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

Compensation versus deterioration across functional networks in amnestic mild cognitive impairment subtypes

Functional connectivity studies to detect neurophysiological correlates of amnestic mild cognitive impairment (aMCI), a prodromal stage of Alzheimer's disease, have generated contradictory results in terms of compensation and deterioration, as most of the studies did not distinguish between the different aMCI subtypes: single-domain aMCI (sd-aMCI) and multiple-domain aMCI (md-aMCI). The present study aimed to characterize the neurophysiological correlates of aMCI subtypes by using resting-state functional magnetic resonance imaging. The study included sd-aMCI (n = 29), md-aMCI (n = 26), and control (n = 30) participants. The data were subjected to independent component analysis (ICA) to explore the default mode network (DMN) and the fronto-parietal control network (FPCN). Additionally, seed-based and moderation analyses were conducted to investigate the connectivity of the medial temporal lobe and functional networks. aMCI subtypes presented differences in functional connectivity relative to the control group: sd-aMCI participants displayed increased FPCN connectivity and reduced connectivity between the posterior parahippocampal gyrus (PHG) and medial structures; md-aMCI participants exhibited lower FPCN connectivity, higher anterior PHG connectivity with frontal structures and lower posterior PHG connectivity with central-parietal and temporo-occipital areas. Additionally, md-aMCI participants showed higher posterior PHG connectivity with structures of the DMN than both control and sd-aMCI participants, potentially indicating more severe cognitive deficits. The results showed gradual and qualitative neurofunctional differences between the aMCI subgroups, suggesting the existence of compensatory (sd-aMCI) and deterioration (md-aMCI) mechanisms in functional networks, mainly originated in the DMN. The findings support consideration of the subgroups as different stages of MCI within the Alzheimer disease continuum.

Mapping Alzheimer's disease stages toward it's progression: A comprehensive cross-sectional and longitudinal study using resting-state fMRI and graph theory

INTRODUCTION

Functional brain connectivity of resting-state networks varies as Alzheimer's disease (AD) progresses. However, our understanding of the dynamic longitudinal changes that occur in the brain over the course of AD is sometimes contradictory and lacking.

MATERIALS AND METHODS

In this study, we analyzed whole-brain networks connectivity using longitudinal resting-state fMRI data from 132 participants from ADNI dataset. The cohort was divided into four groups: 20 AD, 35 CN, 46 Early MCI, and 31 Late MCI Cross-sectional analyses were conducted at baseline and follow-up (approximately two years apart), with longitudinal changes assessed within and between groups.

RESULTS

Cross-sectional analyses revealed that all groups differed significantly from AD in global network properties at both time points, with EMCI also showing disrupted topological metrics compared to CN. Longitudinal analyses highlighted notable changes in small-worldness (σ), global clustering coefficient (Cp), and normalized characteristic path length (λ) across groups. Both EMCI and LMCI groups showed significant alterations in global efficiency (Eglob), Cp, and σ over time. Pairwise comparisons also revealed significant interaction effects, particularly between CN-EMCI and CN-AD groups. All groups showed notable changes in σ, λ, and Cp, according to within-group longitudinal changes. Furthermore, distinct changes in Eglob over time were observed in the LMCI and EMCI groups. Almost all subnetwork attributes demonstrated significant changes between patients at various phases in both time intervals.

CONCLUSION

Our findings emphasize significant connectivity alterations across all groups at both baseline and follow-up, with longitudinal analyses underscoring the progression of these changes. Graph theory metrics provide valuable insights into the transition from normal cognition to AD, potentially serving as biomarkers for disease progression.

fMRI-based Alzheimer's disease detection via functional connectivity analysis: a systematic review

Alzheimer's disease is a common brain disorder affecting many people worldwide. It is the primary cause of dementia and memory loss. The early diagnosis of Alzheimer's disease is essential to provide timely care to AD patients and prevent the development of symptoms of this disease. Various non-invasive techniques can be utilized to diagnose Alzheimer's in its early stages. These techniques include functional magnetic resonance imaging, electroencephalography, positron emission tomography, and diffusion tensor imaging. They are mainly used to explore functional and structural connectivity of human brains. Functional connectivity is essential for understanding the co-activation of certain brain regions co-activation. This systematic review scrutinizes various works of Alzheimer's disease detection by analyzing the learning from functional connectivity of fMRI datasets that were published between 2018 and 2024. This work investigates the whole learning pipeline including data analysis, standard preprocessing phases of fMRI, feature computation, extraction and selection, and the various machine learning and deep learning algorithms that are used to predict the occurrence of Alzheimer's disease. Ultimately, the paper analyzed results on AD and highlighted future research directions in medical imaging. There is a need for an efficient and accurate way to detect AD to overcome the problems faced by patients in the early stages.

