Hippocampal atrophy rates in Alzheimer disease: added value over whole brain volume measures

The effect of intra-nasal co-treatment with insulin and growth factor-rich serum on behavioral defects, hippocampal oxidative-nitrosative stress, and histological changes induced by icv-STZ in a rat model

Impaired insulin and growth factor functions are thought to drive many alterations in neurodegenerative diseases like dementia and seem to contribute to oxidative stress and inflammatory responses. Recent studies revealed that nasal growth factor therapy could induce neuronal and oligodendroglia protection in rodent brain damage induction models. Impairment of several growth factors signaling was reported in neurodegenerative diseases. So, in the present study, we examined the effects of intranasal co-treatment of insulin and a pool of growth factor-rich serum (GFRS) which separated from activated platelets on memory, and behavioral defects induced by intracerebroventricular streptozotocin (icv-STZ) rat model also investigated changes in the hippocampal oxidative-nitrosative state and histology. We found that icv-STZ injection (3 mg/kg bilaterally) impairs spatial learning and memory in Morris Water Maze, leads to anxiogenic-like behavior in the open field arena, and induces oxidative-nitrosative stress, neuroinflammation, and neuronal/oligodendroglia death in the hippocampus. GFRS (1µl/kg, each other day, 9 doses) and regular insulin (4 U/40 µl, daily, 18 doses) treatments improved learning, memory, and anxiogenic behaviors. The present study showed that co-treatment (GFRS + insulin with respective dose) has more robust protection against hippocampal oxidative-nitrosative stress, neuroinflammation, and neuronal/oligodendroglia survival in comparison with the single therapy. Memory and behavioral improvements in the co-treatment of insulin and GFRS could be attributed to their effects on neuronal/oligodendroglia survival and reduction of neuroinflammation in the hippocampus.

Hippocampal transcriptome-wide association study and pathway analysis of mitochondrial solute carriers in Alzheimer's disease

The etiopathogenesis of late-onset Alzheimer's disease (AD) is increasingly recognized as the result of the combination of the aging process, toxic proteins, brain dysmetabolism, and genetic risks. Although the role of mitochondrial dysfunction in the pathogenesis of AD has been well-appreciated, the interaction between mitochondrial function and genetic variability in promoting dementia is still poorly understood. In this study, by tissue-specific transcriptome-wide association study (TWAS) and further meta-analysis, we examined the genetic association between mitochondrial solute carrier family (SLC25) genes and AD in three independent cohorts and identified three AD-susceptibility genes, including SLC25A10, SLC25A17, and SLC25A22. Integrative analysis using neuroimaging data and hippocampal TWAS-predicted gene expression of the three susceptibility genes showed an inverse correlation of SLC25A22 with hippocampal atrophy rate in AD patients, which outweighed the impacts of sex, age, and apolipoprotein E4 (ApoE4). Furthermore, SLC25A22 downregulation demonstrated an association with AD onset, as compared with the other two transcriptome-wide significant genes. Pathway and network analysis related hippocampal SLC25A22 downregulation to defects in neuronal function and development, echoing the enrichment of SLC25A22 expression in human glutamatergic neurons. The most parsimonious interpretation of the results is that we have identified AD-susceptibility genes in the SLC25 family through the prediction of hippocampal gene expression. Moreover, our findings mechanistically yield insight into the mitochondrial cascade hypothesis of AD and pave the way for the future development of diagnostic tools for the early prevention of AD from a perspective of precision medicine by targeting the mitochondria-related genes.

Shared Manifold Regularized Joint Feature Selection for Joint Classification and Regression in Alzheimer's Disease Diagnosis

In Alzheimer's disease (AD) diagnosis, joint feature selection for predicting disease labels (classification) and estimating cognitive scores (regression) with neuroimaging data has received increasing attention. In this paper, we propose a model named Shared Manifold regularized Joint Feature Selection (SMJFS) that performs classification and regression in a unified framework for AD diagnosis. For classification, unlike the existing works that build least squares regression models which are insufficient in the ability of extracting discriminative information for classification, we design an objective function that integrates linear discriminant analysis and subspace sparsity regularization for acquiring an informative feature subset. Furthermore, the local data relationships are learned according to the samples' transformed distances to exploit the local data structure adaptively. For regression, in contrast to previous works that overlook the correlations among cognitive scores, we learn a latent score space to capture the correlations and employ the latent space to design a regression model with l2,1 -norm regularization, facilitating the feature selection in regression task. Moreover, the missing cognitive scores can be recovered in the latent space for increasing the number of available training samples. Meanwhile, to capture the correlations between the two tasks and describe the local relationships between samples, we construct an adaptive shared graph to guide the subspace learning in classification and the latent cognitive score learning in regression simultaneously. An efficient iterative optimization algorithm is proposed to solve the optimization problem. Extensive experiments on three datasets validate the discriminability of the features selected by SMJFS.

Comparison of approaches to control for intracranial volume in research on the association of brain volumes with cognitive outcomes

Most neuroimaging studies linking regional brain volumes with cognition correct for total intracranial volume (ICV), but methods used for this correction differ across studies. It is unknown whether different ICV correction methods yield consistent results. Using a brain-wide association approach in the MRI substudy of UK Biobank (N = 41,964; mean age = 64.5 years), we used regression models to estimate the associations of 58 regional brain volumetric measures with eight cognitive outcomes, comparing no correction and four ICV correction approaches. Approaches evaluated included: no correction; dividing regional volumes by ICV (proportional approach); including ICV as a covariate in the regression (adjustment approach); and regressing the regional volumes against ICV in different normative samples and using calculated residuals to determine associations (residual approach). We used Spearman-rank correlations and two consistency measures to quantify the extent to which associations were inconsistent across ICV correction approaches for each possible brain region and cognitive outcome pair across 2320 regression models. When the association between brain volume and cognitive performance was close to null, all approaches produced similar estimates close to the null. When associations between a regional volume and cognitive test were not null, the adjustment and residual approaches typically produced similar estimates, but these estimates were inconsistent with results from the crude and proportional approaches. For example, when using the crude approach, an increase of 0.114 (95% confidence interval [CI]: 0.103-0.125) in fluid intelligence was associated with each unit increase in hippocampal volume. However, when using the adjustment approach, the increase was 0.055 (95% CI: 0.043-0.068), while the proportional approach showed a decrease of -0.025 (95% CI: -0.035 to -0.014). Different commonly used methods to correct for ICV yielded inconsistent results. The proportional method diverges notably from other methods and results were sometimes biologically implausible. A simple regression adjustment for ICV produced biologically plausible associations.

Cerebral organoids with chromosome 21 trisomy secrete Alzheimer's disease-related soluble aggregates detectable by single-molecule-fluorescence and super-resolution microscopy

Understanding the role of small, soluble aggregates of beta-amyloid (Aβ) and tau in Alzheimer's disease (AD) is of great importance for the rational design of preventative therapies. Here we report a set of methods for the detection, quantification, and characterisation of soluble aggregates in conditioned media of cerebral organoids derived from human iPSCs with trisomy 21, thus containing an extra copy of the amyloid precursor protein (APP) gene. We detected soluble beta-amyloid (Aβ) and tau aggregates secreted by cerebral organoids from both control and the isogenic trisomy 21 (T21) genotype. We developed a novel method to normalise measurements to the number of live neurons within organoid-conditioned media based on glucose consumption. Thus normalised, T21 organoids produced 2.5-fold more Aβ aggregates with a higher proportion of larger (300-2000 nm2) and more fibrillary-shaped aggregates than controls, along with 1.3-fold more soluble phosphorylated tau (pTau) aggregates, increased inflammasome ASC-specks, and a higher level of oxidative stress inducing thioredoxin-interacting protein (TXNIP). Importantly, all this was detectable prior to the appearance of histological amyloid plaques or intraneuronal tau-pathology in organoid slices, demonstrating the feasibility to model the initial pathogenic mechanisms for AD in-vitro using cells from live genetically pre-disposed donors before the onset of clinical disease. Then, using different iPSC clones generated from the same donor at different times in two independent experiments, we tested the reproducibility of findings in organoids. While there were differences in rates of disease progression between the experiments, the disease mechanisms were conserved. Overall, our results show that it is possible to non-invasively follow the development of pathology in organoid models of AD over time, by monitoring changes in the aggregates and proteins in the conditioned media, and open possibilities to study the time-course of the key pathogenic processes taking place.

