Entorhinal cortex: a good biomarker of mild cognitive impairment and mild Alzheimer's disease

Are demographic factors, masticatory performance and structural brain signatures associated with cognitive impairment in older people? A pilot study of cross-sectional neuroimaging data

BACKGROUND

The occurrence of cognitive impairment (CI) is expected to increase within an ageing population. CI is associated with tooth loss, which influences masticatory performance. A decrease in masticatory performance may cause functional and morphological changes in the brain. However, whether CI is associated with masticatory performance, demographics, and structural brain signatures has not been studied yet.

OBJECTIVES

To assess the associations between CI on the one hand, and masticatory performance, demographic factors, and structural brain signatures (i.e. cortical volume and thickness) on the other hand.

METHODS

In total, 18 older adults with CI (mean ± SD age = 72.2 ± 9.5 years) and 68 older adults without CI (65.7 ± 7.5 years) were included in this study. Masticatory performance was quantified using a colour-changeable chewing gum. A Magnetic Resonance Imaging (MRI) scan was used to map structural brain signatures. To study our aim, a multivariate binary logistic regression analysis with backward selection was performed.

RESULTS

The cortical volume of the right entorhinal cortex was negatively associated with CI (p < .01). However, demographic factors, masticatory performance, and the other structural brain signatures under investigation were not associated with CI.

CONCLUSION

A decrease in the volume of the right entorhinal cortex is associated with CI in older people.

Music Therapy as a Complementary Treatment in Patients with Dementia Associated to Alzheimer's Disease: A Systematic Review

Background

Alzheimer's disease (AD) is a complex condition that affects various aspects of a patient's life. Music therapy may be considered a beneficial supplementary tool to traditional therapies, that not fully address the range of AD manifestations.

Objective

The purpose of this systematic review is to investigate whether music therapy can have a positive impact on AD patients and on which symptoms.

Methods

The main research databases employed have been PubMed and Cochrane, using the keywords "dementia", "music therapy", "Alzheimer", "fMRI", "music", and "EEG".

Results

After removing duplicates and irrelevant studies, 23 were screened using set criteria, resulting in the final inclusion of 15 studies. The total number of participants included in these studies has been of 1,196 patients. For the fMRI analysis the search resulted in 28 studies on PubMed, two of which were included in the research; the total number of participants was of 124 individuals. The studies conducted with EEG were found using PubMed. The initial search resulted in 15 studies, but after a more accurate evaluation only 2 have been included in the analysis.

Conclusions

Even though the data currently available is not sufficient to draw conclusions supported by robust statistical power, the impact of music therapy on AD neuropsychiatric symptoms deserves great interest. Further research should be ushered, possibly multicentric studies, led with neuroimaging and other recent techniques, which can eventually open views on the music role in improving the cognitive status in AD.

Greater subjective cognitive decline severity is associated with worse memory performance and lower entorhinal cerebral blood flow in healthy older adults

OBJECTIVE

Subjective cognitive decline (SCD) is a potential early risk marker for Alzheimer's disease (AD), but its utility may vary across individuals. We investigated the relationship of SCD severity with memory function and cerebral blood flow (CBF) in areas of the middle temporal lobe (MTL) in a cognitively normal and overall healthy sample of older adults. Exploratory analyses examined if the association of SCD severity with memory and MTL CBF was different in those with lower and higher cardiovascular disease (CVD) risk status.

METHODS

Fifty-two community-dwelling older adults underwent magnetic resonance imaging, neuropsychological testing, and were administered the Everyday Cognition Scale (ECog) to measure SCD. Regression models investigated whether ECog scores were associated with memory performance and MTL CBF, followed by similar exploratory regressions stratified by CVD risk status (i.e., lower vs higher stroke risk).

RESULTS

Higher ECog scores were associated with lower objective memory performance and lower entorhinal cortex CBF after adjusting for demographics and mood. In exploratory stratified analyses, these associations remained significant in the higher stroke risk group only.

CONCLUSIONS

Our preliminary findings suggest that SCD severity is associated with cognition and brain markers of preclinical AD in otherwise healthy older adults with overall low CVD burden and that this relationship may be stronger for individuals with higher stroke risk, although larger studies with more diverse samples are needed to confirm these findings. Our results shed light on individual characteristics that may increase the utility of SCD as an early risk marker of cognitive decline.

Brain-Cognition Associations in Older Patients With Remitted Major Depressive Disorder or Mild Cognitive Impairment: A Multivariate Analysis of Gray and White Matter Integrity

BACKGROUND

Almost half of older patients with major depressive disorder (MDD) present with cognitive impairment, and one-third meet diagnostic criteria for mild cognitive impairment (MCI). However, mechanisms linking MDD and MCI remain unclear. We investigated multivariate associations between brain structural alterations and cognition in 3 groups of older patients at risk for dementia, remitted MDD (rMDD), MCI, and rMDD+MCI, as well as cognitively healthy nondepressed control participants.

METHODS

We analyzed magnetic resonance imaging data and cognitive domain scores in participants from the PACt-MD (Prevention of Alzheimer's Disease With Cognitive Remediation Plus Transcranial Direct Current Stimulation in Mild Cognitive Impairment and Depression) study. Following quality control, we measured cortical thickness and subcortical volumes of selected regions from 283 T1-weighted scans and fractional anisotropy of white matter tracts from 226 diffusion-weighted scans. We assessed brain-cognition associations using partial least squares regressions in the whole sample and in each subgroup.

RESULTS

In the entire sample, atrophy in the medial temporal lobe and subregions of the motor and prefrontal cortex was associated with deficits in verbal and visuospatial memory, language skills, and, to a lesser extent, processing speed (p < .0001; multivariate r = 0.30, 0.34, 0.26, and 0.18, respectively). Widespread reduced white matter integrity was associated with deficits in executive functioning, working memory, and processing speed (p = .008; multivariate r = 0.21, 0.26, 0.35, respectively). Overall, associations remained significant in the MCI and rMDD+MCI groups, but not the rMDD or healthy control groups.

CONCLUSIONS

We confirm findings of brain-cognition associations previously reported in MCI and extend them to rMDD+MCI, but similar associations in rMDD are not supported. Early-onset and treated MDD might not contribute to structural alterations associated with cognitive impairment.

Associations between white matter hyperintensities, lacunes, entorhinal cortex thickness, declarative memory and leisure activity in cognitively healthy older adults: A 7-year study

INTRODUCTION

Cerebral small vessel disease (cSVD) is a growing epidemic that affects brain health and cognition. Therefore, a more profound understanding of the interplay between cSVD, brain atrophy, and cognition in healthy aging is of great importance. In this study, we examined the association between white matter hyperintensities (WMH) volume, number of lacunes, entorhinal cortex (EC) thickness, and declarative memory in cognitively healthy older adults over a seven-year period, controlling for possible confounding factors. Because there is no cure for cSVD to date, the neuroprotective potential of an active lifestyle has been suggested. Supporting evidence, however, is scarce. Therefore, a second objective of this study is to examine the relationship between leisure activities, cSVD, EC thickness, and declarative memory.

METHODS

We used a longitudinal dataset, which consisted of five measurement time points of structural MRI and psychometric cognitive ability and survey data, collected from a sample of healthy older adults (baseline N = 231, age range: 64-87 years, age M = 70.8 years), to investigate associations between cSVD MRI markers, EC thickness and verbal and figural memory performance. Further, we computed physical, social, and cognitive leisure activity scores from survey-based assessments and examined their associations with brain structure and declarative memory. To provide more accurate estimates of the trajectories and cross-domain correlations, we applied latent growth curve models controlling for potential confounders.

RESULTS

Less age-related thinning of the right (β = 0.92, p<.05) and left EC (β = 0.82, p<.05) was related to less declarative memory decline; and a thicker EC at baseline predicted less declarative memory loss (β = 0.54, p<.05). Higher baseline levels of physical (β = 0.24, p<.05), and social leisure activity (β = 0.27, p<.01) predicted less thinning of right EC. No relation was found between WMH or lacunes and declarative memory or between leisure activity and declarative memory. Higher education was initially related to more physical activity (β = 0.16, p<.05) and better declarative memory (β = 0.23, p<.001), which, however, declined steeper in participants with higher education (β = -.35, p<.05). Obese participants were less physically (β = -.18, p<.01) and socially active (β = -.13, p<.05) and had thinner left EC (β = -.14, p<.05) at baseline. Antihypertensive medication use (β = -.26, p<.05), and light-to-moderate alcohol consumption (β = -.40, p<.001) were associated with a smaller increase in the number of lacunes whereas a larger increase in the number of lacunes was observed in current smokers (β = 0.30, p<.05).

CONCLUSIONS

Our results suggest complex relationships between cSVD MRI markers (total WMH, number of lacunes, right and left EC thickness), declarative memory, and confounding factors such as antihypertensive medication, obesity, and leisure activitiy. Thus, leisure activities and having good cognitive reserve counteracting this neurodegeneration. Several confounding factors seem to contribute to the extent or progression/decline of cSVD, which needs further investigation in the future. Since there is still no cure for cSVD, modifiable confounding factors should be studied more intensively in the future to maintain or promote brain health and thus cognitive abilities in older adults.

Integrated transcriptomics reveals the brain and blood biomarkers in Alzheimer's disease

BACKGROUND

The systematic molecular associations between the peripheral blood cells and brain in Alzheimer's disease (AD) remains unclear, which hinders our understanding of AD pathological mechanisms and the exploration of new diagnostic biomarkers.

METHODS

Here, we performed an integrated analysis of the brain and peripheral blood cells transcriptomics to establish peripheral biomarkers of AD. By employing multiple statistical analyses plus machine learning, we identified and validated multiple regulated central and peripheral network in patients with AD.

RESULTS

By bioinformatics analysis, a total of 243 genes were differentially expressed in the central and peripheral systems, mainly enriched in three modules: immune response, glucose metabolism and lysosome. In addition, lysosome related gene ATP6V1E1 and immune response related genes (IL2RG, OSM, EVI2B TNFRSF1A, CXCR4, STAT5A) were significantly correlated with Aβ or Tau pathology. Finally, receiver operating characteristic (ROC) analysis revealed that ATP6V1E1 showed high-diagnostic potential for AD.

