Parietal cortex matters in Alzheimer's disease: An overview of structural, functional and metabolic findings

Differences between Alzheimer's disease and mild cognitive impairment using brain networks from magnetic resonance texture analysis

Several studies have aimed at identifying biomarkers in the initial phases of Alzheimer's disease (AD). Conversely, texture features, such as those from gray-level co-occurrence matrices (GLCMs), have highlighted important information from several types of medical images. More recently, texture-based brain networks have been shown to provide useful information in characterizing healthy individuals. However, no studies have yet explored the use of this type of network in the context of AD. This work aimed to employ texture brain networks to investigate the distinction between groups of patients with amnestic mild cognitive impairment (aMCI) and mild dementia due to AD, and a group of healthy subjects. Magnetic resonance (MR) images from the three groups acquired at two instances were used. Images were segmented and GLCM texture parameters were calculated for each region. Structural brain networks were generated using regions as nodes and the similarity among texture parameters as links, and graph theory was used to compute five network measures. An ANCOVA was performed for each network measure to assess statistical differences between groups. The thalamus showed significant differences between aMCI and AD patients for four network measures for the right hemisphere and one network measure for the left hemisphere. There were also significant differences between controls and AD patients for the left hippocampus, right superior parietal lobule, and right thalamus-one network measure each. These findings represent changes in the texture of these regions which can be associated with the cortical volume and thickness atrophies reported in the literature for AD. The texture networks showed potential to differentiate between aMCI and AD patients, as well as between controls and AD patients, offering a new tool to help understand these conditions and eventually aid early intervention and personalized treatment, thereby improving patient outcomes and advancing AD research.

Gerstmann Syndrome: What is the Possible Role of Deep Brain Stimulation?

Gerstmann syndrome, characterized by a tetrad of symptoms, which are agraphia, acalculia, left-right disorientation, and finger agnosia, presents challenges in both understanding its pathophysiology and establishing effective treatment modalities. Neuroanatomical studies have highlighted the involvement of the dominant parietal lobe, particularly the inferior parietal lobule, in the development of Gerstmann syndrome. Although current treatment options are largely supportive, recent research suggests a potential role for deep brain stimulation (DBS) in managing this condition. DBS, known for its efficacy in various neurological disorders, has been hypothesized to modulate neuronal pathways associated with Gerstmann syndrome. However, clinical evidence supporting DBS in Gerstmann syndrome remains scarce, posing challenges in patient selection and ethical considerations. Future research should prioritize investigating the efficacy and safety of DBS in Gerstmann syndrome to improve patient outcomes and quality of life.

Torpor induces reversible tau hyperphosphorylation and accumulation in mice expressing human tau

Tau protein hyperphosphorylation and aggregation are key pathological events in neurodegenerative tauopathies such as Alzheimer's disease. Interestingly, seasonal hibernators show extensive tau hyperphosphorylation during torpor, i.e., the hypothermic and hypometabolic state of hibernation, which is completely reversed during arousal. Torpor-associated mechanisms that reverse tau hyperphosphorylation may be of therapeutic relevance, however, it is currently not known to what extent they apply to human tau. Here we addressed this issue using daily torpor in wildtype mice that express mouse tau (mtau) and in mice that lack mtau expression and instead express human tau (htau). AT8, AT100 and Ser396 immunoblotting and immunohistochemistry were used to assess tau (hyper)phosphorylation at clinically relevant phosphorylation sites. We found that torpor robustly and reversibly increases the levels of phosphorylated tau in both mtau and htau mice. Immunohistochemistry revealed four brain areas that show prominent tau phosphorylation: the hippocampus, posterior parietal cortex, piriform cortex and cortical amygdala. Whereas wildtype mice primarily showed increased levels of diffusely organized hyperphosphorylated tau during torpor, htau mice contained clear somato-dendritic accumulations of AT8 reactivity resembling tau pre-tangles as observed in the Alzheimer brain. Interestingly, AT8-positive accumulations disappeared upon arousal, and tau phosphorylation levels at 24 h after arousal were lower than observed at baseline, suggesting a beneficial effect of torpor-arousal cycles on preexisting hyperphosphorylated tau. In conclusion, daily torpor in mice offers a quick and standardized method to study tau phosphorylation, accumulation and clearance in mouse models relevant for neurodegeneration, as well as opportunities to discover new targets for the treatment of human tauopathies.

Appendicular lean mass and the risk of stroke and Alzheimer's disease: a mendelian randomization study

BACKGROUND

Appendicular lean mass (ALM) is a good predictive biomarker for sarcopenia. And previous studies have reported the association between ALM and stroke or Alzheimer's disease (AD), however, the causal relationship is still unclear, The purpose of this study was to evaluate whether genetically predicted ALM is causally associated with the risk of stroke and AD by performing Mendelian randomization (MR) analyses.

METHODS

A two-sample MR study was designed. Genetic variants associated with the ALM were obtained from a large genome-wide association study (GWAS) and utilized as instrumental variables (IVs). Summary-level data for stroke and AD were generated from the corresponding GWASs. We used random-effect inverse-variance weighted (IVW) as the main method for estimating causal effects, complemented by several sensitivity analyses, including the weighted median, MR-Egger, and MR-pleiotropy residual sum and outlier (MR-PRESSO) methods. Multivariable analysis was further conducted to adjust for confounding factors, including body mass index (BMI), type 2 diabetes mellitus (T2DM), low density lipoprotein-C (LDL-C), and atrial fibrillation (AF).

RESULTS

The present MR study indicated significant inverse associations of genetically predicted ALM with any ischemic stroke ([AIS], odds ratio [OR], 0.93; 95% confidence interval [CI], 0.89-0.97; P = 0.002) and AD (OR, 090; 95% CI 0.85-0.96; P = 0.001). Regarding the subtypes of AIS, genetically predicted ALM was related to the risk of large artery stroke ([LAS], OR, 0.86; 95% CI 0.77-0.95; P = 0.005) and small vessel stroke ([SVS], OR, 0.80; 95% CI 0.73-0.89; P < 0.001). Regarding multivariable MR analysis, ALM retained the stable effect on AIS when adjusting for BMI, LDL-C, and AF, while a suggestive association was observed after adjusting for T2DM. And the estimated effect of ALM on LAS was significant after adjustment for BMI and AF, while a suggestive association was found after adjusting for T2DM and LDL-C. Besides, the estimated effects of ALM were still significant on SVS and AD after adjustment for BMI, T2DM, LDL-C, and AF.

CONCLUSIONS

The two-sample MR analysis indicated that genetically predicted ALM was negatively related to AIS and AD. And the subgroup analysis of AIS revealed a negative causal effect of genetically predicted ALM on LAS or SVS. Future studies are required to further investigate the underlying mechanisms.

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

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

Flavonoid-Rich Trianthema decandra Ameliorates Cognitive Dysfunction in the Hyperglycemic Rats

The present study was aimed at the evaluation of neuroprotective ability of methanolic extract of Trianthema decandra (METD) against hyperglycemia-related cognitive impairment in rats. The extract of T. decandra was standardized by TLC and HPTLC methods. To verify the identity and purity of isolated compounds, they were segregated and characterized using various techniques, including UV-visible spectrophotometry, FT-IR, H-NMR, and Mass spectroscopy. α-Amylase and α-glucosidase inhibition property of the extracts were assessed in-vitro. The screening of the neuroprotective effects of METD in hyperglycemic rats was done utilizing Morri's water (MWM) and elevated plus maze (EPM) model, as well as acetylcholinesterase (AChE) activity. The extracts of Trianthema decandra and its chemical constituents, namely quercetin and phytol, demonstrated a significant protective effect on enzymes like α-amylase and α-glucosidase. Methanol and hydroalcoholic extracts have shown the strongest inhibitory activity followed by chloroform extract. Quercetin and phytol were associated with the methanolic and chloroform extracts which were identified using TLC and HPTLC techniques. During the thirty days of the study, the induction of diabetes in the rats exhibited persistent hyperglycemia, hyperlipidemia, higher escape latency during training trials and reduced time spent in target quadrant in probe trial in Morris water maze test, and increased escape latency in EPM task. Regimen of METD (200 and 400 mg/kg) in the diabetic rats reduced the glucose levels in blood, lipid, and liver profile and showed positive results on Morri's water and elevated plus maze tasks. During the investigation, it was determined that Trianthema decandra extracts and the chemical constituent's quercetin and phytol in it had anti-diabetic and neuroprotective activities.