Monitoring Alzheimer's disease via ultraweak photon emission

In an innovative experiment, we detected ultraweak photon emission (UPE) from the hippocampus of male rat brains and found significant correlations between Alzheimer's disease (AD), memory decline, oxidative stress, and UPE intensity. These findings may open up novel methods for screening, detecting, diagnosing, and classifying neurodegenerative diseases, particularly AD. The study suggests that UPE from the brain's neural tissue can serve as a valuable indicator. It also proposes the development of a minimally invasive brain-computer interface (BCI) photonic chip for monitoring and diagnosing AD, offering high spatiotemporal resolution of brain activity. The study used a rodent model of sporadic AD, demonstrating that STZ-induced sAD resulted in increased hippocampal UPE, which was associated with oxidative stress. Treatment with donepezil reduced UPE and improved oxidative stress. These findings support the potential utility of UPE as a screening and diagnostic tool for AD and other neurodegenerative diseases.

Mild Cognitive Impairment Progression and Alzheimer's Disease Risk: A Comprehensive Analysis of 3553 Cases over 203 Months

This study aimed to elucidate the long-term progression of mild cognitive impairment (MCI) within a comprehensive longitudinal dataset, distinguish it from healthy aging, explore the influence of a dementia subtype on this progression, and identify potential contributing factors. Patients with prodromal and preclinical cases underwent regular neuropsychological assessments utilizing various tools. The study included a total of 140 participants with MCI, categorized into Alzheimer's disease (AD) and non-AD subtypes. Our dataset revealed an overall progression rate of 92.8% from MCI to the clinical stage of dementia during the follow-up period, with an annual rate of 15.7%. Notably, all prodromal cases of Lewy body dementia/Parkinson's disease (LBD/PDD) and frontotemporal dementia (FTD) advanced to clinical stages, whereas 7% of vascular dementia (VaD) cases and 8.4% of AD cases remained in the prodromal stage throughout follow-up. Furthermore, we observed a faster progression rate in MCI-AD cases compared to non-AD sufferers (53.9% vs. 35.5%, Entropy: 0.850). This study revealed significant cognitive changes in individuals with MCI over time. The mini-mental state examination (MMSE), global deterioration scale (GDS), and calculation tests were the most effective tests for evaluation of MCI. These findings may offer valuable insights for the development of personalized interventions and management strategies for individuals with MCI.

Disease2Vec: Encoding Alzheimer's progression via disease embedding tree

For decades, a variety of predictive approaches have been proposed and evaluated in terms of their prediction capability for Alzheimer's Disease (AD) and its precursor - mild cognitive impairment (MCI). Most of them focused on prediction or identification of statistical differences among different clinical groups or phases, especially in the context of binary or multi-class classification. The continuous nature of AD development and transition states between successive AD related stages have been typically overlooked. Though a few progression models of AD have been studied recently, they were mainly designed to determine and compare the order of specific biomarkers. How to effectively predict the individual patient's status within a wide spectrum of continuous AD progression has been largely understudied. In this work, we developed a novel learning-based embedding framework to encode the intrinsic relations among AD related clinical stages by a set of meaningful embedding vectors in the latent space (Disease2Vec). We named this process as disease embedding. By Disease2Vec, our framework generates a disease embedding tree (DETree) which effectively represents different clinical stages as a tree trajectory reflecting AD progression and thus can be used to predict clinical status by projecting individuals onto this continuous trajectory. Through this model, DETree can not only perform efficient and accurate prediction for patients at any stages of AD development (across five fine-grained clinical groups instead of typical two groups), but also provide richer status information by examining the projecting locations within a wide and continuous AD progression process. (Code will be available: https://github.com/qidianzl/Disease2Vec.).

Amelioration of Amyloid-β Induced Alzheimer's Disease by Bacopa monnieri through Modulation of Mitochondrial Dysfunction and GSK-3β/Wnt/β-Catenin Signaling

BACKGROUND

Alzheimer's disease (AD) is the most prevalent dementia, affecting a large number of populations. Despite being under scrutiny for decades, an effective therapeutic option is still not available.

METHODS AND RESULTS

This study explores the therapeutic role of a nootropic herb Bacopa monnieri (BM) in AD-like pathological conditions produced by injecting preformed amyloid-β42 (Aβ42 ) fibril bilaterally into hippocampus of Wistar rats, and ethanolic extract of BM is orally administered for 4 weeks. Assessment of behavioral changes reveals that BM treatment ameliorates Aβ42 -induced cognitive impairment and compromised explorative behavior. Supplementation of BM also reduces oxidative stress biomarkers, proinflammatory cytokines, and cholinesterase activity in the AD rats. Additionally, BM treatment restores Bcl-2-associated X protein (Bax)/ B-cell lymphoma 2 (Bcl-2) imbalance, increases neurotrophic factors expression, and prevents neurodegeneration validated by quantifying Nissl-positive hippocampal neurons. Interestingly, BM administration eliminates amyloid plaques in the hippocampal region and normalizes the Aβ42 -induced increase in phospho-tau and total tau expression. Mechanistic investigations reveal that BM interacts with glycogen synthase kinase (GSK-3β) and restores Wnt/β-catenin signaling.

CONCLUSION

BM has been used in diet as a nootropic herb for several centuries. This study highlights the anti-Alzheimer activity of BM from the behavioral to the molecular level by modulating mitochondrial dysfunction, and GSK-3β mediates the Wnt/β-catenin signaling pathway.

Protective Effects of Whey Protein Hydrolysate, Treadmill Exercise, and Their Combination against Scopolamine-Induced Cognitive Deficit in Mice

In this study, the potential of whey protein hydrolysate (WPH) and treadmill exercise to prevent cognitive decline was investigated, along with their neuroprotective mechanisms. Cognitive dysfunction was induced in mice with 1 mg/kg of scopolamine, followed by the administration of WPH at 100 and 200 mg/kg and/or treadmill exercise at 15 m/min for 30 min five days per week. Both WPH administration and treadmill exercise significantly improved the memory of mice with scopolamine-induced cognitive impairment, which was attributed to several key mechanisms, including a reduction in oxidative stress based on decreased levels of reactive oxygen species and malondialdehyde in the brain tissue and an increase in acetylcholine by increasing choline acyltransferase and decreasing acetylcholine esterase levels. Exercise and WPH also exerted neuroprotective effects by inhibiting the hyperphosphorylation of tau proteins, enhancing the expression of the brain-derived neurotrophic factor, and inhibiting apoptosis by reducing the Bax/Bcl2 ratio in conjunction with the downregulation of the mitogen-activated protein kinase pathway. Moreover, the impact of WPH and treadmill exercise extended to the gut microbiome, suggesting a potential link with cognitive improvement. These findings suggest that both WPH intake and treadmill exercise are effective strategies for mitigating cognitive impairment, providing promising avenues for treating neurodegenerative diseases.

Mean Intracranial Volume of Brain among Patients with Normal Magnetic Resonance Imaging Referred to the Department of Radiology and Imaging of a Tertiary Care Centre

Introduction

The measurement of brain volume is an important aspect of the assessment of brain structure and function. However, limited data is available on brain volumetry in the Nepalese population. The study aimed to find the mean intracranial volume of the brain among patients with normal magnetic resonance imaging referred to the Department of Radiology and Imaging of a tertiary care centre.

Methods

A descriptive cross-sectional study was conducted among patients with normal magnetic resonance imaging referred to the Department of Radiology and Imaging in a tertiary care centre. All magnetic resonance imaging of the brain during the study period was reviewed by a radiologist. Magnetic resonance imaging with abnormal findings, clinical signs of neurological deficit, dementia and psychiatric symptoms were excluded from the study. A convenience sampling method was used. The point estimate was calculated at a 95% Confidence Interval.

Results

Among 285 Magnetic Resonance Imaging datasets, the mean intracranial volume was 1286.30±129.88 cc (1271.22-1301.38, 95% of Confidence Interval). The mean cerebral volume was 985.06±106.4 cc, cerebellar volume was 126.99±13.05 cc and brain stem volume was 19.97±2.54 cc.

Conclusions

The mean intracranial volume of the brain among patients with normal magnetic resonance imaging was found to be lower than other studies done in similar settings.

Keywords

brainstem; cerebellum; cerebrum; magnetic resonance imaging.

