95 results match your criteria Brain informatics[Journal]


How Amdahl's Law limits the performance of large artificial neural networks : why the functionality of full-scale brain simulation on processor-based simulators is limited.

Authors:
János Végh

Brain Inform 2019 Apr 11;6(1). Epub 2019 Apr 11.

Kalimános BT, Komlóssy u 26, Debrecen, 4032, Hungary.

With both knowing more and more details about how neurons and complex neural networks work and having serious demand for making performable huge artificial networks, more and more efforts are devoted to build both hardware and/or software simulators and supercomputers targeting artificial intelligence applications, demanding an exponentially increasing amount of computing capacity. However, the inherently parallel operation of the neural networks is mostly simulated deploying inherently sequential (or in the best case: sequential-parallel) computing elements. The paper shows that neural network simulators, (both software and hardware ones), akin to all other sequential-parallel computing systems, have computing performance limitation due to deploying clock-driven electronic circuits, the 70-year old computing paradigm and Amdahl's Law about parallelized computing systems. Read More

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http://dx.doi.org/10.1186/s40708-019-0097-2DOI Listing

The role of artificial intelligence and machine learning in harmonization of high-resolution post-mortem MRI (virtopsy) with respect to brain microstructure.

Brain Inform 2019 Mar 7;6(1). Epub 2019 Mar 7.

Holzinger Group, Institute for Medical Informatics and Statistics, Medical University of Graz, Graz, Austria.

Enhanced resolution of 7 T magnetic resonance imaging (MRI) scanners has considerably advanced our knowledge of structure and function in human and animal brains. Post-industrialized countries are particularly prone to an ever-increasing number of ageing individuals and ageing-associated neurodegenerative diseases. Neurodegenerative diseases are associated with volume loss in the affected brain. Read More

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http://dx.doi.org/10.1186/s40708-019-0096-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6403267PMC
March 2019
1 Read

A machine learning approach to predict perceptual decisions: an insight into face pareidolia.

Brain Inform 2019 Feb 5;6(1). Epub 2019 Feb 5.

Department of Electronics and Electrical Communication Engineering, Indian Institute of Technology, Kharagpur, India.

The perception of an external stimulus not only depends upon the characteristics of the stimulus but is also influenced by the ongoing brain activity prior to its presentation. In this work, we directly tested whether spontaneous electrical brain activities in prestimulus period could predict perceptual outcome in face pareidolia (visualizing face in noise images) on a trial-by-trial basis. Participants were presented with only noise images but with the prior information that some faces would be hidden in these images, while their electrical brain activities were recorded; participants reported their perceptual decision, face or no-face, on each trial. Read More

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http://dx.doi.org/10.1186/s40708-019-0094-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6363645PMC
February 2019

Improved shuffled frog leaping algorithm on system reliability analysis.

Authors:
Yancang Li Zhen Yan

Brain Inform 2019 Jan 31;6(1). Epub 2019 Jan 31.

College of Civil Engineering, Hebei University of Engineering, Handan, China.

With the increase in system complexity, the intelligent heuristic optimization methods have received more and more attention on system reliability analysis. However, the objective functions and constraint conditions of system reliability are nonlinear. Thereby, a hybrid optimization method was proposed, based on the shuffled frog leaping algorithm and bacterial foraging algorithm, to solve the problem of system reliability and redundancy allocation. Read More

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http://dx.doi.org/10.1186/s40708-019-0095-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6357220PMC
January 2019

WaaS architecture-driven depressive mood status quantitative analysis based on forehead EEG and self-rating tool.

Brain Inform 2018 Dec 5;5(2):15. Epub 2018 Dec 5.

Department of Life Science and Informatics, Maebashi Institute of Technology, Maebashi, 3710864, Japan.

Background: Although the objective depression evaluation is a hot topic in recent years, less is known concerning developing a pervasive and objective approach for quantitatively evaluating depression. Driven by the Wisdom as a Service architecture, a quantitative analysis method for rating depressive mood status based on forehead electroencephalograph (EEG) and an electronic diary log application named quantitative log for mental state (Q-Log) is proposed. A regression method based on random forest algorithm is adopted to train the quantitative model, where independent variables are forehead EEG features and the dependent variables are the first principal component (FPC) values of the Q-Log. Read More

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https://braininformatics.springeropen.com/articles/10.1186/s
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http://dx.doi.org/10.1186/s40708-018-0093-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6429167PMC
December 2018
10 Reads

Mental state and emotion detection from musically stimulated EEG.

Brain Inform 2018 Nov 29;5(2):14. Epub 2018 Nov 29.

H.B.T. Medical College and Dr. R.N. Cooper Mun. Gen. Hospital, Mumbai, India.