The tool effect is lower in older adults with or without cognitive impairments than in young adults

Grabbing a phone from a table or stepping over an obstacle on the ground are daily activities that require the brain to take account of both object and the body's parameters. Research has shown that a person's estimated maximum reach is temporarily overestimated after using a tool, even when the tool is no longer in hand. This tool effect reflects the high plasticity of the perceptual-motor system (e.g., body schema updating)-at least in young individuals. The objective of the present study was to determine whether the tool effect is smaller in older adults. Forty-four young adults, 37 older adults without cognitive impairment and 30 older adults with cognitive impairment took part in the experiment. The task consisted in visually estimating the ability to reach (using the index finger) a target positioned at different locations on a table, both before and after using a rake. We observed a strong after-effect of tool use in the young adults only. Conversely, a tool effect was similarly absent in the older adults without and with cognitive impairment. Moreover, even before the tool was used, the maximum reach was overestimated in each of the three groups, although the overestimation was greatest in the two groups of older adults. In summary, we showed that the tool effect, observed in young adults, was absent in older adults; this finding suggests that with advancing age, the perceptual-motor system is less able to adapt to novel sensorimotor contexts. This lack of adaptation might explain (at least in part) the overestimation of motor skills often reported in the elderly.

Resolving heterogeneity in Alzheimer's disease based on individualized structural covariance network

The heterogeneity of Alzheimer's disease (AD) poses a challenge to precision medicine. We aimed to identify distinct subtypes of AD based on the individualized structural covariance network (IDSCN) analysis and to research the underlying neurobiology mechanisms. In this study, 187 patients with AD (age = 73.57 ± 6.00, 50% female) and 143 matched normal controls (age = 74.30 ± 7.80, 44% female) were recruited from the Alzheimer's Disease Neuroimaging Initiative (ADNI) project database, and T1 images were acquired. We utilized the IDSCN analysis to generate individual-level altered structural covariance network and performed k-means clustering to subtype AD based on structural covariance network. Cognition, disease progression, morphological features, and gene expression profiles were further compared between subtypes, to characterize the heterogeneity in AD. Two distinct AD subtypes were identified in a reproducible manner, and we named the two subtypes as slow progression type (subtype 1, n = 104, age = 76.15 ± 6.44, 42% female) and rapid progression type (subtype 2, n = 83, age = 71.98 ± 8.72, 47% female), separately. Subtype 1 had better baseline visuospatial function than subtype 2 (p < 0.05), whereas subtype 2 had better baseline memory function than subtype 1 (p < 0.05). Subtype 2 showed worse progression in memory (p = 0.003), language (p = 0.003), visuospatial function (p = 0.020), and mental state (p = 0.038) than subtype 1. Subtype 1 often shared increased structural covariance network, mainly in the frontal lobe and temporal lobe regions, whereas subtype 2 often shared increased structural covariance network, mainly in occipital lobe regions and temporal lobe regions. Functional annotation further revealed that all differential structural covariance network between the two AD subtypes were mainly implicated in memory, learning, emotion, and cognition. Additionally, differences in gray matter volume (GMV) between AD subtypes were identified, and genes associated with GMV differences were found to be enriched in the terms potassium ion transport, synapse organization, and histone modification and the pathways viral infection, neurodegeneration-multiple diseases, and long-term depression. The two distinct AD subtypes were identified and characterized with neuroanatomy, cognitive trajectories, and gene expression profiles. These comprehensive results have implications for neurobiology mechanisms and precision medicine.

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

Background

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

Objective

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

Methods

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

Results

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

Conclusions

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

Noninvasive Gamma Sensory Stimulation May Reduce White Matter and Myelin Loss in Alzheimer's Disease

BACKGROUND

Patients with Alzheimer's disease (AD) demonstrate progressive white matter atrophy and myelin loss. Restoring myelin content or preventing demyelination has been suggested as a therapeutic approach for AD.

OBJECTIVE

Herein, we investigate the effects of non-invasive, combined visual and auditory gamma-sensory stimulation on white matter atrophy and myelin content loss in patients with AD.

METHODS

In this study, we used the magnetic resonance imaging (MRI) data from the OVERTURE study (NCT03556280), a randomized, controlled, clinical trial in which active treatment participants received daily, non-invasive, combined visual and auditory, 40 Hz stimulation for six months. A subset of OVERTURE participants who meet the inclusion criteria for detailed white matter (N = 38) and myelin content (N = 36) assessments are included in the analysis. White matter volume assessments were performed using T1-weighted MRI, and myelin content assessments were performed using T1-weighted/T2-weighted MRI. Treatment effects on white matter atrophy and myelin content loss were assessed.

RESULTS

Combined visual and auditory gamma-sensory stimulation treatment is associated with reduced total and regional white matter atrophy and myelin content loss in active treatment participants compared to sham treatment participants. Across white matter structures evaluated, the most significant changes were observed in the entorhinal region.

CONCLUSIONS

The study results suggest that combined visual and auditory gamma-sensory stimulation may modulate neuronal network function in AD in part by reducing white matter atrophy and myelin content loss. Furthermore, the entorhinal region MRI outcomes may have significant implications for early disease intervention, considering the crucial afferent connections to the hippocampus and entorhinal cortex.

Structural, static, and dynamic functional MRI predictors for conversion from mild cognitive impairment to Alzheimer's disease: Inter-cohort validation of Shanghai Memory Study and ADNI

Mild cognitive impairment (MCI) is a critical prodromal stage of Alzheimer's disease (AD), and the mechanism underlying the conversion is not fully explored. Construction and inter-cohort validation of imaging biomarkers for predicting MCI conversion is of great challenge at present, due to lack of longitudinal cohorts and poor reproducibility of various study-specific imaging indices. We proposed a novel framework for inter-cohort MCI conversion prediction, involving comparison of structural, static, and dynamic functional brain features from structural magnetic resonance imaging (sMRI) and resting-state functional MRI (fMRI) between MCI converters (MCI_C) and non-converters (MCI_NC), and support vector machine for construction of prediction models. A total of 218 MCI patients with 3-year follow-up outcome were selected from two independent cohorts: Shanghai Memory Study cohort for internal cross-validation, and Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort for external validation. In comparison with MCI_NC, MCI_C were mainly characterized by atrophy, regional hyperactivity and inter-network hypo-connectivity, and dynamic alterations characterized by regional and connectional instability, involving medial temporal lobe (MTL), posterior parietal cortex (PPC), and occipital cortex. All imaging-based prediction models achieved an area under the curve (AUC) > 0.7 in both cohorts, with the multi-modality MRI models as the best with excellent performances of AUC > 0.85. Notably, the combination of static and dynamic fMRI resulted in overall better performance as relative to static or dynamic fMRI solely, supporting the contribution of dynamic features. This inter-cohort validation study provides a new insight into the mechanisms of MCI conversion involving brain dynamics, and paves a way for clinical use of structural and functional MRI biomarkers in future.

Impact of Apolipoprotein E4 on the Locus Coeruleus Functional Connectivity in Preclinical Alzheimer's Disease

Background

Recent interest has surged in the locus coeruleus (LC) for its early involvement in Alzheimer's disease (AD), notably concerning the apolipoprotein ɛ4 allele (APOE4).

Objective

This study aimed to discern LC functional connectivity (FC) variations in preclinical AD subjects, dissecting the roles of APOE4 carrier status and amyloid-β (Aβ) deposition.

Methods

A cohort of 112 cognitively intact individuals, all Aβ-positive, split into 70 APOE4 noncarriers and 42 carriers, underwent functional MRI scans, neuropsychological assessments, and APOE genotyping. The research utilized seed to voxel analysis for illustrating LC rsFC discrepancies between APOE4 statuses and employed a general linear model to examine the interactive influence of APOE4 carrier status and Aβ deposition on LC FC values.

Results

The investigation revealed no significant differences in sex, age, or SUVR between APOE4 carriers and noncarriers. It found diminished LC FC with the occipital cortex in APOE4 carriers and identified a significant interaction between APOE4 carrier status and temporal lobe SUVR in LC FC with the occipital cortex. This interaction suggested a proportional increase in LC FC for APOE4 carriers. Additional notable interactions were observed affecting LC FC with various brain regions, indicating a proportional decrease in LC FC for APOE4 carriers.

Conclusions

These findings confirm that APOE4 carrier status significantly influences LC FC in preclinical AD, showcasing an intricate relationship with regional Aβ deposition. This underscores the critical role of genetic and pathological factors in early AD pathophysiology, offering insights into potential biomarkers for early detection and intervention strategies.