Band-Specific Altered Cortical Connectivity in Early Parkinson's Disease and its Clinical Correlates

BACKGROUND

Functional connectivity (FC) has shown promising results in assessing the pathophysiology and identifying early biomarkers of neurodegenerative disorders, such as Parkinson's disease (PD).

OBJECTIVES

In this study, we aimed to assess possible resting-state FC abnormalities in early-stage PD patients using high-density electroencephalography (EEG) and to detect their clinical relationship with motor and non-motor PD symptoms.

METHODS

We enrolled 26 early-stage levodopa naïve PD patients and a group of 20 healthy controls (HC). Data were recorded with 64-channels EEG system and a source-reconstruction method was used to identify brain-region activity. FC was calculated using the weighted phase-lag index in θ, α, and β bands. Additionally, we quantified the unbalancing between β and lower frequencies through a novel index (β-functional ratio [FR]). Statistical analysis was conducted using a network-based statistical approach.

RESULTS

PD patients showed hypoconnected networks in θ and α band, involving prefrontal-limbic-temporal and frontoparietal areas, respectively, and a hyperconnected network in the β frequency band, involving sensorimotor-frontal areas. The θ FC network was negatively related to Non-Motor Symptoms Scale scores and α FC to the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale part III gait subscore, whereas β FC and β-FR network were positively linked to the bradykinesia subscore. Changes in θ FC and β-FR showed substantial reliability and high accuracy, precision, sensitivity, and specificity in discriminating PD and HC.

CONCLUSIONS

Frequency-specific FC changes in PD likely reflect the dysfunction of distinct cortical networks, which occur from the early stage of the disease. These abnormalities are involved in the pathophysiology of specific motor and non-motor PD symptoms, including gait, bradykinesia, mood, and cognition. © 2023 International Parkinson and Movement Disorder Society.

Structural Network Topology Reveals Higher Brain Resilience in Individuals with Preclinical Alzheimer's Disease

Introduction: The diagnosis of Alzheimer's disease (AD) requires the presence of amyloid and tau pathology, but it remains unclear how they affect the structural network in the pre-clinical stage. We aimed to assess differences in topological properties in cognitively normal (CN) individuals with varying levels of amyloid and tau pathology, as well as their association with AD pathology burden. Methods: A total of 68 CN individuals were included and stratified by normal/abnormal (-/+) amyloid (A) and tau (T) status based on positron emission tomography results, yielding three groups: A-T- (n = 19), A+T- (n = 28), and A+T+ (n = 21). Topological properties were measured from structural connectivity. Group differences and correlations with A and T were evaluated. Results: Compared with the A-T- group, the A+T+ group exhibited changes in the structural network topology. At the global level, higher assortativity was shown in the A+T+ group and was correlated with greater tau burden (r = 0.29, p = 0.02), while no difference in global efficiency was found across the three groups. At the local level, the A+T+ group showed disrupted topological properties in the left hippocampus compared with the A-T- group, characterized by lower local efficiency (p < 0.01) and a lower clustering coefficient (p = 0.014). Conclusions: The increased linkage in the higher level architecture of the white matter network reflected by assortativity may indicate increased brain resilience in the early pathological state. Our results encourage further investigation of the topological properties of the structural network in pre-clinical AD.

Sleep Traits Causally Affect the Brain Cortical Structure: A Mendelian Randomization Study

Background: Brain imaging results in sleep deprived patients showed structural changes in the cerebral cortex; however, the reasons for this phenomenon need to be further explored. Methods: This MR study evaluated causal associations between morningness, ease of getting up, insomnia, long sleep, short sleep, and the cortex structure. Results: At the functional level, morningness increased the surface area (SA) of cuneus with global weighted (beta(b) (95% CI): 32.63 (10.35, 54.90), p = 0.004). Short sleep increased SA of the lateral occipital with global weighted (b (95% CI): 394.37(107.89, 680.85), p = 0.007. Short sleep reduced cortical thickness (TH) of paracentral with global weighted (OR (95% CI): -0.11 (-0.19, -0.03), p = 0.006). Short sleep reduced TH of parahippocampal with global weighted (b (95% CI): -0.25 (-0.42, -0.07), p = 0.006). No pleiotropy was detected. However, none of the Bonferroni-corrected p values of the causal relationship between cortical structure and the five types of sleep traits met the threshold. Conclusions: Our results potentially show evidence of a higher risk association between neuropsychiatric disorders and not only paracentral and parahippocampal brain areas atrophy, but also an increase in the middle temporal zone. Our findings shed light on the associations of cortical structure with the occurrence of five types of sleep traits.

