Grey Matter Loss at Different Stages of Cognitive Decline: A Role for the Thalamus in Developing Alzheimer's Disease

Lifespan development of thalamic nuclei and characterizing thalamic nuclei abnormalities in schizophrenia using normative modeling

Thalamic abnormalities have been repeatedly implicated in the pathophysiology of schizophrenia and other neurodevelopmental disorders. Uncovering the etiology of thalamic abnormalities and how they may contribute to illness phenotypes faces at least two obstacles. First, the typical developmental trajectories of thalamic nuclei and their association with cognition across the lifespan are largely unknown. Second, modest effect sizes indicate marked individual differences and pose a significant challenge to personalized medicine. To address these knowledge gaps, we characterized the development of thalamic nuclei volumes using normative models generated from the Human Connectome Project Lifespan datasets (5-100+ years), then applied them to an independent clinical cohort to determine the frequency of thalamic volume deviations in people with schizophrenia (17-61 years). Normative models revealed diverse non-linear age effects across the lifespan. Association nuclei exhibited negative age effects during youth but stabilized in adulthood until turning negative again with older age. Sensorimotor nuclei volumes remained relatively stable through youth and adulthood until also turning negative with older age. Up to 18% of individuals with schizophrenia exhibited abnormally small (i.e., below the 5th centile) mediodorsal and pulvinar volumes, and the degree of deviation, but not raw volumes, correlated with the severity of cognitive impairment. While case-control differences are robust, only a minority of patients demonstrate unusually small thalamic nuclei volumes. Normative modeling enables the identification of these individuals, which is a necessary step toward precision medicine.

DeepResBat: deep residual batch harmonization accounting for covariate distribution differences

Pooling MRI data from multiple datasets requires harmonization to reduce undesired inter-site variabilities, while preserving effects of biological variables (or covariates). The popular harmonization approach ComBat uses a mixed effect regression framework that explicitly accounts for covariate distribution differences across datasets. There is also significant interest in developing harmonization approaches based on deep neural networks (DNNs), such as conditional variational autoencoder (cVAE). However, current DNN approaches do not explicitly account for covariate distribution differences across datasets. Here, we provide mathematical results, suggesting that not accounting for covariates can lead to suboptimal harmonization. We propose two DNN-based covariate-aware harmonization approaches: covariate VAE (coVAE) and DeepResBat. The coVAE approach is a natural extension of cVAE by concatenating covariates and site information with site- and covariate-invariant latent representations. DeepResBat adopts a residual framework inspired by ComBat. DeepResBat first removes the effects of covariates with nonlinear regression trees, followed by eliminating site differences with cVAE. Finally, covariate effects are added back to the harmonized residuals. Using three datasets from three continents with a total of 2787 participants and 10085 anatomical T1 scans, we find that DeepResBat and coVAE outperformed ComBat, CovBat and cVAE in terms of removing dataset differences, while enhancing biological effects of interest. However, coVAE hallucinates spurious associations between anatomical MRI and covariates even when no association exists. Future studies proposing DNN-based harmonization approaches should be aware of this false positive pitfall. Overall, our results suggest that DeepResBat is an effective deep learning alternative to ComBat. Code for DeepResBat can be found here: https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/harmonization/An2024_DeepResBat .

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.

Structure focused neurodegeneration convolutional neural network for modelling and classification of Alzheimer's disease

Alzheimer's disease (AD), the predominant form of dementia, is a growing global challenge, emphasizing the urgent need for accurate and early diagnosis. Current clinical diagnoses rely on radiologist expert interpretation, which is prone to human error. Deep learning has thus far shown promise for early AD diagnosis. However, existing methods often overlook focal structural atrophy critical for enhanced understanding of the cerebral cortex neurodegeneration. This paper proposes a deep learning framework that includes a novel structure-focused neurodegeneration CNN architecture named SNeurodCNN and an image brightness enhancement preprocessor using gamma correction. The SNeurodCNN architecture takes as input the focal structural atrophy features resulting from segmentation of brain structures captured through magnetic resonance imaging (MRI). As a result, the architecture considers only necessary CNN components, which comprises of two downsampling convolutional blocks and two fully connected layers, for achieving the desired classification task, and utilises regularisation techniques to regularise learnable parameters. Leveraging mid-sagittal and para-sagittal brain image viewpoints from the Alzheimer's disease neuroimaging initiative (ADNI) dataset, our framework demonstrated exceptional performance. The para-sagittal viewpoint achieved 97.8% accuracy, 97.0% specificity, and 98.5% sensitivity, while the mid-sagittal viewpoint offered deeper insights with 98.1% accuracy, 97.2% specificity, and 99.0% sensitivity. Model analysis revealed the ability of SNeurodCNN to capture the structural dynamics of mild cognitive impairment (MCI) and AD in the frontal lobe, occipital lobe, cerebellum, temporal, and parietal lobe, suggesting its potential as a brain structural change digi-biomarker for early AD diagnosis. This work can be reproduced using code we made available on GitHub.

Regional changes in brain metabolism during the progression of mild cognitive impairment: a longitudinal study based on radiomics

BACKGROUND

This study aimed to establish radiomics models based on positron emission tomography (PET) images to longitudinally predict transition from mild cognitive impairment (MCI) to Alzheimer's disease (AD).

METHODS

In our study, 278 MCI patients from the ADNI database were analyzed, where 60 transitioned to AD (pMCI) and 218 remained stable (sMCI) over 48 months. Patients were divided into a training set (n = 222) and a validation set (n = 56). We first employed voxel-based analysis of 18F-FDG PET images to identify brain regions that present significant SUV difference between pMCI and sMCI groups. Radiomic features were extracted from these regions, key features were selected, and predictive models were developed for individual and combined brain regions. The models' effectiveness was evaluated using metrics like AUC to determine the most accurate predictive model for MCI progression.

RESULTS

Voxel-based analysis revealed four brain regions implicated in the progression from MCI to AD. These include ROI1 within the Temporal lobe, ROI2 and ROI3 in the Thalamus, and ROI4 in the Limbic system. Among the predictive models developed for these individual regions, the model utilizing ROI4 demonstrated superior predictive accuracy. In the training set, the AUC for the ROI4 model was 0.803 (95% CI 0.736, 0.865), and in the validation set, it achieved an AUC of 0.733 (95% CI 0.559, 0.893). Conversely, the model based on ROI3 showed the lowest performance, with an AUC of 0.75 (95% CI 0.685, 0.809). Notably, the comprehensive model encompassing all identified regions (ROI total) outperformed the single-region models, achieving an AUC of 0.884 (95% CI 0.845, 0.921) in the training set and 0.816 (95% CI 0.705, 0.909) in the validation set, indicating significantly enhanced predictive capability for MCI progression to AD.

