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Human connectome project

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https://www.readbyqxmd.com/read/28413699/white-matter-connections-of-the-inferior-parietal-lobule-a-study-of-surgical-anatomy
#1
Joshua D Burks, Lillian B Boettcher, Andrew K Conner, Chad A Glenn, Phillip A Bonney, Cordell M Baker, Robert G Briggs, Nathan A Pittman, Daniel L O'Donoghue, Dee H Wu, Michael E Sughrue
INTRODUCTION: Interest in the function of the inferior parietal lobule (IPL) has resulted in increased understanding of its involvement in visuospatial and cognitive functioning, and its role in semantic networks. A basic understanding of the nuanced white-matter anatomy in this region may be useful in improving outcomes when operating in this region of the brain. We sought to derive the surgical relationship between the IPL and underlying major white-matter bundles by characterizing macroscopic connectivity...
April 2017: Brain and Behavior
https://www.readbyqxmd.com/read/28412442/human-brain-mapping-a-systematic-comparison-of-parcellation-methods-for-the-human-cerebral-cortex
#2
REVIEW
Salim Arslan, Sofia Ira Ktena, Antonios Makropoulos, Emma C Robinson, Daniel Rueckert, Sarah Parisot
The macro-connectome elucidates the pathways through which brain regions are structurally connected or functionally coupled to perform a specific cognitive task. It embodies the notion of representing and understanding all connections within the brain as a network, while the subdivision of the brain into interacting functional units is inherent in its architecture. As a result, the definition of network nodes is one of the most critical steps in connectivity network analysis. Although brain atlases obtained from cytoarchitecture or anatomy have long been used for this task, connectivity-driven methods have arisen only recently, aiming to delineate more homogeneous and functionally coherent regions...
April 12, 2017: NeuroImage
https://www.readbyqxmd.com/read/28411159/in-vivo-visualization-of-connections-among-revised-papez-circuit-hubs-using-full-q-space-diffusion-spectrum-imaging-tractography
#3
Peng-Hu Wei, Zhi-Qi Mao, Fei Cong, Fang-Cheng Yeh, Bo Wang, Zhi-Pei Ling, Shu-Li Liang, Lin Chen, Xin-Guang Yu
Structural connections among the hubs of the revised Papez circuit remain to be elucidated in the human brain. As the original Papez circuit failed to explain functional imaging findings, a more detailed investigation is needed to delineate connections among the circuit's key hubs. Here we acquired diffusion spectrum imaging (DSI) from eight normal subjects and used data from the Human Connectome Project (HCP) to elucidate connections among hubs in the retrosplenial gyrus, hippocampus, mammillary bodies, and anterior thalamic nuclei...
April 12, 2017: Neuroscience
https://www.readbyqxmd.com/read/28373838/decoding-time-varying-functional-connectivity-networks-via-linear-graph-embedding-methods
#4
Ricardo P Monti, Romy Lorenz, Peter Hellyer, Robert Leech, Christoforos Anagnostopoulos, Giovanni Montana
An exciting avenue of neuroscientific research involves quantifying the time-varying properties of functional connectivity networks. As a result, many methods have been proposed to estimate the dynamic properties of such networks. However, one of the challenges associated with such methods involves the interpretation and visualization of high-dimensional, dynamic networks. In this work, we employ graph embedding algorithms to provide low-dimensional vector representations of networks, thus facilitating traditional objectives such as visualization, interpretation and classification...
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/28373122/can-brain-state-be-manipulated-to-emphasize-individual-differences-in-functional-connectivity
#5
REVIEW
Emily S Finn, Dustin Scheinost, Daniel M Finn, Xilin Shen, Xenophon Papademetris, R Todd Constable
While neuroimaging studies typically collapse data from many subjects, brain functional organization varies between individuals, and characterizing this variability is crucial for relating brain activity to behavioral phenotypes. Rest has become the default state for probing individual differences, chiefly because it is easy to acquire and a supposed neutral backdrop. However, the assumption that rest is the optimal condition for individual differences research is largely untested. In fact, other brain states may afford a better ratio of within- to between-subject variability, facilitating biomarker discovery...
