Regional coherence changes in the early stages of Alzheimer's disease: a combined structural and resting-state functional MRI study

APOE ε4 influences the dynamic functional connectivity variability and cognitive performance in Alzheimer's disease

BackgroundApolipoprotein E (APOE) ε4 is the most significant genetic risk factor for sporadic Alzheimer's disease (AD). However, its impact on the dynamic changes in resting-state functional connectivity (FC), particularly concerning network formation, interaction, and dissolution over time, remains largely unexplored in AD.ObjectiveThis study aims to explore the effect of APOE ε4 on dynamic FC (dFC) variability and cognitive performance in AD.MethodsWe analyzed the dFC of AD patients, comparing APOE ε4 carriers (n = 33) with non-carriers (n = 41). The whole-brain dFC was assessed by calculating dynamic fractional amplitude of low-frequency fluctuations (dfALFF) and dynamic regional homogeneity (dReHo). To further explore the relationship between cognitive function and dFC in AD patients, we conducted a correlation analysis. Mediation analysis was also performed to determine whether dFC mediates the link between the APOE ε4 and cognitive decline in AD patients.ResultsAD patients carrying the APOE ε4 exhibited more severe cognitive impairment, along with reduced dReHo and dfALFF in both the left and right posterior cerebellar lobes. In these carriers, the dFC analysis showed lower dFC between the left posterior cerebellar lobe and the left middle temporal gyrus, which was positively correlated with executive function and information processing speed. Additionally, mediation analysis indicated that APOE ε4 influences dFC in this brain region, contributing to executive dysfunction in AD.ConclusionsThese findings offer preliminary evidence that APOE ε4 modulates fluctuating communication within the cerebellar lobe and the dFC between the cerebellar lobe and the temporal gyrus in AD.

Altered resting-state brain activity in patients with major depression disorder and bipolar disorder: A regional homogeneity analysis

BACKGROUND

Major Depressive Disorder (MDD) and Bipolar Disorder (BD) exhibit overlapping depressive symptoms, complicating their differentiation in clinical practice. Traditional neuroimaging studies have focused on specific regions of interest, but few have employed whole-brain analyses like regional homogeneity (ReHo). This study aims to differentiate MDD from BD by identifying key brain regions with abnormal ReHo and using advanced machine learning techniques to improve diagnostic accuracy.

METHODS

A total of 63 BD patients, 65 MDD patients, and 70 healthy controls were recruited from the Shanghai Mental Health Center. Resting-state functional MRI (rs-fMRI) was used to analyze ReHo across the brain. We applied Support Vector Machine (SVM) and SVM-Recursive Feature Elimination (SVM-RFE), a robust machine learning model known for its high precision in feature selection and classification, to identify critical brain regions that could serve as biomarkers for distinguishing BD from MDD. SVM-RFE allows for the recursive removal of non-informative features, enhancing the model's ability to accurately classify patients. Correlations between ReHo values and clinical scores were also evaluated.

RESULTS

ReHo analysis revealed significant differences in several brain regions. The study results revealed that, compared to healthy controls, both BD and MDD patients exhibited reduced ReHo in the superior parietal gyrus. Additionally, MDD patients showed decreased ReHo values in the Right Lenticular nucleus, putamen (PUT.R), Right Angular gyrus (ANG.R), and Left Superior occipital gyrus (SOG.L). Compared to the MDD group, BD patients exhibited increased ReHo values in the Left Inferior occipital gyrus (IOG.L). In BD patients only, the reduction in ReHo values in the right superior parietal gyrus and the right angular gyrus was positively correlated with Hamilton Depression Scale (HAMD) scores. SVM-RFE identified the IOG.L, SOG.L, and PUT.R as the most critical features, achieving an area under the curve (AUC) of 0.872, with high sensitivity and specificity in distinguishing BD from MDD.

CONCLUSION

This study demonstrates that BD and MDD patients exhibit distinct patterns of regional brain activity, particularly in the occipital and parietal regions. The combination of ReHo analysis and SVM-RFE provides a powerful approach for identifying potential biomarkers, with the left inferior occipital gyrus, left superior occipital gyrus, and right putamen emerging as key differentiating regions. These findings offer valuable insights for improving the diagnostic accuracy between BD and MDD, contributing to more targeted treatment strategies.

Nebulized seabuckthorn seed oil inhalation attenuates Alzheimer's disease progression in APP/PS1 mice

Seabuckthorn (Hippophae rhamnoides L.) is known for its medicinal properties in treating various diseases, including neurological conditions. However, the therapeutic effect of inhaled seabuckthorn seed oil (SSO) on Alzheimer's disease (AD) remains not fully understood. This study explores the effects of nebulized inhalation of SSO in 9-month-old APP/PS1 mice over 21 days. The results showed that nebulized SSO improved memory and cognition. Using 7.0T MRI to monitor blood oxygenation level dependent (BOLD) signals revealed that SSO altered the Amplitude of Low Frequency Fluctuations (ALFF) and Regional Homogeneity (ReHo) signaling such as in the amygdala and substantia innominate, and hippocampus. Enzyme-linked immuno sorbent assay (ELISA) and pathological analyses indicated reduced neuroinflammation in plasma and brain, decreased neuronal necrosis, lower β-amyloid (Aβ) protein levels, reduced amyloid deposition, and increased tyrosine hydroxylase activity. Additionally, SSO promoted gut microbiota remodeling by increasing alpha diversity and boosting levels of probiotics such as Verrucomicrobia, Bifidobacterium, Prevotella, and Akkermansia, without adverse effects on lung tissue. Nebulized inhalation of SSO may slow AD progression by modulating inflammation and amyloid deposition. Nebulized inhalation offered a potential method for enhancing drug delivery across the blood-brain barrier with reduced systemic side effects.

Subregion-specific associations of the basal forebrain with sleep and cognition in Parkinson's disease

To elucidate the role of basal forebrain (BF) in non-motor symptoms of Parkinson's disease (PD), we investigated the association of sleep quality and cognition with BF volume and functional connectivity. The gray matter volume of the anterior intermediate part (Ch4ai) and the anterior lateral part (Ch4al) of the nucleus basalis Meynert were negatively associated with the PSQI global score. The posterior part of the nucleus basalis Meynert (Ch4p) volume was positively associated with visuospatial, memory, and executive functions. The PSQI global score correlated positively with functional connectivity from the Ch4al to the posterior cingulate, inferior parietal, anterior temporal, and medial prefrontal cortices, which correspond to the default mode network. Our results demonstrated that reduced volume of the anterior portion of the Ch4 and its aberrantly increased functional connectivity with the default mode network were closely related to poor overall sleep quality, while reduced volume of the posterior portion of the Ch4 was associated with decreased cognitive function in PD.

Multi-omics analyses identify gut microbiota-fecal metabolites-brain-cognition pathways in the Alzheimer's disease continuum

BACKGROUND

Gut microbiota dysbiosis is linked to Alzheimer's disease (AD), but our understanding of the molecular and neuropathological bases underlying such association remains fragmentary.

METHODS

Using 16S rDNA amplicon sequencing, untargeted metabolomics, and multi-modal magnetic resonance imaging, we examined group differences in gut microbiome, fecal metabolome, neuroimaging measures, and cognitive variables across 30 patients with AD, 75 individuals with mild cognitive impairment (MCI), and 61 healthy controls (HC). Furthermore, we assessed the associations between these multi-omics changes using correlation and mediation analyses.

RESULTS

There were significant group differences in gut microbial composition, which were driven by 8 microbial taxa (e.g., Staphylococcus and Bacillus) exhibiting a progressive increase in relative abundance from HC to MCI to AD, and 2 taxa (e.g., Anaerostipes) showing a gradual decrease. 26 fecal metabolites (e.g., Arachidonic, Adrenic, and Lithocholic acids) exhibited a progressive increase from HC to MCI to AD. We also observed progressive gray matter atrophy in broadly distributed gray matter regions and gradual micro-structural integrity damage in widespread white matter tracts along the AD continuum. Integration of these multi-omics changes revealed significant associations between microbiota, metabolites, neuroimaging, and cognition. More importantly, we identified two potential mediation pathways: (1) microbiota → metabolites → neuroimaging → cognition, and (2) microbiota → metabolites → cognition.

CONCLUSION

Aside from elucidating the underlying mechanism whereby gut microbiota dysbiosis is linked to AD, our findings may contribute to groundwork for future interventions targeting the microbiota-metabolites-brain-cognition pathways as a therapeutic strategy in the AD continuum.

Integrated cerebellar radiomic-network model for predicting mild cognitive impairment in Alzheimer's disease

INTRODUCTION

Pathological and neuroimaging alterations in the cerebellum of Alzheimer's disease (AD) patients have been documented. However, the role of cerebellum-derived radiomic and structural connectome modeling in the prediction of AD progression remains unclear.

METHODS

Radiomic features were extracted from magnetic resonance imaging (MRI) in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset (n = 1319) and an in-house dataset (n = 308). Integrated machine learning models were developed to predict the conversion risk of normal cognition (NC) to mild cognitive impairment (MCI) over a 6-year follow-up.

RESULTS

The cerebellar models outperformed hippocampal models in distinguishing MCI from NC and in predicting transitions from NC to MCI across both cohorts. Key predictors included textural features in the right III and left I and II lobules, and network properties in Vermis I and II, which were associated with cognitive decline in AD.

DISCUSSION

Cerebellum-derived radiomic-network modeling shows promise as a tool for early identification and prediction of disease progression during the preclinical stage of AD.

HIGHLIGHTS

Altered cerebellar radiomic features and topological networks were identified in the subjects with mild cognitive impairment (MCI). The cerebellar radiomic-network integrated models outperformed hippocampal models in distinguishing MCI from normal cognition. The cerebellar radiomic model effectively predicts MCI risk and can stratify individuals into distinct risk categories. Specific cerebellar radiomic features are associated with cognitive impairment across various stages of amyloid beta and tau pathology.

A fusion analytic framework for investigating functional brain connectivity differences using resting-state fMRI

Introduction

Functional magnetic resonance imaging (fMRI) data is highly complex and high-dimensional, capturing signals from regions of interest (ROIs) with intricate correlations. Analyzing such data is particularly challenging, especially in resting-state fMRI, where patterns are less identifiable without task-specific contexts. Nonetheless, interconnections among ROIs provide essential insights into brain activity and exhibit unique characteristics across groups.

