Multimodal Voxel-Based Meta-Analysis of White Matter Abnormalities in Alzheimer's Disease

Diabetes may contribute to cognitive impairment in Parkinson's disease via damaging white matter tracts

Parkinson's disease with dementia (PDD) severely affects the quality of life of patients with Parkinson's disease (PD) in the later stages. Recently, PD patients with diabetes were found to have a higher risk of cognitive decline and developing dementia with a faster progression, but the underlying mechanism remains unclear. Diabetes-related white matter damage may partially explain the mechanism by which diabetes participates in PDD. Seventy PD patients were included. PD patients underwent diffusion tensor imaging from The Second Affiliated Hospital of Chongqing Medical University were collected and were divided into four groups: PD with diabetes without dementia, PD with dementia without diabetes, PD without dementia and diabetes. Tract-based spatial statistics analysis and region-of-interest-based analysis were performed. Factorial analysis with diabetes and dementia taken as the main effects was performed, and the differences between the white matter fibers of PD patients from the four groups were also analyzed. The interaction between diabetes and dementia in the damage of white matter in PD patients was also analyzed. We found that both diabetes and dementia were found to be related to the damage in internal capsule, corona radiata, and thalamic radiation of the PD patients. There is an interaction between diabetes and dementia in the white matter damage of PD patients. Both diabetes and dementia were found to be related to the damage in internal capsule, corona radiata, and thalamic radiation of the PD patients. Diabetes may participate in cognitive decline in PD patients via damaging cognition-related white matter tracts.

Structural disruption in subjective cognitive decline and mild cognitive impairment

Subjective cognitive decline (SCD) marks the initial stage in Alzheimer's disease continuum. Nonetheless, current research findings regarding brain structural changes in the SCD are inconsistent. In this study, 37 SCD patients, 28 mild cognitive impairment (MCI) patients, and 42 healthy controls (HC) were recruited to investigate structural alterations. Morphological and microstructural differences among the three groups were analyzed based on T1- and diffusion-weighted images, correlating them with neuropsychological assessments. Additionally, classification analysis was performed by using support vector machines (SVM) categorize participants into three groups based on MRI features. Both SCD and MCI showed decreased volume in left inferior parietal lobe (IPL) compared to HC, while SCD showed altered morphologies in the right inferior temporal gyrus (ITG), right insula and right amygdala, and microstructures in fiber tracts of the right ITG, lateral occipital cortex (LOC) and insula relative to MCI. Moreover, the volume in the left IPL, right LOC, right amygdala and diffusivity value in fiber tracts of right LOC were significantly correlated with cognitive functions across all subjects. The classification models achieved an accuracy of > 0.7 (AUC = 0.8) in distinguishing the three groups. Our findings suggest that SCD and MCI share similar atrophy in the IPL but show more differences in morphological and microstructural features of cortical-subcortical areas.

Revolutionizing early Alzheimer's disease and mild cognitive impairment diagnosis: a deep learning MRI meta-analysis

BACKGROUND

 The early diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI) remains a significant challenge in neurology, with conventional methods often limited by subjectivity and variability in interpretation. Integrating deep learning with artificial intelligence (AI) in magnetic resonance imaging (MRI) analysis emerges as a transformative approach, offering the potential for unbiased, highly accurate diagnostic insights.

OBJECTIVE

 A meta-analysis was designed to analyze the diagnostic accuracy of deep learning of MRI images on AD and MCI models.

METHODS

 A meta-analysis was performed across PubMed, Embase, and Cochrane library databases following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, focusing on the diagnostic accuracy of deep learning. Subsequently, methodological quality was assessed using the QUADAS-2 checklist. Diagnostic measures, including sensitivity, specificity, likelihood ratios, diagnostic odds ratio, and area under the receiver operating characteristic curve (AUROC) were analyzed, alongside subgroup analyses for T1-weighted and non-T1-weighted MRI.

RESULTS

 A total of 18 eligible studies were identified. The Spearman correlation coefficient was -0.6506. Meta-analysis showed that the combined sensitivity and specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were 0.84, 0.86, 6.0, 0.19, and 32, respectively. The AUROC was 0.92. The quiescent point of hierarchical summary of receiver operating characteristic (HSROC) was 3.463. Notably, the images of 12 studies were acquired by T1-weighted MRI alone, and those of the other 6 were gathered by non-T1-weighted MRI alone.

CONCLUSION

 Overall, deep learning of MRI for the diagnosis of AD and MCI showed good sensitivity and specificity and contributed to improving diagnostic accuracy.