Decreased beta-band activity in left supramarginal gyrus reflects cognitive decline: Evidence from a large clinical dataset in patients with dementia

Cognitive impairment is a major concern in clinical medicine. It is usually evaluated with neuropsychological assessments, which have inherent limitations. To compensate for them, magnetoencephalography has already come into clinical use to evaluate the level of cognitive impairment. It evaluates global changes in the frequency of resting-state brain activity, which are associated with cognitive status. However, it remains unclear what neural mechanism causes the frequency changes. To understand this, it is important to identify cortical regions that mainly contribute to these changes. We retrospectively analysed the clinical records from 310 individuals with cognitive impairment who visited the outpatient department at our hospital. The analysis included resting-state magnetoencephalography, neuropsychological assessment, and clinical diagnosis data. Regional oscillatory intensities were estimated from the magnetoencephalography data, which were statistically analysed, along with neuropsychological assessment scores, and the severity of cognitive impairment associated with clinical diagnosis. The regional oscillatory intensity covering a wide range of regions and frequencies was significantly associated with neuropsychological assessment scores and differed between healthy individuals and patients with cognitive impairment. However, these associations and differences in all conditions were overlapped by a single change in beta frequency in the left supramarginal gyrus. High frequency oscillatory intensity in the left supramarginal gyrus is associated with cognitive impairment levels among patients who were concerned about dementia. It provides new insights into cognitive status measurements using magnetoencephalography, which is expected to develop as an objective index to be used alongside traditional neuropsychological assessments.

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.

Functional connectivity changes in mild cognitive impairment: A meta-analysis of M/EEG studies

OBJECTIVE

Early synchrony alterations have been observed through electrophysiological techniques in Mild Cognitive Impairment (MCI), which is considered the intermediate phase between healthy aging (HC) and Alzheimer's disease (AD). However, the documented direction (hyper/hypo-synchronization), regions and frequency bands affected are inconsistent. This meta-analysis intended to elucidate existing evidence linked to potential neurophysiological biomarkers of AD.

METHODS

We conducted a random-effects meta-analysis that entailed the unbiased inclusion of Non-statistically Significant Unreported Effect Sizes ("MetaNSUE") of electroencephalogram (EEG) and magnetoencephalogram (MEG) studies investigating functional connectivity changes at rest along the healthy-pathological aging continuum, searched through PubMed, Scopus, Web of Science and PsycINFO databases until June 2023.

RESULTS

Of the 3852 articles extracted, we analyzed 12 papers, and we found an alpha synchrony decrease in MCI compared to HC, specifically between temporal-parietal (d = -0.26) and frontal-parietal areas (d = -0.25).

CONCLUSIONS

Alterations of alpha synchrony are present even at MCI stage.

SIGNIFICANCE

Synchrony measures may be promising for the detection of the first hallmarks of connectivity alterations, even at the prodromal stages of the AD, before clinical symptoms occur.

Apraxia testing to distinguish early Alzheimer's disease from psychiatric causes of cognitive impairment

Objective: Mood- and stress-related disorders commonly cause attentional and memory impairments in middle-aged individuals. In memory testing, these impairments can be mistakenly interpreted as symptoms of dementia; thus, more reliable diagnostic approaches are needed. The present work defines the discriminant accuracy of the Dementia Apraxia Test (DATE) between psychiatric conditions and early-onset Alzheimer's disease (AD) on its own and in combination with memory tests. Method: The consecutive sample included 50-70-year-old patients referred to dementia investigations for recent cognitive and/or affective symptoms. The DATE was administered and scored as a blinded measurement, and a receiver operating curve analysis was used to define the optimal diagnostic cut-off score. Results: A total of 24 patients were diagnosed with probable AD (mean age 61 ± 4) and 23 with a psychiatric condition (mean age 57 ± 4). The AD patients showed remarkable limb apraxia, but the psychiatric patients mainly performed at a healthy level on the DATE. The test showed a total discriminant accuracy of 87% for a total sum cut-off of 47 (sensitivity 79% and specificity 96%). The limb subscale alone reached an accuracy of 91% for a cut-off of 20 (sensitivity 83% and specificity 100%). All memory tests were diagnostically less accurate, while the combination of the limb praxis subscale and a verbal episodic memory test suggested a correct diagnosis in all but one patient. Conclusions: Apraxia testing may improve the accuracy of differentiation between AD and psychiatric aetiologies. Its potential in severe and chronic psychiatric conditions should be examined in the future.

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

INTRODUCTION

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

METHODS

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

RESULTS

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

DISCUSSION

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

HIGHLIGHTS

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

Neural correlates of successful emotion recognition in healthy elderly: a multimodal imaging study

The ageing process is associated with reduced emotional recognition (ER) performance. The ER ability is an essential part of non-verbal communication, and its role is crucial for proper social functioning. Here, using the 'Cambridge Centre for Ageing and Neuroscience cohort sample', we investigated when ER, measured using a facial emotion recognition test, begins to consistently decrease along the lifespan. Moreover, using structural and functional MRI data, we identified the neural correlates associated with ER maintenance in the age groups showing early signs of ER decline (N = 283; age range: 58-89 years). The ER performance was positively correlated with greater volume in the superior parietal lobule, higher white matter integrity in the corpus callosum and greater functional connectivity in the mid-cingulate area. Our results suggest that higher ER accuracy in older people is associated with preserved gray and white matter volumes in cognitive or interconnecting areas, subserving brain regions directly involved in emotional processing.

Alzheimer risk-increasing TREM2 variant causes aberrant cortical synapse density and promotes network hyperexcitability in mouse models

The R47H variant of triggering receptor expressed on myeloid cells 2 (TREM2) increases the risk of Alzheimer's disease (AD). To investigate potential mechanisms, we analyzed knockin mice expressing human TREM2-R47H from one mutant mouse Trem2 allele. TREM2-R47H mice showed increased seizure activity in response to an acute excitotoxin challenge, compared to wildtype controls or knockin mice expressing the common variant of human TREM2. TREM2-R47H also increased spontaneous thalamocortical epileptiform activity in App knockin mice expressing amyloid precursor proteins bearing autosomal dominant AD mutations and a humanized amyloid-β sequence. In mice with or without such App modifications, TREM2-R47H increased the density of putative synapses in cortical regions without amyloid plaques. TREM2-R47H did not affect synaptic density in hippocampal regions with or without plaques. We conclude that TREM2-R47H increases AD-related network hyperexcitability and that it may do so, at least in part, by causing an imbalance in synaptic densities across brain regions.

Sex differences in the structural rich-club connectivity in patients with Alzheimer's disease

Background and objectives

Alzheimer's disease (AD) is more prevalent in women than in men; however, there is a discrepancy in research on sex differences in AD. The human brain is a large-scale network with hub regions forming a central core, the rich-club, which is vital to cognitive functions. However, it is unknown whether alterations in the rich-clubs in AD differ between men and women. We aimed to investigate sex differences in the rich-club organization in the brains of patients with AD.

Methods

In total, 260 cognitively unimpaired individuals with negative amyloid positron emission tomography (PET) scans, 281 with prodromal AD (mild cognitive impairment due to AD) and 285 with AD dementia who confirmed with positive amyloid PET scans participated in the study. We obtained high-resolution T1-weighted and diffusion tensor images and performed network analysis.

Results

We observed sex differences in the rich-club and feeder connections in patients with AD, suggesting lower structural connectivity strength in women than in men. We observed a significant group-by-sex interaction in the feeder connections, particularly in the thalamus. In addition, the connectivity strength of the thalamus in the feeder connections was significantly correlated with general cognitive function in only men with prodromal AD and women with AD dementia.

Conclusion

Our findings provide important evidence for sex-specific alterations in the structural brain network related to AD.

Metacognitive Strategy Training Improves Decision-Making Abilities in Amnestic Mild Cognitive Impairment

Mild cognitive impairment (MCI) is associated with deficits in decision-making, which is of utmost importance for daily functioning. Despite evidence of declined decision-making abilities, research on decision-making interventions for MCI is scarce. As metacognition seems to play an important role in decision-making, the present study's aim was to examine whether a metacognitive strategy training can improve MCI patients' decision-making abilities. Older adults-patients of a day care center, diagnosed with amnestic MCI (n = 55) were randomly allocated in two groups, which were matched in gender, age and educational level. Τhe experimental group (n = 27, 18 women, mean age = 70.63, mean years of education = 13.44) received the metacognitive strategy training in parallel with the cognitive and physical training programs of the day care center, and the active control group (n = 28, 21 women, mean age = 70.86, mean years of education = 13.71) received only the cognitive and physical training of the center. The metacognitive strategy training included three online meeting sessions that took place once per week. The basis of the intervention was using analytical thinking, by answering four metacognitive-strategic questions, to make decisions about everyday situations. To examine the efficacy of the training, the ability to make decisions about everyday decision-making situations and the ability to apply decision rules were measured. Both groups participated in a pre-test session and a post-test session, while the experimental group also participated in a follow-up session, one month after the post-test session. The results showed that the experimental group improved its ability to decide, based on analytical thinking, about economic and healthcare-related everyday decision-making situations after they received the metacognitive strategy training. This improvement was maintained one month later. However, the ability to apply decision rules, which requires high cognitive effort, did not improve. In conclusion, it is important that some aspects of the analytical decision-making ability of amnestic MCI patients were improved due to the present metacognitive intervention.

