scholarly journals The impact of brain-derived neurotrophic factor Val66Met polymorphism on cognition and functional brain networks in patients with intractable partial epilepsy

2018 ◽  
Vol 25 (2) ◽  
pp. 223-232 ◽  
Author(s):  
Meneka K. Sidhu ◽  
Pamela J. Thompson ◽  
Britta Wandschneider ◽  
Alexandra Foulkes ◽  
Jane de Tisi ◽  
...  
Keyword(s):  
Neurotrophic Factor ◽  
Brain Networks ◽  
Brain Derived Neurotrophic Factor ◽  
Partial Epilepsy ◽  
Val66met Polymorphism ◽  
Functional Brain ◽  
Functional Brain Networks ◽  
The Impact

Impact of brain-derived neurotrophic factor Val66Met polymorphism and response to escitalopram or paroxetine in obsessive–compulsive disorder

CNS Spectrums ◽  
2021 ◽  
pp. 1-7
Author(s):  
Ghina Harika-Germaneau ◽  
Nicolas Langbour ◽  
Sylvie Patri ◽  
Marcello Solinas ◽  
Armand Chatard ◽  
...  
Keyword(s):  
Obsessive Compulsive Disorder ◽  
Neurotrophic Factor ◽  
Brain Derived Neurotrophic Factor ◽  
Response To Treatment ◽  
Obsessive Compulsive ◽  
Val66met Polymorphism ◽  
Symptom Dimensions ◽  
Compulsive Disorder ◽  
Symptom Dimension ◽  
The Impact

Abstract Objective Obsessive–compulsive disorder (OCD) is a severe psychiatric disorder characterized by its heterogeneous nature and by different dimensions of obsessive–compulsive (OC) symptoms. Serotonin reuptake inhibitors (SRIs) are used to treat OCD, but up to 40% to 60% of patients do not show a significant improvement with these medications. In this study, we aimed to test the impact of brain-derived neurotrophic factor (BDNF) Val66Met polymorphism on the efficacy of antidepressants in OCD overall, and in relation to the different OC dimensions. Methods In a 6-month prospective treatment study, 69 Caucasian OCD patients were treated with escitalopram for 24 weeks or with escitalopram for 12 weeks followed by paroxetine for an additional 12-week period. Patients were genotyped and assessed for treatment response. The main clinical outcomes were improvement of the Yale-Brown Obsessive–Compulsive Scale score and in different OC symptom dimension scores. Results The Val/Val group comprised 43 (62%) patients, the Val/Met and Met/Met group comprised 26 (38%) patients. Forty-two patients were classified as responders at 12 weeks and 38 at 24 weeks; no significant association was found between BDNF Val66Met and SRIs response at 12 and 24 weeks. In analyses of the different OC symptom dimensions, the Met allele was associated with a slightly reduced score in the aggressive/checking dimension at 6 months (P = .048). Conclusions Our findings do not support the usefulness of BDNF Val66Met genotyping to predict overall response to treatment with SRIs in OCD; they did however suggest a better outcome at 6 months for the aggressive/checking symptom dimension for patients carrying the Met allele.

scholarly journals Sparse Graphical Models for Functional Connectivity Networks: Best Methods and the Autocorrelation Issue

2017 ◽  
Author(s):  
Yunan Zhu ◽  
Ivor Cribben
Keyword(s):  
Graphical Models ◽  
Graphical Model ◽  
Brain Networks ◽  
Information Criterion ◽  
Selection Method ◽  
Absolute Deviation ◽  
Data Set ◽  
Functional Brain ◽  
Functional Brain Networks ◽  
The Impact