Rat Hair Metabolomics Analysis Reveals Perturbations of Unsaturated Fatty Acid Biosynthesis, Phenylalanine, and Arachidonic Acid Metabolism Pathways Are Associated with Amyloid-β-Induced Cognitive Deficits

Hair is a noninvasive valuable biospecimen for the long-term assessment of endogenous metabolic disturbance. Whether the hair is suitable for identifying biomarkers of the Alzheimer's disease (AD) process remains unknown. We aim to investigate the metabolism changes in hair after β-amyloid (Aβ1-42) exposure in rats using ultra-high-performance liquid chromatography-high-resolution mass spectrometry-based untargeted and targeted methods. Thirty-five days after Aβ1-42 induction, rats displayed significant cognitive deficits, and forty metabolites were changed, of which twenty belonged to three perturbed pathways: (1) phenylalanine metabolism and phenylalanine, tyrosine, and tryptophan biosynthesis-L-phenylalanine, phenylpyruvate, ortho-hydroxyphenylacetic acid, and phenyllactic acid are up-regulated; (2) arachidonic acid (ARA) metabolism-leukotriene B4 (LTB4), arachidonyl carnitine, and 5(S)-HPETE are upregulation, but ARA, 14,15-DiHETrE, 5(S)-HETE, and PGB2 are opposite; and (3) unsaturated fatty acid biosynthesis- eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA), FA 18:3 + 1O, and FA 18:3 + 2O are downregulated. Linoleic acid metabolism belonging to the biosynthesis of unsaturated fatty acid includes the upregulation of 8-hydroxy-9,10-epoxystearic acid, 13-oxoODE, and FA 18:2 + 4O, and downregulation of 9(S)-HPODE and dihomo-γ-linolenic acid. In addition, cortisone and dehydroepiandrosterone belonging to steroid hormone biosynthesis are upregulated. These three perturbed metabolic pathways also correlate with cognitive impairment after Aβ1-42 stimulation. Furthermore, ARA, DHA, EPA, L-phenylalanine, and cortisone have been previously implicated in the cerebrospinal fluid of AD patients and show a similar changing trend in Aβ1-42 rats' hair. These data suggest hair can be a useful biospecimen that well reflects the expression of non-polar molecules under Aβ1-42 stimulation, and the five metabolites have the potential to serve as novel AD biomarkers.

Therapeutic Strategies Aimed at Improving Neuroplasticity in Alzheimer Disease

Alzheimer disease (AD) is the most prevalent form of dementia among elderly people. Owing to its varied and multicausal etiopathology, intervention strategies have been highly diverse. Despite ongoing advances in the field, efficient therapies to mitigate AD symptoms or delay their progression are still of limited scope. Neuroplasticity, in broad terms the ability of the brain to modify its structure in response to external stimulation or damage, has received growing attention as a possible therapeutic target, since the disruption of plastic mechanisms in the brain appear to correlate with various forms of cognitive impairment present in AD patients. Several pre-clinical and clinical studies have attempted to enhance neuroplasticity via different mechanisms, for example, regulating glucose or lipid metabolism, targeting the activity of neurotransmitter systems, or addressing neuroinflammation. In this review, we first describe several structural and functional aspects of neuroplasticity. We then focus on the current status of pharmacological approaches to AD stemming from clinical trials targeting neuroplastic mechanisms in AD patients. This is followed by an analysis of analogous pharmacological interventions in animal models, according to their mechanisms of action.

Topologically convergent and divergent large scale complex networks among Alzheimer's disease spectrum patients: A systematic review

Alzheimer's disease (AD) is associated with disruption at the level of a large-scale complex network. To explore the underlying mechanisms in the progression of AD, graph theory was used to quantitatively analyze the topological properties of structural and functional connections. Although an increasing number of studies have shown altered global and nodal network properties, little is known about the topologically convergent and divergent patterns between structural and functional networks among AD-spectrum patients. In this review, we summarized the topological patterns of the large-scale complex networks using multimodal neuroimaging graph theory analysis in AD spectrum patients. Convergent deficits in the connectivity characteristics were primarily in the default mode network (DMN) itself both in the structural and functional networks, while a divergent changes in the neighboring regions of the DMN were also observed between the patient groups. Together, the application of graph theory to large-scale complex brain networks provides quantitative insights into topological principles of brain network organization, which may lead to increasing attention in identifying the underlying neuroimaging pathological changes and predicting the progression of AD.