CONCLUSION

Taken together, our data identified the main pathological pathways in AD progression, particularly the systemic dysregulation of the immune response, and provided peripheral biomarkers for AD diagnosis.

Altered cortical and subcortical morphometric features and asymmetries in the subjective cognitive decline and mild cognitive impairment

Introduction

This study aimed to evaluate morphological changes in cortical and subcortical regions and their asymmetrical differences in individuals with subjective cognitive decline (SCD) and mild cognitive impairment (MCI). These morphological changes may provide valuable insights into the early diagnosis and treatment of Alzheimer's disease (AD).

Methods

We conducted structural MRI scans on a cohort comprising 62 SCD patients, 97 MCI patients, and 70 age-, sex-, and years of education-matched healthy controls (HC). Using Freesurfer, we quantified surface area, thickness, the local gyrification index (LGI) of cortical regions, and the volume of subcortical nuclei. Asymmetry measures were also calculated. Additionally, we explored the correlation between morphological changes and clinical variables related to cognitive decline.

Results

Compared to HC, patients with MCI exhibited predominantly left-sided surface morphological changes in various brain regions, including the transverse temporal gyrus, superior temporal gyrus, insula, and pars opercularis. SCD patients showed relatively minor surface morphological changes, primarily in the insula and pars triangularis. Furthermore, MCI patients demonstrated reduced volumes in the anterior-superior region of the right hypothalamus, the fimbria of the bilateral hippocampus, and the anterior region of the left thalamus. These observed morphological changes were significantly associated with clinical ratings of cognitive decline.

Conclusion

The findings of this study suggest that cortical and subcortical morphometric changes may contribute to cognitive impairment in MCI, while compensatory mechanisms may be at play in SCD to preserve cognitive function. These insights have the potential to aid in the early diagnosis and treatment of AD.

Mitochondrial Complex I and β-Amyloid Peptide Interplay in Alzheimer's Disease: A Critical Review of New and Old Little Regarded Findings

Alzheimer's disease (AD) is the most common neurodegenerative disorder and the main cause of dementia which is characterized by a progressive cognitive decline that severely interferes with daily activities of personal life. At a pathological level, it is characterized by the accumulation of abnormal protein structures in the brain-β-amyloid (Aβ) plaques and Tau tangles-which interfere with communication between neurons and lead to their dysfunction and death. In recent years, research on AD has highlighted the critical involvement of mitochondria-the primary energy suppliers for our cells-in the onset and progression of the disease, since mitochondrial bioenergetic deficits precede the beginning of the disease and mitochondria are very sensitive to Aβ toxicity. On the other hand, if it is true that the accumulation of Aβ in the mitochondria leads to mitochondrial malfunctions, it is otherwise proven that mitochondrial dysfunction, through the generation of reactive oxygen species, causes an increase in Aβ production, by initiating a vicious cycle: there is therefore a bidirectional relationship between Aβ aggregation and mitochondrial dysfunction. Here, we focus on the latest news-but also on neglected evidence from the past-concerning the interplay between dysfunctional mitochondrial complex I, oxidative stress, and Aβ, in order to understand how their interplay is implicated in the pathogenesis of the disease.

Biomarkers of Alzheimer's disease in Black and/or African American Alzheimer's Disease Neuroimaging Initiative (ADNI) participants

Majority of dementia research is conducted in non-Hispanic White participants despite a greater prevalence of dementia in other racial groups. To obtain a better understanding of biomarker presentation of Alzheimer's disease (AD) in the non-Hispanic White population, this study exclusively examined AD biomarker abnormalities in 85 Black and/or African American participants within the Alzheimer's Disease Neuroimaging Initiative (ADNI). Participants were classified by the ADNI into 3 clinical groups: cognitively normal, mild cognitive impairment, or dementia. Data examined included demographics, apolipoprotein E (APOE) ε4, cerebrospinal fluid (CSF) Aβ1-42, CSF total tau (t-tau), CSF phosphorylated tau (p-tau), 3T magnetic resonance imaging (MRI), and measures of cognition and function. Analyses of variance and covariance showed lower cortical thickness in 5 of 7 selected MRI regions, lower hippocampal volume, greater volume of white matter hyperintensities, lower measures of cognition and function, lower measures of CSF Aβ1-42, and greater measures of CSF t-tau and p-tau between clinical groups. Our findings confirmed greater AD biomarker abnormalities between clinical groups in this sample.

Identification of image genetic biomarkers of Alzheimer's disease by orthogonal structured sparse canonical correlation analysis based on a diagnostic information fusion

Alzheimer's disease (AD) is an irreversible neurodegenerative disease, and its incidence increases yearly. Because AD patients will have cognitive impairment and personality changes, it has caused a heavy burden on the family and society. Image genetics takes the structure and function of the brain as a phenotype and studies the influence of genetic variation on the structure and function of the brain. Based on the structural magnetic resonance imaging data and transcriptome data of AD and healthy control samples in the Alzheimer's Disease Neuroimaging Disease database, this paper proposed the use of an orthogonal structured sparse canonical correlation analysis for diagnostic information fusion algorithm. The algorithm added structural constraints to the region of interest (ROI) of the brain. Integrating the diagnostic information of samples can improve the correlation performance between samples. The results showed that the algorithm could extract the correlation between the two modal data and discovered the brain regions most affected by multiple risk genes and their biological significance. In addition, we also verified the diagnostic significance of risk ROIs and risk genes for AD. The code of the proposed algorithm is available at https://github.com/Wanguangyu111/OSSCCA-DIF.

Neuroimaging modalities in the detection of Alzheimer's disease-associated biomarkers

Alzheimer's disease (AD) is the most common cause of dementia. Neuropathological changes in AD patients occur up to 10-20 years before the emergence of clinical symptoms. Specific diagnosis and appropriate intervention strategies are crucial during the phase of mild cognitive impairment (MCI) and AD. The detection of biomarkers has emerged as a promising tool for tracking the efficacy of potential therapies, making an early disease diagnosis, and prejudging treatment prognosis. Specifically, multiple neuroimaging modalities, including magnetic resonance imaging (MRI), positron emission tomography, optical imaging, and single photon emission-computed tomography, have provided a few potential biomarkers for clinical application. The MRI modalities described in this review include structural MRI, functional MRI, diffusion tensor imaging, magnetic resonance spectroscopy, and arterial spin labelling. These techniques allow the detection of presymptomatic diagnostic biomarkers in the brains of cognitively normal elderly people and might also be used to monitor AD disease progression after the onset of clinical symptoms. This review highlights potential biomarkers, merits, and demerits of different neuroimaging modalities and their clinical value in MCI and AD patients. Further studies are necessary to explore more biomarkers and overcome the limitations of multiple neuroimaging modalities for inclusion in diagnostic criteria for AD.

An Optimized Deep Learning Model for Predicting Mild Cognitive Impairment Using Structural MRI

Early diagnosis of mild cognitive impairment (MCI) with magnetic resonance imaging (MRI) has been shown to positively affect patients' lives. To save time and costs associated with clinical investigation, deep learning approaches have been used widely to predict MCI. This study proposes optimized deep learning models for differentiating between MCI and normal control samples. In previous studies, the hippocampus region located in the brain is used extensively to diagnose MCI. The entorhinal cortex is a promising area for diagnosing MCI since severe atrophy is observed when diagnosing the disease before the shrinkage of the hippocampus. Due to the small size of the entorhinal cortex area relative to the hippocampus, limited research has been conducted on the entorhinal cortex brain region for predicting MCI. This study involves the construction of a dataset containing only the entorhinal cortex area to implement the classification system. To extract the features of the entorhinal cortex area, three different neural network architectures are optimized independently: VGG16, Inception-V3, and ResNet50. The best outcomes were achieved utilizing the convolution neural network classifier and the Inception-V3 architecture for feature extraction, with accuracy, sensitivity, specificity, and area under the curve scores of 70%, 90%, 54%, and 69%, respectively. Furthermore, the model has an acceptable balance between precision and recall, achieving an F1 score of 73%. The results of this study validate the effectiveness of our approach in predicting MCI and may contribute to diagnosing MCI through MRI.

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

Introduction

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

Methods

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

Results

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

Discussion

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

Behavioral pattern separation and cognitive flexibility are enhanced in a mouse model of increased lateral entorhinal cortex-dentate gyrus circuit activity

Behavioral pattern separation and cognitive flexibility are essential cognitive abilities that are disrupted in many brain disorders. A better understanding of the neural circuitry involved in these abilities will open paths to treatment. In humans and mice, discrimination and adaptation rely on the integrity of the hippocampal dentate gyrus (DG) which receives glutamatergic input from the entorhinal cortex (EC), including the lateral EC (LEC). An inducible increase of EC-DG circuit activity improves simple hippocampal-dependent associative learning and increases DG neurogenesis. Here, we asked if the activity of LEC fan cells that directly project to the DG (LEC → DG neurons) regulates the relatively more complex hippocampal-dependent abilities of behavioral pattern separation or cognitive flexibility. C57BL/6J male mice received bilateral LEC infusions of a virus expressing shRNA TRIP8b, an auxiliary protein of an HCN channel or a control virus (SCR shRNA). Prior work shows that 4 weeks post-surgery, TRIP8b mice have more DG neurogenesis and greater activity of LEC → DG neurons compared to SCR shRNA mice. Here, 4 weeks post-surgery, the mice underwent testing for behavioral pattern separation and reversal learning (touchscreen-based location discrimination reversal [LDR]) and innate fear of open spaces (elevated plus maze [EPM]) followed by quantification of new DG neurons (doublecortin-immunoreactive cells [DCX+] cells). There was no effect of treatment (SCR shRNA vs. TRIP8b) on performance during general touchscreen training, LDR training, or the 1st days of LDR testing. However, in the last days of LDR testing, the TRIP8b shRNA mice had improved pattern separation (reached the first reversal more quickly and had more accurate discrimination) compared to the SCR shRNA mice, specifically when the load on pattern separation was high (lit squares close together or "small separation"). The TRIP8b shRNA mice were also more cognitively flexible (achieved more reversals) compared to the SCR shRNA mice in the last days of LDR testing. Supporting a specific influence on cognitive behavior, the SCR shRNA and TRIP8b shRNA mice did not differ in total distance traveled or in time spent in the closed arms of the EPM. Supporting an inducible increase in LEC-DG activity, DG neurogenesis was increased. These data indicate that the TRIP8b shRNA mice had better pattern separation and reversal learning and more neurogenesis compared to the SCR shRNA mice. This study advances fundamental and translational neuroscience knowledge relevant to two cognitive functions critical for adaptation and survival-behavioral pattern separation and cognitive flexibility-and suggests that the activity of LEC → DG neurons merits exploration as a therapeutic target to normalize dysfunctional DG behavioral output.

