scholarly journals Mechanisms of functional compensation, delineated by eigenvector centrality mapping, across the pathophysiological continuum of Alzheimer's disease.

2018 ◽  
Author(s):  
Stavros Skouras ◽  
Carles Falcon ◽  
Alan Tucholka ◽  
Lorena Rami ◽  
Raquel Sanchez-Valle ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Memory Performance ◽  
Disease Stage ◽  
Eigenvector Centrality ◽  
Whole Brain ◽  
Functional Compensation ◽  
Mild Dementia ◽  
Preclinical Ad

Background: Mechanisms of functional compensation throughout the progression of Alzheimer's disease (AD) remain largely underspecified. By investigating functional connectomics in relation to cerebrospinal fluid (CSF) biomarkers across the pathophysiological continuum of AD, we identify disease-stage-specific patterns of functional degradation and functional compensation. Methods: Data from a sample of 96 participants, comprised of 49 controls, 11 preclinical AD subjects, 21 patients with mild cognitive impairment (MCI) due to AD and 15 patients with mild dementia due to AD, were analyzed. CSF ratio of phosphorylated tau protein over amyloid beta peptide 42 (p-tau/Aβ42) was computed and used as a marker of progression along the AD continuum. Whole-brain, voxel-wise eigenvector centrality mapping (ECM) was computed from resting-state fMRI and regression against p-tau/Aβ42 was performed. Surviving clusters were used as data-derived seeds in functional connectivity analyses and investigated in relation to memory performance scores (delayed free recall and memory alteration) via complementary regression models. To investigate disease-stage-specific effects, the whole-brain connectivity maps of each cluster were compared between progressive groups. Results: Decreasing centrality in the inferior parietal lobule (IPL) is significantly correlated with the p-tau/Aβ42 ratio and associated to memory function impairment across the AD continuum. The thalamus, anterior cingulate (ACC), midcingulate (MCC) and posterior cingulate cortex (PCC) show the opposite effect. The MCC shows the highest increase in centrality as memory performance decays. In the asymptomatic preclinical group, MCC shows reduced functional connectivity (FC) with the left hippocampus and stronger FC with the precuneus (PCu). Additionally, IPL shows reduced FC with the cerebellum, compensated by stronger FC between cerebellum and PCC. In the MCI group, PCC shows reduced FC with PCu, compensated by stronger FC with the left pars orbitalis, insula and temporal pole, as well as by stronger FC of MCC with its anterior and ventral neighboring areas and the cerebellum. In the mild dementia group, extensive functional decoupling occurs across the entire autobiographical memory network and functional resilience ensues in posterior regions and the cerebellum. Conclusions: Functional decoupling in preclinical AD occurs predominantly in AD-vulnerable regions (e.g. hippocampus, cerebellar lobule VI / Crus I, visual cortex, frontal pole) and coupling between MCC and PCu, as well as between PCC and cerebellum, emerge as intrinsic mechanisms of functional compensation. At the MCI stage, the PCu can no longer compensate for hippocampal decoupling, but the compensatory role of the MCC and PCC ensue into the stage of dementia. These findings shed light on the neural mechanisms of functional compensation across the pathophysiological continuum of AD, highlighting the compensatory roles of several key brain areas.

scholarly journals Significant Associations of the Interplay Between Stress Related Genes With Alzheimer’s Disease

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 640-641
Author(s):  
Anatoliy Yashin ◽  
Deqing Wu ◽  
Konstantin Arbeev ◽  
Olivia Bagley ◽  
Igor Akushevich ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Stress Response ◽  
Cellular Stress ◽  
Experimental Studies ◽  
Cellular Stress Response ◽  
Disease Stage ◽  
Preclinical Ad ◽  
Stress Signals ◽  
Stress Related Genes

Abstract The lack of efficient medication against Alzheimer’s disease (AD) is the most important problem for this health disorder today. One possible reason for this -- the implementing medical interventions “too late in the disease stage” – has been recently addressed in the initiative that defined the preclinical AD stage by measuring changes in preclinical AD biomarkers. According to this definition, beta amyloid (Aβ) is one of the key preclinical AD biomarkers. Experimental studies showed that Aβ results from proteolytic cleavage of APP by β- and γ-secretases. Production of β-secretase involves BACE1 gene, activated by cellular stress response. This suggest that AD might be initiated by cellular stressors and that multifactorial regulation of AD is likely to be driven by genes involved in cellular stress response. In this paper we investigate whether interplay between SNPs from the EIF2AK4 gene involved in sensing cellular stress signals and the APP gene dealing with Aβ production may be associated with AD in human data. For this, we evaluated association of the interactions of the pairs of SNPs from these genes with AD in the analysis of HRS data. We found that interactions between several SNPs have statistically significant associations with AD. The results of this analysis confirm that the interplay between gene served as a sensor of cellular stress and gene involved in production of preclinical AD biomarker in response to stress may influence human AD. This analysis illustrates an important step towards translation of the results of experimental AD studies to human applications.