CONCLUSION

Our findings underscore the Limbic system as the brain region most closely associated with the progression from MCI to AD. Importantly, our study demonstrates that a PET brain radiomics model encompassing multiple brain regions (ROI total) significantly outperforms models based on single brain regions. This comprehensive approach more accurately identifies MCI patients at high risk of progressing to AD, offering valuable insights for non-invasive diagnostics and facilitating early and timely interventions in clinical settings.

Genetic evidence for the causal relationships between migraine, dementia, and longitudinal brain atrophy

BACKGROUND

Migraine is a neurological disease with a significant genetic component and is characterized by recurrent and prolonged episodes of headache. Previous epidemiological studies have reported a higher risk of dementia in migraine patients. Neuroimaging studies have also shown structural brain atrophy in regions that are common to migraine and dementia. However, these studies are observational and cannot establish causality. The present study aims to explore the genetic causal relationship between migraine and dementia, as well as the mediation roles of brain structural changes in this association using Mendelian randomization (MR).

METHODS

We collected the genome-wide association study (GWAS) summary statistics of migraine and its two subtypes, as well as four common types of dementia, including Alzheimer's disease (AD), vascular dementia, frontotemporal dementia, and Lewy body dementia. In addition, we collected the GWAS summary statistics of seven longitudinal brain measures that characterize brain structural alterations with age. Using these GWAS, we performed Two-sample MR analyses to investigate the causal effects of migraine and its two subtypes on dementia and brain structural changes. To explore the possible mediation of brain structural changes between migraine and dementia, we conducted a two-step MR mediation analysis.

RESULTS

The MR analysis demonstrated a significant association between genetically predicted migraine and an increased risk of AD (OR = 1.097, 95% CI = [1.040, 1.158], p = 7.03 × 10- 4). Moreover, migraine significantly accelerated annual atrophy of the total cortical surface area (-65.588 cm2 per year, 95% CI = [-103.112, -28.064], p = 6.13 × 10- 4) and thalamic volume (-9.507 cm3 per year, 95% CI = [-15.512, -3.502], p = 1.91 × 10- 3). The migraine without aura (MO) subtype increased the risk of AD (OR = 1.091, 95% CI = [1.059, 1.123], p = 6.95 × 10- 9) and accelerated annual atrophy of the total cortical surface area (-31.401 cm2 per year, 95% CI = [-43.990, -18.811], p = 1.02 × 10- 6). The two-step MR mediation analysis revealed that thalamic atrophy partly mediated the causal effect of migraine on AD, accounting for 28.2% of the total effect.

DISCUSSION

This comprehensive MR study provided genetic evidence for the causal effect of migraine on AD and identified longitudinal thalamic atrophy as a potential mediator in this association. These findings may inform brain intervention targets to prevent AD risk in migraine patients.

Study on the Relationship between Cerebral Blood Perfusion, Neuronal Cytokines and Cognitive Function in Patients with Alzheimer's Disease

BACKGROUND

Alzheimer's disease (AD) is a common neurodegenerative disorder characterized by the progressive emergence of multiple cognitive deficits. Early diagnosis is of great significance for the intervention and treatment of AD. The objective of this study is to explore the relationship between cerebral blood perfusion, neuronal cytokines and cognitive function in patients with AD.

METHODS

AD patients admitted to the 903 Hospital of the People's Liberation Army Joint Logistics Support Force from June 2020 to January 2023 were retrospectively selected as the study objects, and 65 healthy people who underwent physical examination during the same period were included in the control group. Subjects in both groups underwent 3.0 T magnetic resonance imaging (MRI) to observe their cerebral blood perfusion parameters. The level of cognitive function in both groups was assessed using the Montreal Cognitive Assessment (MoCA). Venous blood was collected from both groups, and the serum levels of brain-derived neuronal factor (BDNF) and glial cell-derived neurotrophic factor (GDNF) were measured by enzyme-linked immunosorbent assay (ELISA). The correlation of serum BDNF and GDNF levels with cerebral blood perfusion parameters and MoCA score in the AD group was analyzed using Spearman analysis.

RESULTS

The cerebral blood flow signal intensity of the left frontal lobe, right frontal lobe, left temporal lobe, right temporal lobe, left parietal lobe, right parietal lobe, left occipital lobe, and right occipital lobe of the observation group was significantly lower than that of the control group (p < 0.001). The visuospatial, executive functions, naming, attention, language function, abstract generalization ability, memory ability, orientation, and total MoCA scale scores were significantly lower than those of the control group (p < 0.001). The serum levels of BDNF and GDNF in the observation group were significantly lower than those in the control group (p < 0.001). The results of Spearman analysis showed that cerebral blood perfusion parameters of the left frontal lobe, right frontal lobe, left temporal lobe, right temporal lobe, left parietal lobe, right parietal lobe, left occipital lobe, and right occipital lobe were positively correlated with cognitive function scores in AD patients, serum BDNF and GDNF levels were positively correlated with cognitive function scores in AD patients, and the correlation was statistically significant (p < 0.05).

CONCLUSION

In AD patients, blood perfusion parameters and serum BDNF and GDNF levels were significantly lower than those of healthy people. Cerebral blood perfusion parameters of the left frontal lobe, right frontal lobe, left temporal lobe, right temporal lobe, left parietal lobe, right parietal lobe, left occipital lobe, and right occipital lobe, and BDNF and GDNF levels were positively correlated with cognitive function scores in AD patients.

Machine Learning Model for Mild Cognitive Impairment Stage Based on Gait and MRI Images

In patients with mild cognitive impairment (MCI), a lower level of cognitive function is associated with a higher likelihood of progression to dementia. In addition, gait disturbances and structural changes on brain MRI scans reflect cognitive levels. Therefore, we aimed to classify MCI based on cognitive level using gait parameters and brain MRI data. Eighty patients diagnosed with MCI from three dementia centres in Gangwon-do, Korea, were recruited for this study. We defined MCI as a Clinical Dementia Rating global score of ≥0.5, with a memory domain score of ≥0.5. Patients were classified as early-stage or late-stage MCI based on their mini-mental status examination (MMSE) z-scores. We trained a machine learning model using gait and MRI data parameters. The convolutional neural network (CNN) resulted in the best classifier performance in separating late-stage MCI from early-stage MCI; its performance was maximised when feature patterns that included multimodal features (GAIT + white matter dataset) were used. The single support time was the strongest predictor. Machine learning that incorporated gait and white matter parameters achieved the highest accuracy in distinguishing between late-stage MCI and early-stage MCI.

