A meta-analysis of voxel-based morphometry studies of white matter volume alterations in Alzheimer's disease

Examining neuroanatomical correlates of win-stay, lose-shift behaviour

This study aimed to better understand the neuroanatomical correlates of decision-making strategies, particularly focusing on win-stay and lose-shift behaviours, using voxel-based morphometry (VBM) in a large cohort of healthy adults. Participants completed a forced-choice card-guessing task designed to elicit behavioural responses to rewards and losses. Using this task, we investigated the relationship between win-stay and lose-shift behaviour and both grey matter volume (GMV) and white matter volume (WMV). The frequency of win-stay and lose-shift behaviours was calculated for each participant and entered into VBM analyses alongside GMV and WMV measures. Our results revealed that increased lose-shift behaviour was associated with reduced GMV in key brain regions, comprising of the left superior temporal gyrus, right middle temporal gyrus, and the bilateral superior lateral occipital cortices. Interestingly, no significant associations were found between GMV or WMV, and win-stay behaviour. These results suggest that specific regions within the temporal and occipital lobes may be involved in modulating decision-making strategies following negative outcomes. Further analyses revealed that increased lose-shift behaviour was also associated with increased WMV in the left superior temporal gyrus. The absence of significant findings in relation to win-stay behaviour and the differential involvement of brain structures in lose-shift responses indicate that decision-making in the face of losses may involve distinct neuroanatomical mechanisms compared to decision-making following wins. This study advances our understanding of the structural brain correlates linked to decision-making strategies and highlights the complexity of brain-behaviour relationships in choice behaviour.

Alzheimer's disease with depression: clinical characteristics and potential mechanisms involving orexin and brain atrophy

This study aimed to explore the clinical characteristics and alteration of orexinergic level in cerebrospinal fluid (CSF) and the volumes of brain grey and white matters, and investigate the roles of orexinergic level on the association between brain atrophy and depression in Alzheimer's disease (AD) patients. The demographic variables of 156 participants were collected. Orexinergic level in CSF and the volumes of brain grey and white matters were evaluated. The correlations of orexinergic level in CSF with depression and brain volume in AD patients were analyzed. The mediating effect of orexinergic level in CSF on the association between brain atrophy and depression in AD patients was investigated. The joint predictive value of orexinergic level in CSF and brain volume for depression in AD patients was established. AD with depression patients showed significantly elevated levels of orexin A and orexin B in CSF; orexin A level in CSF was positively correlated with HAMD score in AD patients. The elevated orexin A level in CSF mediated 49.6% of total association between the decreased grey matter volume of right dorsal medial thalamic nucleus and depression, and 50.3% of total association between the reduced white matter volume of left amygdala and depression. Combinations of above parameters could predict depression in AD patients with a significantly high area under the curve (AUC = 0.841). Therefore, the elevated orexin A level in CSF mediates its effect on the atrophy of the right dorsal medial thalamic nucleus and the white matter of the left amygdala, eventually alleviating depression in AD.

Altered surface-based brain morphometry in type 1 diabetes and neuropathic pain

This study explored surface brain morphometry in type 1 diabetes including focus on painful diabetic peripheral neuropathy (DPN). Brain MRI was obtained from 56 individuals with diabetes (18 without DPN, 19 with painless DPN, 19 with painful DPN) and 20 healthy controls. Cortical thickness, sulcus depth, and gyrification were analysed globally and regionally in each group and in the combined diabetes group. Associations with clinical characteristics and pain were assessed. Globally, cortical thickness was reduced in the combined diabetes group and in painful DPN compared to healthy controls. No differences in sulcus depth and gyrification were found. Several regions, including the middle frontal gyrus showed reduced cortical thickness in the combined diabetes- and painful DPN group. The postcentral gyrus exhibited reduced cortical thickness in painful DPN compared to healthy controls, and reduced sulcus depth compared to painless DPN correlating with higher pain intensity. Cortical thinning manifested across the brain cortex in diabetes, especially for painful DPN. Altered postcentral gyrus morphometry may be associated with neuropathic pain. Assessing cortical morphometry may be critical for comprehending central neuropathy and the manifestation of painful DPN in diabetes.

Deep self-representation learning with hyper-laplacian regularization for brain imaging genetics association analysis

Brain imaging genetics aims to explore the association between genetic factors such as single nucleotide polymorphisms (SNPs) and brain imaging quantitative traits (QTs). However, most existing methods do not consider the nonlinear correlations between genotypic and phenotypic data, as well as potential higher-order relationships among subjects when identifying bi-multivariate associations. In this paper, a novel method called deep hyper-Laplacian regularized self-representation learning based structured association analysis (DHRSAA) is proposed which can learn genotype-phenotype associations and obtain relevant biomarkers. Specifically, a deep neural network is used first to explore the nonlinear relationships among samples. Secondly, self-representation learning based on hyper-Laplacian regularization is utilized to reconstruct the original data. In particular, the introduction of hyper-Laplacian regularization ensures the local structure of the high-dimensional spatial embedding and explores the higher-order relationships among the samples. Moreover, the structural regularization term in the association analysis uncovers chain relationships among SNPs and graphical relationships among imaging QTs, thus making the obtained markers more interpretable and enhancing the biological significance of the method. The performance of the proposed method is validated on real neuroimaging genetics data. Experimental results show that DHRSAA displays better canonical correlation coefficients and recognizes clearer canonical weight patterns compared to several state-of-the-art methods, which suggests that the proposed DHRSAA achieves better performance and identifies disease-related biomarkers.

White Matter Injury in Central Nervous System Disorders

As the aging process accelerates and living conditions improve, central nervous system (CNS) diseases have become a major public health problem. Diseases of the CNS cause not only gray matter damage, which is primarily characterized by the loss of neurons, but also white matter damage. However, most previous studies have focused on grey matter injury (GMI), with fewer studies on white matter injury (WMI). In this article, we will briefly describe the structure and function of white matter, summarize the pathological changes of WMI, and focus on the molecular mechanisms and therapeutic research advances in WMI after ischemic stroke, cerebral hemorrhage, Alzheimer's disease, and multiple sclerosis diseases.

The global research of magnetic resonance imaging in Alzheimer's disease: a bibliometric analysis from 2004 to 2023

Background

Alzheimer's disease (AD) is a common neurodegenerative disorder worldwide and the using of magnetic resonance imaging (MRI) in the management of AD is increasing. The present study aims to summarize MRI in AD researches via bibliometric analysis and predict future research hotspots.

Methods

We searched for records related to MRI studies in AD patients from 2004 to 2023 in the Web of Science Core Collection (WoSCC) database. CiteSpace was applied to analyze institutions, references and keywords. VOSviewer was used for the analysis of countries, authors and journals.

Results

A total of 13,659 articles were obtained in this study. The number of published articles showed overall exponential growth from 2004 to 2023. The top country and institution were the United States and the University of California System, accounting for 40.30% and 9.88% of the total studies, respectively. Jack CR from the United States was the most productive author. The most productive journal was the Journal of Alzheimers Disease. Keyword burst analysis revealed that "machine learning" and "deep learning" were the keywords that frequently appeared in the past 6 years. Timeline views of the references revealed that "#0 tau pathology" and "#1 deep learning" are currently the latest research focuses.

Conclusion

This study provides an in-depth overview of publications on MRI studies in AD. The United States is the leading country in this field with a concentration of highly productive researchers and high-level institutions. The current research hotspot is deep learning, which is being applied to develop noninvasive diagnosis and safer treatment of AD.

Radiomic detection of abnormal brain regions in tuberous sclerosis complex

BACKGROUND

Radiomics refers to the extraction of quantitative information from medical images and is most commonly utilized in oncology to provide ancillary information for solid tumor diagnosis, prognosis, and treatment response. The traditional radiomic pipeline involves segmentation of volumes of interest with comparison to normal brain. In other neurologic disorders, such as epilepsy, lesion delineation may be difficult or impossible due to poor anatomic definition, small size, and multifocal or diffuse distribution. Tuberous sclerosis complex (TSC) is a rare genetic disease in which brain magnetic resonance imaging (MRI) demonstrates multifocal abnormalities with variable imaging and epileptogenic features.