This literature survey attempts to clarify different approaches considered to study the impact of the musical stimulus on the human brain using EEG Modality. Glancing at the field through various aspects of such studies specifically an experimental protocol, the EEG machine, number of channels investigated, feature extracted, categories of emotions, the brain area, the brainwaves, statistical tests, machine learning algorithms used for classification and validation of the developed model. This article comments on how these different approaches have particular weaknesses and strengths. Read More

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http://dx.doi.org/10.1186/s40708-018-0092-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6429168PMC
November 2018

A structural equation model for imaging genetics using spatial transcriptomics.

Brain Inform 2018 Nov 2;5(2):13. Epub 2018 Nov 2.

Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands.

Imaging genetics deals with relationships between genetic variation and imaging variables, often in a disease context. The complex relationships between brain volumes and genetic variants have been explored with both dimension reduction methods and model-based approaches. However, these models usually do not make use of the extensive knowledge of the spatio-anatomical patterns of gene activity. Read More

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https://braininformatics.springeropen.com/articles/10.1186/s
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http://dx.doi.org/10.1186/s40708-018-0091-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6429169PMC
November 2018
9 Reads

Side-channel attacks against the human brain: the PIN code case study (extended version).

Brain Inform 2018 Oct 29;5(2):12. Epub 2018 Oct 29.

UCLouvain, 1348, Louvain-la-Neuve, Belgium.

We revisit the side-channel attacks with brain-computer interfaces (BCIs) first put forward by Martinovic et al. at the USENIX 2012 Security Symposium. For this purpose, we propose a comprehensive investigation of concrete adversaries trying to extract a PIN code from electroencephalogram signals. Read More

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https://braininformatics.springeropen.com/articles/10.1186/s
Publisher Site
http://dx.doi.org/10.1186/s40708-018-0090-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6429170PMC
October 2018
13 Reads

Automated epileptic seizures detection using multi-features and multilayer perceptron neural network.

Brain Inform 2018 Sep 3;5(2):10. Epub 2018 Sep 3.

Institute of Neuroscience, Ramaiah Medical College and Hospitals, Bengaluru, India.

Detection of epileptic seizure activities from long-term multi-channel electroencephalogram (EEG) signals plays a significant role in the timely treatment of the patients with epilepsy. Visual identification of epileptic seizure in long-term EEG is cumbersome and tedious for neurologists, which might also lead to human error. Therefore, an automated tool for accurate detection of seizures in a long-term multi-channel EEG is essential for the clinical diagnosis. Read More

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http://dx.doi.org/10.1186/s40708-018-0088-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6170940PMC
September 2018

Correction to: Two-step verification of brain tumor segmentation using watershed-matching algorithm.

Brain Inform 2018 Aug 29;5(2):11. Epub 2018 Aug 29.

Department of Electrical and Electronic Engineering, Khulna University of Engineering Technology (KUET), Khulna, 9203, Bangladesh.

In the original publication of this article [1], the spelling of second author was incorrect. Read More

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http://dx.doi.org/10.1186/s40708-018-0089-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6170937PMC

The effects of emotional states and traits on time perception.

Brain Inform 2018 Aug 20;5(2). Epub 2018 Aug 20.

Department of Psychology, Rawl Building, East Carolina University, Greenville, NC, 27858, USA.

Background: Models of time perception share an element of scalar expectancy theory known as the internal clock, containing specific mechanisms by which the brain is able to experience time passing and function effectively. A debate exists about whether to treat factors that influence these internal clock mechanisms (e.g. Read More

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http://dx.doi.org/10.1186/s40708-018-0087-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6170941PMC

Two-step verification of brain tumor segmentation using watershed-matching algorithm.

Brain Inform 2018 Aug 14;5(2). Epub 2018 Aug 14.

Department of Electrical and Electronic Engineering, Khulna University of Engineering Technology (KUET), Khulna, 9203, Bangladesh.

Though the modern medical imaging research is advancing at a booming rate, it is still a very challenging task to detect brain tumor perfectly. Medical imaging unlike other imaging system has highest penalty for a minimal error. So, the detection of tumor should be accurate to minimize the error. Read More

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http://dx.doi.org/10.1186/s40708-018-0086-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6170944PMC

Thought Chart: tracking the thought with manifold learning during emotion regulation.

Brain Inform 2018 Jul 19;5(2). Epub 2018 Jul 19.

Department of Psychiatry, Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA.

The Nash embedding theorem demonstrates that any compact manifold can be isometrically embedded in a Euclidean space. Assuming the complex brain states form a high-dimensional manifold in a topological space, we propose a manifold learning framework, termed Thought Chart, to reconstruct and visualize the manifold in a low-dimensional space. Furthermore, it serves as a data-driven approach to discover the underlying dynamics when the brain is engaged in a series of emotion and cognitive regulation tasks. Read More

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http://dx.doi.org/10.1186/s40708-018-0085-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6170936PMC
July 2018
16 Reads

Various epileptic seizure detection techniques using biomedical signals: a review.

