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Frontiers in Neuroinformatics

Nisha A Viswan, Gubbi Vani HarshaRani, Melanie I Stefan, Upinder S Bhalla
Current experiments touch only small but overlapping parts of very complex subcellular signaling networks in neurons. Even with modern optical reporters and pharmacological manipulations, a given experiment can only monitor and control a very small subset of the diverse, multiscale processes of neuronal signaling. We have developed FindSim (Framework for Integrating Neuronal Data and SIgnaling Models) to anchor models to structured experimental datasets. FindSim is a framework for integrating many individual electrophysiological and biochemical experiments with large, multiscale models so as to systematically refine and validate the model...
2018: Frontiers in Neuroinformatics
Muhammad A Kamran, Malik M N Mannann, Myung Yung Jeong
Functional near-infrared spectroscopy (fNIRS) has evolved as a neuro-imaging modality over the course of the past two decades. The removal of superfluous information accompanying the optical signal, however, remains a challenge. A comprehensive analysis of each step is necessary to ensure the extraction of actual information from measured fNIRS waveforms. A slight change in shape could alter the features required for fNIRS-BCI applications. In the present study, the effect of the differential path-length factor (DPF) values on the characteristics of the hemodynamic response function (HRF) was investigated...
2018: Frontiers in Neuroinformatics
Manhua Liu, Danni Cheng, Weiwu Yan
Alzheimer's disease (AD) is an irreversible brain degenerative disorder affecting people aged older than 65 years. Currently, there is no effective cure for AD, but its progression can be delayed with some treatments. Accurate and early diagnosis of AD is vital for the patient care and development of future treatment. Fluorodeoxyglucose positrons emission tomography (FDG-PET) is a functional molecular imaging modality, which proves to be powerful to help understand the anatomical and neural changes of brain related to AD...
2018: Frontiers in Neuroinformatics
Stephen R Pfohl, Renaid B Kim, Grant S Coan, Cassie S Mitchell
Objective: The heterogeneity of amyotrophic lateral sclerosis (ALS) survival duration, which varies from <1 year to >10 years, challenges clinical decisions and trials. Utilizing data from 801 deceased ALS patients, we: (1) assess the underlying complex relationships among common clinical ALS metrics; (2) identify which clinical ALS metrics are the "best" survival predictors and how their predictive ability changes as a function of disease progression. Methods: Analyses included examination of relationships within the raw data as well as the construction of interactive survival regression and classification models (generalized linear model and random forests model)...
2018: Frontiers in Neuroinformatics
Christian Nowke, Sandra Diaz-Pier, Benjamin Weyers, Bernd Hentschel, Abigail Morrison, Torsten W Kuhlen, Alexander Peyser
Simulation models in many scientific fields can have non-unique solutions or unique solutions which can be difficult to find. Moreover, in evolving systems, unique final state solutions can be reached by multiple different trajectories. Neuroscience is no exception. Often, neural network models are subject to parameter fitting to obtain desirable output comparable to experimental data. Parameter fitting without sufficient constraints and a systematic exploration of the possible solution space can lead to conclusions valid only around local minima or around non-minima...
2018: Frontiers in Neuroinformatics
Soheil Keshmiri, Hidenubo Sumioka, Ryuji Yamazaki, Hiroshi Ishiguro
Neuroscience research shows a growing interest in the application of Near-Infrared Spectroscopy (NIRS) in analysis and decoding of the brain activity of human subjects. Given the correlation that is observed between the Blood Oxygen Dependent Level (BOLD) responses that are exhibited by the time series data of functional Magnetic Resonance Imaging (fMRI) and the hemoglobin oxy/deoxy-genation that is captured by NIRS, linear models play a central role in these applications. This, in turn, results in adaptation of the feature extraction strategies that are well-suited for discretization of data that exhibit a high degree of linearity, namely, slope and the mean as well as their combination, to summarize the informational contents of the NIRS time series...
2018: Frontiers in Neuroinformatics
Frederic von Wegner, Helmut Laufs
We present an open-source Python package to compute information-theoretical quantities for electroencephalographic data. Electroencephalography (EEG) measures the electrical potential generated by the cerebral cortex and the set of spatial patterns projected by the brain's electrical potential on the scalp surface can be clustered into a set of representative maps called EEG microstates. Microstate time series are obtained by competitively fitting the microstate maps back into the EEG data set, i.e., by substituting the EEG data at a given time with the label of the microstate that has the highest similarity with the actual EEG topography...