PURPOSE

The purpose of this study was to develop a radiomic workflow for identification of abnormal brain regions in TSC, using a whole-brain atlas-based approach with generation of heatmaps based on signal deviation from normal controls.

METHODS

This was a retrospective pilot study utilizing high-resolution whole-brain 3D FLAIR MRI datasets from retrospective enrollment of tuberous sclerosis complex (TSC) patients and normal controls. Subjects underwent MRI including high-resolution 3D FLAIR sequences. Preprocessing included skull stripping, coregistration, and intensity normalization. Using the Brainnetome and Harvard-Oxford atlases, brain regions were parcellated into 318 discrete regions. Expert neuroradiologists spatially labeled all tubers in TSC patients using ITK-SNAP. The pyradiomics toolbox was used to extract 88 radiomic features based on IBSI guidelines, comparing tuber-affected and non-tuber-affected parenchyma in TSC patients, as well as normal brain tissue in control patients. For model training and validation, regions with tubers from 20 TSC patients and 30 normal control subjects were randomly divided into two training sets (80%) and two validation sets (20%). Additional model testing was performed on a separate group of 20 healthy controls. LASSO (least absolute shrinkage and selection operator) was used to perform variable selection and regularization to identify regions containing tubers. Relevant radiomic features selected by LASSO were combined to produce a radiomic score ω, defined as the sum of squared differences from average control group values. Region-specific ω scores were converted to heat maps and spatially coregistered with brain MRI to reflect overall radiomic deviation from normal.

RESULTS

The proposed radiomic workflow allows for quantification of deviation from normal in 318 regions of the brain with the use of a summative radiomic score ω. This score can be used to generate spatially registered heatmaps to identify brain regions with radiomic abnormalities. The pilot study of TSC showed radiomic scores ω that were statistically different in regions containing tubers from regions without tubers/normal brain (p < 0.0001). Our model exhibits an AUC of 0.81 (95% confidence interval: 0.78-0.84) on the testing set, and the best threshold obtained on the training set, when applied to the testing set, allows us to identify regions with tubers with a specificity of 0.91 and a sensitivity of 0.60.

CONCLUSION

We describe a whole-brain atlas-based radiomic approach to identify abnormal brain regions in TSC patients. This approach may be helpful for identifying specific regions of interest based on relatively greater signal deviation, particularly in clinical scenarios with numerous or poorly defined anatomic lesions.

Impact of Apolipoprotein E ε4 in Alzheimer's Disease: A Meta-Analysis of Voxel-Based Morphometry Studies

BACKGROUND AND PURPOSE

Alzheimer's disease (AD) is the most-prevalent form of dementia and imposes substantial burdens at the personal and societal levels. The apolipoprotein E (APOE) ε4 allele is a genetic factor known to increase AD risk and exacerbate brain atrophy and its symptoms. We aimed to provide a comprehensive review of the impacts of APOE ε4 on brain atrophy in AD as well as in mild cognitive impairment (MCI) as a transitional stage of AD.

METHODS

We performed a coordinate-based meta-analysis of voxel-based morphometry studies to compare gray-matter atrophy patterns between carriers and noncarriers of APOE ε4. We obtained coordinate-based structural magnetic resonance imaging data from 1,135 individuals who met our inclusion criteria among 12 studies reported in PubMed and Google Scholar.

RESULTS

We found that atrophy of the hippocampus and parahippocampus was significantly greater in APOE ε4 carriers than in noncarriers, especially among those with AD and MCI, while there was no significant atrophy in these regions in healthy controls who were also carriers.

CONCLUSIONS

The present meta-analysis has highlighted the significant link between the APOE ε4 allele and hippocampal atrophy in both AD and MCI, which emphasizes the critical influence of the allele on neurodegeneration, especially in the hippocampus. These findings improve the understanding of AD pathology, potentially facilitating progress in early detection, targeted interventions, and personalized care strategies for individuals at risk of AD who carry the APOE ε4 allele.

In vivo effect of LATE-NC on integrity of white matter connections to the hippocampus

INTRODUCTION

TAR DNA-binding protein 43 (TDP-43) is a highly prevalent proteinopathy that is involved in neurodegenerative processes, including axonal damage. To date, no ante mortem biomarkers exist for TDP-43, and few studies have directly assessed its impact on neuroimaging measures utilizing pathologic quantification.

METHODS

Ante mortem diffusion-weighted images were obtained from community-dwelling older adults. Regression models calculated the relationship between post mortem TDP-43 burden and ante mortem fractional anisotropy (FA) within each voxel in connection with the hippocampus, controlling for coexisting Alzheimer's disease and demographics.

RESULTS

Results revealed a significant negative relationship (false discovery rate [FDR] corrected p < .05) between post mortem TDP-43 and ante mortem FA in one cluster within the left medial temporal lobe connecting to the parahippocampal cortex, entorhinal cortex, and cingulate, aligning with the ventral subdivision of the cingulum. FA within this cluster was associated with cognition.

DISCUSSION

Greater TDP-43 burden is associated with lower FA within the limbic system, which may contribute to impairment in learning and memory.

HIGHLIGHTS

Post mortem TDP-43 pathological burden is associated with reduced ante mortem fractional anisotropy. Reduced FA located in the parahippocampal portion of the cingulum. FA in this area was associated with reduced episodic and semantic memory. FA in this area was associated with increased inward hippocampal surface deformation.

The impact of APOE ε4 in Alzheimer's disease: a meta-analysis of voxel-based morphometry studies

Background

Alzheimer's disease (AD) is the most prevalent form of dementia, exerting substantial personal and societal impacts. The apolipoprotein E (APOE) ε4 allele is a known genetic factor that increases the risk of AD, contributing to more severe brain atrophy and exacerbated symptoms.

Purpose

We aim to provide a comprehensive review of the impacts of the APOE ε4 allele on brain atrophy in AD and mild cognitive impairment (MCI) as a transitional stage of AD.

Methods

We performed a coordinate-based meta-analysis of voxel-based morphometry (VBM) studies to identify the patterns of grey matter atrophy in APOE ε4 carriers vs. non-carriers. We obtained coordinate-based structural magnetic resonance imaging (MRI) data for 1135 individuals from 12 studies on PubMed and Google Scholar that met our inclusion criteria.

Results

We found significant atrophy in the hippocampus and parahippocampus of APOE ε4 carriers compared to non-carriers, especially within the AD and MCI groups, while healthy controls showed no significant atrophy in these regions.

Conclusion

Our meta-analysis sheds light on the significant link between the APOE ε4 allele and hippocampal atrophy in both AD and MCI, emphasizing the allele's critical influence on neurodegeneration, especially in the hippocampus. Our findings contribute to the understanding of the disease's pathology, potentially facilitating progress in early detection, targeted interventions, and personalized care strategies for individuals with the APOE ε4 allele who are at risk for Alzheimer's Disease.

Subjective clustering in patients with fronto-temporal dementia

In the behavioral variant of frontotemporal dementia (bvFTD) memory deficits have been traditionally considered as due to difficulties in encoding/retrieval frontal strategies. However, the frontal origin of memory deficits in bvFTD has been questioned and hippocampal dysfunction has been also proposed. Here we analyzed bvFTD patients' proficiency in subjectively organizing memories without an external criterion. Twenty bvFTD patients and 20 healthy individuals were assessed with memory and executive tasks. The ability to subjectively organize memories in the immediate recall of a 15 unrelated word list was measured by calculating the index of subjective clustering (ISC) based on the constancies in response order across the five consecutive free recall trials. Results revealed reduced ISC in bvFTD patients with respect to normal controls. In the bvFTD group, the ISC score correlated with the Corsi span backward score and the number of categories achieved on the Modified Card Sorting Test. The bvFTD patients' reduced ISC and its correlation with executive performance suggest that executive deficits underlie their defective strategic organization of memories. However, as ISC did not predict memory accuracy in these patients, the memory deficit may not be the mere expression of their executive difficulties.