March 31, 2017: NeuroImage
https://www.readbyqxmd.com/read/28365419/mindcontrol-a-web-application-for-brain-segmentation-quality-control
#6
REVIEW
Anisha Keshavan, Esha Datta, Ian McDonough, Christopher R Madan, Kesshi Jordan, Roland G Henry
Tissue classification plays a crucial role in the investigation of normal neural development, brain-behavior relationships, and the disease mechanisms of many psychiatric and neurological illnesses. Ensuring the accuracy of tissue classification is important for quality research and, in particular, the translation of imaging biomarkers to clinical practice. Assessment with the human eye is vital to correct various errors inherent to all currently available segmentation algorithms. Manual quality assurance becomes methodologically difficult at a large scale - a problem of increasing importance as the number of data sets is on the rise...
March 29, 2017: NeuroImage
https://www.readbyqxmd.com/read/28334252/individualized-prediction-of-reading-comprehension-ability-using-gray-matter-volume
#7
Zaixu Cui, Mengmeng Su, Liangjie Li, Hua Shu, Gaolang Gong
Reading comprehension is a crucial reading skill for learning and putatively contains 2 key components: reading decoding and linguistic comprehension. Current understanding of the neural mechanism underlying these reading comprehension components is lacking, and whether and how neuroanatomical features can be used to predict these 2 skills remain largely unexplored. In the present study, we analyzed a large sample from the Human Connectome Project (HCP) dataset and successfully built multivariate predictive models for these 2 skills using whole-brain gray matter volume features...
March 10, 2017: Cerebral Cortex
https://www.readbyqxmd.com/read/28328993/agreement-between-functional-connectivity-and-cortical-thickness-driven-correlation-maps-of-the-medial-frontal-cortex
#8
Hyunjin Park, Yeong-Hun Park, Jungho Cha, Sang Won Seo, Duk L Na, Jong-Min Lee
Parcellation of the human cortex has important implications in neuroscience. Parcellation is often a crucial requirement before meaningful regional analysis can occur. The human cortex can be parcellated into distinct regions based on structural features, such as gyri and sulci. Brain network patterns in a given region with respect to its neighbors, known as connectional fingerprints, can be used to parcellate the cortex. Distinct imaging modalities might provide complementary information for brain parcellation...
2017: PloS One
https://www.readbyqxmd.com/read/28269497/reconstructing-multivariate-causal-structure-between-functional-brain-networks-through-a-laguerre-volterra-based-granger-causality-approach
#9
Andrea Duggento, Gaetano Valenza, Luca Passamonti, Maria Guerrisi, Riccardo Barbieri, Nicola Toschi
Classical multivariate approaches based on Granger causality (GC) which estimate functional connectivity in the brain are almost exclusively based on autoregressive models. Nevertheless, information available from past samples is limited due to both signal autocorrelation and necessarily low model orders. Consequently, multiple time-scales interactions are usually unaccounted for. To overcome these limitations, in this study we propose the use of discrete-time orthogonal Laguerre basis functions within a Wiener-Volterra decomposition of the BOLD signals to perform effective GC assessments of brain functional connectivity...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28267626/bayesian-switching-factor-analysis-for-estimating-time-varying-functional-connectivity-in-fmri
#10
Jalil Taghia, Srikanth Ryali, Tianwen Chen, Kaustubh Supekar, Weidong Cai, Vinod Menon
There is growing interest in understanding the dynamical properties of functional interactions between distributed brain regions. However, robust estimation of temporal dynamics from functional magnetic resonance imaging (fMRI) data remains challenging due to limitations in extant multivariate methods for modeling time-varying functional interactions between multiple brain areas. Here, we develop a Bayesian generative model for fMRI time-series within the framework of hidden Markov models (HMMs). The model is a dynamic variant of the static factor analysis model (Ghahramani and Beal, 2000)...