Neuromodulatory subcortical nucleus integrity is associated with white matter microstructure, tauopathy and APOE status

The neuromodulatory subcortical nuclei within the isodendritic core (IdC) are the earliest sites of tauopathy in Alzheimer's disease (AD). They project broadly throughout the brain's white matter. We investigated the relationship between IdC microstructure and whole-brain white matter microstructure to better understand early neuropathological changes in AD. Using multiparametric quantitative magnetic resonance imaging we observed two covariance patterns between IdC and white matter microstructure in 133 cognitively unimpaired older adults (age 67.9 ± 5.3 years) with familial risk for AD. IdC integrity related to 1) whole-brain neurite density, and 2) neurite orientation dispersion in white matter tracts known to be affected early in AD. Pattern 2 was associated with CSF concentration of phosphorylated-tau, indicating AD specificity. Apolipoprotein-E4 carriers expressed both patterns more strongly than non-carriers. IdC microstructure variation is reflected in white matter, particularly in AD-affected tracts, highlighting an early mechanism of pathological development.

Comprehensive assessment of sleep duration, insomnia, and brain structure within the UK Biobank cohort

STUDY OBJECTIVES

To assess for associations between sleeping more than or less than recommended by the National Sleep Foundation (NSF), and self-reported insomnia, with brain structure.

METHODS

Data from the UK Biobank cohort were analyzed (N between 9K and 32K, dependent on availability, aged 44 to 82 years). Sleep measures included self-reported adherence to NSF guidelines on sleep duration (sleeping between 7 and 9 hours per night), and self-reported difficulty falling or staying asleep (insomnia). Brain structural measures included global and regional cortical or subcortical morphometry (thickness, surface area, volume), global and tract-related white matter microstructure, brain age gap (difference between chronological age and age estimated from brain scan), and total volume of white matter lesions.

RESULTS

Longer-than-recommended sleep duration was associated with lower overall grey and white matter volumes, lower global and regional cortical thickness and volume measures, higher brain age gap, higher volume of white matter lesions, higher mean diffusivity globally and in thalamic and association fibers, and lower volume of the hippocampus. Shorter-than-recommended sleep duration was related to higher global and cerebellar white matter volumes, lower global and regional cortical surface areas, and lower fractional anisotropy in projection fibers. Self-reported insomnia was associated with higher global gray and white matter volumes, and with higher volumes of the amygdala, hippocampus, and putamen.

CONCLUSIONS

Sleeping longer than recommended by the NSF is associated with a wide range of differences in brain structure, potentially indicative of poorer brain health. Sleeping less than recommended is distinctly associated with lower cortical surface areas. Future studies should assess the potential mechanisms of these differences and investigate long sleep duration as a putative marker of brain health.

Identification and validation of supervariants reveal novel loci associated with human white matter microstructure

As an essential part of the central nervous system, white matter coordinates communications between different brain regions and is related to a wide range of neurodegenerative and neuropsychiatric disorders. Previous genome-wide association studies (GWASs) have uncovered loci associated with white matter microstructure. However, GWASs suffer from limited reproducibility and difficulties in detecting multi-single-nucleotide polymorphism (multi-SNP) and epistatic effects. In this study, we adopt the concept of supervariants, a combination of alleles in multiple loci, to account for potential multi-SNP effects. We perform supervariant identification and validation to identify loci associated with 22 white matter fractional anisotropy phenotypes derived from diffusion tensor imaging. To increase reproducibility, we use United Kingdom (UK) Biobank White British (n = 30,842) data for discovery and internal validation, and UK Biobank White but non-British (n = 1927) data, Europeans from the Adolescent Brain Cognitive Development study (n = 4399) data, and Europeans from the Human Connectome Project (n = 319) data for external validation. We identify 23 novel loci on the discovery set that have not been reported in the previous GWASs on white matter microstructure. Among them, three supervariants on genomic regions 5q35.1, 8p21.2, and 19q13.32 have P-values lower than 0.05 in the meta-analysis of the three independent validation data sets. These supervariants contain genetic variants located in genes that have been related to brain structures, cognitive functions, and neuropsychiatric diseases. Our findings provide a better understanding of the genetic architecture underlying white matter microstructure.

An examination of Alzheimer's disease and white matter from 1981 to 2023: a Bibliometric and visual analysis

Background

Alzheimer's disease (AD) is characterized by the presence of gray matter lesions and alterations in white matter. This study aims to investigate the research related to white matter in the context of AD from a Bibliometric standpoint.

Methods

Regular and review articles focusing on the research pertaining to Alzheimer's disease (AD) and white matter were extracted from the Web of Science Core Collection (WOSCC) database, covering the period from its inception to 10th July 2023. The "Bibliometrix" R package was employed to summarize key findings, to quantify the occurrence of top keywords, and to visualize the collaborative network among countries. Furthermore, VOSviewer software was utilized to conduct co-authorship and co-occurrence analyses. CiteSpace was employed to identify the most influential references and keywords based on their citation bursts. The retrieval of AD- and white matter-related publications was conducted by the Web of Science Core Collection. Bibliometric analysis and visualization, including the examination of annual publication distribution, prominent countries, active institutions and authors, core journals, co-cited references, and keywords, were carried out by using VOSviewer, CiteSpace, the Bibliometrix Package, and the ggplot2 Package. The quality and impact of publications were assessed using the total global citation score and total local citation score.

