Networks In Alzheimer's Disease Research Articles

Age-associated proteins explain the role of medial temporal lobe networks in Alzheimer's disease.

The structural connectivity (SC) of the medial temporal lobe and its associated cortical anterior temporal and posterior medial networks (MTL-AT-PM) is linked to pathologies and memory decline in Alzheimer's disease (AD). However, neuroimaging analyses cannot tell us how SC changes occur in AD at the molecular level and do not provide a means of intervening to slow/prevent pathology-related changes in MTL-AT-PM SC. The current study aimed to understand how and where AD-related changes occur within MTL-AT-PM using proteomics. We used a 4-step approach in 101 older adults froma local sample, aiming to understand how proteins and SC in combination at the multivariate level predict AD pathology, and to identify specific proteins related to SC and AD pathology. Separately, we validated the discovered proteins in relation to SC and AD pathology using ADNI sample. We identified 12 latent factors linking proteins and SC; five showed significant relationships with AD pathology and/or episodic memory. Insulin-like growth factor binding proteins and tumor necrosis factor receptors, and hippocampal/parahippocampal edges contributed most to AD-related latent factors. Fast causal inference found protein-protein, protein-SC, and protein-pathology pathways, with seven proteins showing directional links to SC and AD-related neurodegeneration. We validated these results by identifying significant relationships between six available proteins with SC and amyloid-beta and phosphorylated tau in ADNI. We identified multivariate relationships between proteins and MTL-AT-PM networks that add to our understanding of AD pathology and suggest specific non-pathological proteins that warrant further study in relation to brain networks and AD pathology as possible therapeutic targets.

Whole Transcriptome Sequencing of Peripheral Blood Identifies the Alzheimer's Disease-Related circRNA-miRNA-lncRNA Pathway.

Previous studies on transcriptional profiles suggested dysregulation of multiple RNA species in Alzheimer's disease. However, despite recent investigations revealing various aspects of circular RNA (circRNA)-associated competing endogenous RNA (ceRNA) networks in Alzheimer's Disease (AD) pathogenesis, few genome-wide studies have explored circRNA-associated profiles in AD patients exhibiting varying degrees of cognitive loss. To investigate the potential pathogenesis-related molecular biological changes in the various stages of AD progression. Whole transcriptome sequencing was performed on the peripheral blood of 7 normal cognition (NC) subjects, 8 patients with mild cognitive impairment, 8 AD patients with mild dementia (miD), and 7 AD patients with moderate dementia (moD). Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted to predict the potential functions of the maternal genes of microRNAs (miRNAs), circRNAs and long non-coding RNAs (lncRNAs). The construction of ceRNA network was performed between the NC group and each diseased group based on the differently expressed RNAs. In total, 3568 messenger RNAs (mRNAs), 142 miRNAs, 990 lncRNAs, and 183 circRNAs were identified as significantly differentially expressed across the four groups. GO and KEGG enrichment analysis revealed the significant roles of GTPase activity and the MAPK signaling pathway in AD pathogenesis. A circRNA-miRNA-lncRNA ceRNA pathway, characterized by the downregulated hsa-miR-7-5p and upregulated hsa_circ_0001170, was identified based on the differentially expressed RNAs between the NC group and the moD group. The study suggests that circRNAs may be independent of mRNAs in AD pathogenesis and holds promise as potential biomarkers for AD clinical manifestations and pathological changes.

Aberrant morphometric networks in Alzheimer's disease have hemispheric asymmetry and age dependence.

Alzheimer's disease (AD) is associated with abnormal accumulations of hyperphosphorylated tau and amyloid-β proteins, resulting in unique patterns of atrophy in the brain. We aimed to elucidate some characteristics of the AD's morphometric networks constructed by associating different morphometric features among brain areas and evaluating their relationship to Mini-Mental State Examination total score and age. Three-dimensional T1-weighted (3DT1) image data scanned by the same 1.5T magnetic resonance imaging (MRI) were obtained from 62 AD patients and 41 healthy controls (HCs) and were analysed by using FreeSurfer. The associations of the extracted six morphometric features between regions were estimated by correlation coefficients. The global and local graph theoretical measures for this network were evaluated. Associations between graph theoretical measures and age, sex and cognition were evaluated by multiple regression analysis in each group. Global measures of integration: global efficiency and mean information centrality were significantly higher in AD patients. Local measures of integration: node global efficiency and information centrality were significantly higher in the entorhinal cortex, fusiform gyrus and posterior cingulate cortex of AD patients but only in the left hemisphere. All global measures were correlated with age in AD patients but not in HCs. The information centrality was associated with age in AD's broad brain regions. Our results showed that altered morphometric networks due to AD are left-hemisphere dominant, suggesting that AD pathogenesis has a left-right asymmetry. Ageing has a unique impact on the morphometric networks in AD patients. The information centrality is a sensitive graph theoretical measure to detect this association.