Constructing hierarchical attentive functional brain networks for early AD diagnosis

Analyzing functional brain networks (FBN) with deep learning has demonstrated great potential for brain disorder diagnosis. The conventional construction of FBN is typically conducted at a single scale with a predefined brain region atlas. However, numerous studies have identified that the structure and function of the brain are hierarchically organized in nature. This urges the need of representing FBN in a hierarchical manner for more effective analysis of the complementary diagnostic insights at different scales. To this end, this paper proposes to build hierarchical FBNs adaptively within the Transformer framework. Specifically, a sparse attention-based node-merging module is designed to work alongside the conventional network feature extraction modules in each layer. The proposed module generates coarser nodes for further FBN construction and analysis by combining fine-grained nodes. By stacking multiple such layers, a hierarchical representation of FBN can be adaptively learned in an end-to-end manner. The hierarchical structure can not only integrate the complementary information from multiscale FBN for joint analysis, but also reduce the model complexity due to decreasing node sizes. Moreover, this paper argues that the nodes defined by the existing atlases are not necessarily the optimal starting level to build FBN hierarchy and exploring finer nodes may further enrich the FBN representation. In this regard, each predefined node in an atlas is split into multiple sub-nodes, overcoming the scale limitation of the existing atlases. Extensive experiments conducted on various data sets consistently demonstrate the superior performance of the proposed method over the competing methods.

The subcortical default mode network and Alzheimer's disease: a systematic review and meta-analysis

The default mode network is a central cortical brain network suggested to play a major role in several disorders and to be particularly vulnerable to the neuropathological hallmarks of Alzheimer's disease. Subcortical involvement in the default mode network and its alteration in Alzheimer's disease remains largely unknown. We performed a systematic review, meta-analysis and empirical validation of the subcortical default mode network in healthy adults, combined with a systematic review, meta-analysis and network analysis of the involvement of subcortical default mode areas in Alzheimer's disease. Our results show that, besides the well-known cortical default mode network brain regions, the default mode network consistently includes subcortical regions, namely the thalamus, lobule and vermis IX and right Crus I/II of the cerebellum and the amygdala. Network analysis also suggests the involvement of the caudate nucleus. In Alzheimer's disease, we observed a left-lateralized cluster of decrease in functional connectivity which covered the medial temporal lobe and amygdala and showed overlap with the default mode network in a portion covering parts of the left anterior hippocampus and left amygdala. We also found an increase in functional connectivity in the right anterior insula. These results confirm the consistency of subcortical contributions to the default mode network in healthy adults and highlight the relevance of the subcortical default mode network alteration in Alzheimer's disease.

Altered brain morphology and functional connectivity in postmenopausal women: automatic segmentation of whole-brain and thalamic subnuclei and resting-state fMRI

The transition to menopause is associated with various physiological changes, including alterations in brain structure and function. However, menopause-related structural and functional changes are poorly understood. The purpose of this study was not only to compare the brain volume changes between premenopausal and postmenopausal women, but also to evaluate the functional connectivity between the targeted brain regions associated with structural atrophy in postmenopausal women. Each 21 premenopausal and postmenopausal women underwent magnetic resonance imaging (MRI). T1-weighted MRI and resting-state functional MRI data were used to compare the brain volume and seed-based functional connectivity, respectively. In statistical analysis, multivariate analysis of variance, with age and whole brain volume as covariates, was used to evaluate surface areas and subcortical volumes between the two groups. Postmenopausal women showed significantly smaller cortical surface, especially in the left medial orbitofrontal cortex (mOFC), right superior temporal cortex, and right lateral orbitofrontal cortex, compared to premenopausal women (p < 0.05, Bonferroni-corrected) as well as significantly decreased functional connectivity between the left mOFC and the right thalamus was observed (p < 0.005, Monte-Carlo corrected). Although postmenopausal women did not show volume atrophy in the right thalamus, the volume of the right pulvinar anterior, which is one of the distinguished thalamic subnuclei, was significantly decreased (p < 0.05, Bonferroni-corrected). Taken together, our findings suggest that diminished brain volume and functional connectivity may be linked to menopause-related symptoms caused by the lower sex hormone levels.

Systematic comparison of 3D Deep learning and classical machine learning explanations for Alzheimer's Disease detection

Black-box deep learning (DL) models trained for the early detection of Alzheimer's Disease (AD) often lack systematic model interpretation. This work computes the activated brain regions during DL and compares those with classical Machine Learning (ML) explanations. The architectures used for DL were 3D DenseNets, EfficientNets, and Squeeze-and-Excitation (SE) networks. The classical models include Random Forests (RFs), Support Vector Machines (SVMs), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting (LightGBM), Decision Trees (DTs), and Logistic Regression (LR). For explanations, SHapley Additive exPlanations (SHAP) values, Local Interpretable Model-agnostic Explanations (LIME), Gradient-weighted Class Activation Mapping (GradCAM), GradCAM++ and permutation-based feature importance were implemented. During interpretation, correlated features were consolidated into aspects. All models were trained on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. The validation includes internal and external validation on the Australian Imaging and Lifestyle flagship study of Ageing (AIBL) and the Open Access Series of Imaging Studies (OASIS). DL and ML models reached similar classification performances. Regarding the brain regions, both types focus on different regions. The ML models focus on the inferior and middle temporal gyri, and the hippocampus, and amygdala regions previously associated with AD. The DL models focus on a wider range of regions including the optical chiasm, the entorhinal cortices, the left and right vessels, and the 4th ventricle which were partially associated with AD. One explanation for the differences is the input features (textures vs. volumes). Both types show reasonable similarity to a ground truth Voxel-Based Morphometry (VBM) analysis. Slightly higher similarities were measured for ML models.

Effects of sleep quality on diurnal variation of brain volume in older adults: A retrospective cross-sectional study

AIM

Brain volume is influenced by several factors that can change throughout the day. In addition, most of these factors are influenced by sleep quality. This study investigated diurnal variation in brain volume and its relation to overnight sleep quality.