Authors:
Yash Paul

Brain Inform 2018 Jul 10;5(2). Epub 2018 Jul 10.

School of Informatics, Eötvös Loránd University, Budapest, Hungary.

Epilepsy is a chronic chaos of the central nervous system that influences individual's daily life by putting it at risk due to repeated seizures. Epilepsy affects more than 2% people worldwide of which developing countries are affected worse. A seizure is a transient irregularity in the brain's electrical activity that produces disturbing physical symptoms such as a lapse in attention and memory, a sensory illusion, etc. Read More

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http://dx.doi.org/10.1186/s40708-018-0084-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6170938PMC
July 2018
17 Reads

A review and outlook on visual analytics for uncertainties in functional magnetic resonance imaging.

Brain Inform 2018 Jul 3;5(2). Epub 2018 Jul 3.

Biomedical and Multimedia Information Technology Research Group, University of Sydney, Sydney, Australia.

Analysis of functional magnetic resonance imaging (fMRI) plays a pivotal role in uncovering an understanding of the brain. fMRI data contain both spatial volume and temporal signal information, which provide a depiction of brain activity. The analysis pipeline, however, is hampered by numerous uncertainties in many of the steps; often seen as one of the last hurdles for the domain. Read More

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http://dx.doi.org/10.1186/s40708-018-0083-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6170942PMC

Review of EEG-based pattern classification frameworks for dyslexia.

Brain Inform 2018 Jun 15;5(2). Epub 2018 Jun 15.

School of Engineering and Information Technology, Murdoch University, Murdoch, Australia.

Dyslexia is a disability that causes difficulties in reading and writing despite average intelligence. This hidden disability often goes undetected since dyslexics are normal and healthy in every other way. Electroencephalography (EEG) is one of the upcoming methods being researched for identifying unique brain activation patterns in dyslexics. Read More

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http://dx.doi.org/10.1186/s40708-018-0079-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6094381PMC

A 3D stereotactic atlas of the adult human skull base.

Brain Inform 2018 May 31;5(2). Epub 2018 May 31.

Mae Tao Clinic, P.O. Box 67, Mae Sot, Tak, 63110, Thailand.

Background: The skull base region is anatomically complex and poses surgical challenges. Although many textbooks describe this region illustrated well with drawings, scans and photographs, a complete, 3D, electronic, interactive, realistic, fully segmented and labeled, and stereotactic atlas of the skull base has not yet been built. Our goal is to create a 3D electronic atlas of the adult human skull base along with interactive tools for structure manipulation, exploration, and quantification. Read More

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http://dx.doi.org/10.1186/s40708-018-0082-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6170943PMC
May 2018
4 Reads

Brain MRI analysis for Alzheimer's disease diagnosis using an ensemble system of deep convolutional neural networks.

Brain Inform 2018 May 31;5(2). Epub 2018 May 31.

Department of Computer Science, Georgia State University, Atlanta, GA, 30302-5060, USA.

Alzheimer's disease is an incurable, progressive neurological brain disorder. Earlier detection of Alzheimer's disease can help with proper treatment and prevent brain tissue damage. Several statistical and machine learning models have been exploited by researchers for Alzheimer's disease diagnosis. Read More

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http://dx.doi.org/10.1186/s40708-018-0080-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6170939PMC
May 2018
1 Read

DeepNeuron: an open deep learning toolbox for neuron tracing.

Brain Inform 2018 Jun 6;5(2). Epub 2018 Jun 6.

Allen Institute for Brain Science, Seattle, USA.

Reconstructing three-dimensional (3D) morphology of neurons is essential for understanding brain structures and functions. Over the past decades, a number of neuron tracing tools including manual, semiautomatic, and fully automatic approaches have been developed to extract and analyze 3D neuronal structures. Nevertheless, most of them were developed based on coding certain rules to extract and connect structural components of a neuron, showing limited performance on complicated neuron morphology. Read More

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http://dx.doi.org/10.1186/s40708-018-0081-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5990497PMC
June 2018
2 Reads

Removal of muscular artifacts in EEG signals: a comparison of linear decomposition methods.

Brain Inform 2018 Mar 10;5(1):13-22. Epub 2018 Jan 10.

Technische Universität Berlin, Berlin, Germany.