The influence of limbic-predominant age-related TDP-43 encephalopathy on argyrophilic grain disease: A voxel-based morphometry analysis of pathologically confirmed cases

BACKGROUND

The influence of limbic-predominant age-related TAR DNA-binding protein of 43 kDa encephalopathy neuropathological change (LATE-NC) on structural alterations in argyrophilic grain disease (AGD) have not been documented. This study aimed to investigate the morphological impact of LATE-NC on AGD through voxel-based morphometry (VBM) technique.

MATERIALS AND METHODS

Fifteen individuals with pathologically verified AGD, comprising 6 with LATE-NC (comorbid AGD [cAGD]) and 9 without LATE-NC (pure AGD [pAGD]), along with 10 healthy controls (HC) were enrolled. Whole-brain 3D-T1-weighted images were captured and preprocessed utilizing the Computational Anatomy Toolbox 12. VBM was employed to compare gray matter volume among (i) pAGD and HC, (ii) cAGD and HC, and (iii) pAGD and cAGD.

RESULTS

In comparison to HC, the pAGD group exhibited slightly asymmetric gray matter volume loss, particularly in the ambient gyrus, amygdala, hippocampus, anterior cingulate gyrus, and insula. Alternatively, the cAGD group exhibited greater gray matter volume loss, with a predominant focus on the inferolateral regions encompassing the ambient gyrus, amygdala, hippocampus, and the inferior temporal area, including the anterior temporal pole. The atrophy of the bilateral anterior temporal pole and right inferior temporal gyrus persisted when contrasting the pAGD and cAGD groups.

CONCLUSION

Comorbidity with LATE-NC is linked to different atrophic distribution, particularly affecting the inferolateral regions in AGD. Consequently, the consideration of comorbid LATE-NC is crucial in individuals with AGD exhibiting more widespread temporal atrophy.

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.

Diagnosis of Alzheimer's disease by joining dual attention CNN and MLP based on structural MRIs, clinical and genetic data

Alzheimer's disease (AD) is an irreversible central nervous degenerative disease, while mild cognitive impairment (MCI) is a precursor state of AD. Accurate early diagnosis of AD is conducive to the prevention and early intervention treatment of AD. Although some computational methods have been developed for AD diagnosis, most employ only neuroimaging, ignoring other data (e.g., genetic, clinical) that may have potential disease information. In addition, the results of some methods lack interpretability. In this work, we proposed a novel method (called DANMLP) of joining dual attention convolutional neural network (CNN) and multilayer perceptron (MLP) for computer-aided AD diagnosis by integrating multi-modality data of the structural magnetic resonance imaging (sMRI), clinical data (i.e., demographics, neuropsychology), and APOE genetic data. Our DANMLP consists of four primary components: (1) the Patch-CNN for extracting the image characteristics from each local patch, (2) the position self-attention block for capturing the dependencies between features within a patch, (3) the channel self-attention block for capturing dependencies of inter-patch features, (4) two MLP networks for extracting the clinical features and outputting the AD classification results, respectively. Compared with other state-of-the-art methods in the 5CV test, DANMLP achieves 93% and 82.4% classification accuracy for the AD vs. MCI and MCI vs. NC tasks on the ADNI database, which is 0.2%∼15.2% and 3.4%∼26.8% higher than that of other five methods, respectively. The individualized visualization of focal areas can also help clinicians in the early diagnosis of AD. These results indicate that DANMLP can be effectively used for diagnosing AD and MCI patients.

Analysis of the influences of social isolation on cognition and the therapeutic potential of deep brain stimulation in a mouse model

Background

Social interaction is a fundamental human need. Social isolation (SI) can have negative effects on both emotional and cognitive function. However, it is currently unclear how age and the duration of SI affect emotion and recognition function. In addition, there is no specific treatment for the effects of SI.

Methods

The adolescence or adult mice were individually housed in cages for 1, 6 or 12 months and for 2 months to estabolish SI mouse model. We investigated the effects of SI on behavior in mice at different ages and under distinct durations of SI, and we explored the possible underlying mechanisms. Then we performed deep brain stimulation (DBS) to evaluate its influences on SI induced behavioral abnormalities.

Results

We found that social recognition was affected in the short term, while social preference was damaged by extremely long periods of SI. In addition to affecting social memory, SI also affects emotion, short-term spatial ability and learning willingness in mice. Myelin was decreased significantly in the medial prefrontal cortex (mPFC) and dorsal hippocampus of socially isolated mice. Cellular activity in response to social stimulation in both areas was impaired by social isolation. By stimulating the mPFC using DBS, we found that DBS alleviated cellular activation disorders in the mPFC after long-term SI and improved social preference in mice.

Conclusion

Our results suggest that the therapeutic potential of stimulating the mPFC with DBS in individuals with social preference deficits caused by long-term social isolation, as well as the effects of DBS on the cellular activity and density of OPCs.

Assessment of complementary white matter microstructural changes and grey matter atrophy in a preclinical model of Alzheimer's disease

Alzheimer's disease (AD) has been associated with amyloid and tau pathology, as well as neurodegeneration. Beyond these hallmark features, white matter microstructural abnormalities have been observed using MRI. The objective of this study was to assess grey matter atrophy and white matter microstructural changes in a preclinical mouse model of AD (3xTg-AD) using voxel-based morphometry (VBM) and free-water (FW) diffusion tensor imaging (FW-DTI). Compared to controls, lower grey matter density was observed in the 3xTg-AD model, corresponding to the small clusters in the caudate-putamen, hypothalamus, and cortex. DTI-based fractional anisotropy (FA) was decreased in the 3xTg model, while the FW index was increased. Notably, the largest clusters for both FW-FA and FW index were in the fimbria, with other regions including the anterior commissure, corpus callosum, forebrain septum, and internal capsule. Additionally, the presence of amyloid and tau in the 3xTg model was confirmed with histopathology, with significantly higher levels observed across many regions of the brain. Taken together, these results are consistent with subtle neurodegenerative and white matter microstructural changes in the 3xTg-AD model that manifest as increased FW, decreased FW-FA, and decreased grey matter density.

White matter injury, cholesterol dysmetabolism, and APP/Abeta dysmetabolism interact to produce Alzheimer's disease (AD) neuropathology: A hypothesis and review

We postulate that myelin injury contributes to cholesterol release from myelin and cholesterol dysmetabolism which contributes to Abeta dysmetabolism, and combined with genetic and AD risk factors, leads to increased Abeta and amyloid plaques. Increased Abeta damages myelin to form a vicious injury cycle. Thus, white matter injury, cholesterol dysmetabolism and Abeta dysmetabolism interact to produce or worsen AD neuropathology. The amyloid cascade is the leading hypothesis for the cause of Alzheimer's disease (AD). The failure of clinical trials based on this hypothesis has raised other possibilities. Even with a possible new success (Lecanemab), it is not clear whether this is a cause or a result of the disease. With the discovery in 1993 that the apolipoprotein E type 4 allele (APOE4) was the major risk factor for sporadic, late-onset AD (LOAD), there has been increasing interest in cholesterol in AD since APOE is a major cholesterol transporter. Recent studies show that cholesterol metabolism is intricately involved with Abeta (Aβ)/amyloid transport and metabolism, with cholesterol down-regulating the Aβ LRP1 transporter and upregulating the Aβ RAGE receptor, both of which would increase brain Aβ. Moreover, manipulating cholesterol transport and metabolism in rodent AD models can ameliorate pathology and cognitive deficits, or worsen them depending upon the manipulation. Though white matter (WM) injury has been noted in AD brain since Alzheimer's initial observations, recent studies have shown abnormal white matter in every AD brain. Moreover, there is age-related WM injury in normal individuals that occurs earlier and is worse with the APOE4 genotype. Moreover, WM injury precedes formation of plaques and tangles in human Familial Alzheimer's disease (FAD) and precedes plaque formation in rodent AD models. Restoring WM in rodent AD models improves cognition without affecting AD pathology. Thus, we postulate that the amyloid cascade, cholesterol dysmetabolism and white matter injury interact to produce and/or worsen AD pathology. We further postulate that the primary initiating event could be related to any of the three, with age a major factor for WM injury, diet and APOE4 and other genes a factor for cholesterol dysmetabolism, and FAD and other genes for Abeta dysmetabolism.

