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Human connectome project

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https://www.readbyqxmd.com/read/29218892/heritability-estimates-on-resting-state-fmri-data-using-enigma-analysis-pipeline
#1
Bhim M Adhikari, Neda Jahanshad, Dinesh Shukla, David C Glahn, John Blangero, Richard C Reynolds, Robert W Cox, Els Fieremans, Jelle Veraart, Dmitry S Novikov, Thomas E Nichols, L Elliot Hong, Paul M Thompson, Peter Kochunov
Big data initiatives such as the Enhancing NeuroImaging Genetics through Meta-Analysis consortium (ENIGMA), combine data collected by independent studies worldwide to achieve more generalizable estimates of effect sizes and more reliable and reproducible outcomes. Such efforts require harmonized image analyses protocols to extract phenotypes consistently. This harmonization is particularly challenging for resting state fMRI due to the wide variability of acquisition protocols and scanner platforms; this leads to site-to-site variance in quality, resolution and temporal signal-to-noise ratio (tSNR)...
2018: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/29203849/an-integrated-brain-behavior-model-for-working-memory
#2
D A Moser, G E Doucet, A Ing, D Dima, G Schumann, R M Bilder, S Frangou
Working memory (WM) is a central construct in cognitive neuroscience because it comprises mechanisms of active information maintenance and cognitive control that underpin most complex cognitive behavior. Individual variation in WM has been associated with multiple behavioral and health features including demographic characteristics, cognitive and physical traits and lifestyle choices. In this context, we used sparse canonical correlation analyses (sCCAs) to determine the covariation between brain imaging metrics of WM-network activation and connectivity and nonimaging measures relating to sensorimotor processing, affective and nonaffective cognition, mental health and personality, physical health and lifestyle choices derived from 823 healthy participants derived from the Human Connectome Project...
December 5, 2017: Molecular Psychiatry
https://www.readbyqxmd.com/read/29186356/altered-caudate-connectivity-is-associated-with-executive-dysfunction-after-traumatic-brain-injury
#3
Sara De Simoni, Peter O Jenkins, Niall J Bourke, Jessica J Fleminger, Peter J Hellyer, Amy E Jolly, Maneesh C Patel, James H Cole, Robert Leech, David J Sharp
Traumatic brain injury often produces executive dysfunction. This characteristic cognitive impairment often causes long-term problems with behaviour and personality. Frontal lobe injuries are associated with executive dysfunction, but it is unclear how these injuries relate to corticostriatal interactions that are known to play an important role in behavioural control. We hypothesized that executive dysfunction after traumatic brain injury would be associated with abnormal corticostriatal interactions, a question that has not previously been investigated...
November 23, 2017: Brain: a Journal of Neurology
https://www.readbyqxmd.com/read/29175200/quasi-periodic-patterns-of-intrinsic-brain-activity-in-individuals-and-their-relationship-to-global-signal
#4
Behnaz Yousefi, Jaemin Shin, Eric H Schumacher, Shella D Keilholz
Quasiperiodic patterns (QPPs) as reported by Majeed et al., 2011 are prominent features of the brain's intrinsic activity that involve important large-scale networks (default mode, DMN; task positive, TPN) and are likely to be major contributors to widely used measures of functional connectivity. We examined the variability of these patterns in 470 individuals from the Human Connectome Project resting state functional MRI dataset. The QPPs from individuals can be coarsely categorized into two types: one where strong anti-correlation between the DMN and TPN is present, and another where most areas are strongly correlated...
November 21, 2017: NeuroImage
https://www.readbyqxmd.com/read/29158202/dynamic-effective-connectivity-in-resting-state-fmri
#5
Hae-Jeong Park, Karl J Friston, Chongwon Pae, Bumhee Park, Adeel Razi
Context-sensitive and activity-dependent fluctuations in connectivity underlie functional integration in the brain and have been studied widely in terms of synaptic plasticity, learning and condition-specific (e.g., attentional) modulations of synaptic efficacy. This dynamic aspect of brain connectivity has recently attracted a lot of attention in the resting state fMRI community. To explain dynamic functional connectivity in terms of directed effective connectivity among brain regions, we introduce a novel method to identify dynamic effective connectivity using spectral dynamic causal modelling (spDCM)...
November 20, 2017: NeuroImage
https://www.readbyqxmd.com/read/29155843/the-relation-between-statistical-power-and-inference-in-fmri
#6
Henk R Cremers, Tor D Wager, Tal Yarkoni
Statistically underpowered studies can result in experimental failure even when all other experimental considerations have been addressed impeccably. In fMRI the combination of a large number of dependent variables, a relatively small number of observations (subjects), and a need to correct for multiple comparisons can decrease statistical power dramatically. This problem has been clearly addressed yet remains controversial-especially in regards to the expected effect sizes in fMRI, and especially for between-subjects effects such as group comparisons and brain-behavior correlations...
