Voxel-based morphometry in Alzheimers disease and mild cognitive impairment: Systematic review of studies addressing the frontal lobe

White matter changes in Parkinson's disease

Parkinson's disease (PD) is the second most common neurodegenerative disease after Alzheimer's disease (AD). It is characterized by a progressive loss of dopaminergic neurons in the substantia nigra pars compacta (SNc) and the formation of Lewy bodies (LBs). Although PD is primarily considered a gray matter (GM) disease, alterations in white matter (WM) have gained increasing attention in PD research recently. Here we review evidence collected by magnetic resonance imaging (MRI) techniques which indicate WM abnormalities in PD, and discuss the correlations between WM changes and specific PD symptoms. Then we summarize transcriptome and genome studies showing the changes of oligodendrocyte (OLs)/myelin in PD. We conclude that WM abnormalities caused by the changes of myelin/OLs might be important for PD pathology, which could be potential targets for PD treatment.

Effects of long-term exposure to high altitude on brain structure in healthy people: an MRI-based systematic review and meta-analysis

Purpose

To conduct a systematic review and meta-analysis of observational studies of brain MRI, this paper assesses the effects of long-term exposure to high-altitude on brain structures in healthy people.

Methods

Observational studies related to high-altitude, brain and MRI were systematically searched based on data retrieved from PubMed, Embase and Cochrane Library. The timescale for collecting literature was from the establishment of the databases to 2023. NoteExpress 3.2 was used to manage the literature. Two investigators performed literature screening and data extraction based on inclusion criteria, exclusion criteria, and literature quality. The quality of the literature was assessed using the NOS Scale. Finally, meta-analysis of included studies was performed using Reviewer Manager 5.3.

Results

Initially, 3,626 articles were retrieved. After screening, 16 articles (n = 756 participants) were included in the systematic review, and meta-analysis was performed on 6 articles (n = 350 participants). The overall quality of the included articles was at medium level, with a mean NOS score of 5.62. The results of meta-analysis showed that the differences between the HA group and LA group were not statistically significant, in total GM volume (MD: -0.60, 95% CI: -16.78 to 15.58, P = 0.94), WM volume (MD: 3.05, 95% CI: -15.72 to 21.81, P = 0.75) and CSF volume (MD: 5.00, 95% CI: -11.10 to 21.09, P = 0.54).The differences between HA and LA in FA values of frontotemporal lobes were not statistically significant: right frontal lobe (MD: -0.02, 95% CI: -0.07 to 0.03, P = 0.38), left frontal lobe (MD: 0.01, 95% CI: -0.02 to 0.04, P = 0.65), right temporal lobe (MD: -0.00, 95% CI: -0.03 to 0.02, P = 0.78) and left temporal lobe (MD: -0.01, 95% CI: -0.04 to 0.02, P = 0.62). However, there were significant differences in GM volume, GM density and FA values in local brain regions between HA group and LA group.

Conclusion

Compared with LA area, there were no significant differences in total GM, WM and CSF volumes in healthy people living at high-altitude area for long-term, while there were significant differences in GM volume and FA values in local brain regions. Long-term exposure to high-altitude area caused the adaptive structural changes in local brain regions. Since heterogeneity existed between the studies, further studies are needed to uncover the effects of high-altitude on brain of healthy people.

Systematic review registration

https://www.crd.york.ac.uk/prospero/, identifier: CRD42023403491.

Neurostructural and neurocognitive correlates of APOE ε4 in youth bipolar disorder

BACKGROUND

Bipolar disorder (BD) is a clinical risk factor for Alzheimer's disease (AD). Apolipoprotein E ε4 (APOE ε4), a genetic risk factor for AD, has been associated with brain structure and neurocognition in healthy youth.

AIMS

We evaluated whether there was an association between APOE ε4 with neurostructure and neurocognition in youth with BD.

METHODS

Participants included 150 youth (78 BD:19 ε4-carriers, 72 controls:17 ε4-carriers). 3T-magnetic resonance imaging yielded measures of cortical thickness, surface area, and volume. Regions-of-interest (ROI) and vertex-wise analyses of the cortex were conducted. Neurocognitive tests of attention and working memory were examined.

