scholarly journals The HCP 7T Retinotopy Dataset: Description and pRF Analysis

2018 ◽  
Author(s):  
Noah C. Benson ◽  
Keith W. Jamison ◽  
Michael J. Arcaro ◽  
An Vu ◽  
Matthew F. Glasser ◽  
...  
Keyword(s):  
Visual Processing ◽  
Large Scale ◽  
Individual Variability ◽  
7 Tesla ◽  
Individual Subject ◽  
Retinotopic Mapping ◽  
Human Connectome Project ◽  
The Brain ◽  
Model Based Analysis

AbstractAbout a quarter of human cerebral cortex is dedicated mainly to visual processing. The large-scale organization of visual cortex can be measured with functional magnetic resonance imaging (fMRI) while subjects view spatially modulated visual stimuli, also known as ‘retinotopic mapping’. One of the datasets collected by the Human Connectome Project (HCP) involved ultra-high-field (7 Tesla) fMRI retinotopic mapping in 181 healthy young adults (1.6-mm resolution), yielding the largest freely available collection of retinotopy data. Here, we describe the experimental paradigm and the results of model-based analysis of the fMRI data. These results provide estimates of population receptive field position and size. Our analyses include both results from individual subjects as well as results obtained by averaging fMRI time-series across subjects at each cortical and subcortical location and then fitting models. Both the group-average and individual-subject results reveal robust signals across much of the brain, including occipital, temporal, parietal, and frontal cortex as well as subcortical areas. The group-average results agree well with previously published parcellations of visual areas. In addition, split-half analyses show strong within-subject reliability, further demonstrating the high quality of the data. We make publicly available the analysis results for individual subjects and the group average, as well as associated stimuli and analysis code. These resources provide an opportunity for studying fine-scale individual variability in cortical and subcortical organization and the properties of high-resolution fMRI. In addition, they provide a set of observations that can be compared with other HCP measures acquired in these same participants.

scholarly journals Brain hubs defined in the group do not overlap with regions of high inter-individual variability

2021 ◽  
Author(s):  
Derek Martin Smith ◽  
Brian T Kraus ◽  
Ally Dworetsky ◽  
Evan M Gordon ◽  
Caterina Gratton
Keyword(s):  
Functional Connectivity ◽  
Brain Function ◽  
Critical Role ◽  
Brain Regions ◽  
Individual Variability ◽  
Spatial Proximity ◽  
Functional Magnetic Resonance ◽  
Multiple Networks ◽  
Human Connectome Project ◽  
The Brain

Connector 'hubs' are brain regions with links to multiple networks. These regions are hypothesized to play a critical role in brain function. While hubs are often identified based on group-average functional magnetic resonance imaging (fMRI) data, there is considerable inter-subject variation in the functional connectivity profiles of the brain, especially in association regions where hubs tend to be located. Here we investigated how group hubs are related to locations of inter-individual variability, to better understand if hubs are (a) relatively conserved across people, (b) locations with malleable connectivity, leading individuals to show variable hub profiles, or (c) artifacts arising from cross-person variation. To answer this question, we compared the locations of hubs and regions of strong idiosyncratic functional connectivity ("variants") in both the Midnight Scan Club and Human Connectome Project datasets. Group hubs defined based on the participation coefficient did not overlap strongly with variants. These hubs have relatively strong similarity across participants and consistent cross-network profiles. Consistency across participants was further improved when participation coefficient hubs were allowed to shift slightly in local position. Thus, our results demonstrate that group hubs defined with the participation coefficient are generally consistent across people, suggesting they may represent conserved cross-network bridges. More caution is warranted with alternative hub measures, such as community density, which are based on spatial proximity and show higher correspondence to locations of individual variability.

scholarly journals Robust prediction of individual personality from brain functional connectome

2020 ◽  
Vol 15 (3) ◽  
pp. 359-369 ◽  
Author(s):  
Huanhuan Cai ◽  
Jiajia Zhu ◽  
Yongqiang Yu
Keyword(s):  
Personality Traits ◽  
Large Scale ◽  
Five Factor Model ◽  
Brain Regions ◽  
Individual Variability ◽  
Personality Factors ◽  
Imaging Features ◽  
Imaging Data ◽  
Functional Connectome ◽  
Human Connectome Project