Development and validation of deep learning-based automatic brain segmentation for East Asians: A comparison with Freesurfer

Purpose

To develop and validate deep learning-based automatic brain segmentation for East Asians with comparison to data for healthy controls from Freesurfer based on a ground truth.

Methods

A total of 30 healthy participants were enrolled and underwent T1-weighted magnetic resonance imaging (MRI) using a 3-tesla MRI system. Our Neuro I software was developed based on a three-dimensional convolutional neural networks (CNNs)-based, deep-learning algorithm, which was trained using data for 776 healthy Koreans with normal cognition. Dice coefficient (D) was calculated for each brain segment and compared with control data by paired t-test. The inter-method reliability was assessed by intraclass correlation coefficient (ICC) and effect size. Pearson correlation analysis was applied to assess the relationship between D values for each method and participant ages.

Results

The D values obtained from Freesurfer (ver6.0) were significantly lower than those from Neuro I. The histogram of the Freesurfer results showed remarkable differences in the distribution of D values from Neuro I. Overall, D values obtained by Freesurfer and Neuro I showed positive correlations, but the slopes and intercepts were significantly different. It was showed the largest effect sizes ranged 1.07-3.22, and ICC also showed significantly poor to moderate correlations between the two methods (0.498 ≤ ICC ≤ 0.688). For Neuro I, D values resulted in reduced residuals when fitting data to a line of best fit, and indicated consistent values corresponding to each age, even in young and older adults.

Conclusion

Freesurfer and Neuro I were not equivalent when compared to a ground truth, where Neuro I exhibited higher performance. We suggest that Neuro I is a useful alternative for the assessment of the brain volume.

Neuroanatomical and morphometric study of S100β positive astrocytes in the entorhinal cortex during ageing in the 3xTg-Alzehimer's disease mouse model

Astrocytes contribute to the progression of neurodegenerative diseases, including Alzheimer's disease (AD). Here, we report the neuroanatomical and morphometric analysis of astrocytes in the entorhinal cortex (EC) of the aged wild type (WT) and triple transgenic (3xTg-AD) mouse model of AD. Using 3D confocal microscopy, we determined the surface area and volume of positive astrocytic profiles in male mice (WT and 3xTg-AD) from 1 to 18 months of age. We showed that S100β-positive astrocytes were equally distributed throughout the entire EC in both animal types and showed no changes in Nv (number of cells/mm3) nor in their distribution at the different ages studied. These positive astrocytes, demonstrated an age-dependent gradual increase in their surface area and in their volume starting at 3 months of age, in both WT and 3xTg-AD mice. This last group demonstrated a large increase in both surface area and volume at 18 months of age when the burden of pathological hallmarks of AD is present (69.74% to 76.73% in the surface area and the volume, for WT and 3xTg-AD mice respectively). We observed that these changes were due to the enlargement of the cell processes and to less extend the somata. In fact, the volume of the cell body was increased by 35.82% in 18-month-old 3xTg-AD compared to WT. On the other hand, the increase on the astrocytic processes were detected as soon as 9 months of age where we found an increase of surface area and volume (36.56% and 43.73%, respectively) sustained till 18 month of age (93.6% and 113.78%, respectively) when compared age-matched non-Tg mice. Moreover, we demonstrated that these hypertrophic S100β-positive astrocytes were mainly associated with Aβ plaques. Our results show a severe atrophy in GFAP cytoskeleton in all cognitive areas; whilst within the EC astrocytes independent to this atrophy show no changes in GS and S100β; which can play a key role in the memory impairment.

Memory Binding Test and Its Associations With Hippocampal Volume Across the Cognitive Continuum Preceding Dementia

Innovative memory paradigms have been introduced to capture subtle memory changes in early Alzheimer's disease (AD). We aimed to examine the associations between different indexes of the challenging Memory Binding Test (MBT) and hippocampal volume (HV) in a sample of individuals with subjective cognitive decline (SCD; n = 50), amnestic mild cognitive impairment (aMCI) due to AD (n = 31), and cognitively normal (CN) older adults (n = 29) recruited from the Czech Brain Aging Study, in contrast to traditional verbal memory tests. Both MBT free and cued recall scores in immediate and delayed recall conditions were associated with lower HV in both SCD and aMCI due to AD, whereas in traditional verbal memory tests only delayed recall scores were associated with lower HV. In SCD, the associations with lower HV in the immediate recall covered specific cued recall indexes only. In conclusion, the MBT is a promising test for detecting subtle hippocampal-associated memory decline during the predementia continuum.

Comparative survey of multigraph integration methods for holistic brain connectivity mapping

One of the greatest scientific challenges in network neuroscience is to create a representative map of a population of heterogeneous brain networks, which acts as a connectional fingerprint. The connectional brain template (CBT), also named network atlas, presents a powerful tool for capturing the most representative and discriminative traits of a given population while preserving its topological patterns. The idea of a CBT is to integrate a population of heterogeneous brain connectivity networks, derived from different neuroimaging modalities or brain views (e.g., structural and functional), into a unified holistic representation. Here we review current state-of-the-art methods designed to estimate well-centered and representative CBT for populations of single-view and multi-view brain networks. We start by reviewing each CBT learning method, then we introduce the evaluation measures to compare CBT representativeness of populations generated by single-view and multigraph integration methods, separately, based on the following criteria: Centeredness, biomarker-reproducibility, node-level similarity, global-level similarity, and distance-based similarity. We demonstrate that the deep graph normalizer (DGN) method significantly outperforms other multi-graph and all single-view integration methods for estimating CBTs using a variety of healthy and disordered datasets in terms of centeredness, reproducibility (i.e., graph-derived biomarkers reproducibility that disentangle the typical from the atypical connectivity variability), and preserving the topological traits at both local and global graph-levels.

Effect of APOE4 Allele and Gender on the Rate of Atrophy in the Hippocampus, Entorhinal Cortex, and Fusiform Gyrus in Alzheimer's Disease

BACKGROUND

The hippocampus, entorhinal cortex, and fusiform gyrus are brain areas that deteriorate during early-stage Alzheimer's disease (AD). The ApoE4 allele has been identified as a risk factor for AD development, is linked to an increase in the aggregation of amyloid ß (Aß) plaques in the brain, and is responsible for atrophy of the hippocampal area. However, to our knowledge, the rate of deterioration over time in individuals with AD, with or without the ApoE4 allele, has not been investigated.

METHOD

In this study, we, for the first time, analyze atrophy in these brain structures in AD patients with and without the ApoE4 using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset.

RESULTS

It was found that the rate of decrease in the volume of these brain areas over 12 months was related to the presence of ApoE4. Further, we found that neural atrophy was not different for female and male patients, unlike prior studies, suggesting that the presence of ApoE4 is not linked to the gender difference in AD.

CONCLUSION

Our results confirm and extend previous findings, showing that the ApoE4 allele gradually impacts brain regions impacted by AD.

Two Routes to Alzheimer's Disease Based on Differential Structural Changes in Key Brain Regions

BACKGROUND

Alzheimer's disease (AD) is a neurodegenerative disorder with homogenous disease patterns. Neuropathological changes precede symptoms by up to two decades making neuroimaging biomarkers a prime candidate for early diagnosis, prognosis, and patient stratification.

OBJECTIVE

The goal of the study was to discern intermediate AD stages and their precursors based on neuroanatomical features for stratifying patients on their progression through different stages.

METHODS

Data include grey matter features from 14 brain regions extracted from longitudinal structural MRI and cognitive data obtained from 1,017 healthy controls and AD patients of ADNI. AD progression was modeled with a Hidden Markov Model, whose hidden states signify disease stages derived from the neuroanatomical data. To tie the progression in brain atrophy to a behavioral marker, we analyzed the ADAS-cog sub-scores in the stages.

RESULTS

The optimal model consists of eight states with differentiable neuroanatomical features, forming two routes crossing once at a very early point and merging at the final state. The cortical route is characterized by early and sustained atrophy in cortical regions. The limbic route is characterized by early decrease in limbic regions. Cognitive differences between the two routes are most noticeable in the memory domain with subjects from the limbic route experiencing stronger memory impairments.

CONCLUSION

Our findings corroborate that more than one pattern of grey matter deterioration with several discernable stages can be identified in the progression of AD. These neuroanatomical subtypes are behaviorally meaningful and provide a door into early diagnosis of AD and prognosis of the disease's progression.