Methods

To address these challenges, we propose an interpretable fusion analytic framework to identify and understand ROI connectivity differences between two groups, revealing their distinctive features. The framework involves three steps: first, constructing ROI-based Functional Connectivity Networks (FCNs) to manage resting-state fMRI data; second, employing a Self-Attention Deep Learning Model (Self-Attn) for binary classification to generate attention distributions encoding group-level differences; and third, utilizing a Latent Space Item-Response Model (LSIRM) to extract group-representative ROI features, visualized on group summary FCNs.

Results

We applied our framework to analyze four types of cognitive impairments, demonstrating their effectiveness in identifying significant ROIs that contribute to the differences between the two disease groups. The results reveal distinct connectivity patterns and unique ROI features, which differentiate cognitive impairments. Specifically, our framework highlighted group-specific differences in functional connectivity, validating its capability to capture meaningful insights from high-dimensional fMRI data.

Discussion

Our novel interpretable fusion analytic framework addresses the challenges of analyzing high-dimensional, resting-state fMRI data. By integrating FCNs, a Self-Attention Deep Learning Model, and LSIRM, the framework provides an innovative approach to discovering ROI connectivity disparities between groups. The attention distribution and group-representative ROI features offer interpretable insights into brain activity patterns and their variations among cognitive impairment groups. This methodology has significant potential to enhance our understanding of cognitive impairments, paving the way for more targeted therapeutic interventions.

The interplay of age, gender and amyloid on brain and cognition in mid-life and older adults

Deficits in memory are seen as a canonical sign of aging and a prodrome to dementia in older adults. However, our understanding of age-related cognition and brain morphology occurring throughout a broader spectrum of adulthood remains limited. We quantified the relationship between cognitive function and brain morphology (sulcal width, SW) using three cross-sectional observational datasets (PISA, AIBL, ADNI) from mid-life to older adulthood, assessing the influence of age, sex, amyloid (Aβ) and genetic risk for dementia. The data comprised cognitive, genetic and neuroimaging measures of a total of 1570 non-clinical mid-life and older adults (mean age 72, range 49-90 years, 1330 males) and 1365 age- and sex-matched adults with mild cognitive impairment (MCI) or Alzheimer's disease (AD). Among non-clinical adults, we found robust modes of co-variation between regional SW and multidomain cognitive function that differed between the mid-life and older age range. These cortical and cognitive profiles derived from healthy cohorts predicted out-of-sample AD and MCI. Furthermore, Aβ-deposition and educational attainment levels were associated with cognition but not SW. These findings underscoring the complex interplay between factors influencing cognition and brain structure from mid-life onwards, providing valuable insights for future research into neurodegeneration and the development of future screening algorithms.

The unique role of anosognosia in the clinical progression of Alzheimer's disease: a disorder-network perspective

Alzheimer's disease (AD) encompasses a long continuum from a preclinical phase, characterized by neuropathological alterations albeit normal cognition, to a symptomatic phase, marked by its clinical manifestations. Yet, the neural mechanisms responsible for cognitive decline in AD patients remain poorly understood. Here, we posit that anosognosia, emerging from an error-monitoring failure due to early amyloid-β deposits in the posterior cingulate cortex, plays a causal role in the clinical progression of AD by preventing patients from being aware of their deficits and implementing strategies to cope with their difficulties, thus fostering a vicious circle of cognitive decline.

Local functional connectivity abnormalities in mild cognitive impairment and Alzheimer's disease: A meta-analytic investigation using minimum Bayes factor activation likelihood estimation

Functional magnetic resonance imaging research employing regional homogeneity (ReHo) analysis has uncovered aberrant local brain connectivity in individuals with mild cognitive impairment (MCI) and Alzheimer's disease (AD) in comparison with healthy controls. However, the precise localization, extent, and possible overlap of these aberrations are still not fully understood. To bridge this gap, we applied a novel meta-analytic and Bayesian method (minimum Bayes Factor Activation Likelihood Estimation, mBF-ALE) for a systematic exploration of local functional connectivity alterations in MCI and AD brains. We extracted ReHo data via a standardized MEDLINE database search, which included 35 peer-reviewed experiments, 1,256 individuals with AD or MCI, 1,118 healthy controls, and 205 x-y-z coordinates of ReHo variation. We then separated the data into two distinct datasets: one for MCI and the other for AD. Two mBF-ALE analyses were conducted, thresholded at "very strong evidence" (mBF ≥ 150), with a minimum cluster size of 200 mm³. We also assessed the spatial consistency and sensitivity of our Bayesian results using the canonical version of the ALE algorithm. For MCI, we observed two clusters of ReHo decrease and one of ReHo increase. Decreased local connectivity was notable in the left precuneus (Brodmann area - BA 7) and left inferior temporal gyrus (BA 20), while increased connectivity was evident in the right parahippocampal gyrus (BA 36). The canonical ALE confirmed these locations, except for the inferior temporal gyrus. In AD, one cluster each of ReHo decrease and increase were found, with decreased connectivity in the right posterior cingulate cortex (BA 30 extending to BA 23) and increased connectivity in the left posterior cingulate cortex (BA 31). These locations were confirmed by the canonical ALE. The identification of these distinct functional connectivity patterns sheds new light on the complex pathophysiology of MCI and AD, offering promising directions for future neuroimaging-based interventions. Additionally, the use of a Bayesian framework for statistical thresholding enhances the robustness of neuroimaging meta-analyses, broadening its applicability to small datasets.

Atypical intrinsic neural timescale in the left angular gyrus in Alzheimer's disease

Alzheimer's disease is characterized by cognitive impairment and progressive brain atrophy. Recent human neuroimaging studies reported atypical anatomical and functional changes in some regions in the default mode network in patients with Alzheimer's disease, but which brain area of the default mode network is the key region whose atrophy disturbs the entire network activity and consequently contributes to the symptoms of the disease remains unidentified. Here, in this case-control study, we aimed to identify crucial neural regions that mediated the phenotype of Alzheimer's disease, and as such, we examined the intrinsic neural timescales-a functional metric to evaluate the capacity to integrate diverse neural information-and grey matter volume of the regions in the default mode network using resting-state functional MRI images and structural MRI data obtained from individuals with Alzheimer's disease and cognitively typical people. After confirming the atypically short neural timescale of the entire default mode network in Alzheimer's disease and its link with the symptoms of the disease, we found that the shortened neural timescale of the default mode network was associated with the aberrantly short neural timescale of the left angular gyrus. Moreover, we revealed that the shortened neural timescale of the angular gyrus was correlated with the atypically reduced grey matter volume of this parietal region. Furthermore, we identified an association between the neural structure, brain function and symptoms and proposed a model in which the reduced grey matter volume of the left angular gyrus shortened the intrinsic neural time of the region, which then destabilized the entire neural timescale of the default mode network and resultantly contributed to cognitive decline in Alzheimer's disease. These findings highlight the key role of the left angular gyrus in the anatomical and functional aetiology of Alzheimer's disease.

EEG connectivity patterns in response to gaming and learning-based cognitive stimulations in Rett syndrome

BACKGROUND

Functional connectivity is scarcely studied in Rett syndrome (RTT). Explorations revealed associations between RTT's clinical, genetic profiles, and coherence measures, highlighting an unexplored frontier in understanding RTT's neural mechanisms and cognitive processes.

AIMS

To evaluate the effects of diverse cognitive stimulations-learning-focused versus gaming-oriented-on electroencephalography brain connectivity in RTT. The comparison with resting states aimed to uncover potential biomarkers and insights into the neural processes associated with RTT.

METHODS AND PROCEDURES

The study included 15 girls diagnosed with RTT. Throughout sessions lasting about 25 min, participants alternated between active and passive tasks, using an eyetracker device while their brain activity was recorded with a 20-channel EEG. Results revealed significant alterations during cognitive tasks, notably in delta, alpha and beta bands. Both tasks induced spectral pattern changes and connectivity shifts, hinting at enhanced neural processing. Hemispheric asymmetry decreased during tasks, suggesting more balanced neural processing. Linear and nonlinear connectivity alterations were observed in active tasks compared to resting state, while passive tasks showed no significant changes.

CONCLUSIONS AND IMPLICATIONS

Results underscores the potential of cognitive stimulation for heightened cognitive abilities, promoting enhanced brain connectivity and information flow in Rett syndrome. These findings offer valuable markers for evaluating cognitive interventions and suggest gaming-related activities as effective tools for improving learning outcomes.

Multimodal active subspace analysis for computing assessment oriented subspaces from neuroimaging data

BACKGROUND

For successful biomarker discovery, it is essential to develop computational frameworks that summarize high-dimensional neuroimaging data in terms of involved sub-systems of the brain, while also revealing underlying heterogeneous functional and structural changes covarying with specific cognitive and biological traits. However, unsupervised decompositions do not inculcate clinical assessment information, while supervised approaches extract only individual feature importance, thereby impeding qualitative interpretation at the level of subspaces.

NEW METHOD

We present a novel framework to extract robust multimodal brain subspaces associated with changes in a given cognitive or biological trait. Our approach involves active subspace learning on the gradients of a trained machine learning model followed by clustering to extract and summarize the most salient and consistent subspaces associated with the target variable.

RESULTS

Through a rigorous cross-validation procedure on an Alzheimer's disease (AD) dataset, our framework successfully extracts multimodal subspaces specific to a given clinical assessment (e.g., memory and other cognitive skills), and also retains predictive performance in standard machine learning algorithms. We also show that the salient active subspace directions occur consistently across randomly sub-sampled repetitions of the analysis.

COMPARISON WITH EXISTING METHOD(S)

Compared to existing unsupervised decompositions based on principle component analysis, the subspace components in our framework retain higher predictive information.

CONCLUSIONS

As an important step towards biomarker discovery, our framework not only uncovers AD-related brain regions in the associated brain subspaces, but also enables automated identification of multiple underlying structural and functional sub-systems of the brain that collectively characterize changes in memory and proficiency in cognitive skills related to brain disorders like AD.

Altered resting-state functional connectivity and dynamic network properties in cognitive impairment: an independent component and dominant-coactivation pattern analyses study

Introduction

Cognitive impairment (CI) due to Alzheimer's disease (AD) encompasses a decline in cognitive abilities and can significantly impact an individual's quality of life. Early detection and intervention are crucial in managing CI, both in the preclinical and prodromal stages of AD prior to dementia.

