scholarly journals The Impact of Alzheimer’s Disease on the Resting State Functional Connectivity of Brain Regions Modulating Pain: A Cross Sectional Study

2017 ◽  
Vol 57 (1) ◽  
pp. 71-83 ◽  
Author(s):  
Todd B. Monroe ◽  
Paul A. Beach ◽  
Stephen P. Bruehl ◽  
Mary S. Dietrich ◽  
Baxter P. Rogers ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Resting State ◽  
Brain Regions ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Resting State Functional Connectivity ◽  
Cross Sectional ◽  
The Impact

scholarly journals Altered resting-state functional connectivity in late-life depression: A cross-sectional study

2016 ◽  
Vol 189 ◽  
pp. 126-133 ◽  
Author(s):  
Harris A. Eyre ◽  
Hongyu Yang ◽  
Amber M. Leaver ◽  
Kathleen Van Dyk ◽  
Prabha Siddarth ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Late Life ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Late Life Depression ◽  
Resting State Functional Connectivity ◽  
Cross Sectional

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 Perceived stress and depressive symptoms not neuropsychiatric symptoms predict caregiver burden in Alzheimer’s disease: a cross-sectional study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Manee Pinyopornpanish ◽  
Kanokporn Pinyopornpanish ◽  
Atiwat Soontornpun ◽  
Surat Tanprawate ◽  
Angkana Nadsasarn ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Depressive Symptoms ◽  
Caregiver Burden ◽  
Perceived Stress ◽  
Neuropsychiatric Symptoms ◽  
Cross Sectional Study ◽  
Assessment Tools ◽  
Sectional Study ◽  
Cross Sectional

Abstract Background Caregiver burden affects the caregiver’s health and is related to the quality of care received by patients. This study aimed to determine the extent to which caregivers feel burdened when caring for patients with Alzheimer’s Disease (AD) and to investigate the predictors for caregiving burden. Methods A cross-sectional study was conducted. One hundred two caregivers of patients with AD at Maharaj Nakorn Chiang Mai Hospital, a tertiary care hospital, were recruited. Assessment tools included the perceived stress scale (stress), PHQ-9 (depressive symptoms), Zarit Burden Interview-12 (burden), Clinical Dementia Rating (disease severity), Neuropsychiatric Inventory Questionnaires (neuropsychiatric symptoms), and Barthel Activities Daily Living Index (dependency). The mediation analysis model was used to determine any associations. Results A higher level of severity of neuropsychiatric symptoms (r = 0.37, p < 0.01), higher level of perceived stress (r = 0.57, p < 0.01), and higher level of depressive symptoms (r = 0.54, p < 0.01) were related to a higher level of caregiver burden. The direct effect of neuropsychiatric symptoms on caregiver burden was fully mediated by perceived stress and depressive symptoms (r = 0.13, p = 0.177), rendering an increase of 46% of variance in caregiver burden by this parallel mediation model. The significant indirect effect of neuropsychiatric symptoms by these two mediators was (r = 0.21, p = 0.001). Conclusion Caregiver burden is associated with patients’ neuropsychiatric symptoms indirectly through the caregiver’s depressive symptoms and perception of stress. Early detection and provision of appropriate interventions and skills to manage stress and depression could be useful in reducing and preventing caregiver burden.

scholarly journals Frequency and Severity of Adverse Drug Reactions to Medications Prescribed for Alzheimer’s Disease in a Brazilian City: Cross-Sectional Study

2020 ◽  
Vol 11 ◽  
Author(s):  
Tânia Regina Ferreira ◽  
Luciane Cruz Lopes ◽  
Cristiane de Càssia Bergamaschi
Keyword(s):  
Public Health ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Health System ◽  
Psychiatric Disorders ◽  
Public Health System ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Drug Reactions ◽  
Cross Sectional