Results

A total of 5,714 publications addressing the intersection of Alzheimer's disease (AD) and white matter were included in the analysis. The majority of publications originated from the United States, China, and the United Kingdom. Prominent journals were heavily featured in the publication output. In addition to "Alzheimer's disease" and "white matter," "mild cognitive impairment," "MRI" and "atrophy" had been frequently utilized as "keywords."

Conclusion

This Bibliometric investigation delineated a foundational knowledge framework that encompasses countries, institutions, authors, journals, and articles within the AD and white matter research domain spanning from 1981 to 2023. The outcomes provide a comprehensive perspective on the broader landscape of this research field.

Reproducible Abnormalities and Diagnostic Generalizability of White Matter in Alzheimer's Disease

Alzheimer's disease (AD) is associated with the impairment of white matter (WM) tracts. The current study aimed to verify the utility of WM as the neuroimaging marker of AD with multisite diffusion tensor imaging datasets [321 patients with AD, 265 patients with mild cognitive impairment (MCI), 279 normal controls (NC)], a unified pipeline, and independent site cross-validation. Automated fiber quantification was used to extract diffusion profiles along tracts. Random-effects meta-analyses showed a reproducible degeneration pattern in which fractional anisotropy significantly decreased in the AD and MCI groups compared with NC. Machine learning models using tract-based features showed good generalizability among independent site cross-validation. The diffusion metrics of the altered regions and the AD probability predicted by the models were highly correlated with cognitive ability in the AD and MCI groups. We highlighted the reproducibility and generalizability of the degeneration pattern of WM tracts in AD.

Source connectivity patterns in the default mode network differ between elderly golf-novices and non-golfers

Learning to play golf has high demands on attention and therefore may counteract age-related changes of functional brain networks. This cross-sectional study compared source connectivity in the Default Mode Network (DMN) between elderly golf novices and non-golfers. Four-minute resting-state electroencephalography (128 channels) from 22 elderly people (mean age 67 ± 4.3 years, 55% females) were recorded after completing a 22-week golf learning program or after having continued with normal life. Source connectivity was assessed after co-registration of EEG data with native MRI within pre-defined portions of the DMN in the beta band (14-25 Hz). Non-golfers had significantly higher source connectivity values in the anterior DMN compared to non-golfers. Exploratory correlation analyses did not indicate an association to cognitive performance in either group. Inverse correlations between a marker of external attention with source connectivity of the anterior DMN may suggest a trend in the golf group only, but have to be replicated in future studies. Clinical relevance of these findings remains to be elucidated, but the observed difference in the anterior DMN may provide a starting point to further investigate if and how learning golf may have an impact on physiological age-related cognitive changes.

Distance-dependent reconfiguration of hubs in Alzheimer's disease: a cross-tissue functional network study

The hubs of the intra-grey matter (GM) network were sensitive to anatomical distance and susceptible to neuropathological damage. However, few studies examined the hubs of cross-tissue distance-dependent networks and their changes in Alzheimer's disease (AD). Using resting-state fMRI data of 30 AD patients and 37 normal older adults (NC), we constructed the cross-tissue networks based on functional connectivity (FC) between GM and white matter (WM) voxels. In the full-ranged and distance-dependent networks (characterized by gradually increased Euclidean distances between GM and WM voxels), their hubs were identified with weight degree metrics (frWD and ddWD). We compared these WD metrics between AD and NC; using the resultant abnormal WDs as the seeds, we performed seed-based FC analysis. With increasing distance, the GM hubs of distance-dependent networks moved from the medial to lateral cortices, and the WM hubs spread from the projection fibers to longitudinal fascicles. Abnormal ddWD metrics in AD were primarily located in the hubs of distance-dependent networks around 20-100mm. Decreased ddWDs were located in the left corona radiation (CR), which had decreased FCs with the executive network's GM regions in AD. Increased ddWDs were located in the posterior thalamic radiation (PTR) and the temporal-parietal-occipital junction (TPO), and their FCs were larger in AD. Increased ddWDs were shown in the sagittal striatum, which had larger FCs with the salience network's GM regions in AD. The reconfiguration of cross-tissue distance-dependent networks possibly reflected the disruption in the neural circuit of executive function and the compensatory changes in the neural circuits of visuospatial and social-emotional functions in AD.

Effect of sex on the APOE4-aging interaction in the white matter microstructure of cognitively normal older adults using diffusion-tensor MRI with orthogonal-tensor decomposition (DT-DOME)

The influence of the apolipoprotein E ε4 allele (APOE4) on brain microstructure of cognitively normal older adults remains incompletely understood, in part due to heterogeneity within study populations. In this study, we examined white-matter microstructural integrity in cognitively normal older adults as a function of APOE4 carrier status using conventional diffusion-tensor imaging (DTI) and the novel orthogonal-tensor decomposition (DT-DOME), accounting for the effects of age and sex. Age associations with white-matter microstructure did not significantly depend on APOE4 status, but did differ between sexes, emphasizing the importance of accounting for sex differences in APOE research. Moreover, we found the DT-DOME to be more sensitive than conventional DTI metrics to such age-related and sex effects, especially in crossing WM fiber regions, and suggest their use in further investigation of white matter microstructure across the life span in health and disease.