Abstract Neuroimaging studies have linked inter-individual variability in the brain to individualized personality traits. However, only one or several aspects of personality have been effectively predicted based on brain imaging features. The objective of this study was to construct a reliable prediction model of personality in a large sample by using connectome-based predictive modeling (CPM), a recently developed machine learning approach. High-quality resting-state functional magnetic resonance imaging data of 810 healthy young participants from the Human Connectome Project dataset were used to construct large-scale brain networks. Personality traits of the five-factor model (FFM) were assessed by the NEO Five Factor Inventory. We found that CPM successfully and reliably predicted all the FFM personality factors (agreeableness, openness, conscientiousness and neuroticism) other than extraversion in novel individuals. At the neural level, we found that the personality-associated functional networks mainly included brain regions within default mode, frontoparietal executive control, visual and cerebellar systems. Although different feature selection thresholds and parcellation strategies did not significantly influence the prediction results, some findings lost significance after controlling for confounds including age, gender, intelligence and head motion. Our finding of robust personality prediction from an individual’s unique functional connectome may help advance the translation of ‘brain connectivity fingerprinting’ into real-world personality psychological settings.

scholarly journals Subject specificity of the correlation between large-scale structural and functional connectivity

2019 ◽  
Vol 3 (1) ◽  
pp. 90-106 ◽  
Author(s):  
J. Zimmermann ◽  
J. Griffiths ◽  
M. Schirner ◽  
P. Ritter ◽  
A. R. McIntosh
Keyword(s):  
Functional Connectivity ◽  
Large Scale ◽  
Structural Connectivity ◽  
Subject Specificity ◽  
Human Connectome Project ◽  
Subject Specific ◽  
The Brain ◽  
Human Neuroimaging

Structural connectivity (SC), the physical pathways connecting regions in the brain, and functional connectivity (FC), the temporal coactivations, are known to be tightly linked. However, the nature of this relationship is still not understood. In the present study, we examined this relation more closely in six separate human neuroimaging datasets with different acquisition and preprocessing methods. We show that using simple linear associations, the relation between an individual’s SC and FC is not subject specific for five of the datasets. Subject specificity of SC-FC fit is achieved only for one of the six datasets, the multimodal Glasser Human Connectome Project (HCP) parcellated dataset. We show that subject specificity of SC-FC correspondence is limited across datasets due to relatively small variability between subjects in SC compared with the larger variability in FC.

scholarly journals DREAM

2021 ◽  
Author(s):  
Zhu-Qing Gong ◽  
Peng Gao ◽  
Chao Jiang ◽  
Xiu-Xia Xing ◽  
Hao-Ming Dong ◽  
...  
Keyword(s):  
Brain Activity ◽  
Worked Examples ◽  
Individual Variability ◽  
Head Movements ◽  
Neural Oscillations ◽  
Head Motion ◽  
Multiple Frequency ◽  
Frequency Bands ◽  
Human Connectome Project ◽  
The Brain

AbstractRhythms of the brain are generated by neural oscillations across multiple frequencies. These oscillations can be decomposed into distinct frequency intervals associated with specific physiological processes. In practice, the number and ranges of decodable frequency intervals are determined by sampling parameters, often ignored by researchers. To improve the situation, we report on an open toolbox with a graphical user interface for decoding rhythms of the brain system (DREAM). We provide worked examples of DREAM to investigate frequency-specific performance of both neural (spontaneous brain activity) and neurobehavioral (in-scanner head motion) oscillations. DREAM decoded the head motion oscillations and uncovered that younger children moved their heads more than older children across all five frequency intervals whereas boys moved more than girls in the age of 7 to 9 years. It is interesting that the higher frequency bands contain more head movements, and showed stronger age-motion associations but weaker sex-motion interactions. Using data from the Human Connectome Project, DREAM mapped the amplitude of these neural oscillations into multiple frequency bands and evaluated their test-retest reliability. The resting-state brain ranks its spontaneous oscillation’s amplitudes spatially from high in ventral-temporal areas to low in ventral-occipital areas when the frequency band increased from low to high, while those in part of parietal and ventral frontal regions are reversed. The higher frequency bands exhibited more reliable amplitude measurements, implying more inter-individual variability of the amplitudes for the higher frequency bands. In summary, DREAM adds a reliable and valid tool to mapping human brain function from a multiple-frequency window into brain waves.

scholarly journals An extension of the studyforrest dataset for vision research

2016 ◽  
Author(s):  
Ayan Sengupta ◽  
Falko R. Kaule ◽  
J. Swaroop Guntupalli ◽  
Michael B. Hoffmann ◽  
Christian Häusler ◽  
...  
Keyword(s):  
Visual Processing ◽  
Functional Neuroimaging ◽  
Original Cohort ◽  
Mapping Procedure ◽  
Retinotopic Mapping ◽  
Face Area ◽  
Neuroimaging Data ◽  
Imaging Research ◽  
Higher Visual Areas