Genetic basis of anatomical asymmetry and aberrant dynamic functional networks in Alzheimer's disease.

Genetic associations with macroscopic brain networks can provide insights into healthy and aberrant cortical connectivity in disease. However, associations specific to dynamic functional connectivity in Alzheimer's disease are still largely unexplored. Understanding the association between gene expression in the brain and functional networks may provide useful information about the molecular processes underlying variations in impaired brain function. Given the potential of dynamic functional connectivity to uncover brain states associated with Alzheimer's disease, it is interesting to ask: How does gene expression associated with Alzheimer's disease map onto the dynamic functional brain connectivity? If genetic variants associated with neurodegenerative processes involved in Alzheimer's disease are to be correlated with brain function, it is essential to generate such a map. Here, we investigate how the relation between gene expression in the brain and dynamic functional connectivity arises from nodal interactions, quantified by their role in network centrality (i.e. the drivers of the metastability), and the principal component of genetic co-expression across the brain. Our analyses include genetic variations associated with Alzheimer's disease and also genetic variants expressed within the cholinergic brain pathways. Our findings show that contrasts in metastability of functional networks between Alzheimer's and healthy individuals can in part be explained by the two combinations of genetic co-variations in the brain with the confidence interval between 72% and 92%. The highly central nodes, driving the brain aberrant metastable dynamics in Alzheimer's disease, highly correlate with the magnitude of variations from two combinations of genes expressed in the brain. These nodes include mainly the white matter, parietal and occipital brain regions, each of which (or their combinations) are involved in impaired cognitive function in Alzheimer's disease. In addition, our results provide evidence of the role of genetic associations across brain regions in asymmetric changes in ageing. We validated our findings on the same cohort using alternative brain parcellation methods. This work demonstrates how genetic variations underpin aberrant dynamic functional connectivity in Alzheimer's disease.

Alterations via inter-regional connective relationships in Alzheimer's disease.

Disruptions in the inter-regional connective correlation within the brain are believed to contribute to memory impairment. To detect these corresponding correlation networks in Alzheimer's disease (AD), we conducted three types of inter-regional correlation analysis, including structural covariance, functional connectivity and group-level independent component analysis (group-ICA). The analyzed data were obtained from the Alzheimer's Disease Neuroimaging Initiative, comprising 52 cognitively normal (CN) participants without subjective memory concerns, 52 individuals with late mild cognitive impairment (LMCI) and 52 patients with AD. We firstly performed vertex-wise cortical thickness analysis to identify brain regions with cortical thinning in AD and LMCI patients using structural MRI data. These regions served as seeds to construct both structural covariance networks and functional connectivity networks for each subject. Additionally, group-ICA was performed on the functional data to identify intrinsic brain networks at the cohort level. Through a comparison of the structural covariance and functional connectivity networks with ICA networks, we identified several inter-regional correlation networks that consistently exhibited abnormal connectivity patterns among AD and LMCI patients. Our findings suggest that reduced inter-regional connectivity is predominantly observed within a subnetwork of the default mode network, which includes the posterior cingulate and precuneus regions, in both AD and LMCI patients. This disruption of connectivity between key nodes within the default mode network provides evidence supporting the hypothesis that impairments in brain networks may contribute to memory deficits in AD and LMCI.

A meta-analysis of microarray datasets to identify biological regulatory networks in Alzheimer's disease.

