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Human connectome project

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https://www.readbyqxmd.com/read/28491028/resting-state-functional-connectivity-and-network-analysis-of-cerebellum-with-respect-to-crystallized-iq-and-gender
#1
Vasileios C Pezoulas, Michalis Zervakis, Sifis Michelogiannis, Manousos A Klados
During the last years, it has been established that the prefrontal and posterior parietal brain lobes, which are mostly related to intelligence, have many connections to cerebellum. However, there is a limited research investigating cerebellum's relationship with cognitive processes. In this study, the network of cerebellum was analyzed in order to investigate its overall organization in individuals with low and high crystallized Intelligence Quotient (IQ). Functional magnetic resonance imaging (fMRI) data were selected from 136 subjects in resting-state from the Human Connectome Project (HCP) database and were further separated into two IQ groups composed of 69 low-IQ and 67 high-IQ subjects...
2017: Frontiers in Human Neuroscience
https://www.readbyqxmd.com/read/28486710/association-between-reward-reactivity-and-drug-use-severity-is-substance-dependent-preliminary-evidence-from-the-human-connectome-project
#2
Alyssa L Peechatka, Amy C Janes
Introduction: Blunted nucleus accumbens (NAc) reactivity to reward is common across drug users. One theory is that individuals abuse substances due to this reward deficit. However, whether there is a relationship between the amount an individual uses and the severity of NAc dysfunction is unclear. It also is possible that such a relationship is substance specific, as nicotine transiently increases reward system sensitivity while alcohol, another commonly used substance, does not. As smokers may use nicotine to bolster NAc reward function, we hypothesize that NAc reactivity to reward will be related to volume of cigarette use, but not volume of alcohol use...
June 1, 2017: Nicotine & Tobacco Research: Official Journal of the Society for Research on Nicotine and Tobacco
https://www.readbyqxmd.com/read/28461700/connectome-imaging-for-mapping-human-brain-pathways
#3
Y Shi, A W Toga
With the fast advance of connectome imaging techniques, we have the opportunity of mapping the human brain pathways in vivo at unprecedented resolution. In this article we review the current developments of diffusion magnetic resonance imaging (MRI) for the reconstruction of anatomical pathways in connectome studies. We first introduce the background of diffusion MRI with an emphasis on the technical advances and challenges in state-of-the-art multi-shell acquisition schemes used in the Human Connectome Project...
May 2, 2017: Molecular Psychiatry
https://www.readbyqxmd.com/read/28461060/gray-matter-structural-variability-in-the-human-cerebellum-lobule-specific-differences-across-sex-and-hemisphere
#4
REVIEW
Christopher J Steele, M Mallar Chakravarty
Though commonly thought of as a "motor structure", we now know that the cerebellum's reciprocal connections to the cerebral cortex underlie contributions to both motor and non-motor behavior. Further, recent research has shown that cerebellar dysfunction may contribute to a wide range of neuropsychiatric disorders. However, there has been little characterization of normative variability at the level of cerebellar structure that can facilitate and further our understanding of disease biomarkers. In this manuscript we examine normative variation of the cerebellum using data from the Human Connectome Project (HCP)...
April 28, 2017: NeuroImage
https://www.readbyqxmd.com/read/28457575/objective-analysis-of-the-topological-organization-of-the-human-cortical-visual-connectome-suggests-three-visual-pathways
#5
Koen V Haak, Christian F Beckmann
The cortical visual system is composed of many areas serving various visual functions. In non-human primates, these are broadly organised into two distinct processing pathways: a ventral pathway for object recognition, and a dorsal pathway for action. In humans, recent theoretical proposals suggest the possible existence of additional pathways, but direct empirical evidence has yet to be presented. Here, we estimated the connectivity patterns between 22 human visual areas using resting-state functional MRI data of 470 individuals, leveraging the unprecedented data quantity and quality of the Human Connectome Project and a novel probabilistic atlas...
April 7, 2017: Cortex; a Journal Devoted to the Study of the Nervous System and Behavior
https://www.readbyqxmd.com/read/28444225/genetic-influence-on-the-sulcal-pits-on-the-origin-of-the-first-cortical-folds
#6
Yann Le Guen, Guillaume Auzias, Fran├žois Leroy, Marion Noulhiane, Ghislaine Dehaene-Lambertz, Edouard Duchesnay, Jean-Fran├žois Mangin, Olivier Coulon, Vincent Frouin
The influence of genes on cortical structures has been assessed through various phenotypes. The sulcal pits, which are the putative first cortical folds, have for long been assumed to be under tight genetic control, but this was never quantified. We estimated the pit depth heritability in various brain regions using the high quality and large sample size of the Human Connectome Project pedigree cohort. Analysis of additive genetic variance indicated that their heritability ranges between 0.2 and 0.5 and displays a regional genetic control with an overall symmetric pattern between hemispheres...
April 21, 2017: Cerebral Cortex
https://www.readbyqxmd.com/read/28435097/defining-the-most-probable-location-of-the-parahippocampal-place-area-using-cortex-based-alignment-and-cross-validation
#7
REVIEW
Kevin S Weiner, Michael Barnett, Nathan Witthoft, Golijeh Golarai, Anthony Stigliani, Kendrick N Kay, Jesse Gomez, Vaidehi S Natu, Katrin Amunts, Karl Zilles, Kalanit Grill-Spector
The parahippocampal place area (PPA) is a widely studied high-level visual region in the human brain involved in place and scene processing. The goal of the present study was to identify the most probable location of place-selective voxels in medial ventral temporal cortex. To achieve this goal, we first used cortex-based alignment (CBA) to create a probabilistic place-selective region of interest (ROI) from one group of 12 participants. We then tested how well this ROI could predict place selectivity in each hemisphere within a new group of 12 participants...
April 18, 2017: NeuroImage
https://www.readbyqxmd.com/read/28413699/white-matter-connections-of-the-inferior-parietal-lobule-a-study-of-surgical-anatomy
#8
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
#9
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 13, 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
#10
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
#11
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
#12
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
#13
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
#14
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
#15
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
#16
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
#17
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 4, 2017: NeuroImage
https://www.readbyqxmd.com/read/28263925/image-quality-transfer-and-applications-in-diffusion-mri
#18
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
#19
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
#20
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
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