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Frontiers in Neuroinformatics

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https://read.qxmd.com/read/30687056/a-single-cell-level-and-connectome-derived-computational-model-of-the-drosophila-brain
#1
Yu-Chi Huang, Cheng-Te Wang, Ta-Shun Su, Kuo-Wei Kao, Yen-Jen Lin, Chao-Chun Chuang, Ann-Shyn Chiang, Chung-Chuan Lo
Computer simulations play an important role in testing hypotheses, integrating knowledge, and providing predictions of neural circuit functions. While considerable effort has been dedicated into simulating primate or rodent brains, the fruit fly ( Drosophila melanogaster ) is becoming a promising model animal in computational neuroscience for its small brain size, complex cognitive behavior, and abundancy of data available from genes to circuits. Moreover, several Drosophila connectome projects have generated a large number of neuronal images that account for a significant portion of the brain, making a systematic investigation of the whole brain circuit possible...
2018: Frontiers in Neuroinformatics
https://read.qxmd.com/read/30670959/an-empirical-comparison-of-meta-and-mega-analysis-with-data-from-the-enigma-obsessive-compulsive-disorder-working-group
#2
Premika S W Boedhoe, Martijn W Heymans, Lianne Schmaal, Yoshinari Abe, Pino Alonso, Stephanie H Ameis, Alan Anticevic, Paul D Arnold, Marcelo C Batistuzzo, Francesco Benedetti, Jan C Beucke, Irene Bollettini, Anushree Bose, Silvia Brem, Anna Calvo, Rosa Calvo, Yuqi Cheng, Kang Ik K Cho, Valentina Ciullo, Sara Dallaspezia, Damiaan Denys, Jamie D Feusner, Kate D Fitzgerald, Jean-Paul Fouche, Egill A Fridgeirsson, Patricia Gruner, Gregory L Hanna, Derrek P Hibar, Marcelo Q Hoexter, Hao Hu, Chaim Huyser, Neda Jahanshad, Anthony James, Norbert Kathmann, Christian Kaufmann, Kathrin Koch, Jun Soo Kwon, Luisa Lazaro, Christine Lochner, Rachel Marsh, Ignacio Martínez-Zalacaín, David Mataix-Cols, José M Menchón, Luciano Minuzzi, Astrid Morer, Takashi Nakamae, Tomohiro Nakao, Janardhanan C Narayanaswamy, Seiji Nishida, Erika L Nurmi, Joseph O'Neill, John Piacentini, Fabrizio Piras, Federica Piras, Y C Janardhan Reddy, Tim J Reess, Yuki Sakai, Joao R Sato, H Blair Simpson, Noam Soreni, Carles Soriano-Mas, Gianfranco Spalletta, Michael C Stevens, Philip R Szeszko, David F Tolin, Guido A van Wingen, Ganesan Venkatasubramanian, Susanne Walitza, Zhen Wang, Je-Yeon Yun, Paul M Thompson, Dan J Stein, Odile A van den Heuvel, Jos W R Twisk
Objective: Brain imaging communities focusing on different diseases have increasingly started to collaborate and to pool data to perform well-powered meta- and mega-analyses. Some methodologists claim that a one-stage individual-participant data (IPD) mega-analysis can be superior to a two-stage aggregated data meta-analysis, since more detailed computations can be performed in a mega-analysis. Before definitive conclusions regarding the performance of either method can be drawn, it is necessary to critically evaluate the methodology of, and results obtained by, meta- and mega-analyses...
2018: Frontiers in Neuroinformatics
https://read.qxmd.com/read/30631270/integration-of-omics-data-and-phenotypic-data-within-a-unified-extensible-multimodal-framework
#3
Samir Das, Xavier Lecours Boucher, Christine Rogers, Carolina Makowski, François Chouinard-Decorte, Kathleen Oros Klein, Natacha Beck, Pierre Rioux, Shawn T Brown, Zia Mohaddes, Cole Zweber, Victoria Foing, Marie Forest, Kieran J O'Donnell, Joanne Clark, Michael J Meaney, Celia M T Greenwood, Alan C Evans
Analysis of " omics " data is often a long and segmented process, encompassing multiple stages from initial data collection to processing, quality control and visualization. The cross-modal nature of recent genomic analyses renders this process challenging to both automate and standardize; consequently, users often resort to manual interventions that compromise data reliability and reproducibility. This in turn can produce multiple versions of datasets across storage systems. As a result, scientists can lose significant time and resources trying to execute and monitor their analytical workflows and encounter difficulties sharing versioned data...