Background: Alzheimer's Disease (AD) is an age-related progressive neurodegenerative disorder characterized by mental deterioration, memory deficit, and multiple cognitive abnormalities, with an overall prevalence of ∼2% among industrialized countries. Although a proper diagnosis is not yet available, identification of miRNAs and mRNAs could offer valuable insights into the molecular pathways underlying AD's prognosis. Method: This study aims to utilize microarray bioinformatic analysis to identify potential biomarkers of AD, by analyzing six microarray datasets (GSE4757, GSE5281, GSE16759, GSE28146, GSE12685, and GSE1297) of AD patients, and control groups. Furthermore, this study conducted gene ontology, pathways analysis, and protein-protein interaction network to reveal major pathways linked to probable biological events. The datasets were meta-analyzed using bioinformatics tools, to identify significant differentially expressed genes (DEGs) and hub genes and their targeted miRNAs'. Results: According to the findings, CXCR4, TGFB1, ITGB1, MYH11, and SELE genes were identified as hub genes in this study. The analysis of DEGs using GO (gene ontology) revealed that these genes were significantly enriched in actin cytoskeleton regulation, ECM-receptor interaction, and hypertrophic cardiomyopathy. Eventually, hsa-mir-122-5p, hsa-mir-106a-5p, hsa-mir-27a-3p, hsa-mir16-5p, hsa-mir-145-5p, hsa-mir-12-5p, hsa-mir-128-3p, hsa-mir 3200-3p, hsa-mir-103a-3p, and hsa-mir-9-3p exhibited significant interactions with most of the hub genes. Conclusion: Overall, these genes can be considered as pivotal biomarkers for diagnosing the pathogenesis and molecular functions of AD.

Dynamic functional brain networks in Alzheimer’s disease and healthy ageing

AbstractBackgroundBrain network analysis from resting‐state functional MRI (fMRI) has helped us to understand cortical activity in dementia. Likewise, dynamic brain networks' analysis, which takes into account temporal fluctuations in the resting‐state fMRI signal, has helped to reveal patterns of activity, usually averaged out by the conventional functional network analysis. These patterns of activity, or dynamic networks, reveal transient (meta‐stable) dynamics. Dynamic functional networks analysis from fMRI data has shown a potential to unveil clinically relevant information.MethodResting‐state fMRI from 187 participants belonging to Alzheimer’s Disease (AD) and cognitively‐normal Healthy Elderly (HE) from the ADNI‐3 database were analysed. Functional network was constructed from fMRI signal of the 120 cortical and sub‐cortical regions of the Jülich Atlas for each participants, and averaged across the two study groups. Dynamic functional connectivity is calculated using sliding window approach and compared to conventional (‘static’) functional connectivity within each study group and between them.ResultSignificant differences when comparing AD with HE individuals were found in the dynamic, but not static functional networks. These differences were restricted to the white matter regions, and to the inferior and superior parietal, and somatosensory cortices.ConclusionOur results demonstrate the existence of common and distinct patterns of static and dynamic brain connectivity in HE and AD, which highlight the importance of including non‐stationary information into the brain network analysis in Alzheimer’s disease.

EEG resting-state networks in Alzheimer’s disease associated with clinical symptoms

Alzheimer’s disease (AD) is a progressive neuropsychiatric disease affecting many elderly people and is characterized by progressive cognitive impairment of memory, visuospatial, and executive functions. As the elderly population is growing, the number of AD patients is increasing considerably. There is currently growing interest in determining AD’s cognitive dysfunction markers. We used exact low-resolution-brain-electromagnetic-tomography independent-component-analysis (eLORETA-ICA) to assess activities of five electroencephalography resting-state-networks (EEG-RSNs) in 90 drug-free AD patients and 11 drug-free patients with mild-cognitive-impairment due to AD (ADMCI). Compared to 147 healthy subjects, the AD/ADMCI patients showed significantly decreased activities in the memory network and occipital alpha activity, where the age difference between the AD/ADMCI and healthy groups was corrected by linear regression analysis. Furthermore, the age-corrected EEG-RSN activities showed correlations with cognitive function test scores in AD/ADMCI. In particular, decreased memory network activity showed correlations with worse total cognitive scores for both Mini-Mental-State-Examination (MMSE) and Alzheimer’s Disease-Assessment-Scale-cognitive-component-Japanese version (ADAS-J cog) including worse sub-scores for orientation, registration, repetition, word recognition and ideational praxis. Our results indicate that AD affects specific EEG-RSNs and deteriorated network activity causes symptoms. Overall, eLORETA-ICA is a useful, non-invasive tool for assessing EEG-functional-network activities and provides better understanding of the neurophysiological mechanisms underlying the disease.

Single-cell sequencing of entorhinal cortex reveals widespread disruption of neuropeptide networks in Alzheimer's disease.