Graph neural network based unsupervised influential sample selection for brain multigraph population fusion

Graph neural networks (GNNs) have witnessed remarkable proliferation due to the increasing number of applications where data is represented as graphs. GNN-based multigraph population fusion methods for estimating population representative connectional brain templates (CBT) have recently led to improvements, especially in network neuroscience. However, prior studies do not consider how an individual training brain multigraph influences the quality of GNN training for brain multigraph population fusion. To address this issue, we propose two major sample selection methods to quantify the influence of a training brain multigraph on the brain multigraph population fusion task using GNNs, in a fully unsupervised manner: (1) GraphGradIn, in which we use gradients w.r.t GNN weights to trace changes in the centeredness loss of connectional brain template during the training phase; (2) GraphTestIn, in which we exclude a training brain multigraph of interest during the refinement process in the test phase to infer its influence on the CBT centeredness loss. Next, we select the most influential multigraphs to build the training set for brain multigraph population fusion into a CBT. We conducted extensive experiments on brain multigraph datasets to show that using a dataset of influential training samples improves the learned connectional brain template in terms of centeredness, discriminativeness, and topological soundness. Finally, we demonstrate the use of our methods by discovering the connectional fingerprints of healthy and neurologically disordered brain multigraph populations including Alzheimer's disease and Autism spectrum disorder patients. Our source code is available at https://github.com/basiralab/GraphGradIn.

A pilot examination of the validity of stylus and finger drawing on visuomotor-mediated tests on ACEmobile

INTRODUCTION

Cognitive assessments, such as the Addenbrooke's Cognitive Examination (ACE-III) and Montreal Cognitive Assessment (MoCA), have been modified for administration using tablet computers. While this offers important advantages for practice, it may also threaten the test validity. The current study sought to test whether administering visuospatial and writing tests using a tablet (finger or stylus drawing), would demonstrate equivalence to traditional pencil and paper administration on ACEmobile.

METHOD

This study recruited 26 participants with Alzheimer's disease and 23 healthy older adults. Most participants had low familiarity with using a tablet computer. Participants completed ACEmobile in its entirety, after which they repeated the infinity loops, cube, and clock drawing and sentence writing tests by drawing with a stylus and their finger onto an iPad. Performance on the drawing and writing tests using a stylus, finger, and pencil were compared.

RESULTS

Statistically significant differences were observed between the finger and pencil administration on the ACEmobile, with participants performing worse on the finger drawing trials. Differences in scores were most apparent on the sentence writing task. In contrast, no statistical differences were observed between the pencil and stylus administration.

DISCUSSION

The findings of this pilot study have important implications for clinical neuropsychology and demonstrate that administering ACEmobile drawing tests with finger drawing is invalid. However, due to the small sample size, a lack of counterbalancing and the narrow range of scores of the dependent variable, we are unable to confidently interpret the validity of stylus drawing. This is an important consideration for future research.

Preliminary validation of a structural magnetic resonance imaging metric for tracking dementia-related neurodegeneration and future decline

Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by cognitive decline and atrophy in the medial temporal lobe (MTL) and subsequent brain regions. Structural magnetic resonance imaging (sMRI) has been widely used in research and clinical care for diagnosis and monitoring AD progression. However, atrophy patterns are complex and vary by patient. To address this issue, researchers have made efforts to develop more concise metrics that can summarize AD-specific atrophy. Many of these methods can be difficult to interpret clinically, hampering adoption. In this study, we introduce a novel index which we call an "AD-NeuroScore," that uses a modified Euclidean-inspired distance function to calculate differences between regional brain volumes associated with cognitive decline. The index is adjusted for intracranial volume (ICV), age, sex, and scanner model. We validated AD-NeuroScore using 929 older adults from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, with a mean age of 72.7 years (SD = 6.3; 55.1-91.5) and cognitively normal (CN), mild cognitive impairment (MCI), or AD diagnoses. Our validation results showed that AD-NeuroScore was significantly associated with diagnosis and disease severity scores (measured by MMSE, CDR-SB, and ADAS-11) at baseline. Furthermore, baseline AD-NeuroScore was associated with both changes in diagnosis and disease severity scores at all time points with available data. The performance of AD-NeuroScore was equivalent or superior to adjusted hippocampal volume (AHV), a widely used metric in AD research. Further, AD-NeuroScore typically performed as well as or sometimes better when compared to other existing sMRI-based metrics. In conclusion, we have introduced a new metric, AD-NeuroScore, which shows promising results in detecting AD, benchmarking disease severity, and predicting disease progression. AD-NeuroScore differentiates itself from other metrics by being clinically practical and interpretable.

Move Your Body, Boost Your Brain: The Positive Impact of Physical Activity on Cognition across All Age Groups

While the physical improvements from exercise have been well documented over the years, the impact of physical activity on mental health has recently become an object of interest. Physical exercise improves cognition, particularly attention, memory, and executive functions. However, the mechanisms underlying these effects have yet to be fully understood. Consequently, we conducted a narrative literature review concerning the association between acute and chronic physical activity and cognition to provide an overview of exercise-induced benefits during the lifetime of a person. Most previous papers mainly reported exercise-related greater expression of neurotransmitter and neurotrophic factors. Recently, structural and functional magnetic resonance imaging techniques allowed for the detection of increased grey matter volumes for specific brain regions and substantial modifications in the default mode, frontoparietal, and dorsal attention networks following exercise. Here, we highlighted that physical activity induced significant changes in functional brain activation and cognitive performance in every age group and could counteract psychological disorders and neural decline. No particular age group gained better benefits from exercise, and a specific exercise type could generate better cognitive improvements for a selected target subject. Further research should develop appropriate intervention programs concerning age and comorbidity to achieve the most significant cognitive outcomes.

Structural and functional differences in the brains of patients with MCI with and without depressive symptoms and their relations with Alzheimer's disease: an MRI study

Background

The mild cognitive impairment (MCI) stage among elderly individuals is very complex, and the level of diagnostic accuracy is far from ideal. Some studies have tried to improve the 'MCI due to Alzheimer's disease (AD)' classification by further stratifying these patients into subgroups. Depression-related symptoms may play an important role in helping to better define the MCI stage in elderly individuals.

Objective

In this work, we explored functional and structural differences in the brains of patients with nondepressed MCI (nDMCI) and patients with MCI with depressive symptoms (DMCI), and we examined how these groups relate to AD atrophy patterns and cognitive functioning.

Methods

Sixty-five participants underwent MRI exams and were divided into four groups: cognitively normal, nDMCI, DMCI, and AD. We compared the regional brain volumes, cortical thickness, and white matter microstructure measures using diffusion tensor imaging among groups. Additionally, we evaluated changes in functional connectivity using fMRI data.

Results

In comparison to the nDMCI group, the DMCI patients had more pronounced atrophy in the hippocampus and amygdala. Additionally, DMCI patients had asymmetric damage in the limbic-frontal white matter connection. Furthermore, two medial posterior regions, the isthmus of cingulate gyrus and especially the lingual gyrus, had high importance in the structural and functional differentiation between the two groups.

Conclusion

It is possible to differentiate nDMCI from DMCI patients using MRI techniques, which may contribute to a better characterization of subtypes of the MCI stage.

Associations of Midlife Dietary Patterns with Incident Dementia and Brain Structure: Findings from the UK Biobank Study

BACKGROUND

At present, the results on the associations between dietary patterns and the risk of dementia are inconsistent, and studies on the associations between dietary patterns and brain structures are limited.

OBJECTIVE

We aimed to investigate the associations of midlife dietary patterns with incident dementia and brain structures.

METHODS

Based on the UK Biobank Study, we investigated the 1) prospective associations of four healthy dietary pattern indices (healthy plant-based diet index [hPDI], Mediterranean diet score [MDS], Recommended food score [RFS], and Mediterranean-Dietary Approaches to Stop Hypertension Intervention [DASH] Intervention for Neurodegenerative Delay Diet [MIND]) with incident dementia (identified using linked hospital data; N = 114,684; mean age, 56.8 years; 55.5% females) using Cox proportional-hazards regressions and the 2) cross-sectional associations of these dietary pattern indices with brain structures (estimated using magnetic resonance imaging; N = 18,214; mean age, 55.9 years; 53.1% females) using linear regressions. A series of covariates were adjusted, and several sensitivity analyses were conducted.

RESULTS

A total of 481 (0.42%) participants developed dementia during the average 9.4-year follow-up. Although the associations were not statistically significant, all dietary patterns exerted protective effects against incident dementia (all hazard ratios < 1). Furthermore, higher dietary pattern indices were significantly associated with larger regional brain volumes, including volumes of gray matter in the parietal and temporal cortices and volumes of the hippocampus and thalamus. The main results were confirmed via sensitivity analyses.

CONCLUSIONS

Greater adherence to hPDI, MDS, RFS, and MIND was individually associated with larger brain volumes in specific regions. This study shows a comprehensive picture of the consistent associations of midlife dietary patterns with the risk of dementia and brain health, underscoring the potential benefits of a healthy diet in the prevention of dementia.

