Mediation analysis further demonstrated that hippocampal volume mediated the effect of hippocampal gene expression (QPCTL and ERCC2) on AD. This study identifies two novel genes associated with AD by integrating hippocampal gene expression and genome-wide association data and reveals candidate hippocampus-mediated neurobiological pathways from gene expression to AD.

The hippocampus is a potential neuroimaging endophenotype for Alzheimer’s disease (AD). This study identifies genes whose expression in hippocampal tissue is associated with AD and establishes the pathways from hippocampal gene expression to hippocampal volume to AD. We demonstrate that 24 genes are associated with AD in hippocampal tissue, and these genes are enriched for AD-related biological processes of amyloid-beta formation, phosphorylation/dephosphorylation, neuronal apoptosis, neurogenesis and telomerase-related processes. We further show that hippocampal volume mediates the effects of the hippocampal gene expression of QPCTL and ERCC2 on AD. These findings improve our understanding of the genetic and neural mechanisms of AD.

Increased Plasma Heme Oxygenase-1 Levels in Patients With Early-Stage Parkinson's Disease

Introduction: Heme oxygenase-1 (HO-1) is a 32 kDa stress-response protein implicated in the pathogenesis of Parkinson’s disease (PD). Biliverdin is derived from heme through a reaction mediated by HO-1 and protects cells from oxidative stress. However, iron and carbon monoxide produced by the catabolism of HO-1 exert detrimental effects on patients with PD. The purpose of this study was to determine whether plasma HO-1 levels represent a biomarker of PD and to further explore the underlying mechanism of increased HO-1 levels by applying voxel-based morphometry (VBM).Methods: We measured plasma HO-1 levels using an enzyme-linked immunosorbent assay (ELISA) in 156 subjects, including 81 patients with early- and advanced-stage PD and 75 subjects without PD. The analyses were adjusted to control for confounders such as age, sex, and medication. We analyzed T1-weighted magnetic resonance imaging (MRI) data from 74 patients with PD using VBM to elucidate the association between altered brain volumes and HO-1 levels. Then, we compared performance on MMSE sub-items between PD patients with low and high levels of HO-1 using Mann-Whitney U tests.Results: Plasma HO-1 levels were significantly elevated in PD patients, predominantly those with early-stage PD, compared with controls (p < 0.05). The optimal cutoff value for patients with early PD was 2.245 ng/ml HO-1 [area under the curve (AUC) = 0.654]. Plasma HO-1 levels were unaffected by sex, age, and medications (p > 0.05). The right hippocampal volume was decreased in the subset of PD patients with high HO-1 levels (p < 0.05). A weak correlation was observed between right hippocampal volume and plasma HO-1 levels (r = −0.273, p = 0.018). There was no difference in total MMSE scores between the low- and high-HO-1 groups (p > 0.05), but the high-HO-1 group had higher language scores than the low-HO-1 group (p < 0.05).Conclusions: Plasma HO-1 levels may be a promising biomarker of early PD. Moreover, a high plasma concentration of the HO-1 protein is associated with a reduction in right hippocampal volume.

Blood Pressure Variability and Dementia: A State-of-the-Art Review

Accumulating evidence demonstrates that blood pressure variability (BPV) may contribute to target organ damage, causing coronary heart disease, stroke, and renal disease independent of the level of blood pressure (BP). Several lines of evidence have also linked increased BPV to a higher risk of cognitive decline and incident dementia. The estimated number of dementia cases worldwide is nearly 50 million, and this number continues to grow with increasing life expectancy. Because there is no effective treatment to modify the course of dementia, targeting modifiable vascular factors continues as a top priority for dementia prevention. A clear understanding of the role of BPV in dementia may shed light on the etiology, early prevention, and novel therapeutic targets of dementia, and has therefore gained substantial attention from researchers and clinicians. This review summarizes state-of-art evidence on the relationship between BPV and dementia, with a specific focus on the epidemiological evidence, the underlying mechanisms, and potential intervention strategies. We also discuss challenges and opportunities for future research to facilitate optimal BP management and the clinical translation of BPV for the risk assessment and prevention of dementia.

Association of mid-life serum lipid levels with late-life brain volumes: The atherosclerosis risk in communities neurocognitive study (ARICNCS)

BACKGROUND

Limited information exists regarding the association between midlife lipid levels and late-life total and regional brain volumes.

METHODS

We studied 1872 participants in the longitudinal community-based Atherosclerosis Risk in Communities Neurocognitive Study. Serum lipid levels were measured in 1987-1989 (mean age, 53 ± 5 years). Participants underwent 3T brain MRI scans in 2011-2013. Brain volumes were measured using FreeSurfer image analysis software. Linear regression models were used to assess the associations between serum lipids and brain volumes modeled in standard deviation (SD) units, adjusting for potential confounders.