2018: Frontiers in Neuroinformatics
https://read.qxmd.com/read/30631269/bindsnet-a-machine-learning-oriented-spiking-neural-networks-library-in-python
#4
Hananel Hazan, Daniel J Saunders, Hassaan Khan, Devdhar Patel, Darpan T Sanghavi, Hava T Siegelmann, Robert Kozma
The development of spiking neural network simulation software is a critical component enabling the modeling of neural systems and the development of biologically inspired algorithms. Existing software frameworks support a wide range of neural functionality, software abstraction levels, and hardware devices, yet are typically not suitable for rapid prototyping or application to problems in the domain of machine learning. In this paper, we describe a new Python package for the simulation of spiking neural networks, specifically geared toward machine learning and reinforcement learning...
2018: Frontiers in Neuroinformatics
https://read.qxmd.com/read/30622468/national-neuroinformatics-framework-for-canadian-consortium-on-neurodegeneration-in-aging-ccna
#5
Zia Mohaddes, Samir Das, Rida Abou-Haidar, Mouna Safi-Harab, David Blader, Jessica Callegaro, Charlie Henri-Bellemare, Jingla-Fri Tunteng, Leigh Evans, Tara Campbell, Derek Lo, Pierre-Emmanuel Morin, Victor Whitehead, Howard Chertkow, Alan C Evans
The Canadian Institutes for Health Research (CIHR) launched the "International Collaborative Research Strategy for Alzheimer's Disease" as a signature initiative, focusing on Alzheimer's Disease (AD) and related neurodegenerative disorders (NDDs). The Canadian Consortium for Neurodegeneration and Aging (CCNA) was subsequently established to coordinate and strengthen Canadian research on AD and NDDs. To facilitate this research, CCNA uses LORIS, a modular data management system that integrates acquisition, storage, curation, and dissemination across multiple modalities...
2018: Frontiers in Neuroinformatics
https://read.qxmd.com/read/30618704/nfblab-a-versatile-software-for-neurofeedback-and-brain-computer-interface-research
#6
Nikolai Smetanin, Ksenia Volkova, Stanislav Zabodaev, Mikhail A Lebedev, Alexei Ossadtchi
Neurofeedback (NFB) is a real-time paradigm, where subjects learn to volitionally modulate their own brain activity recorded with electroencephalographic (EEG), magnetoencephalographic (MEG) or other functional brain imaging techniques and presented to them via one of sensory modalities: visual, auditory or tactile. NFB has been proposed as an approach to treat neurological conditions and augment brain functions. Although the early NFB studies date back nearly six decades ago, there is still much debate regarding the efficiency of this approach and the ways it should be implemented...
2018: Frontiers in Neuroinformatics
https://read.qxmd.com/read/30618703/real-time-processing-of-two-photon-calcium-imaging-data-including-lateral-motion-artifact-correction
#7
Akinori Mitani, Takaki Komiyama
Two-photon calcium imaging has been extensively used to record neural activity in the brain. It has been long used solely with post-hoc analysis, but the recent efforts began to include closed-loop experiments. Closed-loop experiments pose new challenges because they require fast, real-time image processing without iterative parameter tuning. When imaging awake animals, one of the crucial steps of post hoc image analysis is correction of lateral motion artifacts. In most of the closed-loop experiments, this step has not been implemented and ignored due to technical difficulties...
2018: Frontiers in Neuroinformatics
https://read.qxmd.com/read/30618702/robust-and-fast-markov-chain-monte-carlo-sampling-of-diffusion-mri-microstructure-models
#8
Robbert L Harms, Alard Roebroeck
In diffusion MRI analysis, advances in biophysical multi-compartment modeling have gained popularity over the conventional Diffusion Tensor Imaging (DTI), because they can obtain a greater specificity in relating the dMRI signal to underlying cellular microstructure. Biophysical multi-compartment models require a parameter estimation, typically performed using either the Maximum Likelihood Estimation (MLE) or the Markov Chain Monte Carlo (MCMC) sampling. Whereas, the MLE provides only a point estimate of the fitted model parameters, the MCMC recovers the entire posterior distribution of the model parameters given in the data, providing additional information such as parameter uncertainty and correlations...
