Distinct laterality alterations distinguish mild cognitive impairment and Alzheimer's disease from healthy aging: statistical parametric mapping with high resolution MRI

Bayesian genome-wide TWAS with reference transcriptomic data of brain and blood tissues identified 141 risk genes for Alzheimer's disease dementia

BACKGROUND

Transcriptome-wide association study (TWAS) is an influential tool for identifying genes associated with complex diseases whose genetic effects are likely mediated through transcriptome. TWAS utilizes reference genetic and transcriptomic data to estimate effect sizes of genetic variants on gene expression (i.e., effect sizes of a broad sense of expression quantitative trait loci, eQTL). These estimated effect sizes are employed as variant weights in gene-based association tests, facilitating the mapping of risk genes with genome-wide association study (GWAS) data. However, most existing TWAS of Alzheimer's disease (AD) dementia are limited to studying only cis-eQTL proximal to the test gene. To overcome this limitation, we applied the Bayesian Genome-wide TWAS (BGW-TWAS) method to leveraging both cis- and trans- eQTL of brain and blood tissues, in order to enhance mapping risk genes for AD dementia.

METHODS

We first applied BGW-TWAS to the Genotype-Tissue Expression (GTEx) V8 dataset to estimate cis- and trans- eQTL effect sizes of the prefrontal cortex, cortex, and whole blood tissues. Estimated eQTL effect sizes were integrated with the summary data of the most recent GWAS of AD dementia to obtain BGW-TWAS (i.e., gene-based association test) p-values of AD dementia per gene per tissue type. Then we used the aggregated Cauchy association test to combine TWAS p-values across three tissues to obtain omnibus TWAS p-values per gene.

RESULTS

We identified 85 significant genes in prefrontal cortex, 82 in cortex, and 76 in whole blood that were significantly associated with AD dementia. By combining BGW-TWAS p-values across these three tissues, we obtained 141 significant risk genes including 34 genes primarily due to trans-eQTL and 35 mapped risk genes in GWAS Catalog. With these 141 significant risk genes, we detected functional clusters comprised of both known mapped GWAS risk genes of AD in GWAS Catalog and our identified TWAS risk genes by protein-protein interaction network analysis, as well as several enriched phenotypes related to AD.

CONCLUSION

We applied BGW-TWAS and aggregated Cauchy test methods to integrate both cis- and trans- eQTL data of brain and blood tissues with GWAS summary data, identifying 141 TWAS risk genes of AD dementia. These identified risk genes provide novel insights into the underlying biological mechanisms of AD dementia and potential gene targets for therapeutics development.

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.

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.

Metabolic Asymmetry Relates to Clinical Characteristics and Brain Network Abnormalities in Alzheimer's Disease

BACKGROUND

Metabolic asymmetry has been observed in Alzheimer's disease (AD), but different studies have inconsistent viewpoints.

OBJECTIVE

To analyze the asymmetry of cerebral glucose metabolism in AD and investigate its clinical significance and potential metabolic network abnormalities.

METHODS

Standardized uptake value ratios (SUVRs) were obtained from 18F-FDG positron emission tomography (PET) images of all participants, and the asymmetry indices (AIs) were calculated according to the SUVRs. AD group was divided into left/right-dominant or bilateral symmetric hypometabolism (AD-L/AD-R or AD-BI) when more than half of the AIs of the 20 regions of interest (ROIs) were < -2SD, >2SD, or between±1SD. Differences in clinical features among the three AD groups were compared, and the abnormal network characteristics underlying metabolic asymmetry were explored.

RESULTS

In AD group, the proportions of AD-L, AD-R, and AD-BI were 28.4%, 17.9%, and 18.5%, respectively. AD-L/AD-R groups had younger age of onset and faster rate of cognitive decline than AD-BI group (p < 0.05). The absolute values of AIs in half of the 20 ROIs became higher at follow-up than at baseline (p < 0.05). Compared with those in AD-BI group, metabolic connection strength of network, global efficiency, cluster coefficient, degree centrality and local efficiency were lower, but shortest path length was longer in AD-L and AD-R groups (p < 0.05).

CONCLUSION

Asymmetric and symmetric hypometabolism may represent different clinical subtypes of AD, which may provide a clue for future studies on the heterogeneity of AD and help to optimize the design of clinical trials.

Efficient optimization based thresholding technique for analysis of alzheimer MRIs

Purpose study: Alzheimer is a type of dementia that usually affects older adults by creating memory loss due to damaged brain cells. The damaged brain cells lead to shrinkage in the size of the brain and it is very difficult to extract the grey matter (GM) and white matter (WM). The segmentation of GM and WM is a challenging task due to its homogeneous nature between the neighborhood tissues. In this proposed system, an attempt has been made to extract GM and WM tissues using optimization-based segmentation techniques.Materials and methods: The optimization method is considered for the classification of normal and alzheimer disease (ad) through magnetic resonance images (mri) using a modified cuckoo search algorithm. Gray Level Co-Occurrence Matrix (GLCM) features are calculated from the extracted GM and WM. Principal Component Analysis (PCA) is adopted for selecting the best features from the GLCM features. Support Vector Machine (SVM) is a classifier which is used to classify the normal and abnormal images. Results: The proposed optimization algorithm provides most promising and efficient level of image segmentation compared to fuzzy c means (fcm), otsu, particle swarm optimization (pso) and cuckoo search (cs). The modified cuckoo yields high accuracy of 96%, sensitivity of 97% and specificity of 94% than other methods due to its powerful searching potential for the proper identification of gray and WM tissues.Conclusions: The results of the classification process proved the effectiveness of the proposed technique in identifying alzheimer affected patients due to its very strong optimization ability. The proposed pipeline helps to diagnose early detection of AD and better assessment of the neuroprotective effect of a therapy.

A review on Alzheimer's disease classification from normal controls and mild cognitive impairment using structural MR images

Alzheimer's disease (AD) is an irreversible neurodegenerative brain disorder that degrades the memory and cognitive ability in elderly people. The main reason for memory loss and reduction in cognitive ability is the structural changes in the brain that occur due to neuronal loss. These structural changes are most conspicuous in the hippocampus, cortex, and grey matter and can be assessed by using neuroimaging techniques viz. Positron Emission Tomography (PET), structural Magnetic Resonance Imaging (MRI) and functional MRI (fMRI), etc. Out of these neuroimaging techniques, structural MRI has evolved as the best technique as it indicates the best soft tissue contrast and high spatial resolution which is important for AD detection. Currently, the focus of researchers is on predicting the conversion of Mild Cognitive Impairment (MCI) into AD. MCI represents the transition state between expected cognitive changes with normal aging and Alzheimer's disease. Not every MCI patient progresses into Alzheimer's disease. MCI can develop into stable MCI (sMCI, patients are called non-converters) or into progressive MCI (pMCI, patients are diagnosed as MCI converters). This paper discusses the prognosis of MCI to AD conversion and presents a review of structural MRI-based studies for AD detection. AD detection framework includes feature extraction, feature selection, and classification process. This paper reviews the studies for AD detection based on different feature extraction methods and machine learning algorithms for classification. The performance of various feature extraction methods has been compared and it has been observed that the wavelet transform-based feature extraction method would give promising results for AD classification. The present study indicates that researchers are successful in classifying AD from Normal Controls (N_rmC) but, it still requires a lot of work to be done for MCI/ N_rmC and MCI/AD, which would help in detecting AD at its early stage.

Template-based graph registration network for boosting the diagnosis of brain connectivity disorders

Brain graphs are powerful representations to explore the biological roadmaps of the human brain in its healthy and disordered states. Recently, a few graph neural networks (GNNs) have been designed for brain connectivity synthesis and diagnosis. However, such non-Euclidean deep learning architectures might fail to capture the neural interactions between different brain regions as they are trained without guidance from any prior biological template-i.e., template-free learning. Here we assume that using a population-driven brain connectional template (CBT) that captures well the connectivity patterns fingerprinting a given brain state (e.g., healthy) can better guide the GNN training in its downstream learning task such as classification or regression. To this aim we design a plug-in graph registration network (GRN) that can be coupled with any conventional graph neural network (GNN) so as to boost its learning accuracy and generalizability to unseen samples. Our GRN is a graph generative adversarial network (gGAN), which registers brain graphs to a prior CBT. Next, the registered brain graphs are used to train typical GNN models. Our GRN can be integrated into any GNN working in an end-to-end fashion to boost its prediction accuracy. Our experiments showed that GRN remarkably boosted the prediction accuracy of four conventional GNN models across four neurological datasets.