Methods

In this preliminary study, we investigated differences in resting-state functional connectivity and dynamic network properties between 23 individual with CI due to AD based on clinical assessment and 15 healthy controls (HC) using Independent Component Analysis (ICA) and Dominant-Coactivation Pattern (d-CAP) analysis. The cognitive status of the two groups was also compared, and correlations between cognitive scores and d-CAP switching probability were examined.

Results

Results showed comparable numbers of d-CAPs in the Default Mode Network (DMN), Executive Control Network (ECN), and Frontoparietal Network (FPN) between HC and CI groups. However, the Visual Network (VN) exhibited fewer d-CAPs in the CI group, suggesting altered dynamic properties of this network for the CI group. Additionally, ICA revealed significant connectivity differences for all networks. Spatial maps and effect size analyses indicated increased coactivation and more synchronized activity within the DMN in HC compared to CI. Furthermore, reduced switching probabilities were observed for the CI group in DMN, VN, and FPN networks, indicating less dynamic and flexible functional interactions.

Discussion

The findings highlight altered connectivity patterns within the DMN, VN, ECN, and FPN, suggesting the involvement of multiple functional networks in CI. Understanding these brain processes may contribute to developing targeted diagnostic and therapeutic strategies for CI due to AD.

Continuous inhalation of essential oil increases gray matter volume

Research into the health benefits of scents is on the rise. However, little is known about the effects of continuous inhalation, such as wearing scents on clothing, on brain structure. Therefore, in this study, an intervention study was conducted on a total of 50 healthy female people, 28 in the intervention group and 22 in the control group, asking them to wear a designated rose scent on their clothes for a month. The effect of continuous inhalation of essential oil on the gray matter of the brain was measured by calculating changes in brain images of participants taken before and after the intervention using Magnetic Resonance Imaging (MRI). The results showed that the intervention increased the gray matter volume (GMV) of the whole brain and posterior cingulate cortex (PCC) subregion. On the other hand, the GMV of the amygdala and orbitofrontal cortex (OFC) did not change. This study is the first to show that continuous scent inhalation changes brain structure.

Recent advances in light energy biotherapeutic strategies with photobiomodulation on central nervous system disorders

Transcranial photobiomodulation refers to irradiation of the brain through the skull using low-intensity red or near-infrared light, which is the most commonly studied method of light energy biotherapy for central nervous system disorders. The absorption of photons by specific chromophores within the cell elevates ATP synthesis, reduces oxidative stress damage, alleviates inflammation or mediates the activation of transcription factors and signaling mediators through secondary mediators, which in turn trigger downstream signaling pathways to cause a series of photobiological effects including upregulation of neurotrophic factors. Multiple mechanisms are simultaneously involved in the pathological process of central nervous system disorders. The pleiotropic treatment of transcranial photobiomodulation towards multiple targets plays a beneficial role in improving hemodynamics, neural repair and improving behaviors in central nervous system disorders such as ischemic stroke, traumatic brain injury, neurodegenerative diseases, epilepsy and depression. This review mainly introduces the mechanism and recent preclinical and clinical advances of transcranial photobiomodulation for central nervous system disorders, which will provide a reference for clinicians to understand and engage in related studies, and calls for more and larger studies to validate and develop a wider application of transcranial photobiomodulation in central nervous system.

Self-supervised multimodal learning for group inferences from MRI data: Discovering disorder-relevant brain regions and multimodal links

In recent years, deep learning approaches have gained significant attention in predicting brain disorders using neuroimaging data. However, conventional methods often rely on single-modality data and supervised models, which provide only a limited perspective of the intricacies of the highly complex brain. Moreover, the scarcity of accurate diagnostic labels in clinical settings hinders the applicability of the supervised models. To address these limitations, we propose a novel self-supervised framework for extracting multiple representations from multimodal neuroimaging data to enhance group inferences and enable analysis without resorting to labeled data during pre-training. Our approach leverages Deep InfoMax (DIM), a self-supervised methodology renowned for its efficacy in learning representations by estimating mutual information without the need for explicit labels. While DIM has shown promise in predicting brain disorders from single-modality MRI data, its potential for multimodal data remains untapped. This work extends DIM to multimodal neuroimaging data, allowing us to identify disorder-relevant brain regions and explore multimodal links. We present compelling evidence of the efficacy of our multimodal DIM analysis in uncovering disorder-relevant brain regions, including the hippocampus, caudate, insula, - and multimodal links with the thalamus, precuneus, and subthalamus hypothalamus. Our self-supervised representations demonstrate promising capabilities in predicting the presence of brain disorders across a spectrum of Alzheimer's phenotypes. Comparative evaluations against state-of-the-art unsupervised methods based on autoencoders, canonical correlation analysis, and supervised models highlight the superiority of our proposed method in achieving improved classification performance, capturing joint information, and interpretability capabilities. The computational efficiency of the decoder-free strategy enhances its practical utility, as it saves compute resources without compromising performance. This work offers a significant step forward in addressing the challenge of understanding multimodal links in complex brain disorders, with potential applications in neuroimaging research and clinical diagnosis.

Causal functional connectivity in Alzheimer's disease computed from time series fMRI data

Functional connectivity between brain regions is known to be altered in Alzheimer's disease and promises to be a biomarker for early diagnosis. Several approaches for functional connectivity obtain an un-directed network representing stochastic associations (correlations) between brain regions. However, association does not necessarily imply causation. In contrast, Causal Functional Connectivity (CFC) is more informative, providing a directed network representing causal relationships between brain regions. In this paper, we obtained the causal functional connectome for the whole brain from resting-state functional magnetic resonance imaging (rs-fMRI) recordings of subjects from three clinical groups: cognitively normal, mild cognitive impairment, and Alzheimer's disease. We applied the recently developed Time-aware PC (TPC) algorithm to infer the causal functional connectome for the whole brain. TPC supports model-free estimation of whole brain CFC based on directed graphical modeling in a time series setting. We compared the CFC outcome of TPC with that of other related approaches in the literature. Then, we used the CFC outcomes of TPC and performed an exploratory analysis of the difference in strengths of CFC edges between Alzheimer's and cognitively normal groups, based on edge-wise p-values obtained by Welch's t-test. The brain regions thus identified are found to be in agreement with literature on brain regions impacted by Alzheimer's disease, published by researchers from clinical/medical institutions.

Identifying Alzheimer's disease and mild cognitive impairment with atlas-based multi-modal metrics

Introduction

Multi-modal neuroimaging metrics in combination with advanced machine learning techniques have attracted more and more attention for an effective multi-class identification of Alzheimer's disease (AD), mild cognitive impairment (MCI) and health controls (HC) recently.

Methods

In this paper, a total of 180 subjects consisting of 44 AD, 66 MCI and 58 HC subjects were enrolled, and the multi-modalities of the resting-state functional magnetic resonance imaging (rs-fMRI) and the structural MRI (sMRI) for all participants were obtained. Then, four kinds of metrics including the Hurst exponent (HE) metric and bilateral hippocampus seed independently based connectivity metrics generated from fMRI data, and the gray matter volume (GMV) metric obtained from sMRI data, were calculated and extracted in each region of interest (ROI) based on a newly proposed automated anatomical Labeling (AAL3) atlas after data pre-processing. Next, these metrics were selected with a minimal redundancy maximal relevance (MRMR) method and a sequential feature collection (SFC) algorithm, and only a subset of optimal features were retained after this step. Finally, the support vector machine (SVM) based classification methods and artificial neural network (ANN) algorithm were utilized to identify the multi-class of AD, MCI and HC subjects in single modal and multi-modal metrics respectively, and a nested ten-fold cross-validation was utilized to estimate the final classification performance.

Results

The results of the SVM and ANN based methods indicated the best accuracies of 80.36 and 74.40%, respectively, by utilizing all the multi-modal metrics, and the optimal accuracies for AD, MCI and HC were 79.55, 78.79 and 82.76%, respectively, in the SVM based method. In contrast, when using single modal metric, the SVM based method obtained a best accuracy of 72.62% with the HE metric, and the accuracies for AD, MCI and HC subjects were just 56.82, 80.30 and 75.86%, respectively. Moreover, the overlapping abnormal brain regions detected by multi-modal metrics were mainly located at posterior cingulate gyrus, superior frontal gyrus and cuneus.

Conclusion

Taken together, the SVM based method with multi-modal metrics could provide effective diagnostic information for identifying AD, MCI and HC subjects.

Changes in white matter functional networks across late adulthood

Introduction

The aging brain is characterized by decreases in not only neuronal density but also reductions in myelinated white matter (WM) fibers that provide the essential foundation for communication between cortical regions. Age-related degeneration of WM has been previously characterized by histopathology as well as T2 FLAIR and diffusion MRI. Recent studies have consistently shown that BOLD (blood oxygenation level dependent) effects in WM are robustly detectable, are modulated by neural activities, and thus represent a complementary window into the functional organization of the brain. However, there have been no previous systematic studies of whether or how WM BOLD signals vary with normal aging. We therefore performed a comprehensive quantification of WM BOLD signals across scales to evaluate their potential as indicators of functional changes that arise with aging.

Methods

By using spatial independent component analysis (ICA) of BOLD signals acquired in a resting state, WM voxels were grouped into spatially distinct functional units. The functional connectivities (FCs) within and among those units were measured and their relationships with aging were assessed. On a larger spatial scale, a graph was reconstructed based on the pair-wise connectivities among units, modeling the WM as a complex network and producing a set of graph-theoretical metrics.

Results

The spectral powers that reflect the intensities of BOLD signals were found to be significantly affected by aging across more than half of the WM units. The functional connectivities (FCs) within and among those units were found to decrease significantly with aging. We observed a widespread reduction of graph-theoretical metrics, suggesting a decrease in the ability to exchange information between remote WM regions with aging.

Discussion

Our findings converge to support the notion that WM BOLD signals in specific regions, and their interactions with other regions, have the potential to serve as imaging markers of aging.

