Tsallis statistics and neurodegenerative disorders

2016 ◽  
Vol 25 (3-4) ◽  
pp. 129-139 ◽  
Author(s):  
Aggelos C. Iliopoulos ◽  
Magdalini Tsolaki ◽  
Elias C. Aifantis
Keyword(s):  
Time Series ◽  
Neurodegenerative Disorders ◽  
Complex Dynamics ◽  
Neurodegenerative Disorder ◽  
Epileptic Seizures ◽  
Study Data ◽  
Therapeutic Interventions ◽  
Tsallis Statistics ◽  
Healthy Control ◽  
Non Gaussian

AbstractIn this paper, we perform statistical analysis of time series deriving from four neurodegenerative disorders, namely epilepsy, amyotrophic lateral sclerosis (ALS), Parkinson’s disease (PD), Huntington’s disease (HD). The time series are concerned with electroencephalograms (EEGs) of healthy and epileptic states, as well as gait dynamics (in particular stride intervals) of the ALS, PD and HDs. We study data concerning one subject for each neurodegenerative disorder and one healthy control. The analysis is based on Tsallis non-extensive statistical mechanics and in particular on the estimation of Tsallis q-triplet, namely {qstat, qsen, qrel}. The deviation of Tsallis q-triplet from unity indicates non-Gaussian statistics and long-range dependencies for all time series considered. In addition, the results reveal the efficiency of Tsallis statistics in capturing differences in brain dynamics between healthy and epileptic states, as well as differences between ALS, PD, HDs from healthy control subjects. The results indicate that estimations of Tsallis q-indices could be used as possible biomarkers, along with others, for improving classification and prediction of epileptic seizures, as well as for studying the gait complex dynamics of various diseases providing new insights into severity, medications and fall risk, improving therapeutic interventions.

scholarly journals Recurrence eigenvalues of movements from brain signals

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Tuan D. Pham
Keyword(s):  
Time Series ◽  
Caenorhabditis Elegans ◽  
Behavioral Genetics ◽  
Therapeutic Interventions ◽  
Disease Genes ◽  
Behavioral Dynamics ◽  
Healthy Control ◽  
Largest Eigenvalues ◽  
Human Disease Genes ◽  
Moving Patterns

AbstractThe ability to characterize muscle activities or skilled movements controlled by signals from neurons in the motor cortex of the brain has many useful implications, ranging from biomedical perspectives to brain–computer interfaces. This paper presents the method of recurrence eigenvalues for differentiating moving patterns in non-mammalian and human models. The non-mammalian models of Caenorhabditis elegans have been studied for gaining insights into behavioral genetics and discovery of human disease genes. Systematic probing of the movement of these worms is known to be useful for these purposes. Study of dynamics of normal and mutant worms is important in behavioral genetic and neuroscience. However, methods for quantifying complexity of worm movement using time series are still not well explored. Neurodegenerative diseases adversely affect gait and mobility. There is a need to accurately quantify gait dynamics of these diseases and differentiate them from the healthy control to better understand their pathophysiology that may lead to more effective therapeutic interventions. This paper attempts to explore the potential application of the method for determining the largest eigenvalues of convolutional fuzzy recurrence plots of time series for measuring the complexity of moving patterns of Caenorhabditis elegans and neurodegenerative disease subjects. Results obtained from analyses demonstrate that the largest recurrence eigenvalues can differentiate phenotypes of behavioral dynamics between wild type and mutant strains of Caenorhabditis elegans; and walking patterns among healthy control subjects and patients with Parkinson’s disease, Huntington’s disease, or amyotrophic lateral sclerosis.

Is Brain-Derived Neurotrophic Factor: A Common Link Between Neurodegenerative Disorders and Cancer?