14-3-3 [Formula: see text]-reported early synaptic injury in Alzheimer's disease is independently mediated by sTREM2

INTRODUCTION

Synaptic loss is closely associated with tau aggregation and microglia activation in later stages of Alzheimer's disease (AD). However, synaptic damage happens early in AD at the very early stages of tau accumulation. It remains unclear whether microglia activation independently causes synaptic cleavage before tau aggregation appears.

METHODS

We investigated 104 participants across the AD continuum by measuring 14-3-3 zeta/delta ([Formula: see text]) as a cerebrospinal fluid biomarker for synaptic degradation, and fluid and imaging biomarkers of tau, amyloidosis, astrogliosis, neurodegeneration, and inflammation. We performed correlation analyses in cognitively unimpaired and impaired participants and used structural equation models to estimate the impact of microglia activation on synaptic injury in different disease stages.

RESULTS

14-3-3 [Formula: see text] was increased in participants with amyloid pathology at the early stages of tau aggregation before hippocampal volume loss was detectable. 14-3-3 [Formula: see text] correlated with amyloidosis and tau load in all participants but only with biomarkers of neurodegeneration and memory deficits in cognitively unimpaired participants. This early synaptic damage was independently mediated by sTREM2. At later disease stages, tau and astrogliosis additionally mediated synaptic loss.

CONCLUSIONS

Our results advertise that sTREM2 is mediating synaptic injury at the early stages of tau accumulation, underlining the importance of microglia activation for AD disease propagation.

APOE4 expression confers a mild, persistent reduction in neurovascular function in the visual cortex and hippocampus of awake mice

Vascular factors are known to be early and important players in Alzheimer's disease (AD) development, however the role of the ε4 allele of the Apolipoprotein (APOE) gene (a risk factor for developing AD) remains unclear. APOE4 genotype is associated with early and severe neocortical vascular deficits in anaesthetised mice, but in humans, vascular and cognitive dysfunction are focused on the hippocampal formation and appear later. How APOE4 might interact with the vasculature to confer AD risk during the preclinical phase represents a gap in existing knowledge. To avoid potential confounds of anaesthesia and to explore regions most relevant for human disease, we studied the visual cortex and hippocampus of awake APOE3 and APOE4-TR mice using 2-photon microscopy of neurons and blood vessels. We found mild vascular deficits: vascular density and functional hyperaemia were unaffected in APOE4 mice, and neuronal or vascular function did not decrease up to late middle-age. Instead, vascular responsiveness was lower, arteriole vasomotion was reduced and neuronal calcium signals during visual stimulation were increased. This suggests that, alone, APOE4 expression is not catastrophic but stably alters neurovascular physiology. We suggest this state makes APOE4 carriers more sensitive to subsequent insults such as injury or beta amyloid accumulation.

Multimodal fusion of multiple rest fMRI networks and MRI gray matter via parallel multilink joint ICA reveals highly significant function/structure coupling in Alzheimer's disease

In this article, we focus on estimating the joint relationship between structural magnetic resonance imaging (sMRI) gray matter (GM), and multiple functional MRI (fMRI) intrinsic connectivity networks (ICNs). To achieve this, we propose a multilink joint independent component analysis (ml-jICA) method using the same core algorithm as jICA. To relax the jICA assumption, we propose another extension called parallel multilink jICA (pml-jICA) that allows for a more balanced weight distribution over ml-jICA/jICA. We assume a shared mixing matrix for both the sMRI and fMRI modalities, while allowing for different mixing matrices linking the sMRI data to the different ICNs. We introduce the model and then apply this approach to study the differences in resting fMRI and sMRI data from patients with Alzheimer's disease (AD) versus controls. The results of the pml-jICA yield significant differences with large effect sizes that include regions in overlapping portions of default mode network, and also hippocampus and thalamus. Importantly, we identify two joint components with partially overlapping regions which show opposite effects for AD versus controls, but were able to be separated due to being linked to distinct functional and structural patterns. This highlights the unique strength of our approach and multimodal fusion approaches generally in revealing potentially biomarkers of brain disorders that would likely be missed by a unimodal approach. These results represent the first work linking multiple fMRI ICNs to GM components within a multimodal data fusion model and challenges the typical view that brain structure is more sensitive to AD than fMRI.

Aging-related volume changes in the brain and cerebrospinal fluid using artificial intelligence-automated segmentation

OBJECTIVES

To verify the reliability of the volumes automatically segmented using a new artificial intelligence (AI)-based application and evaluate changes in the brain and CSF volume with healthy aging.

METHODS

The intracranial spaces were automatically segmented in the 21 brain subregions and 5 CSF subregions using the AI-based application on the 3D T1-weighted images in healthy volunteers aged > 20 years. Additionally, the automatically segmented volumes of the total ventricles and subarachnoid spaces were compared with the manually segmented volumes of those extracted from 3D T2-weighted images using the intra-class correlation and Bland-Altman analysis.

RESULTS

In this study, 133 healthy volunteers aged 21-92 years were included. The mean intra-class correlations between the automatically and manually segmented volumes of the total ventricles and subarachnoid spaces were 0.986 and 0.882, respectively. The increase in the CSF volume was estimated to be approximately 30 mL (2%) per decade from 265 mL (18.7%) in the 20s to 488 mL (33.7%) in ages above 80 years; however, the increase in the volume of total ventricles was approximately 20 mL (< 2%) until the 60s and increased in ages above 60 years.

CONCLUSIONS

This study confirmed the reliability of the CSF volumes using the AI-based auto-segmentation application. The intracranial CSF volume increased linearly because of the brain volume reduction with aging; however, the ventricular volume did not change until the age of 60 years and above and then gradually increased. This finding could help elucidate the pathogenesis of chronic hydrocephalus in adults.

KEY POINTS

• The brain and CSF spaces were automatically segmented using an artificial intelligence-based application. • The total subarachnoid spaces increased linearly with aging, whereas the total ventricle volume was around 20 mL (< 2%) until the 60s and increased in ages above 60 years. • The cortical gray matter gradually decreases with aging, whereas the subcortical gray matter maintains its volume, and the cerebral white matter increases slightly until the 40s and begins to decrease from the 50s.

Phenomic Microglia Diversity as a Druggable Target in the Hippocampus in Neurodegenerative Diseases

Phenomics, the complexity of microglia phenotypes and their related functions compels the continuous study of microglia in disease animal models to find druggable targets for neurodegenerative disorders. Activation of microglia was long considered detrimental for neuron survival, but more recently it has become apparent that the real scenario of microglia morphofunctional diversity is far more complex. In this review, we discuss the recent literature on the alterations in microglia phenomics in the hippocampus of animal models of normal brain aging, acute neuroinflammation, ischemia, and neurodegenerative disorders, such as AD. Microglia undergo phenomic changes consisting of transcriptional, functional, and morphological changes that transform them into cells with different properties and functions. The classical subdivision of microglia into M1 and M2, two different, all-or-nothing states is too simplistic, and does not correspond to the variety of phenotypes recently discovered in the brain. We will discuss the phenomic modifications of microglia focusing not only on the differences in microglia reactivity in the diverse models of neurodegenerative disorders, but also among different areas of the brain. For instance, in contiguous and highly interconnected regions of the rat hippocampus, microglia show a differential, finely regulated, and region-specific reactivity, demonstrating that microglia responses are not uniform, but vary significantly from area to area in response to insults. It is of great interest to verify whether the differences in microglia reactivity may explain the differential susceptibility of different brain areas to insults, and particularly the higher sensitivity of CA1 pyramidal neurons to inflammatory stimuli. Understanding the spatiotemporal heterogeneity of microglia phenomics in health and disease is of paramount importance to find new druggable targets for the development of novel microglia-targeted therapies in different CNS disorders. This will allow interventions in three different ways: (i) by suppressing the pro-inflammatory properties of microglia to limit the deleterious effect of their activation; (ii) by modulating microglia phenotypic change to favor anti-inflammatory properties; (iii) by influencing microglia priming early in the disease process.