scholarly journals Amyloid-Beta Influences Memory via Functional Connectivity During Memory Retrieval in Alzheimer's Disease

2021 ◽  
Vol 13 ◽  
Author(s):  
Binyin Li ◽  
Miao Zhang ◽  
Ikbeom Jang ◽  
Guanyu Ye ◽  
Liche Zhou ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Memory Retrieval ◽  
Memory Performance ◽  
Network Connectivity ◽  
Memory Phase ◽  
Β Amyloid ◽  
The Relationship ◽  
Retrieval Phase

Objective: Amnesia in Alzheimer's disease (AD) appears early and could be caused by encoding deficiency, consolidation dysfunction, and/or impairment in the retrieval of stored memory information. The relationship between AD pathology biomarker β-amyloid and memory dysfunction is unclear.Method: The memory task functional MRI and amyloid PET were simultaneously performed to investigate the relationship between memory performance, memory phase-related functional connectivity, and cortical β-amyloid deposition. We clustered functional networks during memory maintenance and compared network connectivity between groups in each memory phase. Mediation analysis was performed to investigate the mediator between β-amyloid and related cognitive performance.Results: Alzheimer's disease was primarily characterized by decreased functional connectivity in a data-driven network composed of an a priori default mode network, limbic network, and frontoparietal network during the memory maintenance (0.205 vs. 0.236, p = 0.04) and retrieval phase (0.159 vs. 0.183, p = 0.017). Within the network, AD had more regions with reduced connectivity during the retrieval than the maintenance and encoding phases (chi-square p = 0.01 and < 0.001). Furthermore, the global cortical β-amyloid negatively correlated with network connectivity during the memory retrieval phase (R = – 0.247, p = 0.032), with this relationship mediating the effect of cortical β-amyloid on memory performance (average causal mediation effect = – 0.05, p = 0.035).Conclusion: We demonstrated that AD had decreased connectivity in specific networks during the memory retrieval phase. Impaired functional connectivity during memory retrieval mediated the adverse effect of β-amyloid on memory. These findings help to elucidate the involvement of cortical β-amyloid (Aβ) in the memory performance in the early stages of AD.

Mapping of impaired functional connectivity during memory phases in Alzheimer's disease and its association with cortical β-amyloid

2020 ◽  
Author(s):  
Binyin Li ◽  
Miao Zhang ◽  
Guanyu Ye ◽  
Liche Zhou ◽  
Guiying He ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Memory Performance ◽  
Network Connectivity ◽  
Posterior Cingulate Cortex ◽  
Frontoparietal Network ◽  
Β Amyloid ◽  
The Relationship ◽  
Retrieval Phase

Abstract Background: Amnesia in Alzheimer's disease (AD) could be due to disrupted encoding, consolidation dysfunction, or an impairment in the retrieval of stored memory information. The different memory phases relate with different parts of functional brain systems. Methods: We combine task functional magnetic resonance imaging and amyloid positron emission tomography in 72 participants (36 AD and 36 controls), to investigate the relationship between memory performance, memory phase-locked functional connectivity, and cortical β-amyloid deposition.Results: We found that AD was mainly characterized by decreased functional connectivity in a new data-driven Network composed of regions from default mode network, limbic network and frontoparietal network during the memory maintenance and retrieval phase. Within the Network, AD had more regions with reduced connectivity during the retrieval phase than other phases, locating mainly in the medial prefrontal cortex, posterior cingulate cortex, middle temporal and inferior parietal cortex of left hemisphere. Furthermore, functional connectivity in the Network related to memory performance. Crucially, the magnitude of the Network connectivity reduction during retrieval negatively correlated with mean cortical β-amyloid, and this relationship mediated the relationship between cortical β-amyloid and memory performance.Conclusions: Our findings show that memory deficiency in AD relates with decreased connectivity in specific network and cortical β-amyloid only during retrieval phase. These findings help to map impaired functional connectivity during memory phases and explain the relationship between memory deficiency and cortical β-amyloid.

scholarly journals Mechanisms of functional compensation, delineated by eigenvector centrality mapping, across the pathophysiological continuum of Alzheimer’s disease

2019 ◽  
Vol 22 ◽  
pp. 101777 ◽  
Author(s):  
Stavros Skouras ◽  
Carles Falcon ◽  
Alan Tucholka ◽  
Lorena Rami ◽  
Raquel Sanchez-Valle ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Eigenvector Centrality ◽  
Functional Compensation

scholarly journals A Whole-Brain Computational Modeling Approach to Explain the Alterations in Resting-State Functional Connectivity during Progression of Alzheimer’s Disease