Automated quantification of brain connectivity in Alzheimer's disease using ClusterMetric

Diffusion magnetic resonance imaging tractography allows investigating brain structural connections in a noninvasive way and has been widely used for understanding neurological disease. Quantification of brain connectivity along with its length by dividing a fiber bundle into multiple segments (node) is a powerful approach to assess biological properties, which is termed as tractometry. However, current tractometry methods face challenges in node identification along with the length of complex bundles whose morphology is difficult to summarize. In addition, the anatomic measure reflecting the macroscopic fiber cross-section has not been followed in previous tractometry. In this paper, we propose an automated fiber bundle quantification, which we refer to as ClusterMetric. The ClusterMetric uses a data-driven approach to identify fiber clusters corresponding to subdivisions of the white matter anatomy and identify consistent space nodes along the length of clusters across individuals. The proposed method is demonstrated by applicating to our collected dataset including 23 Alzheimer's disease (AD) patients and 22 healthy controls (HCs) and a public dataset of ADNI including 53 AD patients and 85 HCs. The altered white matter tracts in AD group are observed using both datasets, which involve several major fiber tracts including the corpus callosum, corona-radiata-frontal, arcuate fasciculus, inferior occipito-frontal fasciculus, uncinate fasciculus, thalamo-frontal, superior longitudinal fasciculus, inferior cerebellar peduncle, cingulum bundle, and extreme capsule. These fiber clusters represent the white matter connections that could be most affected in AD, suggesting the ability of our method in identifying potential abnormalities specific to local regions within a fiber cluster.

Aberrant topological organization and age-related differences in the human connectome in subjective cognitive decline by using regional morphology from magnetic resonance imaging

Subjective cognitive decline (SCD) is characterized by self-experienced deficits in cognitive capacity with normal performance in objective cognitive tests. Previous structural covariance studies showed specific insights into understanding the structural alterations of the brain in neurodegenerative diseases. Moreover, in subjects with neurodegenerative diseases, accelerated brain degeneration with aging was shown. However, the age-related variations in coordinated topological patterns of morphological networks in individuals with SCD remain poorly understood. In this study, 77 individual morphological networks were constructed, including 42 normal controls (NCs) and 35 SCD individuals, from structural magnetic resonance imaging (sMRI). A stepwise linear regression model and partial correlation analysis were constructed to evaluate the differences in age-related alterations of the network properties in individuals with SCD compared with NCs. Compared with NC, the properties of integration and segregation in individuals with SCD were lower, and the aberrant metrics were negatively correlated with age in SCD. The rich-club connections persevered, but the paralimbic system connections were disrupted in individuals with SCD compared with NCs. In addition, age-related differences in nodal global efficiency are distributed mainly in prefrontal cortex regions. In conclusion, the age-related disruption of topological organizations in individuals with SCD may indicate that the degeneration of brain efficiency with aging was accelerated in individuals with SCD.

Neuroimaging and biomarker evidence of neurodegeneration in asthma

BACKGROUND

Epidemiologic studies have shown that Alzheimer's disease (AD) and related dementias (ADRD) are seen more frequently with asthma, especially with greater asthma severity or exacerbation frequency.

OBJECTIVE

To examine the changes in brain structure that may underlie this phenomenon, we examined diffusion-weighted magnetic resonance imaging (dMRI) and blood-based biomarkers of AD (phosphorylated tau 181, p-Tau181), neurodegeneration (neurofilament light chain, NfL), and glial activation (glial fibrillary acidic protein, GFAP).

METHODS

dMRI data were obtained in 111 individuals with asthma, ranging in disease severity from mild to severe, and 135 healthy controls. Regression analyses were used to test the relationships between asthma severity and neuroimaging measures, as well as AD pathology, neurodegeneration, and glial activation, indexed by plasma p-Tau181, NfL, and GFAP, respectively. Additional relationships were tested with cognitive function.

RESULTS

Asthma participants had widespread and large-magnitude differences in several dMRI metrics, which were indicative of neuroinflammation and neurodegeneration, and which were robustly associated with GFAP and, to a lesser extent, NfL. The AD biomarker p-Tau181 was only minimally associated with neuroimaging outcomes. Further, asthma severity was associated with deleterious changes in neuroimaging outcomes, which in turn were associated with slower processing speed, a test of cognitive performance.

CONCLUSIONS

Asthma, particularly when severe, is associated with characteristics of neuroinflammation and neurodegeneration, and may be a potential risk factor for neural injury and cognitive dysfunction. There is a need to determine how asthma may affect brain health and whether treatment directed toward characteristics of asthma associated with these risks can mitigate these effects.

Pattern of Altered Magnetization Transfer Rate in Alzheimer's Disease

BACKGROUND

Biomarkers for Alzheimer's disease (AD) are crucial for early diagnosis and treatment monitoring once disease modifying therapies become available.