March 3, 2017: NeuroImage
https://www.readbyqxmd.com/read/28263925/image-quality-transfer-and-applications-in-diffusion-mri
#11
Daniel C Alexander, Darko Zikic, Aurobrata Ghosh, Ryutaro Tanno, Viktor Wottschel, Jiaying Zhang, Enrico Kaden, Tim B Dyrby, Stamatios N Sotiropoulos, Hui Zhang, Antonio Criminisi
This paper introduces a new computational imaging technique called image quality transfer (IQT). IQT uses machine learning to transfer the rich information available from one-off experimental medical imaging devices to the abundant but lower-quality data from routine acquisitions. The procedure uses matched pairs to learn mappings from low-quality to corresponding high-quality images. Once learned, these mappings then augment unseen low quality images, for example by enhancing image resolution or information content...
March 3, 2017: NeuroImage
https://www.readbyqxmd.com/read/28255221/latent-variable-graphical-model-selection-using-harmonic-analysis-applications-to-the-human-connectome-project-hcp
#12
Won Hwa Kim, Hyunwoo J Kim, Nagesh Adluru, Vikas Singh
A major goal of imaging studies such as the (ongoing) Human Connectome Project (HCP) is to characterize the structural network map of the human brain and identify its associations with covariates such as genotype, risk factors, and so on that correspond to an individual. But the set of image derived measures and the set of covariates are both large, so we must first estimate a 'parsimonious' set of relations between the measurements. For instance, a Gaussian graphical model will show conditional independences between the random variables, which can then be used to setup specific downstream analyses...
June 2016: Proceedings
https://www.readbyqxmd.com/read/28246033/jive-integration-of-imaging-and-behavioral-data
#13
Qunqun Yu, Benjamin B Risk, Kai Zhang, J S Marron
A major goal in neuroscience is to understand the neural pathways underlying human behavior. We introduce the recently developed Joint and Individual Variation Explained (JIVE) method to the neuroscience community to simultaneously analyze imaging and behavioral data from the Human Connectome Project. Motivated by recent computational and theoretical improvements in the JIVE approach, we simultaneously explore the joint and individual variation between and within imaging and behavioral data. In particular, we demonstrate that JIVE is an effective and efficient approach for integrating task fMRI and behavioral variables using three examples: one example where task variation is strong, one where task variation is weak and a reference case where the behavior is not directly related to the image...
February 27, 2017: NeuroImage
https://www.readbyqxmd.com/read/28242315/individual-differences-and-time-varying-features-of-modular-brain-architecture
#14
Xuhong Liao, Miao Cao, Mingrui Xia, Yong He
Recent studies have suggested that human brain functional networks are topologically organized into functionally specialized but inter-connected modules to facilitate efficient information processing and highly flexible cognitive function. However, these studies have mainly focused on group-level network modularity analyses using "static" functional connectivity approaches. How these extraordinary modular brain structures vary across individuals and spontaneously reconfigure over time remain largely unknown...
February 24, 2017: NeuroImage
https://www.readbyqxmd.com/read/28231395/functional-connectivity-in-amygdalar-sensory-pre-motor-networks-at-rest-new-evidence-from-the-human-connectome-project
#15
Nicola Toschi, Andrea Duggento, Luca Passamonti
The word "e-motion" derives from the Latin word "ex-moveo" which literally means "moving away from something / somebody". Emotions are thus fundamental to prime action and goal-directed behavior with obvious implications for individual's survival. However, the brain mechanisms underlying the interactions between emotional and motor cortical systems remain poorly understood. A recent diffusion tensor imaging study in humans has reported the existence of direct anatomical connections between the amygdala and sensory/(pre)motor cortices, corroborating an initial observation in animal research...
