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https://www.readbyqxmd.com/read/28645840/autoreject-automated-artifact-rejection-for-meg-and-eeg-data
#1
Mainak Jas, Denis A Engemann, Yousra Bekhti, Federico Raimondo, Alexandre Gramfort
We present an automated algorithm for unified rejection and repair of bad trials in magnetoencephalography (MEG) and electroencephalography (EEG) signals. Our method capitalizes on cross-validation in conjunction with a robust evaluation metric to estimate the optimal peak-to-peak threshold - a quantity commonly used for identifying bad trials in M/EEG. This approach is then extended to a more sophisticated algorithm which estimates this threshold for each sensor yielding trial-wise bad sensors. Depending on the number of bad sensors, the trial is then repaired by interpolation or by excluding it from subsequent analysis...
June 20, 2017: NeuroImage
https://www.readbyqxmd.com/read/28643948/investigating-the-separate-and-interactive-associations-of-trauma-and-depression-on-brain-structure-implications-for-cognition-and-aging
#2
Aimee J Karstens, Olusola Ajilore, Leah H Rubin, Shaolin Yang, Aifeng Zhang, Alex Leow, Anand Kumar, Melissa Lamar
OBJECTIVE: Trauma and depression are associated with brain structural alterations; their combined effects on these outcomes are unclear. We previously reported a negative effect of trauma, independent of depression, on verbal learning and memory; less is known about underlying structural associates. We investigated separate and interactive associations of trauma and depression on brain structure. METHODS: Adults aged 30-89 (N = 203) evaluated for depression (D+) and trauma history (T+) using structured clinical interviews were divided into 53 D+T+, 42 D+T-, 50 D-T+, and 58 D-T-...
June 23, 2017: International Journal of Geriatric Psychiatry
https://www.readbyqxmd.com/read/28643354/diffusion-mri-microstructure-models-with-in-vivo-human-brain-connectome-data-results-from-a-multi-group-comparison
#3
Uran Ferizi, Benoit Scherrer, Torben Schneider, Mohammad Alipoor, Odin Eufracio, Rutger H J Fick, Rachid Deriche, Markus Nilsson, Ana K Loya-Olivas, Mariano Rivera, Dirk H J Poot, Alonso Ramirez-Manzanares, Jose L Marroquin, Ariel Rokem, Christian Pötter, Robert F Dougherty, Ken Sakaie, Claudia Wheeler-Kingshott, Simon K Warfield, Thomas Witzel, Lawrence L Wald, José G Raya, Daniel C Alexander
A large number of mathematical models have been proposed to describe the measured signal in diffusion-weighted (DW) magnetic resonance imaging (MRI). However, model comparison to date focuses only on specific subclasses, e.g. compartment models or signal models, and little or no information is available in the literature on how performance varies among the different types of models. To address this deficiency, we organized the 'White Matter Modeling Challenge' during the International Symposium on Biomedical Imaging (ISBI) 2015 conference...
June 23, 2017: NMR in Biomedicine
https://www.readbyqxmd.com/read/28642938/correlation-weighted-sparse-group-representation-for-brain-network-construction-in-mci-classification
#4
Renping Yu, Han Zhang, Le An, Xiaobo Chen, Zhihui Wei, Dinggang Shen
Analysis of brain functional connectivity network (BFCN) has shown great potential in understanding brain functions and identifying biomarkers for neurological and psychiatric disorders, such as Alzheimer's disease and its early stage, mild cognitive impairment (MCI). In all these applications, the accurate construction of biologically meaningful brain network is critical. Due to the sparse nature of the brain network, sparse learning has been widely used for complex BFCN construction. However, the conventional l1-norm penalty in the sparse learning equally penalizes each edge (or link) of the brain network, which ignores the link strength and could remove strong links in the brain network...
October 2016: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/28641247/modeling-task-fmri-data-via-deep-convolutional-autoencoder
#5
Heng Huang, Xintao Hu, Yu Zhao, Milad Makkie, Qinglin Dong, Shijie Zhao, Lei Guo, Tianming Liu
Task-based fMRI (tfMRI) has been widely used to study functional brain networks under task performance. Modeling tfMRI data is challenging due to at least two problems: the lack of the ground truth of underlying neural activity and the highly complex intrinsic structure of tfMRI data. To better understand brain networks based on fMRI data, data-driven approaches have been proposed, for instance, Independent Component Analysis (ICA) and Sparse Dictionary Learning (SDL). However, both ICA and SDL only build shallow models, and they are under the strong assumption that original fMRI signal could be linearly decomposed into time series components with their corresponding spatial maps...