2016 ◽  
Author(s):  
Murat Demirtaş ◽  
Carles Falcon ◽  
Alan Tucholka ◽  
Juan Domingo Gispert ◽  
José Luis Molinuevo ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Computational Modeling ◽  
Resting State ◽  
Functional Neuroimaging ◽  
Brain Regions ◽  
Resting State Functional Connectivity ◽  
Whole Brain ◽  
Modeling Approach

AbstractUnderstanding the mechanisms behind Alzheimer’s disease (AD) is one of the most challenging problems in neuroscience. Recent efforts provided valuable insights on the genetic, biochemical and neuronal correlates of AD. The advances in structural and functional neuroimaging provided massive evidence for the AD related alterations in brain connectivity. In this study, we investigated the whole-brain resting state functional connectivity (FC) and variability in dynamic functional connectivity (v-FC) of the subjects with preclinical condition (PC), mild cognitive impairment (MCI) and Alzheimer’s disease (AD). The synchronization in the whole-brain was monotonously decreasing during the course of the progression. However, only in the AD group the reduced synchronization produced significant widespread effects in FC. Furthermore, we found elevated variability of FC in PC group, which was reversed in AD group. We proposed a whole-brain computational modeling approach to study the mechanisms behind these alterations. We estimated the effective connectivity (EC) between brain regions in the model to reproduce observed FC of each subject. First, we compared ECs between groups to identify the changes in underlying connectivity structure. We found that the significant EC changes were restricted to temporal lobe. Then, based on healthy control subjects we systematically manipulated the dynamics in the model to investigate its effect on FC. The model showed FC alterations similar to those observed in clinical groups providing a mechanistic explanation to AD progression.

scholarly journals A Whole-Brain Functional Connectivity Model of Alzheimer’s Disease Pathology

2021 ◽  
Author(s):  
Ruchika S. Prakash ◽  
Michael R. McKenna ◽  
Oyetunde Gbadeyan ◽  
Anita R. Shankar ◽  
Rebecca Andridge ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Predictive Modeling ◽  
Large Scale ◽  
Learning Algorithm ◽  
Supervised Machine Learning ◽  
Whole Brain ◽  
Functional Connections ◽  
Model Of Alzheimer’S Disease

AbstractEarly detection of Alzheimer’s disease (AD) is a necessity as prognosis is poor upon symptom onset. Although previous work diagnosing AD from protein-based biomarkers has been encouraging, cerebrospinal (CSF) biomarker measurement of AD proteins requires invasive lumbar puncture, whereas assessment of direct accumulation requires radioactive substance exposure in positron emission tomography (PET) imaging. Functional magnetic resonance imaging (fMRI)-based neuromarkers, offers an alternative, especially those built by capitalizing on variance distributed across the entire human connectome. In this study, we employed connectome-based predictive modeling (CPM) to build a model of functional connections that would predict CSF p-tau/Aβ42 (PATH-fc model) in individuals diagnosed with Mild Cognitive Impairment (MCI) and AD dementia. fMRI, CSF-based biomarker data, and longitudinal data from neuropsychological testing from the Alzheimer’s Disease NeuroImaging Initiative (ADNI) were utilized to build the PATH-fc model. Our results provide support for successful in-sample fit of the PATH-fc model in predicting AD pathology in MCI and AD dementia individuals. The PATH-fc model, distributed across all ten canonical networks, additionally predicted cognitive decline on composite measures of global cognition and executive functioning. Our highly distributed pathology-based model of functional connectivity disruptions had a striking overlap with the spatial affinities of amyloid and tau pathology, and included the default mode network as the hub of such network-based disruptions in AD. Future work validating this model in other external datasets, and to midlife adults and older adults with no known diagnosis, will critically extend this neuromarker development work using fMRI.Significance StatementAlzheimer’s disease (AD) is clinical-pathological syndrome with multi-domain amnestic symptoms considered the hallmark feature of the disease. However, accumulating evidence from autopsy studies evince support for the onset of pathophysiological processes well before the onset of symptoms. Although CSF- and PET-based biomarkers provide indirect and direct estimates of AD pathology, both methodologies are invasive. In here, we implemented a supervised machine learning algorithm – connectome-based predictive modeling – on fMRI data and found support for a whole-brain model of functional connectivity to predict AD pathology and decline in cognitive functioning over a two-year period. Our study provides support for AD pathology dependent functional connectivity disturbances in large-scale functional networks to influence the trajectory of key cognitive domains in MCI and AD patients.

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