METHODS

We enrolled 1,003 healthy Koreans without any psychiatric disorders aged 60 years or older. We assessed sleep quality and average wake time using the Pittsburgh Sleep Quality Index, and divided sleep quality into good, moderate, and poor groups. We estimated the whole and regional brain volumes from three-dimensional T1-weighted brain MRI scans. We divided the interval between average wake-up time and MRI acquisition time (INT) into tertile groups: short (INT1), medium (INT2), and long (INT3).

RESULTS

Whole and regional brain volumes showed no significance with respect to INT. However, the `interaction between INT and sleep quality showed significance for whole brain, cerebral gray matter, and cerebrospinal fluid volumes (p < .05). The INT2 group showed significantly lower volumes of whole brain, whole gray matter, cerebral gray matter, cortical gray matter, subcortical gray matter, and cerebrospinal fluid than the INT1 and INT3 groups only in the individuals with good sleep quality.

CONCLUSION

Human brain volume changes significantly within a day associated with overnight sleep in the individuals with good sleep quality.

Dementia risk and thalamic nuclei volumetry in healthy midlife adults: the PREVENT Dementia study

A reduction in the volume of the thalamus and its nuclei has been reported in Alzheimer's disease, mild cognitive impairment and asymptomatic individuals with risk factors for early-onset Alzheimer's disease. Some studies have reported thalamic atrophy to occur prior to hippocampal atrophy, suggesting thalamic pathology may be an early sign of cognitive decline. We aimed to investigate volumetric differences in thalamic nuclei in middle-aged, cognitively unimpaired people with respect to dementia family history and apolipoprotein ε4 allele carriership and the relationship with cognition. Seven hundred participants aged 40-59 years were recruited into the PREVENT Dementia study. Individuals were stratified according to dementia risk (approximately half with and without parental dementia history). The subnuclei of the thalamus of 645 participants were segmented on T1-weighted 3 T MRI scans using FreeSurfer 7.1.0. Thalamic nuclei were grouped into six regions: (i) anterior, (ii) lateral, (iii) ventral, (iv) intralaminar, (v) medial and (vi) posterior. Cognitive performance was evaluated using the computerized assessment of the information-processing battery. Robust linear regression was used to analyse differences in thalamic nuclei volumes and their association with cognitive performance, with age, sex, total intracranial volume and years of education as covariates and false discovery rate correction for multiple comparisons. We did not find significant volumetric differences in the thalamus or its subregions, which survived false discovery rate correction, with respect to first-degree family history of dementia or apolipoprotein ε4 allele status. Greater age was associated with smaller volumes of thalamic subregions, except for the medial thalamus, but only in those without a dementia family history. A larger volume of the mediodorsal medial nucleus (Pfalse discovery rate = 0.019) was associated with a faster processing speed in those without a dementia family history. Larger volumes of the thalamus (P = 0.016) and posterior thalamus (Pfalse discovery rate = 0.022) were associated with significantly worse performance in the immediate recall test in apolipoprotein ε4 allele carriers. We did not find significant volumetric differences in thalamic subregions in relation to dementia risk but did identify an interaction between dementia family history and age. Larger medial thalamic nuclei may exert a protective effect on cognitive performance in individuals without a dementia family history but have little effect on those with a dementia family history. Larger volumes of posterior thalamic nuclei were associated with worse recall in apolipoprotein ε4 carriers. Our results could represent initial dysregulation in the disease process; further study is needed with functional imaging and longitudinal analysis.

Gray and White Matter Voxel-Based Morphometry of Alzheimer's Disease With and Without Significant Cerebrovascular Pathologies

Alzheimer's disease (AD) is the most common type of dementia, and AD individuals often present significant cerebrovascular disease (CVD) symptomology. AD with significant levels of CVD is frequently labeled mixed dementia (or sometimes AD-CVD), and the differentiation of these two neuropathologies (AD, AD-CVD) from each other is challenging, especially at early stages. In this study, we compared the gray matter (GM) and white matter (WM) volumes in AD (n = 83) and AD-CVD (n = 37) individuals compared with those of cognitively healthy controls (n = 85) using voxel-based morphometry (VBM) of their MRI scans. The control individuals, matched for age and sex with our two dementia groups, were taken from the ADNI. The VBM analysis showed widespread patterns of significantly lower GM and WM volume in both dementia groups compared to the control group (P < .05, family-wise error corrected). While comparing with AD-CVD, the AD group mainly demonstrated a trend of lower volumes in the GM of the left putamen and right hippocampus and WM of the right thalamus (uncorrected P < .005 with cluster threshold, K = 10). The AD-CVD group relative to AD tended to present lower GM and WM volumes, mainly in the cerebellar lobules and right brainstem regions, respectively (uncorrected P < .005 with cluster threshold, K = 10). Although finding a discriminatory feature in structural MRI data between AD and AD-CVD neuropathologies is challenging, these results provide preliminary evidence that demands further investigation in a larger sample size.

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.

Nonalcoholic fatty liver disease, liver fibrosis, and structural brain imaging: The Cross-Cohort Collaboration

BACKGROUND AND PURPOSE

Prior studies reported conflicting findings regarding the association of nonalcoholic fatty liver disease (NAFLD) and liver fibrosis with measures of brain health. We examined whether NAFLD and liver fibrosis are associated with structural brain imaging measures in middle- and old-age adults.

METHODS

In this cross-sectional study among dementia- and stroke-free individuals, data were pooled from the Offspring and Third Generation cohorts of the Framingham Heart Study (FHS), the Rotterdam Study (RS), and the Study of Health in Pomerania. NAFLD was assessed through abdominal imaging. Transient hepatic elastography (FibroScan) was used to assess liver fibrosis in FHS and RS. Linear regression models were used to explore the relation of NAFLD and liver fibrosis with brain volumes, including total brain, gray matter, hippocampus, and white matter hyperintensities, adjusting for potential confounders. Results were combined using fixed effects meta-analysis.

RESULTS

In total, 5660 and 3022 individuals were included for NAFLD and liver fibrosis analyses, respectively. NAFLD was associated with smaller volumes of total brain (β = -3.5, 95% confidence interval [CI] = -5.4 to -1.7), total gray matter (β = -1.9, 95% CI = -3.4 to -0.3), and total cortical gray matter (β = -1.9, 95% CI = -3.7 to -0.01). In addition, liver fibrosis (defined as liver stiffness measure ≥8.2 kPa) was related to smaller total brain volumes (β = -7.3, 95% CI = -11.1 to -3.5). Heterogeneity between studies was low.