Generative adversarial network constrained multiple loss autoencoder: A deep learning-based individual atrophy detection for Alzheimer's disease and mild cognitive impairment

Exploring individual brain atrophy patterns is of great value in precision medicine for Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, the current individual brain atrophy detection models are deficient. Here, we proposed a framework called generative adversarial network constrained multiple loss autoencoder (GANCMLAE) for precisely depicting individual atrophy patterns. The GANCMLAE model was trained using normal controls (NCs) from the Alzheimer's Disease Neuroimaging Initiative cohort, and the Xuanwu cohort was employed to validate the robustness of the model. The potential of the model for identifying different atrophy patterns of MCI subtypes was also assessed. Furthermore, the clinical application potential of the GANCMLAE model was investigated. The results showed that the model can achieve good image reconstruction performance on the structural similarity index measure (0.929 ± 0.003), peak signal-to-noise ratio (31.04 ± 0.09), and mean squared error (0.0014 ± 0.0001) with less latent loss in the Xuanwu cohort. The individual atrophy patterns extracted from this model are more precise in reflecting the clinical symptoms of MCI subtypes. The individual atrophy patterns exhibit a better discriminative power in identifying patients with AD and MCI from NCs than those of the t-test model, with areas under the receiver operating characteristic curve of 0.867 (95%: 0.837-0.897) and 0.752 (95%: 0.71-0.790), respectively. Similar findings are also reported in the AD and MCI subgroups. In conclusion, the GANCMLAE model can serve as an effective tool for individualised atrophy detection.

Neuroanatomical and neurocognitive changes associated with subjective cognitive decline

Introduction

Subjective Cognitive Decline (SCD) can progress to mild cognitive impairment (MCI) and Alzheimer's disease (AD) dementia and thus may represent a preclinical stage of the AD continuum. However, evidence about structural changes observed in the brain during SCD remains inconsistent.

Materials and methods

This cross-sectional study aimed to evaluate, in subjects recruited from the CompAS project, neurocognitive and neurostructural differences between a group of forty-nine control subjects and forty-nine individuals who met the diagnostic criteria for SCD and exhibited high levels of subjective cognitive complaints (SCCs). Structural magnetic resonance imaging was used to compare neuroanatomical differences in brain volume and cortical thickness between both groups.

Results

Relative to the control group, the SCD group displayed structural changes involving frontal, parietal, and medial temporal lobe regions of critical importance in AD etiology and functionally related to several cognitive domains, including executive control, attention, memory, and language.

Conclusion

Despite the absence of clinical deficits, SCD may constitute a preclinical entity with a similar (although subtle) pattern of neuroanatomical changes to that observed in individuals with amnestic MCI or AD dementia.

Behavioral pattern separation and cognitive flexibility are enhanced in a mouse model of increased lateral entorhinal cortex-dentate gyrus circuit activity

Behavioral pattern separation and cognitive flexibility are essential cognitive abilities which are disrupted in many brain disorders. Better understanding of the neural circuitry involved in these abilities will open paths to treatment. In humans and mice, discrimination and adaptation rely on integrity of the hippocampal dentate gyrus (DG) which both receive glutamatergic input from the entorhinal cortex (EC), including the lateral EC (LEC). Inducible increase of EC-DG circuit activity improves simple hippocampal-dependent associative learning and increases DG neurogenesis. Here we asked if the activity of LEC fan cells that directly project to the DG (LEC➔DG neurons) regulates behavioral pattern separation or cognitive flexibility. C57BL6/J male mice received bilateral LEC infusions of a virus expressing shRNA TRIP8b, an auxiliary protein of an HCN channel or a control virus (SCR shRNA); this approach increases the activity of LEC➔DG neurons. Four weeks later, mice underwent testing for behavioral pattern separation and reversal learning (touchscreen-based Location Discrimination Reversal [LDR] task) and innate fear of open spaces (elevated plus maze [EPM]) followed by counting of new DG neurons (doublecortin-immunoreactive cells [DCX+] cells). TRIP8b and SCR shRNA mice performed similarly in general touchscreen training and LDR training. However, in late LDR testing, TRIP8b shRNA mice reached the first reversal more quickly and had more accurate discrimination vs. SCR shRNA mice, specifically when pattern separation was challenging (lit squares close together or "small separation"). Also, TRIP8b shRNA mice achieved more reversals in late LDR testing vs. SCR shRNA mice. Supporting a specific influence on cognitive behavior, SCR shRNA and TRIP8b shRNA mice did not differ in total distance traveled or in time spent in the closed arms of the EPM. Supporting an inducible increase in LEC-DG activity, DG neurogenesis was increased. These data indicate TRIP8b shRNA mice had better pattern separation and reversal learning and more neurogenesis vs. SCR shRNA mice. This work advances fundamental and translational neuroscience knowledge relevant to two cognitive functions critical for adaptation and survival - behavioral pattern separation and cognitive flexibility - and suggests the activity of LEC➔DG neurons merits exploration as a therapeutic target to normalize dysfunctional DG behavioral output.

The Inflammatory Gene PYCARD of the Entorhinal Cortex as an Early Diagnostic Target for Alzheimer's Disease

The incidence of Alzheimer's disease (AD) is increasing year by year, which brings great challenges to human health. However, the pathogenesis of AD is still unclear, and it lacks early diagnostic targets. The entorhinal cortex (EC) is a key brain region for the occurrence of AD neurodegeneration, and neuroinflammation plays a significant role in EC degeneration in AD. This study aimed to reveal the close relationship between inflammation-related genes in the EC and AD by detecting key differentially expressed genes (DEGs) via gene function enrichment pathway analysis. GSE4757 and GSE21779 gene expression profiles of AD were downloaded from the Gene Expression Omnibus (GEO) database. R language was used for the standardization and differential analysis of DEGs. Then, significantly enriched Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were analyzed to predict the potential biological functions of the DEGs. Finally, the significant expressions of identified DEGs were verified, and the therapeutic values were detected by a receiver operating characteristic (ROC) curve. The results showed that eight up-regulated genes (SLC22A2, ITGB2-AS1, NIT1, FGF14-AS2, SEMA3E, PYCARD, PRORY, ADIRF) and two down-regulated genes (AKAIN1, TRMT2B) may have a potential diagnostic value for AD, and participate in inflammatory pathways. The area under curve (AUC) results of the ten genes showed that they had potential diagnostic value for AD. The AUC of PYCARD was 0.95, which had the most significant diagnostic value, and it is involved in inflammatory processes such as the inflammasome complex adaptor protein. The DEGs screened, and subsequent pathway analysis revealed a close relationship between inflammation-related PYCARD and AD, thus providing a new basis for an early diagnostic target for AD.

Hormone replacement therapy is associated with improved cognition and larger brain volumes in at-risk APOE4 women: results from the European Prevention of Alzheimer's Disease (EPAD) cohort

BACKGROUND

The risk of dementia is higher in women than men. The metabolic consequences of estrogen decline during menopause accelerate neuropathology in women. The use of hormone replacement therapy (HRT) in the prevention of cognitive decline has shown conflicting results. Here we investigate the modulating role of APOE genotype and age at HRT initiation on the heterogeneity in cognitive response to HRT.

METHODS

The analysis used baseline data from participants in the European Prevention of Alzheimer's Dementia (EPAD) cohort (total n= 1906, women= 1178, 61.8%). Analysis of covariate (ANCOVA) models were employed to test the independent and interactive impact of APOE genotype and HRT on select cognitive tests, such as MMSE, RBANS, dot counting, Four Mountain Test (FMT), and the supermarket trolley test (SMT), together with volumes of the medial temporal lobe (MTL) regions by MRI. Multiple linear regression models were used to examine the impact of age of HRT initiation according to APOE4 carrier status on these cognitive and MRI outcomes.

RESULTS

APOE4 HRT users had the highest RBANS delayed memory index score (P-APOE*HRT interaction = 0.009) compared to APOE4 non-users and to non-APOE4 carriers, with 6-10% larger entorhinal (left) and amygdala (right and left) volumes (P-interaction= 0.002, 0.003, and 0.005 respectively). Earlier introduction of HRT was associated with larger right (standardized β= -0.555, p=0.035) and left hippocampal volumes (standardized β= -0.577, p=0.028) only in APOE4 carriers.

CONCLUSION

HRT introduction is associated with improved delayed memory and larger entorhinal and amygdala volumes in APOE4 carriers only. This may represent an effective targeted strategy to mitigate the higher life-time risk of AD in this large at-risk population subgroup. Confirmation of findings in a fit for purpose RCT with prospective recruitment based on APOE genotype is needed to establish causality.

Relationship between finger movement characteristics and brain voxel-based morphometry

Background

Aging is the most significant risk factor for dementia. Alzheimer’s disease (AD) accounts for approximately 60–80% of all dementia cases in older adults. This study aimed to examine the relationship between finger movements and brain volume in AD patients using a voxel-based reginal analysis system for Alzheimer’s disease (VSRAD) software.

Methods

Patients diagnosed with AD at the Center for Comprehensive Care and Research on Memory Disorders were included. The diagnostic criteria were based on the National Institute on Aging-Alzheimer’s Association. A finger-tapping device was used for all measurements. Participants performed the tasks in the following order: with their non-dominant hand, dominant hand, both hands simultaneously, and alternate hands. Movements were measured for 15 s each. The relationship between distance and output was measured. Magnetic resonance imaging measurements were performed, and VSRAD was conducted using sagittal section 3D T1-weighted images. The Z-score was used to calculate the severity of medial temporal lobe atrophy. Pearson’s product-moment correlation coefficient analyzed the relationship between the severity of medial temporal lobe atrophy and mean values of the parameters in the finger-tapping movements. The statistical significance level was set at <5%. The calculated p-values were corrected using the Bonferroni method.

Results

Sixty-two patients were included in the study. Comparison between VSRAD and MoCA-J scores corrected for p-values showed a significant negative correlation with the extent of gray matter atrophy (r = -0. 52; p< 0.001). A positive correlation was observed between the severity of medial temporal lobe atrophy and standard deviation (SD) of the distance rate of velocity peak in extending movements in the non-dominant hand (r = 0. 51; p< 0.001).

Conclusions

The SD of distance rate of velocity peak in extending movements extracted from finger taps may be a useful parameter for the early detection of AD and diagnosis of its severity.

