Investigating the Effect of Modafinil on Marked Brain Regions’ Functional Connectivity While Resting in Young, Healthy Individuals, Using Variance Component Longitudinal Model

Introduction: In recent years, investigating the differences in Functional Connectivity (FC) network in different brain regions in Functional Magnetic Resonance Imagining (fMRI) has appealed to neurological researchers. Examining the functional connectivity differences between two groups can assist in improving neurological disorders cure. The present study explores the differences in functional connectivity between two groups, one using Modafinil and the other placebo, as to consider the impact of this medicine, concerning functional connectivity of regions of interests among young, healthy people. Materials and Methods: Data was downloaded from website "Open fMRI." Downloaded data included 26 young, healthy men with no history of mental disease. They are divided into two groups of 13. The first group received 100 mgr Modafinil, and the second group 100mgr placebo. Three scans were taken from each group during the time. The data were analyzed through a longitudinal model, using a variance component. Results: Exploring the functional connectivity difference between the two groups, using intervention and placebo in the baseline effect did not show a significant statistical difference, but investigating the functional connectivity difference between the two groups in longitudinal trends showed a significant statistical difference in Inter-Hemispheric and Right- Brainstem. Conclusion: According to the present study's findings, Modafinil did not increase functional connectivity in most investigated regions.   &nbsp

A Novel Joint Brain Network Analysis Using Longitudinal Alzheimer's Disease Data.

There is well-documented evidence of brain network differences between individuals with Alzheimer's disease (AD) and healthy controls (HC). To date, imaging studies investigating brain networks in these populations have typically been cross-sectional, and the reproducibility of such findings is somewhat unclear. In a novel study, we use the longitudinal ADNI data on the whole brain to jointly compute the brain network at baseline and one-year using a state of the art approach that pools information across both time points to yield distinct visit-specific networks for the AD and HC cohorts, resulting in more accurate inferences. We perform a multiscale comparison of the AD and HC networks in terms of global network metrics as well as at the more granular level of resting state networks defined under a whole brain parcellation. Our analysis illustrates a decrease in small-worldedness in the AD group at both the time points and also identifies more local network features and hub nodes that are disrupted due to the progression of AD. We also obtain high reproducibility of the HC network across visits. On the other hand, a separate estimation of the networks at each visit using standard graphical approaches reveals fewer meaningful differences and lower reproducibility

Methods for Analyzing Multi-Subject Resting-State Neuroimaging Time Series Data

University of Minnesota Ph.D. dissertation. May 2019. Major: Biostatistics. Advisors: Mark Fiecas, Lynn Eberly. 1 computer file (PDF); xv, 107 pages.Resting-state neuroimaging modalities such as electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) collect data in the form of time series which represent the activity in the brain at rest. This resting-state behavior can be analyzed in different ways to address different research questions and is thought to represent the intrinsic activity of the brain. We discuss three potential avenues of analysis. First, we propose a permutation-based method which tests the longitudinal functional connectivity of fMRI data collected from cognitively normal participants and Alzheimer’s patients. Next, we propose a Bayesian nonparametric model to jointly perform spectral time series analysis on EEG data from 1,116 twins from the Minnesota Twin Family Study (MTFS) and discuss a novel heritability estimator for features of the estimated spectral density curves. Finally, we propose another Bayesian nonparametric model to perform EEG microstate analysis of the MTFS data at the twin pair level. Each method discussed views the resting-state time series data from a different angle. Additionally, in each of these scenarios, we jointly analyze data collected from many different participants while accounting for the design of the study in which the data was collected. Regardless of the analysis method chosen, accounting for the within and between-participant dependence structure yields improved results

Características cognitivas y neurofisiológicas en ancianos sanos con factores de riesgo genético de Enfermedad de Alzheimer

A pesar de la gran cantidad de investigaciones que se han llevado a cabo en relación a la Enfermedad de Alzheimer (EA) en los últimos 20 años, aún no se ha desarrollado un tratamiento eficaz para detener o retrasar su progresión. En la actualidad, uno de los temas de mayor interés consiste en la búsqueda de marcadores tempranos que permitan predecir la EA, tanto para el diagnóstico precoz, como para su prevención mediante la intervención farmacológica y/o la estimulación cognitiva. En este sentido, los factores de riesgo genético cobran especial protagonismo al contribuir en más de un 60% al desarrollo de esta enfermedad.Cada vez es mayor el número de trabajos focalizados en la búsqueda de genes candidatos, estudios de asociación del genoma completo (GWAS en inglés), etc., con el fin de proporcionar nuevas perspectivas sobre su patogénesis y los beneficios que podrían derivarse de un abordaje precoz de la misma. Gracias a ello se sabe que el ser portador del alelo ε4 del gen de la apolipoproteína E (APOE ε4) es el factor de riesgo genético más importante de la EA de origen tardío. Asimismo, se han descubierto polimorfismos genéticos de diversos genes (CLU, PICALM, CR1 o BDNF) que también parecen estar asociados con la enfermedad..

Crosstalk between Depression, Anxiety, and Dementia: Comorbidity in Behavioral Neurology and Neuropsychiatry

This Special Issue highlights the most recent research on depression, anxiety and dementia, with attention to comorbidity in a range of diseases. The symptoms of depression, anxiety and dementia are the most common comorbid manifestations present in patients suffering from neurodegenerative and psychiatric diseases. Together, these illnesses constitute an extremely complex and challenging research field due to their inherent multifactorial causative factors, heterogeneous pathogenesis, and mental and behavioral manifestations. This Special Issue covers laboratory, clinical and statistical studies on the crosstalk between depression, anxiety, dementia, Alzheimer’s disease, multiple sclerosis, schizophrenia, diabetes mellitus, Down’s syndrome, and/or compulsive disorders. It contains contributions from 71 authors, has been reviewed by 25 referees, and edited by three academic editors and one managing editor