OBJECTIVE

This study aims to quantify the forward magnetization transfer rate (kfor) map from brain tissue water to macromolecular protons and use it to identify the brain regions with abnormal kfor in AD and AD progression.

METHODS

From the Cardiovascular Health Study (CHS) cognition study, magnetization transfer imaging (MTI) was acquired at baseline from 63 participants, including 20 normal controls (NC), 18 with mild cognitive impairment (MCI), and 25 AD subjects. Of those, 53 participants completed a follow-up MRI scan and were divided into four groups: 15 stable NC, 12 NC-to-MCI, 12 stable MCI, and 14 MCI/AD-to-AD subjects. kfor maps were compared across NC, MCI, and AD groups at baseline for the cross-sectional study and across four longitudinal groups for the longitudinal study.

RESULTS

We found a lower kfor in the frontal gray matter (GM), parietal GM, frontal corona radiata (CR) white matter (WM) tracts, frontal and parietal superior longitudinal fasciculus (SLF) WM tracts in AD relative to both NC and MCI. Further, we observed progressive decreases of kfor in the frontal GM, parietal GM, frontal and parietal CR WM tracts, and parietal SLF WM tracts in stable MCI. In the parietal GM, parietal CR WM tracts, and parietal SLF WM tracts, we found trend differences between MCI/AD-to-AD and stable NC.

CONCLUSION

Forward magnetization transfer rate is a promising biomarker for AD diagnosis and progression.

Systematic Review of Different Neuroimaging Correlates in Mild Cognitive Impairment and Alzheimer's Disease

Alzheimer's disease (AD) is a heterogeneous progressive neurocognitive disorder. Although different neuroimaging modalities have been used for the identification of early diagnostic and prognostic factors of AD, there is no consolidated view of the findings from the literature. Here, we aim to provide a comprehensive account of different neural correlates of cognitive dysfunction via magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), functional MRI (fMRI) (resting-state and task-related), positron emission tomography (PET) and magnetic resonance spectroscopy (MRS) modalities across the cognitive groups i.e., normal cognition, mild cognitive impairment (MCI), and AD. A total of 46 meta-analyses met the inclusion criteria, including relevance to MCI, and/or AD along with neuroimaging modality used with quantitative and/or functional data. Volumetric MRI identified early anatomical changes involving transentorhinal cortex, Brodmann area 28, followed by the hippocampus, which differentiated early AD from healthy subjects. A consistent pattern of disruption in the bilateral precuneus along with the medial temporal lobe and limbic system was observed in fMRI, while DTI substantiated the observed atrophic alterations in the corpus callosum among MCI and AD cases. Default mode network hypoconnectivity in bilateral precuneus (PCu)/posterior cingulate cortices (PCC) and hypometabolism/hypoperfusion in inferior parietal lobules and left PCC/PCu was evident. Molecular imaging revealed variable metabolite concentrations in PCC. In conclusion, the use of different neuroimaging modalities together may lead to identification of an early diagnostic and/or prognostic biomarker for AD.

Cognitive dysfunction and cerebral volumetric deficits in individuals with Alzheimer's disease, alcohol use disorder, and dual diagnosis

Epidemiological surveys suggest that excessive drinking is associated with higher risk of Alzheimer's disease (AD). The present study utilized data from the National Alzheimer's Coordinating Center to examine cognition as well as gray/white matter and ventricular volumes among participants with AD and alcohol use disorder (AD/AUD, n = 52), AD only (n = 701), AUD only (n = 67), and controls (n = 1283). AUD diagnosis was associated with higher Clinical Dementia Rating Scale Sum of Boxes (CDR-SB) in AD than in non-AD. AD performed worse on semantic fluency and Trail Making Test A + B (TMT A + B) and showed smaller total GMV, WMV, and larger ventricular volume than non-AD. AD had smaller regional GMV in the inferior/superior parietal cortex, hippocampal formation, occipital cortex, inferior frontal gyrus, posterior cingulate cortex, and isthmus cingulate cortex than non-AD. AUD had significantly smaller somatomotor cortical GMV and showed a trend towards smaller volume in the hippocampal formation, relative to non-AUD participants. Misuse of alcohol has an additive effect on dementia severity among AD participants. Smaller hippocampal volume is a common feature of both AD and AUD. Although AD is associated with more volumetric deficits overall, AD and AUD are associated with atrophy in largely distinct brain regions.