Abnormalities of neuropeptides (NPs) that play important roles in modulating neuronal activities are commonly observed in Alzheimer's disease (AD). We hypothesize that NP network disruption is widespread in AD brains. Single-cell transcriptomic data from the entorhinal cortex (EC) were used to investigate the NP network disruption in AD. Bulk RNA-sequencing data generated from the temporal cortex by independent groups and machine learning were employed to identify key NPs involved in AD. The relationship between aging and AD-associated NP (ADNP) expression was studied using GTEx data. The proportion of cells expressing NPs but not their receptors decreased significantly in AD. Neurons expressing higher level and greater diversity of NPs were disproportionately absent in AD. Increased age coincides with decreased ADNP expression in the hippocampus. NP network disruption is widespread in AD EC. Neurons expressing more NPs may be selectively vulnerable to AD. Decreased expression of NPs participates in early AD pathogenesis. We predict that the NP network can be harnessed for treatment and/or early diagnosis of AD.

Genome-wide methylomic regulation of multiscale gene networks in Alzheimer's disease.

Recent studies revealed the association of abnormal methylomic changes with Alzheimer's disease (AD) but there is a lack of systematic study of the impact of methylomic alterations over the molecular networks underlying AD. We profiled genome-wide methylomic variations in the parahippocampal gyrus from 201 post mortem control, mild cognitive impaired, and AD brains. We identified 270 distinct differentially methylated regions (DMRs) associated with AD. We quantified the impact of these DMRs on each gene and each protein as well as gene and protein co-expression networks. DNA methylation had aprofound impact on both AD-associated gene/protein modules and their key regulators. We further integrated the matched multi-omics data to show the impact of DNA methylation on chromatin accessibility, which further modulates gene and protein expression. The quantified impact of DNA methylation on gene and protein networks underlying AD identified potential upstream epigenetic regulators of AD. A cohort of DNA methylation data in the parahippocampal gyrus was developed from 201 post mortem control, mild cognitive impaired, and Alzheimer's disease (AD) brains. Two hundred seventy distinct differentially methylated regions (DMRs) were found to be associated with AD compared to normal control. A metric was developed to quantify methylation impact on each gene and each protein. DNA methylation was found to have aprofound impact on not only the AD-associated gene modules but also key regulators of the gene and protein networks. Key findings were validated in an independent multi-omics cohort in AD. The impact of DNA methylation on chromatin accessibility was also investigated by integrating the matched methylomic, epigenomic, transcriptomic, and proteomic data.

Effective Connectivity Evaluation of Resting-State Brain Networks in Alzheimer's Disease, Amnestic Mild Cognitive Impairment, and Normal Aging: An Exploratory Study.

(1) Background: Alzheimer's disease (AD) is a neurodegenerative disease with a high prevalence. Despite the cognitive tests to diagnose AD, there are pitfalls in early diagnosis. Brain deposition of pathological markers of AD can affect the direction and intensity of the signaling. The study of effective connectivity allows the evaluation of intensity flow and signaling pathways in functional regions, even in the early stage, known as amnestic mild cognitive impairment (aMCI). (2) Methods: 16 aMCI, 13 AD, and 14 normal subjects were scanned using resting-state fMRI and T1-weighted protocols. After data pre-processing, the signal of the predefined nodes was extracted, and spectral dynamic causal modeling analysis (spDCM) was constructed. Afterward, the mean and standard deviation of the Jacobin matrix of each subject describing effective connectivity was calculated and compared. (3) Results: The maps of effective connectivity in the brain networks of the three groups were different, and the direction and strength of the causal effect with the progression of the disease showed substantial changes. (4) Conclusions: Impaired information flow in the resting-state networks of the aMCI and AD groups was found versus normal groups. Effective connectivity can serve as a potential marker of Alzheimer's pathophysiology, even in the early stages of the disease.