2019 ◽  
Vol 16 (4) ◽  
pp. 344-352
Author(s):  
Radhika Khosla ◽  
Avijit Banik ◽  
Sushant Kaushal ◽  
Priya Battu ◽  
Deepti Gupta ◽  
...  
Keyword(s):  
Neurodegenerative Disorders ◽  
Neurotrophic Factor ◽  
Animal Studies ◽  
Neurodegenerative Disorder ◽  
Indirect Evidence ◽  
Brain Derived Neurotrophic Factor ◽  
Common Disease ◽  
Inverse Association ◽  
Bdnf Expression ◽  
Strong Inverse Association

Background: Cancer is a common disease caused by the excessive proliferation of cells, and neurodegenerative diseases are the disorders caused due to the degeneration of neurons. Both can be considered as diseases caused by the dysregulation of cell cycle events. A recent data suggests that there is a strong inverse association between cancer and neurodegenerative disorders. There is indirect evidence to postulate Brain-derived Neurotrophic Factor (BDNF) as a potential molecular link in this association. Discussion: The BDNF levels are found to be downregulated in many neurodegenerative disorders and are found to be upregulated in various kinds of cancers. The lower level of BDNF in Alzheimer’s and Parkinson’s disease has been found to be related to cognitive and other neuropsychological impairments, whereas, its higher levels are associated with the tumour growth and metastasis and poor survival rate in the cancer patients. Conclusion: In this review, we propose that variance in BDNF levels is critical in determining the course of cellular pathophysiology and the development of cancer or neurodegenerative disorder. We further propose that an alternative therapeutic strategy that can modulate BDNF expression, can rescue or prevent above said pathophysiological course. Larger studies that examine this link through animal studies are imperative to understand the putative biochemical and molecular link to wellness and disease.

scholarly journals Multimodal in vivo recording using transparent graphene microelectrodes illuminates spatiotemporal seizure dynamics at the microscale

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Nicolette Driscoll ◽  
Richard E. Rosch ◽  
Brendan B. Murphy ◽  
Arian Ashourvan ◽  
Ramya Vishnubhotla ◽  
...  
Keyword(s):  
Temporal Resolution ◽  
Spatial Scales ◽  
Epileptic Seizures ◽  
Therapeutic Interventions ◽  
State Transitions ◽  
Integrated Analysis ◽  
High Temporal Resolution ◽  
Seizure Onset ◽  
Acute Seizures

AbstractNeurological disorders such as epilepsy arise from disrupted brain networks. Our capacity to treat these disorders is limited by our inability to map these networks at sufficient temporal and spatial scales to target interventions. Current best techniques either sample broad areas at low temporal resolution (e.g. calcium imaging) or record from discrete regions at high temporal resolution (e.g. electrophysiology). This limitation hampers our ability to understand and intervene in aberrations of network dynamics. Here we present a technique to map the onset and spatiotemporal spread of acute epileptic seizures in vivo by simultaneously recording high bandwidth microelectrocorticography and calcium fluorescence using transparent graphene microelectrode arrays. We integrate dynamic data features from both modalities using non-negative matrix factorization to identify sequential spatiotemporal patterns of seizure onset and evolution, revealing how the temporal progression of ictal electrophysiology is linked to the spatial evolution of the recruited seizure core. This integrated analysis of multimodal data reveals otherwise hidden state transitions in the spatial and temporal progression of acute seizures. The techniques demonstrated here may enable future targeted therapeutic interventions and novel spatially embedded models of local circuit dynamics during seizure onset and evolution.

Mechanisms of neuronal death in disease: defining the models and the players

2008 ◽  
Vol 415 (2) ◽  
pp. 165-182 ◽  
Author(s):  
Elena M. Ribe ◽  
Esther Serrano-Saiz ◽  
Nsikan Akpan ◽  
Carol M. Troy
Keyword(s):  
Neurodegenerative Diseases ◽  
Neuronal Death ◽  
Neurological Disease ◽  
Neurodegenerative Disorder ◽  
Cellular Level ◽  
Therapeutic Interventions ◽  
Common Cause ◽  
Life And Death ◽  
Caspase Family ◽  
Common Neurodegenerative Disorder