A Comparative Multimodal Meta-analysis of Anisotropy and Volume Abnormalities in White Matter in People Suffering From Bipolar Disorder or Schizophrenia

Schizophrenia (SZ) and bipolar disorder (BD) share some similarities in terms of genetic-risk genes and abnormalities of gray-matter structure in the brain, but white matter (WM) abnormalities have not been studied in depth. We undertook a comparative multimodal meta-analysis to identify common and disorder-specific abnormalities in WM structure between SZ and BD. Anisotropic effect size-signed differential mapping software was used to conduct a comparative meta-analysis of 68 diffusion tensor imaging (DTI) and 34 voxel-based morphometry (VBM) studies comparing fractional anisotropy (FA) and white matter volume (WMV), respectively, between patients with SZ (DTI: N = 1543; VBM: N = 1068) and BD (DTI: N = 983; VBM: N = 518) and healthy controls (HCs). The bilateral corpus callosum (extending to the anterior and superior corona radiata) showed shared decreased WMV and FA in SZ and BD. Compared with BD patients, SZ patients showed remarkable disorder-specific WM abnormalities: decreased FA and increased WMV in the left cingulum, and increased FA plus decreased WMV in the right anterior limb of the internal capsule. SZ patients showed more extensive alterations in WM than BD cases, which may be the pathophysiological basis for the clinical continuity of both disorders. The disorder-specific regions in the left cingulum and right anterior limb of the internal capsule provided novel insights into both disorders. Our study adds value to further understanding of the pathophysiology, classification, and differential diagnosis of SZ and BD.

Implicating causal brain imaging endophenotypes in Alzheimer's disease using multivariable IWAS and GWAS summary data

Recent evidence suggests the existence of many undiscovered heritable brain phenotypes involved in Alzheimer's Disease (AD) pathogenesis. This finding necessitates methods for the discovery of causal brain changes in AD that integrate Magnetic Resonance Imaging measures and genotypic data. However, existing approaches for causal inference in this setting, such as the univariate Imaging Wide Association Study (UV-IWAS), suffer from inconsistent effect estimation and inflated Type I errors in the presence of genetic pleiotropy, the phenomenon in which a variant affects multiple causal intermediate risk phenotypes. In this study, we implement a multivariate extension to the IWAS model, namely MV-IWAS, to consistently estimate and test for the causal effects of multiple brain imaging endophenotypes from the Alzheimer's Disease Neuroimaging Initiative (ADNI) in the presence of pleiotropic and possibly correlated SNPs. We further extend MV-IWAS to incorporate variant-specific direct effects on AD, analogous to the existing Egger regression Mendelian Randomization approach, which allows for testing of remaining pleiotropy after adjusting for multiple intermediate pathways. We propose a convenient approach for implementing MV-IWAS that solely relies on publicly available GWAS summary data and a reference panel. Through simulations with either individual-level or summary data, we demonstrate the well controlled Type I errors and superior power of MV-IWAS over UV-IWAS in the presence of pleiotropic SNPs. We apply the summary statistic based tests to 1578 heritable imaging derived phenotypes (IDPs) from the UK Biobank. MV-IWAS detected numerous IDPs as possible false positives by UV-IWAS while uncovering many additional causal neuroimaging phenotypes in AD which are strongly supported by the existing literature.

Characterizing white matter connectivity in Alzheimer's disease and mild cognitive impairment: An automated fiber quantification analysis with two independent datasets

Alzheimer's disease (AD) is a chronic neurodegenerative disease characterized by progressive dementia. Diffusion tensor imaging (DTI) has been widely used to show structural integrity and delineate white matter (WM) degeneration in AD. The automated fiber quantification (AFQ) method is a fully automated approach that can rapidly and reliably identify major WM fiber tracts and evaluate WM properties. The main aim of this study was to assess WM integrity and abnormities in a cohort of patients with amnestic mild cognitive impairment (aMCI) and AD as well as normal controls (NCs). For this purpose, we first used AFQ to identify 20 major WM tracts and assessed WM integrity and abnormalities in a cohort of 120 subjects (39 NCs, 34 aMCI patients and 47 AD patients) in a discovery dataset and 122 subjects (43 NCs, 37 aMCI patients and 42 AD patients) in a replicated dataset. Pointwise differences along WM tracts were identified in the discovery dataset and simultaneously confirmed in the replicated dataset. Next, we investigated the utility of DTI measures along WM tracts as features to distinguish patients with AD from NCs via multilevel cross validation using a support vector machine. Correlation analysis revealed the identified microstructural WM alterations and classification output to be highly associated with cognitive ability in the patient groups, suggesting that they may be a robust biomarker of AD. This systematic study provides a pipeline to examine WM integrity and its potential clinical application in AD and may be useful for studying other neurological and psychiatric disorders.

Longitudinal analysis of brain atrophy in Alzheimer's disease and frontotemporal dementia

Objectives

Alzheimer’s disease (AD) and behavioral variant frontotemporal dementia (bvFTD) are among the leading causes of early-onset dementia. This study aimed to assess the rate of whole brain atrophy by comparing bvFTD and AD.

Methods

Two patients (one man with AD, and one woman with bvFTD) had neuropsychological and neuroimaging assessment by using automated techniques for cross-sectional and longitudinal atrophy measurements.