CONCLUSIONS

NAFLD and liver fibrosis may be directly related to brain aging. Larger and prospective studies are warranted to validate these findings and identify liver-related preventive strategies for neurodegeneration.

"Back to Braak": Role of Nucleus Reuniens and Subcortical Pathways in Alzheimer's Disease Progression

BACKGROUND

Patients with Alzheimer's Disease (AD) exhibit structural alterations of the thalamus that correlate with clinical symptoms. However, given the anatomical complexity of this brain structure, it is still unclear whether atrophy affects specific thalamic nuclei and modulates the clinical progression from a prodromal stage, known as Mild Cognitive Impairment (MCI), to full-fledged AD.

OBJECTIVES

To characterize the structural integrity of distinct thalamic nuclei across the AD spectrum, testing whether MCI patients who convert to AD (c-MCI) show a distinctive pattern of thalamic structural alterations compared to patients who remain stable (s-MCI).

DESIGN

Investigating between-group differences in the volumetric features of distinct thalamic nuclei across the AD spectrum.

SETTING

Prodromal and clinical stages of AD.

PARTICIPANTS

We analyzed data from 84 healthy control subjects (HC), 58 individuals with MCI, and 102 AD patients. The dataset was obtained from the AD Neuroimaging Initiative (ADNI-3) database. The MCI group was further divided into two subgroups depending on whether patients remained stable (s-MCI, n=22) or progressed to AD (s-MCI, n=36) in the 48 months following the diagnosis.

MEASUREMENTS

A multivariate analysis of variance (MANOVA) assessed group differences in the volumetric features of distinct thalamic nuclei obtained from magnetic resonance (MR) images. A stepwise discriminant function analysis identified which feature most effectively predicted the conversion to AD. The corresponding predictive performance was evaluated through a Receiver Operating Characteristic approach.

RESULTS

AD and c-MCI patients showed generalized atrophy of thalamic nuclei compared to HC. In contrast, no significant structural differences were observed between s-MCI and HC subjects. Compared to s-MCI, c-MCI individuals displayed significant atrophy of the nucleus reuniens and a trend toward significant atrophy in the anteroventral and laterodorsal nuclei. The discriminant function analysis confirmed the nucleus reuniens as a significant predictor of AD conversion, with a sensitivity of 0.73 and a specificity of 0.69.

CONCLUSIONS

In line with the pathophysiological relevance of the nucleus reuniens proposed by seminal post-mortem studies on patients with AD, we confirm the pivotal role of this nucleus as a critical hub in the clinical progression to AD. We also propose a theoretical model to explain the evolving dysfunction of subcortical brain networks in the disease process.

Higher fresh fruit intake relates to larger grey matter volumes in areas involved in dementia and depression: A UK Biobank study

The benefits of consuming fruits and vegetables are widely accepted. While previous studies suggest a protective role of fruits and vegetables against a variety of diseases such as dementia and depression, the biological mechanisms/effects remain unclear. Here we investigated the effect of fruit and vegetable consumption on brain structure. Particularly on grey matter (GM) and white matter (WM) volumes, regional GM volumes and subcortical volumes. Cross-sectional imaging data from UK Biobank cohort was used. A total of 9925 participants (Mean age 62.4 ± 7.5 years, 51.1 % men) were included in the present analysis. Measures included fruit and vegetable intake, other dietary patterns and a number of selected lifestyle factors and clinical data. Brain volumes were derived from structural brain magnetic resonance imaging. General linear model was used to study the associations between brain volumes and fruit/vegetable intakes. After adjusting for selected confounding factors, salad/raw vegetable intake showed a positive association with total white matter volume, fresh fruit intake showed a negative association with total grey matter (GM) volume. Regional GM analyses showed that higher fresh fruit intake was associated with larger GM volume in the left hippocampus, right temporal occipital fusiform cortex, left postcentral gyrus, right precentral gyrus, and right juxtapositional lobule cortex. We conclude that fruit and vegetable consumption seems to specifically modulate brain volumes. In particular, fresh fruit intake may have a protective role in specific cortical areas such as the hippocampus, areas robustly involved in the pathophysiology of dementia and depression.

Disentangling in-vivo microstructural changes of white and gray matter in mild cognitive impairment and Alzheimer's disease: a systematic review and meta-analysis

The microstructural characteristics of white and gray matter in mild cognitive impairment (MCI) and the early-stage of Alzheimer's disease (AD) remain unclear. This study aimed to systematically identify the microstructural damages of MCI/AD in studies using neurite orientation dispersion and density imaging (NODDI), and explore their correlations with cognitive performance. Multiple databases were searched for eligible studies. The 10 eligible NODDI studies were finally included. Patients with MCI/AD showed overall significant reductions in neurite density index (NDI) of specific white matter structures in bilateral hemispheres (left hemisphere: -0.40 [-0.53, -0.27], P < 0.001; right: -0.33 [-0.47, -0.19], P < 0.001), involving the bilateral superior longitudinal fasciculus (SLF), uncinate fasciculus (UF), the left posterior thalamic radiation (PTR), and the left cingulum. White matter regions exhibited significant increased orientation dispersion index (ODI) (left: 0.25 [0.02, 0.48], P < 0.05; right: 0.27 [0.07, 0.46], P < 0.05), including the left cingulum, the right UF, and the bilateral parahippocampal cingulum (PHC), and PTR. Additionally, the ODI of gray matter showed significant reduction in bilateral hippocampi (left: -0.97 [-1.42, -0.51], P < 0.001; right: -0.90 [-1.35, -0.45], P < 0.001). The cognitive performance in MCI/AD was significantly associated with NDI (r = 0.50, P < 0.001). Our findings highlight the microstructural changes in MCI/AD were characterized by decreased fiber orientation dispersion in the hippocampus, and decreased neurite density and increased fiber orientation dispersion in specific white matter tracts, including the cingulum, UF, and PTR. Moreover, the decreased NDI may indicate the declined cognitive level of MCI/AD patients.

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.