Longitudinal Assessment of Intravoxel Incoherent Motion Diffusion-Weighted MRI Metrics in Cognitive Decline

BACKGROUND

Advanced diffusion-based MRI biomarkers may provide insight into microstructural and perfusion changes associated with neurodegeneration and cognitive decline.

PURPOSE

To assess longitudinal microstructural and perfusion changes using apparent diffusion coefficient (ADC) and intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) parameters in cognitively impaired (CI) and healthy control (HC) groups.

STUDY TYPE

Prospective/longitudinal.

POPULATION

Twelve CI patients (75% female) and 13 HC subjects (69% female).

FIELD STRENGTH/SEQUENCE

3 T; Spin-Echo-IVIM-DWI.

ASSESSMENT

Two MRI scans were performed with a 12-month interval. ADC and IVIM-DWI metrics (diffusion coefficient [D] and perfusion fraction [f]) were generated from monoexponential and biexponential fits, respectively. Additionally, voxel-based correlations were evaluated between change in Montreal Cognitive Assessment (ΔMoCA) and baseline imaging parameters.

STATISTICAL TESTS

Analysis of covariance with sex and age as covariates was performed for main effects of group and time (false discovery rate [FDR] corrected) with post hoc comparisons using Bonferroni correction. Partial-η² and Hedges' g were used for effect-size analysis. Spearman's correlations (FDR corrected) were used for the relationship between ΔMoCA score and imaging. P < 0.05 was considered statistically significant.

RESULTS

Significant differences were found for the main effects of group (HC vs. CI) and time. For group effects, higher ADC, IVIM-D, and IVIM-f were observed in the CI group compared to HC (ADC: 1.23 ± 0.08^. 10^-3 vs. 1.09 ± 0.07^. 10^-3  mm² /sec; IVIM-D: 0.82 ± 0.01^. 10^-3 vs. 0.73 ± 0.01^. 10^-3  mm² /sec; and IVIM-f: 0.317 ± 0.008 vs. 0.253 ± 0.009). Significantly higher ADC, IVIM-D, and IVIM-f values were observed in the CI group after 12 months (ADC: 1.45 ± 0.05^. 10^-3 vs. 1.50 ± 0.07^. 10^-3  mm² /sec; IVIM-D: 0.87 ± 0.01^. 10^-3 vs. 0.94 ± 0.02^. 10^-3  mm² /sec; and IVIM-f: 0.303 ± 0.007 vs. 0.332 ± 0.008), but not in the HC group at large effect size. ADC, IVIM-D, and IVIM-f negatively correlated with ΔMoCA score (ρ = -0.49, -0.51, and -0.50, respectively).

DATA CONCLUSION

These findings demonstrate that longitudinal differences between CI and HC cohorts can be measured using IVIM-based metrics.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY STAGE: 2.

Older people with severe loneliness have an atrophied thalamus, hippocampus, and entorhinal cortex

OBJECTIVES

Loneliness has been shown to increase the risk of dementia. However, it is unclear why greater loneliness is associated with greater susceptibility to dementia. Herein, we aimed to examine the morphological characteristics of the brain associated with loneliness in older people concerned about cognitive dysfunction.

METHODS

In this retrospective study, 110 participants (80 with amnestic mild cognitive impairment, and 30 cognitively healthy individuals) were included. Participants were assessed using the revised University of California at Los Angeles (UCLA) loneliness scale and had undergone magnetic resonance imaging. Spearman correlation analysis and Mann-Whitney U tests were used to examine the clinical factors associated with loneliness. Multiple regression was performed to examine the relationship between the revised UCLA loneliness scale score and regional gray matter (GM) volume on voxel-based morphometry.

RESULTS

The revised UCLA loneliness scale scores were not significantly correlated with age, sex, or mini-mental state examination (MMSE) scores. Multiple regression using age, sex, MMSE score, and total brain volume as covariates showed negative correlations of the revised UCLA loneliness scale scores with the grey matter volume in regions centered on the bilateral thalamus, left hippocampus and left parahippocampal gyrus, and left entorhinal area.

CONCLUSIONS

Subjective loneliness was associated with decreased GM volume in the bilateral thalamus, left hippocampus, and left entorhinal cortex of the brain in the older people, thereby providing a morphological basis for the increased risk of dementia associated with greater loneliness.

Distinct Atrophy Pattern of Hippocampal Subfields in Patients with Progressive and Stable Mild Cognitive Impairment: A Longitudinal MRI Study

BACKGROUND

Predicting the prognosis of mild cognitive impairment (MCI) has outstanding clinical value, and the hippocampal volume is a reliable imaging biomarker of AD diagnosis.

OBJECTIVE

We aimed to longitudinally assess hippocampal sub-regional difference (volume and asymmetry) among progressive MCI (pMCI), stable MCI (sMCI) patients, and normal elderly.

METHODS

We identified 29 pMCI, 52 sMCI, and 102 normal controls (NC) from the ADNI database. All participants underwent neuropsychological assessment and 3T MRI scans three times. The time interval between consecutive MRI sessions was about 1 year. Volumes of hippocampal subfield were measured by Freesurfer. Based on the analysis of variance, repeated measures analyses, and receiver operating characteristic curves, we compared cross-sectional and longitudinal alteration sub-regional volume and asymmetry index.

RESULTS

Compared to NC, both MCI groups showed significant atrophy in all subfields. At baseline, pMCI have a smaller volume than sMCI in the bilateral subiculum, molecular layer (ML), the molecular and granule cell layers of the dentate gyrus, cornu ammonis 4, and right tail. Furthermore, repeated measures analyses revealed that pMCI patients showed a faster volume loss than sMCI in bilateral subiculum and ML. After controlling for age, gender, and education, most results remained unchanged. However, none of the hippocampal sub-regional volumes performed better than the whole hippocampus in ROC analyses, and no asymmetric difference between pMCI and sMCI was found.

CONCLUSION

The faster volume loss in subiculum and ML suggest a higher risk of disease progression in MCI patients. The hippocampal asymmetry may have smaller value in predicting the MCI prognosis.

Decoupling of Global Brain Activity and Cerebrospinal Fluid Flow in Parkinson's Disease Cognitive Decline

BACKGROUND

Deposition and spreading of misfolded proteins (α-synuclein and tau) have been linked to Parkinson's disease cognitive dysfunction. The glymphatic system may play an important role in the clearance of these toxic proteins via cerebrospinal fluid (CSF) flow through perivascular and interstitial spaces. Recent studies discovered that sleep-dependent global brain activity is coupled to CSF flow, which may reflect glymphatic function.

OBJECTIVE

The objective of this current study was to determine if the decoupling of brain activity-CSF flow is linked to Parkinson's disease cognitive dysfunction.

METHODS

Functional and structural MRI data, clinical motor (Unified Parkinson's Disease Rating Scale), and cognitive (Montreal Cognitive Assessment [MoCA]) scores were collected from 60 Parkinson's disease and 58 control subjects. Parkinson's disease patients were subgrouped into those with mild cognitive impairment (MoCA < 26), n = 31, and those without mild cognitive impairment (MoCA ≥ 26), n = 29. The coupling strength between the resting-state global blood-oxygen-level-dependent signal and associated CSF flow was quantified, compared among groups, and associated with clinical and structural measurements.

RESULTS

Global blood-oxygen-level-dependent signal-CSF coupling decreased significantly (P < 0.006) in Parkinson's disease patients showing mild cognitive impairment, compared with those without mild cognitive impairment and controls. Reduced global blood-oxygen-level-dependent signal-CSF coupling was associated with decreased MoCA scores present in Parkinson's disease patients (P = 0.005) but not in controls (P = 0.65). Weaker global blood-oxygen-level-dependent signal-CSF coupling in Parkinson's disease patients also was associated with a thinner right entorhinal cortex (Spearman's correlation, -0.36; P = 0.012), an early structural change often seen in Alzheimer's disease.

CONCLUSIONS

The decoupling between global brain activity and associated CSF flow is related to Parkinson's disease cognitive impairment. © 2021 International Parkinson and Movement Disorder Society.