2018: Frontiers in Neuroinformatics
Luis G Hernández, Oscar Martinez Mozos, José M Ferrández, Javier M Antelis
The anticipatory recognition of braking is essential to prevent traffic accidents. For instance, driving assistance systems can be useful to properly respond to emergency braking situations. Moreover, the response time to emergency braking situations can be affected and even increased by different driver's cognitive states caused by stress, fatigue, and extra workload. This work investigates the detection of emergency braking from driver's electroencephalographic (EEG) signals that precede the brake pedal actuation...
2018: Frontiers in Neuroinformatics
Jens K Boldsen, Thorbjørn S Engedal, Salvador Pedraza, Tae-Hee Cho, Götz Thomalla, Norbert Nighoghossian, Jean-Claude Baron, Jens Fiehler, Leif Østergaard, Kim Mouridsen
Stroke is the second most common cause of death worldwide, responsible for 6.24 million deaths in 2015 (about 11% of all deaths). Three out of four stroke survivors suffer long term disability, as many cannot return to their prior employment or live independently. Eighty-seven percent of strokes are ischemic. As an increasing volume of ischemic brain tissue proceeds to permanent infarction in the hours following the onset, immediate treatment is pivotal to increase the likelihood of good clinical outcome for the patient...
2018: Frontiers in Neuroinformatics
Guilin Meng, Xiulin Meng, Xiaoye Ma, Gengping Zhang, Xiaolin Hu, Aiping Jin, Yanxin Zhao, Xueyuan Liu
Alzheimer's disease (AD) is an increasing concern in human health. Despite significant research, highly effective drugs to treat AD are lacking. The present study describes the text mining process to identify drug candidates from a traditional Chinese medicine (TCM) database, along with associated protein target mechanisms. We carried out text mining to identify literatures that referenced both AD and TCM and focused on identifying compounds and protein targets of interest. After targeting one potential TCM candidate, corresponding protein-protein interaction (PPI) networks were assembled in STRING to decipher the most possible mechanism of action...
2018: Frontiers in Neuroinformatics
Grzegorz M Wojcik, Jolanta Masiak, Andrzej Kawiak, Piotr Schneider, Lukasz Kwasniewicz, Nikodem Polak, Anna Gajos-Balinska
The interview is still the main and most important tool in psychiatrist's work. The neuroimaging methods such as CT or MRI are widely used in other fields of medicine, for instance neurology. However, psychiatry lacks effective quantitative methods to support of diagnosis. A novel neuroinformatic approach to help clinical patients by means of electroencephalographic technology in order to build foundations for finding neurophysiological biomarkers of psychiatric disorders is proposed. A cohort of 30 right-handed patients (21 males, 9 females) with psychiatric disorders (mainly with panic and anxiety disorder, Asperger syndrome as well as with phobic anxiety disorders, schizophrenia, bipolar affective disorder, obsessive-compulsive disorder, nonorganic hypersomnia, and moderate depressive episode) were examined using the dense array EEG amplifier in the P300 experiment...
2018: Frontiers in Neuroinformatics
Anthony L Vaccarino, Moyez Dharsee, Stephen Strother, Don Aldridge, Stephen R Arnott, Brendan Behan, Costas Dafnas, Fan Dong, Kenneth Edgecombe, Rachad El-Badrawi, Khaled El-Emam, Tom Gee, Susan G Evans, Mojib Javadi, Francis Jeanson, Shannon Lefaivre, Kristen Lutz, F Chris MacPhee, Jordan Mikkelsen, Tom Mikkelsen, Nicholas Mirotchnick, Tanya Schmah, Christa M Studzinski, Donald T Stuss, Elizabeth Theriault, Kenneth R Evans
Historically, research databases have existed in isolation with no practical avenue for sharing or pooling medical data into high dimensional datasets that can be efficiently compared across databases. To address this challenge, the Ontario Brain Institute's "Brain-CODE" is a large-scale neuroinformatics platform designed to support the collection, storage, federation, sharing and analysis of different data types across several brain disorders, as a means to understand common underlying causes of brain dysfunction and develop novel approaches to treatment...