Emotional Dysregulation in Mild Behavioral Impairment Is Associated with Reduced Cortical Thickness in the Right Supramarginal Gyrus

BACKGROUND

Mild behavioral impairment (MBI) has attracted attention as a possible precursor symptom of dementia, but its neural basis has not been fully investigated.

OBJECTIVE

We aimed to investigate the relationship between MBI and surface area, cortical thickness, and volume in the temporal and parietal lobes, which are strongly associated with dementia and emotional disorders.

METHODS

This retrospective study evaluated 123 participants: 90 with mild cognitive impairment (MCI), 13 with subjective cognitive decline (SCD), and 20 cognitively healthy (CH). Using analysis of covariance (ANCOVA) with sex, age, and MMSE score as covariates, cortical thickness, surface area, and volume in 10 regions were compared between groups with and without MBI. Groups with MBI emotional dysregulation were also compared with groups without MBI.

RESULTS

ANCOVA revealed significantly smaller cortical thickness in the MBI group's right parahippocampal (p = 0.01) and supramarginal gyri (p = 0.002). After multiple comparison correction, only the right supramarginal gyrus was significantly smaller (p = 0.02). When considering only MBI emotional dysregulation, the right parahippocampal and supramarginal gyrus' cortical thicknesses were significantly smaller in this MBI group (p = 0.03, 0.01). However, multiple comparison correction identified no significant differences (p = 0.14, 0.11).

CONCLUSION

Overall MBI and the emotional dysregulation domains were associated with reduced cortical thickness in the right parahippocampal and supramarginal gyri. Since neurodegeneration in the medial temporal and parietal lobe precedes early Alzheimer's disease (AD), MBI, particularly emotion dysregulation, may predict early AD below the diagnostic threshold.

Genetic Risk for Alzheimer Disease and Plasma Tau Are Associated With Accelerated Parietal Cortex Thickness Change in Middle-Aged Adults

Background and Objectives

Neuroimaging and biomarker studies in Alzheimer disease (AD) have shown well-characterized patterns of cortical thinning and altered biomarker concentrations of tau and β-amyloid (Aβ). However, earlier identification of AD has great potential to advance clinical care and determine candidates for drug trials. The extent to which AD risk markers relate to cortical thinning patterns in midlife is unknown. The first objective of this study was to examine cortical thickness change associated with genetic risk for AD among middle-aged military veterans. The second objective was to determine the relationship between plasma tau and Aβ and change in brain cortical thickness among veterans stratified by genetic risk for AD.

Methods

Participants consisted of post-9/11 veterans (N = 155) who were consecutively enrolled in the Translational Research Center for TBI and Stress Disorders prospective longitudinal cohort and were assessed for mild traumatic brain injury (TBI) and posttraumatic disorder (PTSD). Genome-wide polygenic risk scores (PRSs) for AD were calculated using summary results from the International Genomics of Alzheimer's Disease Project. T-tau and Aβ40 and Aβ42 plasma assays were run using Simoa technology. Whole-brain MRI cortical thickness change estimates were obtained using the longitudinal stream of FreeSurfer. Follow-up moderation analyses examined the AD PRS × plasma interaction on change in cortical thickness in AD-vulnerable regions.

Results

Higher AD PRS, signifying greater genetic risk for AD, was associated with accelerated cortical thickness change in a right hemisphere inferior parietal cortex cluster that included the supramarginal gyrus, angular gyrus, and intraparietal sulcus. Higher tau, but not Aβ42/40 ratio, was associated with greater cortical thickness change among those with higher AD PRS. Mild TBI and PTSD were not associated with cortical thickness change.

Discussion

Plasma tau, particularly when combined with genetic stratification for AD risk, can be a useful indicator of brain change in midlife. Accelerated inferior parietal cortex changes in midlife may be an important factor to consider as a marker of AD-related brain alterations.

Gray Matter Volume as Evidence for Cognitive Reserve in Bilinguals With Mild Cognitive Impairment

BACKGROUND

Compared with monolinguals, bilinguals have a later onset of mild cognitive impairment (MCI) and Alzheimer disease symptoms and greater neuropathology at similar cognitive and clinical levels. The present study follows a previous report showing the faster conversion from MCI to Alzheimer disease for bilingual patients than comparable monolinguals, as predicted by a cognitive reserve (CR).

PURPOSE

Identify whether the increased CR found for bilinguals in the previous study was accompanied by greater gray matter (GM) atrophy than was present for the monolinguals.

METHODS

A novel deep-learning technique based on convolutional neural networks was used to enhance clinical scans into 1 mm MPRAGEs and analyze the GM volume at the time of MCI diagnosis in the earlier study.

PATIENTS

Twenty-four bilingual and 24 monolingual patients were diagnosed with MCI at a hospital memory clinic.

RESULTS

Bilingual patients had more GM loss than monolingual patients in areas related to language processing, attention, decision-making, motor function, and episodic memory retrieval. Bilingualism and age were the strongest predictors of atrophy after other variables such as immigration and education were included in a multivariate model.

DISCUSSION

CR from bilingualism is evident in the initial stages of neurodegeneration after MCI has been diagnosed.

Discriminative patterns of white matter changes in Alzheimer's

Changes in structural connectivity of the Alzheimer's brain have not been widely studied utilizing cutting-edge methodologies. This study develops an efficient structural connectome-based convolutional neural network (CNN) to classify the AD and uses explanations of CNNs' choices in classification to pinpoint the discriminative changes in white matter connectivity in AD. A CNN architecture has been developed to classify normal control (NC) and AD subjects from the weighted structural connectome. Then, the CNN classification decision is visually analyzed using gradient-based localization techniques to identify the discriminative changes in white matter connectivity in Alzheimer's. The cortical regions involved in the identified discriminative structural connectivity changes in AD are highly covered in the temporal/subcortical regions. A specific pattern is identified in the discriminative changes in structural connectivity of AD, where the white matter changes are revealed within the temporal/subcortical regions and from the temporal/subcortical regions to the frontal and parietal regions in both left and right hemispheres.  The proposed approach has the potential to comprehensively analyze the discriminative structural connectivity differences in AD, change the way of detecting biomarkers, and help clinicians better understand the structural changes in AD and provide them with more confidence in automated diagnostic systems.

Animal models of developmental dyslexia

As some critics have stated, the term “developmental dyslexia” refers to a strictly human disorder, relating to a strictly human capacity – reading – so it cannot be modeled in experimental animals, much less so in lowly rodents. However, two endophenotypes associated with developmental dyslexia are eminently suitable for animal modeling: Cerebral Lateralization, as illustrated by the association between dyslexia and non-righthandedness, and Cerebrocortical Dysfunction, as illustrated by the described abnormal structural anatomy and/or physiology and functional imaging of the dyslexic cerebral cortex. This paper will provide a brief review of these two endophenotypes in human beings with developmental dyslexia and will describe the animal work done in my laboratory and that of others to try to shed light on the etiology of and neural mechanisms underlying developmental dyslexia. Some thought will also be given to future directions of the research.

Explainable Tensor Multi-Task Ensemble Learning Based on Brain Structure Variation for Alzheimer's Disease Dynamic Prediction

Machine learning approaches for predicting Alzheimer's disease (AD) progression can substantially assist researchers and clinicians in developing effective AD preventive and treatment strategies. This study proposes a novel machine learning algorithm to predict the AD progression utilising a multi-task ensemble learning approach. Specifically, we present a novel tensor multi-task learning (MTL) algorithm based on similarity measurement of spatio-temporal variability of brain biomarkers to model AD progression. In this model, the prediction of each patient sample in the tensor is set as one task, where all tasks share a set of latent factors obtained through tensor decomposition. Furthermore, as subjects have continuous records of brain biomarker testing, the model is extended to ensemble the subjects' temporally continuous prediction results utilising a gradient boosting kernel to find more accurate predictions. We have conducted extensive experiments utilising data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to evaluate the performance of the proposed algorithm and model. Results demonstrate that the proposed model have superior accuracy and stability in predicting AD progression compared to benchmarks and state-of-the-art multi-task regression methods in terms of the Mini Mental State Examination (MMSE) questionnaire and The Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog) cognitive scores. Brain biomarker correlation information can be utilised to identify variations in individual brain structures and the model can be utilised to effectively predict the progression of AD with magnetic resonance imaging (MRI) data and cognitive scores of AD patients at different stages.

Different loneliness types, cognitive function, and brain structure in midlife: Findings from the Framingham Heart Study

Background

It remains unclear whether persistent loneliness is related to brain structures that are associated with cognitive decline and development of Alzheimer's disease (AD). This study aimed to investigate the relationships between different loneliness types, cognitive functioning, and regional brain volumes.