AbstractSparse graphical models are frequently used to explore both static and dynamic functional brain networks from neuroimaging data. However, the practical performance of the models has not been studied in detail for brain networks. In this work, we have two objectives. First, we compare several sparse graphical model estimation procedures and several selection criteria under various experimental settings, such as different dimensions, sample sizes, types of data, and sparsity levels of the true model structures. We discuss in detail the superiority and deficiency of each combination. Second, in the same simulation study, we show the impact of autocorrelation and whitening on the estimation of functional brain networks. We apply the methods to a resting-state functional magnetic resonance imaging (fMRI) data set. Our results show that the best sparse graphical model, in terms of detection of true connections and having few false-positive connections, is the smoothly clipped absolute deviation (SCAD) estimating method in combination with the Bayesian information criterion (BIC) and cross-validation (CV) selection method. In addition, the presence of autocorrelation in the data adversely affects the estimation of networks but can be helped by using the CV selection method. These results question the validity of a number of fMRI studies where inferior graphical model techniques have been used to estimate brain networks.

scholarly journals Impact of Transcutaneous Auricular Vagus Nerve Stimulation on Large-Scale Functional Brain Networks: From Local to Global

2021 ◽  
Vol 12 ◽  
Author(s):  
Thorsten Rings ◽  
Randi von Wrede ◽  
Timo Bröhl ◽  
Sophia Schach ◽  
Christoph Helmstaedter ◽  
...  
Keyword(s):  
Vagus Nerve ◽  
Nerve Stimulation ◽  
Mode Of Action ◽  
Vagus Nerve Stimulation ◽  
Large Scale ◽  
Brain Networks ◽  
Short Term ◽  
Functional Brain ◽  
Functional Brain Networks ◽  
The Impact

Transcutaneous auricular vagus nerve stimulation (taVNS) is a novel non-invasive brain stimulation technique considered as a potential supplementary treatment option for a wide range of diseases. Although first promising findings were obtained so far, the exact mode of action of taVNS is not fully understood yet. We recently developed an examination schedule to probe for immediate taVNS-induced modifications of large-scale epileptic brain networks. With this schedule, we observed short-term taVNS to have a topology-modifying, robustness- and stability-enhancing immediate effect on large-scale functional brain networks from subjects with focal epilepsies. We here expand on this study and investigate the impact of short-term taVNS on various local and global characteristics of large-scale evolving functional brain networks from a group of 30 subjects with and without central nervous system diseases. Our findings point to differential, at first glance counterintuitive, taVNS-mediated alterations of local and global topological network characteristics that result in a reconfiguration of networks and a modification of their stability and robustness properties. We propose a model of a stimulation-related stretching and compression of evolving functional brain networks that may help to better understand the mode of action of taVNS.

scholarly journals Time in Brain: How Biological Rhythms Impact on EEG Signals and on EEG-Derived Brain Networks

2021 ◽  
Vol 1 ◽  
Author(s):  
Klaus Lehnertz ◽  
Thorsten Rings ◽  
Timo Bröhl
Keyword(s):  
Spectral Characteristics ◽  
Brain Networks ◽  
Biological Rhythms ◽  
Brain Dynamics ◽  
Eeg Signals ◽  
Functional Brain ◽  
Non Invasive ◽  
Functional Brain Networks ◽  
Eeg Recordings ◽  
The Impact

Electroencephalography (EEG) is a widely employed tool for exploring brain dynamics and is used extensively in various domains, ranging from clinical diagnosis via neuroscience, cognitive science, cognitive psychology, psychophysiology, neuromarketing, neurolinguistics, and pharmacology to research on brain computer interfaces. EEG is the only technique that enables the continuous recording of brain dynamics over periods of time that range from a few seconds to hours and days and beyond. When taking long-term recordings, various endogenous and exogenous biological rhythms may impinge on characteristics of EEG signals. While the impact of the circadian rhythm and of ultradian rhythms on spectral characteristics of EEG signals has been investigated for more than half a century, only little is known on how biological rhythms influence characteristics of brain dynamics assessed with modern EEG analysis techniques. At the example of multiday, multichannel non-invasive and invasive EEG recordings, we here discuss the impact of biological rhythms on temporal changes of various characteristics of human brain dynamics: higher-order statistical moments and interaction properties of multichannel EEG signals as well as local and global characteristics of EEG-derived evolving functional brain networks. Our findings emphasize the need to take into account the impact of biological rhythms in order to avoid erroneous statements about brain dynamics and about evolving functional brain networks.

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