The most common approach to reduce muscle artifacts in electroencephalographic signals is to linearly decompose the signals in order to separate artifactual from neural sources, using one of several variants of independent component analysis (ICA). Here we compare three of the most commonly used ICA methods (extended Infomax, FastICA and TDSEP) with two other linear decomposition methods (Fourier-ICA and spatio-spectral decomposition) suitable for the extraction of oscillatory activity. We evaluate the methods' ability to remove event-locked muscle artifacts while maintaining event-related desynchronization in data from 18 subjects who performed self-paced foot movements. Read More

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http://dx.doi.org/10.1007/s40708-017-0074-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5893498PMC
March 2018
5 Reads

Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network.

Brain Inform 2018 Mar 8;5(1):23-30. Epub 2018 Jan 8.

Department of CS&E, MSRIT, Bangalore, India.

The identification, segmentation and detection of infecting area in brain tumor MRI images are a tedious and time-consuming task. The different anatomy structure of human body can be visualized by an image processing concepts. It is very difficult to have vision about the abnormal structures of human brain using simple imaging techniques. Read More

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http://dx.doi.org/10.1007/s40708-017-0075-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5893499PMC
March 2018
2 Reads

An efficient scheme for mental task classification utilizing reflection coefficients obtained from autocorrelation function of EEG signal.

Brain Inform 2018 Mar 9;5(1):1-12. Epub 2017 Dec 9.

Bangladesh University of Engineering and Technology (BUET), Dhaka, 1000, Bangladesh.

Classification of different mental tasks using electroencephalogram (EEG) signal plays an imperative part in various brain-computer interface (BCI) applications. In the design of BCI systems, features extracted from lower frequency bands of scalp-recorded EEG signals are generally considered to classify mental tasks and higher frequency bands are mostly ignored as noise. However, in this paper, it is demonstrated that high frequency components of EEG signal can provide accommodating data for enhancing the classification performance of the mental task-based BCI. Read More

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http://dx.doi.org/10.1007/s40708-017-0073-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5893497PMC

Bioplausible multiscale filtering in retino-cortical processing as a mechanism in perceptual grouping.

Brain Inform 2017 Dec 8;4(4):271-293. Epub 2017 Sep 8.

College of Science and Engineering, Flinders University, GPO Box 2100, Adelaide, SA, 5001, Australia.

Why does our visual system fail to reconstruct reality, when we look at certain patterns? Where do Geometrical illusions start to emerge in the visual pathway? How far should we take computational models of vision with the same visual ability to detect illusions as we do? This study addresses these questions, by focusing on a specific underlying neural mechanism involved in our visual experiences that affects our final perception. Among many types of visual illusion, 'Geometrical' and, in particular, 'Tilt Illusions' are rather important, being characterized by misperception of geometric patterns involving lines and tiles in combination with contrasting orientation, size or position. Over the last decade, many new neurophysiological experiments have led to new insights as to how, when and where retinal processing takes place, and the encoding nature of the retinal representation that is sent to the cortex for further processing. Read More

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http://link.springer.com/10.1007/s40708-017-0072-8
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http://dx.doi.org/10.1007/s40708-017-0072-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5709283PMC
December 2017
24 Reads

Brain explorer for connectomic analysis.

Brain Inform 2017 Dec 23;4(4):253-269. Epub 2017 Aug 23.

Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA.

Visualization plays a vital role in the analysis of multimodal neuroimaging data. A major challenge in neuroimaging visualization is how to integrate structural, functional, and connectivity data to form a comprehensive visual context for data exploration, quality control, and hypothesis discovery. We develop a new integrated visualization solution for brain imaging data by combining scientific and information visualization techniques within the context of the same anatomical structure. Read More

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http://link.springer.com/10.1007/s40708-017-0071-9
Publisher Site
http://dx.doi.org/10.1007/s40708-017-0071-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5709282PMC
December 2017
28 Reads

Optimized statistical parametric mapping procedure for NIRS data contaminated by motion artifacts : Neurometric analysis of body schema extension.

Authors:
Satoshi Suzuki

Brain Inform 2017 Sep 29;4(3):171-182. Epub 2017 Jul 29.

Department of Robotics and Mechatronics, Tokyo Denki University, 5 Asahi-Chou, Senju, Adachi-ku, Tokyo, 120-8551, Japan.

This study investigated the spatial distribution of brain activity on body schema (BS) modification induced by natural body motion using two versions of a hand-tracing task. In Task 1, participants traced Japanese Hiragana characters using the right forefinger, requiring no BS expansion. In Task 2, participants performed the tracing task with a long stick, requiring BS expansion. Read More

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http://link.springer.com/10.1007/s40708-017-0070-x
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http://dx.doi.org/10.1007/s40708-017-0070-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5563303PMC
September 2017
1 Read

Emotion recognition based on EEG features in movie clips with channel selection.

Brain Inform 2017 Dec 15;4(4):241-252. Epub 2017 Jul 15.

Electrical and Electronics Engineering, Mus Alparslan University, 49000, Muş, Turkey.