Results

In the patient with AD, magnetic resonance imaging (MRI) showed decreased bilateral hippocampal and mesial-temporal volume. However, conventional images showed no difference between baseline (T0) and after 1 year (T1). In the patient with bvFTD, MRI showed bilateral frontotemporal lobe atrophy and a moderate increase in atrophy between T0 and T1, particularly in the temporal lobes. A cross-sectional cerebral volume examination showed a considerable reduction in brain volume in the patient with bvFDT and a moderate reduction in the patient with AD. A longitudinal cerebral volume examination showed a lower percentage brain volume change in the patient with bvFTS compared with the patient with AD.

Conclusions

Our results suggest that bvFTD has more neurodegenerative progression. MRI findings should be considered as a reliable marker of disease progression in the brain. Our findings offer potential for monitoring treatment outcomes.

Associations between modifiable risk factors and white matter of the aging brain: insights from diffusion tensor imaging studies

There is increasing interest in factors that may modulate white matter (WM) breakdown and, consequentially, age-related cognitive and behavioral deficits. Recent diffusion tensor imaging studies have examined the relationship of such factors with WM microstructure. This review summarizes the evidence regarding the relationship between WM microstructure and recognized modifiable factors, including hearing loss, hypertension, diabetes, obesity, smoking, depressive symptoms, physical (in) activity, and social isolation, as well as sleep disturbances, diet, cognitive training, and meditation. Current cross-sectional evidence suggests a clear link between loss of WM integrity (lower fractional anisotropy and higher mean diffusivity) and hypertension, obesity, diabetes, and smoking; a relationship that seems to hold for hearing loss, social isolation, depressive symptoms, and sleep disturbances. Physical activity, cognitive training, diet, and meditation, on the other hand, may protect WM with aging. Preliminary evidence from cross-sectional studies of treated risk factors suggests that modification of factors could slow down negative effects on WM microstructure. Careful intervention studies are needed for this literature to contribute to public health initiatives going forward.

Atrophy in midbrain & cerebral/cerebellar pedunculi is characteristic for progressive supranuclear palsy - A double-validation whole-brain meta-analysis

OBJECTIVE

Progressive supranuclear palsy (PSP) is an atypical parkinsonian syndrome characterized by vertical gaze palsy and postural instability. Midbrain atrophy is suggested as a hallmark, but it has not been validated systematically in whole-brain imaging.

METHODS

We conducted whole-brain meta-analyses identifying disease-related atrophy in structural MRI. Eighteen studies were identified (N = 315 PSP, 393 controls) and separated into gray or white matter analyses (15/12). All patients were diagnosed according to the National Institute of Neurological Disorders and Stroke and the Society for PSP (NINDS-SPSP criteria, Litvan et al. (1996a)), which are now considered as PSP-Richardson syndrome (Höglinger et al., 2017). With overlay analyses, we double-validated two meta-analytical algorithms: anatomical likelihood estimation and seed-based D mapping. Additionally, we conducted region-of-interest effect size meta-analyses on radiological biomarkers and subtraction analyses differentiating PSP from Parkinson's disease.

RESULTS

Whole brain meta-analyses revealed consistent gray matter atrophy in bilateral thalamus, anterior insulae, midbrain, and left caudate nucleus. White matter alterations were consistently detected in bilateral superior/middle cerebellar pedunculi, cerebral pedunculi, and midbrain atrophy. Region-of-interest meta-analyses demonstrated that midbrain metrics generally perform very well in distinguishing PSP from other parkinsonian syndromes with strong effect sizes. Subtraction analyses identified the midbrain as differentiating between PSP and Parkinson's disease.

CONCLUSIONS

Our meta-analyses identify gray matter atrophy of the midbrain and white matter atrophy of the cerebral/cerebellar pedunculi and midbrain as characteristic for PSP. Results support the incorporation of structural MRI data, and particularly these structures, into the revised PSP diagnostic criteria.

Use of Multimodal Magnetic Resonance Imaging Techniques to Explore Cognitive Impairment in Leukoaraiosis

Leukoaraiosis, also referred to as white-matter hyperintensities (WMHs) or age-related white matter changes, is the most frequently seen lesion on brain magnetic resonance images (MRI) in the elderly. LA is a subject of intense research interest, and is correlated with stroke, cognitive impairment or dementia, disturbances, affective disorders, and poor prognoses. Rapid advances in neuroimaging have enabled greater understanding of LA associated with aging-related cognitive decline or dementia. Recently, the techniques of multimodal MRI, such as structural MRI (sMRI), resting-state functional MRI (rs-MRI), cerebrovascular reactivity (CVR), diffusion tensor imaging (DTI), magnetic resonance spectroscopy (MRS), and dynamic contrast-enhanced MRI (DCE-MRI), have been used to explore the underlying mechanism of cognitive impairment in patients with LA. These multimodal MRI techniques may provide further insights into the structural and functional changes of LA with cognitive dysfunction.