Hierarchical based classification method based on fusion of Gaussian map descriptors for Alzheimer diagnosis using T1-weighted magnetic resonance imaging

Alzheimer's disease (AD) is considered one of the most spouting elderly diseases. In 2015, AD is reported the US's sixth cause of death. Substantially, non-invasive imaging is widely employed to provide biomarkers supporting AD screening, diagnosis, and progression. In this study, Gaussian descriptors-based features are proposed to be efficient new biomarkers using Magnetic Resonance Imaging (MRI) T1-weighted images to differentiate between Alzheimer's disease (AD), Mild Cognitive Impairment (MCI), and Normal controls (NC). Several Gaussian map-based features are extracted such as Gaussian shape operator, Gaussian curvature, and mean curvature. The aforementioned features are then introduced to the Support Vector Machine (SVM). They were, first, calculated separately for the Hippocampus and Amygdala. Followed by the fusion of the features. Moreover, Fusion of the regions before feature extraction was also employed. Alzheimer's disease Neuroimaging Initiative (ADNI) dataset, formed of 45, 55, and 65 cases for AD, MCI, and NC respectively, is appointed in this study. The shape operator feature outperformed the other features, with 74.6%, and 98.9% accuracy in the case of normal vs. abnormal, and AD vs. MCI classification respectively.

Pathophysiological Role of Microglial Activation Induced by Blood-Borne Proteins in Alzheimer's Disease

The blood-brain barrier (BBB) restricts entry of neurotoxic plasma components, blood cells, and pathogens into the brain, leading to proper neuronal functioning. BBB impairment leads to blood-borne protein infiltration such as prothrombin, thrombin, prothrombin kringle-2, fibrinogen, fibrin, and other harmful substances. Thus, microglial activation and release of pro-inflammatory mediators commence, resulting in neuronal damage and leading to impaired cognition via neuroinflammatory responses, which are important features observed in the brain of Alzheimer's disease (AD) patients. Moreover, these blood-borne proteins cluster with the amyloid beta plaque in the brain, exacerbating microglial activation, neuroinflammation, tau phosphorylation, and oxidative stress. These mechanisms work in concert and reinforce each other, contributing to the typical pathological changes in AD in the brain. Therefore, the identification of blood-borne proteins and the mechanisms involved in microglial activation and neuroinflammatory damage can be a promising therapeutic strategy for AD prevention. In this article, we review the current knowledge regarding the mechanisms of microglial activation-mediated neuroinflammation caused by the influx of blood-borne proteins into the brain via BBB disruption. Subsequently, the mechanisms of drugs that inhibit blood-borne proteins, as a potential therapeutic approach for AD, along with the limitations and potential challenges of these approaches, are also summarized.

Insulin‐like growth factor‐2 is a promising candidate for the treatment and prevention of Alzheimer's disease

Alzheimer's disease (AD) is the most common form of dementia. Current AD treatments slow the rate of cognitive decline, but do not restore lost function. One reason for the low efficacy of current treatments is that they fail to target neurotrophic processes, which are thought to be essential for functional recovery. Bolstering neurotrophic processes may also be a viable strategy for preventative treatment, since structural losses are thought to underlie cognitive decline in AD. The challenge of identifying presymptomatic patients who might benefit from preventative treatment means that any such treatment must meet a high standard of safety and tolerability. The neurotrophic peptide insulin-like growth factor-2 (IGF2) is a promising candidate for both treating and preventing AD-induced cognitive decline. Brain IGF2 expression declines in AD patients. In rodent models of AD, exogenous IGF2 modulates multiple aspects of AD pathology, resulting in (1) improved cognitive function; (2) stimulation of neurogenesis and synaptogenesis; and, (3) neuroprotection against cholinergic dysfunction and beta amyloid-induced neurotoxicity. Preclinical evidence suggests that IGF2 is likely to be safe and tolerable at therapeutic doses. In the preventative treatment context, the intranasal route of administration is likely to be the preferred method for achieving the therapeutic effect without risking adverse side effects. For patients already experiencing AD dementia, routes of administration that deliver IGF2 directly access the CNS may be necessary. Finally, we discuss several strategies for improving the translational validity of animal models used to study the therapeutic potential of IGF2.

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.

Multimodal fusion of multiple rest fMRI networks and MRI gray matter via multilink joint ICA reveals highly significant function/structure coupling in Alzheimer's disease

In this paper we focus on estimating the joint relationship between structural MRI (sMRI) gray matter (GM) and multiple functional MRI (fMRI) intrinsic connectivity networks (ICN) using a novel approach called multi-link joint independent component analysis (ml-jICA). The proposed model offers several improvements over the existing joint independent component analysis (jICA) model. We assume a shared mixing matrix for both the sMRI and fMRI modalities, while allowing for different mixing matrices linking the sMRI data to the different ICNs. We introduce the model and then apply this approach to study the differences in resting fMRI and sMRI data from patients with Alzheimer's disease (AD) versus controls. The results yield significant differences with large effect sizes that include regions in overlapping portions of default mode network, and also hippocampus and thalamus. Importantly, we identify two joint components with partially overlapping regions which show opposite effects for Alzheimer's disease versus controls, but were able to be separated due to being linked to distinct functional and structural patterns. This highlights the unique strength of our approach and multimodal fusion approaches generally in revealing potentially biomarkers of brain disorders that would likely be missed by a unimodal approach. These results represent the first work linking multiple fMRI ICNs to gray matter components within a multimodal data fusion model and challenges the typical view that brain structure is more sensitive to AD than fMRI.

Predicting Cognitive Decline in Older Adults Using Baseline Metrics of AD Pathologies, Cerebrovascular Disease, and Neurodegeneration

BACKGROUND AND OBJECTIVES

Dementia is a growing socioeconomic challenge that requires early intervention. Identifying biomarkers that reliably predict clinical progression early in the disease process would better aid selection of individuals for future trial participation. Here, we compared the ability of baseline, single time-point biomarkers (CSF amyloid 1-42, CSF ptau-181, white matter hyperintensities (WMH), cerebral microbleeds, whole-brain volume, and hippocampal volume) to predict decline in cognitively normal individuals who later converted to mild cognitive impairment (MCI) (CNtoMCI) and those with MCI who later converted to an Alzheimer disease (AD) diagnosis (MCItoAD).

METHODS

Standardized baseline biomarker data from AD Neuroimaging Initiative 2 (ADNI2)/GO and longitudinal diagnostic data (including ADNI3) were used. Cox regression models assessed biomarkers in relation to time to change in clinical diagnosis using all follow-up time points available. Models were fit for biomarkers univariately and together in a multivariable model. Hazard ratios (HRs) were compared to evaluate biomarkers. Analyses were performed separately in CNtoMCI and MCItoAD groups.

RESULTS

For CNtoMCI (n = 189), there was strong evidence that higher WMH volume (individual model: HR 1.79, p = 0.002; fully adjusted model: HR 1.98, p = 0.003) and lower hippocampal volume (individual: HR 0.54, p = 0.001; fully adjusted: HR 0.40, p < 0.001) were associated with conversion to MCI individually and independently. For MCItoAD (n = 345), lower hippocampal (individual model: HR 0.45, p < 0.001; fully adjusted model: HR 0.55, p < 0.001) and whole-brain volume (individual: HR 0.31, p < 0.001; fully adjusted: HR 0.48, p = 0.02), increased CSF ptau (individual: HR 1.88, p < 0.001; fully adjusted: HR 1.61, p < 0.001), and lower CSF amyloid (individual: HR 0.37, p < 0.001; fully adjusted: HR 0.62, p = 0.008) were most strongly associated with conversion to AD individually and independently.

DISCUSSION

Lower hippocampal volume was a consistent predictor of clinical conversion to MCI and AD. CSF and brain volume biomarkers were predictive of conversion to AD from MCI, whereas WMH were predictive of conversion to MCI from cognitively normal. The predictive ability of WMH in the CNtoMCI group may be interpreted as some being on a different pathologic pathway, such as vascular cognitive impairment.

Causal effects of serum testosterone levels on brain volume: a sex-stratified Mendelian randomization study

PURPOSE

To assess the causal effects of serum testosterone and sex hormone-binding globulin levels on brain volumetric measurements in women and men.

METHODS

We performed a sex-stratified two-sample Mendelian randomization study using the random-effects inverse variance-weighted method as the primary analysis method. Sex-specific genetic instruments were obtained from a study with up to 194,453 men and 230,454 women. For testosterone, variants with dominant effects on both total and bioavailable testosterone but no aggregate effect on sex hormone-binding globulin were used as the main genetic instruments. Sex-specific summary-level data for magnetic resonance imaging brain volumetric measurements were obtained from a study with 11,624 women and 10,514 men.

