scholarly journals Detecting early changes in Alzheimer’s disease with graph theory

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Joana B Pereira
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Graph Theory ◽  
Alzheimer Disease ◽  
Grey Matter ◽  
Autosomal Dominant ◽  
Single Subject ◽  
Disease Course

This scientific commentary refers to ‘Single-subject grey matter network trajectories over the disease course of autosomal dominant Alzheimer disease’, by Vermunt et al. (https://doi.org/10.1093/braincomms/fcaa102).

scholarly journals Single-subject grey matter network trajectories over the disease course of autosomal dominant Alzheimer’s disease

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Lisa Vermunt ◽  
Ellen Dicks ◽  
Guoqiao Wang ◽  
Aylin Dincer ◽  
Shaney Flores ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Symptom Onset ◽  
Grey Matter ◽  
Autosomal Dominant ◽  
Single Subject ◽  
Mutation Carriers ◽  
Autosomal Dominant Alzheimer’S Disease ◽  
Network Properties ◽  
Over Time

Abstract Structural grey matter covariance networks provide an individual quantification of morphological patterns in the brain. The network integrity is disrupted in sporadic Alzheimer’s disease, and network properties show associations with the level of amyloid pathology and cognitive decline. Therefore, these network properties might be disease progression markers. However, it remains unclear when and how grey matter network integrity changes with disease progression. We investigated these questions in autosomal dominant Alzheimer’s disease mutation carriers, whose conserved age at dementia onset allows individual staging based upon their estimated years to symptom onset. From the Dominantly Inherited Alzheimer Network observational cohort, we selected T1-weighted MRI scans from 269 mutation carriers and 170 non-carriers (mean age 38 ± 15 years, mean estimated years to symptom onset −9 ± 11), of whom 237 had longitudinal scans with a mean follow-up of 3.0 years. Single-subject grey matter networks were extracted, and we calculated for each individual the network properties which describe the network topology, including the size, clustering, path length and small worldness. We determined at which time point mutation carriers and non-carriers diverged for global and regional grey matter network metrics, both cross-sectionally and for rate of change over time. Based on cross-sectional data, the earliest difference was observed in normalized path length, which was decreased for mutation carriers in the precuneus area at 13 years and on a global level 12 years before estimated symptom onset. Based on longitudinal data, we found the earliest difference between groups on a global level 6 years before symptom onset, with a greater rate of decline of network size for mutation carriers. We further compared grey matter network small worldness with established biomarkers for Alzheimer disease (i.e. amyloid accumulation, cortical thickness, brain metabolism and cognitive function). We found that greater amyloid accumulation at baseline was associated with faster decline of small worldness over time, and decline in grey matter network measures over time was accompanied by decline in brain metabolism, cortical thinning and cognitive decline. In summary, network measures decline in autosomal dominant Alzheimer’s disease, which is alike sporadic Alzheimer’s disease, and the properties show decline over time prior to estimated symptom onset. These data suggest that single-subject networks properties obtained from structural MRI scans form an additional non-invasive tool for understanding the substrate of cognitive decline and measuring progression from preclinical to severe clinical stages of Alzheimer’s disease.

scholarly journals Correction: Single-Subject Grey Matter Graphs in Alzheimer's Disease

PLoS ONE ◽  
2013 ◽  
Vol 8 (9) ◽  
Author(s):  
Betty M. Tijms ◽  
Christiane Möller ◽  
Hugo Vrenken ◽  
Alle Meije Wink ◽  
Willem de Haan ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Grey Matter ◽  
Single Subject

scholarly journals Single-Subject Grey Matter Graphs in Alzheimer's Disease

PLoS ONE ◽  
2013 ◽  
Vol 8 (3) ◽  
pp. e58921 ◽  
Author(s):  
Betty M. Tijms ◽  
Christiane Möller ◽  
Hugo Vrenken ◽  
Alle Meije Wink ◽  
Willem de Haan ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Grey Matter ◽  
Single Subject

Contribution of Graph Theory Applied to EEG Data Analysis for Alzheimer’s Disease versus Vascular Dementia Diagnosis

2021 ◽  
pp. 1-9
Author(s):  
Fabrizio Vecchio ◽  
Francesca Miraglia ◽  
Francesca Alú ◽  
Alessandro Orticoni ◽  
Elda Judica ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Graph Theory ◽  
Vascular Dementia ◽  
High Frequency ◽  
Clinical Symptoms ◽  
Low Frequency ◽  
Brain Diseases ◽  
Single Subject ◽  
Eeg Data

