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Human connectome project

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https://www.readbyqxmd.com/read/28816791/resting-state-functional-connectivity-in-the-human-connectome-project-current-status-and-relevance-to-understanding-psychopathology
#1
Deanna M Barch
A key tenet of modern psychiatry is that psychiatric disorders arise from abnormalities in brain circuits that support human behavior. Our ability to examine hypotheses around circuit-level abnormalities in psychiatric disorders has been made possible by advances in human neuroimaging technologies. These advances have provided the basis for recent efforts to develop a more complex understanding of the function of brain circuits in health and of their relationship to behavior-providing, in turn, a foundation for our understanding of how disruptions in such circuits contribute to the development of psychiatric disorders...
August 16, 2017: Harvard Review of Psychiatry
https://www.readbyqxmd.com/read/28816657/a-dynamic-regression-approach-for-frequency-domain-partial-coherence-and-causality-analysis-of-functional-brain-networks
#2
Lipeng Ning, Yogesh Rathi
Coherence and causality measures are often used to analyze the influence of one region on another during analysis of functional brain networks. The analysis methods usually involve a regression problem where the signal of interest is decomposed into a mixture of regressor and a residual signal. In this paper, we revisit this basic problem and present solutions that provide the minimal-entropy residuals for different types of regression filters, such as causal, instantaneously causal and noncausal filters. Using optimal prediction theory, we derive several novel frequency-domain expressions for partial coherence, causality and conditional causality analysis...
August 14, 2017: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28794427/neuroimaging-biomarkers-to-associate-obesity-and-negative-emotions
#3
Bo-Yong Park, Jisu Hong, Hyunjin Park
Obesity is a serious medical condition highly associated with health problems such as diabetes, hypertension, and stroke. Obesity is highly associated with negative emotional states, but the relationship between obesity and emotional states in terms of neuroimaging has not been fully explored. We obtained 196 emotion task functional magnetic resonance imaging (t-fMRI) from the Human Connectome Project database using a sampling scheme similar to a bootstrapping approach. Brain regions were specified by automated anatomical labeling atlas and the brain activity (z-statistics) of each brain region was correlated with body mass index (BMI) values...
August 9, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28771867/motion-corrected-k-space-reconstruction-for-interleaved-epi-diffusion-imaging
#4
Zijing Dong, Fuyixue Wang, Xiaodong Ma, Erpeng Dai, Zhe Zhang, Hua Guo
PURPOSE: To develop a new approach to correct for physiological and macroscopic motion in multishot, interleaved echo-planar diffusion imaging. THEORY: This work built on the previous SPIRiT (iterative self-consistent parallel imaging reconstruction) based reconstruction for physiological motion correction in multishot diffusion-weighted imaging to account for macroscopic motion. In-plane rotation, translation correction, data rejection, and weighted combination are integrated in SPIRiT-based reconstruction to correct for ghosting artifacts, blurring, altered b-matrix, and residual artifacts caused by motion...
August 3, 2017: Magnetic Resonance in Medicine: Official Journal of the Society of Magnetic Resonance in Medicine
https://www.readbyqxmd.com/read/28760438/elucidating-functional-differences-between-cortical-gyri-and-sulci-via-sparse-representation-hcp-grayordinate-fmri-data
#5
Huan Liu, Xi Jiang, Tuo Zhang, Yudan Ren, Xintao Hu, Lei Guo, Junwei Han, Tianming Liu
The highly convoluted cerebral cortex is characterized by two different topographic structures: convex gyri and concave sulci. Increasing studies have demonstrated that cortical gyri and sulci exhibit different structural connectivity patterns. Inspired by the intrinsic structural differences between gyri and sulci, in this paper, we present a data-driven framework based on sparse representation of fMRI data for functional network inferences, then examine the interactions within and across gyral and sulcal functional networks and finally elucidate possible functional differences using graph theory based properties...
July 28, 2017: Brain Research
https://www.readbyqxmd.com/read/28751860/pseudo-bootstrap-network-analysis-an-application-in-functional-connectivity-fingerprinting
#6
Hu Cheng, Ao Li, Andrea A Koenigsberger, Chunfeng Huang, Yang Wang, Jinhua Sheng, Sharlene D Newman
Brain parcellation divides the brain's spatial domain into small regions, which are represented by nodes within the network analysis framework. While template-based parcellations are widely used, the parcels on the template do not necessarily match individual's functional nodes. A new method is developed to overcome the inconsistent network analysis results by by-passing the difficulties of parcellating the brain into functionally meaningful areas. First, roughly equal-sized parcellations are obtained. Second, these random parcellations are applied to individual subjects multiple times and a pseudo-bootstrap (PBS) of the network is obtained for statistical inferences...