2018: Frontiers in Neuroinformatics
Javaria Ashraf, Jamil Ahmad, Amjad Ali, Zaheer Ul-Haq
Alzheimer's Disease (AD) is the most common neuro-degenerative disorder in the elderly that leads to dementia. The hallmark of AD is senile lesions made by abnormal aggregation of amyloid beta in extracellular space of brain. One of the challenges in AD treatment is to better understand the mechanism of action of key proteins and their related pathways involved in neuronal cell death in order to identify adequate therapeutic targets. This study focuses on the phenomenon of aggregation of amyloid beta into plaques by considering the signal transduction pathways of Calpain-Calpastatin (CAST) regulation system and Amyloid Precursor Protein (APP) processing pathways along with Ca2+ channels...
2018: Frontiers in Neuroinformatics
Hao Guo, Yao Li, Yong Xu, Yanyi Jin, Jie Xiang, Junjie Chen
Brain network analysis has been widely applied in neuroimaging studies. A hyper-network construction method was previously proposed to characterize the high-order relationships among multiple brain regions, where every edge is connected to more than two brain regions and can be represented by a hyper-graph. A brain functional hyper-network is constructed by a sparse linear regression model using resting-state functional magnetic resonance imaging (fMRI) time series, which in previous studies has been solved by the lasso method...
2018: Frontiers in Neuroinformatics
Stavros I Dimitriadis, Avraam D Marimpis
A brain-computer interface (BCI) is a channel of communication that transforms brain activity into specific commands for manipulating a personal computer or other home or electrical devices. In other words, a BCI is an alternative way of interacting with the environment by using brain activity instead of muscles and nerves. For that reason, BCI systems are of high clinical value for targeted populations suffering from neurological disorders. In this paper, we present a new processing approach in three publicly available BCI data sets: (a) a well-known multi-class ( N = 6) coded-modulated Visual Evoked potential (c-VEP)-based BCI system for able-bodied and disabled subjects; (b) a multi-class ( N = 32) c-VEP with slow and fast stimulus representation; and (c) a steady-state Visual Evoked potential (SSVEP) multi-class ( N = 5) flickering BCI system...
2018: Frontiers in Neuroinformatics
Tiina Manninen, Jugoslava Aćimović, Riikka Havela, Heidi Teppola, Marja-Leena Linne
The possibility to replicate and reproduce published research results is one of the biggest challenges in all areas of science. In computational neuroscience, there are thousands of models available. However, it is rarely possible to reimplement the models based on the information in the original publication, let alone rerun the models just because the model implementations have not been made publicly available. We evaluate and discuss the comparability of a versatile choice of simulation tools: tools for biochemical reactions and spiking neuronal networks, and relatively new tools for growth in cell cultures...
2018: Frontiers in Neuroinformatics
Francisco Naveros, Jesus A Garrido, Richard R Carrillo, Eduardo Ros, Niceto R Luque
[This corrects the article on p. 7 in vol. 11, PMID: 28223930.].
2018: Frontiers in Neuroinformatics
Dong Wen, Zhenhao Wei, Yanhong Zhou, Guolin Li, Xu Zhang, Wei Han
No abstract text is available yet for this article.
2018: Frontiers in Neuroinformatics
Ruben A Tikidji-Hamburyan, Vikram Narayana, Zeki Bozkus, Tarek A El-Ghazawi
[This corrects the article on p. 46 in vol. 11, PMID: 28775687.].
2018: Frontiers in Neuroinformatics
Lealem Mulugeta, Andrew Drach, Ahmet Erdemir, C A Hunt, Marc Horner, Joy P Ku, Jerry G Myers, Rajanikanth Vadigepalli, William W Lytton
Modeling and simulation in computational neuroscience is currently a research enterprise to better understand neural systems. It is not yet directly applicable to the problems of patients with brain disease. To be used for clinical applications, there must not only be considerable progress in the field but also a concerted effort to use best practices in order to demonstrate model credibility to regulatory bodies, to clinics and hospitals, to doctors, and to patients. In doing this for neuroscience, we can learn lessons from long-standing practices in other areas of simulation (aircraft, computer chips), from software engineering, and from other biomedical disciplines...
2018: Frontiers in Neuroinformatics
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