Right entorhinal cortical thickness is associated with Mini-Mental State Examination scores from multi-country datasets using MRI

PURPOSE

To discover common biomarkers correlating with the Mini-Mental State Examination (MMSE) scores from multi-country MRI datasets.

METHODS

The first dataset comprised 112 subjects (49 men, 63 women; range, 46-94 years) at the National Hospital Organization Kyushu Medical Center. A second dataset comprised 300 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (177 men, 123 women; range, 57-91 years). Three-dimensional T1-weighted MR images were collected from both datasets. In total, 14 deep gray matter volumes and 70 cortical thicknesses were obtained from MR images using FreeSurfer software. Total hippocampal volume and the ratio of hippocampus to cerebral volume were also calculated. Correlations between each variable and MMSE scores were assessed using Pearson's correlation coefficient. Parameters with moderate correlation coefficients (r > 0.3) from each dataset were determined as independent variables and evaluated using general linear model (GLM) analyses.

RESULTS

In Pearson's correlation coefficient, total and bilateral hippocampal volumes, right amygdala volume, and right entorhinal cortex (ERC) thickness showed moderate correlation coefficients (r > 0.3) with MMSE scores from the first dataset. The ADNI dataset showed moderate correlations with MMSE scores in more variables, including bilateral ERC thickness and hippocampal volume. GLM analysis revealed that right ERC thickness correlated significantly with MMSE score in both datasets. Cortical thicknesses of the left parahippocampal gyrus, left inferior parietal lobe, and right fusiform gyrus also significantly correlated with MMSE score in the ADNI dataset (p < 0.05).

CONCLUSION

A positive correlation between right ERC thickness and MMSE score was identified from multi-country datasets.

MCI Detection Using Kernel Eigen-Relative-Power Features of EEG Signals

Classification between individuals with mild cognitive impairment (MCI) and healthy controls (HC) based on electroencephalography (EEG) has been considered a challenging task to be addressed for the purpose of its early detection. In this study, we proposed a novel EEG feature, the kernel eigen-relative-power (KERP) feature, for achieving high classification accuracy of MCI versus HC. First, we introduced the relative powers (RPs) between pairs of electrodes across 21 different subbands of 2-Hz width as the features, which have not yet been used in previous MCI-HC classification studies. Next, the Fisher’s class separability criterion was applied to determine the best electrode pairs (five electrodes) as well as the frequency subbands for extracting the most sensitive RP features. The kernel principal component analysis (kernel PCA) algorithm was further performed to extract a few more discriminating nonlinear principal components from the optimal RPs, and these components form a KERP feature vector. Results carried out on 51 participants (24 MCI and 27 HC) show that the newly introduced subband RP feature showed superior classification performance to commonly used spectral power features, including the band power, single-electrode relative power, and also the RP based on the conventional frequency bands. A high leave-one-participant-out cross-validation (LOPO-CV) classification accuracy 86.27% was achieved by the RP feature, using a simple linear discriminant analysis (LDA) classifier. Moreover, with the same classifier, the proposed KERP further improved the accuracy to 88.24%. Finally, cascading the KERP feature to a nonlinear classifier, the support vector machine (SVM), yields a high MCI-HC classification accuracy of 90.20% (sensitivity = 87.50% and specificity = 92.59%). The proposed method demonstrated a high accuracy and a high usability (only five electrodes are required), and therefore, has great potential to further develop an EEG-based computer-aided diagnosis system that can be applied for the early detection of MCI.

Neuroimage Biomarker Identification of the Conversion of Mild Cognitive Impairment to Alzheimer's Disease

An efficient method to identify whether mild cognitive impairment (MCI) has progressed to Alzheimer's disease (AD) will be beneficial to patient care. Previous studies have shown that magnetic resonance imaging (MRI) has enabled the assessment of AD progression based on imaging findings. The present work aimed to establish an algorithm based on three features, namely, volume, surface area, and surface curvature within the hippocampal subfields, to model variations, including atrophy and structural changes to the cortical surface. In this study, a new biomarker, the ratio of principal curvatures (RPC), was proposed to characterize the folding patterns of the cortical gyrus and sulcus. Along with volumes and surface areas, these morphological features associated with the hippocampal subfields were assessed in terms of their sensitivity to the changes in cognitive capacity by two different feature selection methods. Either the extracted features were statistically significantly different, or the features were selected through a random forest model. The identified subfields and their structural indices that are sensitive to the changes characteristic of the progression from MCI to AD were further assessed with a multilayer perceptron classifier to help facilitate the diagnosis. The accuracy of the classification based on the proposed method to distinguish whether a MCI patient enters the AD stage amounted to 79.95%, solely using the information from the features selected by a logical feature selection method.

Asymmetric thinning of the cerebral cortex across the adult lifespan is accelerated in Alzheimer's disease

Aging and Alzheimer's disease (AD) are associated with progressive brain disorganization. Although structural asymmetry is an organizing feature of the cerebral cortex it is unknown whether continuous age- and AD-related cortical degradation alters cortical asymmetry. Here, in multiple longitudinal adult lifespan cohorts we show that higher-order cortical regions exhibiting pronounced asymmetry at age ~20 also show progressive asymmetry-loss across the adult lifespan. Hence, accelerated thinning of the (previously) thicker homotopic hemisphere is a feature of aging. This organizational principle showed high consistency across cohorts in the Lifebrain consortium, and both the topological patterns and temporal dynamics of asymmetry-loss were markedly similar across replicating samples. Asymmetry-change was further accelerated in AD. Results suggest a system-wide dedifferentiation of the adaptive asymmetric organization of heteromodal cortex in aging and AD.

Abnormal cortical regions and subsystems in whole brain functional connectivity of mild cognitive impairment and Alzheimer's disease: a preliminary study

The disease roots of Alzheimer's disease (AD) are unknown. Functional connection (FC) methodology based on functional MRI data is an effective lever to investigate macroscopic neural activity patterns. However, regional properties of brain architecture have been less investigated by special markers of graph indexes in general mental disorders. In terms of the set of the abnormal edges in the FCs matrix, this paper introduces the strength index (S-scores) of region centrality on the principle of holism. Then, the important process is to investigate the S-scores of regions and subsystems in 36 healthy controls, 38 mild cognitive impairment (MCI) patients and 34 AD patients. At the edge level, abnormal FCs is numerically increasing progressively from MCI to AD brains. At the region level, the CUN.L, PAL.R, THA.L, and TPOsup.R regions are highlighted with abnormal S-scores in MCI patients. By comparison, more regions are abnormal in AD patients, which are PreCG.L, INS.R, DCG.L, AMYG.R, IOG.R, FFG.L, PoCG.L, PCUN.R, TPOsup.L, MTG.L, and TPOmid.L. Importantly, the regions in DMN have abnormal S-scores in AD groups. At the module level, the S-scores of frontal, parietal, occipital lobe, and cerebellum are found in MCI and AD patients. Meanwhile, the abnormal lateralization is inferred because of the S-scores of left and top hemisphere in the AD group. Though this is strictly a contrastive study, the S-score may be a meaningful imaging marker for excavating AD psychopathology.

Comparing cortical signatures of atrophy between late-onset and autosomal dominant Alzheimer disease

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Cortical signatures selective to AD could provide an early MRI biomarker.

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Autosomal dominant Alzheimer disease (ADAD) may model an ideal AD signature.

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ADAD and late-onset maps overlap in parietal cortex but contain unique features.

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Signatures predicted increasing amyloid within their own, but not across cohorts.

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These results indicate atrophy in AD can take multiple spatial patterns.

Defining a signature of cortical regions of interest preferentially affected by Alzheimer disease (AD) pathology may offer improved sensitivity to early AD compared to hippocampal volume or mesial temporal lobe alone. Since late-onset Alzheimer disease (LOAD) participants tend to have age-related comorbidities, the younger-onset age in autosomal dominant AD (ADAD) may provide a more idealized model of cortical thinning in AD. To test this, the goals of this study were to compare the degree of overlap between the ADAD and LOAD cortical thinning maps and to evaluate the ability of the ADAD cortical signature regions to predict early pathological changes in cognitively normal individuals.