Inhibiting amyloid beta (1–42) peptide-induced mitochondrial dysfunction prevents the degradation of synaptic proteins in the entorhinal cortex

Increasing evidence suggests that mitochondrial dysfunction and aberrant release of mitochondrial reactive oxygen species (ROS) play crucial roles in early synaptic perturbations and neuropathology that drive memory deficits in Alzheimer’s disease (AD). We recently showed that solubilized human amyloid beta peptide 1–42 (hAβ_1–42) causes rapid alterations at glutamatergic synapses in the entorhinal cortex (EC) through the activation of both GluN2A- and GluN2B-containing NMDA receptors. However, whether disruption of mitochondrial dynamics and increased ROS contributes to mechanisms mediating hAβ_1–42-induced synaptic perturbations in the EC is unknown. Here we assessed the impact of hAβ_1–42 on mitochondrial respiratory functions, and the expression of key mitochondrial and synaptic proteins in the EC. Measurements of mitochondrial respiratory function in wild-type EC slices exposed to 1 μM hAβ_1–42 revealed marked reductions in tissue oxygen consumption and energy production efficiency relative to control. hAβ_1–42 also markedly reduced the immunoexpression of both mitochondrial superoxide dismutase (SOD2) and mitochondrial-cytochrome c protein but had no significant impact on cytosolic-cytochrome c expression, voltage-dependent anion channel protein (a marker for mitochondrial density/integrity), and the immunoexpression of protein markers for all five mitochondrial complexes. The rapid impairments in mitochondrial functions induced by hAβ_1–42 were accompanied by reductions in the presynaptic marker synaptophysin, postsynaptic density protein (PSD95), and the vesicular acetylcholine transporter, with no significant changes in the degradative enzyme acetylcholinesterase. We then assessed whether reducing hAβ_1–42-induced increases in ROS could prevent dysregulation of entorhinal synaptic proteins, and found that synaptic impairments induced by hAβ_1–42 were prevented by the mitochondria-targeted antioxidant drug mitoquinone mesylate, and by the SOD and catalase mimetic EUK134. These findings indicate that hAβ_1–2 can rapidly disrupt mitochondrial functions and increase ROS in the entorhinal, and that this may contribute to synaptic dysfunctions that may promote early AD-related neuropathology.

Cortical thinning 3 years after ischaemic stroke is associated with cognitive impairment and APOE ε4

Cortical thinning has been described in many neurodegenerative diseases and used for both diagnosis and disease monitoring. The imaging signatures of post-stroke vascular cognitive impairment have not been well described. We investigated the trajectory of cortical thickness over 3 years following ischaemic stroke compared to healthy stroke-free age- and sex-matched controls. We also compared cortical thickness between cognitively normal and impaired stroke survivors, and between APOE ɛ4 carriers and non-carriers. T1-weighted MRI and cognitive data for 90 stroke survivors and 36 controls from the Cognition And Neocortical Volume After Stroke (CANVAS) study were used. Cortical thickness was estimated using FreeSurfer volumetric reconstruction according to the Desikan-Killiany parcellation atlas. Segmentation inaccuracies were manually corrected and infarcted ipsilesional vertices in cortical thickness maps were identified and excluded using stroke lesion masks traced a-priori. Mixed-effects regression was used to compare cortical thickness cross-sectionally between groups and longitudinally between timepoints. Healthy control and stroke groups did not differ on demographics and most clinical characteristics, though controls were less likely to have atrial fibrillation. Age was negatively associated with global mean cortical thickness independent of sex or group, with women in both groups having significantly thicker cortex. Three months post-stroke, cortical thinning was limited and focal. From 3 months to 3 years, the rate of cortical thinning in stroke was faster compared to that in healthy controls. However, this difference in cortical thinning rate could not survive family-wise correction for multiple comparisons. Yet, cortical thinning at 3 years was found more spread especially in ipsilesional hemispheres in regions implicated in motor, sensory, and memory processing and recovery. The cognitively impaired stroke survivors showed greater cortical thinning, compared to controls, than those who were cognitively normal at 3 years. Also, carriers of the APOE ɛ4 allele in stroke exhibited greater cortical thinning independent of cognitive status. The temporal changes of cortical thickness in both healthy and stroke cohorts followed previously reported patterns of cortical thickness asymmetry loss across the human adult life. However, this loss of thickness asymmetry was amplified in stroke. The post-stroke trajectories of cortical thickness reported in this study may contribute to our understanding of imaging signatures of vascular cognitive impairment.

The lateral entorhinal cortex is a hub for local and global dysfunction in early Alzheimer's disease states

Functional network activity alterations are one of the earliest hallmarks of Alzheimer's disease (AD), detected prior to amyloidosis and tauopathy. Better understanding the neuronal underpinnings of such network alterations could offer mechanistic insight into AD progression. Here, we examined a mouse model (3xTgAD mice) recapitulating this early AD stage. We found resting functional connectivity loss within ventral networks, including the entorhinal cortex, aligning with the spatial distribution of tauopathy reported in humans. Unexpectedly, in contrast to decreased connectivity at rest, 3xTgAD mice show enhanced fMRI signal within several projection areas following optogenetic activation of the entorhinal cortex. We corroborate this finding by demonstrating neuronal facilitation within ventral networks and synaptic hyperexcitability in projection targets. 3xTgAD mice, thus, reveal a dichotomic hypo-connected:resting versus hyper-responsive:active phenotype. This strong homotopy between the areas affected supports the translatability of this pathophysiological model to tau-related, early-AD deficits in humans.

Functional Connectome Dynamics After Mild Traumatic Brain Injury According to Age and Sex

Neural and cognitive deficits after mild traumatic brain injury (mTBI) are paralleled by changes in resting state functional correlation (FC) networks that mirror post-traumatic pathophysiology effects on functional outcomes. Using functional magnetic resonance images acquired both acutely and chronically after injury (∼1 week and ∼6 months post-injury, respectively), we map post-traumatic FC changes across 136 participants aged 19-79 (52 females), both within and between the brain's seven canonical FC networks: default mode, dorsal attention, frontoparietal, limbic, somatomotor, ventral attention, and visual. Significant sex-dependent FC changes are identified between (A) visual and limbic, and between (B) default mode and somatomotor networks. These changes are significantly associated with specific functional recovery patterns across all cognitive domains (p < 0.05, corrected). Changes in FC between default mode, somatomotor, and ventral attention networks, on the one hand, and both temporal and occipital regions, on the other hand, differ significantly by age group (p < 0.05, corrected), and are paralleled by significant sex differences in cognitive recovery independently of age at injury (p < 0.05, corrected). Whereas females' networks typically feature both significant (p < 0.036, corrected) and insignificant FC changes, males more often exhibit significant FC decreases between networks (e.g., between dorsal attention and limbic, visual and limbic, default-mode and somatomotor networks, p < 0.0001, corrected), all such changes being accompanied by significantly weaker recovery of cognitive function in males, particularly older ones (p < 0.05, corrected). No significant FC changes were found across 35 healthy controls aged 66-92 (20 females). Thus, male sex and older age at injury are risk factors for significant FC alterations whose patterns underlie post-traumatic cognitive deficits. This is the first study to map, systematically, how mTBI impacts FC between major human functional networks.

Brain structural alterations detected by an automatic quantified tool as an indicator for MCI diagnosing in type 2 diabetes mellitus patients: a magnetic resonance imaging study

Background and objectives

Type 2 diabetes mellitus (T2DM) is an important risk factors for mild cognitive impairment (MCI). Structural magnetic resonance imaging (sMRI) is an effective and widely used method to investigate brain pathomorphological injury in neural diseases. In present study, we aimed to determine the brain regional alterations that correlated to the incidence of MCI in T2DM patients.

Materials and methods

Eighteen T2DM patients with and without MCI (DMCI/T2DM) respectively, and eighteen age/gender-matched healthy controls (HC) were recruited. Brain MRI imagines of all the individuals were subjected to automatic quantified brain sub-structure volume segmentation and measurement by Dr. brain ™ software. The relative volume of total gray matter (TGM), total white matter (TWM), and 68 pairs (left and right) of brain sub-structures were compared between the three groups. Cognitive function correlation analysis and receiver operating characteristic (ROC) curve analysis were conducted in the MCI-related brain regions in T2DM patients, and we utilized a machine learning method to classify the three group of subjects.

Results

10 and 27 brain sub-structures with significant relative volumetric alterations were observed in T2DM patients without MCI and T2DM patients with MCI, respectively (p < 0.05). Compared with T2DM patients without MCI, eight critical regions include right anterior orbital gyrus, right calcarine and cerebrum, left cuneus, left entorhinal area, left frontal operculum, right medial orbital gyrus, right occipital pole, left temporal pole had significant lower volumetric ratio in T2DM patients with MCI (p < 0.05). Among them, the decrease of volumetric ratio in several regions had a positive correlation with Montreal Cognitive Assessment (MoCA) scores and Mini-Mental State Examination (MMSE) scores. The classification results conducted based on these regions as features by random forest algorithm yielded good accuracies of T2DM/HC 69.4%, DMCI/HC 72.2% and T2DM/DMCI 69.4%.

Conclusions

Certain brain regional structural lesions occurred in patients with T2DM, and this condition was more serious in T2DM patients combined with MCI. A systematic way of segmenting and measuring the whole brain has a potential clinical value for predicting the presence of MCI for T2DM patients.

Disrupted connectivity in the olfactory bulb-entorhinal cortex-dorsal hippocampus circuit is associated with recognition memory deficit in Alzheimer's disease model

Neural synchrony in brain circuits is the mainstay of cognition, including memory processes. Alzheimer's disease (AD) is a progressive neurodegenerative disorder that disrupts neural synchrony in specific circuits, associated with memory dysfunction before a substantial neural loss. Recognition memory impairment is a prominent cognitive symptom in the early stages of AD. The entorhinal–hippocampal circuit is critically engaged in recognition memory and is known as one of the earliest circuits involved due to AD pathology. Notably, the olfactory bulb is closely connected with the entorhinal–hippocampal circuit and is suggested as one of the earliest regions affected by AD. Therefore, we recorded simultaneous local field potential from the olfactory bulb (OB), entorhinal cortex (EC), and dorsal hippocampus (dHPC) to explore the functional connectivity in the OB-EC-dHPC circuit during novel object recognition (NOR) task performance in a rat model of AD. Animals that received amyloid-beta (Aβ) showed a significant impairment in task performance and a marked reduction in OB survived cells. We revealed that Aβ reduced coherence and synchrony in the OB-EC-dHPC circuit at theta and gamma bands during NOR performance. Importantly, our results exhibit that disrupted functional connectivity in the OB-EC-dHPC circuit was correlated with impaired recognition memory induced by Aβ. These findings can elucidate dynamic changes in neural activities underlying AD, helping to find novel diagnostic and therapeutic targets.

