Abnormal Functional Brain Networks in Mild Cognitive Impairment and Alzheimer's Disease: A Minimum Spanning Tree Analysis

Effects of computerized cognitive training on structure‒function coupling and topology of multiple brain networks in people with mild cognitive impairment: a randomized controlled trial

BACKGROUND

People with mild cognitive impairment (MCI) experience a loss of cognitive functions, whose mechanism is characterized by aberrant structure‒function (SC-FC) coupling and topological attributes of multiple networks. This study aimed to reveal the network-level SC-FC coupling and internal topological changes triggered by computerized cognitive training (CCT) to explain the therapeutic effects of this training in individuals with MCI.

METHODS

In this randomized block experiment, we recruited 60 MCI individuals and randomly divided them into an 8-week multidomain CCT group and a health education control group. The neuropsychological outcome measures were the Montreal Cognitive Assessment (MoCA), Chinese Auditory Verbal Learning Test (CAVLT), Chinese Stroop Color-Word Test (SCWT), and Rey-Osterrieth Complex Figure Test (Rey CFT). The brain imaging outcome measures were SC-FC coupling and topological attributes using functional MRI and diffusion tensor imaging methods. We applied linear model analysis to assess the differences in the outcome measures and identify the correspondence between the changes in the brain networks and cognitive functions before and after the CCT.

RESULTS

Fifty participants were included in the analyses after the exclusion of three dropouts and seven participants with low-quality MRI scans. Significant group × time effects were found on the changes in the MoCA, CAVLT, and Rey CFT recall scores. The changes in the SC-FC coupling values of the default mode network (DMN) and somatomotor network (SOM) were higher in the CCT group than in the control group (P(unc.) = 0.033, P(unc.) = 0.019), but opposite effects were found on the coupling values of the visual network (VIS) (P(unc.) = 0.039). Increasing clustering coefficients in the functional DMN and SOM and subtle changes in the nodal degree centrality and nodal efficiency of the right dorsal medial prefrontal cortex, posterior cingulate cortex, left parietal lobe, somatomotor area, and visual cortex were observed in the CCT group (P < 0.05, Bonferroni correction). Significant correspondences were found between global cognitive function and DMN coupling values (P(unc.) = 0.007), between immediate memory and SOM as well as FPC coupling values (P(unc.) = 0.037, P(unc.) = 0.030), between delayed memory and SOM coupling values (P(unc.) = 0.030), and between visual memory and VIS coupling values (P(unc.) = 0.007).

CONCLUSIONS

Eight weeks of CCT effectively improved global cognitive and memory functions; these changes were correlated with increases in SC-FC coupling and changes in the topography of the DMN and SOM in individuals with MCI. The CCT regimen also modulated the clustering coefficient and the capacity for information transformation in functional networks; these effects appeared to underlie the cognitive improvement associated with CCT.

TRIAL REGISTRATION

Chinese Clinical Trial Registry, ChiCTR2000034012. Registered on 21 June 2020.

Altered higher-order coupling between brain structure and function with embedded vector representations of connectomes in schizophrenia

It has been shown that the functional dependency of the brain exists in both direct and indirect regional relationships. Therefore, it is necessary to map higher-order coupling in brain structure and function to understand brain dynamic. However, how to quantify connections between not directly regions remains unknown to schizophrenia. The word2vec is a common algorithm through create embeddings of words to solve these problems. We apply the node2vec embedding representation to characterize features on each node, their pairwise relationship can give rise to correspondence relationships between brain regions. Then we adopt pearson correlation to quantify the higher-order coupling between structure and function in normal controls and schizophrenia. In addition, we construct direct and indirect connections to quantify the coupling between their respective functional connections. The results showed that higher-order coupling is significantly higher in schizophrenia. Importantly, the anomalous cause of coupling mainly focus on indirect structural connections. The indirect structural connections play an essential role in functional connectivity–structural connectivity (SC–FC) coupling. The similarity between embedded representations capture more subtle network underlying information, our research provides new perspectives for understanding SC–FC coupling. A strong indication that the structural backbone of the brain has an intimate influence on the resting-state functional.

Graph Analysis of Functional Brain Topology Using Minimum Spanning Tree in Subjective Cognitive Decline

BACKGROUND

Subjects with subjective cognitive decline (SCD) are proposed as a potential population to screen for Alzheimer's disease (AD).

OBJECTIVE

Investigating brain topologies would help to mine the neuromechanisms of SCD and provide new insights into the pathogenesis of AD.