We defined and analyzed the LOAD cortical maps of cortical thickness in 588 participants from the Knight Alzheimer Disease Research Center (Knight ADRC) and the ADAD cortical maps in 269 participants from the Dominantly Inherited Alzheimer Network (DIAN) observational study. Both cohorts were divided into three groups: cognitively normal controls (n_ADRC = 381; n_DIAN = 145), preclinical (n_ADRC = 153; n_DIAN = 76), and cognitively impaired (n_ADRC = 54; n_DIAN = 48). Both cohorts underwent clinical assessments, 3T MRI, and amyloid PET imaging with either ¹¹C-Pittsburgh compound B or ¹⁸F-florbetapir.

To generate cortical signature maps of cortical thickness, we performed a vertex-wise analysis between the cognitively normal controls and impaired groups within each cohort using six increasingly conservative statistical thresholds to determine significance. The optimal cortical map among the six statistical thresholds was determined from a receiver operating characteristic analysis testing the performance of each map in discriminating between the cognitively normal controls and preclinical groups. We then performed within-cohort and cross-cohort (e.g. ADAD maps evaluated in the Knight ADRC cohort) analyses to examine the sensitivity of the optimal cortical signature maps to the amyloid levels using only the cognitively normal individuals (cognitively normal controls and preclinical groups) in comparison to hippocampal volume.

We found the optimal cortical signature maps were sensitive to early increases in amyloid for the asymptomatic individuals within their respective cohorts and were significant beyond the inclusion of hippocampus volume, but the cortical signature maps performed poorly when analyzing across cohorts. These results suggest the cortical signature maps are a useful MRI biomarker of early AD-related neurodegeneration in preclinical individuals and the pattern of decline differs between LOAD and ADAD.

Parallels between retinal and brain pathology and response to immunotherapy in old, late-stage Alzheimer's disease mouse models

Despite growing evidence for the characteristic signs of Alzheimer's disease (AD) in the neurosensory retina, our understanding of retina-brain relationships, especially at advanced disease stages and in response to therapy, is lacking. In transgenic models of AD (APPSWE/PS1∆E9; ADtg mice), glatiramer acetate (GA) immunomodulation alleviates disease progression in pre- and early-symptomatic disease stages. Here, we explored the link between retinal and cerebral AD-related biomarkers, including response to GA immunization, in cohorts of old, late-stage ADtg mice. This aged model is considered more clinically relevant to the age-dependent disease. Levels of synaptotoxic amyloid β-protein (Aβ)1-42, angiopathic Aβ1-40, non-amyloidogenic Aβ1-38, and Aβ42/Aβ40 ratios tightly correlated between paired retinas derived from oculus sinister (OS) and oculus dexter (OD) eyes, and between left and right posterior brain hemispheres. We identified lateralization of Aβ burden, with one-side dominance within paired retinal and brain tissues. Importantly, OS and OD retinal Aβ levels correlated with their cerebral counterparts, with stronger contralateral correlations and following GA immunization. Moreover, immunomodulation in old ADtg mice brought about reductions in cerebral vascular and parenchymal Aβ deposits, especially of large, dense-core plaques, and alleviation of microgliosis and astrocytosis. Immunization further enhanced cerebral recruitment of peripheral myeloid cells and synaptic preservation. Mass spectrometry analysis identified new parallels in retino-cerebral AD-related pathology and response to GA immunization, including restoration of homeostatic glutamine synthetase expression. Overall, our results illustrate the viability of immunomodulation-guided CNS repair in old AD model mice, while shedding light onto similar retino-cerebral responses to intervention, providing incentives to explore retinal AD biomarkers.

Older, non-demented apolipoprotein ε4 carrier males show hyperactivation and structural differences in odor memory regions: a blood-oxygen-level-dependent and structural magnetic resonance imaging study

The current study sought to examine the interaction of sex and Apolipoprotein ε₄ status on olfactory recognition memory within non-demented, older individuals. We separated 39 participants into groups based on ε₄ status and sex. Each participant completed an olfactory memory recognition task during 2 functional magnetic resonance imaging scans and 1 structural scan. The ε₄ carriers had greater functional recruitment of memory regions during false positives relative to ε₄ non-carriers. During hits, the male ε₄ carriers showed greater functional recruitment compared to female ε₄ carriers. The ε₄ carriers had larger bilateral putamen volumes relative to ε₄ non-carriers. Neuroimaging data were significantly associated with Dementia Rating Scale scores solely in males. Results suggest differential olfactory memory processing in relation to sex and ε₄ status. Male ε₄ carriers in particular, demonstrated hyperactivation during recognition memory, which we suspect reflects neuronal compensation to maintain functional performance. Future studies should consider examining underlying mechanisms that contribute to these sex differences within ε₄ carriers.

Entorhinal cortex volume, thickness, surface area and curvature trajectories over the adult lifespan

The entorhinal cortex (ERC) acts as a connection between the hippocampus and temporal cortex and plays a key role in memory retrieval and navigation. The morphology of this brain region changes with age. However, there are few quantitative magnetic resonance imaging studies of ERC morphology across the healthy adult lifespan. In this study, we quantified ERC volume, thickness, surface area, and curvature in a large number of subjects spanning seven decades of life. Using structural MRI data from 563 healthy subjects ranging from 19 to 86 years of age, we explored the adult lifespan trajectory of ERC volume, thickness, surface and curvature. ERC volume, thickness, and surface area initially increased with age, reaching a peak at about 32 years, 40 years, and 50 years of age, respectively, after which they decreased with age. ERC volume and surface area were hemispherically leftward asymmetric, whereas ERC thickness was hemispherically rightward asymmetric, with no gender differences. The direction of asymmetry differed across the measures. This informs previous inconsistencies in reports of ERC asymmetry. ERC aging began in mid-adulthood. At this stage of life, it may be important to adopt some strategies to reduce the effects of aging on cognition.

Asymmetrical atrophy of thalamic subnuclei in Alzheimer's disease and amyloid-positive mild cognitive impairment is associated with key clinical features

Introduction

Although widespread cortical asymmetries have been identified in Alzheimer's disease (AD), thalamic asymmetries and their relevance to clinical severity in AD remain unclear.

Methods

Lateralization indices were computed for individual thalamic subnuclei of 65 participants (33 healthy controls, 14 amyloid-positive patients with mild cognitive impairment, and 18 patients with AD dementia). We compared lateralization indices across diagnostic groups and correlated them with clinical measures.

Results

Although overall asymmetry of the thalamus did not differ between groups, greater leftward lateralization of atrophy in the ventral nuclei was demonstrated in AD, compared with controls and amyloid-positive mild cognitive impairment. Increased posterior ventrolateral and ventromedial nuclei asymmetry were associated with worse cognitive dysfunction, informant-reported neuropsychiatric symptoms, and functional ability.

Discussion

Leftward ventral thalamic atrophy was associated with disease severity in AD. Our findings suggest the clinically relevant involvement of thalamic nuclei in the pathophysiology of AD.

Exploration of time-frequency reassignment and homologous inter-hemispheric asymmetry analysis of MCI-AD brain activity

Background

In this study, nonlinear based time–frequency (TF) and time domain investigations are employed for the analysis of electroencephalogram (EEG) signals of mild cognitive impairment–Alzheimer’s disease (MCI–AD) patients and healthy controls. This study attempts to comprehend the cognitive decline of MCI–AD under both resting and cognitive task conditions.

Results

Wavelet-based synchrosqueezing transform (SST) alleviates the smearing of energy observed in the spectrogram around the central frequencies in short-time Fourier transform (STFT), and continuous wavelet transform (CWT). A precise TF representation is assured due to the reassignment of scale variable to the frequency variable. It is discerned from the studies of time domain measures encompassing fractal dimension (FD) and approximate entropy (ApEn), that the parietal lobe is the most affected in MCI–AD under both resting and cognitive states. Alterations in asymmetry in the brain hemispheres are analysed using the homologous areas inter-hemispheric symmetry (HArS).

Conclusion

Time and time–frequency domain analysis of EEG signals have been used for distinguishing various brain states. Therefore, EEG analysis is highly useful for the screening of AD in its prodromal phase.