Construction of a New Protein–Protein Interaction and Molecular Biomarkers Networks in Alzheimer’s Disease Patients by Bioinformatics Screening

This study aimed to construct the molecular biomarkers of autophagy and endoplasmic reticulum stress (ERS), as well as their corresponding protein interaction network in Alzheimer’s disease (AD) patients with different levels of physical activity (PA) by bioinformatics methods. The expression profiles of the genes were downloaded from Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) between AD samples with low, moderate and high levels of PA were studied. The autophagy and ERS-related genes (AERSRGs) were extracted from GeneCards and MsigDB databases. The functional enrichment analysis was conducted to determine the function of DEGs. To explore the proteins, miRNAs and transcription factors (TF) interacting with DEGs, the protein–protein network, mRNA-miRNA network and mRNA-TF network were built using Cytoscape software. Then the receiver operating characteristic (ROC) analysis were conducted to verify the diagnostic performance of hub genes. A total of 533 AERSRGs were identified in Group H and 150 AERSRGs were screened in Group M. Functional enrichment analysis suggested genes of AD play vital roles in some biological process (e.g., cell cycle phase transition, mitochondrion organization, proteasomal protein catabolic process). KEGG enrichment analysis suggested that sarcopenia involves the pathways (e.g., GABA, P2Y receptors, serotonin release cycle). A total of 5 hub genes were screened in Group H and 9 were identified in Group M. ROC analysis suggested that several hub genes exhibited a relatively high sensitivity and specificity in both groups of AD. The hub genes screened in this study are closely correlated with autophagy and ERS in AD and can differentiate AD with different levels of PA. SRC, MAPK3 and MAP2K1 exhibit relatively high sensitivity and specificity in diagnosis in Group H; MCM2, CDC42, HNRNPM, ASF1A, NCBP2, SNRNP70 and MCM6 exhibit relatively high sensitivity and specificity in diagnosis in Group M.

UNRAVELING THE AMBIGUITY BETWEEN COGNITION AND DEPRESSION: A LONGITUDINAL ANALYSIS OF OLDER ADULTS

Abstract Research on directionality of the relationship between cognitive functioning and depression is ambiguous, especially when considering implications for patients with mild cognitive impairment or Alzheimer disease (AD). Previous research suggests that depression in late life could be a pre-clinical manifestation of AD before other cognitive symptoms are detectable. Some research supports the hypothesis that level of depression can independently predict level of cognition. Other research suggests that depression is a risk factor for developing dementia or AD late in life. Further research on the impact of subjective cognitive decline versus objective cognitive performance in association with depressive symptoms is a critical area to explore. Using data from the Social Networks in Alzheimer Disease (SNAD) study and the Indiana Alzheimer Disease Research Center (IADRC), we conducted a preliminary longitudinal analysis of 196 focal subjects (Mage = 71.6 years, Pfemale = 63%). Interviews conducted one year apart were leveraged to elucidate the bidirectional relationship between depressive symptomology and cognition. Using a lagged dependent variable approach controlling for age, sex, race, and education, the results indicate that only executive function predicts depression at timepoint 2 (-0.31 SD, p< 0.001). However, depression predicts focal Cognitive Change Index (CCI) (0.06 SD, p< 0.05), processing speed (-0.04 SD, p< 0.05), and episodic memory (-0.04 SD, p< 0.05). Other cognitive domains examined, including attention, language, visual/spatial skills, MoCA score, and informant-rated CCI, were not significant as predictors or as outcomes. These results suggest that depression may be a more robust predictor of cognition than cognition is of depression.

Disrupted topological organization of functional brain networks in Alzheimer’s disease patients with depressive symptoms

BackgroundDepression is a common symptom of Alzheimer’s disease (AD), but the underlying neural mechanism is unknown. The aim of this study was to explore the topological properties of AD patients with depressive symptoms (D-AD) using graph theoretical analysis.MethodsWe obtained 3-Tesla rsfMRI data from 24 D-AD patients, 20 non-depressed AD patients (nD-AD), and 20 normal controls (NC). Resting state networks were identified using graph theory analysis. ANOVA with a two-sample t-test post hoc analysis in GRETNA was used to assess the topological measurements.ResultsOur results demonstrate that the three groups show characteristic properties of a small-world network. NCs showed significantly larger global and local efficiency than D-AD and nD-AD patients. Compared with nD-AD patients, D-AD patients showed decreased nodal centrality in the pallidum, putamen, and right superior temporal gyrus. They also showed increased nodal centrality in the right superior parietal gyrus, the medial orbital portion of the right superior frontal gyrus, and the orbital portion of the right superior frontal gyrus. Compared with nD-AD patients, NC showed decreased nodal betweenness in the right superior temporal gyrus, and increased nodal betweenness in medial orbital part of the right superior frontal gyrus.ConclusionsThese results indicate that D-AD is associated with alterations of topological structure. Our study provides new insights into the brain mechanisms underlying D-AD.