METHODS

Objectively cognitively unimpaired subjects from communities who underwent resting-state BOLD-fMRI and clinical assessments were included. The subjects were categorized into SCD and normal control (NC) groups according to whether they exhibited self-perceived cognitive decline and were worried about it. The minimum spanning tree (MST) of the functional brain network was calculated for each subject, based on which the efficiency and centrality of the brain network organization were explored. Hippocampal/parahippocampal volumes were also detected to reveal whether the early neurodegeneration of AD could be seen in SCD.

RESULTS

A total of 49 subjects in NC and 95 subjects in SCD group were included in this study. We found the efficiency and centrality of brain network organization, as well as the hippocampal/parahippocampal volume were preserved in SCD. Besides, SCD exhibited normal cognitions, including memory, language, and execution, but increased depressive and anxious levels. Interestingly, language and execution, instead of memory, showed a significant positive correlation with the maximum betweenness centrality of the functional brain organization and hippocampal/parahippocampal volume. Neither depressive nor anxious scales exhibited correlations with the brain functional topologies or hippocampal/parahippocampal volume.

CONCLUSION

SCD exhibited preserved efficiency and centrality of brain organization. In clinical practice, language and execution as well as depression and anxiety should be paid attention in SCD.

Spontaneous brain activity, graph metrics, and head motion related to prospective post-traumatic stress disorder trauma-focused therapy response

Introduction

Trauma-focused psychotherapy for post-traumatic stress disorder (PTSD) is effective in about half of all patients. Investigating biological systems related to prospective treatment response is important to gain insight in mechanisms predisposing patients for successful intervention. We studied if spontaneous brain activity, brain network characteristics and head motion during the resting state are associated with future treatment success.

Methods

Functional magnetic resonance imaging scans were acquired from 46 veterans with PTSD around the start of treatment. Psychotherapy consisted of trauma-focused cognitive behavioral therapy (tf-CBT), eye movement desensitization and reprocessing (EMDR), or a combination thereof. After intervention, 24 patients were classified as treatment responders and 22 as treatment resistant. Differences between groups in spontaneous brain activity were evaluated using amplitude of low-frequency fluctuations (ALFF), while global and regional brain network characteristics were assessed using a minimum spanning tree (MST) approach. In addition, in-scanner head motion was assessed.

Results

No differences in spontaneous brain activity and global network characteristics were observed between the responder and non-responder group. The right inferior parietal lobule, right putamen and left superior parietal lobule had a more central position in the network in the responder group compared to the non-responder group, while the right dorsolateral prefrontal cortex (DLPFC), right inferior frontal gyrus and left inferior temporal gyrus had a less central position. In addition, responders showed less head motion.

Discussion

These results show that areas involved in executive functioning, attentional and action processes, learning, and visual-object processing, are related to prospective PTSD treatment response in veterans. In addition, these findings suggest that involuntary micromovements may be related to future treatment success.

Global Network Analysis of Alzheimer’s Disease with Minimum Spanning Trees

Background: A minimum spanning tree (MST) is a unique efficient network comprising the necessary connections needed to connect all regions in a network while retaining the lowest possible cost of connection weight. Objective: This study aimed to utilize functional near-infrared spectroscopy (fNIRS) to analyze brain activity in different regions and then construct MST-based regions to characterize the brain topologies of participants with Alzheimer’s disease (AD), mild cognitive impairment (MCI), and normal controls (NC). Methods: A 46 channel fNIRS setup was used on all participants, with correlation being calculated for each channel pair. An MST was constructed from the resulting correlation matrix, from which graph theory measures were calculated. The average number of connections within a lobe in the left versus right hemisphere was calculated to identify which lobes displayed and abnormal amount of connectivity. Results: Compared to those in the MCI group, the AD group showed a less integrated network structure, with a higher characteristic path length, but lower leaf fraction, maximum degree, and degree divergence. The AD group also showed a higher number of connections in the frontal lobe within the left hemisphere and a lower number between hemispheric frontal lobes as compared to MCI. Conclusion: These results indicate a deviation in network structure and connectivity within patient groups that is consistent with the theory of dysconnectivity for AD. Additionally, the AD group showed strong correlations between the Hamilton depression rating scale and different graph metrics, suggesting a link between network organization and the recurrence of depression in AD.