2018: Frontiers in Neuroinformatics
https://read.qxmd.com/read/30618701/initial-dip-existence-and-estimation-in-relation-to-dpf-and-data-drift
#9
Muhammad A Kamran, Malik M Naeem Mannan, Myung-Yung Jeong
Early de-oxygenation (initial dip) is an indicator of the primal cortical activity source in functional neuro-imaging. In this study, initial dip's existence and its estimation in relation to the differential pathlength factor (DPF) and data drift were investigated in detail. An efficient algorithm for estimation of drift in fNIRS data is proposed. The results favor the shifting of the fNIRS signal to a transformed coordinate system to infer correct information. Additionally, in this study, the effect of the DPF on initial dip was comprehensively analyzed...
2018: Frontiers in Neuroinformatics
https://read.qxmd.com/read/30618700/epileptic-seizure-detection-based-on-eeg-signals-and-cnn
#10
Mengni Zhou, Cheng Tian, Rui Cao, Bin Wang, Yan Niu, Ting Hu, Hao Guo, Jie Xiang
Epilepsy is a neurological disorder that affects approximately fifty million people according to the World Health Organization. While electroencephalography (EEG) plays important roles in monitoring the brain activity of patients with epilepsy and diagnosing epilepsy, an expert is needed to analyze all EEG recordings to detect epileptic activity. This method is obviously time-consuming and tedious, and a timely and accurate diagnosis of epilepsy is essential to initiate antiepileptic drug therapy and subsequently reduce the risk of future seizures and seizure-related complications...
2018: Frontiers in Neuroinformatics
https://read.qxmd.com/read/30618699/an-automated-pipeline-for-the-analysis-of-pet-data-on-the-cortical-surface
#11
Arnaud Marcoux, Ninon Burgos, Anne Bertrand, Marc Teichmann, Alexandre Routier, Junhao Wen, Jorge Samper-González, Simona Bottani, Stanley Durrleman, Marie-Odile Habert, Olivier Colliot
We present a fully automatic pipeline for the analysis of PET data on the cortical surface. Our pipeline combines tools from FreeSurfer and PETPVC, and consists of (i) co-registration of PET and T1-w MRI (T1) images, (ii) intensity normalization, (iii) partial volume correction, (iv) robust projection of the PET signal onto the subject's cortical surface, (v) spatial normalization to a template, and (vi) atlas statistics. We evaluated the performance of the proposed workflow by performing group comparisons and showed that the approach was able to identify the areas of hypometabolism characteristic of different dementia syndromes: Alzheimer's disease (AD) and both the semantic and logopenic variants of primary progressive aphasia...
2018: Frontiers in Neuroinformatics
https://read.qxmd.com/read/30618698/mapping-histological-slice-sequences-to-the-allen-mouse-brain-atlas-without-3d-reconstruction
#12
Jing Xiong, Jing Ren, Liqun Luo, Mark Horowitz
Histological brain slices are widely used in neuroscience to study the anatomical organization of neural circuits. Systematic and accurate comparisons of anatomical data from multiple brains, especially from different studies, can benefit tremendously from registering histological slices onto a common reference atlas. Most existing methods rely on an initial reconstruction of the volume before registering it to a reference atlas. Because these slices are prone to distortions during the sectioning process and often sectioned with non-standard angles, reconstruction is challenging and often inaccurate...
2018: Frontiers in Neuroinformatics
https://read.qxmd.com/read/30618697/multimodal-modeling-of-neural-network-activity-computing-lfp-ecog-eeg-and-meg-signals-with-lfpy-2-0
#13
Espen Hagen, Solveig Næss, Torbjørn V Ness, Gaute T Einevoll
Recordings of extracellular electrical, and later also magnetic, brain signals have been the dominant technique for measuring brain activity for decades. The interpretation of such signals is however nontrivial, as the measured signals result from both local and distant neuronal activity. In volume-conductor theory the extracellular potentials can be calculated from a distance-weighted sum of contributions from transmembrane currents of neurons. Given the same transmembrane currents, the contributions to the magnetic field recorded both inside and outside the brain can also be computed...
2018: Frontiers in Neuroinformatics
https://read.qxmd.com/read/30618696/reproducible-neural-network-simulations-statistical-methods-for-model-validation-on-the-level-of-network-activity-data
#14
Robin Gutzen, Michael von Papen, Guido Trensch, Pietro Quaglio, Sonja Grün, Michael Denker
Computational neuroscience relies on simulations of neural network models to bridge the gap between the theory of neural networks and the experimentally observed activity dynamics in the brain. The rigorous validation of simulation results against reference data is thus an indispensable part of any simulation workflow. Moreover, the availability of different simulation environments and levels of model description require also validation of model implementations against each other to evaluate their equivalence...
