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.

scholarly journals The heterogeneous functional architecture of the posteromedial cortex is associated with selective functional connectivity differences in Alzheimer’s disease

2019 ◽  
Author(s):  
Wasim Khan ◽  
Ali Amad ◽  
Vincent Giampietro ◽  
Emilio Werden ◽  
Sara De Simoni ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Disease Progression ◽  
Large Scale ◽  
Posterior Cingulate Cortex ◽  
Subjective Memory ◽  
Functional Architecture ◽  
Human Connectome Project ◽  
Data Driven Approach

AbstractThe posteromedial cortex (PMC) is a key region involved in the development and progression of Alzheimer’s disease (AD). Previous studies have demonstrated a heterogenous functional architecture of the region, with different subdivisions reflecting distinct connectivity profiles. However, little is understood about PMC functional connectivity and its differential vulnerability to AD pathogenesis. Using a data-driven approach, we applied a constrained independent component analysis (ICA) on healthy adults from the Human Connectome Project (HCP) to characterise the distinct functional subdivisions and unique functional-anatomic connectivity patterns of the PMC. These connectivity profiles were subsequently quantified in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study, to examine functional connectivity differences in (1) AD patients and cognitively normal (CN) participants and (2) the entire AD pathological spectrum, ranging from CN participants and participants with subjective memory complaints (SMC), through to those with mild cognitive impairment (MCI), and finally, patients diagnosed with AD. Our findings revealed decreased functional connectivity in the anterior precuneus, dorsal posterior cingulate cortex, and the central precuneus in AD patients compared to CN participants. Functional abnormalities in these subdivisions were also related to high amyloid burden and lower hippocampal volumes. Across the entire AD spectrum, functional connectivity of the central precuneus was associated with disease progression and specific deficits in memory and executive function. These findings provide new evidence showing that specific vulnerabilities in PMC functional connectivity are associated with large-scale network disruptions in AD and that these patterns may be useful for elucidating potential biomarkers for measuring disease progression in future work.

scholarly journals Whole brain vascular imaging in a mouse model of Alzheimer’s disease with two-photon microscopy

2018 ◽  
Vol 23 (07) ◽  
pp. 1 ◽  
Author(s):  
Patrick Delafontaine-Martel ◽  
Joel Lefebvre ◽  
Pier-Luc Tardif ◽  
Bernard I. Lévy ◽  
Philippe Pouliot ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Mouse Model ◽  
Vascular Imaging ◽  
Whole Brain ◽  
Two Photon Microscopy ◽  
Two Photon ◽  
Model Of Alzheimer’S Disease

scholarly journals Differences Changes in Cerebellar Functional Connectivity Between Mild Cognitive Impairment and Alzheimer's Disease: A Seed-Based Approach

2021 ◽  
Vol 12 ◽  
Author(s):  
Fanyu Tang ◽  
Donglin Zhu ◽  
Wenying Ma ◽  
Qun Yao ◽  
Qian Li ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Functional Connectivity ◽  
Parietal Lobe ◽  
Inferior Frontal Gyrus ◽  
Superior Frontal Gyrus ◽  
Functional Connections ◽  
The Right

Background: Recent studies have discovered that functional connections are impaired among patients with Alzheimer's disease (AD), even at the preclinical stage. The cerebellum has been implicated as playing a role in cognitive processes. However, functional connectivity (FC) among cognitive sub-regions of the cerebellum in patients with AD and mild cognitive impairment (MCI) remains to be further elucidated.Objective: Our study aims to investigate the FC changes of the cerebellum among patients with AD and MCI, compared to healthy controls (HC). Additionally, we explored the role of cerebellum FC changes in the cognitive performance of all subjects.Materials: Resting-state functional magnetic resonance imaging (rs-fMRI) data from three different groups (28 AD patients, 26 MCI patients, and 30 HC) was collected. We defined cerebellar crus II and lobule IX as seed regions to assess the intragroup differences of cortico-cerebellar connectivity. Bias correlational analysis was performed to investigate the relationship between changes in FC and neuropsychological performance.Results: Compared to HC, AD patients had decreased FC within the caudate, limbic lobe, medial frontal gyrus (MFG), middle temporal gyrus, superior frontal gyrus, parietal lobe/precuneus, inferior temporal gyrus, and posterior cingulate gyrus. Interestingly, MCI patients demonstrated increased FC within inferior parietal lobe, and MFG, while they had decreased FC in the thalamus, inferior frontal gyrus, and superior frontal gyrus. Further analysis indicated that FC changes between the left crus II and the right thalamus, as well as between left lobule IX and the right parietal lobe, were both associated with cognitive decline in AD. Disrupted FC between left crus II and right thalamus, as well as between left lobule IX and right parietal lobe, was associated with attention deficit among subjects with MCI.Conclusion: These findings indicate that cortico-cerebellar FC in MCI and AD patients was significantly disrupted with different distributions, particularly in the default mode networks (DMN) and fronto-parietal networks (FPN) region. Increased activity within the fronto-parietal areas of MCI patients indicated a possible compensatory role for the cerebellum in cognitive impairment. Therefore, alterations in the cortico-cerebellar FC represent a novel approach for early diagnosis and a potential therapeutic target for early intervention.

scholarly journals Functional connectivity of white matter as a biomarker of cognitive decline in Alzheimer’s disease

2020 ◽  
Author(s):  
Yurui Gao ◽  
Anirban Sengupta ◽  
Muwei Li ◽  
Zhongliang Zu ◽  
Baxter P. Rogers ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
White Matter ◽  
Functional Connectivity ◽  
Cognitive Decline ◽  
Supervised Machine Learning ◽  
Cognitive Measures ◽  
White Matter Tracts ◽  
Functional Changes ◽  
Dementia Patients

AbstractObjectiveIn vivo functional changes in white matter during the progression of Alzheimer’s disease (AD) have not been previously reported. Our objectives are to measure changes in white matter functional connectivity (FC) in an aging population undergoing cognitive decline as AD develops, to establish their relationship to neuropsychological scores of cognitive abilities, and to assess their performance as predictors of AD.MethodsAnalyses were conducted using resting state functional MRI (rsfMRI) and neuropsychological data from 383 ADNI participants, including 136 cognitive normal (CN) controls, 46 with significant memory concern, 83 with early mild cognitive impairment (MCI), 37 with MCI, 46 with late MCI, and 35 with AD dementia. We used novel analyses of whole brain rsfMRI data to derive FC metrics between white matter tracts and discrete cortical volumes, as well as FC metrics between different white matter tracts, and their relationship to 6 cognitive measures. We then implemented supervised machine learning on white matter FCs to classify the participants and evaluated the performance.ResultsSignificant decreases were found in white matter FCs with prominent, specific, regional deficits appearing in late MCI and AD dementia patients relative to CN. These changes significantly correlated with behavioral measurements of impairments in cognition and memory. The sensitivity and specificity for distinguishing AD dementia and CN using white matter FCs were 0.83 and 0.81 respectively.Conclusions and RelevanceThe white matter FC decreased in late MCI and AD dementia patients compared to CN participants, and the white matter FC correlates with cognitive measures. White matter FC based classification shows promise for differentiating AD patients from CN. It is suggested that white matter FC may be a novel imaging biomarker of AD progression.

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