Using deep Siamese neural networks for detection of brain asymmetries associated with Alzheimer's Disease and Mild Cognitive Impairment

In recent studies, neuroanatomical volume and shape asymmetries have been seen during the course of Alzheimer's Disease (AD) and could potentially be used as preclinical imaging biomarkers for the prediction of Mild Cognitive Impairment (MCI) and AD dementia. In this study, a deep learning framework utilizing Siamese neural networks trained on paired lateral inter-hemispheric regions is used to harness the discriminative power of whole-brain volumetric asymmetry. The method uses the MRICloud pipeline to yield low-dimensional volumetric features of pre-defined atlas brain structures, and a novel non-linear kernel trick to normalize these features to reduce batch effects across datasets and populations. By working with the low-dimensional features, Siamese networks were shown to yield comparable performance to studies that utilize whole-brain MR images, with the advantage of reduced complexity and computational time, while preserving the biological information density. Experimental results also show that Siamese networks perform better in certain metrics by explicitly encoding the asymmetry in brain volumes, compared to traditional prediction methods that do not use the asymmetry, on the ADNI and BIOCARD datasets.

Brain Network and Abnormal Hemispheric Asymmetry Analyses to Explore the Marginal Differences in Glucose Metabolic Distributions Among Alzheimer's Disease, Parkinson's Disease Dementia, and Lewy Body Dementia

Facilitating accurate diagnosis and ensuring appropriate treatment of dementia subtypes, including Alzheimer's disease (AD), Parkinson's disease dementia (PDD), and Lewy body dementia (DLB), is clinically important. However, the differences in glucose metabolic distribution among these three dementia subtypes are minor, which can result in difficulties in diagnosis by visual assessment or traditional quantification methods. Here, we explored this issue using novel approaches, including brain network and abnormal hemispheric asymmetry analyses. We generated 18F-labeled fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) images from patients with AD, PDD, and DLB, and healthy control (HC) subjects (n = 22, 18, 22, and 22, respectively) from Huashan hospital, Shanghai, China. Brain network properties were measured and between-group differences evaluated using graph theory. We also calculated and explored asymmetry indices for the cerebral hemispheres in the four groups, to explore whether differences between the two hemispheres were characteristic of each group. Our study revealed significant differences in the network properties of the HC and AD groups (small-world coefficient, 1.36 vs. 1.28; clustering coefficient, 1.48 vs. 1.59; characteristic path length, 1.57 vs. 1.64). In addition, differing hub regions were identified in the different dementias. We also identified rightward asymmetry in the hemispheric brain networks of patients with AD and DLB, and leftward asymmetry in the hemispheric brain networks of patients with PDD, which were attributable to aberrant topological properties in the corresponding hemispheres.

Asymmetrical subcortical plasticity entails cognitive progression in older individuals

Structural brain asymmetries have been associated with cognition. However, it is not known to what extent neuropsychological parameters and structural laterality covary with aging. Seventy‐five subjects drawn from a larger normal aging cohort were evaluated in terms of MRI and neuropsychological parameters at two moments (M1 and M2), 18 months apart. In this time frame, asymmetry as measured by structural laterality index (ΔLI) was stable regarding both direction and magnitude in all areas. However, a significantly higher dispersion for this variation was observed in subcortical over cortical areas. Subjects with extreme increase in rightward lateralization of the caudate revealed increased M1 to M2 Stroop interference scores, but also a worsening of general cognition (MMSE). In contrast, subjects showing extreme increase in leftward lateralization of the thalamus presented higher increase in Stroop interference scores. In conclusion, while a decline in cognitive function was observed in the entire sample, regional brain asymmetries were relatively stable. Neuropsychological trajectories were associated with laterality changes in subcortical regions.

Brain Networks Reorganization During Maturation and Healthy Aging-Emphases for Resilience

Maturation and aging are important life periods that are linked to drastic brain reorganization processes which are essential for mental health. However, the development of generalized theories for delimiting physiological and pathological brain remodeling through life periods linked to healthy states and resilience on one side or mental dysfunction on the other remains a challenge. Furthermore, important processes of preservation and compensation of brain function occur continuously in the cerebral brain networks and drive physiological responses to life events. Here, we review research on brain reorganization processes across the lifespan, demonstrating brain circuits remodeling at the structural and functional level that support mental health and are parallelized by physiological trajectories during maturation and healthy aging. We show evidence that aberrations leading to mental disorders result from the specific alterations of cerebral networks and their pathological dynamics leading to distinct excitability patterns. We discuss how these series of large-scale responses of brain circuits can be viewed as protective or malfunctioning mechanisms for the maintenance of mental health and resilience.

MRI Asymmetry Index of Hippocampal Subfields Increases Through the Continuum From the Mild Cognitive Impairment to the Alzheimer's Disease

Objective: It is well-known that the hippocampus presents significant asymmetry in Alzheimer's disease (AD) and that difference in volumes between left and right exists and varies with disease progression. However, few works investigated whether the asymmetry degree of subfields of hippocampus changes through the continuum from Mild Cognitive Impairment (MCI) to AD. Thus, aim of the present work was to evaluate the Asymmetry Index (AI) of hippocampal substructures as possible MRI biomarkers of Dementia. Moreover, we aimed to assess whether the subfields presented peculiar differences between left and right hemispheres. We also investigated the relationship between the asymmetry magnitude in hippocampal subfields and the decline of verbal memory as assessed by Rey's auditory verbal learning test (RAVLT). Methods: Four-hundred subjects were selected from ADNI, equally divided into healthy controls (HC), AD, stable MCI (sMCI), and progressive MCI (pMCI). The structural baseline T1s were processed with FreeSurfer 6.0 and volumes of whole hippocampus (WH) and 12 subfields were extracted. The AI was calculated as: (|Left-Right|/(Left+Right))*100. ANCOVA was used for evaluating AI differences between diagnoses, while paired t-test was applied for assessing changes between left and right volumes, separately for each group. Partial correlation was performed for exploring relationship between RAVLT summary scores (Immediate, Learning, Forgetting, Percent Forgetting) and hippocampal substructures AI. The statistical threshold was Bonferroni corrected p < 0.05/13 = 0.0038. Results: We found a general trend of increased degree of asymmetry with increasing severity of diagnosis. Indeed, AD presented the higher magnitude of asymmetry compared with HC, sMCI and pMCI, in the WH (AI mean 5.13 ± 4.29 SD) and in each of its twelve subfields. Moreover, we found in AD a significant negative correlation (r = -0.33, p = 0.00065) between the AI of parasubiculum (mean 12.70 ± 9.59 SD) and the RAVLT Learning score (mean 1.70 ± 1.62 SD). Conclusions: Our findings showed that hippocampal subfields AI varies differently among the four groups HC, sMCI, pMCI, and AD. Moreover, we found-for the first time-that hippocampal substructures had different sub-patterns of lateralization compared with the whole hippocampus. Importantly, the severity in learning rate was correlated with pathological high degree of asymmetry in parasubiculum of AD patients.

Neurophysiological signatures of Alzheimer's disease and frontotemporal lobar degeneration: pathology versus phenotype

The disruption of brain networks is characteristic of neurodegenerative dementias. However, it is controversial whether changes in connectivity reflect only the functional anatomy of disease, with selective vulnerability of brain networks, or the specific neurophysiological consequences of different neuropathologies within brain networks. We proposed that the oscillatory dynamics of cortical circuits reflect the tuning of local neural interactions, such that different pathologies are selective in their impact on the frequency spectrum of oscillations, whereas clinical syndromes reflect the anatomical distribution of pathology and physiological change. To test this hypothesis, we used magnetoencephalography from five patient groups, representing dissociated pathological subtypes and distributions across frontal, parietal and temporal lobes: amnestic Alzheimer's disease, posterior cortical atrophy, and three syndromes associated with frontotemporal lobar degeneration. We measured effective connectivity with graph theory-based measures of local efficiency, using partial directed coherence between sensors. As expected, each disease caused large-scale changes of neurophysiological brain networks, with reductions in local efficiency compared to controls. Critically however, the frequency range of altered connectivity was consistent across clinical syndromes that shared a likely underlying pathology, whilst the localization of changes differed between clinical syndromes. Multivariate pattern analysis of the frequency-specific topographies of local efficiency separated the disorders from each other and from controls (accuracy 62% to 100%, according to the groups' differences in likely pathology and clinical syndrome). The data indicate that magnetoencephalography has the potential to reveal specific changes in neurophysiology resulting from neurodegenerative disease. Our findings confirm that while clinical syndromes have characteristic anatomical patterns of abnormal connectivity that may be identified with other methods like structural brain imaging, the different mechanisms of neurodegeneration also cause characteristic spectral signatures of physiological coupling that are not accessible with structural imaging nor confounded by the neurovascular signalling of functional MRI. We suggest that these spectral characteristics of altered connectivity are the result of differential disruption of neuronal microstructure and synaptic physiology by Alzheimer's disease versus frontotemporal lobar degeneration.