Clustering Analysis of the Care Problems of People with Dementia Based on the Minimum Spanning Tree Algorithm: A Cross-Sectional Study

Background: Recognizing the correlations between care problems of people with dementia could help clinicians choose treatment methods because related symptom groups might respond to the same treatment intervention. Objective: This study aimed to evaluate the prevalence of various care problems in people with dementia and to explore the core care problems and correlations between care problems of people with dementia. Methods: This cross-sectional study recruited family caregivers of people with dementia through memory clinics and WeChat groups. Care problems of people with dementia were measured using a care problems evaluation sheet, which involved three aspects: daily living care problems, behavioral and psychological symptoms, and safety risks. Clustering analysis of the care problems based on Kruskal’s minimum spanning tree (MST) algorithm was performed in the Jupyter Notebook software to explore the core care problems and their correlations. Results: A total of 687 carer-patient pairs were included in the analysis. In general, the prevalence of having difficulty in language performance, agitated behavior, and incidence of falls was relatively higher than other care problems in people with dementia, which distressed their family caregivers. Through clustering analysis, the 63 care problems were clustered into 7 clusters and 7 core care problems were identified. Conclusion: The prevalence of various care problems of people living with dementia in China was relatively high. The information regarding correlations in clusters among care problems will help practitioners and policymakers to identify the core care problems and optimize more rational treatments for people with dementia.

Minimum spanning tree analysis of brain networks: A systematic review of network size effects, sensitivity for neuropsychiatric pathology and disorder specificity

Brain network characteristics’ potential to serve as a neurological and psychiatric pathology biomarker has been hampered by the so-called thresholding problem. The minimum spanning tree (MST) is increasingly applied to overcome this problem. It is yet unknown whether this approach leads to more consistent findings across studies and converging outcomes of either disease-specific biomarkers or transdiagnostic effects. We performed a systematic review on MST analysis in neurophysiological and neuroimaging studies (N = 43) to study consistency of MST metrics between different network sizes and assessed disease specificity and transdiagnostic sensitivity of MST metrics for neurological and psychiatric conditions. Analysis of data from control groups (12 studies) showed that MST leaf fraction but not diameter decreased with increasing network size. Studies showed a broad range in metric values, suggesting that specific processing pipelines affect MST topology. Contradicting findings remain in the inconclusive literature of MST brain network studies, but some trends were seen: (1) a more linelike organization characterizes neurodegenerative disorders across pathologies, and is associated with symptom severity and disease progression; (2) neurophysiological studies in epilepsy show frequency band specific MST alterations that normalize after successful treatment; and (3) less efficient MST topology in alpha band is found across disorders associated with attention impairments.

The potential of brain network characteristics to serve as biomarker of neurological and psychiatric pathology has been hampered by the so-called thresholding problem. The minimum spanning tree (MST) is increasingly applied to overcome this problem. We performed a systematic review on MST analysis in neurophysiological and neuroimaging studies and assessed disease specificity and transdiagnostic sensitivity of MST metrics for neurological and psychiatric conditions. MST leaf fraction but not diameter decreased with increasing network size. Contradicting findings remain in the literature on MST brain network studies, but some trends were seen: (1) a more linelike organization characterizes neurodegenerative disorders; (2) in epilepsy there are frequency band specific MST alterations that normalize after successful treatment; and (3) less efficient MST topology is found across disorders associated with attention impairments.

EEG Alpha and Beta Band Functional Connectivity and Network Structure Mark Hub Overload in Mild Cognitive Impairment During Memory Maintenance

Background: While decreased alpha and beta-band functional connectivity (FC) and changes in network topology have been reported in Alzheimer’s disease, it is not yet entirely known whether these differences can mark cognitive decline in the early stages of the disease. Our study aimed to analyze electroencephalography (EEG) FC and network differences in the alpha and beta frequency band during visuospatial memory maintenance between Mild Cognitive Impairment (MCI) patients and healthy elderly with subjective memory complaints.

Methods: Functional connectivity and network structure of 17 MCI patients and 20 control participants were studied with 128-channel EEG during a visuospatial memory task with varying memory load. FC between EEG channels was measured by amplitude envelope correlation with leakage correction (AEC-c), while network analysis was performed by applying the Minimum Spanning Tree (MST) approach, which reconstructs the critical backbone of the original network.

Results: Memory load (increasing number of to-be-learned items) enhanced the mean AEC-c in the control group in both frequency bands. In contrast to that, after an initial increase, the MCI group showed significantly (p < 0.05) diminished FC in the alpha band in the highest memory load condition, while in the beta band this modulation was absent. Moreover, mean alpha and beta AEC-c correlated significantly with the size of medial temporal lobe structures in the entire sample. The network analysis revealed increased maximum degree, betweenness centrality, and degree divergence, and decreased diameter and eccentricity in the MCI group compared to the control group in both frequency bands independently of the memory load. This suggests a rerouted network in the MCI group with a more centralized topology and a more unequal traffic load distribution.