June 15, 2017: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28641239/automatic-recognition-of-fmri-derived-functional-networks-using-3d-convolutional-neural-networks
#6
Yu Zhao, Qinglin Dong, Shu Zhang, Wei Zhang, Hanbo Chen, Xi Jiang, Lei Guo, Xintao Hu, Junwei Han, Tianming Liu
Current fMRI data modeling techniques such as Independent Component Analysis (ICA) and Sparse Coding methods can effectively reconstruct dozens or hundreds of concurrent interacting functional brain networks simultaneously from the whole brain fMRI signals. However, such reconstructed networks have no correspondences across different subjects. Thus, automatic, effective and accurate classification and recognition of these large numbers of fMRI-derived functional brain networks are very important for subsequent steps of functional brain analysis in cognitive and clinical neuroscience applications...
June 15, 2017: IEEE Transactions on Bio-medical Engineering
https://www.readbyqxmd.com/read/28640803/brain-network-eigenmodes-provide-a-robust-and-compact-representation-of-the-structural-connectome-in-health-and-disease
#7
Maxwell B Wang, Julia P Owen, Pratik Mukherjee, Ashish Raj
Recent research has demonstrated the use of the structural connectome as a powerful tool to characterize the network architecture of the brain and potentially generate biomarkers for neurologic and psychiatric disorders. In particular, the anatomic embedding of the edges of the cerebral graph have been postulated to elucidate the relative importance of white matter tracts to the overall network connectivity, explaining the varying effects of localized white matter pathology on cognition and behavior. Here, we demonstrate the use of a linear diffusion model to quantify the impact of these perturbations on brain connectivity...
June 2017: PLoS Computational Biology
https://www.readbyqxmd.com/read/28638896/a-scalable-cyberinfrastructure-for-interactive-visualization-of-terascale-microscopy-data
#8
A Venkat, C Christensen, A Gyulassy, B Summa, F Federer, A Angelucci, V Pascucci
The goal of the recently emerged field of connectomics is to generate a wiring diagram of the brain at different scales. To identify brain circuitry, neuroscientists use specialized microscopes to perform multichannel imaging of labeled neurons at a very high resolution. CLARITY tissue clearing allows imaging labeled circuits through entire tissue blocks, without the need for tissue sectioning and section-to-section alignment. Imaging the large and complex non-human primate brain with sufficient resolution to identify and disambiguate between axons, in particular, produces massive data, creating great computational challenges to the study of neural circuits...
August 2016: N Y Sci Data Summit NYSDS
https://www.readbyqxmd.com/read/28637203/morphological-diversity-strongly-constrains-synaptic-connectivity-and-plasticity
#9
Michael W Reimann, Anna-Lena Horlemann, Srikanth Ramaswamy, Eilif B Muller, Henry Markram
Synaptic connectivity between neurons is naturally constrained by the anatomical overlap of neuronal arbors, the space on the axon available for synapses, and by physiological mechanisms that form synapses at a subset of potential synapse locations. What is not known is how these constraints impact emergent connectivity in a circuit with diverse morphologies. We investigated the role of morphological diversity within and across neuronal types on emergent connectivity in a model of neocortical microcircuitry...
June 20, 2017: Cerebral Cortex
https://www.readbyqxmd.com/read/28631354/cross-population-myelination-covariance-of-human-cerebral-cortex
#10
Zhiwei Ma, Nanyin Zhang
Cross-population covariance of brain morphometric quantities provides a measure of interareal connectivity, as it is believed to be determined by the coordinated neurodevelopment of connected brain regions. Although useful, structural covariance analysis predominantly employed bulky morphological measures with mixed compartments, whereas studies of the structural covariance of any specific subdivisions such as myelin are rare. Characterizing myelination covariance is of interest, as it will reveal connectivity patterns determined by coordinated development of myeloarchitecture between brain regions...