Detecting Biomarkers of Alzheimer's Disease Based on Multi-constrained Uncertainty-Aware Adaptive Sparse Multi-view Canonical Correlation Analysis

Image genetics mainly explores the pathogenesis of Alzheimer's disease (AD) by studying the relationship between genetic data (such as SNP, gene expression data, and DNA methylation) and imaging data (such as structural MRI (sMRI), fMRI, and PET). Most of the existing research on brain imaging genomics uses two-way or three-way bi-multivariate methods to explore the correlation analysis between genes and brain imaging. However, many of these methods are still affected by the gradient domination or cannot take into account the effect of feature redundancy on the results, so that the typical correlation coefficient and program running speed are not significantly improved. In order to solve the above problems, this paper proposes a multi-constrained uncertainty-aware adaptive sparse multi-view canonical correlation analysis method (MC-unAdaSMCCA) to explore associations among SNPs, gene expression data, and sMRI; that is, based on traditional unAdaSMCCA, orthogonal constraints are imposed on the weights of the three data features through linear programming, which can reduce the redundancy of feature weights to improve the correlation between the data and reduce the complexity of the algorithm to significantly speed up the running speed of the program. Three adaptive sparse multi-view canonical correlation analysis methods are used as benchmarks to evaluate the difference between real neuroimaging data and synthetic data. Compared with the other three methods, our proposed method has obtained better or comparable typical correlation coefficients and typical weights. Moreover, the following experimental results show that the MC-unAdaSMCCA method cannot only identify biomarkers related to AD and mild cognitive impairment (MCI), but also has a strong ability to resist noise and process high-dimensional data. Therefore, our proposed method provides a reliable approach to multi-modal imaging genetic researches.

Structural Alteration of Medial Temporal Lobe Subfield in the Amnestic Mild Cognitive Impairment Stage of Alzheimer’s Disease

Objective. Volume reduction and structural abnormality is the most replicated finding in neuroimaging studies of Alzheimer’s disease (AD). Amnestic mild cognitive impairment (aMCI) is the early stage of AD development. Thus, it is necessary to investigate the link between atrophy of regions of interest (ROIs) in medial temporal lobe, the variation trend of ROI densities and volumes among patients with cognitive impairment, and the distribution characteristics of ROIs in the aMCI group, Alzheimer’s disease (AD) group, and normal control (NC) group. Methods. 30 patients with aMCI, 16 patients with AD, and 30 NC are recruited; magnetic resonance imaging (MRI) brain scans are conducted. Voxel-based morphometry was employed to conduct the quantitative measurement of gray matter densities of the hippocampus, amygdala, entorhinal cortex, and mammillary body (MB). FreeSurfer was utilized to automatically segment the hippocampus into 21 subregions and the amygdala into 9 subregions. Then, their subregion volumes and total volume were calculated. Finally, the ANOVA and multiple comparisons were performed on the above-mentioned data from these three groups. Results. AD had lower GM densities than MCI, and MCI had lower GM densities than NC, but not all of the differences were statistically significant. In the comparisons of AD-aMCI-NC, AD-aMCI, and AD-NC, the hippocampus, amygdala, and entorhinal cortex showed differences in the gray matter densities ( $p<0.05$ ); the differences of mammillary body densities were not significant in the random comparison between these three groups ( $p>0.05$ ). The hippocampus densities and volumes of the subjects from the aMCI group and the AD group were bilaterally symmetric. The gray matter densities of the right side of the entorhinal cortex inside each group and the hippocampus from the NC group were higher than those of the left side ( $p<0.05$ ), and the gray matter densities of the amygdala and mammillary body were bilaterally symmetric in the three groups ( $p>0.05$ ). There were no gender differences of four ROIs in the AD, aMCI, and NC groups ( $p>0.05$ ). The volume differences of the hippocampus presubiculum-body and parasubiculum manifest no statistical significance ( $p>0.05$ ) in the random comparison between these three groups. Volume differences of the left amygdala basal nucleus, the left lateral nucleus, the left cortical amygdala transitional area, the left paravamnion nucleus, and bilateral hippocampal amygdala transition area (HATA) had statistical differences only between the AD group and the NC group ( $p<0.05$ ). Conclusion. Structural defects of medial temporal lobe subfields were revealed in the aMCI and AD groups. Decreased gray matter densities of the hippocampus, entorhinal cortex, and amygdala could distinguish patients with early stage of AD between aMCI and NC. Volume decline of the hippocampus and amygdala subfields could only distinguish AD between NC.

Study Design and Baseline Results in a Cohort Study to Identify Predictors for the Clinical Progression to Mild Cognitive Impairment or Dementia From Subjective Cognitive Decline (CoSCo) Study

Background and Purpose

Subjective cognitive decline (SCD) refers to the self-perception of cognitive decline with normal performance on objective neuropsychological tests. SCD, which is the first help-seeking stage and the last stage before the clinical disease stage, can be considered to be the most appropriate time for prevention and treatment. This study aimed to compare characteristics between the amyloid positive and amyloid negative groups of SCD patients.

Methods

A cohort study to identify predictors for the clinical progression to mild cognitive impairment (MCI) or dementia from subjective cognitive decline (CoSCo) study is a multicenter, prospective observational study conducted in the Republic of Korea. In total, 120 people aged 60 years or above who presented with a complaint of persistent cognitive decline were selected, and various risk factors were measured among these participants. Continuous variables were analyzed using the Wilcoxon rank-sum test, and categorical variables were analyzed using the χ² test or Fisher’s exact test. Logistic regression models were used to assess the predictors of amyloid positivity.

Results

The multivariate logistic regression model indicated that amyloid positivity on PET was related to a lack of hypertension, atrophy of the left temporal lateral and entorhinal cortex, low body mass index, low waist circumference, less body and visceral fat, fast gait speed, and the presence of the apolipoprotein E ε4 allele in amnestic SCD patients.

Conclusions

The CoSCo study is still in progress, and the authors aim to identify the risk factors that are related to the progression of MCI or dementia in amnestic SCD patients through a two-year follow-up longitudinal study.

Entorhinal Subfield Vulnerability to Neurofibrillary Tangles in Aging and the Preclinical Stage of Alzheimer's Disease

BACKGROUND

Neurofibrillary tangle (NFT) accumulation in the entorhinal cortex (EC) precedes the transformation from cognitive controls to mild cognitive impairment and Alzheimer's disease (AD). While tauopathy has been described in the EC before, the order and degree to which the individual subfields within the EC are engulfed by NFTs in aging and the preclinical AD stage is unknown.

OBJECTIVE

We aimed to investigate substructures within the EC to map the populations of cortical neurons most vulnerable to tau pathology in aging and the preclinical AD stage.

METHODS

We characterized phosphorylated tau (CP13) in 10 cases at eight well-defined anterior-posterior levels and assessed NFT density within the eight entorhinal subfields (described by Insausti and colleagues) at the preclinical stages of AD. We validated with immunohistochemistry and labeled the NFT density ratings on ex vivo MRIs. We measured subfield cortical thickness and reconstructed the labels as three-dimensional isosurfaces, resulting in anatomically comprehensive, histopathologically validated tau "heat maps."

RESULTS

We found the lateral EC subfields ELc, ECL, and ECs (lateral portion) to have the highest tau density in semi-quantitative scores and quantitative measurements. We observed significant stepwise higher tau from anterior to posterior levels (p < 0.001). We report an age-dependent anatomically-specific vulnerability, with all cases showing posterior tau pathology, yet older individuals displaying an additional anterior tau burden. Finally, cortical thickness of each subfield negatively correlated with respective tau scores (p < 0.05).

CONCLUSION

Our findings indicate that posterior-lateral subfields within the EC are the most vulnerable to early NFTs and atrophy in aging and preclinical AD.

Cortical thickness across the lifespan: Data from 17,075 healthy individuals aged 3-90 years

Delineating the association of age and cortical thickness in healthy individuals is critical given the association of cortical thickness with cognition and behavior. Previous research has shown that robust estimates of the association between age and brain morphometry require large-scale studies. In response, we used cross-sectional data from 17,075 individuals aged 3-90 years from the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to infer age-related changes in cortical thickness. We used fractional polynomial (FP) regression to quantify the association between age and cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma method. Interindividual variability was estimated using meta-analysis and one-way analysis of variance. For most regions, their highest cortical thickness value was observed in childhood. Age and cortical thickness showed a negative association; the slope was steeper up to the third decade of life and more gradual thereafter; notable exceptions to this general pattern were entorhinal, temporopolar, and anterior cingulate cortices. Interindividual variability was largest in temporal and frontal regions across the lifespan. Age and its FP combinations explained up to 59% variance in cortical thickness. These results may form the basis of further investigation on normative deviation in cortical thickness and its significance for behavioral and cognitive outcomes.