RESULTS

In adjusted analyses, one SD higher low-density lipoprotein cholesterol (LDL) levels were associated with larger total brain volumes (β 0.033, 95% CI 0.006-0.060) as well as larger volumes of the temporal (β 0.038, 95% CI 0.003-0.074) and parietal lobes (β 0.044, 95% CI 0.009-0.07) and Alzheimer disease-related region (β 0.048, 95% CI 0.048-0.085). Higher triglyceride levels were associated with smaller total brain volumes (β -0.033, 95% CI -0.060, -0.007). The associations between LDL levels and brain volumes were modified by age (P for interaction <0.001), with higher LDL levels associated with larger total and regional brain volumes only among adults >53 years at baseline, and were attenuated after application of weights to account for informative attrition, although associations with the parietal and Alzheimer's disease-related region remained significant. High-density lipoprotein cholesterol was not associated with brain volumes.

CONCLUSION

Higher LDL levels in late midlife were associated with larger brain volumes later in life, while higher triglyceride levels were associated with smaller brain volumes. These associations were driven by adults >53 years at baseline.

Implicating causal brain imaging endophenotypes in Alzheimer's disease using multivariable IWAS and GWAS summary data

Recent evidence suggests the existence of many undiscovered heritable brain phenotypes involved in Alzheimer's Disease (AD) pathogenesis. This finding necessitates methods for the discovery of causal brain changes in AD that integrate Magnetic Resonance Imaging measures and genotypic data. However, existing approaches for causal inference in this setting, such as the univariate Imaging Wide Association Study (UV-IWAS), suffer from inconsistent effect estimation and inflated Type I errors in the presence of genetic pleiotropy, the phenomenon in which a variant affects multiple causal intermediate risk phenotypes. In this study, we implement a multivariate extension to the IWAS model, namely MV-IWAS, to consistently estimate and test for the causal effects of multiple brain imaging endophenotypes from the Alzheimer's Disease Neuroimaging Initiative (ADNI) in the presence of pleiotropic and possibly correlated SNPs. We further extend MV-IWAS to incorporate variant-specific direct effects on AD, analogous to the existing Egger regression Mendelian Randomization approach, which allows for testing of remaining pleiotropy after adjusting for multiple intermediate pathways. We propose a convenient approach for implementing MV-IWAS that solely relies on publicly available GWAS summary data and a reference panel. Through simulations with either individual-level or summary data, we demonstrate the well controlled Type I errors and superior power of MV-IWAS over UV-IWAS in the presence of pleiotropic SNPs. We apply the summary statistic based tests to 1578 heritable imaging derived phenotypes (IDPs) from the UK Biobank. MV-IWAS detected numerous IDPs as possible false positives by UV-IWAS while uncovering many additional causal neuroimaging phenotypes in AD which are strongly supported by the existing literature.

Processing moving visual scenes during upright stance in elderly patients with mild cognitive impairment

Background

The ability to maintain balance in an upright stance gradually worsens with age and is even more difficult for patients with cognitive disorders. Cognitive impairment plays a probable role in the worsening of stability. The purpose of this study was to expose subjects with mild cognitive impairment (MCI) and healthy, age-matched controls to moving visual scenes in order to examine their postural adaptation abilities.

Methods

We observed postural responses to moving visual stimulation while subjects stood on a force platform. The visual disturbance was created by interposing a moving picture in four directions (forward, backward, right, and left). The pre-stimulus (a static scene for 10 s), stimulus (a dynamic visual scene for 20 seconds) and post-stimulus (a static scene for 20 seconds) periods were evaluated. We separately analyzed the total path (TP) of the center of pressure (COP) and the root mean square (RMS) of the COP displacement in all four directions.

Results

We found differences in the TP of the COP during the post-stimulus period for all stimulus directions except in motion towards the subject (left p = 0.006, right p = 0.004, and away from the subject p = 0.009). Significant RMS differences between groups were also observed during the post-stimulus period in all directions except when directed towards the subject (left p = 0.002, right p = 0.007, and away from the subject p = 0.014).

Conclusion

Exposing subjects to a moving visual scene induced greater destabilization in MCI subjects compared to healthy elderly controls. Surprisingly, the moving visual scene also induced significant aftereffects in the MCI group. Our findings indicate that the MCI group had diminished adaptation to the dynamic visual scene and recovery. These results suggest that even mild cognitive deficits can impair sensory information integration and alter the sensory re-weighing process.