Early Detection of Alzheimer's Disease in Postmenopausal Women Using Thalamic Subnuclear Volumetry

Alzheimer's disease (AD) and aging are intrinsically interconnected with each other and are mediated by molecular, cellular, and biological systems. In particular, a specific pattern of brain volume atrophy is the most profound risk factor for cognitive impairment, including AD, that is directly linked to aging. Thus, this study aimed to investigate knowledge on the early detection of AD in postmenopausal women, focusing on the volume changes of the subcortical regions, including the thalamic subnuclei, in women with AD vs. postmenopausal women. Twenty-one women with AD and twenty-one postmenopausal women without AD underwent magnetic resonance imaging (MRI). Women with AD showed significantly reduced volumes in the hippocampus, thalamus, and amygdala compared with postmenopausal women (p < 0.05, FWE-corrected). After adjustments for age, the right hippocampal volume was found to be significantly lower in the women with AD, but the volumes of the thalamus and amygdala were relatively unaffected. The women with AD exhibited significantly reduced volume in the right laterodorsal nucleus of the thalamus compared with the postmenopausal women (p < 0.05, Bonferroni-corrected). Our findings suggest that the reduced volume of both the right laterodorsal thalamic nucleus and right hippocampus may serve as a potential biomarker for the early detection of AD in postmenopausal women.

Tele-supervised home-based transcranial alternating current stimulation (tACS) for Alzheimer's disease: a pilot study

Background

Over 55 million people worldwide are currently diagnosed with Alzheimer's disease (AD) and live with debilitating episodic memory deficits. Current pharmacological treatments have limited efficacy. Recently, transcranial alternating current stimulation (tACS) has shown memory improvement in AD by normalizing high-frequency neuronal activity. Here we investigate the feasibility, safety, and preliminary effects on episodic memory of an innovative protocol where tACS is administered within the homes of older adults with AD with the help of a study companion (HB-tACS).

Methods

Eight participants diagnosed with AD underwent multiple consecutive sessions of high-definition HB-tACS (40 Hz, 20-min) targeting the left angular gyrus (AG), a key node of the memory network. The Acute Phase comprised 14-weeks of HB-tACS with at least five sessions per week. Three participants underwent resting state electroencephalography (EEG) before and after the 14-week Acute Phase. Subsequently, participants completed a 2-3-month Hiatus Phase not receiving HB-tACS. Finally, in the Taper phase, participants received 2-3 sessions per week over 3-months. Primary outcomes were safety, as determined by the reporting of side effects and adverse events, and feasibility, as determined by adherence and compliance with the study protocol. Primary clinical outcomes were memory and global cognition, measured with the Memory Index Score (MIS) and Montreal Cognitive Assessment (MoCA), respectively. Secondary outcome was EEG theta/gamma ratio. Results reported as mean ± SD.

Results

All participants completed the study, with an average of 97 HB-tACS sessions completed by each participant; reporting mild side effects during 25% of sessions, moderate during 5%, and severe during 1%. Acute Phase adherence was 98 ± 6.8% and Taper phase was 125 ± 22.3% (rates over 100% indicates participants completed more than the minimum of 2/week). After the Acute Phase, all participants showed memory improvement, MIS of 7.25 ± 3.77, sustained during Hiatus 7.00 ± 4.90 and Taper 4.63 ± 2.39 Phases compared to baseline. For the three participants that underwent EEG, a decreased theta/gamma ratio in AG was observed. Conversely, participants did not show improvement in the MoCA, 1.13 ± 3.80 after the Acute Phase, and there was a modest decrease during the Hiatus -0.64 ± 3.28 and Taper -2.56 ± 5.03 Phases.

Conclusion

This pilot study shows that the home-based, remotely-supervised, study companion administered, multi-channel tACS protocol for older adults with AD was feasible and safe. Further, targeting the left AG, memory in this sample was improved. These are preliminary results that warrant larger more definite trials to further elucidate tolerability and efficacy of the HB-tACS intervention. NCT04783350.

Clinical trial registration

https://clinicaltrials.gov/ct2/show/NCT04783350?term=NCT04783350&draw=2&rank=1, identifier NCT04783350.

Baseline grey matter volumes and white matter hyperintensities predict decline in functional activities in older adults over a 5-year follow-up period

INTRODUCTION

Functional independence is an essential predictor of quality of life in aging, yet few accessible predictors of functional decline have been identified. This study examined associations between baseline structural neuroimaging markers and longitudinal functional status.

METHODS

Linear mixed effects models with follow-up time interaction terms related baseline grey matter volume and white matter hyperintensities (WMHs) to functional trajectory, adjusting for demographic and medical covariates. Subsequent models assessed interactions with cognitive status and apolipoprotein E (APOE) ε4 status.

RESULTS

Smaller baseline grey matter volumes, particularly in regions commonly affected by Alzheimer's disease (AD), and greater baseline WMHs were associated with faster functional decline over a mean 5-year follow-up. Effects were stronger in APOE-ε4 carriers on grey matter variables. Cognitive status interacted with most MRI variables.

DISCUSSION

Greater atrophy in AD-related regions and higher WMH burden at study entry were associated with faster functional decline, particularly among participants at increased risk of AD.

Automatic Detection of Alzheimer's Disease using Deep Learning Models and Neuro-Imaging: Current Trends and Future Perspectives

Deep learning algorithms have a huge influence on tackling research issues in the field of medical image processing. It acts as a vital aid for the radiologists in producing accurate results toward effective disease diagnosis. The objective of this research is to highlight the importance of deep learning models in the detection of Alzheimer's Disease (AD). The main objective of this research is to analyze different deep learning methods used for detecting AD. This study examines 103 research articles published in various research databases. These articles have been selected based on specific criteria to find the most relevant findings in the field of AD detection. The review was carried out based on deep learning techniques such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transfer Learning (TL). To propose accurate methods for the detection, segmentation, and severity grading of AD, the radiological features need to be examined in greater depth. This review attempts to analyze different deep learning methods applied for AD detection using neuroimaging modalities like Positron Emission Tomography (PET), Magnetic Resonance Imaging (MRI), etc. The focus of this review is restricted to deep learning works based on radiological imaging data for AD detection. There are a few works that have utilized other biomarkers to understand the effect of AD. Also, articles published in English were alone considered for analysis. This work concludes by highlighting the key research issues towards effective AD detection. Though several methods have yielded promising results in AD detection, the progression from Mild Cognitive Impairment (MCI) to AD need to be analyzed in greater depth using DL models.