Cerebral glucose metabolic prediction from amnestic mild cognitive impairment to Alzheimer's dementia: a meta-analysis

Brain ¹⁸F-fluorodeoxyglucose positron emission tomography (FDG-PET) has been utilized to monitor disease conversion from amnestic mild cognitive impairment (aMCI) to Alzheimer’s dementia (AD). However, the conversion patterns of FDG-PET metabolism across studies are not conclusive. We conducted a voxel-wise meta-analysis using Seed-based d Mapping that included 10 baseline voxel-wise FDG-PET comparisons between 93 aMCI converters and 129 aMCI non-converters from nine longitudinal studies. The most robust and reliable metabolic alterations that predicted conversion from aMCI to AD were localized in the left posterior cingulate cortex (PCC)/precuneus. Furthermore, meta-regression analyses indicated that baseline mean age and severity of cognitive impairment, and follow-up duration were significant moderators for metabolic alterations in aMCI converters. Our study revealed hypometabolism in the left PCC/precuneus as an early feature in the development of AD. This finding has important implications in understanding the neural substrates for AD conversion and could serve as a potential imaging biomarker for early detection of AD as well as for tracking disease progression at the predementia stage.

Morphological Biomarker Differentiating MCI Converters from Nonconverters: Longitudinal Evidence Based on Hemispheric Asymmetry

Identifying subjects with mild cognitive impairment (MCI) who may probably progress to Alzheimer's disease (AD) is important for better understanding the disease mechanisms and facilitating early treatments. In addition to the direct volumetric and thickness measurement based on high-resolution magnetic resonance imaging (MRI), hemispheric asymmetry could be a potential index to detect morphological variations in MCI patients with a high risk of conversion to AD. The present study collected a set of longitudinal MRI data from 53 MCI converters and nonconverters and investigated the asymmetry differences between groups. Asymmetry variation was observed in the medial temporal lobe, especially in the entorhinal cortex, between converters and nonconverters 3 years before the former developed AD. The proposed asymmetry analysis was observed to be sensitive to detect morphological changes between groups as compared to the methods of voxel-based morphometry (VBM) and thickness measurement. Hemispheric asymmetry in specific brain regions as a neuroimaging biomarker can provide helpful information for prediction of MCI conversion.

Transcranial Magnetic Stimulation (TMS) in the Elderly

PURPOSE OF REVIEW

This article aims to review select applications of Transcranial Magnetic Stimulation (TMS) that have significant relevance in geriatric psychiatry.

RECENT FINDINGS

Small study sizes and parameter variability limit the generalizability of many TMS studies in geriatric patients. Additionally, geriatric patients have unique characteristics that can moderate the efficacy of TMS. Nonetheless, several promising experimental applications in addition to the FDA-approved indication for major depression have emerged. Cognitive impairment, neuropathic pain, and smoking cessation are experimental applications with special significance to the elderly. Cognitive impairment has been researched the most in this population and evidence thus far suggests that TMS has potential therapeutic benefit. There is also evidence to suggest benefit from TMS for neuropathic pain and smoking cessation in working age adults. TMS is consistently reported as a safe and well-tolerated treatment modality with no adverse cognitive side effects. TMS is a safe treatment modality that can be effective for certain applications in the elderly. Additional research that specifically includes older subjects is needed to replicate findings and to optimize treatment protocols for this population.

Changes in Brain Lateralization in Patients with Mild Cognitive Impairment and Alzheimer's Disease: A Resting-State Functional Magnetic Resonance Study from Alzheimer's Disease Neuroimaging Initiative

Purpose

To detect changes in brain lateralization in patients with mild cognitive impairment (MCI) and Alzheimer’s disease (AD) using resting-state functional magnetic resonance imaging (fMRI).

Materials and methods

Data from 61 well-matched right-handed subjects were obtained from the Alzheimer’s Disease Neuroimaging Initiative, including 19 healthy controls (HCs), 25 patients with MCI, and 17 patients with AD. First, we divided 256 pairs of seed regions from each hemisphere covering the entire cerebral gray matter. Then, we used the intrinsic laterality index (iLI) approach to quantify the functional laterality using fMRI. One-way ANOVA was employed to estimate the differences in iLI among the three groups. The sum, number and mean value of the iLI were calculated within the thresholds of 0 < |iLI| < 0.2, 0.2 ≤ |iLI| < 0.4, 0.4 ≤ |iLI| < 0.8, and |iLI| ≥ 0.8, to explore the changes in the lateralization of resting-state brain function in patients with MCI and AD.

Results

One-way ANOVA revealed that the iLIs of the three groups were significantly different. The HCs showed a significant leftward interhemispheric difference within |iLI| ≥ 0.8. Compared with the HCs, the patients with MCI manifested a distinct abnormal rightward interhemispheric asymmetry, mainly within the thresholds of 0.2 ≤ |iLI| < 0.4 and 0.4 ≤ |iLI| < 0.8; in the patients with AD, the normal leftward lateralization that was observed in the HCs disappeared, and an abnormal rightward laterality was expressed within 0.4 ≤ |iLI| < 0.8. By directly comparing the patients with MCI with the patients with AD, an exclusive abnormal rightward laterality was observed in the patients with MCI within the 0.2 ≤ |iLI| < 0.4 threshold, and the normal leftward asymmetry vanished in the patients with AD within the |iLI| ≥ 0.8 threshold.

Conclusion

Global brain lateralization was different among three groups. The abnormal rightward dominance observed in the patients with MCI and AD may indicate that these patients use additional brain resources to compensate for the loss of cognitive function, and the observed disappearance of the leftward laterality in the patients with AD was likely associated with the damage in the left hemisphere. The observed disappearance of the rightward asymmetry in the patients with AD using the 0.2 ≤ |iLI| < 0.4 threshold was likely a sign of decompensation. Our study provides new insights that may improve our understanding of MCI and AD.

Mismatch Negativity and Ear Laterality in Alzheimer's Disease and in Mild Cognitive Impairment

BACKGROUND

Cortical auditory event-related potentials (ERPs) were studied in order to measure mismatch negativity (MMN). Three groups of subjects were studied: patients with Alzheimer's disease (AD, n = 32), mild cognitive impairment (MCI, n = 44), and subjective memory complaints without cognitive decline (SMC, n = 27). A bottom up strategy was applied, and the right and left ears were stimulated monaurally.

OBJECTIVE

To investigate MMN in AD and MCI, and in a clinical reference group.

METHODS

ERPs were carried out with 500 tone pulses at 80 dBnHL. Each sequence included 80% standard tones (500 Hz) (f), and 20% deviant tones (1000 Hz) (r). MMN measurements were carried out by comparing the amplitudes of (f) and (r) recordings and to calculate the amplitude difference in μV for each group. The right and the left ears were analyzed separately.

RESULTS

A left ear advantage (LEA) of MMN amplitude was demonstrated in the two groups with better cognition (the MCI and the SMC groups), but not in the AD group.

DISCUSSION

The absence of MMN asymmetry in the AD group is possibly caused by a dysfunction to apprehend changes of tonal stimuli.

The Pathoconnectivity Profile of Alzheimer's Disease: A Morphometric Coalteration Network Analysis

Gray matter alterations are typical features of brain disorders. However, they do not impact on the brain randomly. Indeed, it has been suggested that neuropathological processes can selectively affect certain assemblies of neurons, which typically are at the center of crucial functional networks. Because of their topological centrality, these areas form a core set that is more likely to be affected by neuropathological processes. In order to identify and study the pattern formed by brain alterations in patients’ with Alzheimer’s disease (AD), we devised an innovative meta-analytic method for analyzing voxel-based morphometry data. This methodology enabled us to discover that in AD gray matter alterations do not occur randomly across the brain but, on the contrary, follow identifiable patterns of distribution. This alteration pattern exhibits a network-like structure composed of coaltered areas that can be defined as coatrophy network. Within the coatrophy network of AD, we were able to further identify a core subnetwork of coaltered areas that includes the left hippocampus, left and right amygdalae, right parahippocampal gyrus, and right temporal inferior gyrus. In virtue of their network centrality, these brain areas can be thought of as pathoconnectivity hubs.