Conclusion: Alpha- and beta-band FC measured by AEC-c correlates with cognitive load-related modulation, with subtle medial temporal lobe atrophy, and with the disruption of hippocampal fiber integrity in the earliest stages of cognitive decline. The more integrated network topology of the MCI group is in line with the “hub overload and failure” framework and might be part of a compensatory mechanism or a consequence of neural disinhibition.

Brain network construction and analysis for patients with mild cognitive impairment and Alzheimer's disease based on a highly-available nodes approach

INTRODUCTION

Using brain network and graph theory methods to analyze the Alzheimer's disease (AD) and mild cognitive impairment (MCI) abnormal brain function is more and more popular. Plenty of potential methods have been proposed, but the representative signal of each brain region in these methods remains poor performance.

METHODS

We propose a highly-available nodes approach for constructing brain network of patients with MCI and AD. With resting-state functional magnetic resonance imaging (rs-fMRI) data of 84 AD subjects, 81 MCI subjects, and 82 normal control (NC) subjects from the Alzheimer's Disease Neuroimaging Initiative Database, we construct connected weighted brain networks based on the different sparsity and minimum spanning tree. Support Vector Machine of Radial Basis Function kernel was selected as classifier.

RESULTS

Accuracies of 74.09% and 77.58% in classification of MCI and AD from NC, respectively. We also performed a hub node analysis and found 18 significant brain regions were identified as hub nodes.

CONCLUSIONS

The findings of this study provide insights for helping understanding the progress of the AD. The proposed method highlights the effectively representative time series of brain regions of rs-fMRI data for construction and topology analysis brain network.

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

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

Phosphodiesterase 4D Gene Modifies the Functional Network of Patients With Mild Cognitive Impairment and Alzheimer's Disease

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that is affected by several genetic variants. It has been demonstrated that genetic variants affect brain organization and function. In this study, using whole genome-wide association studies (GWAS), we analyzed the functional magnetic resonance imaging and genetic data from the Alzheimer’s Disease Neuroimaging Initiative dataset (ADNI) dataset and identified genetic variants associated with the topology of the functional brain network http://www.adni-info.org. We found three novel loci (rs2409627, rs9647533, and rs11955845) in an intron of the phosphodiesterase 4D (PDE4D) gene that contribute to abnormalities in the topological organization of the functional network. In particular, compared to the wild-type genotype, the subjects carrying the PDE4D variants had altered network properties, including a significantly reduced clustering coefficient, small-worldness, global and local efficiency, a significantly enhanced path length and a normalized path length. In addition, we found that all global brain network attributes were affected by PDE4D variants to different extents as the disease progressed. Additionally, brain regions with alterations in nodal efficiency due to the variations in PDE4D were predominant in the limbic lobe, temporal lobe and frontal lobes. PDE4D has a great effect on memory consolidation and cognition through long-term potentiation (LTP) effects and/or the promotion of inflammatory effects. PDE4D variants might be a main reasons underlyling for the abnormal topological properties and cognitive impairment. Furthermore, we speculated that PDE4D is a risk factor for neural degenerative diseases and provided important clues for the earlier detection and therapeutic intervention for AD.

Abnormal Anatomical Rich-Club Organization and Structural-Functional Coupling in Mild Cognitive Impairment and Alzheimer's Disease

Emerging research indicates interruptions in the wiring organization of the brain network in Mild cognitive impairment (MCI) and Alzheimer's disease (AD). Due to the important role of rich-club organization in distinguishing abnormalities of AD patients and the close relationship between structural connectivity (SC) and functional connectivity (FC), our study examined whether changes in SC-FC coupling and the relationship with abnormal rich-club organizations during the development of diseases may contribute to the pathophysiology of AD. Structural diffusion-tensor imaging (DTI) and resting-state functional magnetic resonance imaging (fMRI) were performed in 38 normal controls (NCs), 40 MCI patients and 19 AD patients. Measures of the rich-club structure and its role in global structural-functional coupling were administered. Our study found decreased levels of feeder and local connectivity in MCI and AD patients, which were the main contributing factors to the lower efficiency of the brain structural network. Another important finding was that we have more accurately characterized the changing pattern of functional brain dynamics. The enhanced coupling between SC and FC in MCI and AD patients might be due to disruptions in optimal structural organization. More interestingly, we also found increases in the SC-FC coupling for feeder and local connections in MCI and AD patients. SC-FC coupling also showed significant differences between MCI and AD patients, mainly between the abnormal feeder connections. The connection density and coupling strength were significantly correlated with clinical metrics in patients. The present findings enhanced our understanding of the neurophysiologic mechanisms associated with MCI and AD.