Apathy in Alzheimer's disease: A neurocircuitry based perspective

In addition to memory deficits and other cognitive disturbances, patients with Alzheimer's disease (AD) experience neuropsychiatric symptoms, notably apathy, which is a state of impaired motivation observed by deficits in goal directed behavior. Apathy is a multifaceted neuropsychiatric condition and appears to be a prognostic indicator, correlating with the progression of AD. Strikingly, recent studies point out that the neurodegenerative pathology of AD may drive apathy independent of cognitive decline. These studies also highlight that neuropsychiatric symptoms, in particular apathy, might manifest early in AD. Here, we review the current understanding of the neurobiological underpinnings of apathy as a neuropsychiatric symptom of AD. Specifically, we highlight the neural circuits and brain regions recognized to be correlated with the apathetic symptomatology. We also discuss the current evidence that supports the notion that apathy and cognitive deficits may develop as independent but concurrent phenomena driven by AD pathology, suggesting its efficacy as an additional outcome measure in Alzheimer's disease clinical trials. The current and prospective therapeutic interventions for apathy in AD from a neurocircuitry based perspective are also reviewed.

Progressive structural and covariance connectivity abnormalities in patients with Alzheimer's disease

Background

Alzheimer's disease (AD) is one of most prevalent neurodegenerative diseases worldwide and characterized by cognitive decline and brain structure atrophy. While studies have reported substantial grey matter atrophy related to progression of AD, it remains unclear about brain regions with progressive grey matter atrophy, covariance connectivity, and the associations with cognitive decline in AD patients.

Objective

This study aims to investigate the grey matter atrophy, structural covariance connectivity abnormalities, and the correlations between grey matter atrophy and cognitive decline during AD progression.

Materials

We analyzed neuroimaging data of healthy controls (HC, n = 45) and AD patients (n = 40) at baseline (AD-T1) and one-year follow-up (AD-T2) obtained from the Alzheimer's Disease Neuroimaging Initiative. We investigated AD-related progressive changes of grey matter volume, covariance connectivity, and the clinical relevance to further understand the pathological progression of AD.

Results

The results showed clear patterns of grey matter atrophy in inferior frontal gyrus, prefrontal cortex, lateral temporal gyrus, posterior cingulate cortex, insula, hippocampus, caudate, and thalamus in AD patients. There was significant atrophy in bilateral superior temporal gyrus (STG) and left caudate in AD patients over a one-year period, and the grey matter volume decrease in right STG and left caudate was correlated with cognitive decline. Additionally, we found reduced structural covariance connectivity between right STG and left caudate in AD patients. Using AD-related grey matter atrophy as features, there was high discrimination accuracy of AD patients from HC, and AD patients at different time points.

Characterization of the angular gyrus in an older adult population: a multimodal multilevel approach

The angular gyrus (AG) has been associated with multiple cognitive functions, such as language, spatial and memory functions. Since the AG is thought to be a cross-modal hub region suffering from significant age-related structural atrophy, it may also play a key role in age-related cognitive decline. However, the exact relation between structural atrophy of the AG and cognitive decline in older adults is not fully understood, which may be related to two aspects: First, the AG is cytoarchitectonically divided into two areas, PGa and PGp, potentially sub-serving different cognitive functions. Second, the older adult population is characterized by high between-subjects variability which requires targeting individual phenomena during the aging process. We therefore performed a multimodal (gray matter volume [GMV], resting-state functional connectivity [RSFC] and structural connectivity [SC]) characterization of AG subdivisions PGa and PGp in a large older adult population, together with relations to age, cognition and lifestyle on the group level. Afterwards, we switched the perspective to the individual, which is especially important when it comes to the assessment of individual patients. The AG can be considered a heterogeneous structure in of the older brain: we found the different AG parts to be associated with different patterns of whole-brain GMV associations as well as their associations with RSFC, and SC patterns. Similarly, differential effects of age, cognition and lifestyle on the GMV of AG subdivisions were observed. This suggests each region to be structurally and functionally differentially involved in the older adult's brain network architecture, which was supported by differential molecular and genetic patterns, derived from the EBRAINS multilevel atlas framework. Importantly, individual profiles deviated considerably from the global conclusion drawn from the group study. Hence, general observations within the older adult population need to be carefully considered, when addressing individual conditions in clinical practice.

Coupling Between Hippocampal Parenchymal Fraction and Cortical Grey Matter Atrophy at Different Stages of Cognitive Decline

BACKGROUND

Hippocampal atrophy is a significant brain marker of pathology in Alzheimer's disease (AD). The hippocampal parenchymal fraction (HPF) was recently developed to better assess the hippocampal volumetric integrity, and it has been shown to be a sensitive measure of hippocampal atrophy in AD.

OBJECTIVE

To investigate the clinical relevance of hippocampal volumetric integrity as measured by the HPF and the coupling between the HPF and brain atrophy during AD progression.

METHODS

We included data from 143 cognitively normal (CN), 101 mild cognitive impairment (MCI), and 125 AD participants. We examined group differences in the HPF, associations between HPF and cognitive ability, and coupling between the HPF and cortical grey matter volume in the CN, MCI, and AD groups.

RESULTS

We observed progressive decreases in HPF from CN to MCI and from MCI to AD, and increases in the asymmetry of HPF, with the lowest asymmetry index (AI) in the CN group and the highest AI in the AD group. There was a significant association between HPF and cognitive ability across participants. The coupling between HPF and cortical regions was observed in bilateral hippocampus, parahippocampal gyrus, temporal, frontal, and occipital regions, thalamus, and amygdala in CN, MCI, and AD groups, with a greater involvement of temporal, occipital, frontal, and subcortical regions in MCI and AD patients, especially in AD patients.

CONCLUSION

This study provides novel evidence for the neuroanatomical basis of cognitive decline and brain atrophy during AD progression, which may have important clinical implications for the prognosis of AD.

Spatio-temporal metabolic rewiring in the brain of TgF344-AD rat model of Alzheimer's disease