Methods

Loneliness was measured longitudinally, using the item from the Center for Epidemiologic Studies Depression Scale in the Framingham Heart Study, Generation 3, with participants' average age of 46·3 ± 8·6 years. Robust regression models tested the association between different loneliness types with longitudinal neuropsychological performance (n = 2,609) and regional magnetic resonance imaging brain data (n = 1,829) (2002-2019). Results were stratified for sex, depression, and Apolipoprotein E4 (ApoE4).

Findings

Persistent loneliness, but not transient loneliness, was strongly associated with cognitive decline, especially memory and executive function. Persistent loneliness was negatively associated with temporal lobe volume (β = -0.18, 95%CI [-0.32, -0.04], P = 0·01). Among women, persistent loneliness was associated with smaller frontal lobe (β = -0.19, 95%CI [-0.38, -0.01], P = 0·04), temporal lobe (β = -0.20, 95%CI [-0.37, -0.03], P = 0·02), and hippocampus volumes (β = -0.23, 95%CI [-0.40, -0.06], P = 0·007), and larger lateral ventricle volume (β = 0.15, 95%CI [0.02, 0.28], P = 0·03). The higher cumulative loneliness scores across three exams, the smaller parietal, temporal, and hippocampus volumes and larger lateral ventricle were evident, especially in the presence of ApoE4.

Interpretation

Persistent loneliness in midlife was associated with atrophy in brain regions responsible for memory and executive dysfunction. Interventions to reduce the chronicity of loneliness may mitigate the risk of age-related cognitive decline and AD.

Funding

US National Institute on Aging.

Impact of age on the cerebrospinal fluid spaces: high-convexity and medial subarachnoid spaces decrease with age

Background

Impaired cerebrospinal fluid (CSF) dynamics may contribute to the pathophysiology of neurodegenerative diseases, and play a crucial role in brain health in older people; nonetheless, such age-related changes have not been well elucidated. Disproportionately enlarged subarachnoid-space hydrocephalus (DESH) is a neuroimaging phenotype of idiopathic normal-pressure hydrocephalus, originating from impaired CSF dynamics, and closely associated with aging. This study aimed to investigate the pathophysiology of DESH and determine age-related changes in CSF dynamics.

Methods

Using magnetic resonance imaging, we investigated the pathophysiology of DESH by quantitatively evaluating the volumes of DESH-related regions (ventricles [VS], Sylvian fissure [SF], and subarachnoid spaces at high convexity and midline [SHM]) and brain parenchyma in community-dwelling individuals aged  ≥ 65 years. DESH-related regions were assessed using a visual rating scale, and volumes measured using voxel-based morphometry. Brain parenchyma volumes were measured using FreeSurfer software.

Results

Data from 1,356 individuals were analyzed, and 25 (1.8%) individuals had DESH. Regarding the relationships between the volume of each CSF space and age, VS and SF volumes increased with age, whereas SHM volume did not increase. VS and SF volumes increased as the whole brain volume decreased, whereas SHM volume did not increase even if the whole brain volume decreased; that is, SHM did not expand even if brain atrophy progressed. Moreover, lower Mini-Mental State Examination scores were significantly associated with lower SHM volume and higher VS volume. These associations remained significant even when individuals with DESH were excluded.

Conclusions

This study showed that the volume of high-convexity and medial subarachnoid spaces did not expand and tended to decrease with age; the human brain continuously progresses toward a “DESH-like” morphology with aging in community-dwelling older persons (i.e., DESH might be an “accelerated aging stage” rather than an “age-related disorder”). Our results indicated that brain atrophy may be associated with the development of “DESH-like” morphology. In addition, this morphological change, as well as brain atrophy, is an important condition associated with cognitive decline in older adults. Our findings highlight the importance of investigating the aging process of CSF dynamics in the human brain to preserve brain health in older people.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12987-022-00381-5.

Quantitative Assessment of Resting-State Functional Connectivity MRI to Differentiate Amnestic Mild Cognitive Impairment, Late-Onset Alzheimer's Disease From Normal Subjects

BACKGROUND

Alzheimer disease (AD) is a neurological disorder with brain network dysfunction. Investigation of the brain network functional connectivity (FC) alterations using resting-state functional MRI (rs-fMRI) can provide valuable information about the brain network pattern in early AD diagnosis.

PURPOSE

To quantitatively assess FC patterns of resting-state brain networks and graph theory metrics (GTMs) to identify potential features for differentiation of amnestic mild cognitive impairment (aMCI) and late-onset AD from normal.

STUDY TYPE

Prospective.

SUBJECTS

A total of 14 normal, 16 aMCI, and 13 late-onset AD.

FIELD STRENGTH/SEQUENCE

A 3.0 T; rs-fMRI: single-shot 2D-EPI and T1-weighted structure: MPRAGE.

ASSESSMENT

By applying bivariate correlation coefficient and Fisher transformation on the time series of predefined ROIs' pairs, correlation coefficient matrixes and ROI-to-ROI connectivity (RRC) were extracted. By thresholding the RRC matrix (with a threshold of 0.15), a graph adjacency matrix was created to compute GTMs.

STATISTICAL TESTS

Region of interest (ROI)-based analysis: parametric multivariable statistical analysis (PMSA) with a false discovery rate using (FDR)-corrected P < 0.05 cluster-level threshold together with posthoc uncorrected P < 0.05 connection-level threshold. Graph-theory analysis (GTA): P-FDR-corrected < 0.05. One-way ANOVA and Chi-square tests were used to compare clinical characteristics.

RESULTS

PMSA differentiated AD from normal, with a significant decrease in FC of default mode, salience, dorsal attention, frontoparietal, language, visual, and cerebellar networks. Furthermore, significant increase in overall FC of visual and language networks was observed in aMCI compared to normal. GTA revealed a significant decrease in global-efficiency (28.05 < 45), local-efficiency (22.98 < 24.05), and betweenness-centrality (14.60 < 17.39) for AD against normal. Moreover, a significant increase in local-efficiency (33.46 > 24.05) and clustering-coefficient (25 > 20.18) were found in aMCI compared to normal.

DATA CONCLUSION

This study demonstrated resting-state FC potential as an indicator to differentiate AD, aMCI, and normal. GTA revealed brain integration and breakdown by providing concise and comprehensible statistics.

EVIDENCE LEVEL

1 TECHNICAL EFFICACY: Stage 2.

Enhanced temporal complexity of EEG signals in older individuals with high cognitive functions

Recent studies suggest that the maintenance of cognitive function in the later life of older people is an essential factor contributing to mental wellbeing and physical health. Particularly, the risk of depression, sleep disorders, and Alzheimer's disease significantly increases in patients with mild cognitive impairment. To develop early treatment and prevention strategies for cognitive decline, it is necessary to individually identify the current state of cognitive function since the progression of cognitive decline varies among individuals. Therefore, the development of biomarkers that allow easier measurement of cognitive function in older individuals is relevant for hyperaged societies. One of the methods used to estimate cognitive function focuses on the temporal complexity of electroencephalography (EEG) signals. The characteristics of temporal complexity depend on the time scale, which reflects the range of neuron functional interactions. To capture the dynamics, composed of multiple time scales, multiscale entropy (MSE) analysis is effective for comprehensively assessing the neural activity underlying cognitive function in the brain. Thus, we hypothesized that EEG complexity analysis could serve to assess a wide range of cognitive functions in older adults. To validate our hypothesis, we divided older participants into two groups based on their cognitive function test scores: a high cognitive function group and a low cognitive function group, and applied MSE analysis to the measured EEG data of all participants. The results of the repeated-measures analysis of covariance using age and sex as a covariate in the MSE profile showed a significant difference between the high and low cognitive function groups (F = 10.18, p = 0.003) and the interaction of the group × electrodes (F = 3.93, p = 0.002). Subsequently, the results of the post-hoct-test showed high complexity on a slower time scale in the frontal, parietal, and temporal lobes in the high cognitive function group. This high complexity on a slow time scale reflects the activation of long-distance neural interactions among various brain regions to achieve high cognitive functions. This finding could facilitate the development of a tool for diagnosis of cognitive decline in older individuals.

Abnormal information interaction in multilayer directed network based on cross-frequency integration of mild cognitive impairment and Alzheimer’s disease

Mild cognitive impairment (MCI) and Alzheimer’s disease (AD) have been reported to result in abnormal cross-frequency integration. However, previous studies have failed to consider specific abnormalities in receiving and outputting information among frequency bands during integration. Here, we investigated heterogeneity in receiving and outputting information during cross-frequency integration in patients. The results showed that during cross-frequency integration, information interaction first increased and then decreased, manifesting in the heterogeneous distribution of inter-frequency nodes for receiving information. A possible explanation was that due to damage to some inter-frequency hub nodes, intra-frequency nodes gradually became new inter-frequency nodes, whereas original inter-frequency nodes gradually became new inter-frequency hub nodes. Notably, damage to the brain regions that receive information between layers was often accompanied by a strengthened ability to output information and the emergence of hub nodes for outputting information. Moreover, an important compensatory mechanism assisted in the reception of information in the cingulo-opercular and auditory networks and in the outputting of information in the visual network. This study revealed specific abnormalities in information interaction and compensatory mechanism during cross-frequency integration, providing important evidence for understanding cross-frequency integration in patients with MCI and AD.