EEG Feature Extraction Based on a Bilevel Network: Minimum Spanning Tree and Regional Network

Feature extraction is essential for classifying different motor imagery (MI) tasks in a brain–computer interface (BCI). Although the methods of brain network analysis have been widely studied in the BCI field, these methods are limited by differences in network size, density, and standardization. To address this issue and improve classification accuracy, we propose a novel method, in which the hybrid features of the brain function based on the bilevel network are extracted. Minimum spanning tree (MST) based on electroencephalogram (EEG) signal nodes in different MIs is constructed as the first network layer to solve the global network connectivity problem. In addition, the regional network in different movement patterns is constructed as the second network layer to determine the network characteristics, which is consistent with the correspondence between limb movement patterns and cerebral cortex in neurophysiology. We attempt to apply MST to the classification of the MI EEG signals, and the bilevel network has better interpretability. Thereafter, a vector is formed by combining the MST fundamental features with the directional features of the regional network. Our method is validated using the BCI Competition IV Dataset I. Experimental results verify the feasibility of the bilevel network framework. Furthermore, the average classification performance of the proposed method reaches 89.50%, which is higher than that of other competing methods, thereby indicating that the bilevel network is effective for MI classification.

Changes in Centrality Frequency of the Default Mode Network in Individuals With Subjective Cognitive Decline

Despite subjective cognitive decline (SCD), a preclinical stage of Alzheimer's disease (AD), being widely studied in recent years, studies on centrality frequency in individuals with SCD are lacking. This study aimed to investigate the differences in centrality frequency between individuals with SCD and normal controls (NCs). Forty individuals with SCD and 53 well-matched NCs underwent a resting-state functional magnetic resonance imaging scan. We assessed individual dynamic functional connectivity using sliding window correlations. In each time window, brain regions with a high degree centrality were defined as hubs. Across the entire time window, the proportion of time that the hub appeared was characterized as centrality frequency. The centrality frequency correlated with cognitive performance differently in individuals with SCD and NCs. Our results revealed that in individuals with SCD, compared with NCs, correlations between centrality frequency of the anterior cortical regions and cognitive performance decreased (79.2% for NCs and 43.5% for individuals with SCD). In contrast, correlations between centrality frequency of the posterior cortical regions and cognitive performance increased in SCD individuals compared with NCs (20.8% for NCs and 56.5% for individuals with SCD). Moreover, the changes mainly focused on the anterior (93.3% for NCs and 45.5% for individuals with SCD) and posterior (6.7% for NCs and 54.5% for individuals with SCD) regions associated with the default mode network (DMN). In addition, we used absolute thresholds (correlation efficient r = 0.2, 0.25) and proportional thresholds (sparsity = 0.2, 0.25) to verify the results. Dynamic results are relative stable at absolute thresholds while static results are relative stable at proportional thresholds. Converging findings provide a new framework for the detection of the changes occurring in individuals with SCD via centrality frequency of the DMN.

Functional Connectivity Changes Across the Spectrum of Subjective Cognitive Decline, Amnestic Mild Cognitive Impairment and Alzheimer's Disease

The abnormality occurs at molecular, cellular as well as network levels in patients with Alzheimer’s disease (AD) prior to diagnosis. Most previous connectivity studies were conducted at 1 out of 3 (local, meso and global) scales in subjects covering only part of the entire AD spectrum (subjective cognitive decline, SCD; amnestic mild cognitive impairment, aMCI; and then fully manifest AD). Data interpretation within the framework of disease progression is therefore difficult. The current study included 3 age- and sex-matched cohorts: SCD (n = 32), aMCI (n = 37) and fully-established AD (n = 30). A group of healthy elderly subjects (n = 40) were included as a normal control (NC). Network connectivity was examined at the local (degree centrality), meso [subgraph centrality (SC)], and global (eigenvector and page-rank centralities) levels. As compared to NC, SCD subjects had isolated decrease of SC in primary (somatomotor and visual) networks. aMCI subjects had decreased centralities at all three scales in associative (frontoparietal control, dorsal attention, limbic and default) networks. AD subjects had increased centrality at the global scale in all seven networks. There was a positive association between Montreal Cognitive Assessment (MoCA) scores and DC in the frontoparietal control network in SCD, a negative relationship between Mini-Mental State Examination (MMSE) scores and EC in the somatomotor network in AD. These findings suggest that the primary network is impaired as early as in SCD. Impairment in the associative network also starts at the local level at this stage and may contribute to the cognitive decline. As associative network impairment extends from local to meso and global scales in aMCI, compensatory mechanisms in the primary network are activated.