June 20, 2017: Human Brain Mapping
https://www.readbyqxmd.com/read/28624504/proinsulin-protects-against-age-related-cognitive-loss-through-anti-inflammatory-convergent-pathways
#11
Rubén Corpas, Alberto M Hernández-Pinto, David Porquet, Catalina Hernández-Sánchez, Fatima Bosch, Arantxa Ortega-Aznar, Francesc Comellas, Enrique J de la Rosa, Coral Sanfeliu
Brain inflammaging is increasingly considered as contributing to age-related cognitive loss and neurodegeneration. Despite intensive research in multiple models, no clinically effective pharmacological treatment has been found yet. Here, in the mouse model of brain senescence SAMP8, we tested the effects of proinsulin, a promising neuroprotective agent that was previously proven to be effective in mouse models of retinal neurodegeneration. Proinsulin is the precursor of the hormone insulin but also upholds developmental physiological effects, particularly as a survival factor for neural cells...
June 14, 2017: Neuropharmacology
https://www.readbyqxmd.com/read/28617222/an-extensive-assessment-of-network-alignment-algorithms-for-comparison-of-brain-connectomes
#12
Marianna Milano, Pietro Hiram Guzzi, Olga Tymofieva, Duan Xu, Christofer Hess, Pierangelo Veltri, Mario Cannataro
BACKGROUND: Recently the study of the complex system of connections in neural systems, i.e. the connectome, has gained a central role in neurosciences. The modeling and analysis of connectomes are therefore a growing area. Here we focus on the representation of connectomes by using graph theory formalisms. Macroscopic human brain connectomes are usually derived from neuroimages; the analyzed brains are co-registered in the image domain and brought to a common anatomical space. An atlas is then applied in order to define anatomically meaningful regions that will serve as the nodes of the network - this process is referred to as parcellation...
June 6, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28615277/alterations-in-the-brain-s-connectome-during-recovery-from-severe-traumatic-brain-injury-protocol-for-a-longitudinal-prospective-study
#13
Virginia Conde, Sara Hesby Andreasen, Tue Hvass Petersen, Karen Busted Larsen, Karine Madsen, Kasper Winther Andersen, Irina Akopian, Kristoffer Hougaard Madsen, Christian Pilebæk Hansen, Ingrid Poulsen, Lars Peter Kammersgaard, Hartwig Roman Siebner
INTRODUCTION: Traumatic brain injury (TBI) is considered one of the most pervasive causes of disability in people under the age of 45. TBI often results in disorders of consciousness, and clinical assessment of the state of consciousness in these patients is challenging due to the lack of behavioural responsiveness. Functional neuroimaging offers a means to assess these patients without the need for behavioural signs, indicating that brain connectivity plays a major role in consciousness emergence and maintenance...
June 14, 2017: BMJ Open
https://www.readbyqxmd.com/read/28611614/functional-and-structural-network-recovery-after-mild-traumatic-brain-injury-a-1-year-longitudinal-study
#14
Patrizia Dall'Acqua, Sönke Johannes, Ladislav Mica, Hans-Peter Simmen, Richard Glaab, Javier Fandino, Markus Schwendinger, Christoph Meier, Erika J Ulbrich, Andreas Müller, Hansruedi Baetschmann, Lutz Jäncke, Jürgen Hänggi
Brain connectivity after mild traumatic brain injury (mTBI) has not been investigated longitudinally with respect to both functional and structural networks together within the same patients, crucial to capture the multifaceted neuropathology of the injury and to comprehensively monitor the course of recovery and compensatory reorganizations at macro-level. We performed a prospective study with 49 mTBI patients at an average of 5 days and 1 year post-injury and 49 healthy controls. Neuropsychological assessments as well as resting-state functional and diffusion-weighted magnetic resonance imaging were obtained...
2017: Frontiers in Human Neuroscience
https://www.readbyqxmd.com/read/28610804/mechanisms-of-connectome-development
#15
REVIEW
Marcus Kaiser
At the centenary of D'Arcy Thompson's seminal work 'On Growth and Form', pioneering the description of principles of morphological changes during development and evolution, recent experimental advances allow us to study change in anatomical brain networks. Here, we outline potential principles for connectome development. We will describe recent results on how spatial and temporal factors shape connectome development in health and disease. Understanding the developmental origins of brain diseases in individuals will be crucial for deciding on personalized treatment options...
