scholarly journals Identifying Dynamic Functional Connectivity Changes in Dementia with Lewy Bodies Based on Product Hidden Markov Models

2016 ◽  
Vol 10 ◽  
Author(s):  
Marion Sourty ◽  
Laurent Thoraval ◽  
Daniel Roquet ◽  
Jean-Paul Armspach ◽  
Jack Foucher ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Hidden Markov Models ◽  
Dementia With Lewy Bodies ◽  
Markov Models ◽  
Hidden Markov ◽  
Lewy Bodies ◽  
Dynamic Functional Connectivity

scholarly journals Dynamic functional connectivity changes in dementia with Lewy bodies and Alzheimer's disease

2019 ◽  
Vol 22 ◽  
pp. 101812 ◽  
Author(s):  
Julia Schumacher ◽  
Luis R. Peraza ◽  
Michael Firbank ◽  
Alan J. Thomas ◽  
Marcus Kaiser ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Dementia With Lewy Bodies ◽  
Lewy Bodies ◽  
Dynamic Functional Connectivity

scholarly journals Dynamic functional connectivity changes in dementia with Lewy bodies and Alzheimer’s disease

2018 ◽  
Author(s):  
Julia Schumacher ◽  
Luis R. Peraza ◽  
Michael Firbank ◽  
Alan J. Thomas ◽  
Marcus Kaiser ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Functional Connectivity ◽  
Resting State ◽  
Dementia With Lewy Bodies ◽  
Lewy Bodies ◽  
Brain Network ◽  
Visual Hallucinations ◽  
Dynamic Functional Connectivity ◽  
Dynamic Connectivity ◽  
Cognitive Fluctuations

AbstractWe studied the dynamic functional connectivity profile of dementia with Lewy bodies (DLB) and Alzheimer’s disease (AD) and the relationship between dynamic connectivity and the temporally transient symptoms of cognitive fluctuations and visual hallucinations in DLB.Resting state fMRI data from 31 DLB, 29 AD, and 31 healthy control participants were analysed using dual regression to determine between-network functional connectivity. We used a sliding window approach followed by k-means clustering and dynamic network analyses to study dynamic functional connectivity changes associated with AD and DLB. Network measures that showed significant group differences were tested for correlations with clinical symptom severity.AD and DLB patients spent more time than controls in sparse connectivity configurations with absence of strong positive and negative connections and a relative isolation of motor networks from other networks. Additionally, DLB patients spent less time in a more strongly connected state and the variability of global brain network efficiency was reduced in DLB compared to controls. However, there were no significant correlations between dynamic connectivity measures and clinical scores.The loss of global efficiency variability in DLB might indicate the presence of an abnormally rigid brain network and the lack of economical dynamics, factors which could contribute to an inability to respond appropriately to situational demands. However, the absence of significant clinical correlations indicates that the severity of transient cognitive symptoms such as cognitive fluctuations and visual hallucinations might not be directly related to these dynamic connectivity changes observed during a short resting state scan.

scholarly journals Dynamic Functional Connectivity Changes in Dementia with Lewy Bodies and Alzheimer's Disease

2018 ◽  
Author(s):  
Julia Schumacher ◽  
Luis R. Peraza ◽  
Michael Firbank ◽  
Alan J. Thomas ◽  
Marcus Kaiser ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Dementia With Lewy Bodies ◽  
Lewy Bodies ◽  
Dynamic Functional Connectivity

Estimating Personality Impression from Speech Record Using Hidden Markov Models

2015 ◽  
Vol 135 (12) ◽  
pp. 1517-1523 ◽  
Author(s):  
Yicheng Jin ◽  
Takuto Sakuma ◽  
Shohei Kato ◽  
Tsutomu Kunitachi
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov

Hidden Markov Processes

2014 ◽  
Author(s):  
M. Vidyasagar
Keyword(s):  
Hidden Markov Models ◽  
Markov Processes ◽  
Viterbi Algorithm ◽  
Markov Models ◽  
Hidden Markov ◽  
Local Alignment ◽  
Biological Applications ◽  
Standard Material ◽  
Hidden Markov Processes ◽  
Genomics And Proteomics

This book explores important aspects of Markov and hidden Markov processes and the applications of these ideas to various problems in computational biology. It starts from first principles, so that no previous knowledge of probability is necessary. However, the work is rigorous and mathematical, making it useful to engineers and mathematicians, even those not interested in biological applications. A range of exercises is provided, including drills to familiarize the reader with concepts and more advanced problems that require deep thinking about the theory. Biological applications are taken from post-genomic biology, especially genomics and proteomics. The topics examined include standard material such as the Perron–Frobenius theorem, transient and recurrent states, hitting probabilities and hitting times, maximum likelihood estimation, the Viterbi algorithm, and the Baum–Welch algorithm. The book contains discussions of extremely useful topics not usually seen at the basic level, such as ergodicity of Markov processes, Markov Chain Monte Carlo (MCMC), information theory, and large deviation theory for both i.i.d and Markov processes. It also presents state-of-the-art realization theory for hidden Markov models. Among biological applications, it offers an in-depth look at the BLAST (Basic Local Alignment Search Technique) algorithm, including a comprehensive explanation of the underlying theory. Other applications such as profile hidden Markov models are also explored.

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