Brain damage associated with Alzheimer's disease (AD) occurs even decades before the symptomatic onset, raising the need to investigate its progression from prodromal stages. In this context, animal models that progressively display AD pathological hallmarks (e.g. TgF344-AD) become crucial. Translational technologies, such as magnetic resonance spectroscopy (MRS), enable the longitudinal metabolic characterization of this disease. However, an integrative approach is required to unravel the complex metabolic changes underlying AD progression, from early to advanced stages. TgF344-AD and wild-type (WT) rats were studied in vivo on a 7 Tesla MRI scanner, for longitudinal quantitative assessment of brain metabolic profile changes using MRS. Disease progression was investigated at 4 time points, from 9 to 18 months of age, and in 4 regions: cortex, hippocampus, striatum, and thalamus. Compared to WT, TgF344-AD rats replicated common findings in AD patients, including decreased N-acetylaspartate in the cortex, hippocampus and thalamus, and decreased glutamate in the thalamus and striatum. Different longitudinal evolution of metabolic concentration was observed between TgF344-AD and WT groups. Namely, age-dependent trajectories differed between groups for creatine in the cortex and thalamus and for taurine in cortex, with significant decreases in Tg344-AD animals; whereas myo-inositol in the thalamus and striatum showed greater increase along time in the WT group. Additional analysis revealed divergent intra- and inter-regional metabolic coupling in each group. Thus, in cortex, strong couplings of N-acetylaspartate and creatine with myo-inositol in WT, but with taurine in TgF344-AD rats were observed; whereas in the hippocampus, myo-inositol, taurine and choline compounds levels were highly correlated in WT but not in TgF344-AD animals. Furthermore, specific cortex-hippocampus-striatum metabolic crosstalks were found for taurine levels in the WT group but for myo-inositol levels in the TgF344-AD rats. With a systems biology perspective of metabolic changes in AD pathology, our results shed light into the complex spatio-temporal metabolic rewiring in this disease, reported here for the first time. Age- and tissue-dependent imbalances between myo-inositol, taurine and other metabolites, such as creatine, unveil their role in disease progression, while pointing to the inadequacy of the latter as an internal reference for quantification.

A model-based constrained deep learning clustering approach for spatially resolved single-cell data

Spatially resolved scRNA-seq (sp-scRNA-seq) technologies provide the potential to comprehensively profile gene expression patterns in tissue context. However, the development of computational methods lags behind the advances in these technologies, which limits the fulfillment of their potential. In this study, we develop a deep learning approach for clustering sp-scRNA-seq data, named Deep Spatially constrained Single-cell Clustering (DSSC). In this model, we integrate the spatial information of cells into the clustering process in two steps: (1) the spatial information is encoded by using a graphical neural network model, and (2) cell-to-cell constraints are built based on the spatial expression pattern of the marker genes and added in the model to guide the clustering process. Then, a deep embedding clustering is performed on the bottleneck layer of autoencoder by Kullback-Leibler (KL) divergence along with the learning of feature representation. DSSC is the first model that can use information from both spatial coordinates and marker genes to guide cell/spot clustering. Extensive experiments on both simulated and real data sets show that DSSC boosts clustering performance significantly compared with the state-of-the-art methods. It has robust performance across different data sets with various cell type/tissue organization and/or cell type/tissue spatial dependency. We conclude that DSSC is a promising tool for clustering sp-scRNA-seq data.

Gray matter microstructural alterations in manganese-exposed welders: a preliminary neuroimaging study

OBJECTIVES

Chronic occupational manganese (Mn) exposure is characterized by motor and cognitive dysfunction. This study aimed to investigate structural abnormalities in Mn-exposed welders compared to healthy controls (HCs).

METHODS

Thirty-five HCs and forty Mn-exposed welders underwent magnetic resonance imaging (MRI) scans in this study. Based on T1-weighted MRI, the voxel-based morphometry (VBM), structural covariance, and receiver operating characteristic (ROC) curve were applied to examine whole-brain structural changes in Mn-exposed welders.

RESULTS

Compared to HCs, Mn-exposed welders had altered gray matter volume (GMV) mainly in the medial prefrontal cortex, lentiform nucleus, hippocampus, and parahippocampus. ROC analysis indicated the potential highest classification power of the hippocampus/parahippocampus. Moreover, distinct structural covariance patterns in the two groups were associated with regions, mainly including the thalamus, insula, amygdala, sensorimotor area, and middle temporal gyrus. No significant relationships were found between the findings and clinical characteristics.

CONCLUSIONS

Our findings showed Mn-exposed welders had changed GMV and structural covariance patterns in some regions, which implicated in motivative response, cognitive control, and emotional regulation. These results might provide preliminary evidence for understanding the pathophysiology of Mn overexposure.

KEY POINTS

• Chronic Mn exposure might be related to abnormal brain structural neural mechanisms. • Mn-exposed welders had morphological changes in brain regions implicated in emotional modulation, cognitive control, and motor-related response. • Altered gray matter volume in the hippocampus/parahippocampus and putamen might serve as potential biomarkers for Mn overexposure.

Automated High-Definition MRI Processing Routine Robustly Detects Longitudinal Morphometry Changes in Alzheimer's Disease Patients

Longitudinal MRI studies are of increasing importance to document the time course of neurodegenerative diseases as well as neuroprotective effects of a drug candidate in clinical trials. However, manual longitudinal image assessments are time consuming and conventional assessment routines often deliver unsatisfying study outcomes. Here, we propose a profound analysis pipeline that consists of the following coordinated steps: (1) an automated and highly precise image processing stream including voxel and surface based morphometry using latest highly detailed brain atlases such as the HCP MMP 1.0 atlas with 360 cortical ROIs; (2) a profound statistical assessment using a multiplicative model of annual percent change (APC); and (3) a multiple testing correction adopted from genome-wide association studies that is optimally suited for longitudinal neuroimaging studies. We tested this analysis pipeline with 25 Alzheimer's disease patients against 25 age-matched cognitively normal subjects with a baseline and a 1-year follow-up conventional MRI scan from the ADNI-3 study. Even in this small cohort, we were able to report 22 significant measurements after multiple testing correction from SBM (including cortical volume, area and thickness) complementing only three statistically significant volume changes (left/right hippocampus and left amygdala) found by VBM. A 1-year decrease in brain morphometry coincided with an increasing clinical disability and cognitive decline in patients measured by MMSE, CDR GLOBAL, FAQ TOTAL and NPI TOTAL scores. This work shows that highly precise image assessments, APC computation and an adequate multiple testing correction can produce a significant study outcome even for small study sizes. With this, automated MRI processing is now available and reliable for routine use and clinical trials.

Hypertension With High Homocysteine Is Associated With Default Network Gray Matter Loss

Hypertension with high homocysteine (Hcy, ≥10 μmol/L) is also known as H-type hypertension (HHT) and proposed as an independent risk factor for stroke and cognitive impairment. Although previous studies have established the relationships among hypertension, Hcy levels, and cognitive impairment, how they affect brain neuroanatomy remains unclear. Thus, we aimed to investigate whether and to what extent hypertension and high Hcy may affect gray matter volume in 52 middle-aged HHT patients and 51 demographically matched normotensive subjects. Voxel-based morphological analysis suggested that HHT patients experienced significant gray matter loss in the default network. The default network atrophy was significantly correlated with Hcy level and global cognitive function. These findings provide, to our knowledge, novel insights into how HHT affects brain gray matter morphology through blood pressure and Hcy.