RESULTS

Analyses showed per standard deviation (approximately 3.7 nmol/L) higher testosterone levels in men were suggestively associated with larger gray matter volume (beta = 0.208, 95% confidence interval = 0.067 to 0.349, p = 0.004). The association remained in sensitivity analyses and multivariable analyses. Further analyses showed the effect was mainly act on peripheral cortical gray matter, but not on subcortical gray matter. Testosterone in men was not associated with hippocampal volume. Testosterone in women and sex hormone binding globulin in both sexes had no effect on all outcomes.

CONCLUSION

Our findings overall support previous evidence that testosterone might have neuroprotective properties in elderly men. Future larger trials with long duration of intervention are warranted to assess the efficacy of testosterone for elderly men with cognitive impairment, especially in those with hypoandrogenism.

Longitudinal changes in cerebral blood flow and their relation with cognitive decline in patients with dementia: Current knowledge and future directions

The pathophysiology underlying cognitive decline is multifactorial, with increasing literature suggesting a role for cerebrovascular health. Cerebral blood flow (CBF) is an important element of cerebrovascular health, which raises questions regarding the relation between CBF and cognitive decline. Cross-sectional studies demonstrate lower CBF in patients with cognitive decline compared to healthy age-matched peers. Remarkably, longitudinal studies do not support a link between CBF reductions and cognitive decline. These studies, however, are often limited by small sample sizes and may therefore be underpowered to detect small effect sizes. Therefore, through a systematic review and meta-analysis of longitudinal studies, we examined whether longitudinal changes in global CBF are related to cognitive decline in subjects with Alzheimer's disease, and qualitatively described findings on regional CBF. Considering the growing impact of dementia and the lack of treatment options, it is important to understand the role of CBF as a prognostic biomarker and/or treatment target in dementia.

Orthogonal latent space learning with feature weighting and graph learning for multimodal Alzheimer's disease diagnosis

Recent studies have shown that multimodal neuroimaging data provide complementary information of the brain and latent space-based methods have achieved promising results in fusing multimodal data for Alzheimer's disease (AD) diagnosis. However, most existing methods treat all features equally and adopt nonorthogonal projections to learn the latent space, which cannot retain enough discriminative information in the latent space. Besides, they usually preserve the relationships among subjects in the latent space based on the similarity graph constructed on original features for performance boosting. However, the noises and redundant features significantly corrupt the graph. To address these limitations, we propose an Orthogonal Latent space learning with Feature weighting and Graph learning (OLFG) model for multimodal AD diagnosis. Specifically, we map multiple modalities into a common latent space by orthogonal constrained projection to capture the discriminative information for AD diagnosis. Then, a feature weighting matrix is utilized to sort the importance of features in AD diagnosis adaptively. Besides, we devise a regularization term with learned graph to preserve the local structure of the data in the latent space and integrate the graph construction into the learning processing for accurately encoding the relationships among samples. Instead of constructing a similarity graph for each modality, we learn a joint graph for multiple modalities to capture the correlations among modalities. Finally, the representations in the latent space are projected into the target space to perform AD diagnosis. An alternating optimization algorithm with proved convergence is developed to solve the optimization objective. Extensive experimental results show the effectiveness of the proposed method.

Increased Hippocampal-Inferior Temporal Gyrus White Matter Connectivity following Donepezil Treatment in Patients with Early Alzheimer's Disease: A Diffusion Tensor Probabilistic Tractography Study

The incidence of Alzheimer's disease (AD) has been increasing each year, and a defective hippocampus has been primarily associated with an early stage of AD. However, the effect of donepezil treatment on hippocampus-related networks is unknown. Thus, in the current study, we evaluated the hippocampal white matter (WM) connectivity in patients with early-stage AD before and after donepezil treatment using probabilistic tractography, and we further determined the WM integrity and changes in brain volume. Ten patients with early-stage AD (mean age = 72.4 ± 7.9 years; seven females and three males) and nine healthy controls (HC; mean age = 70.7 ± 3.5 years; six females and three males) underwent a magnetic resonance (MR) examination. After performing the first MR examination, the patients received donepezil treatment for 6 months. The brain volumes and diffusion tensor imaging scalars of 11 regions of interest (the superior/middle/inferior frontal gyrus, the superior/middle/inferior temporal gyrus, the amygdala, the caudate nucleus, the hippocampus, the putamen, and the thalamus) were measured using MR imaging and DTI, respectively. Seed-based structural connectivity analyses were focused on the hippocampus. The patients with early AD had a lower hippocampal volume and WM connectivity with the superior frontal gyrus and higher mean diffusivity (MD) and radial diffusivity (RD) in the amygdala than HC (p < 0.05, Bonferroni-corrected). However, brain areas with a higher (or lower) brain volume and WM connectivity were not observed in the HC compared with the patients with early AD. After six months of donepezil treatment, the patients with early AD showed increased hippocampal-inferior temporal gyrus (ITG) WM connectivity (p < 0.05, Bonferroni-corrected).

Multiclass prediction of different dementia syndromes based on multi-centric volumetric MRI imaging

INTRODUCTION

Dementia syndromes can be difficult to diagnose. We aimed at building a classifier for multiple dementia syndromes using magnetic resonance imaging (MRI).

METHODS

Atlas-based volumetry was performed on T1-weighted MRI data of 426 patients and 51 controls from the multi-centric German Research Consortium of Frontotemporal Lobar Degeneration including patients with behavioral variant frontotemporal dementia, Alzheimer's disease, the three subtypes of primary progressive aphasia, i.e., semantic, logopenic and nonfluent-agrammatic variant, and the atypical parkinsonian syndromes progressive supranuclear palsy and corticobasal syndrome. Support vector machine classification was used to classify each patient group against controls (binary classification) and all seven diagnostic groups against each other in a multi-syndrome classifier (multiclass classification).

RESULTS

The binary classification models reached high prediction accuracies between 71 and 95% with a chance level of 50%. Feature importance reflected disease-specific atrophy patterns. The multi-syndrome model reached accuracies of more than three times higher than chance level but was far from 100%. Multi-syndrome model performance was not homogenous across dementia syndromes, with better performance in syndromes characterized by regionally specific atrophy patterns. Whereas diseases generally could be classified vs controls more correctly with increasing severity and duration, differentiation between diseases was optimal in disease-specific windows of severity and duration.

DISCUSSION

Results suggest that automated methods applied to MR imaging data can support physicians in diagnosis of dementia syndromes. It is particularly relevant for orphan diseases beside frequent syndromes such as Alzheimer's disease.

Anti-neuroinflammatory Effects and Brain Pharmacokinetic Properties of Selonsertib, an Apoptosis signal-regulating Kinase 1 Inhibitor, in mice

Selonsertib is a first-in-class apoptosis signal-regulating kinase 1 (ASK1) inhibitor in clinical trials for treating NASH and diabetic kidney disease due to its anti-inflammatory and anti-fibrotic activities. In the present study, we investigated the anti-neuroinflammatory effects and brain pharmacokinetic properties of selonsertib. It inhibited inflammatory cytokines and NO production by suppressing phosphorylated ASK1 in the LPS-stimulated microglial cell line, BV2 cells. Consistent with the in vitro results, selonsertib attenuated plasma and brain TNF-α levels in the LPS-induced murine neuroinflammation model. In vitro and in vivo pharmacokinetic studies of selonsertib were conducted in support of central nervous system (CNS) drug discovery. In both Caco-2 and MDR-MDCK cells, selonsertib exhibited a high efflux ratio, showing that it is a P-gp substrate. Selonsertib was rapidly and effectively absorbed into the systemic circulation after oral treatment, with a T_max of 0.5 h and oral bioavailability of 74%. In comparison with high systemic exposure with C_max of 16.2 µg/ml and AUC of 64 µg·h/mL following oral dosing of 10 mg/kg, the brain disposition of selonsertib was limited, with C_max of 0.08 µg/g and Kp value of 0.004. This study demonstrates that selonsertib can be a therapeutic agent for neuroinflammatory diseases.