Gray matter asymmetries in aging and neurodegeneration: A review and meta-analysis

Inter-hemispheric asymmetries are a common phenomenon of the human brain. Some evidence suggests that neurodegeneration related to aging and disease may preferentially affect the left-usually language- and motor-dominant-hemisphere. Here, we used activation likelihood estimation meta-analysis to assess gray matter (GM) loss and its lateralization in healthy aging and in neurodegeneration, namely, mild cognitive impairment (MCI), Alzheimer's dementia (AD), Parkinson's disease (PD), and Huntington's disease (HD). This meta-analysis, comprising 159 voxel-based morphometry publications (enrolling 4,469 patients and 4,307 controls), revealed that GM decline appeared to be asymmetric at trend levels but provided no evidence for increased left-hemisphere vulnerability. Regions with asymmetric GM decline were located in areas primarily affected by neurodegeneration. In HD, the left putamen showed converging evidence for more pronounced atrophy, while no consistent pattern was found in PD. In MCI, the right hippocampus was more atrophic than its left counterpart, a pattern that reversed in AD. The stability of these findings was confirmed using permutation tests. However, due to the lenient threshold used in the asymmetry analysis, further work is needed to confirm our results and to provide a better understanding of the functional role of GM asymmetries, for instance in the context of cognitive reserve and compensation. Hum Brain Mapp 38:5890-5904, 2017. © 2017 Wiley Periodicals, Inc.

The Abnormality of Topological Asymmetry between Hemispheric Brain White Matter Networks in Alzheimer's Disease and Mild Cognitive Impairment

A large number of morphology-based studies have previously reported a variety of regional abnormalities in hemispheric asymmetry in Alzheimer’s disease (AD). Recently, neuroimaging studies have revealed changes in the topological organization of the structural network in AD. However, little is known about the alterations in topological asymmetries. In the present study, we used diffusion tensor image tractography to construct the hemispheric brain white matter networks of 25 AD patients, 95 mild cognitive impairment (MCI) patients, and 48 normal control (NC) subjects. Graph theoretical approaches were then employed to estimate hemispheric topological properties. Rightward asymmetry in both global and local network efficiencies were observed between the two hemispheres only in AD patients. The brain regions/nodes exhibiting increased rightward asymmetry in both AD and MCI patients were primarily located in the parahippocampal gyrus and cuneus. The observed rightward asymmetry was attributed to changes in the topological properties of the left hemisphere in AD patients. Finally, we found that the abnormal hemispheric asymmetries of brain network properties were significantly correlated with memory performance (Rey’s Auditory Verbal Learning Test). Our findings provide new insights into the lateralized nature of hemispheric disconnectivity and highlight the potential for using hemispheric asymmetry of brain network measures as biomarkers for AD.

Enhanced Feedback-Related Negativity in Alzheimer's Disease

Alzheimer’s disease (AD), the most common cause of dementia in the elderly, results in the impairment of executive function, including that of performance monitoring. Feedback-related negativity (FRN) is an electrophysiological measure reflecting the activity of this monitoring system via feedback signals, and is generated from the anterior cingulate cortex. However, there have been no reports on FRN in AD. Based on prior aging studies, we hypothesized that FRN would decrease in AD patients. To assess this, FRN was measured in healthy individuals and those with AD during a simple gambling task involving positive and negative feedback stimuli. Contrary to our hypothesis, FRN amplitude increased in AD patients, compared with the healthy elderly. We speculate that this may reflect the existence of a compensatory mechanism against the decline in executive function. Also, there was a significant association between FRN amplitude and depression scores in AD, and the FRN amplitude tended to increase insomuch as the Self-rating Depression Scale (SDS) was higher. This result suggests the existence of a negative bias in the affective state in AD. Thus, the impaired functioning monitoring system in AD is a more complex phenomenon than we thought.

Whole-brain analysis reveals increased neuroanatomical asymmetries in dementia for hippocampus and amygdala

Structural magnetic resonance imaging data are frequently analysed to reveal morphological changes of the human brain in dementia. Most contemporary imaging biomarkers are scalar values, such as the volume of a structure, and may miss the localized morphological variation of early presymptomatic disease progression. Neuroanatomical shape descriptors, however, can represent complex geometric information of individual anatomical regions and may demonstrate increased sensitivity in association studies. Yet, they remain largely unexplored. In this article, we introduce a novel technique to study shape asymmetries of neuroanatomical structures across subcortical and cortical brain regions. We demonstrate that neurodegeneration of subcortical structures in Alzheimer's disease is not symmetric. The hippocampus shows a significant increase in asymmetry longitudinally and both hippocampus and amygdala show a significantly higher asymmetry cross-sectionally concurrent with disease severity above and beyond an ageing effect. Our results further suggest that the asymmetry in these structures is undirectional and that primarily the anterior hippocampus becomes asymmetric. Based on longitudinal asymmetry measures we subsequently study the progression from mild cognitive impairment to dementia, demonstrating that shape asymmetry in hippocampus, amygdala, caudate and cortex is predictive of disease onset. The same analyses on scalar volume measurements did not produce any significant results, indicating that shape asymmetries, potentially induced by morphometric changes in subnuclei, rather than size asymmetries are associated with disease progression and can yield a powerful imaging biomarker for the early presymptomatic classification and prediction of Alzheimer's disease. Because literature has focused on contralateral volume differences, subcortical disease lateralization may have been overlooked thus far.

Electroencephalographic Fractal Dimension in Healthy Ageing and Alzheimer's Disease

Brain activity is complex; a reflection of its structural and functional organization. Among other measures of complexity, the fractal dimension is emerging as being sensitive to neuronal damage secondary to neurological and psychiatric diseases. Here, we calculated Higuchi’s fractal dimension (HFD) in resting-state eyes-closed electroencephalography (EEG) recordings from 41 healthy controls (age: 20–89 years) and 67 Alzheimer’s Disease (AD) patients (age: 50–88 years), to investigate whether HFD is sensitive to brain activity changes typical in healthy aging and in AD. Additionally, we considered whether AD-accelerating effects of the copper fraction not bound to ceruloplasmin (also called “free” copper) are reflected in HFD fluctuations. The HFD measure showed an inverted U-shaped relationship with age in healthy people (R² = .575, p < .001). Onset of HFD decline appeared around the age of 60, and was most evident in central-parietal regions. In this region, HFD decreased with aging stronger in the right than in the left hemisphere (p = .006). AD patients demonstrated reduced HFD compared to age- and education-matched healthy controls, especially in temporal-occipital regions. This was associated with decreasing cognitive status as assessed by mini-mental state examination, and with higher levels of non-ceruloplasmin copper. Taken together, our findings show that resting-state EEG complexity increases from youth to maturity and declines in healthy, aging individuals. In AD, brain activity complexity is further reduced in correlation with cognitive impairment. In addition, elevated levels of non-ceruloplasmin copper appear to accelerate the reduction of neural activity complexity. Overall, HDF appears to be a proper indicator for monitoring EEG-derived brain activity complexity in healthy and pathological aging.

Cognitive dysfunction in patients with very mild Alzheimer's disease and amnestic mild cognitive impairment showing hemispheric asymmetries of hypometabolism on ¹⁸F-FDG PET

OBJECTIVE

We investigated cognitive dysfunction in patients with Alzheimer's disease (AD) and amnestic mild cognitive impairment (aMCI) who present hemispheric asymmetries of cerebral metabolic rate of glucose (CMRglc) decrease on (18) F-fluorodeoxyglucose positron emission tomography.

METHODS

Based on the hemispheric asymmetries of CMRglc decrease in the posterior cingulate cortex, precuneus, and parietotemporal cortex, the patients were divided into three groups (a left-dominant hypometabolism group, a right-dominant hypometabolism group, and a non-dominant hypometabolism group). CMRglc decrease in the whole brain was controlled among the three groups. All the patients underwent mini-mental state examination (MMSE), Wechsler Memory Scale-Revised (WMS-R), and Wechsler Adult Intelligent Scale-Third (WAIS-III).