Emotion plays an important role in human interaction. People can explain their emotions in terms of word, voice intonation, facial expression, and body language. However, brain-computer interface (BCI) systems have not reached the desired level to interpret emotions. Read More

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http://dx.doi.org/10.1007/s40708-017-0069-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5709281PMC
December 2017
9 Reads

Brain connectivity during encoding and retrieval of spatial information: individual differences in navigation skills.

Brain Inform 2017 Sep 16;4(3):207-217. Epub 2017 May 16.

Instrumentation and Control Engineering Department, NSIT, Dwarka, Delhi, 110078, India.

Emerging evidence suggests that the variations in the ability to navigate through any real or virtual environment are accompanied by distinct underlying cortical activations in multiple regions of the brain. These activations may appear due to the use of different frame of reference (FOR) for representing an environment. The present study investigated the brain dynamics in the good and bad navigators using Graph Theoretical analysis applied to low-density electroencephalography (EEG) data. Read More

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http://dx.doi.org/10.1007/s40708-017-0066-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5563302PMC
September 2017
29 Reads

The effect of anger expression style on cardiovascular responses to lateralized cognitive stressors.

Brain Inform 2017 Dec 15;4(4):231-239. Epub 2017 May 15.

Department of Psychology, Behavioral Neuroscience Laboratory, Williams Hall, Virginia Tech, Blacksburg, VA, 24061-0436, USA.

To determine the effects of self-reported anger expression style on cerebrally lateralized physiological responses to neuropsychological stressors, changes in systolic blood pressure and heart rate were examined in response to a verbal fluency task and a figural fluency task among individuals reporting either "anger in" or "anger out" expression styles. Significant group by trial interaction effects was found for systolic blood pressure following administration of verbal fluency [F(1,54) = 5.86, p < 0. Read More

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http://dx.doi.org/10.1007/s40708-017-0068-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5709280PMC
December 2017
21 Reads

Multiscale modeling in the clinic: diseases of the brain and nervous system.

Brain Inform 2017 Dec 9;4(4):219-230. Epub 2017 May 9.

USC, Los Angeles, CA, USA.

Computational neuroscience is a field that traces its origins to the efforts of Hodgkin and Huxley, who pioneered quantitative analysis of electrical activity in the nervous system. While also continuing as an independent field, computational neuroscience has combined with computational systems biology, and neural multiscale modeling arose as one offshoot. This consolidation has added electrical, graphical, dynamical system, learning theory, artificial intelligence and neural network viewpoints with the microscale of cellular biology (neuronal and glial), mesoscales of vascular, immunological and neuronal networks, on up to macroscales of cognition and behavior. Read More

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http://dx.doi.org/10.1007/s40708-017-0067-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5709279PMC
December 2017
12 Reads

Preoperative prediction of language function by diffusion tensor imaging.

Brain Inform 2017 Sep 4;4(3):201-205. Epub 2017 May 4.

Department of Neurosurgery, Medical University of Innsbruck, Anichstrasse 35, 6020, Innsbruck, Austria.

For surgery of eloquent tumors in language areas, the accepted gold standard is functional mapping through direct cortical stimulation (DCS) in awake patients. Ever since, neuroscientists are searching for reliable noninvasive detection of function in the human brain, with variable success. The potential of diffusion tensor imaging (DTI) in combination with computational cortical parcellation to predict functional areas in language eloquent tumors has not been assessed so far. Read More

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http://dx.doi.org/10.1007/s40708-017-0064-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5563300PMC
September 2017

Machine learning-XGBoost analysis of language networks to classify patients with epilepsy.

Brain Inform 2017 Sep 22;4(3):159-169. Epub 2017 Apr 22.

CNRS LPNC UMR 5105, Univ. Grenoble Alpes, 380000, Grenoble, France.

Our goal was to apply a statistical approach to allow the identification of atypical language patterns and to differentiate patients with epilepsy from healthy subjects, based on their cerebral activity, as assessed by functional MRI (fMRI). Patients with focal epilepsy show reorganization or plasticity of brain networks involved in cognitive functions, inducing 'atypical' (compared to 'typical' in healthy people) brain profiles. Moreover, some of these patients suffer from drug-resistant epilepsy, and they undergo surgery to stop seizures. Read More

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http://dx.doi.org/10.1007/s40708-017-0065-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5563301PMC
September 2017
2 Reads

Fast assembling of neuron fragments in serial 3D sections.

Brain Inform 2017 Sep 1;4(3):183-186. Epub 2017 Apr 1.

Allen Institute for Brain Science, Seattle, WA, USA.