Medial Temporal Lobe Subregional Atrophy in Aging and Alzheimer's Disease: A Longitudinal Study

Medial temporal lobe (MTL) atrophy is a key feature of Alzheimer's disease (AD), however, it also occurs in typical aging. To enhance the clinical utility of this biomarker, we need to better understand the differential effects of age and AD by encompassing the full AD-continuum from cognitively unimpaired (CU) to dementia, including all MTL subregions with up-to-date approaches and using longitudinal designs to assess atrophy more sensitively. Age-related trajectories were estimated using the best-fitted polynomials in 209 CU adults (aged 19–85). Changes related to AD were investigated among amyloid-negative (Aβ−) (n = 46) and amyloid-positive (Aβ+) (n = 14) CU, Aβ+ patients with mild cognitive impairment (MCI) (n = 33) and AD (n = 31). Nineteen MCI-to-AD converters were also compared with 34 non-converters. Relationships with cognitive functioning were evaluated in 63 Aβ+ MCI and AD patients. All participants were followed up to 47 months. MTL subregions, namely, the anterior and posterior hippocampus (aHPC/pHPC), entorhinal cortex (ERC), Brodmann areas (BA) 35 and 36 [as perirhinal cortex (PRC) substructures], and parahippocampal cortex (PHC), were segmented from a T1-weighted MRI using a new longitudinal pipeline (LASHiS). Statistical analyses were performed using mixed models. Adult lifespan models highlighted both linear (PRC, BA35, BA36, PHC) and nonlinear (HPC, aHPC, pHPC, ERC) trajectories. Group comparisons showed reduced baseline volumes and steeper volume declines over time for most of the MTL subregions in Aβ+ MCI and AD patients compared to Aβ− CU, but no differences between Aβ− and Aβ+ CU or between Aβ+ MCI and AD patients (except in ERC). Over time, MCI-to-AD converters exhibited a greater volume decline than non-converters in HPC, aHPC, and pHPC. Most of the MTL subregions were related to episodic memory performances but not to executive functioning or speed processing. Overall, these results emphasize the benefits of studying MTL subregions to distinguish age-related changes from AD. Interestingly, MTL subregions are unequally vulnerable to aging, and those displaying non-linear age-trajectories, while not damaged in preclinical AD (Aβ+ CU), were particularly affected from the prodromal stage (Aβ+ MCI). This volume decline in hippocampal substructures might also provide information regarding the conversion from MCI to AD-dementia. All together, these findings provide new insights into MTL alterations, which are crucial for AD-biomarkers definition.

Regional cerebral perfusion in patients with amnestic mild cognitive impairment: effect of cerebral small vessel disease

OBJECTIVE

To explore the effort of cerebral small vessel disease (CSVD) on regional cerebral perfusion in patients with mild cognitive impairment (MCI) using NeuroGam™ software and evaluate the capability of brain perfusion single photon emission computed tomography (SPECT) in distinguishing MCI with and without CSVD.

METHODS

34 amnestic MCI subjects entered this study, conducting neuropsychological tests, MRI and ^99mTechnetium ethyl cystine dimer brain perfusion SPECT imaging. All subjects were divided into those with CSVD and those without CSVD. Perfusion value was measured with Brodmann area (BA) mapping in these two groups. Automated software (NeuroGam™) was used for semi-quantitative analyses of perfusion value and comparison with normal database.

RESULTS

Compared with normal database, perfusion levels in BAs 23-left, 28 and 36-left of MCI without CSVD group had great deviations, while perfusion levels in BAs 21, 23, 24, 25, 28, 36, 38 and 47-left of MCI with CSVD group had great deviations. Furthermore, compared with CSVD group, there was significantly lower perfusion value in BA 7-left (P < 0.001) in MCI without CSVD group.

CONCLUSIONS

CSVD could interact with pathological changes related to AD, exacerbating hypoperfusion in BAs 21, 23, 28, 36, 38 while compensating for cerebral blood perfusion disorder in BA 7-left in MCI patients. Meanwhile, MCI patients with CSVD shared similar hypoperfusion with vascular cognitive impairment (VCI) in BAs 24, 25 and 47L. Brain perfusion SPECT may help improve our ability to differentiate MCI with and without CSVD.

T2 heterogeneity as an in vivo marker of microstructural integrity in medial temporal lobe subfields in ageing and mild cognitive impairment

A better understanding of early brain changes that precede loss of independence in diseases like Alzheimer's disease (AD) is critical for development of disease-modifying therapies. Quantitative MRI, such as T2 relaxometry, can identify microstructural changes relevant to early stages of pathology. Recent evidence suggests heterogeneity of T2 may be a more informative MRI measure of early pathology than absolute T2. Here we test whether T2 markers of brain integrity precede the volume changes we know are present in established AD and whether such changes are most marked in medial temporal lobe (MTL) subfields known to be most affected early in AD. We show that T2 heterogeneity was greater in people with mild cognitive impairment (MCI; n = 49) compared to healthy older controls (n = 99) in all MTL subfields, but this increase was greatest in MTL cortices, and smallest in dentate gyrus. This reflects the spatio-temporal progression of neurodegeneration in AD. T2 heterogeneity in CA1-3 and entorhinal cortex and volume of entorhinal cortex showed some ability to predict cognitive decline, where absolute T2 could not, however further studies are required to verify this result. Increases in T2 heterogeneity in MTL cortices may reflect localised pathological change and may present as one of the earliest detectible brain changes prior to atrophy. Finally, we describe a mechanism by which memory, as measured by accuracy and reaction time on a paired associate learning task, deteriorates with age. Age-related memory deficits were explained in part by lower subfield volumes, which in turn were directly associated with greater T2 heterogeneity. We propose that tissue with high T2 heterogeneity represents extant tissue at risk of permanent damage but with the potential for therapeutic rescue. This has implications for early detection of neurodegenerative diseases and the study of brain-behaviour relationships.

Amyloid-β (1-42) peptide induces rapid NMDA receptor-dependent alterations at glutamatergic synapses in the entorhinal cortex

The hippocampus and entorhinal cortex (EC) accumulate amyloid beta peptides (Aβ) that promote neuropathology in Alzheimer's disease, but the early effects of Aβ on excitatory synaptic transmission in the EC have not been well characterized. To assess the acute effects of Aβ1-42 on glutamatergic synapses, acute brain slices from wildtype rats were exposed to Aβ1-42 or control solution for 3 hours, and tissue was analyzed using protein immunoblotting and quantitative PCR. Presynaptically, Aβ1-42 induced marked reductions in synaptophysin, synapsin-2a mRNA, and mGluR3 mRNA, and increased both VGluT2 protein and Ca2+-activated channel KCa2.2 mRNA levels. Postsynaptically, Aβ1-42 reduced PSD95 and GluN2B protein, and also downregulated GluN2B and GluN2A mRNA, without affecting scaffolding elements SAP97 and PICK1. mGluR5 mRNA was strongly increased, while mGluR1 mRNA was unaffected. Blocking either GluN2A- or GluN2B-containing NMDA receptors did not significantly prevent synaptic changes induced by Aβ1-42, but combined blockade did prevent synaptic alterations. These findings demonstrate that Aβ1-42 rapidly disrupts glutamatergic transmission in the EC through mechanisms involving concurrent activation of GluN2A- and GluN2B-containing NMDA receptors.

Magnetic Resonance Imaging Measurement of Entorhinal Cortex in the Diagnosis and Differential Diagnosis of Mild Cognitive Impairment and Alzheimer's Disease

Several magnetic resonance imaging studies have shown that the entorhinal cortex (ERC) is the first brain area related to pathologic changes in Alzheimer’s disease (AD), even before atrophy of the hippocampus (HP). However, change in ERC morphology (thickness, surface area and volume) in the progression from aMCI to AD, especially in the subtypes of aMCI (single-domain and multiple-domain: aMCI-s and aMCI-m), however, is still unclear. ERC thickness, surface area and volume were measured in 29 people with aMCI-s, 22 people with aMCI-m, 18 patients with AD and 26 age-/sex-matched healthy controls. Group comparisons of the ERC geometry measurements (including thickness, volume and surface area) were performed using analyses of covariance (ANCOVA). Furthermore, receiver operator characteristic (ROC) analyses and the area under the curve (AUC) were employed to investigate classification ability (HC, aMCI-s, aMCI-m and AD from each other). There was a significant decreasing tendency in ERC thickness from HC to aMCI-s to aMCI-m to finally AD in both the left and the right hemispheres (left hemisphere: HC > aMCI-s > AD; right hemisphere: aMCI-s > aMCI-m > AD). For ERC volume, both the AD group and the aMCI-m group showed significantly decreased volume on both sides compared with the HC group. In addition, the AD group also had significantly decreased volume on both sides compared with the aMCI-s group. As for the ERC surface area, no significant difference was identified among the four groups. Furthermore, the AUC results demonstrate that combined ERC parameters (thickness and volume) can better discriminate the four groups from each other than ERC thickness alone. Finally, and most importantly, relative to HP volume, the capacity of combined ERC parameters was better at discriminating between HC and aMCI-s, as well as aMCI-m and AD. ERC atrophy, particularly the combination of ERC thickness and volume, might be regarded as a promising candidate biomarker in the diagnosis and differential diagnosis of aMCI and AD.

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

Background

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

Methods

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

Results

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

Conclusions

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

Supplementary Information

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

Key Disease Mechanisms Linked to Alzheimer's Disease in the Entorhinal Cortex

Alzheimer’s disease (AD) is a chronic, neurodegenerative brain disorder affecting millions of Americans that is expected to increase in incidence with the expanding aging population. Symptomatic AD patients show cognitive decline and often develop neuropsychiatric symptoms due to the accumulation of insoluble proteins that produce plaques and tangles seen in the brain at autopsy. Unexpectedly, some clinically normal individuals also show AD pathology in the brain at autopsy (asymptomatic AD, AsymAD). In this study, SWItchMiner software was used to identify key switch genes in the brain’s entorhinal cortex that lead to the development of AD or disease resilience. Seventy-two switch genes were identified that are differentially expressed in AD patients compared to healthy controls. These genes are involved in inflammation, platelet activation, and phospholipase D and estrogen signaling. Peroxisome proliferator-activated receptor γ (PPARG), zinc-finger transcription factor (YY1), sterol regulatory element-binding transcription factor 2 (SREBF2), and early growth response 1 (EGR1) were identified as transcription factors that potentially regulate switch genes in AD. Comparing AD patients to AsymAD individuals revealed 51 switch genes; PPARG as a potential regulator of these genes, and platelet activation and phospholipase D as critical signaling pathways. Chemical–protein interaction analysis revealed that valproic acid is a therapeutic agent that could prevent AD from progressing.