Background: Most common progressive brain diseases in the elderly are Alzheimer’s disease (AD) and vascular dementia (VaD). They present with relatively similar clinical symptoms of cognitive decline, but the underlying pathophysiological mechanisms are different. Objective: The aim is to explore the brain connectivity differences between AD and VaD patients compared to mild cognitive impairment (MCI) and normal elderly (Nold) subjects applying graph theory, in particular the Small World (SW) analysis. Methods: 274 resting state EEGs were analyzed in 100 AD, 80 MCI, 40 VaD, and 54 Nold subjects. Graph theory analyses were applied to undirected and weighted networks obtained by lagged linear coherence evaluated by eLORETA tool. Results: VaD and AD patients presented more ordered low frequency structure (lower value of SW) than Nold and MCI subjects, and more random organization (higher value of SW) in low and high frequency alpha rhythms. Differences between patients have been found in high frequency alpha rhythms in VaD (higher value of SW) with respect to AD, and in theta band with a trend which is more similar to MCI and Nold than to AD. MCI subjects presented a network organization which is intermediate, in low frequency bands, between Nold and patients. Conclusion: Graph theory applied to EEG data has proved very useful in identifying differences in brain network patterns in subjects with dementia, proving to be a valid tool for differential diagnosis. Future studies will aim to validate this method to diagnose especially in the early stages of the disease and at single subject level.

scholarly journals Disease progression modelling from preclinical Alzheimer’s disease (AD) to AD dementia

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Soo Hyun Cho ◽  
Sookyoung Woo ◽  
Changsoo Kim ◽  
Hee Jin Kim ◽  
Hyemin Jang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Disease Progression ◽  
Disease Assessment ◽  
Time Interval ◽  
Disease Course ◽  
Preclinical Alzheimer’S Disease ◽  
Apoe Ε4 ◽  
Preclinical Ad ◽  
Cognitive Subscale

AbstractTo characterize the course of Alzheimer’s disease (AD) over a longer time interval, we aimed to construct a disease course model for the entire span of the disease using two separate cohorts ranging from preclinical AD to AD dementia. We modelled the progression course of 436 patients with AD continuum and investigated the effects of apolipoprotein E ε4 (APOE ε4) and sex on disease progression. To develop a model of progression from preclinical AD to AD dementia, we estimated Alzheimer’s Disease Assessment Scale-Cognitive Subscale 13 (ADAS-cog 13) scores. When calculated as the median of ADAS-cog 13 scores for each cohort, the estimated time from preclinical AD to MCI due to AD was 7.8 years and preclinical AD to AD dementia was 15.2 years. ADAS-cog 13 scores deteriorated most rapidly in women APOE ε4 carriers and most slowly in men APOE ε4 non-carriers (p < 0.001). Our results suggest that disease progression modelling from preclinical AD to AD dementia may help clinicians to estimate where patients are in the disease course and provide information on variation in the disease course by sex and APOE ε4 status.

Subjective Cognitive Decline and its Relation to Verbal Memory and Sex in Cognitively Unimpaired Individuals from a Colombian Cohort with Autosomal-Dominant Alzheimer’s Disease

2021 ◽  
pp. 1-9
Author(s):  
Jairo E. Martinez ◽  
Enmanuelle Pardilla-Delgado ◽  
Edmarie Guzmán-Vélez ◽  
Clara Vila-Castelar ◽  
Rebecca Amariglio ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Sex Differences ◽  
Cognitive Decline ◽  
Verbal Memory ◽  
Autosomal Dominant ◽  
Memory Performance ◽  
Subjective Cognitive Decline ◽  
Mutation Carriers ◽  
Study Partner

Abstract Objective: Subjective Cognitive Decline (SCD) may be an early indicator of risk for Alzheimer’s disease (AD). Findings regarding sex differences in SCD are inconsistent. Studying sex differences in SCD within cognitively unimpaired individuals with autosomal-dominant AD (ADAD), who will develop dementia, may inform sex-related SCD variations in preclinical AD. We examined sex differences in SCD within cognitively unimpaired mutation carriers from the world’s largest ADAD kindred and sex differences in the relationship between SCD and memory performance. Methods: We included 310 cognitively unimpaired Presenilin-1 (PSEN-1) E280A mutation carriers (51% females) and 1998 noncarrier family members (56% females) in the study. Subjects and their study partners completed SCD questionnaires and the CERAD word list delayed recall test. ANCOVAs were conducted to examine group differences in SCD, sex, and memory performance. In carriers, partial correlations were used to examine associations between SCD and memory performance covarying for education. Results: Females in both groups had greater self-reported and study partner-reported SCD than males (all p < 0.001). In female mutation carriers, greater self-reported (p = 0.02) and study partner-reported SCD (p < 0.001) were associated with worse verbal memory. In male mutation carriers, greater self-reported (p = 0.03), but not study partner-reported SCD (p = 0.11) was associated with worse verbal memory. Conclusions: Study partner-reported SCD may be a stronger indicator of memory decline in females versus males in individuals at risk for developing dementia. Future studies with independent samples and preclinical trials should consider sex differences when recruiting based on SCD criteria.

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