Understanding the multidimensional cognitive deficits of logopenic variant primary progressive aphasia

The logopenic variant of primary progressive aphasia is characterized by early deficits in language production and phonological short-term memory, attributed to left-lateralized temporoparietal, inferior parietal and posterior temporal neurodegeneration. Despite patients primarily complaining of language difficulties, emerging evidence points to performance deficits in non-linguistic domains. Temporoparietal cortex, and functional brain networks anchored to this region, are implicated as putative neural substrates of non-linguistic cognitive deficits in logopenic variant primary progressive aphasia, suggesting that degeneration of a shared set of brain regions may result in co-occurring linguistic and non-linguistic dysfunction early in the disease course. Here, we provide a Review aimed at broadening the understanding of logopenic variant primary progressive aphasia beyond the lens of an exclusive language disorder. By considering behavioural and neuroimaging research on non-linguistic dysfunction in logopenic variant primary progressive aphasia, we propose that a significant portion of multidimensional cognitive features can be explained by degeneration of temporal/inferior parietal cortices and connected regions. Drawing on insights from normative cognitive neuroscience, we propose that these regions underpin a combination of domain-general and domain-selective cognitive processes, whose disruption results in multifaceted cognitive deficits including aphasia. This account explains the common emergence of linguistic and non-linguistic cognitive difficulties in logopenic variant primary progressive aphasia, and predicts phenotypic diversification associated with progression of pathology in posterior neocortex.

A Role for PGC-1a in the Control of Abnormal Mitochondrial Dynamics in Alzheimer’s Disease

Emerging evidence suggests that the proper control of mitochondrial dynamics provides a window for therapeutic intervention for Alzheimer’s disease (AD) progression. The transcriptional coactivator peroxisome proliferator activated receptor gamma coactivator 1 (PGC-1a) has been shown to regulate mitochondrial biogenesis in neurons. Thus far, the roles of PGC-1a in Alzheimer’s disease and its potential value for restoring mitochondrial dysfunction remain largely unknown. In the present study, we explored the impacts of PGC-1a on AD pathology and neurobehavioral dysfunction and its potential mechanisms with a particular focus on mitochondrial dynamics. Paralleling AD-related pathological deposits, neuronal apoptosis, abnormal mitochondrial dynamics and lowered membrane potential, a remarkable reduction in the expression of PGC-1a was shown in the cortex of APP/PS1 mice at 6 months of age. By infusing AAV-Ppargc1α into the lateral parietal association (LPtA) cortex of the APP/PS1 brain, we found that PGC-1a ameliorated AD-like behavioral abnormalities, such as deficits in spatial reference memory, working memory and sensorimotor gating. Notably, overexpressed PGC-1a in LPtA rescued mitochondrial swelling and damage in neurons, likely through correcting the altered balance in mitochondrial fission–fusion and its abnormal distribution. Our findings support the notion that abnormal mitochondrial dynamics is likely an important mechanism that leading to mitochondrial dysfunction and AD-related pathological and cognitive impairments, and they indicate the potential value of PGC-1a for restoring mitochondrial dynamics as an innovative therapeutic target for AD.

The Parietal Lobe in Alzheimer’s Disease and Blindness

The progressive aging of the population will notably increase the burden of those diseases which leads to a disabling situation, such as Alzheimer’s disease (AD) and ophthalmological diseases that cause a visual impairment (VI). Eye diseases that cause a VI raise neuroplastic processes in the parietal lobe. Meanwhile, the aforementioned lobe suffers a severe decline throughout AD. From this perspective, diving deeper into the particularities of the parietal lobe is of paramount importance. In this article, we discuss the functions of the parietal lobe, review the parietal anatomical and pathophysiological peculiarities in AD, and also describe some of the changes in the parietal region that occur after VI. Although the alterations in the hippocampus and the temporal lobe have been well documented in AD, the alterations of the parietal lobe have been less thoroughly explored. Recent neuroimaging studies have revealed that some metabolic and perfusion impairments along with a reduction of the white and grey matter could take place in the parietal lobe during AD. Conversely, it has been speculated that blinding ocular diseases induce a remodeling of the parietal region which is observable through the improvement of the integration of multimodal stimuli and in the increase of the volume of this cortical region. Based on current findings concerning the parietal lobe in both pathologies, we hypothesize that the increased activity of the parietal lobe in people with VI may diminish the neurodegeneration of this brain region in those who are visually impaired by oculardiseases.

Aspects of cognition that impact aging-in-place and long-term care planning

BACKGROUND

Older adults frequently defer decisions about their aging-in-place/long-term care (AIP-LTC) needs. As a result, when older adults experience worsening Alzheimer's disease, family members/friends become surrogate decision makers. We sought to understand what aspects of cognition impact older adult AIP-LTC planning.

METHODS

As part of the PlanYourLifespan (PYL)-LitCog study, we longitudinally examined AIP-LTC decision-making among a cohort (LitCog) of community-based older adults (65 years and older) recruited from hospital-associated primary care clinics in Chicago, Illinois, with extensive cognitive testing. PlanYourLifespan.org (PYL) is an evidence-based online intervention that facilitates AIP-LTC planning. Subjects underwent baseline testing, received the PYL online intervention, and then were surveyed at 1, 6, and 12 months about AIP-LTC decision-making. Cross-sectional logistic regression analysis was conducted examining cognitive variables that impacted AIP-LTC decision-making.

RESULTS

Of the 293 older adults interviewed (mean age 73.0 years, 40.4% non-White), subjects were more likely to have made AIP-LTC decisions if they had adequate inductive reasoning (ETS letter sets total-OR = 1.14 (95% CI = 1.03-1.27; p < 0.05)) and adequate working memory (size judgment span total-OR = 1.76 (95% CI = 1.13-2.73; p < 0.05)). There were no differences in decision-making observed in verbal abilities, long-term memory, or processing speed. All analyses were adjusted for participant gender, race, age, and decision-making response at baseline.

CONCLUSION

Inductive reasoning and working memory are critical to AIP-LTC decision-making. Screening routinely for these specific cognitive domains is important in targeting and helping older adults prepare in time for their future AIP-LTC needs.

Sparse Interpretation of Graph Convolutional Networks for Multi-Modal Diagnosis of Alzheimer's Disease

The interconnected quality of brain regions in neurological disease has immense importance for the development of biomarkers and diagnostics. While Graph Convolutional Network (GCN) methods are fundamentally compatible with discovering the connected role of brain regions in disease, current methods apply limited consideration for node features and their connectivity in brain network analysis. In this paper, we propose a sparse interpretable GCN framework (SGCN) for the identification and classification of Alzheimer's disease (AD) using brain imaging data with multiple modalities. SGCN applies an attention mechanism with sparsity to identify the most discriminative subgraph structure and important node features for the detection of AD. The model learns the sparse importance probabilities for each node feature and edge with entropy, ℓ 1, and mutual information regularization. We then utilized this information to find signature regions of interest (ROIs), and emphasize the disease-specific brain network connections by detecting the significant difference of connectives between regions in healthy control (HC), and AD groups. We evaluated SGCN on the ADNI database with imaging data from three modalities, including VBM-MRI, FDG-PET, and AV45-PET, and observed that the important probabilities it learned are effective for disease status identification and the sparse interpretability of disease-specific ROI features and connections. The salient ROIs detected and the most discriminative network connections interpreted by our method show a high correspondence with previous neuroimaging evidence associated with AD.

Identification and validation of a gray matter volume network in Alzheimer's disease

OBJECTIVE

This study aims to identify and validate a gray matter volume network in patients with Alzheimer's disease (AD).

METHODS

To identify a disease-related network, a principal component analysis-based algorithm, Scaled Subprofile Model, was applied to gray matter volume data derived from structural T1-weighted magnetic resonance imaging of the training sample that consisted of nine patients with AD (women, four; dementia, seven; mild cognitive impairment, two; age, 66.7 ± 8.8 [mean ± SD] years) with positive ¹⁸F-flutemetamol amyloid positron emission tomography and eight age-matched healthy controls obtained on-site. The network expression scores were calculated by topographic profile rating in the validation sample obtained via the Open Access Series of Imaging Studies and comprised 12 patients with AD dementia (women, four; age, 70.0 ± 3.7 years) and 12 age-matched healthy controls.