2017: Frontiers in Human Neuroscience
https://www.readbyqxmd.com/read/28751859/probabilistic-white-matter-atlases-of-human-auditory-basal-ganglia-language-precuneus-sensorimotor-visual-and-visuospatial-networks
#7
Teresa D Figley, Behnoush Mortazavi Moghadam, Navdeep Bhullar, Jennifer Kornelsen, Susan M Courtney, Chase R Figley
Background: Despite the popularity of functional connectivity analyses and the well-known topology of several intrinsic cortical networks, relatively little is known about the white matter regions (i.e., structural connectivity) underlying these networks. In the current study, we have therefore performed fMRI-guided diffusion tensor imaging (DTI) tractography to create probabilistic white matter atlases for eight previously identified functional brain networks, including the Auditory, Basal Ganglia, Language, Precuneus, Sensorimotor, Primary Visual, Higher Visual and Visuospatial Networks...
2017: Frontiers in Human Neuroscience
https://www.readbyqxmd.com/read/28749606/perceived-stress-is-associated-with-increased-rostral-middle-frontal-gyrus-cortical-thickness-a-family-based-and-discordant-sibling-investigation
#8
L J Michalski, C H Demers, D A A Baranger, D M Barch, M P Harms, G C Burgess, R Bogdan
BACKGROUND: Elevated stress perception and depression commonly co-occur, suggesting that they share a common neurobiology. Cortical thickness of the rostral middle frontal gyrus (RMFG), a region critical for executive function, has been associated with depression- and stress-related phenotypes. Here, we examined whether RMFG cortical thickness is associated with these phenotypes in a large family-based community sample. METHODS: RMFG cortical thickness was estimated using FreeSurfer among participants (n=879) who completed the ongoing Human Connectome Project...
July 27, 2017: Genes, Brain, and Behavior
https://www.readbyqxmd.com/read/28745584/the-heritability-of-multi-modal-connectivity-in-human-brain-activity
#9
Giles L Colclough, Stephen M Smith, Tom E Nichols, Anderson M Winkler, Stamatios N Sotiropoulos, Matthew F Glasser, David C Van Essen, Mark W Woolrich
Patterns of intrinsic human brain activity exhibit a profile of functional connectivity that is associated with behaviour and cognitive performance, and deteriorates with disease. This paper investigates the relative importance of genetic factors and the common environment between twins in determining this functional connectivity profile. Using functional magnetic resonance imaging (fMRI) on 820 subjects from the Human Connectome Project, and magnetoencephalographic (MEG) recordings from a subset, the heritability of connectivity between 39 cortical regions was estimated...
July 26, 2017: ELife
https://www.readbyqxmd.com/read/28716967/contextual-and-developmental-differences-in-the-neural-architecture-of-cognitive-control
#10
Raluca Petrican, Cheryl L Grady
Since both development and context impact functional brain architecture, the neural connectivity signature of a cognitive or affective predisposition may similarly vary across different ages and circumstances. To test this hypothesis, we investigated the effects of age and cognitive versus social-affective context on the stable and time-varying neural architecture of inhibition, the putative core cognitive control component, in a subsample (N= 359 [22-36 yrs], 174 men) of the Human Connectome Project. Among younger individuals, a neural signature of superior inhibition emerged in both stable and dynamic connectivity analyses...
July 17, 2017: Journal of Neuroscience: the Official Journal of the Society for Neuroscience
https://www.readbyqxmd.com/read/28716714/functional-density-and-edge-maps-characterizing-functional-architecture-in-individuals-and-improving-cross-subject-registration
#11
Tong Tong, Iman Aganj, Tian Ge, Jonathan R Polimeni, Bruce Fischl
Population-level inferences and individual-level analyses are two important aspects in functional magnetic resonance imaging (fMRI) studies. Extracting reliable and informative features from fMRI data that capture biologically meaningful inter-subject variation is critical for aligning and comparing functional networks across subjects, and connecting the properties of functional brain organization with variations in behavior, cognition and genetics. In this study, we derive two new measures, which we term functional density map and edge map, and demonstrate their usefulness in characterizing the function of individual brains...