AbstractThe studyforrest (http://studyforrest.org) dataset is likely the largest neuroimag-ing dataset on natural language and story processing publicly available today. In this article, along with a companion publication, we present an update of this dataset that extends its scope to vision and multi-sensory research. 15 participants of the original cohort volunteered for a series of additional studies: a clinical examination of visual function, a standard retinotopic mapping procedure, and a localization of higher visual areas — such as the fusiform face area. The combination of this update, the previous data releases for the dataset, and the companion publication, which includes neuroimaging and eye tracking data from natural stimulation with a motion picture, form an extremely versatile and comprehensive resource for brain imaging research — with almost six hours of functional neuroimaging data across five different stimulation paradigms for each participant. Furthermore, we describe employed paradigms and present results that document the quality of the data for the purpose of characterising major properties of participants’ visual processing stream.

scholarly journals Heritability of individualized cortical network topography

2020 ◽  
Author(s):  
Kevin Michael Anderson ◽  
Tian Ge ◽  
Ru Kong ◽  
Lauren M Patrick ◽  
R. Nathan Spreng ◽  
...  
Keyword(s):  
Genetic Control ◽  
Large Scale ◽  
Posterior Parietal Cortex ◽  
Genetic Factors ◽  
Population Level ◽  
Individual Variability ◽  
Cortical Networks ◽  
Brain Organization ◽  
Human Connectome Project ◽  
Sensory Motor

Human cortex is patterned by a complex and interdigitated web of large-scale functional networks. Recent methodological breakthroughs reveal variation in the size, shape, and spatial topography of cortical networks across individuals. While spatial network organization emerges across development, is stable over time, and predictive of behavior, it is not yet clear to what extent genetic factors underlie inter-individual differences in network topography. Here, leveraging a novel non-linear multi-dimensional estimation of heritability, we provide evidence that individual variability in the size and topographic organization of cortical networks are under genetic control. Using twin and family data from the Human Connectome Project (n=1,023), we find increased variability and reduced heritability in the size of heteromodal association networks (h2: M=0.33, SD=0.071), relative to unimodal sensory/motor cortex (h2: M=0.44, SD=0.051). We then demonstrate that the spatial layout of cortical networks is influenced by genetics, using our multi-dimensional estimation of heritability (h2-multi; M=0.14, SD=0.015). However, topographic heritability did not differ between heteromodal and unimodal networks. Genetic factors had a regionally variable influence on brain organization, such that the heritability of network topography was greatest in prefrontal, precuneus, and posterior parietal cortex. Taken together, these data are consistent with relaxed genetic control of association cortices relative to primary sensory/motor regions, and have implications for understanding population-level variability in brain functioning, guiding both individualized prediction and the interpretation of analyses that integrate genetics and neuroimaging.

scholarly journals Human brain atlasing: past, present and future

2017 ◽  
Vol 30 (6) ◽  
pp. 504-519 ◽  
Author(s):  
Wieslaw L Nowinski
Keyword(s):  
Human Brain ◽  
Large Scale ◽  
State Of The Art ◽  
The State ◽  
Brain Atlas ◽  
Future Directions ◽  
Human Connectome Project ◽  
Health And Disease ◽  
The Brain ◽  
Brain Atlases

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. For over two decades with my R&D lab I developed 35 brain atlases, licensed to 67 companies and made available in about 100 countries. This paper has two objectives. First, it provides an overview of the state of the art in brain atlasing. Second, as it is already 20 years from the release of our first brain atlas, I summarise my past and present efforts, share my experience in atlas creation, validation and commercialisation, compare with the state of the art, and propose future directions.

scholarly journals Heritability of individualized cortical network topography

2021 ◽  
Vol 118 (9) ◽  
pp. e2016271118
Author(s):  
Kevin M. Anderson ◽  
Tian Ge ◽  
Ru Kong ◽  
Lauren M. Patrick ◽  
R. Nathan Spreng ◽  
...  
Keyword(s):  
Genetic Control ◽  
Large Scale ◽  
Posterior Parietal Cortex ◽  
Genetic Factors ◽  
Population Level ◽  
Individual Variability ◽  
Cortical Networks ◽  
Brain Organization ◽  
Human Connectome Project ◽  
Sensory Motor