2017: PloS One
https://www.readbyqxmd.com/read/29143409/chronnectome-fingerprinting-identifying-individuals-and-predicting-higher-cognitive-functions-using-dynamic-brain-connectivity-patterns
#7
Jin Liu, Xuhong Liao, Mingrui Xia, Yong He
The human brain is a large, interacting dynamic network, and its architecture of coupling among brain regions varies across time (termed the "chronnectome"). However, very little is known about whether and how the dynamic properties of the chronnectome can characterize individual uniqueness, such as identifying individuals as a "fingerprint" of the brain. Here, we employed multiband resting-state functional magnetic resonance imaging data from the Human Connectome Project (N = 105) and a sliding time-window dynamic network analysis approach to systematically examine individual time-varying properties of the chronnectome...
November 15, 2017: Human Brain Mapping
https://www.readbyqxmd.com/read/29135806/fluid-intelligence-relates-to-the-resting-state-amplitude-of-low-frequency-fluctuation-and-functional-connectivity-a-multivariate-pattern-analysis
#8
Changjun Li, Guocheng Yang, Meiling Li, Bo Li
The goal of this study was to investigate the relationship between fluid intelligence (gF) and the pattern of the functional characteristics in the resting state in adults using multivariate pattern analysis. Resting-state functional images from 100 participants in the Human Connectome Project data set were analyzed. The amplitude of low-frequency fluctuation (ALFF) was first calculated, and a support vector regression approach was used to identify the association with gF. To discover whether the connectivity of the gF-associated areas was also related to gF, we further checked the seed-based functional connectivity using the seeds from the ALFF...
November 13, 2017: Neuroreport
https://www.readbyqxmd.com/read/29104968/a-sparse-bayesian-learning-algorithm-for-white-matter-parameter-estimation-from-compressed-multi-shell-diffusion-mri
#9
Pramod Kumar Pisharady, Stamatios N Sotiropoulos, Guillermo Sapiro, Christophe Lenglet
We propose a sparse Bayesian learning algorithm for improved estimation of white matter fiber parameters from compressed (under-sampled q-space) multi-shell diffusion MRI data. The multi-shell data is represented in a dictionary form using a non-monoexponential decay model of diffusion, based on continuous gamma distribution of diffusivities. The fiber volume fractions with predefined orientations, which are the unknown parameters, form the dictionary weights. These unknown parameters are estimated with a linear un-mixing framework, using a sparse Bayesian learning algorithm...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/29104529/bayesian-rician-regression-for-neuroimaging
#10
Bertil Wegmann, Anders Eklund, Mattias Villani
It is well-known that data from diffusion weighted imaging (DWI) follow the Rician distribution. The Rician distribution is also relevant for functional magnetic resonance imaging (fMRI) data obtained at high temporal or spatial resolution. We propose a general regression model for non-central χ (NC-χ) distributed data, with the heteroscedastic Rician regression model as a prominent special case. The model allows both parameters in the Rician distribution to be linked to explanatory variables, with the relevant variables chosen by Bayesian variable selection...
2017: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/29102809/spatio-temporal-modeling-of-connectome-scale-brain-network-interactions-via-time-evolving-graphs
#11
REVIEW
Jing Yuan, Xiang Li, Jinhe Zhang, Liao Luo, Qinglin Dong, Jinglei Lv, Yu Zhao, Xi Jiang, Shu Zhang, Wei Zhang, Tianming Liu
Many recent literature studies have revealed interesting dynamics patterns of functional brain networks derived from fMRI data. However, it has been rarely explored how functional networks spatially overlap (or interact) and how such connectome-scale network interactions temporally evolve. To explore these unanswered questions, this paper presents a novel framework for spatio-temporal modeling of connectome-scale functional brain network interactions via two main effective computational methodologies. First, to integrate, pool and compare brain networks across individuals and their cognitive states under task performances, we designed a novel group-wise dictionary learning scheme to derive connectome-scale consistent brain network templates that can be used to define the common reference space of brain network interactions...
November 9, 2017: NeuroImage
https://www.readbyqxmd.com/read/29100937/anatomicuts-hierarchical-clustering-of-tractography-streamlines-based-on-anatomical-similarity
#12
Viviana Siless, Ken Chang, Bruce Fischl, Anastasia Yendiki
Diffusion MRI tractography produces massive sets of streamlines that contain a wealth of information on brain connections. The size of these datasets creates a need for automated clustering methods to group the streamlines into meaningful bundles. Conventional clustering techniques group streamlines based on their spatial coordinates. Neuroanatomists, however, define white-matter bundles based on the anatomical structures that they go through or next to, rather than their spatial coordinates. Thus we propose a similarity measure for clustering streamlines based on their position relative to cortical and subcortical brain regions...
November 1, 2017: NeuroImage
https://www.readbyqxmd.com/read/29096577/human-brain-atlasing-past-present-and-future
#13
Wieslaw L Nowinski
We have recently witnessed an explosion of large-scale initiatives and projects addressing mapping, modeling, simulation and atlasing of the human brain, including the BRAIN Initiative, the Human Brain Project, the Human Connectome Project (HCP), the Big Brain, the Blue Brain Project, the Allen Brain Atlas, the Brainnetome, among others. Besides these large and international initiatives, there are numerous mid-size and small brain atlas-related projects. My contribution to these global efforts has been to create adult human brain atlases in health and disease, and to develop atlas-based applications...