Neuroanatomical and neurocognitive changes associated with subjective cognitive decline

Introduction

Subjective Cognitive Decline (SCD) can progress to mild cognitive impairment (MCI) and Alzheimer's disease (AD) dementia and thus may represent a preclinical stage of the AD continuum. However, evidence about structural changes observed in the brain during SCD remains inconsistent.

Materials and methods

This cross-sectional study aimed to evaluate, in subjects recruited from the CompAS project, neurocognitive and neurostructural differences between a group of forty-nine control subjects and forty-nine individuals who met the diagnostic criteria for SCD and exhibited high levels of subjective cognitive complaints (SCCs). Structural magnetic resonance imaging was used to compare neuroanatomical differences in brain volume and cortical thickness between both groups.

Results

Relative to the control group, the SCD group displayed structural changes involving frontal, parietal, and medial temporal lobe regions of critical importance in AD etiology and functionally related to several cognitive domains, including executive control, attention, memory, and language.

Conclusion

Despite the absence of clinical deficits, SCD may constitute a preclinical entity with a similar (although subtle) pattern of neuroanatomical changes to that observed in individuals with amnestic MCI or AD dementia.

Longitudinal White and Gray Matter Response to Precision Medicine-Guided Intervention for Alzheimer's Disease

BACKGROUND

Alzheimer's disease (AD) is a debilitating condition that is widely known to adversely affect gray matter (GM) and white matter (WM) tracts within the brain. Recently, precision medicine has shown promise in alleviating the clinical and gross morphological trajectories of patients with AD. However, regional morphological changes have not yet been adequately characterized.

OBJECTIVE

Investigate regional morphological responses to a precision medicine-guided intervention with regards to white and gray matter in AD and mild cognitive impairment (MCI).

METHODS

Clinical and neuroimaging data were compiled over a 9-month period from 25 individuals who were diagnosed with AD or MCI receiving individualized treatment plans. Structural T1-weighted MRI scans underwent segmentation and volumetric quantifications via Neuroreader. Longitudinal changes were calculated via annualized percent change of WM or GM ratios.

RESULTS

Montreal Cognitive Assessment scores (p < 0.001) and various domains of the Computerized Neurocognitive Screening Vital Signs significantly improved from baseline to 9-month follow-up. There was regional variability in WM and GM atrophy or hypertrophy, but none of these observed changes were statistically significant after correction for multiple comparisons.

From attention-deficit hyperactivity disorder to sporadic Alzheimer's disease-Wnt/mTOR pathways hypothesis

Alzheimer's disease (AD) is the most common neurodegenerative disorder with the majority of patients classified as sporadic AD (sAD), in which etiopathogenesis remains unresolved. Though sAD is argued to be a polygenic disorder, apolipoprotein E (APOE) ε4, was found three decades ago to pose the strongest genetic risk for sAD. Currently, the only clinically approved disease-modifying drugs for AD are aducanumab (Aduhelm) and lecanemab (Leqembi). All other AD treatment options are purely symptomatic with modest benefits. Similarly, attention-deficit hyperactivity disorder (ADHD), is one of the most common neurodevelopmental mental disorders in children and adolescents, acknowledged to persist in adulthood in over 60% of the patients. Moreover, for ADHD whose etiopathogenesis is not completely understood, a large proportion of patients respond well to treatment (first-line psychostimulants, e.g., methylphenidate/MPH), however, no disease-modifying therapy exists. Interestingly, cognitive impairments, executive, and memory deficits seem to be common in ADHD, but also in early stages of mild cognitive impairment (MCI), and dementia, including sAD. Therefore, one of many hypotheses is that ADHD and sAD might have similar origins or that they intercalate with one another, as shown recently that ADHD may be considered a risk factor for sAD. Intriguingly, several overlaps have been shown between the two disorders, e.g., inflammatory activation, oxidative stress, glucose and insulin pathways, wingless-INT/mammalian target of rapamycin (Wnt/mTOR) signaling, and altered lipid metabolism. Indeed, Wnt/mTOR activities were found to be modified by MPH in several ADHD studies. Wnt/mTOR was also found to play a role in sAD and in animal models of the disorder. Moreover, MPH treatment in the MCI phase was shown to be successful for apathy including some improvement in cognition, according to a recent meta-analysis. In several AD animal models, ADHD-like behavioral phenotypes have been observed indicating a possible interconnection between ADHD and AD. In this concept paper, we will discuss the various evidence in human and animal models supporting the hypothesis in which ADHD might increase the risk for sAD, with common involvement of the Wnt/mTOR-pathway leading to lifespan alteration at the neuronal levels.

Brain Atrophy and Clinical Characterization of Adults With Mild Cognitive Impairment and Different Cerebrospinal Fluid Biomarker Profiles According to the AT(N) Research Framework of Alzheimer’s Disease

Introduction

This study aimed to evaluate, in adults with mild cognitive impairment (MCI), the brain atrophy that may distinguish between three AT(N) biomarker-based profiles, and to determine its clinical value.

Methods

Structural MRI (sMRI) was employed to evaluate the volume and cortical thickness differences in MCI patients with different AT(N) profiles, namely, A−T−(N)−: normal AD biomarkers; A+T−(N)−: AD pathologic change; and A+T+(N)+: prodromal AD. Sensitivity and specificity of these changes were also estimated.

Results

An initial atrophy in medial temporal lobe (MTL) areas was found in the A+T−(N)− and A+T+(N)+ groups, spreading toward the parietal and frontal regions in A+T+(N)+ patients. These structural changes allowed distinguishing AT(N) profiles within the AD continuum; however, the profiles and their pattern of neurodegeneration were unsuccessful to determine the current clinical status.

Conclusion

sMRI is useful in the determination of the specific brain structural changes of AT(N) profiles along the AD continuum, allowing differentiation between MCI adults with or without pathological AD biomarkers.

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.

Effect of Acupuncture on Neuroplasticity of Stroke Patients with Motor Dysfunction: A Meta-Analysis of fMRI Studies

Objective

To analyze the pattern of intrinsic brain activity variability that is altered by acupuncture compared with conventional treatment in stroke patients with motor dysfunction, thus providing the mechanism of stroke treatment by acupuncture.

Methods

Chinese and English articles published up to May 2020 were searched in the PubMed, Web of Science, EMBASE, and Cochrane Library databases, China National Knowledge Infrastructure, Chongqing VIP, and Wanfang Database. We only included randomized controlled trials (RCTs) using resting-state fMRI to observe the effect of acupuncture on stroke patients with motor dysfunction. R software was used to analyze the continuous variables, and Seed-based d Mapping with Permutation of Subject Images (SDM-PSI) was used to perform an analysis of fMRI data. Findings. A total of 7 studies comprising 143 patients in the treatment group and 138 in the control group were included in the meta-analysis. The results suggest that acupuncture treatment helps the healing process of motor dysfunction in stroke patients and exhibits hyperactivation in the bilateral basal ganglia and insula and hypoactivation in motor-related areas (especially bilateral BA6 and left BA4).