Adaptive and pathological connectivity responses in Parkinson's disease brain networks

Functional imaging has been used extensively to identify and validate disease-specific networks as biomarkers in neurodegenerative disorders. It is not known, however, whether the connectivity patterns in these networks differ with disease progression compared to the beneficial adaptations that may also occur over time. To distinguish the 2 responses, we focused on assortativity, the tendency for network connections to link nodes with similar properties. High assortativity is associated with unstable, inefficient flow through the network. Low assortativity, by contrast, involves more diverse connections that are also more robust and efficient. We found that in Parkinson's disease (PD), network assortativity increased over time. Assoratitivty was high in clinically aggressive genetic variants but was low for genes associated with slow progression. Dopaminergic treatment increased assortativity despite improving motor symptoms, but subthalamic gene therapy, which remodels PD networks, reduced this measure compared to sham surgery. Stereotyped changes in connectivity patterns underlie disease progression and treatment responses in PD networks.

Application of machine learning and complex network measures to an EEG dataset from ayahuasca experiments

Ayahuasca is a blend of Amazonian plants that has been used for traditional medicine by the inhabitants of this region for hundreds of years. Furthermore, this plant has been demonstrated to be a viable therapy for a variety of neurological and mental diseases. EEG experiments have found specific brain regions that changed significantly due to ayahuasca. Here, we used an EEG dataset to investigate the ability to automatically detect changes in brain activity using machine learning and complex networks. Machine learning was applied at three different levels of data abstraction: (A) the raw EEG time series, (B) the correlation of the EEG time series, and (C) the complex network measures calculated from (B). Further, at the abstraction level of (C), we developed new measures of complex networks relating to community detection. As a result, the machine learning method was able to automatically detect changes in brain activity, with case (B) showing the highest accuracy (92%), followed by (A) (88%) and (C) (83%), indicating that connectivity changes between brain regions are more important for the detection of ayahuasca. The most activated areas were the frontal and temporal lobe, which is consistent with the literature. F3 and PO4 were the most important brain connections, a significant new discovery for psychedelic literature. This connection may point to a cognitive process akin to face recognition in individuals during ayahuasca-mediated visual hallucinations. Furthermore, closeness centrality and assortativity were the most important complex network measures. These two measures are also associated with diseases such as Alzheimer's disease, indicating a possible therapeutic mechanism. Moreover, the new measures were crucial to the predictive model and suggested larger brain communities associated with the use of ayahuasca. This suggests that the dissemination of information in functional brain networks is slower when this drug is present. Overall, our methodology was able to automatically detect changes in brain activity during ayahuasca consumption and interpret how these psychedelics alter brain networks, as well as provide insights into their mechanisms of action.

Temporal and Spatial Analysis of Alzheimer's Disease Based on an Improved Convolutional Neural Network and a Resting-State FMRI Brain Functional Network

Most current research on Alzheimer's disease (AD) is based on transverse measurements. Given the nature of neurodegeneration in AD progression, observing longitudinal changes in the structural features of brain networks over time may improve the accuracy of the predicted transformation and provide a good measure of the progression of AD. Currently, there is no cure for patients with existing AD dementia, but patients with mild cognitive impairment (MCI) in the prodromal stage of AD dementia may be diagnosed. The study of the early diagnosis of MCI and the prediction of MCI to AD transformation is of great significance for the monitoring of the MCI to AD transformation process. Despite the high rate of MCI conversion to AD, the neuropathological cause of MCI is heterogeneous. However, many people with MCI remain stable. Treatment options are different for patients with stable MCI and those with underlying dementia. Therefore, it is of great significance for clinical practice to predict whether patients with MCI will develop AD dementia. This paper proposes an improved algorithm that is based on a convolution neural network (CNN) with residuals combined with multi-layer long short-term memory (LSTM) to diagnose AD and predict MCI. Firstly, multi-time resting-state fMRI images were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database for preprocessing, and then an AAL brain partition template was used to construct a 90 × 90 functional connectivity (FC) network matrix of a whole-brain region of interest (ROI). Secondly, the diversity of training samples was increased by generating an adversarial network (GAN). Finally, a CNN with residuals and a multi-layer LSTM model were constructed to automatically classify and predict the functional adjacency matrix. This method can not only distinguish Alzheimer's disease from normal health conditions at multiple time points, but can also predict progressive MCI (pMCI) and stable MCI (sMCI) at multiple time points. The classification accuracies in AD vs. NC and sMCI vs.pMCI reached 93.5% and 75.5%, respectively.