Reconstructing neurons from 3D image-stacks of serial sections of thick brain tissue is very time-consuming and often becomes a bottleneck in high-throughput brain mapping projects. We developed NeuronStitcher, a software suite for stitching non-overlapping neuron fragments reconstructed in serial 3D image sections. With its efficient algorithm and user-friendly interface, NeuronStitcher has been used successfully to reconstruct very large and complex human and mouse neurons. Read More

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http://dx.doi.org/10.1007/s40708-017-0063-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5563299PMC
September 2017
4 Reads

An ontology-based search engine for digital reconstructions of neuronal morphology.

Brain Inform 2017 Jun 23;4(2):123-134. Epub 2017 Mar 23.

Center for Neural Informatics, Structures and Plasticity, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA.

Neuronal morphology is extremely diverse across and within animal species, developmental stages, brain regions, and cell types. This diversity is functionally important because neuronal structure strongly affects synaptic integration, spiking dynamics, and network connectivity. Digital reconstructions of axonal and dendritic arbors are thus essential to quantify and model information processing in the nervous system. Read More

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http://dx.doi.org/10.1007/s40708-017-0062-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5413594PMC
June 2017
1 Read

Pattern recognition of spectral entropy features for detection of alcoholic and control visual ERP's in multichannel EEGs.

Brain Inform 2017 Jun 21;4(2):147-158. Epub 2017 Jan 21.

Department of Medical Electronics, M.S. Ramaiah Institute of Technology (An Autonomous Institute, Affiliated to Visvesvaraya Technological University), Bangalore, Karnataka, 560054, India.

This paper presents a novel ranking method to select spectral entropy (SE) features that discriminate alcoholic and control visual event-related potentials (ERP'S) in gamma sub-band (30-55 Hz) derived from a 64-channel electroencephalogram (EEG) recording. The ranking is based on a t test statistic that rejects the null hypothesis that the group means of SE values in alcoholics and controls are identical. The SE features with high ranks are indicative of maximal separation between their group means. Read More

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http://dx.doi.org/10.1007/s40708-017-0061-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5413593PMC
June 2017
7 Reads

Optshrink LR + S: accelerated fMRI reconstruction using non-convex optimal singular value shrinkage.

Brain Inform 2017 Mar 10;4(1):65-83. Epub 2017 Jan 10.

SBILab, Department of Electronics and Communication Engineering, IIIT-Delhi, New Delhi, India.

This paper presents a new accelerated fMRI reconstruction method, namely, OptShrink LR + S method that reconstructs undersampled fMRI data using a linear combination of low-rank and sparse components. The low-rank component has been estimated using non-convex optimal singular value shrinkage algorithm, while the sparse component has been estimated using convex l minimization. The performance of the proposed method is compared with the existing state-of-the-art algorithms on real fMRI dataset. Read More

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http://dx.doi.org/10.1007/s40708-016-0059-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5319953PMC
March 2017
1 Read

Test-retest reliability of brain morphology estimates.

Brain Inform 2017 Jun 5;4(2):107-121. Epub 2017 Jan 5.

Department of Psychology, Boston College, McGuinn 300, 140 Commonwealth Ave., Chestnut Hill, MA, 02467, USA.

Metrics of brain morphology are increasingly being used to examine inter-individual differences, making it important to evaluate the reliability of these structural measures. Here we used two open-access datasets to assess the intersession reliability of three cortical measures (thickness, gyrification, and fractal dimensionality) and two subcortical measures (volume and fractal dimensionality). Reliability was generally good, particularly with the gyrification and fractal dimensionality measures. Read More

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http://dx.doi.org/10.1007/s40708-016-0060-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5413592PMC
June 2017
10 Reads

Spreading activation in nonverbal memory networks.

Brain Inform 2017 Sep 28;4(3):187-199. Epub 2016 Nov 28.

San Francisco Clinical Neurosciences and University of California, San Francisco, USA.

Theories of spreading activation primarily involve semantic memory networks. However, the existence of separate verbal and visuospatial memory networks suggests that spreading activation may also occur in visuospatial memory networks. The purpose of the present investigation was to explore this possibility. Read More

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http://dx.doi.org/10.1007/s40708-016-0058-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5563298PMC
September 2017
6 Reads

Improved diagonal queue medical image steganography using Chaos theory, LFSR, and Rabin cryptosystem.

Brain Inform 2017 Jun 9;4(2):95-106. Epub 2016 Sep 9.

Department of Neuro Surgery, School of Medicine, University of Maryland, Baltimore, MD, USA.

In this article, we have proposed an improved diagonal queue medical image steganography for patient secret medical data transmission using chaotic standard map, linear feedback shift register, and Rabin cryptosystem, for improvement of previous technique (Jain and Lenka in Springer Brain Inform 3:39-51, 2016). The proposed algorithm comprises four stages, generation of pseudo-random sequences (pseudo-random sequences are generated by linear feedback shift register and standard chaotic map), permutation and XORing using pseudo-random sequences, encryption using Rabin cryptosystem, and steganography using the improved diagonal queues. Security analysis has been carried out. Read More

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http://dx.doi.org/10.1007/s40708-016-0057-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5413591PMC
June 2017
1 Read

Fuzzy clustering-based feature extraction method for mental task classification.