Interpretable brain disease classification and relevance-guided deep learning

Deep neural networks are increasingly used for neurological disease classification by MRI, but the networks' decisions are not easily interpretable by humans. Heat mapping by deep Taylor decomposition revealed that (potentially misleading) image features even outside of the brain tissue are crucial for the classifier's decision. We propose a regularization technique to train convolutional neural network (CNN) classifiers utilizing relevance-guided heat maps calculated online during training. The method was applied using T1-weighted MR images from 128 subjects with Alzheimer's disease (mean age = 71.9 ± 8.5 years) and 290 control subjects (mean age = 71.3 ± 6.4 years). The developed relevance-guided framework achieves higher classification accuracies than conventional CNNs but more importantly, it relies on less but more relevant and physiological plausible voxels within brain tissue. Additionally, preprocessing effects from skull stripping and registration are mitigated. With the interpretability of the decision mechanisms underlying CNNs, these results challenge the notion that unprocessed T1-weighted brain MR images in standard CNNs yield higher classification accuracy in Alzheimer's disease than solely atrophy.

Can hippocampal subfield measures supply information that could be used to improve the diagnosis of Alzheimer's disease?

The diagnosis of Alzheimer's disease (AD) needs to be improved. We investigated if hippocampal subfield volume measured by structural imaging, could supply information, so that the diagnosis of AD could be improved. In this study, subjects were classified based on clinical, neuropsychological, and amyloid positivity or negativity using PET scans. Data from 478 elderly Korean subjects grouped as cognitively unimpaired β-amyloid-negative (NC), cognitively unimpaired β-amyloid-positive (aAD), mild cognitively impaired β-amyloid-positive (pAD), mild cognitively impaired-specific variations not due to dementia β-amyloid-negative (CIND), severe cognitive impairment β-amyloid-positive (ADD+) and severe cognitive impairment β-amyloid-negative (ADD-) were used. NC and aAD groups did not show significant volume differences in any subfields. The CIND did not show significant volume differences when compared with either the NC or the aAD (except L-HATA). However, pAD showed significant volume differences in Sub, PrS, ML, Tail, GCMLDG, CA1, CA4, HATA, and CA3 when compared with the NC and aAD. The pAD group also showed significant differences in the hippocampal tail, CA1, CA4, molecular layer, granule cells/molecular layer/dentate gyrus, and CA3 when compared with the CIND group. The ADD- group had significantly larger volumes than the ADD+ group in the bilateral tail, SUB, PrS, and left ML. The results suggest that early amyloid depositions in cognitive normal stages are not accompanied by significant bilateral subfield volume atrophy. There might be intense and accelerated subfield volume atrophy in the later stages associated with the cognitive impairment in the pAD stage, which subsequently could drive the progression to AD dementia. Early subfield volume atrophy associated with the β-amyloid burden may be characterized by more symmetrical atrophy in CA regions than in other subfields. We conclude that the hippocampal subfield volumetric differences from structural imaging show promise for improving the diagnosis of Alzheimer's disease.

A causal association of ANKRD37 with human hippocampal volume

Human hippocampal volume has been separately associated with single nucleotide polymorphisms (SNPs), DNA methylation and gene expression, but their causal relationships remain largely unknown. Here, we aimed at identifying the causal relationships of SNPs, DNA methylation, and gene expression that are associated with hippocampal volume by integrating cross-omics analyses with genome editing, overexpression and causality inference. Based on structural neuroimaging data and blood-derived genome, transcriptome and methylome data, we prioritized a possibly causal association across multiple molecular phenotypes: rs1053218 mutation leads to cg26741686 hypermethylation, thus leads to overactivation of the associated ANKRD37 gene expression in blood, a gene involving hypoxia, which may result in the reduction of human hippocampal volume. The possibly causal relationships from rs1053218 to cg26741686 methylation to ANKRD37 expression obtained from peripheral blood were replicated in human hippocampal tissue. To confirm causality, we performed CRISPR-based genome and epigenome-editing of rs1053218 homologous alleles and cg26741686 methylation in mouse neural stem cell differentiation models, and overexpressed ANKRD37 in mouse hippocampus. These in-vitro and in-vivo experiments confirmed that rs1053218 mutation caused cg26741686 hypermethylation and ANKRD37 overexpression, and cg26741686 hypermethylation favored ANKRD37 overexpression, and ANKRD37 overexpression reduced hippocampal volume. The pairwise relationships of rs1053218 with hippocampal volume, rs1053218 with cg26741686 methylation, cg26741686 methylation with ANKRD37 expression, and ANKRD37 expression with hippocampal volume could be replicated in an independent healthy young (n = 443) dataset and observed in elderly people (n = 194), and were more significant in patients with late-onset Alzheimer's disease (n = 76). This study revealed a novel causal molecular association mechanism of ANKRD37 with human hippocampal volume, which may facilitate the design of prevention and treatment strategies for hippocampal impairment.

Associations between cardiorespiratory fitness, monocyte polarization, and exercise-related changes in mnemonic discrimination performance in older adults

Biological aging is accompanied by a chronic pro-inflammatory state that may facilitate losses in hippocampal-dependent mnemonic discrimination. Aerobic exercise training promotes adaptations that include improved immune competency, higher cardiorespiratory fitness, and maintenance of hippocampal function. However, it is poorly understood whether, in active older adults, baseline immune cell profiles and cardiorespiratory fitness are possible mechanisms that facilitate the long-term benefits to hippocampal dependent mnemonic discrimination performance. This within-subjects study with counterbalanced conditions aimed to investigate whether baseline monocyte polarization and cardiorespiratory fitness influenced performance in the mnemonic similarity task (MST) and related Lure Discrimination Index (LDI) score after an acute bout of exercise. Twenty-one active older adults (M = 68 ± 5 yrs) underwent baseline testing in which blood samples were collected and cardiorespiratory fitness measured. Participants then returned and completed a seated rest or moderate intensity aerobic exercise condition in which the MST was proctored prior to and 5 min after each condition. A linear mixed effects model was used in which Participant ID was a random effect and Condition (rest v. exercise), Time (pre- v post-), and order were fixed main effects. Simple linear regression models were used to determine the variance accounted for by monocyte phenotypes and cardiorespiratory fitness for LDI scores post-condition. Post-rest LDI scores were significantly lower than post-exercise LDI scores (t(20) = -2.65, p < 0.02, d = -0.57). Intermediate monocytes were significant predictors of the change in pre- to post-exercise LDI scores (F(1, 19) = 6.03, p = 0.024, R² = 0.24) and cardiorespiratory fitness was a significant predictor of the difference between post-condition LDI scores (F(1, 19) = 6.71, p = 0.018, R² = 0.26). Our results suggest baseline cardiorespiratory fitness and intermediate monocytes may relate to the integrity of hippocampal-dependent mnemonic discrimination performance, and possibly the degree of responsiveness to aerobic exercise interventions.

Perivascular spaces as a potential biomarker of Alzheimer's disease

Alzheimer's disease (AD) is a highly damaging disease that affects one's cognition and memory and presents an increasing societal and economic burden globally. Considerable research has gone into understanding AD; however, there is still a lack of effective biomarkers that aid in early diagnosis and intervention. The recent discovery of the glymphatic system and associated Perivascular Spaces (PVS) has led to the theory that enlarged PVS (ePVS) may be an indicator of AD progression and act as an early diagnostic marker. Visible on Magnetic Resonance Imaging (MRI), PVS appear to enlarge when known biomarkers of AD, amyloid-β and tau, accumulate. The central goal of ePVS and AD research is to determine when ePVS occurs in AD progression and if ePVS are causal or epiphenomena. Furthermore, if ePVS are indeed causative, interventions promoting glymphatic clearance are an attractive target for research. However, it is necessary first to ascertain where on the pathological progression of AD ePVS occurs. This review aims to examine the knowledge gap that exists in understanding the contribution of ePVS to AD. It is essential to understand whether ePVS in the brain correlate with increased regional tau distribution and global or regional Amyloid-β distribution and to determine if these spaces increase proportionally over time as individuals experience neurodegeneration. This review demonstrates that ePVS are associated with reduced glymphatic clearance and that this reduced clearance is associated with an increase in amyloid-β. However, it is not yet understood if ePVS are the outcome or driver of protein accumulation. Further, it is not yet clear if ePVS volume and number change longitudinally. Ultimately, it is vital to determine early diagnostic criteria and early interventions for AD to ease the burden it presents to the world; ePVS may be able to fulfill this role and therefore merit further research.