Conclusion

Acupuncture plays a role in promoting neuroplasticity in subcortical regions that are commonly affected by stroke and cortical motor areas that may compensate for motor deficits, which may provide a possible mechanism underlying the therapeutic effect of acupuncture.

Relationship between Brain Tissue Changes and Blood Biomarkers of Cyclophilin A, Heme Oxygenase-1, and Inositol-Requiring Enzyme 1 in Patients with Alzheimer's Disease

Cyclophilin A (CypA), heme oxygenase-1 (HO-1), and inositol-requiring enzyme 1 (IRE1) are believed to be associated with Alzheimer's disease (AD). In this study, we investigated the association between gray matter volume (GMV) changes and blood levels of CypA, HO-1, and IRE1 in cognitively normal (CN) subjects and those with amnestic mild cognitive impairment (aMCI) and AD. Forty-five elderly CN, 34 aMCI, and 39 AD subjects were enrolled in this study. The results of voxel-based multiple regression analysis showed that blood levels of CypA, HO-1, and IRE1 were correlated with GMV on brain magnetic resonance imaging (MRI) in the entire population (p = 0.0005). The three serum protein levels were correlated with GMV of signature AD regions in the population as a whole. CypA values increased with increasing GMV in the occipital gyrus (r = 0.387, p < 0.0001) and posterior cingulate (r = 0.196, p = 0.034). HO-1 values increased with increasing GMV at the uncus (r = 0.307, p = 0.0008), lateral globus pallidus and putamen (r = 0.287, p = 0.002), and hippocampus (r = 0.197, p = 0.034). IRE1 values decreased with increasing GMV at the uncus (r = -0.239, p = 0.010) and lateral globus pallidus and putamen (r = -0.335, p = 0.0002). Associations between the three serum protein levels and regional GMV indicate that the blood levels of these biomarkers may reflect the pathological mechanism of AD in the brain.

White Matter Abnormalities in Patients With Parkinson's Disease: A Meta-Analysis of Diffusion Tensor Imaging Using Tract-Based Spatial Statistics

Background: Tract-based spatial statistics (TBSS) studies based on diffusion tensor imaging (DTI) have revealed extensive abnormalities in white matter (WM) fibers of Parkinson's disease (PD); however, the results were inconsistent. Therefore, a meta-analytical approach was used in this study to find the most prominent and replicable WM abnormalities of PD. Methods: Online databases were systematically searched for all TBSS studies comparing fractional anisotropy (FA) between patients with PD and controls. Subsequently, we performed the meta-analysis using a coordinate-based meta-analytic software called seed-based d mapping. Meanwhile, meta-regression was performed to explore the potential correlation between the alteration of FA and the clinical characteristics of PD. Results: Out of a total of 1,701 studies that were identified, 23 studies were included. Thirty datasets, including 915 patients (543 men) with PD and 836 healthy controls (449 men), were included in the current study. FA reduction was identified in the body of the corpus callosum (CC; 245 voxels; z = -1.739; p < 0.001) and the left inferior fronto-occipital fasciculus (IFOF) 118 voxels; z = -1.182; p < 0.001). Both CC and IFOF maintained significance in the sensitivity analysis. No increase in FA was identified, but the percentage of male patients with PD was positively associated with the value of FA in the body of the CC. Conclusions: Although some limitations exist, DTI is regarded as a valid way to identify the pathophysiology of PD. It could be more beneficial to integrate DTI parameters with other MRI techniques to explore brain degeneration in PD.

Impact of emotionally negative information on attentional processes in normal aging and Alzheimer's disease

Impairments of emotional processing have been reported in Alzheimer's disease (AD), consistently with the existence of early amygdala atrophy in the pathology. In this study, we hypothesized that patients with AD might show a deficit of orientation toward emotional information under conditions of visual search. Eighteen patients with AD, 24 age-matched controls, and 35 young controls were eye-tracked while they performed a visual search task on a computer screen. The target was a vehicle with implicit (negative or neutral) emotional content, presented concurrently with one, three, or five non-vehicle neutral distractors. The task was to find the target and to report whether a break in the target frame was on the left or on the right side. Both control groups detected negative targets more efficiently than they detected neutral targets, showing facilitated engagement toward negative information. In contrast, patients with AD showed no influence of emotional information on engagement delays. However, all groups reported the frame break location more slowly for negative than for neutral targets (after accounting for the last fixation delay), showing a more difficult disengagement from negative information. These findings are the first to highlight a selective lack of emotional influence on engagement processes in patients with AD. The involvement of amygdala alterations in this behavioral impairment remains to be investigated.

Correlation Between the Wechsler Adult Intelligence Scale- 3 rd Edition Metrics and Brain Structure in Healthy Individuals: A Whole-Brain Magnetic Resonance Imaging Study

Background

The Wechsler Adult Intelligence Scale, 3^rd edition (WAIS-III) is widely used to evaluate the intelligence quotient (IQ). We aimed to investigate the correlation between the WAIS-III metrics and whole-brain structures using magnetic resonance imaging.

Methods

The participants were 266 healthy, right-handed individuals (age: 45.6 ± 12.9 years, 98 males and 168 females). IQs were evaluated using the WAIS-III and Japanese Adult Reading Test (JART). Voxel-based morphometry and diffusion tensor imaging were performed to analyze the correlation of the WAIS-III metrics and JART score with the gray matter volume and white matter integrity, respectively.

Results

The verbal IQ significantly and positively correlated with the left gyrus rectus and anterior cingulate gyrus, left posterior insula and planum polare, and left superior and middle frontal gyri volumes (p < 0.05, corrected). The verbal comprehension group index significantly and positively correlated with the left superior and middle frontal gyri, left gyrus rectus and anterior cingulate gyrus, and left middle frontal gyrus volumes, while the processing speed group index significantly and positively correlated with the bilateral various regional white matter fractional anisotropy values (p < 0.05, corrected). In contrast, the JART score showed no correlation with any brain structure.

Conclusion

These results suggested the neurostructural bases of the WAIS-III IQs and group indices in the brain of healthy individuals.

The brain structure and function alterations in tension-type headache: A protocol for systematic review and meta analysis

OBJECTIVE

The aim of this systematic review and meta-analysis is to improve the understanding of the pathophysiology of tension-type headache (TTH), as well as propose avenues for future neuroimaging studies of TTH.

METHODS

From the inception dates to May 1, 2020, a systematic literature will search in Medline (Ovid SP), Embase (Ovid SP), Cochrane Central Register of Controlled Trials, Web of Science, and 4 Chinese databases without limitation on language or publication. Additionally, International Clinical Trials Registry Platform , reference lists, and relevant gray literatures will be searched. After screening of eligible references, included studies will be determined according to included criteria, and then data extraction and a methodological quality assessment with a customized checklist will be conducted. Each process will be independently implemented by 2 reviewers, any disagreement will be resolved by consensus to the third researcher. If the extracted data is feasible, anisotropic effect-size version of signed differential mapping will be conducted to perform the meta-analysis of the structural and functional brain alterations in TTH patients.

Distinct Components in the Right Extended Frontal Aslant Tract Mediate Language and Working Memory Performance: A Tractography-Informed VBM Study

The extended frontal aslant tract (exFAT) is a tractography-based extension of the frontal aslant tract (FAT) which has been shown to be related with language and working memory performance in healthy human adults, but whether those functional implications map to structurally separate regions along its trajectory is still an open question. We present a tractography-informed Voxel-Based Morphometry procedure capable of detecting local tract-specific structural differences in white matter regions and apply it in two maximum variation sampling studies by comparing local differences in diffusion-derived microstructural parameters and fiber density along the exFAT territory between top performers and bottom performers in language and working memory tasks. In the right hemisphere we were able to detect, without prior constraints, a vertical frontal aslant component approximating the original FAT trajectory whose fiber density was significantly correlated with language (but not working memory) performance and an anterior cluster component corresponding to a distinct anterior frontal aslant component whose fiber density was significantly correlated with working memory (but not language) performance. The reported sub-division of the exFAT territory describes a set of frontal connections that are compatible with previously reported results on the Broca’s territory and frontal cortex hierarchical organization along an anterior-posterior gradient, suggesting that the exFAT could be part of a common neuroanatomical scaffold where language and working memory functions are integrated in the healthy human brain.