RESULTS

Vertex-wise analyses revealed clusters with a diagnosis-by-APOE ε4 interaction effect for surface area (p = 0.002) and volume (p = 0.046) in pars triangularis (BD ε4-carriers > BD noncarriers), and surface area (p = 0.03) in superior frontal gyrus (controls ε4-carriers > other groups). ROI analyses were not significant. A significant interaction effect for working memory (p = 0.001) appeared to be driven by nominally poorer performance in BD ε4-carriers but not control ε4-carriers; however, post hoc contrasts were not significant.

CONCLUSIONS

APOE ε4 was associated with larger neurostructural metrics in BD and controls, however, the regional association of APOE ε4 with neurostructure differed between groups. The role of APOE ε4 on neurodevelopmental processes is a plausible explanation for the observed differences. Future studies should evaluate the association of APOE ε4 with pars triangularis and its neurofunctional implications among youth with BD.

Predicting Conversion from Subjective Cognitive Decline to Mild Cognitive Impairment and Alzheimer's Disease Dementia Using Ensemble Machine Learning

BACKGROUND

Subjective cognitive decline (SCD) may represent a preclinical stage of Alzheimer's disease (AD). Predicting progression of SCD patients is of great importance in AD-related research but remains a challenge.

OBJECTIVE

To develop and implement an ensemble machine learning (ML) algorithm to identify SCD subjects at risk of conversion to mild cognitive impairment (MCI) or AD.

METHODS

Ninety-nine SCD patients were included. Thirty-two progressed to MCI/AD, while 67 remained stable. To minimize the effect of class imbalance, both classes were balanced, and sensitivity was taken as evaluation metric. Bagging and boosting ML models were developed by using socio-demographic and clinical information, Mini-Mental State Examination and Geriatric Depression Scale (GDS) scores (feature-set 1a); socio-demographic characteristics and neuropsychological tests scores (feature-set 1b) and regional magnetic resonance imaging grey matter volumes (feature-set 2). The most relevant variables were combined to find the best model.

RESULTS

Good prediction performances were obtained with feature-sets 1a and 2. The most relevant variables (variable importance exceeding 20%) were: Age, GDS, and grey matter volumes measured in four cortical regions of interests. Their combination provided the optimal classification performance (highest sensitivity and specificity) ensemble ML model, Extreme Gradient Boosting with over-sampling of the minority class, with performance metrics: sensitivity = 1.00, specificity = 0.92 and area-under-the-curve = 0.96. The median values based on fifty random train/test splits were sensitivity = 0.83 (interquartile range (IQR) = 0.17), specificity = 0.77 (IQR = 0.23) and area-under-the-curve = 0.75 (IQR = 0.11).

CONCLUSION

A high-performance algorithm that could be translatable into practice was able to predict SCD conversion to MCI/AD by using only six predictive variables.

Neurodegenerative Changes in the Brains of the 5xFAD Alzheimer’s Disease Model Mice Investigated by High-Field and High-Resolution Magnetic Resonance Imaging and Multi-Nuclei Magnetic Resonance Spectroscopy

This study aimed to investigate morphological and metabolic changes in the brains of 5xFAD mice. Structural magnetic resonance imaging (MRI) and 1H magnetic resonance spectroscopy (MRS) were obtained in 10- and 14-month-old 5xFAD and wild-type (WT) mice, while 31P MRS scans were acquired in 11-month-old mice. Significantly reduced gray matter (GM) was identified by voxel-based morphometry (VBM) in the thalamus, hypothalamus, and periaqueductal gray areas of 5xFAD mice compared to WT mice. Significant reductions in N-acetyl aspartate and elevation of myo-Inositol were revealed by the quantification of MRS in the hippocampus of 5xFAD mice, compared to WT. A significant reduction in NeuN-positive cells and elevation of Iba1- and GFAP-positive cells supported this observation. The reduction in phosphomonoester and elevation of phosphodiester was observed in 11-month-old 5xFAD mice, which might imply a sign of disruption in the membrane synthesis. Commonly reported 1H MRS features were replicated in the hippocampus of 14-month-old 5xFAD mice, and a sign of disruption in the membrane synthesis and elevation of breakdown were revealed in the whole brain of 5xFAD mice by 31P MRS. GM volume reduction was identified in the thalamus, hypothalamus, and periaqueductal gray areas of 5xFAD mice.

