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Human connectome project

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https://www.readbyqxmd.com/read/28334252/individualized-prediction-of-reading-comprehension-ability-using-gray-matter-volume
#1
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
#2
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
#3
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
#4
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
#5
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
#6
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
#7
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
#8
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
#9
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
#10
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
#11
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
#12
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
#13
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
#14
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
https://www.readbyqxmd.com/read/28161314/groupwise-structural-parcellation-of-the-whole-cortex-a-logistic-random-effects-model-based-approach
#15
Guillermo Gallardo, William Wells, Rachid Deriche, Demian Wassermann
Current theories hold that brain function is highly related to long-range physical connections through axonal bundles, namely extrinsic connectivity. However, obtaining a groupwise cortical parcellation based on extrinsic connectivity remains challenging. Current parcellation methods are computationally expensive; need tuning of several parameters or rely on ad-hoc constraints. Furthermore, none of these methods present a model for the cortical extrinsic connectivity of the cortex. To tackle these problems, we propose a parsimonious model for the extrinsic connectivity and an efficient parceling technique based on clustering of tractograms...
February 1, 2017: NeuroImage
https://www.readbyqxmd.com/read/28127590/motion-robust-reconstruction-based-on-simultaneous-multi-slice-registration-for-diffusion-weighted-mri-of-moving-subjects
#16
Bahram Marami, Benoit Scherrer, Onur Afacan, Simon K Warfield, Ali Gholipour
Simultaneous multi-slice (SMS) echo-planar imaging has had a huge impact on the acceleration and routine use of diffusion-weighted MRI (DWI) in neuroimaging studies in particular the human connectome project; but also holds the potential to facilitate DWI of moving subjects, as proposed by the new technique developed in this paper. We present a novel registration-based motion tracking technique that takes advantage of the multi-plane coverage of the anatomy by simultaneously acquired slices to enable robust reconstruction of neural microstructure from SMS DWI of moving subjects...
October 2016: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/28122961/surface-based-morphometry-reveals-the-neuroanatomical-basis-of-the-five-factor-model-of-personality
#17
Roberta Riccelli, Nicola Toschi, Salvatore Nigro, Antonio Terracciano, Luca Passamonti
The five-factor model (FFM) is a widely used taxonomy of human personality; yet its neuro anatomical basis remains unclear. This is partly because past associations between gray-matter volume and FFM were driven by different surface-based morphometry (SBM) indices (i.e. cortical thickness, surface area, cortical folding or any combination of them). To overcome this limitation, we used Free-Surfer to study how variability in SBM measures was related to the FFM in n = 507 participants from the Human Connectome Project...
January 24, 2017: Social Cognitive and Affective Neuroscience
https://www.readbyqxmd.com/read/28119342/elevated-body-mass-index-is-associated-with-increased-integration-and-reduced-cohesion-of-sensory-driven-and-internally-guided-resting-state-functional-brain-networks
#18
Gaelle E Doucet, Natalie Rasgon, Bruce S McEwen, Nadia Micali, Sophia Frangou
Elevated body mass index (BMI) is associated with increased multi-morbidity and mortality. The investigation of the relationship between BMI and brain organization has the potential to provide new insights relevant to clinical and policy strategies for weight control. Here, we quantified the association between increasing BMI and the functional organization of resting-state brain networks in a sample of 496 healthy individuals that were studied as part of the Human Connectome Project. We demonstrated that higher BMI was associated with changes in the functional connectivity of the default-mode network (DMN), central executive network (CEN), sensorimotor network (SMN), visual network (VN), and their constituent modules...
January 23, 2017: Cerebral Cortex
https://www.readbyqxmd.com/read/28113234/synergetic-and-redundant-information-flow-detected-by-unnormalized-granger-causality-application-to-resting-state-fmri
#19
Sebastiano Stramaglia, Leonardo Angelini, Guorong Wu, Jesus M Cortes, Luca Faes, Daniele Marinazzo
OBJECTIVES: We develop a framework for the analysis of synergy and redundancy in the pattern of information flow between subsystems of a complex network. METHODS: The presence of redundancy and/or synergy in multivariate time series data renders difficult to estimate the neat flow of information from each driver variable to a given target. We show that adopting an unnormalized definition of Granger causality one may put in evidence redundant multiplets of variables influencing the target by maximizing the total Granger causality to a given target, over all the possible partitions of the set of driving variables...
April 28, 2016: IEEE Transactions on Bio-medical Engineering
https://www.readbyqxmd.com/read/28113218/temporal-dynamics-assessment-of-spatial-overlap-pattern-of-functional-brain-networks-reveals-novel-functional-architecture-of-cerebral-cortex
#20
Xi Jiang, Xiang Li, Jinglei Lv, Shijie Zhao, Shu Zhang, Wei Zhang, Tuo Zhang, Junwei Han, Lei Guo, Tianming Liu
OBJECTIVE: Various studies in the brain mapping field have demonstrated that there exist multiple concurrent functional networks that are spatially overlapped and interacting with each other during specific task performance to jointly realize the total brain function. Assessing such spatial overlap patterns of functional networks (SOPFNs) based on fMRI has thus received increasing interest for brain function studies. However, there are still two crucial issues to be addressed. First, the SOPFNs are assessed over the entire fMRI scan assuming the temporal stationarity, while possibly time-dependent dynamics of the SOPFNs is not sufficiently explored...
August 10, 2016: IEEE Transactions on Bio-medical Engineering
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