2018: Frontiers in Neuroinformatics
https://read.qxmd.com/read/30618695/analytic-tools-for-post-traumatic-epileptogenesis-biomarker-search-in-multimodal-dataset-of-an-animal-model-and-human-patients
#15
Dominique Duncan, Giuseppe Barisano, Ryan Cabeen, Farshid Sepehrband, Rachael Garner, Adebayo Braimah, Paul Vespa, Asla Pitkänen, Meng Law, Arthur W Toga
Epilepsy is among the most common serious disabling disorders of the brain, and the global burden of epilepsy exerts a tremendous cost to society. Most people with epilepsy have acquired forms of the disorder, and the development of antiepileptogenic interventions could potentially prevent or cure epilepsy in many of them. However, the discovery of potential antiepileptogenic treatments and clinical validation would require a means to identify populations of patients at very high risk for epilepsy after a potential epileptogenic insult, to know when to treat and to document prevention or cure...
2018: Frontiers in Neuroinformatics
https://read.qxmd.com/read/30559658/complex-dynamics-in-simplified-neuronal-models-reproducing-golgi-cell-electroresponsiveness
#16
Alice Geminiani, Claudia Casellato, Francesca Locatelli, Francesca Prestori, Alessandra Pedrocchi, Egidio D'Angelo
Brain neurons exhibit complex electroresponsive properties - including intrinsic subthreshold oscillations and pacemaking, resonance and phase-reset - which are thought to play a critical role in controlling neural network dynamics. Although these properties emerge from detailed representations of molecular-level mechanisms in "realistic" models, they cannot usually be generated by simplified neuronal models (although these may show spike-frequency adaptation and bursting). We report here that this whole set of properties can be generated by the extended generalized leaky integrate-and-fire (E-GLIF) neuron model...
2018: Frontiers in Neuroinformatics
https://read.qxmd.com/read/30546301/a-cell-atlas-for-the-mouse-brain
#17
Csaba Erö, Marc-Oliver Gewaltig, Daniel Keller, Henry Markram
Despite vast numbers of studies of stained cells in the mouse brain, no current brain atlas provides region-by-region neuron counts. In fact, neuron numbers are only available for about 4% of brain of regions and estimates often vary by as much as 3-fold. Here we provide a first 3D cell atlas for the whole mouse brain, showing cell positions constructed algorithmically from whole brain Nissl and gene expression stains, and compared against values from the literature. The atlas provides the densities and positions of all excitatory and inhibitory neurons, astrocytes, oligodendrocytes, and microglia in each of the 737 brain regions defined in the AMBA...
2018: Frontiers in Neuroinformatics
https://read.qxmd.com/read/30534067/xolotl-an-intuitive-and-approachable-neuron-and-network-simulator-for-research-and-teaching
#18
Srinivas Gorur-Shandilya, Alec Hoyland, Eve Marder
Conductance-based models of neurons are used extensively in computational neuroscience. Working with these models can be challenging due to their high dimensionality and large number of parameters. Here, we present a neuron and network simulator built on a novel automatic type system that binds object-oriented code written in C++ to objects in MATLAB. Our approach builds on the tradition of uniting the speed of languages like C++ with the ease-of-use and feature-set of scientific programming languages like MATLAB...
2018: Frontiers in Neuroinformatics
https://read.qxmd.com/read/30534066/rigorous-neural-network-simulations-a-model-substantiation-methodology-for-increasing-the-correctness-of-simulation-results-in-the-absence-of-experimental-validation-data
#19
Guido Trensch, Robin Gutzen, Inga Blundell, Michael Denker, Abigail Morrison
The reproduction and replication of scientific results is an indispensable aspect of good scientific practice, enabling previous studies to be built upon and increasing our level of confidence in them. However, reproducibility and replicability are not sufficient: an incorrect result will be accurately reproduced if the same incorrect methods are used. For the field of simulations of complex neural networks, the causes of incorrect results vary from insufficient model implementations and data analysis methods, deficiencies in workmanship (e...
2018: Frontiers in Neuroinformatics
https://read.qxmd.com/read/30515089/deep-synthesis-of-realistic-medical-images-a-novel-tool-in-clinical-research-and-training
#20
Evgeniy Bart, Jay Hegdé
Making clinical decisions based on medical images is fundamentally an exercise in statistical decision-making. This is because in this case, the decision-maker must distinguish between image features that are clinically diagnostic (i.e., signal) from a large amount of non-diagnostic features. (i.e., noise). To perform this task, the decision-maker must have learned the underlying statistical distributions of the signal and noise to begin with. The same is true for machine learning algorithms that perform a given diagnostic task...
2018: Frontiers in Neuroinformatics
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