Creation of an anthropomorphic CT head phantom for verification of image segmentation

Purpose

Many methods are available to segment structural magnetic resonance (MR) images of the brain into different tissue types. These have generally been developed for research purposes but there is some clinical use in the diagnosis of neurodegenerative diseases such as dementia. The potential exists for computed tomography (CT) segmentation to be used in place of MRI segmentation, but this will require a method to verify the accuracy of CT processing, particularly if algorithms developed for MR are used, as MR has notably greater tissue contrast.

Methods

To investigate these issues we have created a three‐dimensional (3D) printed brain with realistic Hounsfield unit (HU) values based on tissue maps segmented directly from an individual T1 MRI scan of a normal subject. Several T1 MRI scans of normal subjects from the ADNI database were segmented using SPM12 and used to create stereolithography files of different tissues for 3D printing. The attenuation properties of several material blends were investigated, and three suitable formulations were used to print an object expected to have realistic geometry and attenuation properties. A skull was simulated by coating the object with plaster of Paris impregnated bandages. Using two CT scanners, the realism of the phantom was assessed by the measurement of HU values, SPM12 segmentation and comparison with the source data used to create the phantom.

Results

Realistic relative HU values were measured although a subtraction of 60 was required to obtain equivalence with the expected values (gray matter 32.9–35.8 phantom, 29.9–34.2 literature). Segmentation of images acquired at different kVps/mAs showed excellent agreement with the source data (Dice Similarity Coefficient 0.79 for gray matter). The performance of two scanners with two segmentation methods was compared, with the scanners found to have similar performance and with one segmentation method clearly superior to the other.

Conclusion

The ability to use 3D printing to create a realistic (in terms of geometry and attenuation properties) head phantom has been demonstrated and used in an initial assessment of CT segmentation accuracy using freely available software developed for MRI.

Narrative impairment, white matter damage and CSF biomarkers in the Alzheimer's disease spectrum

BACKGROUND

Narrative discourse (ND) refers to one's ability to verbally reproduce a sequence of temporally and logically-linked events. Impairments in ND may occur in subjects with Amnestic Mild Cognitive Impairment (aMCI) and Alzheimer's Disease (AD), but correlates across this function, neuroimaging and cerebrospinal fluid (CSF) AD biomarkers remain understudied.

OBJECTIVES

We sought to measure correlates among ND, Diffusion Tensor Imaging (DTI) indexes and AD CSF biomarkers in patients within the AD spectrum.

RESULTS

Groups differed in narrative production (NProd) and comprehension. aMCI and AD presented poorer inference abilities than controls. AD subjects were more impaired than controls and aMCI regarding WB (p<0.01). ROIs DTI assessment distinguished the three groups. Mean Diffusivity (MD) in the uncinate, bilateral parahippocampal cingulate and left inferior occipitofrontal fasciculi negatively correlated with NProd. Changes in specific tracts correlated with T-tau/Aβ1-42 ratio in CSF.

CONCLUSIONS

AD and aMCI patients presented more ND impairments than controls. Those findings were associated with changes in ventral language-associated and in the inferior parahippocampal pathways. The latest were correlated with biomarkers' levels in the CSF.

METHODS

AD (N=14), aMCI (N=31) and Control (N=39) groups were compared for whole brain (WB) and regions of interest (ROI) DTI parameters, ND and AD CSF biomarkers.

Microstructural White Matter Alterations in Mild Cognitive Impairment and Alzheimer's Disease : Study Based on Neurite Orientation Dispersion and Density Imaging (NODDI)

PURPOSE

To investigate microstructural alterations in white matter in mild cognitive impairment (MCI) and Alzheimer's disease (AD) using neurite orientation dispersion and density imaging (NODDI) and to assess the potential diagnostic performance of NODDI-derived parameters.

METHODS

In this study 14 MCI patients, 14 AD patients, and 14 healthy controls (HC) were recruited. The diffusion tensor imaging(DTI)-derived fractional anisotropy (FA) and NODDI-derived neurite density index (NDI), orientation dispersion index (ODI), and volume fraction of isotropic water molecules (Viso) were calculated from the diffusion data. The tract-based spatial statistics (TBSS) method was used for statistical analysis with one-way ANOVA. The correlations between the parameter values and mini-mental state examination (MMSE) and Montreal cognitive assessment (MoCA) scores were examined. A receiver operating characteristic (ROC) curve was conducted to assess the diagnostic performance of different parameters.

RESULTS

Compared with the HC group, the NDI and ODI values decreased significantly and the Viso values were significantly increased in the MCI and AD groups (p < 0.01, threshold-free cluster enhancement (TFCE)-corrected); however, there were no significant differences in FA values in the MCI group. The NDI, ODI, and Viso values of multiple fibers were significantly correlated with MMSE and MoCA scores. For the diagnosis of AD, the area under the ROC curve (AUC) for the NDI value of the splenium of corpus callosum was larger than the FA value (AUC = 0.885, 0.714, p = 0.042). The AUC of the Viso value of the right cerebral peduncle was larger than FA value (AUC = 0.934, 0.531, p = 0.004).

CONCLUSION

The NDI is more sensitive to white matter microstructural changes than FA and NODDI could be superior to DTI in the diagnosis of AD.

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.

White matter volume loss in amyotrophic lateral sclerosis: A meta-analysis of voxel-based morphometry studies

Structural neuroimaging studies of white matter (WM) volume in amyotrophic lateral sclerosis (ALS) using voxel-based morphometry (VBM) have yielded inconsistent findings. This study aimed to perform a quantitative voxel-based meta-analysis using effect-size signed differential mapping (ES-SDM) to establish a statistical consensus between published studies for WM volume alterations in ALS. The pooled meta-analysis revealed significant WM volume losses in the bilateral supplementary motor areas (SMAs), bilateral precentral gyri (PGs), left middle cerebellar peduncle and right cerebellum in patients with ALS, involving the corticospinal tract (CST), interhemispheric fibers, subcortical arcuate fibers, projection fibers to the striatum and cortico-ponto-cerebellar tract. The meta-regression showed that the ALS functional rating scale-revised (ALSFRS-R) was positively correlated with decreased WM volume in the bilateral SMAs, whereas illness duration was negatively correlated with WM volume reduction in the right SMA. This study provides a thorough profile of WM volume loss in ALS and robust evidence that ALS is a multisystem neurodegenerative disease that involves a variety of subcortical WM tracts extending beyond motor cortex involvement.

A Bayesian Spatial Model to Predict Disease Status Using Imaging Data From Various Modalities

Relating disease status to imaging data stands to increase the clinical significance of neuroimaging studies. Many neurological and psychiatric disorders involve complex, systems-level alterations that manifest in functional and structural properties of the brain and possibly other clinical and biologic measures. We propose a Bayesian hierarchical model to predict disease status, which is able to incorporate information from both functional and structural brain imaging scans. We consider a two-stage whole brain parcellation, partitioning the brain into 282 subregions, and our model accounts for correlations between voxels from different brain regions defined by the parcellations. Our approach models the imaging data and uses posterior predictive probabilities to perform prediction. The estimates of our model parameters are based on samples drawn from the joint posterior distribution using Markov Chain Monte Carlo (MCMC) methods. We evaluate our method by examining the prediction accuracy rates based on leave-one-out cross validation, and we employ an importance sampling strategy to reduce the computation time. We conduct both whole-brain and voxel-level prediction and identify the brain regions that are highly associated with the disease based on the voxel-level prediction results. We apply our model to multimodal brain imaging data from a study of Parkinson's disease. We achieve extremely high accuracy, in general, and our model identifies key regions contributing to accurate prediction including caudate, putamen, and fusiform gyrus as well as several sensory system regions.