Structural and functional activities of brain in patients with vascular cognitive impairment: A case-controlled magnetic resonance imaging study

This study aimed to identify abnormal brain regions and imaging indices of vascular cognitive impairment (VCI) and explore specific imaging diagnostic markers of VCI. In this study, 24 patients with VCI were allocated to the VCI group and 25 healthy subjects were assigned to the healthy control (HC) group. Demographic data and neuropsychological test scores were compared using SPSS 25.0. The structural and functional imaging data were post-processed and statistically analyzed using CAT12, DPARSF and SPM12 software, based on the MATLAB platform. The structural and functional indices of gray matter volume (GMV) and regional homogeneity (ReHo) were obtained, and inter-group data were analyzed using an independent-sample t test. Sex, age, years of education, and total brain volume were used as covariates. Compared to the HC group, the GMV of VCI in the VCI group decreased significantly in the rectus muscles of the bilateral gyrus, left superior temporal gyrus, left supplementary motor area (SMA), right insula, right superior temporal gyrus, right anterior cuneiform lobe, and right anterior central gyrus (PRECG) (P < .05, FWE correction), without GMV enlargement in the brain area. ReHo decreased in the right inferior temporal gyrus (ITG), right parahippocampal gyrus, and left temporal pole (middle temporal gyrus, right lingual gyrus, left posterior central gyrus, and right middle temporal gyrus), the areas of increased ReHo were the left caudate nucleus, left rectus gyrus, right anterior cingulate gyrus and lateral cingulate gyrus (P < .05, FWE correction). Correlation analysis showed that the GMV of the left superior temporal gyrus was positively correlated with the Montreal Cognitive Assessment (MoCA) score (P < .05), and the GMV of the right insula was positively correlated with the MESE and long delayed memory scores (P < .05). There was a significant positive correlation between the ReHo and short-term delayed memory scores in the middle temporal gyrus of the left temporal pole (P < .05). The volume of GMV and ReHo decreased in VCI patients, suggesting that impairment of brain structure and function in specific regions is the central mechanism of cognitive impairment in these patients. Meanwhile, the functional indices of some brain regions were increased, which may be a compensatory mechanism for the cognitive impairment associated with VCI.

Multimodality neuroimaging in vascular mild cognitive impairment: A narrative review of current evidence

The vascular mild cognitive impairment (VaMCI) is generally accepted as the premonition stage of vascular dementia (VaD). However, most studies are focused mainly on VaD as a diagnosis in patients, thus neglecting the VaMCI stage. VaMCI stage, though, is easily diagnosed by vascular injuries and represents a high-risk period for the future decline of patients' cognitive functions. The existing studies in China and abroad have found that magnetic resonance imaging technology can provide imaging markers related to the occurrence and development of VaMCI, which is an important tool for detecting the changes in microstructure and function of VaMCI patients. Nevertheless, most of the existing studies evaluate the information of a single modal image. Due to the different imaging principles, the data provided by a single modal image are limited. In contrast, multi-modal magnetic resonance imaging research can provide multiple comprehensive data such as tissue anatomy and function. Here, a narrative review of published articles on multimodality neuroimaging in VaMCI diagnosis was conducted，and the utilization of certain neuroimaging bio-markers in clinical applications was narrated. These markers include evaluation of vascular dysfunction before tissue damages and quantification of the extent of network connectivity disruption. We further provide recommendations for early detection, progress, prompt treatment response of VaMCI, as well as optimization of the personalized treatment plan.

The characteristics of glucose metabolism and functional connectivity in posterior default network during nondemented aging: relationship with executive function performance

BACKGROUND

Understanding the characteristics of intrinsic connectivity networks (ICNs) in terms of both glucose metabolism and functional connectivity (FC) is important for revealing cognitive aging and neurodegeneration, but the relationships between these two aspects during aging has not been well established in older adults.

OBJECTIVE

This study is to assess the relationship between age-related glucose metabolism and FC in key ICNs, and their direct or indirect effects on cognitive deficits in older adults.

METHODS

We estimated the individual-level standard uptake value ratio (SUVr) and FC of eleven ICNs in 59 cognitively unimpaired older adults, then analyzed the associations of SUVr and FC of each ICN and their relationships with cognitive performance.

RESULTS

The results showed both the SUVr and FC in the posterior default mode network (pDMN) had a significant decline with age, and the association between them was also significant. Moreover, both decline of metabolism and FC in the pDMN were significantly correlated with executive function decline. Finally, mediation analysis revealed the glucose metabolism mediated the FC decline with age and FC mediated the executive function deficits.

CONCLUSIONS

Our findings indicated that covariance between glucose metabolism and FC in the pDMN is one of the main routes that contributes to age-related executive function decline.

Linking resting-state network fluctuations with systems of coherent synaptic density: A multimodal fMRI and 11C-UCB-J PET study

Introduction: Resting-state network (RSN) connectivity is a widely used measure of the brain's functional organization in health and disease; however, little is known regarding the underlying neurophysiology of RSNs. The aim of the current study was to investigate associations between RSN connectivity and synaptic density assessed using the synaptic vesicle glycoprotein 2A radioligand 11C-UCB-J PET. Methods: Independent component analyses (ICA) were performed on resting-state fMRI and PET data from 34 healthy adult participants (16F, mean age: 46 ± 15 years) to identify a priori RSNs of interest (default-mode, right frontoparietal executive-control, salience, and sensorimotor networks) and select sources of 11C-UCB-J variability (medial prefrontal, striatal, and medial parietal). Pairwise correlations were performed to examine potential intermodal associations between the fractional amplitude of low-frequency fluctuations (fALFF) of RSNs and subject loadings of 11C-UCB-J source networks both locally and along known anatomical and functional pathways. Results: Greater medial prefrontal synaptic density was associated with greater fALFF of the anterior default-mode, posterior default-mode, and executive-control networks. Greater striatal synaptic density was associated with greater fALFF of the anterior default-mode and salience networks. Post-hoc mediation analyses exploring relationships between aging, synaptic density, and RSN activity revealed a significant indirect effect of greater age on fALFF of the anterior default-mode network mediated by the medial prefrontal 11C-UCB-J source. Discussion: RSN functional connectivity may be linked to synaptic architecture through multiple local and circuit-based associations. Findings regarding healthy aging, lower prefrontal synaptic density, and lower default-mode activity provide initial evidence of a neurophysiological link between RSN activity and local synaptic density, which may have relevance in neurodegenerative and psychiatric disorders.

Accurate module induced brain network construction for mild cognitive impairment identification with functional MRI

Introduction

Functional brain networks (FBNs) estimated from functional magnetic resonance imaging (fMRI) data has become a potentially useful way for computer-aided diagnosis of neurological disorders, such as mild cognitive impairment (MCI), a prodromal stage of Alzheimer's Disease (AD). Currently, Pearson's correlation (PC) is the most widely-used method for constructing FBNs. Despite its popularity and simplicity, the conventional PC-based method usually results in dense networks where regions-of-interest (ROIs) are densely connected. This is not accordance with the biological prior that ROIs may be sparsely connected in the brain. To address this issue, previous studies proposed to employ a threshold or l_1-regularizer to construct sparse FBNs. However, these methods usually ignore rich topology structures, such as modularity that has been proven to be an important property for improving the information processing ability of the brain.

Methods

To this end, in this paper, we propose an accurate module induced PC (AM-PC) model to estimate FBNs with a clear modular structure, by including sparse and low-rank constraints on the Laplacian matrix of the network. Based on the property that zero eigenvalues of graph Laplacian matrix indicate the connected components, the proposed method can reduce the rank of the Laplacian matrix to a pre-defined number and obtain FBNs with an accurate number of modules.

Results

To validate the effectiveness of the proposed method, we use the estimated FBNs to classify subjects with MCI from healthy controls. Experimental results on 143 subjects from Alzheimer's Disease Neuroimaging Initiative (ADNI) with resting-state functional MRIs show that the proposed method achieves better classification performance than previous methods.

The neuroprotective effects of oxygen therapy in Alzheimer’s disease: a narrative review

Alzheimer’s disease (AD) is a degenerative neurological disease that primarily affects the elderly. Drug therapy is the main strategy for AD treatment, but current treatments suffer from poor efficacy and a number of side effects. Non-drug therapy is attracting more attention and may be a better strategy for treatment of AD. Hypoxia is one of the important factors that contribute to the pathogenesis of AD. Multiple cellular processes synergistically promote hypoxia, including aging, hypertension, diabetes, hypoxia/obstructive sleep apnea, obesity, and traumatic brain injury. Increasing evidence has shown that hypoxia may affect multiple pathological aspects of AD, such as amyloid-beta metabolism, tau phosphorylation, autophagy, neuroinflammation, oxidative stress, endoplasmic reticulum stress, and mitochondrial and synaptic dysfunction. Treatments targeting hypoxia may delay or mitigate the progression of AD. Numerous studies have shown that oxygen therapy could improve the risk factors and clinical symptoms of AD. Increasing evidence also suggests that oxygen therapy may improve many pathological aspects of AD including amyloid-beta metabolism, tau phosphorylation, neuroinflammation, neuronal apoptosis, oxidative stress, neurotrophic factors, mitochondrial function, cerebral blood volume, and protein synthesis. In this review, we summarized the effects of oxygen therapy on AD pathogenesis and the mechanisms underlying these alterations. We expect that this review can benefit future clinical applications and therapy strategies on oxygen therapy for AD.

Separation of memory span and learning rate: Evidence from behavior and spontaneous brain activity in older adults

It is unclear how the ability to initially acquire information in a first learning trial relates to learning rate in subsequent repeated trials. The separation of memory span and learning rate is an important psychological dilemma that remains unaddressed. Given the potential effects of aging on memory and learning, this study investigated the separation of memory span and learning rate from behavior and spontaneous brain activity in older adults. We enrolled a total of 758 participants, including 707 healthy older adults and 51 mild cognitive impairment (MCI) patients. Sixty-five participants out of 707 completed resting-state functional magnetic resonance imaging (fMRI) scanning. Behaviorally, memory span and learning rate were not correlated with each other in the paired-associative learning test (PALT) but were negatively correlated in the auditory verbal learning test (AVLT). This indicated that the relationship between memory span and learning rate for item memory might be differentially affected by aging. Interaction analysis confirmed that these two capacities were differentially affected by test type (associative memory vs. item memory). Additionally, at three progressive brain activity indexes (ALFF, ReHo, and DC), the right brain regions (right inferior temporal gyrus and right middle frontal gyrus) were more negatively correlated with memory span, whereas, the left precuneus was more positively correlated with learning rate. Regarding pathological aging, none of the correlations between memory span and learning rate were significant in either PALT or AVLT in MCI. This study provides novel evidence for the dissociation of memory span and learning rate at behavioral and brain activity levels, which may have useful applications in detecting cognitive deficits or conducting cognitive interventions.