Human cortex is patterned by a complex and interdigitated web of large-scale functional networks. Recent methodological breakthroughs reveal variation in the size, shape, and spatial topography of cortical networks across individuals. While spatial network organization emerges across development, is stable over time, and is predictive of behavior, it is not yet clear to what extent genetic factors underlie interindividual differences in network topography. Here, leveraging a nonlinear multidimensional estimation of heritability, we provide evidence that individual variability in the size and topographic organization of cortical networks are under genetic control. Using twin and family data from the Human Connectome Project (n = 1,023), we find increased variability and reduced heritability in the size of heteromodal association networks (h2: M = 0.34, SD = 0.070), relative to unimodal sensory/motor cortex (h2: M = 0.40, SD = 0.097). We then demonstrate that the spatial layout of cortical networks is influenced by genetics, using our multidimensional estimation of heritability (h2-multi; M = 0.14, SD = 0.015). However, topographic heritability did not differ between heteromodal and unimodal networks. Genetic factors had a regionally variable influence on brain organization, such that the heritability of network topography was greatest in prefrontal, precuneus, and posterior parietal cortex. Taken together, these data are consistent with relaxed genetic control of association cortices relative to primary sensory/motor regions and have implications for understanding population-level variability in brain functioning, guiding both individualized prediction and the interpretation of analyses that integrate genetics and neuroimaging.

Assessment of quality of life in patients with the effects of transient ischemic stroke

2016 ◽  
pp. 37-39
Author(s):  
A. Babirad
Keyword(s):  
Quality Of Life ◽  
Ischemic Stroke ◽  
Large Scale ◽  
Cerebrovascular Diseases ◽  
Control Measures ◽  
Sociological Research ◽  
Cerebrovascular Events ◽  
Sf 36 ◽  
Angioplasty And Stenting

Cerebrovascular diseases are a problem of the world today, and according to the forecast, the problem of the near future arises. The main risk factors for the development of ischemic disorders of the cerebral circulation include oblique and aging, arterial hypertension, smoking, diabetes mellitus and heart disease. An effective strategy for the prevention of cerebrovascular events is based on the implementation of large-scale risk control measures, including the use of antiagregant and anticoagulant therapy, invasive interventions such as atheromectomy, angioplasty and stenting. In this connection, the efforts of neurologists, cardiologists, angiosurgery, endocrinologists and other specialists are the basis for achieving an acceptable clinical outcome. A review of the SF-36 method for assessing the quality of life in patients with the effects of transient ischemic stroke is presented. The assessment of quality of life is recognized in world medical practice and research, an indicator that is also used to assess the quality of the health system and in general sociological research.

Classification of methods for generating pseudo-CT from MRI images for MRI-alone RT

2017 ◽  
Vol 1 (3) ◽  
pp. 54
Author(s):  
BOUKELLOUZ Wafa ◽  
MOUSSAOUI Abdelouahab
Keyword(s):  
Performance Metrics ◽  
Radiation Treatment ◽  
Hybrid Methods ◽  
Dose Calculation ◽  
Absolute Error ◽  
Ct Images ◽  
General Technique ◽  
Therapy Field ◽  
The Brain

Background: Since the last decades, research have been oriented towards an MRI-alone radiation treatment planning (RTP), where MRI is used as the primary modality for imaging, delineation and dose calculation by assigning to it the needed electron density (ED) information. The idea is to create a computed tomography (CT) image or so-called pseudo-CT from MRI data. In this paper, we review and classify methods for creating pseudo-CT images from MRI data. Each class of methods is explained and a group of works in the literature is presented in detail with statistical performance. We discuss the advantages, drawbacks and limitations of each class of methods. Methods: We classified most recent works in deriving a pseudo-CT from MR images into four classes: segmentation-based, intensity-based, atlas-based and hybrid methods. We based the classification on the general technique applied in the approach. Results: Most of research focused on the brain and the pelvis regions. The mean absolute error (MAE) ranged from 80 HU to 137 HU and from 36.4 HU to 74 HU for the brain and pelvis, respectively. In addition, an interest in the Dixon MR sequence is increasing since it has the advantage of producing multiple contrast images with a single acquisition. Conclusion: Radiation therapy field is emerging towards the generalization of MRI-only RT thanks to the advances in techniques for generation of pseudo-CT images. However, a benchmark is needed to set in common performance metrics to assess the quality of the generated pseudo-CT and judge on the efficiency of a certain method.

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