Diverging Progression of Network Disruption and Atrophy in Alzheimer's Disease and Semantic Dementia

The progression of cognitive deficits in Alzheimer's disease and semantic dementia is accompanied by grey matter atrophy and white matter deterioration. The impact of neuronal loss on the structural network connectivity in these dementia subtypes is, however, not well understood. In order to gain a more refined knowledge of the topological organization of white matter alterations in dementia, we used a network-based approach to analyze the brain's structural connectivity network. Diffusion-weighted and anatomical MRI images of groups with eighteen Alzheimer's disease and six semantic dementia patients, as well as twenty-one healthy controls were recorded to reconstruct individual connectivity networks. Additionally, voxel-based morphometry, using grey and white matter volume, served to relate atrophy to altered structural connectivity. The analyses showed that Alzheimer's disease is characterized by decreased connectivity strength in various cortical regions. An overlap with grey matter loss was found only in the inferior frontal and superior temporal areas. In semantic dementia, significantly reduced network strength was found in the temporal lobes, which converged with grey and white matter atrophy. Therefore, this study demonstrated that the structural disconnection in early Alzheimer's disease goes beyond grey matter atrophy and is independent of white matter volume loss, an observation that was not found in semantic dementia.

Meta-analysis of regional white matter volume in bipolar disorder with replication in an independent sample using coordinates, T-maps, and individual MRI data

Converging evidence suggests that bipolar disorder (BD) is associated with white matter (WM) abnormalities. Meta-analyses of voxel based morphometry (VBM) data is commonly performed using published coordinates, however this method is limited since it ignores non-significant data. Obtaining statistical maps from studies (T-maps) as well as raw MRI datasets increases accuracy and allows for a comprehensive analysis of clinical variables. We obtained coordinate data (7-studies), T-Maps (12-studies, including unpublished data) and raw MRI datasets (5-studies) and analysed the 24 studies using Seed-based d Mapping (SDM). A VBM analysis was conducted to verify the results in an independent sample. The meta-analysis revealed decreased WM volume in the posterior corpus callosum extending to WM in the posterior cingulate cortex. This region was significantly reduced in volume in BD patients in the independent dataset (p=0.003) but there was no association with clinical variables. We identified a robust WM volume abnormality in BD patients that may represent a trait marker of the disease and used a novel methodology to validate the findings.

Aberrant pattern of regional cerebral blood flow in Alzheimer's disease: a voxel-wise meta-analysis of arterial spin labeling MR imaging studies

Many studies have applied arterial spin labeling (ASL) to characterize cerebral perfusion patterns of Alzheimer's disease (AD). However, findings across studies are not conclusive. A quantitatively voxel-wise meta-analysis to pool the resting-state ASL studies that measure regional cerebral blood flow (rCBF) alterations in AD was conducted to identify the most consistent and replicable perfusion pattern using seed-based d mapping. The meta-analysis, including 17 ASL studies encompassing 327 AD patients and 357 healthy controls, demonstrated that decreased rCBF in AD patients relative to healthy controls were consistently identified in the bilateral posterior cingulate cortices (PCC)/precuneus, bilateral inferior parietal lobules (IPLs), and left dorsolateral prefrontal cortex. The meta-regression analysis showed that more severe cognitive impairment in the AD samples correlated with greater decreases of rCBF in the bilateral PCC and left IPL. This study characterizes an aberrant ASL-rCBF perfusion pattern of AD involving the posterior default mode network and executive network, which are implicated in its pathophysiology and hold promise for developing imaging biomarkers.

Insular atrophy at the prodromal stage of dementia with Lewy bodies: a VBM DARTEL study

Diffuse atrophy including the insula was previously demonstrated in dementia with Lewy bodies (DLB) patients but little is known about the prodromal stage of DLB (pro-DLB). In this prospective study, we used SPM8-DARTEL to measure gray matter (GM) and white matter (WM) atrophy in pro-DLB patients (n = 54), prodromal Alzheimer’s disease (pro-AD) patients (n = 16), DLB patients at the stage of dementia (mild-DLB) (n = 15), and Alzheimer’s disease patients at the stage of dementia (mild-AD) (n = 28), and compared them with healthy elderly controls (HC, n = 22). Diminished GM volumes were found in bilateral insula in pro-DLB patients, a trend to significance in right hippocampus and parahippocampal gyrus in pro-AD patients, in left insula in mild-DLB patients, and in medial temporal lobes and insula in mild-AD patients. The comparison between prodromal groups did not showed any differences. The comparison between groups with dementia revealed atrophy around the left middle temporal gyrus in mild-AD patients. Reduced WM volume was observed in mild-DLB in the pons. The insula seems to be a key region in DLB as early as the prodromal stage. MRI studies looking at perfusion, and functional and anatomical connectivity are now needed to better understand the role of this region in DLB.

Structured and Sparse Canonical Correlation Analysis as a Brain-Wide Multi-Modal Data Fusion Approach

Multi-modal data fusion has recently emerged as a comprehensive neuroimaging analysis approach, which usually uses canonical correlation analysis (CCA). However, the current CCA-based fusion approaches face problems like high-dimensionality, multi-collinearity, unimodal feature selection, asymmetry, and loss of spatial information in reshaping the imaging data into vectors. This paper proposes a structured and sparse CCA (ssCCA) technique as a novel CCA method to overcome the above problems. To investigate the performance of the proposed algorithm, we have compared three data fusion techniques: standard CCA, regularized CCA, and ssCCA, and evaluated their ability to detect multi-modal data associations. We have used simulations to compare the performance of these approaches and probe the effects of non-negativity constraint, the dimensionality of features, sample size, and noise power. The results demonstrate that ssCCA outperforms the existing standard and regularized CCA-based fusion approaches. We have also applied the methods to real functional magnetic resonance imaging (fMRI) and structural MRI data of Alzheimer's disease (AD) patients (n = 34) and healthy control (HC) subjects (n = 42) from the ADNI database. The results illustrate that the proposed unsupervised technique differentiates the transition pattern between the subject-course of AD patients and HC subjects with a p-value of less than 1×10^-6 . Furthermore, we have depicted the brain mapping of functional areas that are most correlated with the anatomical changes in AD patients relative to HC subjects.

Abnormalities of regional brain function in Parkinson's disease: a meta-analysis of resting state functional magnetic resonance imaging studies

There is convincing evidence that abnormalities of regional brain function exist in Parkinson’s disease (PD). However, many resting-state functional magnetic resonance imaging (rs-fMRI) studies using amplitude of low-frequency fluctuations (ALFF) have reported inconsistent results about regional spontaneous neuronal activity in PD. Therefore, we conducted a comprehensive meta-analysis using the Seed-based d Mapping and several complementary analyses. We searched PubMed, Embase, and Web of Science databases for eligible whole-brain rs-fMRI studies that measured ALFF differences between patients with PD and healthy controls published from January 1st, 2000 until June 24, 2016. Eleven studies reporting 14 comparisons, comparing 421 patients and 381 healthy controls, were included. The most consistent and replicable findings in patients with PD compared with healthy controls were identified, including the decreased ALFFs in the bilateral supplementary motor areas, left putamen, left premotor cortex, and left inferior parietal gyrus, and increased ALFFs in the right inferior parietal gyrus. The altered ALFFs in these brain regions are related to motor deficits and compensation in PD, which contribute to understanding its neurobiological underpinnings and could serve as specific regions of interest for further studies.