Characterizing Disrupted Cellular Crosstalk Signaling Networks in Alzheimer’s Disease Using Single‐Nuclei Transcriptomics

AbstractBackgroundGenetics and molecular studies have implicated multiple genes, pathways, and cell types in Alzheimer’s disease (AD) risk and progression. How these genes and pathways perturb and are modulated by brain cellular crosstalk networks have not been deeply characterized.MethodWe generated single‐nuclei transcriptomic profiles (snRNA‐seq) of parietal lobes from 67 donors from the Knight ADRC and DIAN brain banks, representing neuropathological‐free controls and AD cases from pre‐symptomatic and AD[1]. We estimated cellular crosstalk patterns among the brain cell types based on the expression of known ligand‐receptor pairs[2] and reconstructed the co‐expression network upstream and downstream of these ligand‐receptor pairs to determine how these signals are propagated across cells[3].ResultWe analyzed ∼294K high‐quality nuclei and identified six major cell populations[1]. We observed changes in cellular crosstalk patterns between controls and AD, with the largest involving microglial interactions (increased in AD, OR = 1.31, p = 8.94e‐11). Cellular interactions directly involving AD‐related genes[4] as either the receptor or the ligand were enriched for neuron‐microglia pairs (OR = 2.74, p = 4.41e‐15), and the majority (64.9%) codified for microglial cell membrane receptors, supporting the role for these cells in AD. We observed an increase in the frequency of a subset of interactions involving AD‐related genes in microglia when comparing pre‐symptomatic and AD individuals, suggesting correlation with pathological burden. These included TREM2‐semaphorin (neuron‐microglia, 4.38‐fold increase). We performed a comprehensive study of the microglial co‐expression networks up/downstream of these interactions and provided additional evidence of their association with Braak stage. The TREM2‐semaphorin neuron‐microglia interaction is predicted to directly propagate signals into a sub‐network associated with microglia activation, involving four genes from the HLA family previously implicated in AD. Furthermore, the neuronal co‐expression network had significant enrichment for AD‐related genes including CELF2 (OR = 2.10, p = 5.32e‐3), consistent with AD‐related genes in neurons propagating signals into microglia. We replicated these results in public snRNA‐seq studies and are currently validating these findings experimentally.ConclusionThis study reveals the role of cellular crosstalk in AD biology and identifies disruptions in neuron‐microglia interactions as an important component of AD pathology. In addition, we show that cellular crosstalk networks can directly modulate genes previously associated with AD (Figure 1).

Neuropsychiatric symptoms associated multimodal brain networks in Alzheimer's disease.

Concomitant neuropsychiatric symptoms (NPS) are associated with accelerated Alzheimer's disease (AD) progression. Identifying multimodal brain imaging patterns associated with NPS may help understand pathophysiology correlates AD. Based on the AD continuum, a supervised learning strategy was used to guide four-way multimodal neuroimaging fusion (Amyloid, Tau, gray matter volume, brain function) by using NPS total score as the reference. Loadings of the identified multimodal patterns were compared across the AD continuum. Then, regression analyses were performed to investigate its predictability of longitudinal cognition performance. Furthermore, the fusion analysis was repeated in the four NPS subsyndromes. Here, an NPS-associated pathological-structural-functional covaried pattern was observed in the frontal-subcortical limbic circuit, occipital, and sensor-motor region. Loading of this multimodal pattern showed a progressive increase with the development of AD. The pattern significantly correlates with multiple cognitive domains and could also predict longitudinal cognitive decline. Notably, repeated fusion analysis using subsyndromes as references identified similar patterns with some unique variations associated with different syndromes. Conclusively, NPS was associated with a multimodal imaging pattern involving complex neuropathologies, which could effectively predict longitudinal cognitive decline. These results highlight the possible neural substrate of NPS in AD, which may provide guidance for clinical management.

Aberrant protein networks in Alzheimer disease.

Aberrant protein networks in Alzheimer disease.