Butyrate ameliorates quinolinic acid-induced cognitive decline in obesity models

Obesity is a risk factor for neurodegenerative disease associated with cognitive dysfunction, including Alzheimer's disease. Low-grade inflammation is common in obesity, but the mechanism between inflammation and cognitive impairment in obesity is unclear. Accumulative evidence shows that quinolinic acid (QA), a neuroinflammatory neurotoxin, is involved in the pathogenesis of neurodegenerative processes. We investigated the role of QA in obesity-induced cognitive impairment and the beneficial effect of butyrate in counteracting impairments of cognition, neural morphology, and signaling. We show that in human obesity, there was a negative relationship between serum QA levels and cognitive function and decreased cortical gray matter. Diet-induced obese mice had increased QA levels in the cortex associated with cognitive impairment. At single-cell resolution, we confirmed that QA impaired neurons, altered the dendritic spine's intracellular signal, and reduced brain-derived neurotrophic factor (BDNF) levels. Using Caenorhabditis elegans models, QA induced dopaminergic and glutamatergic neuron lesions. Importantly, the gut microbiota metabolite butyrate was able to counteract those alterations, including cognitive impairment, neuronal spine loss, and BDNF reduction in both in vivo and in vitro studies. Finally, we show that butyrate prevented QA-induced BDNF reductions by epigenetic enhancement of H3K18ac at BDNF promoters. These findings suggest that increased QA is associated with cognitive decline in obesity and that butyrate alleviates neurodegeneration.

Application of diffusion tensor imaging in Alzheimer’s disease: quantification of white matter microstructural changes

Background

Alzheimer’s disease (AD) is the most common cause of dementia in the aging population, responsible for 60–70% of all demented cases. Diffusion tensor imaging (DTI) is a very recent technique that allows the mapping of white matter (WM) microstructure changes in neurological disorders. The current study was conducted to compare DTI parameters between AD patients and healthy elderly subjects and to determine whether DTI can act as a potential biomarker for AD.

Results

There were significant differences in Modified Mini-Mental State Examination (MMMSE) and Clinical Dementia Rating (CDR) between the two groups. As regards the DTI parameters, significant differences were found between AD patients versus healthy subjects, in the mean diffusivity (MD) of the splenium [(1.05 ± 0.19) vs. (0.92 ± 0.22) , P=0.03], the MD of the right uncinate fasciculus [(0.92 ± 0.04) vs. (0.87 ± 0.05), P= 0.01],  and MD of the right arcuate fasciculus (AF) [(0.83 ± 0.04) vs. (0.79 ± 0.04) P =0.01],  as well as the MD of the right and left inferior fronto-occipital fasiculus (IFOF) [(0.89 ± 0.06) vs. (0.83 ± 0.04), P=0.01]. In addition, there were significant differences in the fractional anisotropy (FA) of the right and left cingulum between both groups [(0.45 ± 0.02) vs. (0.47 ± 0.03), P=0.01 and (0.45 ± 0.03) vs. 0.49± 0.04), P=0.01, respectively]. The overall accuracy of the aforementioned parameters ranged between 73 and 81% with the MD of the left cingulum revealing the highest accuracy.

Conclusion

DTI proofed to be a useful tool in differentiating AD patients from healthy subjects. In our study, we found that the splenium, cingulum, IFOF, and the right UF and right AF are the main tracts involved in AD. The left cingulum provided the highest accuracy in differentiating AD from normal subjects.