July 14, 2017: NeuroImage
https://www.readbyqxmd.com/read/28710040/evaluating-the-replicability-specificity-and-generalizability-of-connectome-fingerprints
#12
Lea Waller, Henrik Walter, Johann D Kruschwitz, Lucia Reuter, Sabine Müller, Susanne Erk, Ilya M Veer
Establishing reliable, robust, and unique brain signatures from neuroimaging data is a prerequisite for precision psychiatry, and therefore a highly sought-after goal in contemporary neuroscience. Recently, the procedure of connectome fingerprinting, using brain functional connectivity profiles as such signatures, was shown to be able to accurately identify individuals from a group of 126 subjects from the Human Connectome Project (HCP). However, the specificity and generalizability of this procedure were not tested...
July 11, 2017: NeuroImage
https://www.readbyqxmd.com/read/28687517/comparing-test-retest-reliability-of-dynamic-functional-connectivity-methods
#13
Ann S Choe, Mary Beth Nebel, Anita D Barber, Jessica R Cohen, Yuting Xu, James J Pekar, Brian Caffo, Martin A Lindquist
Due to the dynamic, condition-dependent nature of brain activity, interest in estimating rapid functional connectivity (FC) changes that occur during resting-state functional magnetic resonance imaging (rs-fMRI) has recently soared. However, studying dynamic FC is methodologically challenging, due to the low signal-to-noise ratio of the blood oxygen level dependent (BOLD) signal in fMRI and the massive number of data points generated during the analysis. Thus, it is important to establish methods and summary measures that maximize reliability and the utility of dynamic FC to provide insight into brain function...
July 5, 2017: NeuroImage
https://www.readbyqxmd.com/read/28684331/fiberprint-a-subject-fingerprint-based-on-sparse-code-pooling-for-white-matter-fiber-analysis
#14
Kuldeep Kumar, Christian Desrosiers, Kaleem Siddiqi, Olivier Colliot, Matthew Toews
White matter characterization studies use the information provided by diffusion magnetic resonance imaging (dMRI) to draw cross-population inferences. However, the structure, function, and white matter geometry vary across individuals. Here, we propose a subject fingerprint, called Fiberprint, to quantify the individual uniqueness in white matter geometry using fiber trajectories. We learn a sparse coding representation for fiber trajectories by mapping them to a common space defined by a dictionary. A subject fingerprint is then generated by applying a pooling function for each bundle, thus providing a vector of bundle-wise features describing a particular subject's white matter geometry...
July 3, 2017: NeuroImage
https://www.readbyqxmd.com/read/28676985/functional-connectivity-density-mapping-comparing-multiband-and-conventional-epi-protocols
#15
Alexander D Cohen, Dardo Tomasi, Ehsan Shokri-Kojori, Andrew S Nencka, Yang Wang
Functional connectivity density mapping (FCDM) is a newly developed data-driven technique that quantifies the number of local and global functional connections for each voxel in the brain. In this study, we evaluated reproducibility, sensitivity, and specificity of both local functional connectivity density (lFCD) and global functional connectivity density (gFCD). We compared these metrics using the human connectome project (HCP) compatible high-resolution (2 mm isotropic, TR = 0.8 s) multiband (MB), and more typical, lower resolution (3...
July 4, 2017: Brain Imaging and Behavior
https://www.readbyqxmd.com/read/28675490/the-significance-of-negative-correlations-in-brain-connectivity
#16
Liang Zhan, Lisanne M Jenkins, Ouri E Wolfson, Johnson Jonaris GadElkarim, Kevin Nocito, Paul M Thompson, Olusola A Ajilore, Moo K Chung, Alex D Leow
Understanding the modularity of functional magnetic resonance imaging (fMRI)-derived brain networks or "connectomes" can inform the study of brain function organization. However, fMRI connectomes additionally involve negative edges, which may not be optimally accounted for by existing approaches to modularity that variably threshold, binarize, or arbitrarily weight these connections. Consequently, many existing Q maximization-based modularity algorithms yield variable modular structures. Here, we present an alternative complementary approach that exploits how frequent the blood-oxygen-level-dependent (BOLD) signal correlation between two nodes is negative...
July 4, 2017: Journal of Comparative Neurology
https://www.readbyqxmd.com/read/28645840/autoreject-automated-artifact-rejection-for-meg-and-eeg-data
#17
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/28641247/modeling-task-fmri-data-via-deep-convolutional-autoencoder
#18
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
#19
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/28631354/cross-population-myelination-covariance-of-human-cerebral-cortex
#20
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...
September 2017: Human Brain Mapping
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