Molecular mechanisms of neurodegeneration in the entorhinal cortex that underlie its selective vulnerability during the pathogenesis of Alzheimer's disease

The entorhinal cortex (EC) is a vital component of the medial temporal lobe, and its contributions to cognitive processes and memory formation are supported through its extensive interconnections with the hippocampal formation. During the pathogenesis of Alzheimer's disease (AD), many of the earliest degenerative changes are seen within the EC. Neurodegeneration in the EC and hippocampus during AD has been clearly linked to impairments in memory and cognitive function, and a growing body of evidence indicates that molecular and functional neurodegeneration within the EC may play a primary role in cognitive decline in the early phases of AD. Defining the mechanisms underlying molecular neurodegeneration in the EC is crucial to determining its contributions to the pathogenesis of AD. Surprisingly few studies have focused on understanding the mechanisms of molecular neurodegeneration and selective vulnerability within the EC. However, there have been advancements indicating that early dysregulation of cellular and molecular signaling pathways in the EC involve neurodegenerative cascades including oxidative stress, neuroinflammation, glia activation, stress kinases activation, and neuronal loss. Dysfunction within the EC can impact the function of the hippocampus, which relies on entorhinal inputs, and further degeneration within the hippocampus can compound this effect, leading to severe cognitive disruption. This review assesses the molecular and cellular mechanisms underlying early degeneration in the EC during AD. These mechanisms may underlie the selective vulnerability of neuronal subpopulations in this brain region to the disease development and contribute both directly and indirectly to cognitive loss.This paper has an associated Future Leader to Watch interview with the first author of the article.

A multivariate model of time to conversion from mild cognitive impairment to Alzheimer's disease

The present study was aimed at determining which combination of demographic, genetic, cognitive, neurophysiological, and neuroanatomical factors may predict differences in time to progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD). To this end, a sample of 121 MCIs was followed up during a 5-year period. According to their clinical outcome, MCIs were divided into two subgroups: (i) the "progressive" MCI group (n = 46; mean time to progression 17 ± 9.73 months) and (ii) the "stable" MCI group (n = 75; mean time of follow-up 31.37 ± 14.58 months). Kaplan-Meier survival analyses were applied to explore each variable's relationship with the progression to AD. Once potential predictors were detected, Cox regression analyses were utilized to calculate a parsimonious model to estimate differences in time to progression. The final model included three variables (in order of relevance): left parahippocampal volume (corrected by intracranial volume, LP_ ICV), delayed recall (DR), and left inferior occipital lobe individual alpha peak frequency (LIOL_IAPF). Those MCIs with LP_ICV volume, DR score, and LIOL_IAPF value lower than the defined cutoff had 6 times, 5.5 times, and 3 times higher risk of progression to AD, respectively. Besides, when the categories of the three variables were "unfavorable" (i.e., values below the cutoff), 100% of cases progressed to AD at the end of follow-up. Our results highlighted the relevance of neurophysiological markers as predictors of conversion (LIOL_IAPF) and the importance of multivariate models that combine markers of different nature to predict time to progression from MCI to dementia.

Structural Analysis of Brain Hub Region Volume and Cortical Thickness in Patients with Mild Cognitive Impairment and Dementia

Background and Objectives: A complex network of axonal pathways interlinks the human brain cortex. Brain networks are not distributed evenly, and brain regions making more connections with other parts are defined as brain hubs. Our objective was to analyze brain hub region volume and cortical thickness and determine the association with cognitive assessment scores in patients with mild cognitive impairment (MCI) and dementia. Materials and Methods: In this cross-sectional study, we included 11 patients (5 mild cognitive impairment; 6 dementia). All patients underwent neurological examination, and Montreal Cognitive Assessment (MoCA) test scores were recorded. Scans with a 3T MRI scanner were done, and cortical thickness and volumetric data were acquired using Freesurfer 7.1.0 software. Results: By analyzing differences between the MCI and dementia groups, MCI patients had higher hippocampal volumes (p < 0.05) and left entorhinal cortex thickness (p < 0.05). There was a significant positive correlation between MoCA test scores and left hippocampus volume (r = 0.767, p < 0.01), right hippocampus volume (r = 0.785, p < 0.01), right precuneus cortical thickness (r = 0.648, p < 0.05), left entorhinal cortex thickness (r = 0.767, p < 0.01), and right entorhinal cortex thickness (r = 0.612, p < 0.05). Conclusions: In our study, hippocampal volume and entorhinal cortex showed significant differences in the MCI and dementia patient groups. Additionally, we found a statistically significant positive correlation between MoCA scores, hippocampal volume, entorhinal cortex thickness, and right precuneus. Although other brain hub regions did not show statistically significant differences, there should be additional research to evaluate the brain hub region association with MCI and dementia.

Structure and function of the perivascular fluid compartment and vertebral venous plexus: Illumining a novel theory on mechanisms underlying the pathogenesis of Alzheimer's, cerebral small vessel, and neurodegenerative diseases

Blood dynamically and richly supplies the cerebral tissue via microvessels invested in pia matter perforating the cerebral substance. Arteries penetrating the cerebral substance derive an investment from one or two successive layers of pia mater, luminally apposed to the pial-glial basal lamina of the microvasculature and abluminally apposed to a series of aquaporin IV-studded astrocytic end feet constituting the soi-disant glia limitans. The full investment of successive layers forms the variably continuous walls of the periarteriolar, pericapillary, and perivenular divisions of the perivascular fluid compartment. The pia matter disappears at the distal periarteriolar division of the perivascular fluid compartment. Plasma from arteriolar blood sequentially transudates into the periarteriolar division of the perivascular fluid compartment and subarachnoid cisterns in precession to trickling into the neural interstitium. Fluid from the neural interstitium successively propagates into the venules through the subarachnoid cisterns and perivenular division of the perivascular fluid compartment. Fluid fluent within the perivascular fluid compartment flows gegen the net direction of arteriovenular flow. Microvessel oscillations at the central tendency of the cerebral vasomotion generate corresponding oscillations of within the surrounding perivascular fluid compartment, interposed betwixt the abluminal surface of the vessels and internal surface of the pia mater. The precise microanatomy of this most fascinating among designable spaces has eluded the efforts of various investigators to interrogate its structure, though most authors non-consensusly concur the investing layers effectively and functionally segregate the perivascular and subarachnoid fluid compartments. Enlargement of the perivascular fluid compartment in a variety of neurological disorders, including senile dementia of the Alzheimer's type and cerebral small vessel disease, may alternately or coordinately constitute a correlative marker of disease severity and a possible cause implicated in the mechanistic pathogenesis of these conditions. Venular pressures modulating oscillatory dynamic flow within the perivascular fluid compartment may similarly contribute to the development of a variety among neurological disorders. An intimate understanding of subtle features typifying microanatomy and microphysiology of the investing structures and spaces of the cerebral microvasculature may powerfully inform mechanistic pathophysiology mediating a variety of neurovascular ischemic, neuroinfectious, neuroautoimmune, and neurodegenerative diseases.

Assessment of Alzheimer's Disease Based on Texture Analysis of the Entorhinal Cortex

Alzheimer’s disease (AD) brain magnetic resonance imaging (MRI) biomarkers based on larger-scale tissue neurodegeneration changes, such as atrophy, are currently widely used. Texture analysis evaluates the statistical properties of the tissue image quantitatively; therefore, it could detect smaller-scale changes of neurodegeneration. Entorhinal cortex is the first region affected, and no study has investigated texture analysis on this region before. This study aims to differentiate AD patients from Normal Control (NC) and Mild Cognitive Impairment (MCI) subjects using entorhinal cortex texture features. Furthermore, it was evaluated whether texture has association to MCI beyond that of volume, to evaluate if atrophy development may precede. Texture features were extracted from 194 NC, 200 MCI, 84 MCI who converted to AD (MCIc), and 130 AD subjects. Receiving operating characteristic curves determined the performance of the various features in discriminating the groups, and a predictive model was used to predict conversion of MCIc subjects to AD. An area under the curve (AUC) of 0.872, 0.710, 0.730, and 0.764 was seen between NC vs. AD, NC vs. MCI, MCI vs. MCIc, and MCI vs. AD subjects, respectively. Including entorhinal cortex volume improved the AUCs to 0.914, 0.740, 0.756, and 0.780, respectively. For the disease prediction, binary logistic regression was applied on five randomly selected test groups and achieved on average AUC’s of 0.760 and 0.764 on the training and validation cohorts, respectively. Entorhinal cortex texture features were significantly different between the four groups and in many cases provided better results compared to other methods such as volumetry.

Characterization of astrocytes throughout life in wildtype and APP/PS1 mice after early-life stress exposure

Background

Early-life stress (ES) is an emerging risk factor for later life development of Alzheimer’s disease (AD). We have previously shown that ES modulates amyloid-beta pathology and the microglial response to it in the APPswe/PS1dE9 mouse model. Because astrocytes are key players in the pathogenesis of AD, we studied here if and how ES affects astrocytes in wildtype (WT) and APP/PS1 mice and how these relate to the previously reported amyloid pathology and microglial profile.

Methods

We induced ES by limiting nesting and bedding material from postnatal days (P) 2–9. We studied in WT mice (at P9, P30, and 6 months) and in APP/PS1 mice (at 4 and 10 months) (i) GFAP coverage, cell density, and complexity in hippocampus (HPC) and entorhinal cortex (EC); (ii) hippocampal gene expression of astrocyte markers; and (iii) the relationship between astrocyte, microglia, and amyloid markers.

Results

In WT mice, ES increased GFAP coverage in HPC subregions at P9 and decreased it at 10 months. APP/PS1 mice at 10 months exhibited both individual cell as well as clustered GFAP signals. APP/PS1 mice when compared to WT exhibited reduced total GFAP coverage in HPC, which is increased in the EC, while coverage of the clustered GFAP signal in the HPC was increased and accompanied by increased expression of several astrocytic genes. While measured astrocytic parameters in APP/PS1 mice appear not be further modulated by ES, analyzing these in the context of ES-induced alterations to amyloid pathology and microglial shows alterations at both 4 and 10 months of age.

Conclusions

Our data suggest that ES leads to alterations to the astrocytic response to amyloid-β pathology.