February 23, 2017: European Journal of Neuroscience
https://www.readbyqxmd.com/read/28227749/reconstructing-multivariate-causal-structure-between-functional-brain-networks-through-a-laguerre-volterra-based-granger-causality-approach
#16
Andrea Duggento, Gaetano Valenza, Luca Passamonti, Maria Guerrisi, Riccardo Barbieri, Nicola Toschi, Andrea Duggento, Gaetano Valenza, Luca Passamonti, Maria Guerrisi, Riccardo Barbieri, Nicola Toschi, Andrea Duggento, Nicola Toschi, Maria Guerrisi, Luca Passamonti, Riccardo Barbieri, Gaetano Valenza
Classical multivariate approaches based on Granger causality (GC) which estimate functional connectivity in the brain are almost exclusively based on autoregressive models. Nevertheless, information available from past samples is limited due to both signal autocorrelation and necessarily low model orders. Consequently, multiple time-scales interactions are usually unaccounted for. To overcome these limitations, in this study we propose the use of discrete-time orthogonal Laguerre basis functions within a Wiener-Volterra decomposition of the BOLD signals to perform effective GC assessments of brain functional connectivity...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28214981/neural-mechanism-underling-comprehension-of-narrative-speech-and-its-heritability-study-in-a-large-population
#17
Abbas Babajani-Feremi
Comprehension of narratives constitutes a fundamental part of our everyday life experience. Although the neural mechanism of auditory narrative comprehension has been investigated in some studies, the neural correlates underlying this mechanism and its heritability remain poorly understood. We investigated comprehension of naturalistic speech in a large, healthy adult population (n = 429; 176/253 M/F; 22-36 years of age) consisting of 192 twin pairs (49 monozygotic and 47 dizygotic pairs) and 237 of their siblings...
February 18, 2017: Brain Topography
https://www.readbyqxmd.com/read/28209314/connection-between-bilateral-temporal-regions-tractography-using-human-connectome-data-and-diffusion-spectrum-imaging
#18
Peng-Hu Wei, Zhi-Qi Mao, Fei Cong, Bo Wang, Zhi-Pei Ling, Shu-Li Liang, Xin-Guang Yu
Temporal lobe epilepsy often propagates inter-hemispherically. Although the pathway of the propagation was verified by electrophysiology, the trajectory remains poorly defined. DTI can depict fiber trajectory but it has limited angular resolution and cannot adequately assess cortical regions. We visualized potential pathways of bitemporal epilepsy propagation using diffusion spectrum imaging (DSI) with data consisting of 8 groups of 514 directions and diffusion templates of 842 subjects from the human connectome project (HCP)...
February 10, 2017: Journal of Clinical Neuroscience: Official Journal of the Neurosurgical Society of Australasia
https://www.readbyqxmd.com/read/28176164/whole-brain-high-resolution-structural-connectome-inter-subject-validation-and-application-to-the-anatomical-segmentation-of-the-striatum
#19
Pierre Besson, Nicolas Carrière, S Kathleen Bandt, Marc Tommasi, Xavier Leclerc, Philippe Derambure, Renaud Lopes, Louise Tyvaert
The present study describes extraction of high-resolution structural connectome (HRSC) in 99 healthy subjects, acquired and made available by the Human Connectome Project. Single subject connectomes were then registered to the common surface space to allow assessment of inter-individual reproducibility of this novel technique using a leave-one-out approach. The anatomic relevance of the surface-based connectome was examined via a clustering algorithm, which identified anatomic subdivisions within the striatum...
February 7, 2017: Brain Topography
https://www.readbyqxmd.com/read/28174617/parameterizable-consensus-connectomes-from-the-human-connectome-project-the-budapest-reference-connectome-server-v3-0
#20
Balázs Szalkai, Csaba Kerepesi, Bálint Varga, Vince Grolmusz
Connections of the living human brain, on a macroscopic scale, can be mapped by a diffusion MR imaging based workflow. Since the same anatomic regions can be corresponded between distinct brains, one can compare the presence or the absence of the edges, connecting the very same two anatomic regions, among multiple cortices. Previously, we have constructed the consensus braingraphs on 1015 vertices first in five, then in 96 subjects in the Budapest Reference Connectome Server v1.0 and v2.0, respectively. Here we report the construction of the version 3...
February 2017: Cognitive Neurodynamics
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