RESULTS

A significant network from the training sample, for which subject expression differed between the groups (permutation test, P = 0.006; sensitivity and specificity, 100%; area under the curve, 1), was identified. This network was represented by the principal components 1, 2, and 3 and showed a relative decrease in the inferior parietal lobule including angular gyrus, inferior temporal gyrus, premotor cortex, amygdala, hippocampus, and precuneus. It significantly differed between the groups with a sensitivity, specificity, and area under the curve of 83%, 91%, and 0.85, respectively, in the validation sample (P = 0.003).

CONCLUSIONS

An AD-related gray matter volume network that captured relevant regions was identified in amyloid positron emission tomography-positive patients and validated in an independent sample.

Differential Neuropathology, Genetics, and Transcriptomics in Two Kindred Cases with Alzheimer’s Disease and Lewy Body Dementia

Alzheimer’s disease (AD) and Lewy body dementia (LBD) are two different forms of dementia, but their pathology may involve the same cortical areas with overlapping cognitive manifestations. Nonetheless, the clinical phenotype is different due to the topography of the lesions driven by the different underlying molecular processes that arise apart from genetics, causing diverse neurodegeneration. Here, we define the commonalities and differences in the pathological processes of dementia in two kindred cases, a mother and a son, who developed classical AD and an aggressive form of AD/LBD, respectively, through a neuropathological, genetic (next-generation sequencing), and transcriptomic (RNA-seq) comparison of four different brain areas. A genetic analysis did not reveal any pathogenic variants in the principal AD/LBD-causative genes. RNA sequencing highlighted high transcriptional dysregulation within the substantia nigra in the AD/LBD case, while the AD case showed lower transcriptional dysregulation, with the parietal lobe being the most involved brain area. The hippocampus (the most degenerated area) and basal ganglia (lacking specific lesions) expressed the lowest level of dysregulation. Our data suggest that there is a link between transcriptional dysregulation and the amount of tissue damage accumulated across time, assessed through neuropathology. Moreover, we highlight that the molecular bases of AD and LBD follow very different pathways, which underlie their neuropathological signatures. Indeed, the transcriptome profiling through RNA sequencing may be an important tool in flanking the neuropathological analysis for a deeper understanding of AD and LBD pathogenesis.

MRI findings suggestive of Alzheimer's disease in patients with primary open angle glaucoma - a single sequence analysis using rapid 3D T1 spoiled gradient echo

OBJECTIVE

Primary open-angle glaucoma (POAG) is a degenerative optic neuropathy disease which has somewhat similar pathophysiology to Alzheimer's disease (AD). This study aims to determine the presence of medial temporal atrophy and parietal lobe atrophy in patients with POAG compared to normal controls using medial temporal atrophy (MTA) scoring and posterior cortical atrophy (PCA) scoring system on T1 magnetization-prepared rapid gradient-echo.

METHODS

50 POAG patients and 50 normal subjects were recruited and an MRI brain with T1-magnetization-prepared rapid gradient-echo was performed. Medial temporal lobe and parietal lobe atrophy were by MTA and PCA/Koedam scoring. The score of the PCA and MTA were compared between the POAG group and the controls.

RESULTS

There was a significant statistical difference between PCA score in POAG and the healthy control group (p-value = 0.026). There is no statistical difference between MTA score in POAG compared to the healthy control group (p-value = 0.58).

CONCLUSION

This study suggests a correlation between POAG and PCA score. Potential application of this scoring method in clinical diagnosis and monitoring of POAG patients.

ADVANCES IN KNOWLEDGE

The scoring method used in AD may also be applied in the diagnosis and monitoring of POAGMRI brain, specifically rapid volumetric T1 spoiled gradient echo sequence, may be applied in POAG assessment.

Decoupling of regional neural activity and inter-regional functional connectivity in Alzheimer's disease: a simultaneous PET/MR study

PURPOSE

Alzheimer's disease (AD) and mild cognitive impairment (MCI) are characterized by both aberrant regional neural activity and disrupted inter-regional functional connectivity (FC). However, the effect of AD/MCI on the coupling between regional neural activity (measured by regional fluorodeoxyglucose imaging (rFDG)) and inter-regional FC (measured by resting-state functional magnetic resonance imaging (rs-fMRI)) is poorly understood.

METHODS

We scanned 19 patients with MCI, 33 patients with AD, and 26 healthy individuals by simultaneous FDG-PET/rs-fMRI and assessed rFDG and inter-regional FC metrics (i.e., clustering coefficient and degree centrality). Next, we examined the potential moderating effect of disease status (MCI or AD) on the link between rFDG and inter-regional FC metrics using hierarchical moderated multiple regression analysis. We also tested this effect by considering interaction between disease status and inter-regional FC metrics, as well as interaction between disease status and rFDG.

RESULTS

Our findings revealed that both rFDG and inter-regional FC metrics were disrupted in MCI and AD. Moreover, AD altered the relationship between rFDG and inter-regional FC metrics. In particular, we found that AD moderated the effect of inter-regional FC metrics of the caudate, parahippocampal gyrus, angular gyrus, supramarginal gyrus, frontal pole, inferior temporal gyrus, middle frontal, lateral occipital, supramarginal gyrus, precuneus, and thalamus on predicting their rFDG. On the other hand, AD moderated the effect of rFDG of the parietal operculum on predicting its inter-regional FC metric.

CONCLUSION

Our findings demonstrated that AD decoupled the link between regional neural activity and functional segregation and global connectivity across particular brain regions.

Barriers to Effective Memory Assessments for Alzheimer's Disease

Recently, Alzheimer's Disease International (ADI) stressed that around 75% of people living with dementia globally are still not receiving a diagnosis. In this commentary, I reflect on how efforts towards better cognitive assessments, particularly of memory, can be aligned and harmonized to contribute to such needs. I highlight some barriers that ongoing collaborations and trials are facing and their potential drivers. I suggest some strategies that can help overcome them and in so doing, integrate research agendas. We need to ignite the debate towards strategies that can help level the playfield to tackle Alzheimer's disease with true global solutions.

Reduced parietal activation in participants with mild cognitive impairments during visual-spatial processing measured with functional near-infrared spectroscopy

Functional Near Infrared Spectroscopy (fNIRS) may be a suitable, simple, and cost-effective brain imaging technique for detecting divergent neuronal patterns at an early stage of neurodegeneration. In course of Mild Cognitive Impairment (MCI) or Alzheimer's disease (AD), a deficit in visual-spatial processing, located in the parietal cortex, is a reliable risk factor. Earlier, we established the application of the clock-hand-angle-discrimination task (ADT) during fNIRS to identify neuronal correlates of the visual-spatial processing in a healthy elderly sample. In this analysis, we aimed to measure and find out differences in the hemodynamic response in MCI participants compared to matched healthy controls. As expected, MCI participants showed more errors over all conditions of pointer length and a higher reaction time in the long and middle pointer length condition. Moreover, results revealed a significant reduction of cortical activation in MCI patients. There was a generally increased activity in both the right as compared to the left hemisphere and the superior parietal brain region as compared to the inferior parietal brain region in both groups. In summary, fNIRS can be implemented in the measurement of visual-spatial processing in MCI patients and healthy elderly based on ADT. MCI participants had difficulties to cope with the ADT. Since neuronal hypoactivity occurs with concomitant behavioral deficits, an additional analysis was performed on a subgroup of MCI patients who performed as well as the control group in behavior. This subgroup analysis also showed a hypoactivation of the parietal cortex, without evidence of a compensatory activation. Therefore, we assume that MCI patients are characterized by a deficit in the parietal cortex. Overall, these findings confirm our hypothesis that hemodynamic deficits in visual-spatial processing, localized in the parietal cortex, are reliable and early diagnostic markers for cognitive decline in risk groups for the development of AD.

Apolipoprotein E Genotype Moderation of the Association Between Physical Activity and Brain Health. A Systematic Review and Meta-Analysis

Introduction

Possession of one or two e4 alleles of the apolipoprotein E (APOE) gene is associated with cognitive decline and dementia risk. Some evidence suggests that physical activity may benefit carriers of the e4 allele differently.

Method

We conducted a systematic review and meta-analysis of studies which assessed APOE differences in the association between physical activity and: lipid profile, Alzheimer's disease pathology, brain structure and brain function in healthy adults. Searches were carried out in PubMed, SCOPUS, Web of Science and PsycInfo.

Results

Thirty studies were included from 4,896 papers screened. Carriers of the e4 allele gained the same benefit from physical activity as non-carriers on most outcomes. For brain activation, e4 carriers appeared to gain a greater benefit from physical activity on task-related and resting-state activation and resting-state functional connectivity compared to non-carriers. Post-hoc analysis identified possible compensatory mechanisms allowing e4 carriers to maintain cognitive function.

Discussion

Though there is evidence suggesting physical activity may benefit e4 carriers differently compared to non-carriers, this may vary by the specific brain health outcome, perhaps limited to brain activation. Further research is required to confirm these findings and elucidate the mechanisms.