Alterations of voxel-wise spontaneous activity and corresponding brain functional networks in multiple system atrophy patients with mild cognitive impairment

Emerging evidence has indicated that cognitive impairment is an underrecognized feature of multiple system atrophy (MSA). Mild cognitive impairment (MCI) is related to a high risk of dementia. However, the mechanism underlying MCI in MSA remains controversial. In this study, we conducted the amplitude of low-frequency fluctuation (ALFF) and seed-based functional connectivity (FC) analyses to detect the characteristics of local neural activity and corresponding network alterations in MSA patients with MCI (MSA-MCI). We enrolled 80 probable MSA patients classified as cognitively normal (MSA-NC, n = 36) and MSA-MCI (n = 44) and 40 healthy controls. Compared with MSA-NC, MSA-MCI exhibited decreased ALFF in the right dorsal lateral prefrontal cortex (RDLPFC) and increased ALFF in the right cerebellar lobule IX and lobule IV-V. In the secondary FC analyses, decreased FC in the left inferior parietal lobe (IPL) was observed when we set the RDLPFC as the seed region. Decreased FC in the bilateral cuneus, left precuneus, and left IPL and increased FC in the right middle temporal gyrus were shown when we set the right cerebellar lobule IX as the seed region. Furthermore, FC of DLPFC-IPL and cerebello-cerebral circuit, as well as ALFF alterations, were significantly correlated with Montreal Cognitive Assessment scores in MSA patients. We also employed whole-brain voxel-based morphometry analysis, but no gray matter atrophy was detected between the patient subgroups. Our findings indicate that altered spontaneous activity in the DLPFC and the cerebellum and disrupted DLPFC-IPL, cerebello-cerebral networks are possible biomarkers of early cognitive decline in MSA patients.

Altered dynamic intrinsic brain activity of the default mode network in Alzheimer's disease: A resting-state fMRI study

Objective

Static regional homogeneity (ReHo) based on the resting-state functional magnetic resonance imaging (rs-fMRI) has been used to study intrinsic brain activity (IBA) in Alzheimer's disease (AD). However, few studies have examined dynamic ReHo (dReHo) in AD. In this study, we used rs-fMRI and dReHo to investigate the alterations in dynamic IBA in patients with AD to uncover dynamic imaging markers of AD.

Method

In total, 111 patients with AD, 29 patients with mild cognitive impairment (MCI), and 73 healthy controls (HCs) were recruited for this study ultimately. After the rs-fMRI scan, we calculated the dReHo values using the sliding window method. ANOVA and post hoc two-sample t-tests were used to detect the differences among the three groups. We used the mini-mental state examination (MMSE) and Montreal Cognitive Assessment (MoCA) to evaluate the cognitive function of the subjects. The associations between the MMSE score, MoCA score, and dReHo were assessed by the Pearson correlation analysis.

Results

Significant dReHo variability in the right middle frontal gyrus (MFG) and right posterior cingulate gyrus (PCG) was detected in the three groups through ANOVA. In post hoc analysis, the AD group exhibited significantly greater dReHo variability in the right MFG than the MCI group. Compared with the HC group, the AD group exhibited significantly increased dReHo variability in the right PCG. Furthermore, dReHo variability in the right PCG was significantly negatively correlated with the MMSE and MoCA scores of patients with AD.

Conclusion

Disrupted dynamic IBA in the DMN might be an important characteristic of AD and could be a potential biomarker for the diagnosis or prognosis of AD.

Deep learning of MRI contrast enhancement for mapping cerebral blood volume from single-modal non-contrast scans of aging and Alzheimer's disease brains

While MRI contrast agents such as those based on Gadolinium are needed for high-resolution mapping of brain metabolism, these contrast agents require intravenous administration, and there are rising concerns over their safety and invasiveness. Furthermore, non-contrast MRI scans are more commonly performed than those with contrast agents and are readily available for analysis in public databases such as the Alzheimer's Disease Neuroimaging Initiative (ADNI). In this article, we hypothesize that a deep learning model, trained using quantitative steady-state contrast-enhanced structural MRI datasets, in mice and humans, can generate contrast-equivalent information from a single non-contrast MRI scan. The model was first trained, optimized, and validated in mice, and was then transferred and adapted to humans. We observe that the model can substitute for Gadolinium-based contrast agents in approximating cerebral blood volume, a quantitative representation of brain activity, at sub-millimeter granularity. Furthermore, we validate the use of our deep-learned prediction maps to identify functional abnormalities in the aging brain using locally obtained MRI scans, and in the brain of patients with Alzheimer's disease using publicly available MRI scans from ADNI. Since it is derived from a commonly-acquired MRI protocol, this framework has the potential for broad clinical utility and can also be applied retrospectively to research scans across a host of neurological/functional diseases.

SVR-based Multimodal Active Subspace Analysis for the Brain using Neuroimaging Data.. [DOI: 10.1101/2022.07.28.501879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Understanding the patterns of changes in brain function and structure due to various disorders and diseases is of utmost importance. There have been numerous efforts toward successful biomarker discovery for complex brain disorders by evaluating neuroimaging datasets with novel analytical frameworks. However, due to the multi-faceted nature of the disorders involving a wide and overlapping range of symptoms as well as complex changes in structural and functional brain networks, it is increasingly important to devise computational frameworks that can consider the underlying patterns of heterogeneous changes with specific target assessments, at the same time producing a summarizing output from the high-dimensional neuroimaging data. While various machine learning approaches focus on diagnostic prediction, many learning frameworks analyze important features at the level of brain regions involved in prediction using supervised methods. Unsupervised learning methods have also been utilized to break down the neuroimaging features into lower dimensional components. However, most learning frameworks either do not consider the target assessment information while extracting brain subspaces, or can extract only higher dimensional importance associations as an ordered list of involved features, making manual interpretation at the level of subspaces difficult. We present a novel multimodal active subspace learning framework to understand various subspaces within the brain that are associated with changes in particular biological and cognitive traits. For a given cognitive or biological trait, our framework performs a decomposition of the feature importances to extract robust multimodal subspaces that define the most significant change in the given trait. Through a rigorous cross-validation procedure on an Alzheimer’s disease (AD) dataset, we show that our framework can extract subspaces covering both functional and structural modalities, which are specific to a given clinical assessment (like memory and other cognitive skills) and also retain predictive performance in standard machine learning algorithms. We show that our framework not only uncovers AD-related brain regions (e.g., hippocampus, entorhinal cortex) in the associated brain subspaces, but also enables an automated identification of multiple underlying structural and functional sub-systems of the brain that collectively characterize changes in memory and cognitive skill proficiency related to brain disorders like AD.

The fractional amplitude of low-frequency fluctuations signals related to amyloid uptake in high-risk populations—A pilot fMRI study

Background

Patients with type 2 diabetes mellitus (T2DM) and subjective cognitive decline (SCD) have a higher risk to develop Alzheimer's Disease (AD). Resting-state-functional magnetic resonance imaging (rs-fMRI) was used to document neurological involvement in the two groups from the aspect of brain dysfunction. Accumulation of amyloid-β (Aβ) starts decades ago before the onset of clinical symptoms and may already have been associated with brain function in high-risk populations. However, this study aims to compare the patterns of fractional amplitude of low-frequency fluctuations (fALFF) maps between cognitively normal high-risk groups (SCD and T2DM) and healthy elderly and evaluate the association between regional amyloid deposition and local fALFF signals in certain cortical regions.

Materials and methods

A total of 18 T2DM, 11 SCD, and 18 healthy elderlies were included in this study. The differences in the fALFF maps were compared between HC and high-risk groups. Regional amyloid deposition and local fALFF signals were obtained and further correlated in two high-risk groups.

Results

Compared to HC, the altered fALFF signals of regions were shown in SCD such as the left posterior cerebellum, left putamen, and cingulate gyrus. The T2DM group illustrated altered neural activity in the superior temporal gyrus, supplementary motor area, and precentral gyrus. The correlation between fALFF signals and amyloid deposition was negative in the left anterior cingulate cortex for both groups. In the T2DM group, a positive correlation was shown in the right occipital lobe and left mesial temporal lobe.

Conclusion

The altered fALFF signals were demonstrated in high-risk groups compared to HC. Very early amyloid deposition in SCD and T2DM groups was observed to affect the neural activity mainly involved in the default mode network (DMN).

Functional Connectivity Dynamics Altered of the Resting Brain in Subjective Cognitive Decline

Background

Subjective cognitive decline (SCD) appears in the preclinical stage of the Alzheimer's disease continuum. In this stage, dynamic features are more sensitive than static features to reflect early subtle changes in functional brain connectivity. Therefore, we studied local and extended dynamic connectivity of the resting brain of people with SCD to determine their intrinsic brain changes.

Methods

We enrolled cognitively normal older adults from the communities and divided them into SCD and normal control (NC) groups. We used mean dynamic amplitude of low-frequency fluctuation (mdALFF) to evaluate region of interest (ROI)-wise local dynamic connectivity of resting-state functional MRI. The dynamic functional connectivity (dFC) between ROIs was tested by whole-brain-based statistics.

Results

When comparing SCD (N = 40) with NC (N = 45), mdALFFmean decreased at right inferior parietal lobule (IPL) of the frontoparietal network (FPN). Still, it increased at the right middle temporal gyrus (MTG) of the ventral attention network (VAN) and right calcarine of the visual network (VIS). Also, the mdALFFvar (variance) increased at the left superior temporal gyrus of AUD, right MTG of VAN, right globus pallidum of the cingulo-opercular network (CON), and right lingual gyrus of VIS. Furthermore, mdALFFmean at right IPL of FPN are correlated negatively with subjective complaints and positively with objective cognitive performance. In the dFC seeded from the ROIs with local mdALFF group differences, SCD showed a generally lower dFCmean and higher dFCvar (variance) to other regions of the brain. These weakened and unstable functional connectivity appeared among FPN, CON, the default mode network, and the salience network, the large-scale networks of the triple network model for organizing neural resource allocations.