December 2017: Neuroradiology Journal
https://www.readbyqxmd.com/read/29071309/kernel-regularized-ica-for-computing-functional-topography-from-resting-state-fmri
#14
Junyan Wang, Yonggang Shi
Topographic regularity is a fundamental property in brain connectivity. In this work, we present a novel method for studying topographic regularity of functional connectivity based on resting-state fMRI (rfMRI), which is widely available and easy to acquire in large-scale studies. The main idea in our method is the incorporation of topographically regular structural connectivity for independent component analysis (ICA). This is enabled by the recent development of novel tractography and tract filtering algorithms that can generate highly organized fiber bundles connecting different brain regions...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/29067135/the-braingraph-org-database-of-high-resolution-structural-connectomes-and-the-brain-graph-tools
#15
Csaba Kerepesi, Balázs Szalkai, Bálint Varga, Vince Grolmusz
Based on the data of the NIH-funded Human Connectome Project, we have computed structural connectomes of 426 human subjects in five different resolutions of 83, 129, 234, 463 and 1015 nodes and several edge weights. The graphs are given in anatomically annotated GraphML format that facilitates better further processing and visualization. For 96 subjects, the anatomically classified sub-graphs can also be accessed, formed from the vertices corresponding to distinct lobes or even smaller regions of interests of the brain...
October 2017: Cognitive Neurodynamics
https://www.readbyqxmd.com/read/29060865/simultaneous-estimation-of-the-in-mean-and-in-variance-causal-connectomes-of-the-human-brain
#16
A Duggento, L Passamonti, M Guerrisi, N Toschi
In recent years, the study of the human connectome (i.e. of statistical relationships between non spatially contiguous neurophysiological events in the human brain) has been enormously fuelled by technological advances in high-field functional magnetic resonance imaging (fMRI) as well as by coordinated world wide data-collection efforts like the Human Connectome Project (HCP). In this context, Granger Causality (GC) approaches have recently been employed to incorporate information about the directionality of the influence exerted by a brain region on another...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/29060609/resting-state-brain-correlates-of-instantaneous-autonomic-outflow
#17
G Valenza, A Duggento, L Passamonti, S Diciotti, C Tessa, R Barbieri, N Toschi
A prominent pathway of brain-heart interaction is represented by autonomic nervous system (ANS) heartbeat modulation. While within-brain resting state networks have been the object of intense functional Magnetic Resonance Imaging (fMRI) research, technological and methodological limitations have hampered research on the central correlates of cardiovascular control dynamics. Here we combine the high temporal and spatial resolution as well as data volume afforded by the Human Connectome Project with a probabilistic model of heartbeat dynamics to characterize central correlates of sympathetic and parasympathetic ANS activity at rest...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/29060607/resting-state-brain-correlates-of-cardiovascular-complexity
#18
G Valenza, A Duggento, L Passamonti, S Diciotti, C Tessa, N Toschi, R Barbieri
While estimates of complex heartbeat dynamics have provided effective prognostic and diagnostic markers for a wide range of pathologies, brain correlates of complex cardiac measures in general and of complex sympatho-vagal dynamics in particular are still unknown. In this study we combine resting state functional Magnetic Resonance Imaging (fMRI) and physiological signal acquisition from 34 healthy subjects selected from the Human Connectome Project (HCP) repository with inhomogeneous point-process approximate and sample heartbeat entropy measures (ipApEn and ipSampEn) to investigate brain areas involved in complex cardiovascular control...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/29060606/dynamic-inter-network-connectivity-in-the-human-brain
#19
R Riccelli, L Passamonti, A Duggento, M Guerrisi, I Indovina, N Toschi
Recently, the field of functional brain connectivity has shifted its attention on studying how functional connectivity (FC) between remote regions changes over time. It is becoming increasingly evident that the human "connectome" is a dynamical entity whose variations are effected over very short timescales and reflect crucial mechanisms which underline the physiological functioning of the brain. In this study, we employ ad-hoc statistical and surrogate data generation methods to quantify whether and which brain networks displayed dynamic behaviors in a very large sample of healthy subjects provided by the Human Connectome Project (HCP)...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/29060604/dynamical-brain-connectivity-estimation-using-garch-models-an-application-to-personality-neuroscience
#20
R Riccelli, L Passamonti, A Duggento, M Guerrisi, I Indovina, A Terracciano, N Toschi
It has recently become evident that the functional connectome of the human brain is a dynamical entity whose time evolution carries important information underpinning physiological brain function as well as its disease-related aberrations. While simple sliding window approaches have had some success in estimating dynamical brain connectivity in a functional MRI (fMRI) context, these methods suffer from limitations related to the arbitrary choice of window length and limited time resolution. Recently, Generalized autoregressive conditional heteroscedastic (GARCH) models have been employed to generate dynamical covariance models which can be applied to fMRI...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
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