Brain Inform 2017 Jun 3;4(2):135-145. Epub 2016 Sep 3.

School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, 110067, India.

A brain computer interface (BCI) is a communication system by which a person can send messages or requests for basic necessities without using peripheral nerves and muscles. Response to mental task-based BCI is one of the privileged areas of investigation. Electroencephalography (EEG) signals are used to represent the brain activities in the BCI domain. Read More

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http://dx.doi.org/10.1007/s40708-016-0056-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5413590PMC

Workload regulation by Sudarshan Kriya: an EEG and ECG perspective.

Brain Inform 2017 Mar 18;4(1):13-25. Epub 2016 Jul 18.

Netaji Subhas Institute of Technology (NSIT), Dwarka, Delhi, India.

Sudarshan Kriya Yoga (SKY) is a type of rhythmic breathing activity, trivially a form of Pranayama that stimulates physical, mental, emotional, and social well-being. The objective of the present work is to verify the effect of meditation in optimizing task efficiency and regulating stress. It builds on to quantitatively answer if SKY will increase workload tolerance for divided attention tasks in the people sank in it. Read More

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http://dx.doi.org/10.1007/s40708-016-0055-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5319952PMC
March 2017
30 Reads

Spreading activation in emotional memory networks and the cumulative effects of somatic markers.

Brain Inform 2017 Jun 15;4(2):85-93. Epub 2016 Jul 15.

Virginia Polytechnic Institute, Blacksburg, USA.

The theory of spreading activation proposes that the activation of a semantic memory node may spread along bidirectional associative links to other related nodes. Although this theory was originally proposed to explain semantic memory networks, a similar process may be said to exist with episodic or emotional memory networks. The Somatic Marker hypothesis proposes that remembering an emotional memory activates the somatic sensations associated with the memory. Read More

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http://dx.doi.org/10.1007/s40708-016-0054-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5413589PMC
June 2017
1 Read

Name-calling in the hippocampus (and beyond): coming to terms with neuron types and properties.

Brain Inform 2017 Mar 9;4(1):1-12. Epub 2016 Jun 9.

Center for Neural Informatics, Structure, & Plasticity, Molecular Neuroscience Dept., Krasnow Institute for Advanced Study, MS2A1, George Mason University, Fairfax, VA, 22030-4444, USA.

Widely spread naming inconsistencies in neuroscience pose a vexing obstacle to effective communication within and across areas of expertise. This problem is particularly acute when identifying neuron types and their properties. Hippocampome. Read More

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http://dx.doi.org/10.1007/s40708-016-0053-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5319951PMC
March 2017
1 Read

Two-dimensional enrichment analysis for mining high-level imaging genetic associations.

Brain Inform 2017 Mar 13;4(1):27-37. Epub 2016 May 13.

Radiology and Imaging Sciences, Indiana University School of Medicine, 355 West 16th Street Suite 4100, Indianapolis, IN, USA.

Enrichment analysis has been widely applied in the genome-wide association studies, where gene sets corresponding to biological pathways are examined for significant associations with a phenotype to help increase statistical power and improve biological interpretation. In this work, we expand the scope of enrichment analysis into brain imaging genetics, an emerging field that studies how genetic variation influences brain structure and function measured by neuroimaging quantitative traits (QT). Given the high dimensionality of both imaging and genetic data, we propose to study Imaging Genetic Enrichment Analysis (IGEA), a new enrichment analysis paradigm that jointly considers meaningful gene sets (GS) and brain circuits (BC) and examines whether any given GS-BC pair is enriched in a list of gene-QT findings. Read More

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http://dx.doi.org/10.1007/s40708-016-0052-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5118198PMC
March 2017
8 Reads

Familiarity effects in EEG-based emotion recognition.

Brain Inform 2017 Mar 29;4(1):39-50. Epub 2016 Apr 29.

Institute of Scientific and Industrial Research (ISIR), Osaka University, Ibaraki-shi, Osaka, 567-0047, Japan.

Although emotion detection using electroencephalogram (EEG) data has become a highly active area of research over the last decades, little attention has been paid to stimulus familiarity, a crucial subjectivity issue. Using both our experimental data and a sophisticated database (DEAP dataset), we investigated the effects of familiarity on brain activity based on EEG signals. Focusing on familiarity studies, we allowed subjects to select the same number of familiar and unfamiliar songs; both resulting datasets demonstrated the importance of reporting self-emotion based on the assumption that the emotional state when experiencing music is subjective. Read More

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http://dx.doi.org/10.1007/s40708-016-0051-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5319949PMC
March 2017
3 Reads

It's not what you expect: feedback negativity is independent of reward expectation and affective responsivity in a non-probabilistic task.