Conclusion

The local dynamic connectivity of SCD decreased in brain regions of cognitive executive control. Meanwhile, compensatory visual efforts and bottom-up attention rose. Mixed decrease and compensatory increase of dynamics of intrinsic brain activity suggest the transitional nature of SCD. The FPN local dynamics balance subjective and objective cognition and maintain cognitive preservation in preclinical dementia. Aberrant triple network model features the dFC alternations of SCD. Finally, the right lateralization phenomenon emerged early in the dementia continuum and affected local dynamic connectivity.

Investigation of Underlying Association Between Whole Brain Regions and Alzheimer’s Disease: A Research Based on an Artificial Intelligence Model

Alzheimer’s disease (AD) is the most common form of dementia, causing progressive cognitive decline. Radiomic features obtained from structural magnetic resonance imaging (sMRI) have shown a great potential in predicting this disease. However, radiomic features based on the whole brain segmented regions have not been explored yet. In our study, we collected sMRI data that include 80 patients with AD and 80 healthy controls (HCs). For each patient, the T1 weighted image (T1WI) images were segmented into 106 subregions, and radiomic features were extracted from each subregion. Then, we analyzed the radiomic features of specific brain subregions that were most related to AD. Based on the selective radiomic features from specific brain subregions, we built an integrated model using the best machine learning algorithms, and the diagnostic accuracy was evaluated. The subregions most relevant to AD included the hippocampus, the inferior parietal lobe, the precuneus, and the lateral occipital gyrus. These subregions exhibited several important radiomic features that include shape, gray level size zone matrix (GLSZM), and gray level dependence matrix (GLDM), among others. Based on the comparison among different algorithms, we constructed the best model using the Logistic regression (LR) algorithm, which reached an accuracy of 0.962. Conclusively, we constructed an excellent model based on radiomic features from several specific AD-related subregions, which could give a potential biomarker for predicting AD.

Alzheimer's Disease Diagnosis and Biomarker Analysis Using Resting-State Functional MRI Functional Brain Network With Multi-Measures Features and Hippocampal Subfield and Amygdala Volume of Structural MRI

Accurate diagnosis of the initial phase of Alzheimer's disease (AD) is essential and crucial. The objective of this research was to employ efficient biomarkers for the diagnostic analysis and classification of AD based on combining structural MRI (sMRI) and resting-state functional MRI (rs-fMRI). So far, several anatomical MRI imaging markers for AD diagnosis have been identified. The use of cortical and subcortical volumes, the hippocampus, and amygdala volume, as well as genetic patterns, has proven to be beneficial in distinguishing patients with AD from the healthy population. The fMRI time series data have the potential for specific numerical information as well as dynamic temporal information. Voxel and graphical analyses have gained popularity for analyzing neurodegenerative diseases, such as Alzheimer's and its prodromal phase, mild cognitive impairment (MCI). So far, these approaches have been utilized separately for the diagnosis of AD. In recent studies, the classification of cases of MCI into those that are not converted for a certain period as stable MCI (MCIs) and those that converted to AD as MCIc has been less commonly reported with inconsistent results. In this study, we verified and validated the potency of a proposed diagnostic framework to identify AD and differentiate MCIs from MCIc by utilizing the efficient biomarkers obtained from sMRI, along with functional brain networks of the frequency range .01-.027 at the resting state and the voxel-based features. The latter mainly included default mode networks (amplitude of low-frequency fluctuation [ALFF], fractional ALFF [ALFF], and regional homogeneity [ReHo]), degree centrality (DC), and salience networks (SN). Pearson's correlation coefficient for measuring fMRI functional networks has proven to be an efficient means for disease diagnosis. We applied the graph theory to calculate nodal features (nodal degree [ND], nodal path length [NL], and between centrality [BC]) as a graphical feature and analyzed the connectivity link between different brain regions. We extracted three-dimensional (3D) patterns to calculate regional coherence and then implement a univariate statistical t-test to access a 3D mask that preserves voxels showing significant changes. Similarly, from sMRI, we calculated the hippocampal subfield and amygdala nuclei volume using Freesurfer (version 6). Finally, we implemented and compared the different feature selection algorithms to integrate the structural features, brain networks, and voxel features to optimize the diagnostic identifications of AD using support vector machine (SVM) classifiers. We also compared the performance of SVM with Random Forest (RF) classifiers. The obtained results demonstrated the potency of our framework, wherein a combination of the hippocampal subfield, the amygdala volume, and brain networks with multiple measures of rs-fMRI could significantly enhance the accuracy of other approaches in diagnosing AD. The accuracy obtained by the proposed method was reported for binary classification. More importantly, the classification results of the less commonly reported MCIs vs. MCIc improved significantly. However, this research involved only the AD Neuroimaging Initiative (ADNI) cohort to focus on the diagnosis of AD advancement by integrating sMRI and fMRI. Hence, the study's primary disadvantage is its small sample size. In this case, the dataset we utilized did not fully reflect the whole population. As a result, we cannot guarantee that our findings will be applicable to other populations.

Training-Specific Changes in Regional Spontaneous Neural Activity Among Professional Chinese Chess Players

Background

Our previous reports reflected some aspects of neuroplastic changes from long-term Chinese chess training but were mainly based on large-scale intrinsic connectivity. In contrast to functional connectivity among remote brain areas, synchronization of local intrinsic activity demonstrates functional connectivity among regional areas. Until now, local connectivity changes in professional Chinese chess players (PCCPs) have been reported only at specific hubs; whole-brain-based local connectivity and its relation to training profiles has not been revealed.

Objectives

To investigate whole-brain local connectivity changes and their relation to training profiles in PCCPs.

Methods

Regional homogeneity (ReHo) analysis of rs-fMRI data from 22 PCCPs versus 21 novices was performed to determine local connectivity changes and their relation to training profiles.

Results

Compared to novices, PCCPs showed increased regional spontaneous activity in the posterior lobe of the left cerebellum, the left temporal pole, the right amygdala, and the brainstem but decreased ReHo in the right precentral gyrus. From a whole-brain perspective, local activity in areas such as the posterior lobe of the right cerebellum and the caudate correlated with training profiles.

Conclusion

Regional homogeneity changes in PCCPs were consistent with the classical view of automaticity in motor control and learning. Related areas in the pattern indicated an enhanced capacity for emotion regulation, supporting cool and focused attention during gameplay. The possible participation of the basal ganglia-cerebellar-cerebral networks, as suggested by these correlation results, expands our present knowledge of the neural substrates of professional chess players. Meanwhile, ReHo change occurred in an area responsible for the pronunciation and reading of Chinese characters. Additionally, professional Chinese chess training was associated with change in a region that is affected by Alzheimer's disease (AD).

Impact of APOE ε4 Carrier Status on Associations Between Subthreshold, Positive Amyloid-β Deposition, Brain Function, and Cognitive Performance in Cognitively Normal Older Adults: A Prospective Study

Background

A growing body of evidence suggests a deteriorating effect of subthreshold amyloid-beta (Aβ) accumulation on cognition before the onset of clinical symptoms of Alzheimer's disease (AD). Despite the association between the Aβ-dependent pathway and the APOE ε4 allele, the impact of this allele on the progression from the subthreshold Aβ deposits to cognitive function impairment is unclear. Furthermore, the comparative analysis of positive Aβ accumulation in the preclinical phase is lacking.

Objective

This study aimed to explore the differential effect of the APOE ε4 carrier status on the association between Aβ deposition, resting-state brain function, and cognitive performance in cognitively normal (CN) older adults, depending on the Aβ burden status.

Methods

One hundred and eighty-two older CN adults underwent resting-state functional magnetic resonance imaging, [18F] flutemetamol (FMM) positron emission tomography, a neuropsychological battery, and APOE genotyping. We evaluated the resting-state brain function by measuring the local and remote functional connectivity (FC) and measured the remote FC in the default-mode network (DMN), central-executive network (CEN), and salience network (SN). In addition, the subjects were dichotomized into those with subthreshold and positive Aβ deposits using a neocortical standardized uptake value ratio with the cut-off value of 0.62, which was calculated with respect to the pons.

Results

The present result showed that APOE ε4 carrier status moderated the relationship between Aβ deposition, local and remote resting-state brain function, and cognitive performance in each CN subthreshold and positive Aβ group. We observed the following: (i) the APOE ε4 carrier status-Aβ deposition and APOE ε4 carrier status-local FC interaction for the executive and memory function; (ii) the APOE ε4 carrier status-regional Aβ accumulation interaction for the local FC; and (iv) the APOE ε4 carrier status-local FC interaction for the remote inter-network FC between the DMN and CEN, contributing higher cognitive performance in the APOE ε4 carrier with higher inter-network FC. Finally, these results were modulated according to Aβ positivity.

Conclusion

This study is the first attempt to thoroughly examine the influence of the APOE ε4 carrier status from the subthreshold to positive Aβ accumulation during the preclinical phase.

Altered Spontaneous Brain Activity in Patients With Diabetic Osteoporosis Using Regional Homogeneity: A Resting-State Functional Magnetic Resonance Imaging Study

Background

The pathophysiological mechanism of cognitive impairment by osteoporosis in type 2 diabetes mellitus (T2DM) remains unclear. This study aims to further investigate the regional spontaneous brain activity changes of patients with diabetic osteoporosis (DOP), and the correlation between abnormal brain regions and bone metabolites.

Methods

A total of 29 subjects with T2DM were recruited, including fourteen patients with DOP and thirteen patients without osteoporosis (Control group). Based on the resting-state functional magnetic resonance imaging (rs-fMRI) datasets acquired from all the subjects, a two-sample t-test was performed on individual normalized regional homogeneity (ReHo) maps. Spearman correlation analysis was performed between the abnormal ReHo regions with the clinical parameters and Montreal Cognitive Assessment (MOCA) scores.

Results

In the DOP group, we demonstrated the significantly increased ReHo values in the left middle temporal gyrus (MTG), right superior occipital gyrus (SOG), aright superior parietal lobule (SPL), right angular gyrus (AG), and left precuneus (PE). Additionally, we also found a significant positive correlation between increased ReHo values in the left MTG and the average bone mineral density (BMD AVG), and average T scores (T AVG). The ReHo values of the right SOG and right SPL showed a negative correlation with MOCA scores, as well as a negative correlation between increased ReHo values in the right SPL and osteocalcin (OC) level.

Conclusion

Patients with DOP showed increased spontaneous activity in multiple brain regions. The results indicated that osteoporosis exacerbated cognitive impairment and brain damage. Also, the OC might be considered as a bone marker to track the progression of cognitive impairment.