Background: There is lack of national studies that assess the risks associated with the drugs provided under the Brazilian public health system for treating Alzheimer’s disease. Then, this study determined the prevalence and severity of self-reported adverse drug reactions (ADRs) prescribed to patients with Alzheimer’s disease in the Brazilian public health system.Methods: A cross-sectional study was carried out based on public data from the MEDEX system (information on dispensing data, known as exceptional dispensing medications) and interviews with patients and/or caregivers who get access to Alzheimer’s drugs at a public pharmacy in a large Brazilian city, between July and September 2017, inquiring about ADRs and serious adverse events (SAEs).Results: The subjects were asked about ADRs and SAEs related to the use of donepezil, galantamine, rivastigmine and memantine. Out of 285 patients enrolled on the database, 250 participated in the study (87.7%). Among the participants, approximately 63.0% were female, 70.3% aged ≥75 years and 70.3% had comorbidities. Overall, 209 patients (83.6%) reported at least one ADR (total 1,149 ADRs) and rivastigmine was associated with the largest number of ADRs per patient (7.9 ADRs/patient). The predominant adverse effects were psychiatric disorders with common frequency (57.1%) and mild severity (89.0%). Six patients (2.4%) had SAEs that required hospitalization. The use of antipsychotics was the variable associated with ADR (OR = 4.95; 95% CI: 1.45–16.93; p = 0.011).Conclusion: There was a large number of reported ADRs and most of them were of common frequency and mild severity, being mainly related to psychiatric disorders. Considering the fragility of these patients, it is important to improve safety-related care in the use of drugs for treating this disease.

scholarly journals Predicting dysfunctional age-related task activations from resting-state network alterations

2019 ◽  
Author(s):  
Ravi D. Mill ◽  
Brian A. Gordon ◽  
David A. Balota ◽  
Jeffrey M. Zacks ◽  
Michael W. Cole
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
At Risk ◽  
Functional Connectivity ◽  
Resting State ◽  
Cognitive Task ◽  
Low Risk ◽  
Flow Modeling ◽  
Resting State Functional Connectivity ◽  
Age Related

AbstractAlzheimer’s disease (AD) is linked to changes in fMRI task activations and fMRI resting-state functional connectivity (restFC), which can emerge early in the timecourse of illness. Study of these fMRI correlates of unhealthy aging has been conducted in largely separate subfields. Taking inspiration from neural network simulations, we propose a unifying mechanism wherein restFC network alterations associated with Alzheimer’s disease disrupt the ability for activations to flow between brain regions, leading to aberrant task activations. We apply this activity flow modeling framework in a large sample of clinically unimpaired older adults, which was segregated into healthy (low-risk) and at-risk subgroups based on established imaging (positron emission tomography amyloid) and genetic (apolipoprotein) risk factors for AD. We identified healthy task activations in individuals at low risk for AD, and then by estimating activity flow using at-risk AD restFC data we were able to predict the altered at-risk AD task activations. Thus, modeling the flow of healthy activations over at-risk AD connectivity effectively transformed the healthy aged activations into unhealthy aged activations. These results provide evidence that activity flow over altered intrinsic functional connections may act as a mechanism underlying Alzheimer’s-related dysfunction, even in very early stages of the illness. Beyond these mechanistic insights linking restFC with cognitive task activations, this approach has potential clinical utility as it enables prediction of task activations and associated cognitive dysfunction in individuals without requiring them to perform in-scanner cognitive tasks.Significance StatementDeveloping analytic approaches that can reliably predict features of Alzheimer’s disease is a major goal for cognitive and clinical neuroscience, with particular emphasis on identifying such diagnostic features early in the timeline of disease. We demonstrate the utility of an activity flow modeling approach, which predicts fMRI cognitive task activations in subjects identified as at-risk for Alzheimer’s disease. The approach makes activation predictions by transforming a healthy aged activation template via the at-risk subjects’ individual pattern of fMRI resting-state functional connectivity (restFC). The observed prediction accuracy supports activity flow as a mechanism linking age-related alterations in restFC and task activations, thereby providing a theoretical basis for incorporating restFC into imaging biomarker and personalized medicine interventions.

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