Enhanced mitochondrial DNA editing in mice using nuclear-exported TALE-linked deaminases and nucleases

We present two methods for enhancing the efficiency of mitochondrial DNA (mtDNA) editing in mice with DddA-derived cytosine base editors (DdCBEs). First, we fused DdCBEs to a nuclear export signal (DdCBE-NES) to avoid off-target C-to-T conversions in the nuclear genome and improve editing efficiency in mtDNA. Second, mtDNA-targeted TALENs (mitoTALENs) are co-injected into mouse embryos to cleave unedited mtDNA. We generated a mouse model with the m.G12918A mutation in the MT-ND5 gene, associated with mitochondrial genetic disorders in humans. The mutant mice show hunched appearances, damaged mitochondria in kidney and brown adipose tissues, and hippocampal atrophy, resulting in premature death.

Hippocampal morphological atrophy and distinct patterns of structural covariance network in Alzheimer's disease and mild cognitive impairment

Elucidating distinct morphological atrophy patterns of Alzheimer's disease (AD) and its prodromal stage, namely, mild cognitive impairment (MCI) helps to improve early diagnosis and medical intervention of AD. On that account, we aimed to obtain distinct patterns of voxel-wise morphological atrophy and its further perturbation on structural covariance network in AD and MCI compared with healthy controls (HCs). T1-weighted anatomical images of matched AD, MCI, and HCs were included in this study. Gray matter volume was obtained using voxel-based morphometry and compared among three groups. In addition, structural covariance network of identified brain regions exhibiting morphological difference was constructed and compared between pairs of three groups. Thus, patients with AD have a reduced hippocampal volume and an increased rate of atrophy compared with MCI and HCs. MCI exhibited a decreased trend in bilateral hippocampal volume compared with HCs and the accelerated right hippocampal atrophy rate than HCs. In AD, the hippocampus further exhibited increased structural covariance connected to reward related brain regions, including the anterior cingulate cortex, the putamen, the caudate, and the insula compared with HCs. In addition, the patients with AD exhibited increased structural covariance of left hippocampus with the bilateral insula, the inferior frontal gyrus, the superior temporal gyrus, and the cerebellum than MCI. These results reveal distinct patterns of morphological atrophy in AD and MCI, providing new insights into pathology of AD.

The association of reproductive stage with lobular cerebellar network connectivity across female adulthood

Sex-specific differences in the aging cerebellum may be related to hormone changes with menopause. We evaluated the association between reproductive stage and lobular cerebellar network connectivity using data from the Cambridge Centre for Ageing and Neuroscience repository. We used raw structural and resting state neuroimaging data and information regarding age, sex, and menopause-related variables. Crus I and II and Lobules V and VI were our cerebellar seeds of interest. We characterized reproductive stage using the Stages of Reproductive Aging Workshop criteria. Results show that postmenopausal females have lower cerebello-striatal and cerebello-cortical connectivity, particularly in frontal regions, along with lower connectivity within the cerebellum, compared to reproductive females. Postmenopausal females also exhibit greater connectivity in some brain areas as well. Differences begin to emerge across transitional stages of menopause. Further, results reveal sex-specific differences in connectivity between female reproductive groups and age-matched male control groups. This suggests that menopause may be associated with cerebellar network connectivity in aging females, and sex differences in the aging brain may be related to this biological process.

Plasma biomarkers predict cognitive trajectories in an ethnoracially and clinically diverse cohort: Mediation with hippocampal volume

Introduction

We examine whether the association between key plasma biomarkers (amyloid β [aβ] 42/40, total tau (t-tau), neurofilament light [NfL]) and cognitive trajectories (executive function [EF] and episodic memory [EM]) is mediated through neurodegeneration.

Methods

All participants were recruited from the University of California, Davis-Alzheimer's Disease Research Center (n = 473; baseline age range = 49-95 years, 60% women). We applied an accelerated longitudinal design to test latent growth models for EF and EM, and path and mediation analyses. Age was centered at 75 years, and all models were adjusted for sex, education, and ethnicity.

Results

HV differentially mediated the association aβ 42/40 and NfL on EF and EM level and change. Hippocampal volume (HV) did not mediate the association between t-tau and cognitive performance.

Discussion

Neurodegeneration as represented with HV selectively mediates the association between key non-invasive plasma biomarkers and cognitive trajectories in an ethnoracially and clinically diverse community-based sample.

Association between Type 2 Diabetes Mellitus and Brain Atrophy: A Meta-Analysis

BACKGROUND

Type 2 diabetes mellitus (T2DM) is known to be associated with cognitive decline and brain structural changes. This study systematically reviews and estimates human brain volumetric differences and atrophy associated with T2DM.

METHODS

PubMed, PsycInfo and Cochrane Library were searched for brain imaging studies reporting on brain volume differences between individuals with T2DM and healthy controls. Data were examined using meta-analysis, and association between age, sex, diabetes characteristics and brain volumes were tested using meta-regression.

RESULTS

A total of 14,605 entries were identified; after title, abstract and full-text screening applying inclusion and exclusion criteria, 64 studies were included and 42 studies with compatible data contributed to the meta-analysis (n=31,630; mean age 71.0 years; 44.4% male; 26,942 control; 4,688 diabetes). Individuals with T2DM had significantly smaller total brain volume, total grey matter volume, total white matter volume and hippocampal volume (approximately 1% to 4%); meta-analyses of smaller samples focusing on other brain regions and brain atrophy rate in longitudinal investigations also indicated smaller brain volumes and greater brain atrophy associated with T2DM. Meta-regression suggests that diabetes-related brain volume differences start occurring in early adulthood, decreases with age and increases with diabetes duration.

CONCLUSION

T2DM is associated with smaller total and regional brain volume and greater atrophy over time. These effects are substantial and highlight an urgent need to develop interventions to reduce the risk of T2DM for brain health.

An overview on microglial origin, distribution, and phenotype in Alzheimer's disease

Alzheimer's disease (AD) is the most common neurodegenerative disease that is responsible for about one-third of dementia cases worldwide. It is believed that AD is initiated with the deposition of Ab plaques in the brain. Genetic studies have shown that a high number of AD risk genes are expressed by microglia, the resident macrophages of brain. Common mode of action by microglia cells is neuroinflammation and phagocytosis. Moreover, it has been discovered that inflammatory marker levels are increased in AD patients. Recent studies advocate that neuroinflammation plays a major role in AD progression. Microglia have different activation profiles depending on the region of brain and stimuli. In different activation, profile microglia can generate either pro-inflammatory or anti-inflammatory responses. Microglia defend brain cells from pathogens and respond to injuries; also, microglia can lead to neuronal death along the way. In this review, we will bring the different roles played by microglia and microglia-related genes in the progression of AD.

Polygenic score for Alzheimer’s disease identifies differential atrophy in hippocampal subfield volumes

Hippocampal subfield atrophy is a prime structural change in the brain, associated with cognitive aging and neurodegenerative diseases such as Alzheimer’s disease. Recent developments in genome-wide association studies (GWAS) have identified genetic loci that characterize the risk of hippocampal volume loss based on the processes of normal and abnormal aging. Polygenic risk scores are the genetic proxies mimicking the genetic role of the pre-existing vulnerabilities of the underlying mechanisms influencing these changes. Discriminating the genetic predispositions of hippocampal subfield atrophy between cognitive aging and neurodegenerative diseases will be helpful in understanding the disease etiology. In this study, we evaluated the polygenic risk of Alzheimer’s disease (AD PGRS) for hippocampal subfield atrophy in 1,086 individuals (319 cognitively normal (CN), 591 mild cognitively impaired (MCI), and 176 Alzheimer’s disease dementia (ADD)). Our results showed a stronger association of AD PGRS effect on the left hemisphere than on the right hemisphere for all the hippocampal subfield volumes in a mixed clinical population (CN+MCI+ADD). The subfields CA1, CA4, hippocampal tail, subiculum, presubiculum, molecular layer, GC-ML-DG, and HATA showed stronger AD PGRS associations with the MCI+ADD group than with the CN group. The subfields CA3, parasubiculum, and fimbria showed moderately higher AD PGRS associations with the MCI+ADD group than with the CN group. Our findings suggest that the eight subfield regions, which were strongly associated with AD PGRS are likely involved in the early stage ADD and a specific focus on the left hemisphere could enhance the early prediction of ADD.

Obesity and the Brain

Innate and adaptive immunity are essential for neurodevelopment and central nervous system (CNS) homeostasis; however, the fragile equilibrium between immune and brain cells can be disturbed by any immune dysregulation and cause detrimental effects. Accumulating evidence indicates that, despite the blood-brain barrier (BBB), overactivation of the immune system leads to brain vulnerability that increases the risk of neuropsychiatric disorders, particularly upon subsequent exposure later in life. Disruption of microglial function in later life can be triggered by various environmental and psychological factors, including obesity-driven chronic low-grade inflammation and gut dysbiosis. Increased visceral adiposity has been recognized as an important risk factor for multiple neuropsychiatric conditions. The review aims to present our current understanding of the topic.