White matter tauopathy: Transient functional loss and novel myelin remodeling

Early white matter (WM) changes are common in dementia and may contribute to functional decline. We here examine this phenomenon in an induced dementia model for the first time. We report a novel and selective form of myelin injury as the first manifestation of tauopathy in the adult central nervous system. Myelin pathology rapidly followed the induction of a P301 tau mutation associated with fronto-temporal dementia in humans (rTG4510 line). Damage involved focal disruption of the ad-axonal myelin lamella and internal oligodendrocyte tongue process, followed by myelin remodeling with features of re-myelination that included myelin thinning and internodal shortening. The evolution of the re-myelinated phenotype was complete in the molecular layer of the dentate gyrus after 1 month and in the optic nerve (ON) after 9 months of transgene induction and proceeded in the absence of actual demyelination, reactive glial changes or inflammatory response. The initial rapid myelin pathology was associated with loss of WM function and performance decline in a novel recognition test and both these effects largely reversed during the myelin re-modeling phase. The initial phase of myelin injury was accompanied by disruption of the vesicle population present in the axoplasm of hippocampal and ON axons. Axoplasmic vesicle release is significant for the regulation of myelin plasticity and disruption of this pathway may underlie the myelin damage and remodeling evoked by tauopathy. WM dysfunction early in tauopathy will disorder neural circuits, the current findings suggest this event may make a significant contribution to early clinical deficit in dementia.

Brain gray matter changes in type 2 diabetes mellitus: A meta-analysis of whole-brain voxel-based morphometry study

AIMS

We aimed to identify alterations in global gray matter volumes (GMV) and consistent regional abnormalities in T2DM patients via meta-analysis.

METHODS

A systematic search for relevant studies indexed in the PubMed and Embase databases was conducted. A quantitative meta-analysis of volumetric and whole-brain VBM data was conducted using STATA v.12.0 and AES-SDM software packages, respectively.

RESULTS

A total of 15 volumetric studies and five VBM studies of GM in T2DM patients vs. healthy controls (HCs) were identified. The volumetric meta-analysis showed that the GMV of patients with T2DM is lower than in HCs (SMD = -0.56, 95% CI = -0.81 to -0.31, P 0.01). The whole-brain VBM meta-analysis revealed GM reductions in the left superior temporal gyrus, the right middle temporal gyrus, the right rolandic operculum, and the left fusiform gyrus in T2DM patients compared with HCs. Meta-regression analysis showed that Mini-Mental State Examination (MMSE) scores have a positive relationship with GMV in the right insula.

CONCLUSIONS

The results showed a reduced volume of whole and regional GM in T2DM patients, which may indicate a risk for dementia. Further longitudinal research is needed to confirm GM changes, cognitive dysfunction, and their relationship in T2DM.

White matter microstructural abnormalities in amnestic mild cognitive impairment: A meta-analysis of whole-brain and ROI-based studies

Studies that examined white matter (WM) alterations in amnestic mild cognitive impairment (aMCI) abound. This timely meta-analysis aims to synthesize the results of these studies. Seventy-seven studies (totalN_aMCI=1844) were included. Fourteen region-of-interest-based (ROI-based) (k≥8;N_aMCI≥284 per ROI) and two activation likelihood estimation (ALE) meta-analyses (fractional anisotropy [FA]: k=15;N_aMCI=463; mean diffusivity [MD]: k=8;N_aMCI=193) were carried out. Among the many significant ROI-related findings, reliable FA and MD alterations in the fornix, uncinate fasciculus, and parahippocampal cingulum were observed in aMCI. Larger effects were observed in MD relative to FA. The ALE meta-analysis revealed a significant FA decrease among aMCI subjects in the posterior corona radiata. These results provide robust evidence of the presence of WM abnormalities in aMCI. Our findings also highlight the importance of carrying out both ROI-based and whole-brain-based research to obtain a complete picture of WM microstructural alterations associated with the condition..

Age-related differences in the topological efficiency of the brain structural connectome in amnestic mild cognitive impairment

Amnestic mild cognitive impairment (aMCI) is accompanied by the accelerated cognitive decline and rapid brain degeneration with aging. However, the age-related alterations of the topological organization of the brain connectome in aMCI patients remained largely unknown. In this study, we constructed the brain structural connectome in 51 aMCI patients and 51 healthy controls by diffusion magnetic resonance imaging and deterministic tractography. The different age-related alteration patterns of the global and regional network metrics between aMCI patients and healthy controls were assessed by a linear regression model. Compared with healthy controls, significantly decreased global and local network efficiency in aMCI patients were found. When correlating network efficiency with age, we observed a significant decline in network efficiency with aging in the aMCI patients, while not in the healthy controls. The age-related decreases of nodal efficiency in aMCI patients were mainly distributed in the key regions of the default-mode network, such as precuneus, anterior cingulate gyrus, and parahippocampal gyrus. In addition, age-related decreases in the connection strength of the edges between peripheral nodes were observed in aMCI patients. Moreover, the decreased regional efficiency of the parahippocampal gyrus was correlated with impaired memory performances in patients. The present study suggests an age-related disruption of the topological organization of the brain structural connectome in aMCI patients, which may provide evidence for different neural mechanisms underlying aging in aMCI and may serve as a potential imaging marker for the early diagnosis of Alzheimer's disease.