Sensitive and reproducible MEG resting-state metrics of functional connectivity in Alzheimer\u2019s disease

BackgroundAnalysis of functional brain networks in Alzheimer’s disease (AD) has been hampered by a lack of reproducible, yet valid metrics of functional connectivity (FC). This study aimed to assess both the sensitivity and reproducibility of the corrected amplitude envelope correlation (AEC-c) and phase lag index (PLI), two metrics of FC that are insensitive to the effects of volume conduction and field spread, in two separate cohorts of patients with dementia due to AD versus healthy elderly controls.MethodsSubjects with a clinical diagnosis of AD dementia with biomarker proof, and a control group of subjective cognitive decline (SCD), underwent two 5-min resting-state MEG recordings. Data consisted of a test (AD = 28; SCD = 29) and validation (AD = 29; SCD = 27) cohort. Time-series were estimated for 90 regions of interest (ROIs) in the automated anatomical labelling (AAL) atlas. For each of five canonical frequency bands, the AEC-c and PLI were calculated between all 90 ROIs, and connections were averaged per ROI. General linear models were constructed to compare the global FC differences between the groups, assess the reproducibility, and evaluate the effects of age and relative power. Reproducibility of the regional FC differences was assessed using the Mann-Whitney U tests, with correction for multiple testing using the false discovery rate (FDR).ResultsThe AEC-c showed significantly and reproducibly lower global FC for the AD group compared to SCD, in the alpha (8–13 Hz) and beta (13–30 Hz) bands, while the PLI revealed reproducibly lower FC for the AD group in the delta (0.5–4 Hz) band and higher FC for the theta (4–8 Hz) band. Regionally, the beta band AEC-c showed reproducibility for almost all ROIs (except for 13 ROIs in the frontal and temporal lobes). For the other bands, the AEC-c and PLI did not show regional reproducibility after FDR correction. The theta band PLI was susceptible to the effect of relative power.ConclusionFor MEG, the AEC-c is a sensitive and reproducible metric, able to distinguish FC differences between patients with AD dementia and cognitively healthy controls. These two measures likely reflect different aspects of neural activity and show differential sensitivity to changes in neural dynamics.

Cognitively stimulating environments and cognitive reserve: the case of personal social networks

Cognitively stimulating environments are thought to be protective of cognitive decline and onset of Alzheimer's disease and related dementias (ADRD) through the development of cognitive reserve (CR). CR refers to cognitive adaptability that buffers the impact of brain pathology on cognitive function. Despite the critical need to identify cognitively stimulating environments to build CR, there is no consensus regarding which environmental determinants are most effective. Rather, most studies use education as proxies for CR and little is known about the association between older adults’ personal social networks and CR. Using neuroimaging data from 135 older adults participating in the Social Networks in Alzheimer Disease (SNAD) study, this article adopted a residual method for measuring CR and found that large network size, high network diversity, and loosely connected networks were positively associated with greater CR. These results suggest that expansive social networks in later life may constitute cognitively stimulating environments which can be leveraged to build CR and reduce the burden of ADRD.

Cognitive impairment networks in Alzheimer's disease: Analysis of three double-blind randomized, placebo-controlled, clinical trials of donepezil

Psychometric network analysis is an alternative theoretically-driven analytic approach that has the potential to conceptualize cognitive impairment in Alzheimer's disease differently than was previously assumed and consequently detect unknown treatment effects. Based on individual participant data, extracted from three double-blind, randomized placebo-controlled clinical trials, psychometric networks were computed on observed Alzheimer's Disease Assessment Scale Cognitive Subscale scores at baseline (N=1,554) and on predicted change scores at 24 weeks of follow-up for participants who received donepezil (N=797) or placebo (N=484). A novel conceptualization of cognitive impairment in Alzheimer's disease was displayed through the baseline network, that had 90% (n=27) positive statistically significant (p<0.05) associations, and a most central aspect of ideational praxis. Following 24 weeks, treatment effects emerged via the differences between the change score networks. The donepezil network had more statistically significant (p<0.05) positive associations and a higher global strength (n=15; S=1.22; p=0.03), than the placebo network (n=8; S=0.57). This suggests that for those who were treated with donepezil compared with placebo, cognition is a more unified construct. The main aspects of change in cognitive impairment were comprehension of spoken language for the donepezil network and spoken language ability for the placebo network. Comprehension of spoken language apears to be most sensitive to psychopharmaceutical interventions and should therefore be closely monitored. Overall, our psychometric network analysis presents a new conceptualization of cognitive impairment in Alzheimer's disease, points to previously unknown treatment effects and highlights well-defined aspects of cognitive impairment that may translate into future treatment targets.