Functional Brain Connectivity in Mild Cognitive Impairment With Sleep Disorders: A Study Based on Resting-State Functional Magnetic Resonance Imaging

Purpose

To investigate the effect of sleep disorder (SD) on the changes of brain network dysfunction in mild cognitive impairment (MCI), we compared network connectivity patterns among MCI, SD, and comorbid MCI and sleep disorders (MCI-SD) patients using resting state functional magnetic resonance imaging (RS-fMRI).

Patients and Methods

A total of 60 participants were included in this study, 20 each with MCI, SD, or MCI-SD. And all participants underwent structural and functional MRI scanning. The default-mode network (DMN) was extracted by independent component analysis (ICA), and regional functional connectivity strengths were calculated and compared among groups.

Results

Compared to MCI patients, The DMN of MCI-SD patients demonstrated weaker functional connectivity with left middle frontal gyrus, right superior marginal gyrus, but stronger connectivity with the left parahippocampus, left precuneus and left middle temporal gyrus. Compared to the SD group, MCI-SD patients demonstrated weaker functional connectivity with right transverse temporal gyrus (Heschl’s gyrus), right precentral gyrus, and left insula, but stronger connectivity with posterior cerebellum, right middle occipital gyrus, and left precuneus.

Conclusion

Patients with MCI-SD show unique changes in brain network connectivity patterns compared to MCI or SD alone, likely reflecting a broader functional disconnection and the need to recruit more brain regions for functional compensation.

Subcortical and Cerebellar Neural Correlates of Prodromal Alzheimer’s Disease with Prolonged Sleep Latency

Background: Despite the important associations among sleep, Alzheimer’s disease (AD), subcortical structures, and the cerebellum, structural and functional magnetic resonance imaging (MRI) with regard to these regions and sleep on patients in AD trajectory are scarce. Objective: This study aimed to evaluate the influence of prolonged sleep latency on the structural and functional alterations in the subcortical and cerebellar neural correlates in amyloid-β positive amnestic mild cognitive impairment patients (Aβ+aMCI). Methods: A total of 60 patients with aMCI who were identified as amyloid positive ([18F] flutemetamol+) were recruited in the study, 24 patients with normal sleep latency (aMCI-n) and 36 patients prolonged sleep latency (aMCI-p). Cortical thickness and volumes between the two groups were compared. Volumetric analyses were implemented on the brainstem, thalamus, and hippocampus. Subcortical and cerebellar resting state functional connectivity (FC) differences were measured between the both groups through seed-to-voxel analysis. Additionally, group x Aβ interactive effects on FC values were tested with a general linear model. Result: There was a significantly decreased brainstem volume in aMCI-p subjects. We observed a significant reduction of the locus coeruleus (LC) FC with frontal, temporal, insular cortices, hippocampus, and left thalamic FC with occipital cortex. Moreover, the LC FC with occipital cortex and left hippocampal FC with frontal cortex were increased in aMCI-p subjects. In addition, there was a statistically significant group by regional standardized uptake value ratio interactions discovered in cerebro-cerebellar networks. Conclusion: The aforementioned findings suggest that prolonged sleep latency may be a detrimental factor in compromising structural and functional correlates of subcortical structures and the cerebellum, which may accelerate AD pathophysiology.

Diagnosis of Alzheimer's Disease Severity with fMRI Images Using Robust Multitask Feature Extraction Method and Convolutional Neural Network (CNN)

The automatic diagnosis of Alzheimer's disease plays an important role in human health, especially in its early stage. Because it is a neurodegenerative condition, Alzheimer's disease seems to have a long incubation period. Therefore, it is essential to analyze Alzheimer's symptoms at different stages. In this paper, the classification is done with several methods of machine learning consisting of K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), linear discrimination analysis (LDA), and random forest (RF). Moreover, novel convolutional neural network (CNN) architecture is presented to diagnose Alzheimer's severity. The relationship between Alzheimer's patients' functional magnetic resonance imaging (fMRI) images and their scores on the MMSE is investigated to achieve the aim. The feature extraction is performed based on the robust multitask feature learning algorithm. The severity is also calculated based on the Mini-Mental State Examination score, including low, mild, moderate, and severe categories. Results show that the accuracy of the KNN, SVM, DT, LDA, RF, and presented CNN method is 77.5%, 85.8%, 91.7%, 79.5%, 85.1%, and 96.7%, respectively. Moreover, for the presented CNN architecture, the sensitivity of low, mild, moderate, and severe status of Alzheimer patients is 98.1%, 95.2%,89.0%, and 87.5%, respectively. Based on the findings, the presented CNN architecture classifier outperforms other methods and can diagnose the severity and stages of Alzheimer's disease with maximum accuracy.