Exploratory imaging outcomes of a phase 1b/2a clinical trial of allopregnanolone as a regenerative therapeutic for Alzheimer's disease: Structural effects and functional connectivity outcomes

Introduction

Allopregnanolone (ALLO), an endogenous neurosteroid, promoted neurogenesis and oligogenesis and restored cognitive function in animal models of Alzheimer's disease (AD). Based on these discovery research findings, we conducted a randomized-controlled phase 1b/2a multiple ascending dose trial of ALLO in persons with early AD (NCT02221622) to assess safety, tolerability, and pharmacokinetics. Exploratory imaging outcomes to determine whether ALLO impacted hippocampal structure, white matter integrity, and functional connectivity are reported.

Methods

Twenty-four individuals participated in the trial (n = 6 placebo; n = 18 ALLO) and underwent brain magnetic resonance imaging (MRI) before and after 12 weeks of treatment. Hippocampal atrophy rate was determined from volumetric MRI, computed as rate of change, and qualitatively assessed between ALLO and placebo sex, apolipoprotein E (APOE) ε4 allele, and ALLO dose subgroups. White matter microstructural integrity was compared between placebo and ALLO using fractional and quantitative anisotropy (QA). Changes in local, inter-regional, and network-level functional connectivity were also compared between groups using resting-state functional MRI.

Results

Rate of decline in hippocampal volume was slowed, and in some cases reversed, in the ALLO group compared to placebo. Gain of hippocampal volume was evident in APOE ε4 carriers (range: 0.6% to 7.8% increased hippocampal volume). Multiple measures of white matter integrity indicated evidence of preserved or improved integrity. ALLO significantly increased fractional anisotropy (FA) in 690 of 690 and QA in 1416 of 1888 fiber tracts, located primarily in the corpus callosum, bilateral thalamic radiations, and bilateral corticospinal tracts. Consistent with structural changes, ALLO strengthened local, inter-regional, and network level functional connectivity in AD-vulnerable regions, including the precuneus and posterior cingulate, and network connections between the default mode network and limbic system.

Discussion

Indicators of regeneration from previous preclinical studies and these exploratory MRI-based outcomes from this phase 1b/2a clinical cohort support advancement to a phase 2 proof-of-concept efficacy clinical trial of ALLO as a regenerative therapeutic for mild AD (REGEN-BRAIN study; NCT04838301).

Thalamic altered spontaneous activity and connectivity in obstructive sleep apnea syndrome

BACKGROUND AND PURPOSE

Obstructive sleep apnea (OSA) syndrome is a sleep disorder characterized by excessive snoring, repetitive apneas, and nocturnal arousals, that leads to fragmented sleep and intermittent nocturnal hypoxemia. Morphometric and functional brain alterations in cortical and subcortical structures have been documented in these patients via magnetic resonance imaging (MRI), even if correlational data between the alterations in the brain and cognitive and clinical indexes are still not reported.

METHODS

We examined the impact of OSA on brain spontaneous activity by measuring the fractional amplitude of low-frequency fluctuations (fALFF) in resting-state functional MRI data of 20 drug-naïve patients with OSA syndrome and 20 healthy controls matched for age, gender, and body mass index.

RESULTS

Patients showed a pattern of significantly abnormal subcortical functional activity as compared to controls, with increased activity selectively involving the thalami, specifically their intrinsic nuclei connected to somatosensory and motor-premotor cortical regions. Using these nuclei as seed regions, the subsequent functional connectivity analysis highlighted an increase in patients' thalamocortical connectivity at rest. Additionally, the correlation between fALFF and polysomnographic data revealed a possible link between OSA severity and fALFF of regions belonging to the central autonomic network.

CONCLUSIONS

Our results suggest a hyperactivation in thalamic diurnal activity in patients with OSA syndrome, which we interpret as a possible consequence of increased thalamocortical circuitry activation during nighttime due to repeated arousals.

Test-retest stability of spontaneous brain activity and functional connectivity in the core resting-state networks assessed with ultrahigh field 7-Tesla resting-state functional magnetic resonance imaging

The growing demand for precise and reliable biomarkers in psychiatry is fueling research interest in the hope that identifying quantifiable indicators will improve diagnoses and treatment planning across a range of mental health conditions. The individual properties of brain networks at rest have been highlighted as a possible source for such biomarkers, with the added advantage that they are relatively straightforward to obtain. However, an important prerequisite for their consideration is their reproducibility. While the reliability of resting‐state (RS) measurements has often been studied at standard field strengths, they have rarely been investigated using ultrahigh‐field (UHF) magnetic resonance imaging (MRI) systems. We investigated the intersession stability of four functional MRI RS parameters—amplitude of low‐frequency fluctuations (ALFF) and fractional ALFF (fALFF; representing the spontaneous brain activity), regional homogeneity (ReHo; measure of local connectivity), and degree centrality (DC; measure of long‐range connectivity)—in three RS networks, previously shown to play an important role in several psychiatric diseases—the default mode network (DMN), the central executive network (CEN), and the salience network (SN). Our investigation at individual subject space revealed a strong stability for ALFF, ReHo, and DC in all three networks, and a moderate level of stability in fALFF. Furthermore, the internetwork connectivity between each network pair was strongly stable between CEN/SN and moderately stable between DMN/SN and DMN/SN. The high degree of reliability and reproducibility in capturing the properties of the three major RS networks by means of UHF‐MRI points to its applicability as a potentially useful tool in the search for disease‐relevant biomarkers.

Extracting BOLD signals based on time-constrained multiset canonical correlation analysis for brain functional network estimation and classification

Brain functional network (BFN), usually estimated from blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI), has been proven to be a powerful tool to study the organization of the brain and discover biomarkers for diagnosis of brain disorders. Prior to BFN estimation and classification, extracting representative BOLD signals from brain regions of interest (ROIs) is a critical step. Traditional extraction methods include averaging, peaking operation and dimensionality reduction, often leading to signal cancellation and information loss. In this paper, we propose a novel method, namely time-constrained multiset canonical correlation analysis (TMCCA), to extract representative BOLD signals for subsequent BFN estimation and classification. Different from traditional methods that equally treat all BOLD signals in a ROI, the proposed method assigns weights to different BOLD signals, and learns the optimal weights to make the extracted representative signals jointly maximize the multiple correlations between ROIs. Importantly, time-constraint is incorporated into our proposed method, which can effectively encode nonlinear relationship among BOLD signals. To evaluate the effectiveness of the proposed method, the extracted BOLD signals is used to estimate BFN and, in turn, identify brain disorders, including mild cognitive impairment (MCI) and autistic spectrum disorder (ASD). Experimental results demonstrate that our proposed TMCCA can lead to better performance than traditional methods.

Machine Learning Based Multimodal Neuroimaging Genomics Dementia Score for Predicting Future Conversion to Alzheimer's Disease

BACKGROUND

The increasing availability of databases containing both magnetic resonance imaging (MRI) and genetic data allows researchers to utilize multimodal data to better understand the characteristics of dementia of Alzheimer's type (DAT).

OBJECTIVE

The goal of this study was to develop and analyze novel biomarkers that can help predict the development and progression of DAT.

METHODS

We used feature selection and ensemble learning classifier to develop an image/genotype-based DAT score that represents a subject's likelihood of developing DAT in the future. Three feature types were used: MRI only, genetic only, and combined multimodal data. We used a novel data stratification method to better represent different stages of DAT. Using a pre-defined 0.5 threshold on DAT scores, we predicted whether a subject would develop DAT in the future.

RESULTS

Our results on Alzheimer's Disease Neuroimaging Initiative (ADNI) database showed that dementia scores using genetic data could better predict future DAT progression for currently normal control subjects (Accuracy = 0.857) compared to MRI (Accuracy = 0.143), while MRI can better characterize subjects with stable mild cognitive impairment (Accuracy = 0.614) compared to genetics (Accuracy = 0.356). Combining MRI and genetic data showed improved classification performance in the remaining stratified groups.

CONCLUSION

MRI and genetic data can contribute to DAT prediction in different ways. MRI data reflects anatomical changes in the brain, while genetic data can detect the risk of DAT progression prior to the symptomatic onset. Combining information from multimodal data appropriately can improve prediction performance.

The central executive network and executive function in healthy and persons with schizophrenia groups: a meta-analysis of structural and functional MRI

This meta-analysis evaluated the extent to which executive function can be understood with structural and functional magnetic resonance imaging. Studies included structural in schizophrenia (k = 8; n = 241) and healthy controls (k = 12; n = 1660), and functional in schizophrenia (k = 4; n = 104) and healthy controls (k = 12; n = 712). Results revealed a positive association in the brain behavior relationship when pooled across schizophrenia and control samples for structural (pr = 0.27) and functional (pr = 0.29) modalities. Subgroup analyses revealed no significant difference for functional neuroimaging (pr = .43, 95%CI = -.08-.77, p = .088) but with structural neuroimaging (pr = .37, 95%CI = -.08-.69, p = .015) the association to executive functions is lower in the control group. Subgroup analyses also revealed no significant differences in the strength of the brain-behavior relationship in the schizophrenia group (pr = .59, 95%CI = .58-.61, p = .881) or the control group (pr = 0.19, 95%CI = 0.18-0.19, p = 0.920), suggesting concordance.

Dynamics of task-induced modulation of spontaneous brain activity and functional connectivity in the triple resting-state networks assessed using the visual oddball paradigm

The default mode network (DMN), the salience network (SN), and the central executive network (CEN) are considered as the core resting-state brain networks (RSN) due to their involvement in a wide range of cognitive tasks. Despite the large body of knowledge related to their regional spontaneous activity (RSA) and functional connectivity (FC) of these networks, less is known about the dynamics of the task-associated modulation on these parameters and the task-induced interaction between these three networks. We have investigated the effects of the visual-oddball paradigm on three fMRI measures (amplitude of low-frequency fluctuations for RSA, regional homogeneity for local FC, and degree centrality for global FC) in these three core RSN. A rest-task-rest paradigm was used and the RSNs were identified using independent component analysis (ICA) on the resting-state data. The observed patterns of change differed noticeably between the networks and were tightly associated with the task-related brain activity and the distinct involvement of the networks in the performance of the single subtasks. Furthermore, the inter-network analysis showed an increased synchronization of CEN with the DMN and the SN immediately after the task, but not between the DMN and SN. Higher pre-task inter-network synchronization between the DMN and the CEN was associated with shorter reaction times and thus better performance. Our results provide some additional insights into the dynamics within and between the triple RSN. Further investigations are required in order to understand better their functional importance and interplay.