Neuroimaging Research on Dementia in Brazil in the Last Decade: Scientometric Analysis, Challenges, and Peculiarities

The last years have evinced a remarkable growth in neuroimaging studies around the world. All these studies have contributed to a better understanding of the cerebral outcomes of dementia, even in the earliest phases. In low- and middle-income countries, studies involving structural and functional neuroimaging are challenging due to low investments and heterogeneous populations. Outstanding the importance of diagnosing mild cognitive impairment and dementia, the purpose of this paper is to offer an overview of neuroimaging dementia research in Brazil. The review includes a brief scientometric analysis of quantitative information about the development of this field over the past 10 years. Besides, discusses some peculiarities and challenges that have limited neuroimaging dementia research in this big and heterogeneous country of Latin America. We systematically reviewed existing neuroimaging literature with Brazilian authors that presented outcomes related to a dementia syndrome, published from 2010 to 2020. Briefly, the main neuroimaging methods used were morphometrics, followed by fMRI, and DTI. The major diseases analyzed were Alzheimer's disease, mild cognitive impairment, and vascular dementia, respectively. Moreover, research activity in Brazil has been restricted almost entirely to a few centers in the Southeast region, and funding could be the main driver for publications. There was relative stability concerning the number of publications per year, the citation impact has historically been below the world average, and the author's gender inequalities are not relevant in this specific field. Neuroimaging research in Brazil is far from being developed and widespread across the country. Fortunately, increasingly collaborations with foreign partnerships contribute to the impact of Brazil's domestic research. Although the challenges, neuroimaging researches performed in the native population regarding regional peculiarities and adversities are of pivotal importance.

Alterations of Brain Networks in Alzheimer's Disease and Mild Cognitive Impairment: A Resting State fMRI Study Based on a Population-specific Brain Template

This study aimed to investigate the alterations in brain networks in patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI) based on a population-specific brain template. Previous studies on AD brain networks using graph theory rarely adopted brain templates specific for certain ethnicities. In this study, patients were divided into 3 groups: AD (n = 24), MCI (n = 27), and healthy controls (HCs, n = 33), and all of the subjects are Chinese. Functional brain networks were constructed for each group based on a Chinese brain template using resting-state functional magnetic resonance imaging (rs-fMRI) data; several graph metrics were calculated. Graph metrics with significant differences after false discovery rate (FDR) correction were analyzed with respect to correlations with four neuropsychological test scores: Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Activities of Daily Living (ADL), and Clinical Dementia Rating (CDR), which assessed the subjects' cognitive functions and ability to engage in ADL. Graph metrics including assortativity coefficient, nodal degree centrality, nodal clustering coefficient, nodal efficiency, and nodal local efficiency of the frontal gyrus and cerebellum were significantly altered in AD and MCI compared with HC. Several graph metrics were significantly correlated with cognitive function and the ability to engage in daily activities. The findings suggest that altered graph metrics in the frontal gyrus may reflect brain plasticity, and that patients with MCI may have unique graph metric alterations in the cerebellum. Future graph analysis studies on functional brain networks in AD and MCI based on population-specific brain atlases for particular ethnicities may prove valuable.

Fusion of ULS Group Constrained High- and Low-Order Sparse Functional Connectivity Networks for MCI Classification

Functional connectivity networks, derived from resting-state fMRI data, have been found as effective biomarkers for identifying mild cognitive impairment (MCI) from healthy elderly. However, the traditional functional connectivity network is essentially a low-order network with the assumption that the brain activity is static over the entire scanning period, ignoring temporal variations among the correlations derived from brain region pairs. To overcome this limitation, we proposed a new type of sparse functional connectivity network to precisely describe the relationship of temporal correlations among brain regions. Specifically, instead of using the simple pairwise Pearson's correlation coefficient as connectivity, we first estimate the temporal low-order functional connectivity for each region pair based on an ULS Group constrained-UOLS regression algorithm, where a combination of ultra-least squares (ULS) criterion with a Group constrained topology structure detection algorithm is applied to detect the topology of functional connectivity networks, aided by an Ultra-Orthogonal Least Squares (UOLS) algorithm to estimate connectivity strength. Compared to the classical least squares criterion which only measures the discrepancy between the observed signals and the model prediction function, the ULS criterion takes into consideration the discrepancy between the weak derivatives of the observed signals and the model prediction function and thus avoids the overfitting problem. By using a similar approach, we then estimate the high-order functional connectivity from the low-order connectivity to characterize signal flows among the brain regions. We finally fuse the low-order and the high-order networks using two decision trees for MCI classification. Experimental results demonstrate the effectiveness of the proposed method on MCI classification.