Dissociating Normal Aging from Alzheimer's Disease: A View from Cognitive Neuroscience

Both normal aging and Alzheimer's disease (AD) are associated with changes in cognition, grey and white matter volume, white matter integrity, neural activation, functional connectivity, and neurotransmission. Obviously, all of these changes are more pronounced in AD and proceed faster providing the basis for an AD diagnosis. Since these differences are quantitative, however, it was hypothesized that AD might simply reflect an accelerated aging process. The present article highlights the different neurocognitive changes associated with normal aging and AD and shows that, next to quantitative differences, there are multiple qualitative differences as well. These differences comprise different neurocognitive dissociations as different cognitive deficit profiles, different weights of grey and white matter atrophy, and different gradients of structural decline. These qualitative differences clearly indicate that AD cannot be simply described as accelerated aging process but on the contrary represents a solid entity.

Rey's Auditory Verbal Learning Test scores can be predicted from whole brain MRI in Alzheimer's disease

Rey's Auditory Verbal Learning Test (RAVLT) is a powerful neuropsychological tool for testing episodic memory, which is widely used for the cognitive assessment in dementia and pre-dementia conditions. Several studies have shown that an impairment in RAVLT scores reflect well the underlying pathology caused by Alzheimer's disease (AD), thus making RAVLT an effective early marker to detect AD in persons with memory complaints. We investigated the association between RAVLT scores (RAVLT Immediate and RAVLT Percent Forgetting) and the structural brain atrophy caused by AD. The aim was to comprehensively study to what extent the RAVLT scores are predictable based on structural magnetic resonance imaging (MRI) data using machine learning approaches as well as to find the most important brain regions for the estimation of RAVLT scores. For this, we built a predictive model to estimate RAVLT scores from gray matter density via elastic net penalized linear regression model. The proposed approach provided highly significant cross-validated correlation between the estimated and observed RAVLT Immediate (R = 0.50) and RAVLT Percent Forgetting (R = 0.43) in a dataset consisting of 806 AD, mild cognitive impairment (MCI) or healthy subjects. In addition, the selected machine learning method provided more accurate estimates of RAVLT scores than the relevance vector regression used earlier for the estimation of RAVLT based on MRI data. The top predictors were medial temporal lobe structures and amygdala for the estimation of RAVLT Immediate and angular gyrus, hippocampus and amygdala for the estimation of RAVLT Percent Forgetting. Further, the conversion of MCI subjects to AD in 3-years could be predicted based on either observed or estimated RAVLT scores with an accuracy comparable to MRI-based biomarkers.

Repeatability of Brain Volume Measurements Made with the Atlas-based Method from T1-weighted Images Acquired Using a 0.4 Tesla Low Field MR Scanner

PURPOSE

An understanding of the repeatability of measured results is important for both the atlas-based and voxel-based morphometry (VBM) methods of magnetic resonance (MR) brain volumetry. However, many recent studies that have investigated the repeatability of brain volume measurements have been performed using static magnetic fields of 1-4 tesla, and no study has used a low-strength static magnetic field. The aim of this study was to investigate the repeatability of measured volumes using the atlas-based method and a low-strength static magnetic field (0.4 tesla).

MATERIALS AND METHODS

Ten healthy volunteers participated in this study. Using a 0.4 tesla magnetic resonance imaging (MRI) scanner and a quadrature head coil, three-dimensional T1-weighted images (3D-T1WIs) were obtained from each subject, twice on the same day. VBM8 software was used to construct segmented normalized images [gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) images]. The regions-of-interest (ROIs) of GM, WM, CSF, hippocampus (HC), orbital gyrus (OG), and cerebellum posterior lobe (CPL) were generated using WFU PickAtlas. The percentage change was defined as[100 × (measured volume with first segmented image - mean volume in each subject)/(mean volume in each subject)]The average percentage change was calculated as the percentage change in the 6 ROIs of the 10 subjects.

RESULTS

The mean of the average percentage changes for each ROI was as follows: GM, 0.556%; WM, 0.324%; CSF, 0.573%; HC, 0.645%; OG, 1.74%; and CPL, 0.471%. The average percentage change was higher for the orbital gyrus than for the other ROIs.

CONCLUSION

We consider that repeatability of the atlas-based method is similar between 0.4 and 1.5 tesla MR scanners. To our knowledge, this is the first report to show that the level of repeatability with a 0.4 tesla MR scanner is adequate for the estimation of brain volume change by the atlas-based method.

The superior fault tolerance of artificial neural network training with a fault/noise injection-based genetic algorithm

Artificial neural networks (ANNs) are powerful computational tools that are designed to replicate the human brain and adopted to solve a variety of problems in many different fields. Fault tolerance (FT), an important property of ANNs, ensures their reliability when significant portions of a network are lost. In this paper, a fault/noise injection-based (FIB) genetic algorithm (GA) is proposed to construct fault-tolerant ANNs. The FT performance of an FIB-GA was compared with that of a common genetic algorithm, the back-propagation algorithm, and the modification of weights algorithm. The FIB-GA showed a slower fitting speed when solving the exclusive OR (XOR) problem and the overlapping classification problem, but it significantly reduced the errors in cases of single or multiple faults in ANN weights or nodes. Further analysis revealed that the fit weights showed no correlation with the fitting errors in the ANNs constructed with the FIB-GA, suggesting a relatively even distribution of the various fitting parameters. In contrast, the output weights in the training of ANNs implemented with the use the other three algorithms demonstrated a positive correlation with the errors. Our findings therefore indicate that a combination of the fault/noise injection-based method and a GA is capable of introducing FT to ANNs and imply that the distributed ANNs demonstrate superior FT performance.

Quantitative T1 and T2 MRI signal characteristics in the human brain: different patterns of MR contrasts in normal ageing

Objective

The objective of this study was to examine age-dependent changes in both T₁-weighted and T₂-weighted image contrasts and spin-echo T₂ relaxation time in the human brain during healthy ageing.

Methods

A total of 37 participants between the ages of 49 and 87 years old were scanned with a 3 Tesla system, using T₁-weighted, T₂ weighted and quantitative spin-echo T₂ imaging. Contrast between image intensities and T₂ values was calculated for various regions, including between individual hippocampal subfields.

Results

The T₁ contrast-to-noise (CNR) and gray:white signal intensity ratio (GWR) did not change in the hippocampus, but it declined in the cingulate cortex with age. In contrast, T₂ CNR and GWR declined in both brain regions. T₂ relaxation time was almost constant in gray matter and most (but not all) hippocampal subfields, but increased substantially in white matter, pointing to an age effect on water relaxation in white matter.

Conclusions

Changes in T₁ and T₂ MR characteristics influence the appearance of brain images in later life and should be considered in image analyses of aged subjects. It is speculated that alterations at the cell biology level, with concomitant alterations to the local magnetic environment, reduce dephasing and subsequently prolong spin-echo T₂ through reduced diffusion effects in later life.

Electronic supplementary material

The online version of this article (doi:10.1007/s10334-016-0573-0) contains supplementary material, which is available to authorized users.

Brain imaging of neurovascular dysfunction in Alzheimer's disease

Neurovascular dysfunction, including blood-brain barrier (BBB) breakdown and cerebral blood flow (CBF) dysregulation and reduction, are increasingly recognized to contribute to Alzheimer's disease (AD). The spatial and temporal relationships between different pathophysiological events during preclinical stages of AD, including cerebrovascular dysfunction and pathology, amyloid and tau pathology, and brain structural and functional changes remain, however, still unclear. Recent advances in neuroimaging techniques, i.e., magnetic resonance imaging (MRI) and positron emission tomography (PET), offer new possibilities to understand how the human brain works in health and disease. This includes methods to detect subtle regional changes in the cerebrovascular system integrity. Here, we focus on the neurovascular imaging techniques to evaluate regional BBB permeability (dynamic contrast-enhanced MRI), regional CBF changes (arterial spin labeling- and functional-MRI), vascular pathology (structural MRI), and cerebral metabolism (PET) in the living human brain, and examine how they can inform about neurovascular dysfunction and vascular pathophysiology in dementia and AD. Altogether, these neuroimaging approaches will continue to elucidate the spatio-temporal progression of vascular and neurodegenerative processes in dementia and AD and how they relate to each other.