Voxel-based morphometry and a deep learning model for the diagnosis of early Alzheimer's disease based on cerebral gray matter changes

This study aimed to analyse cerebral grey matter changes in mild cognitive impairment (MCI) using voxel-based morphometry and to diagnose early Alzheimer's disease using deep learning methods based on convolutional neural networks (CNNs) evaluating these changes. Participants (111 MCI, 73 normal cognition) underwent 3-T structural magnetic resonance imaging. The obtained images were assessed using voxel-based morphometry, including extraction of cerebral grey matter, analyses of statistical differences, and correlation analyses between cerebral grey matter and clinical cognitive scores in MCI. The CNN-based deep learning method was used to extract features of cerebral grey matter images. Compared to subjects with normal cognition, participants with MCI had grey matter atrophy mainly in the entorhinal cortex, frontal cortex, and bilateral frontotemporal lobes (p < 0.0001). This atrophy was significantly correlated with the decline in cognitive scores (p < 0.01). The accuracy, sensitivity, and specificity of the CNN model for identifying participants with MCI were 80.9%, 88.9%, and 75%, respectively. The area under the curve of the model was 0.891. These findings demonstrate that research based on brain morphology can provide an effective way for the clinical, non-invasive, objective evaluation and identification of early Alzheimer's disease.

A Targeted Association Study of Blood-Brain Barrier Gene SNPs and Brain Atrophy

Background: The blood-brain barrier (BBB) is formed by a high-density lining of endothelial cells, providing a border between circulating blood and the brain interstitial fluid. This structure plays a key role in protecting the brain microenvironment by restricting passage of certain molecules and circulating pathogens. Objective: To identify associations between brain volumetric changes and a set of 355 BBB-related single nucleotide polymorphisms (SNP). Method: In a population of 721 unrelated individuals, linear mixed effect models were used to assess if specific variants were linked to regional rates of atrophy over a 12-year time span. Four brain regions were investigated, including cortical grey matter, cortical white matter, ventricle, and hippocampus. Further, we also investigated the potential impact of history of hypertension, diabetes, and the incidence of stroke on regional brain volume change. Results: History of hypertension, diabetes, and stroke was not associated with longitudinal brain volume change. However, we identified a series of genetic variants associated with regional brain volume changes. The associations were independent of variation due to the APOEɛ4 allele and were significant post correction for multiple comparisons. Conclusion: This study suggests that key genes involved in the regulation of BBB integrity may be associated with longitudinal changes in specific brain regions. The derived polygenic risk scores indicate that these interactions are multigenic. Further research needs to be conducted to investigate how BBB functions maybe compromised by genetic variation.

The oxidatively damaged DNA and amyloid-β oligomer hypothesis of Alzheimer's disease

The amyloid-β (Aβ) oligomer hypothesis of Alzheimer's disease (AD) still dominates the field, yet the clinical trial evidence does not robustly support it. A falsifiable prediction of the hypothesis is that Aβ oligomer levels should be elevated in the brain regions and at the disease stages where and when neuron death and synaptic protein loss begin and are the most severe, but we review previous evidence to demonstrate that this is not consistently the case. To rescue the Aβ oligomer hypothesis from falsification, we propose the novel ad-hoc hypothesis that the exceptionally vulnerable hippocampus may normally produce Aβ peptides even in healthily aging individuals, and hippocampal oxidatively damaged DNA, pathogen DNA, and metal ions such as zinc may initiate and drive Aβ peptide aggregation into oligomers and spreading, neuron death, synaptic dysfunction, and other aspects of AD neurodegeneration. We highlight additional evidence consistent with the underwhelming efficacy of Aβ oligomer-lowering agents, such as aducanumab, and of antioxidants, such as vitamin E, versus the so far isolated case report that DNase-I treatment for 2 months resulted in a severe AD patient's Mini-Mental State Exam score increasing from 3 to 18, reversing his diagnosis to moderate AD, according to the Mini-Mental State Exam.

Distinct profiles of functional connectivity density aberrance in Alzheimer's disease and mild cognitive impairment

INTRODUCTION

Investigating the neuroimaging changes from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is of great significance. However, the details about the distinct functional characteristics of AD and MCI remain unknown.

METHODS

In this study, we investigated distinct profiles of functional connectivity density (FCD) differences between AD and MCI compared with the normal population, aiming to depict the progressive brain changes from MCI to AD. As a data-driven method, FCD measures the profiles of FC for the given voxel at different scales. Resting-state functional magnetic resonance imaging (fMRI) images were obtained from patients with AD and MCI and matched healthy controls (HCs). One-way ANCOVA was used to investigate (global, long-range, and local) FCD differences among the three groups followed by post-hoc analysis controlling age, sex, and head motion.

RESULTS

The three groups exhibited significant global FCD differences in the superior frontal gyrus. The post-hoc results further showed that patients with AD had a significant increase in global FCD values than those with MCI and HCs. Patients with MCI exhibited an increased trend compared with HCs. We further identified brain regions contributing to the observed global FCD differences by conducting seed-based FC analysis. We also identified that the observed global FCD differences were the additive effects of altered FC between the superior frontal gyrus and the posterior default model network.

DISCUSSION

These results depicted the global information communication capability impairment in AD and MCI providing a new insight into the progressive brain changes from MCI to AD.

A Machine Learning-Based Holistic Approach to Predict the Clinical Course of Patients within the Alzheimer's Disease Spectrum

BACKGROUND

Alzheimer's disease (AD) is a neurodegenerative condition driven by multifactorial etiology. Mild cognitive impairment (MCI) is a transitional condition between healthy aging and dementia. No reliable biomarkers are available to predict the conversion from MCI to AD.

OBJECTIVE

To evaluate the use of machine learning (ML) on a wealth of data offered by the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Alzheimer's Disease Metabolomics Consortium (ADMC) database in the prediction of the MCI to AD conversion.

METHODS

We implemented an ML-based Random Forest (RF) algorithm to predict conversion from MCI to AD. Data related to the study population (587 MCI subjects) were analyzed by RF as separate or combined features and assessed for classification power. Four classes of variables were considered: neuropsychological test scores, AD-related cerebrospinal fluid (CSF) biomarkers, peripheral biomarkers, and structural magnetic resonance imaging (MRI) variables.

RESULTS

The ML-based algorithm exhibited 86% accuracy in predicting the AD conversion of MCI subjects. When assessing the features that helped the most, neuropsychological test scores, MRI data, and CSF biomarkers were the most relevant in the MCI to AD prediction. Peripheral parameters were effective when employed in association with neuropsychological test scores. Age and sex differences modulated the prediction accuracy. AD conversion was more effectively predicted in females and younger subjects.

CONCLUSION

Our findings support the notion that AD-related neurodegenerative processes result from the concerted activity of multiple pathological mechanisms and factors that act inside and outside the brain and are dynamically affected by age and sex.

Local Brain-Age: A U-Net Model

We propose a new framework for estimating neuroimaging-derived "brain-age" at a local level within the brain, using deep learning. The local approach, contrary to existing global methods, provides spatial information on anatomical patterns of brain ageing. We trained a U-Net model using brain MRI scans from n = 3,463 healthy people (aged 18-90 years) to produce individualised 3D maps of brain-predicted age. When testing on n = 692 healthy people, we found a median (across participant) mean absolute error (within participant) of 9.5 years. Performance was more accurate (MAE around 7 years) in the prefrontal cortex and periventricular areas. We also introduce a new voxelwise method to reduce the age-bias when predicting local brain-age "gaps." To validate local brain-age predictions, we tested the model in people with mild cognitive impairment or dementia using data from OASIS3 (n = 267). Different local brain-age patterns were evident between healthy controls and people with mild cognitive impairment or dementia, particularly in subcortical regions such as the accumbens, putamen, pallidum, hippocampus, and amygdala. Comparing groups based on mean local brain-age over regions-of-interest resulted in large effects sizes, with Cohen's d values >1.5, for example when comparing people with stable and progressive mild cognitive impairment. Our local brain-age framework has the potential to provide spatial information leading to a more mechanistic understanding of individual differences in patterns of brain ageing in health and disease.

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.