RESULTS

There were no significant differences in MMSE and WAIS-III scores among the three groups. In WMS-R, the results indicated that the left-dominant group demonstrated significantly lower scores in verbal memory than the other two groups. Furthermore, the left-dominant group had a greater tendency to be diagnosed with AD rather than aMCI.

CONCLUSIONS

Patients with AD and aMCI showing left-dominant hypometabolism tend to show severer impairment in verbal memory function and to be diagnosed with AD dementia.

Entorhinal Cortex Thickness across the Human Lifespan

BACKGROUND AND PURPOSE

Human entorhinal cortex (ERC) connects the temporal neocortex with hippocampus and is essential for memory retrieval and navigation. Markedly, there have been only few quantitative MRI works on the ERC geometric measurements in pediatric and adult healthy subjects across the lifespan. Here, we sought to fill this gap in knowledge by quantifying the ERC thickness in a very large cohort of subjects spanning 9 decades of life.

METHODS

Using magnetic resonance imaging data from multiple centers (IXI, MMRR, NKI, OASIS combined with the NIH-Child Dev database and locally recruited healthy subjects), we analyzed the lifespan trajectory of ERC thickness in 1,660 healthy controls ranging from 2 to 94 years of age.

RESULTS

The ERC thickness increased with age, reached a peak at about 44 years, and then decreased with age. ERC thickness is hemispherically rightward-asymmetric with no gender differences. Mean ERC thickness was found to vary between 2.943 ± .438 mm and 3.525 ± .355 mm across different age populations. Also, more pronounced loss of the ERC thickness in healthy aging men was noticeable.

DISCUSSION

Our report with high spatial resolution brain MRI data from 1,660 healthy controls provided important clues about ERC thickness across lifespan. We believe that our report will pave the way for the future studies investigating distinct neural pathologies related with cognitive dysfunctions.

Permutation and parametric tests for effect sizes in voxel-based morphometry of gray matter volume in brain structural MRI

Permutation testing has been widely implemented in voxel-based morphometry (VBM) tools. However, this type of non-parametric inference has yet to be thoroughly compared with traditional parametric inference in VBM studies of brain structure. Here we compare both types of inference and investigate what influence the number of permutations in permutation testing has on results in an exemplar study of how gray matter proportion changes with age in a group of working age adults. High resolution T1-weighted volume scans were acquired from 80 healthy adults aged 25-64years. Using a validated VBM procedure and voxel-based permutation testing for Pearson product-moment coefficient, the effect sizes of changes in gray matter proportion with age were assessed using traditional parametric and permutation testing inference with 100, 500, 1000, 5000, 10000 and 20000 permutations. The statistical significance was set at P<0.05 and false discovery rate (FDR) was used to correct for multiple comparisons. Clusters of voxels with statistically significant (PFDR<0.05) declines in gray matter proportion with age identified with permutation testing inference (N≈6000) were approximately twice the size of those identified with parametric inference (N=3221voxels). Permutation testing with 10000 (N=6251voxels) and 20000 (N=6233voxels) permutations produced clusters that were generally consistent with each other. However, with 1000 permutations there were approximately 20% more statistically significant voxels (N=7117voxels) than with ≥10000 permutations. Permutation testing inference may provide a more sensitive method than traditional parametric inference for identifying age-related differences in gray matter proportion. Based on the results reported here, at least 10000 permutations should be used in future univariate VBM studies investigating age related changes in gray matter to avoid potential false findings. Additional studies using permutation testing in large imaging databanks are required to address the impact of model complexity, multivariate analysis, number of observations, sampling bias and data quality on the accuracy with which subtle differences in brain structure associated with normal aging can be identified.

Dynamic cortical lateralization during olfactory discrimination learning

Bilateral cortical circuits are not necessarily symmetrical. Asymmetry, or cerebral lateralization, allows functional specialization of bilateral brain regions and has been described in humans for such diverse functions as perception, memory and emotion. There is also evidence for asymmetry in the human olfactory system, although evidence in non-human animal models is lacking. In the present study, we took advantage of the known changes in olfactory cortical local field potentials that occur over the course of odour discrimination training to test for functional asymmetry in piriform cortical activity during learning. Both right and left piriform cortex local field potential activities were recorded. The results obtained demonstrate a robust interhemispheric asymmetry in anterior piriform cortex activity that emerges during specific stages of odour discrimination learning, with a transient bias toward the left hemisphere. This asymmetry is not apparent during error trials. Furthermore, functional connectivity (coherence) between the bilateral anterior piriform cortices is learning- and context-dependent. Steady-state interhemispheric anterior piriform cortex coherence is reduced during the initial stages of learning and then recovers as animals acquire competent performance. The decrease in coherence is seen relative to bilateral coherence expressed in the home cage, which remains stable across conditioning days. Similarly, transient, trial-related interhemispheric coherence increases with task competence. Taken together, the results demonstrate transient asymmetry in piriform cortical function during odour discrimination learning until mastery, suggesting that each piriform cortex may contribute something unique to odour memory.

Abnormal changes of multidimensional surface features using multivariate pattern classification in amnestic mild cognitive impairment patients

Previous studies have suggested that amnestic mild cognitive impairment (aMCI) is associated with changes in cortical morphological features, such as cortical thickness, sulcal depth, surface area, gray matter volume, metric distortion, and mean curvature. These features have been proven to have specific neuropathological and genetic underpinnings. However, most studies primarily focused on mass-univariate methods, and cortical features were generally explored in isolation. Here, we used a multivariate method to characterize the complex and subtle structural changing pattern of cortical anatomy in 24 aMCI human participants and 26 normal human controls. Six cortical features were extracted for each participant, and the spatial patterns of brain abnormities in aMCI were identified by high classification weights using a support vector machine method. The classification accuracy in discriminating the two groups was 76% in the left hemisphere and 80% in the right hemisphere when all six cortical features were used. Regions showing high weights were subtle, spatially complex, and predominately located in the left medial temporal lobe and the supramarginal and right inferior parietal lobes. In addition, we also found that the six morphological features had different contributions in discriminating the two groups even for the same region. Our results indicated that the neuroanatomical patterns that discriminated individuals with aMCI from controls were truly multidimensional and had different effects on the morphological features. Furthermore, the regions identified by our method could potentially be useful for clinical diagnosis.

Discerning mild cognitive impairment and Alzheimer Disease from normal aging: morphologic characterization based on univariate and multivariate models

RATIONALE AND OBJECTIVES

Differentiating mild cognitive impairment (MCI) and Alzheimer Disease (AD) from healthy aging remains challenging. This study aimed to explore the cerebral structural alterations of subjects with MCI or AD as compared to healthy elderly based on the individual and collective effects of cerebral morphologic indices using univariate and multivariate analyses.

MATERIALS AND METHODS

T1-weighted images (T1WIs) were retrieved from Alzheimer Disease Neuroimaging Initiative database for 116 subjects who were categorized into groups of healthy aging, MCI, and AD. Analysis of covariance (ANCOVA) and multivariate analysis of covariance (MANCOVA) were performed to explore the intergroup morphologic alterations indexed by surface area, curvature index, cortical thickness, and subjacent white matter volume with age and sex controlled as covariates, in 34 parcellated gyri regions of interest (ROIs) for both cerebral hemispheres based on the T1WI. Statistical parameters were mapped on the anatomic images to facilitate visual inspection.

RESULTS

Global rather than region-specific structural alterations were revealed in groups of MCI and AD relative to healthy elderly using MANCOVA. ANCOVA revealed that the cortical thickness decreased more prominently in entorhinal, temporal, and cingulate cortices and was positively correlated with patients' cognitive performance in AD group but not in MCI. The temporal lobe features marked atrophy of white matter during the disease dynamics. Significant intercorrelations were observed among the morphologic indices with univariate analysis for given ROIs.

CONCLUSIONS

Significant global structural alterations were identified in MCI and AD based on MANCOVA model with improved sensitivity. The intercorrelation among the morphologic indices may dampen the use of individual morphological parameter in featuring cerebral structural alterations. Decrease in cortical thickness is not reflective of the cognitive performance at the early stage of AD.