Dysregulation of life and death at the cellular level leads to a variety of diseases. In the nervous system, aberrant neuronal death is an outstanding feature of neurodegenerative diseases. Since the discovery of the caspase family of proteases, much effort has been made to determine how caspases function in disease, including neurodegenerative diseases. Although many papers have been published examining caspases in neuronal death and disease, the pathways have not been fully clarified. In the present review, we examine the potential players in the death pathways, the current tools for examining these players and the models for studying neurological disease. Alzheimer's disease, the most common neurodegenerative disorder, and cerebral ischaemia, the most common cause of neurological death, are used to illustrate our current understanding of death signalling in neurodegenerative diseases. A better understanding of the neuronal death pathways would provide targets for the development of therapeutic interventions for these diseases.

scholarly journals Chaotic Patterns in Aeroelastic Signals

2009 ◽  
Vol 2009 ◽  
pp. 1-19 ◽  
Author(s):  
F. D. Marques ◽  
R. M. G. Vasconcellos
Keyword(s):  
Time Series ◽  
State Space ◽  
Complex Dynamics ◽  
Chaotic Behavior ◽  
Separated Flow ◽  
Direct Analysis ◽  
Surrogate Data ◽  
The State ◽  
Aeroelastic System ◽  
Value Decomposition

This work presents the analysis of nonlinear aeroelastic time series from wing vibrations due to airflow separation during wind tunnel experiments. Surrogate data method is used to justify the application of nonlinear time series analysis to the aeroelastic system, after rejecting the chance for nonstationarity. The singular value decomposition (SVD) approach is used to reconstruct the state space, reducing noise from the aeroelastic time series. Direct analysis of reconstructed trajectories in the state space and the determination of Poincaré sections have been employed to investigate complex dynamics and chaotic patterns. With the reconstructed state spaces, qualitative analyses may be done, and the attractors evolutions with parametric variation are presented. Overall results reveal complex system dynamics associated with highly separated flow effects together with nonlinear coupling between aeroelastic modes. Bifurcations to the nonlinear aeroelastic system are observed for two investigations, that is, considering oscillations-induced aeroelastic evolutions with varying freestream speed, and aeroelastic evolutions at constant freestream speed and varying oscillations. Finally, Lyapunov exponent calculation is proceeded in order to infer on chaotic behavior. Poincaré mappings also suggest bifurcations and chaos, reinforced by the attainment of maximum positive Lyapunov exponents.

scholarly journals Estimating feedforward and feedback effective connections from fMRI time series: Assessments of statistical methods

2019 ◽  
Vol 3 (2) ◽  
pp. 274-306 ◽  
Author(s):  
Ruben Sanchez-Romero ◽  
Joseph D. Ramsey ◽  
Kun Zhang ◽  
Madelyn R. K. Glymour ◽  
Biwei Huang ◽  
...  
Keyword(s):  
Time Series ◽  
Statistical Methods ◽  
Permutation Test ◽  
Causal Structure ◽  
Multiple Model ◽  
New Methods ◽  
Test Models ◽  
Non Gaussian ◽  
True Structure ◽  
Better Than

We test the adequacies of several proposed and two new statistical methods for recovering the causal structure of systems with feedback from synthetic BOLD time series. We compare an adaptation of the first correct method for recovering cyclic linear systems; Granger causal regression; a multivariate autoregressive model with a permutation test; the Group Iterative Multiple Model Estimation (GIMME) algorithm; the Ramsey et al. non-Gaussian methods; two non-Gaussian methods by Hyvärinen and Smith; a method due to Patel et al.; and the GlobalMIT algorithm. We introduce and also compare two new methods, Fast Adjacency Skewness (FASK) and Two-Step, both of which exploit non-Gaussian features of the BOLD signal. We give theoretical justifications for the latter two algorithms. Our test models include feedback structures with and without direct feedback (2-cycles), excitatory and inhibitory feedback, models using experimentally determined structural connectivities of macaques, and empirical human resting-state and task data. We find that averaged over all of our simulations, including those with 2-cycles, several of these methods have a better than 80% orientation precision (i.e., the probability of a directed edge is in the true structure given that a procedure estimates it to be so) and the two new methods also have better than 80% recall (probability of recovering an orientation in the true structure).

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