Deep Learning Approach for Early Detection of Alzheimer's Disease

Alzheimer's disease (AD) is a chronic, irreversible brain disorder, no effective cure for it till now. However, available medicines can delay its progress. Therefore, the early detection of AD plays a crucial role in preventing and controlling its progression. The main objective is to design an end-to-end framework for early detection of Alzheimer's disease and medical image classification for various AD stages. A deep learning approach, specifically convolutional neural networks (CNN), is used in this work. Four stages of the AD spectrum are multi-classified. Furthermore, separate binary medical image classifications are implemented between each two-pair class of AD stages. Two methods are used to classify the medical images and detect AD. The first method uses simple CNN architectures that deal with 2D and 3D structural brain scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset based on 2D and 3D convolution. The second method applies the transfer learning principle to take advantage of the pre-trained models for medical image classifications, such as the VGG19 model. Due to the COVID-19 pandemic, it is difficult for people to go to hospitals periodically to avoid gatherings and infections. As a result, Alzheimer's checking web application is proposed using the final qualified proposed architectures. It helps doctors and patients to check AD remotely. It also determines the AD stage of the patient based on the AD spectrum and advises the patient according to its AD stage. Nine performance metrics are used in the evaluation and the comparison between the two methods. The experimental results prove that the CNN architectures for the first method have the following characteristics: suitable simple structures that reduce computational complexity, memory requirements, overfitting, and provide manageable time. Besides, they achieve very promising accuracies, 93.61% and 95.17% for 2D and 3D multi-class AD stage classifications. The VGG19 pre-trained model is fine-tuned and achieved an accuracy of 97% for multi-class AD stage classifications.

Regional homogeneity alterations in multi-frequency bands in tension-type headache: a resting-state fMRI study

OBJECTIVES

In this study, we aimed to investigate the spontaneous neural activity in the conventional frequency band (0.01-0.08 Hz) and two sub-frequency bands (slow-4: 0.027-0.073 Hz, and slow-5: 0.01-0.027 Hz) in tension-type headache (TTH) patients with regional homogeneity (ReHo) analyses.

METHODS

Thirty-eight TTH patients and thirty-eight healthy controls (HCs) underwent resting-state functional magnetic resonance imaging (RS-fMRI) scanning to investigate abnormal spontaneous neural activity using ReHo analysis in conventional frequency band (0.01-0.08 Hz) and two sub-frequency bands (slow-4: 0.027-0.073 Hz and slow-5: 0.01-0.027 Hz).

RESULTS

In comparison with the HC group, patients with TTH exhibited ReHo increases in the right medial superior frontal gyrus in the conventional frequency band (0.01-0.08 Hz). The between group differences in the slow-5 band (0.01-0.027 Hz) highly resembled the differences in the conventional frequency band (0.01-0.08 Hz); even the voxels with increased ReHo were spatially more extensive, including the right medial superior frontal gyrus and the middle frontal gyrus. In contrast, no region showed significant between-group differences in the slow-4 band (0.027-0.073 Hz). The correlation analyses showed no correlation between the ReHo values in TTH patients and VAS scores, course of disease and number of seizures per month in conventional band (0.01-0.08 Hz), slow-4 band (0.027-0.073 Hz), as well as in slow-5 band (0.01-0.027 Hz).

CONCLUSIONS

The results showed that the superior frontal gyrus and middle frontal gyrus were involved in the integration and processing of pain signals. In addition, the abnormal spontaneous neural activity in TTH patients was frequency-specific. Namely, slow-5 band (0.01-0.027 Hz) might contain additional useful information in comparison to slow-4 band (0.027-0.073 Hz). This preliminary exploration might provide an objective imaging basis for the understanding of the pathophysiological mechanism of TTH.

Simultaneous PET/fMRI Detects Distinctive Alterations in Functional Connectivity and Glucose Metabolism of Precuneus Subregions in Alzheimer's Disease

As a central hub in the interconnected brain network, the precuneus has been reported showing disrupted functional connectivity and hypometabolism in Alzheimer's disease (AD). However, as a highly heterogeneous cortical structure, little is known whether individual subregion of the precuneus is uniformly or differentially involved in the progression of AD. To this end, using a hybrid PET/fMRI technique, we compared resting-state functional connectivity strength (FCS) and glucose metabolism in dorsal anterior (DA_pcu), dorsal posterior (DP_pcu) and ventral (V_pcu) subregions of the precuneus among 20 AD patients, 23 mild cognitive impairment (MCI) patients, and 27 matched cognitively normal (CN) subjects. The sub-parcellation of precuneus was performed using a K-means clustering algorithm based on its intra-regional functional connectivity. For the whole precuneus, decreased FCS (p = 0.047) and glucose hypometabolism (p = 0.006) were observed in AD patients compared to CN subjects. For the subregions of the precuneus, decreased FCS was found in DP_pcu of AD patients compared to MCI patients (p = 0.011) and in V_pcu for both MCI (p = 0.006) and AD (p = 0.008) patients compared to CN subjects. Reduced glucose metabolism was found in DP_pcu of AD patients compared to CN subjects (p = 0.038) and in V_pcu of AD patients compared to both MCI patients (p = 0.045) and CN subjects (p < 0.001). For both FCS and glucose metabolism, DA_pcu remained relatively unaffected by AD. Moreover, only in V_pcu, disruptions in FCS (r = 0.498, p = 0.042) and hypometabolism (r = 0.566, p = 0.018) were significantly correlated with the cognitive decline of AD patients. Our results demonstrated a distinctively disrupted functional and metabolic pattern from ventral to dorsal precuneus affected by AD, with V_pcu and DA_pcu being the most vulnerable and conservative subregion, respectively. Findings of this study extend our knowledge on the differential roles of precuneus subregions in AD.

Multiparametric MRI for the improved diagnostic accuracy of Alzheimer's disease and mild cognitive impairment: Research protocol of a case-control study design

Background

Alzheimer’s disease (AD) is a major neurocognitive disorder identified by memory loss and a significant cognitive decline based on previous level of performance in one or more cognitive domains that interferes in the independence of everyday activities. The accuracy of imaging helps to identify the neuropathological features that differentiate AD from its common precursor, mild cognitive impairment (MCI). Identification of early signs will aid in risk stratification of disease and ensures proper management is instituted to reduce the morbidity and mortality associated with AD. Magnetic resonance imaging (MRI) using structural MRI (sMRI), functional MRI (fMRI), diffusion tensor imaging (DTI), and magnetic resonance spectroscopy (¹H-MRS) performed alone is inadequate. Thus, the combination of multiparametric MRI is proposed to increase the accuracy of diagnosing MCI and AD when compared to elderly healthy controls.

Methods

This protocol describes a non-interventional case control study. The AD and MCI patients and the healthy elderly controls will undergo multi-parametric MRI. The protocol consists of sMRI, fMRI, DTI, and single-voxel proton MRS sequences. An eco-planar imaging (EPI) will be used to perform resting-state fMRI sequence. The structural images will be analysed using Computational Anatomy Toolbox-12, functional images will be analysed using Statistical Parametric Mapping-12, DPABI (Data Processing & Analysis for Brain Imaging), and Conn software, while DTI and ¹H-MRS will be analysed using the FSL (FMRIB’s Software Library) and Tarquin respectively. Correlation of the MRI results and the data acquired from the APOE genotyping, neuropsychological evaluations (i.e. Montreal Cognitive Assessment [MoCA], and Mini–Mental State Examination [MMSE] scores) will be performed. The imaging results will also be correlated with the sociodemographic factors. The diagnosis of AD and MCI will be standardized and based on the DSM-5 criteria and the neuropsychological scores.

Discussion

The combination of sMRI, fMRI, DTI, and MRS sequences can provide information on the anatomical and functional changes in the brain such as regional grey matter volume atrophy, impaired functional connectivity among brain regions, and decreased metabolite levels specifically at the posterior cingulate cortex/precuneus. The combination of multiparametric MRI sequences can be used to stratify the management of MCI and AD patients. Accurate imaging can decide on the frequency of follow-up at memory clinics and select classifiers for machine learning that may aid in the disease identification and prognostication. Reliable and consistent quantification, using standardised protocols, are crucial to establish an optimal diagnostic capability in the early detection of Alzheimer’s disease.

Effects of APOE ε2 on the Fractional Amplitude of Low-Frequency Fluctuation in Mild Cognitive Impairment: A Study Based on the Resting-State Functional MRI

BACKGROUND

Apolipoprotein E (APOE) ε2 is a protective genetic factor for Alzheimer's disease (AD). However, the potential interaction effects between the APOE ε2 allele and disease status on the intrinsic brain activity remain elusive.

METHODS

We identified 73 healthy control (HC) with APOE ε3/ε3, 61 mild cognitive impairment (MCI) subjects with APOE ε3/ε3, 24 HC with APOE ε2/ε3, and 10 MCI subjects with APOE ε2/ε3 from the ADNI database. All subjects underwent a resting-state functional MRI and Fluoro-deoxy-glucose positron emission tomography (FDG-PET). We used a fractional amplitude of low-frequency fluctuation (fALFF) to explore the spontaneous brain activity. Based on the mixed-effects analysis, we explored the interaction effects between the APOE ε2 allele versus disease status on brain activity and metabolism in a voxel-wise fashion (GRF corrected, p < 0.01), followed by post hoc two-sample t-tests (Bonferroni corrected, p < 0.05). We then investigated the relationship between the mean imaging metrics and cognitive abilities.

RESULTS

There are no significant differences in gender, age, or education among the four groups. The interaction effect on brain activity was located in the inferior parietal lobule (IPL). Post hoc analysis showed that APOE ε2/ε3 MCI had an increased IPL fALFF than APOE ε3/ε3 MCI. Regarding the APOE ε2 allele effects, we found that ε2 carriers had a decreased fALFF in the transverse temporal gyrus than non-carriers. Also, FDG-PET results showed a lower SUVR of the frontal lobe in APOE ε2 carriers than non-carriers. Furthermore, fALFF of IPL was correlated with the visuospatial function (r = -0.16, p < 0.05).

CONCLUSION

APOE ε2 carriers might have a better brain reservation when coping with AD-related pathologies.