Brain Inform 2017 Mar 18;4(1):51-63. Epub 2016 Apr 18.

Department of Psychology, East Carolina University, 238 Rawl Building, East 5th St., Greenville, NC, 27858, USA.

ERP studies commonly utilize gambling-based reinforcement tasks to elicit feedback negativity (FN) responses. This study used a pattern learning task in order to limit gambling-related fallacious reasoning and possible affective responses to gambling, while investigating relationships between the FN components between high and low reward expectation conditions. Eighteen undergraduates completed measures of reinforcement sensitivity, trait and state affect, and psychophysiological recording. Read More

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http://dx.doi.org/10.1007/s40708-016-0050-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5319948PMC
March 2017
7 Reads

Visual analytics for concept exploration in subspaces of patient groups : Making sense of complex datasets with the Doctor-in-the-loop.

Brain Inform 2016 Dec 21;3(4):233-247. Epub 2016 Mar 21.

Research Unit HCI-KDD, Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Graz, Austria.

Medical doctors and researchers in bio-medicine are increasingly confronted with complex patient data, posing new and difficult analysis challenges. These data are often comprising high-dimensional descriptions of patient conditions and measurements on the success of certain therapies. An important analysis question in such data is to compare and correlate patient conditions and therapy results along with combinations of dimensions. Read More

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http://dx.doi.org/10.1007/s40708-016-0043-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5106406PMC
December 2016
1 Read

A tamper-proof audit and control system for the doctor in the loop.

Brain Inform 2016 Dec 19;3(4):269-279. Epub 2016 Mar 19.

Graz University of Technology, Graz, Austria.

The "doctor in the loop" is a new paradigm in information-driven medicine, picturing the doctor as authority inside a loop supplying an expert system with information on actual patients, treatment results, and possible additional (side-)effects, including general information in order to enhance data-driven medical science, as well as giving back treatment advice to the doctor himself. While this approach can be very beneficial for new medical approaches like P4 medicine (personal, predictive, preventive, and participatory), it also relies heavily on the authenticity of the data and thus increases the need for secure and reliable databases. In this paper, we propose a solution in order to protect the doctor in the loop against responsibility derived from manipulated data, thus enabling this new paradigm to gain acceptance in the medical community. Read More

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http://dx.doi.org/10.1007/s40708-016-0046-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5106408PMC
December 2016
2 Reads

Automatic screening and classification of diabetic retinopathy and maculopathy using fuzzy image processing.

Brain Inform 2016 Dec 16;3(4):249-267. Epub 2016 Mar 16.

Faculty of Engineering, Environment and Computing, Coventry University, Priory Street, Coventry, CV1 5FB, UK.

Digital retinal imaging is a challenging screening method for which effective, robust and cost-effective approaches are still to be developed. Regular screening for diabetic retinopathy and diabetic maculopathy diseases is necessary in order to identify the group at risk of visual impairment. This paper presents a novel automatic detection of diabetic retinopathy and maculopathy in eye fundus images by employing fuzzy image processing techniques. Read More

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http://dx.doi.org/10.1007/s40708-016-0045-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5106407PMC
December 2016
11 Reads

The structure function as new integral measure of spatial and temporal properties of multichannel EEG.

Authors:
Mikhail Trifonov

Brain Inform 2016 Dec 25;3(4):211-220. Epub 2016 Feb 25.

IM Sechenov Institute of Evolutionary Physiology and Biochemistry of the Russian Academy of Sciences, Saint-Petersburg, Russia.

The first-order temporal structure functions (SFs), i.e., the first-order statistical moment of absolute increments of scaled multichannel resting state EEG signals in healthy children and teenagers over a wide range of temporal separation (time lags) are computed. Read More

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http://dx.doi.org/10.1007/s40708-016-0040-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5106404PMC
December 2016
5 Reads

Reconstructing the brain: from image stacks to neuron synthesis.

Brain Inform 2016 Dec 24;3(4):205-209. Epub 2016 Feb 24.

Allen Institute for Brain Science, Seattle, WA, 98109, USA.

Large-scale brain initiatives such as the US BRAIN initiative and the European Human Brain Project aim to marshall a vast amount of data and tools for the purpose of furthering our understanding of brains. Fundamental to this goal is that neuronal morphologies must be seamlessly reconstructed and aggregated on scales up to the whole rodent brain. The experimental labor needed to manually produce this number of digital morphologies is prohibitively large. Read More

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http://dx.doi.org/10.1007/s40708-016-0041-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5106405PMC
December 2016
13 Reads
1 Citation