June 10, 2017: Trends in Cognitive Sciences
https://www.readbyqxmd.com/read/28608763/identifying-clinical-risk-in-low-grade-gliomas-and-appropriate-treatment-strategies-with-special-emphasis-on-the-role-of-surgery
#16
Sébastien Boissonneau, Hugues Duffau
Diffuse low-grade glioma (DLGG) is a chronic tumoral disease that ineluctably grows, migrates along white matter pathways, and progresses to a higher grade of malignancy. Areas covered: To determine the best individualized treatment attitude for each DLGG patient, and to redefine it over the years, i.e. to optimize the "onco-functional balance" of serial and multimodal therapies, the understanding of the natural history of this chronic disease is crucial but not sufficient. A paradigmatic shift is to tailor the individual management according to the dynamic relationships between DLGG course and neural remodeling...
June 13, 2017: Expert Review of Anticancer Therapy
https://www.readbyqxmd.com/read/28608616/whole-brain-structural-connectivity-in-dyskinetic-cerebral-palsy-and-its-association-with-motor-and-cognitive-function
#17
Júlia Ballester-Plané, Ruben Schmidt, Olga Laporta-Hoyos, Carme Junqué, Élida Vázquez, Ignacio Delgado, Leire Zubiaurre-Elorza, Alfons Macaya, Pilar Póo, Esther Toro, Marcel A de Reus, Martijn P van den Heuvel, Roser Pueyo
Dyskinetic cerebral palsy (CP) has long been associated with basal ganglia and thalamus lesions. Recent evidence further points at white matter (WM) damage. This study aims to identify altered WM pathways in dyskinetic CP from a standardized, connectome-based approach, and to assess structure-function relationship in WM pathways for clinical outcomes. Individual connectome maps of 25 subjects with dyskinetic CP and 24 healthy controls were obtained combining a structural parcellation scheme with whole-brain deterministic tractography...
June 13, 2017: Human Brain Mapping
https://www.readbyqxmd.com/read/28605376/anatomically-inspired-three-dimensional-micro-tissue-engineered-neural-networks-for-nervous-system-reconstruction-modulation-and-modeling
#18
Laura A Struzyna, Dayo O Adewole, Wisberty J Gordián-Vélez, Michael R Grovola, Justin C Burrell, Kritika S Katiyar, Dmitriy Petrov, James P Harris, D Kacy Cullen
Functional recovery rarely occurs following injury or disease-induced degeneration within the central nervous system (CNS) due to the inhibitory environment and the limited capacity for neurogenesis. We are developing a strategy to simultaneously address neuronal and axonal pathway loss within the damaged CNS. This manuscript presents the fabrication protocol for micro-tissue engineered neural networks (micro-TENNs), implantable constructs consisting of neurons and aligned axonal tracts spanning the extracellular matrix (ECM) lumen of a preformed hydrogel cylinder hundreds of microns in diameter that may extend centimeters in length...
May 31, 2017: Journal of Visualized Experiments: JoVE
https://www.readbyqxmd.com/read/28604722/webknossos-efficient-online-3d-data-annotation-for-connectomics
#19
Kevin M Boergens, Manuel Berning, Tom Bocklisch, Dominic Bräunlein, Florian Drawitsch, Johannes Frohnhofen, Tom Herold, Philipp Otto, Norman Rzepka, Thomas Werkmeister, Daniel Werner, Georg Wiese, Heiko Wissler, Moritz Helmstaedter
We report webKnossos, an in-browser annotation tool for 3D electron microscopic data. webKnossos provides flight mode, a single-view egocentric reconstruction method enabling trained annotator crowds to reconstruct at a speed of 1.5 ± 0.6 mm/h for axons and 2.1 ± 0.9 mm/h for dendrites in 3D electron microscopic data from mammalian cortex. webKnossos accelerates neurite reconstruction for connectomics by 4- to 13-fold compared with current state-of-the-art tools, thus extending the range of connectomes that can realistically be mapped in the future...
June 12, 2017: Nature Methods
https://www.readbyqxmd.com/read/28602945/the-energy-landscape-underpinning-module-dynamics-in-the-human-brain-connectome
#20
Arian Ashourvan, Shi Gu, Marcelo G Mattar, Jean M Vettel, Danielle S Bassett
Human brain dynamics can be viewed through the lens of statistical mechanics, where neurophysiological activity evolves around and between local attractors representing mental states. Many physically-inspired models of these dynamics define brain states based on instantaneous measurements of regional activity. Yet, recent work in network neuroscience has provided evidence that the brain might also be well-characterized by time-varying states composed of locally coherent activity or functional modules. We study this network-based notion of brain state to understand how functional modules dynamically interact with one another to perform cognitive functions...
June 7, 2017: NeuroImage
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