scholarly journals Group-Level Ranking-Based Hubness Analysis of Human Brain Connectome Reveals Significant Interhemispheric Asymmetry and Intraparcel Heterogeneities

2021 ◽  
Vol 15 ◽  
Author(s):  
Sahin Hanalioglu ◽  
Siyar Bahadir ◽  
Ilkay Isikay ◽  
Pinar Celtikci ◽  
Emrah Celtikci ◽  
...  
Keyword(s):  
Human Brain ◽  
Diffusion Imaging ◽  
Structural Connectivity ◽  
Population Level ◽  
Similar Distribution ◽  
Group Level ◽  
Imaging Data ◽  
Brain Connectome ◽  
Human Connectome Project ◽  
Intuitive Method

Objective: Graph theory applications are commonly used in connectomics research to better understand connectivity architecture and characterize its role in cognition, behavior and disease conditions. One of the numerous open questions in the field is how to represent inter-individual differences with graph theoretical methods to make inferences for the population. Here, we proposed and tested a simple intuitive method that is based on finding the correlation between the rank-ordering of nodes within each connectome with respect to a given metric to quantify the differences/similarities between different connectomes.Methods: We used the diffusion imaging data of the entire HCP-1065 dataset of the Human Connectome Project (HCP) (n = 1,065 subjects). A customized cortical subparcellation of HCP-MMP atlas (360 parcels) (yielding a total of 1,598 ROIs) was used to generate connectivity matrices. Six graph measures including degree, strength, coreness, betweenness, closeness, and an overall “hubness” measure combining all five were studied. Group-level ranking-based aggregation method (“measure-then-aggregate”) was used to investigate network properties on population level.Results: Measure-then-aggregate technique was shown to represent population better than commonly used aggregate-then-measure technique (overall rs: 0.7 vs 0.5). Hubness measure was shown to highly correlate with all five graph measures (rs: 0.88–0.99). Minimum sample size required for optimal representation of population was found to be 50 to 100 subjects. Network analysis revealed a widely distributed set of cortical hubs on both hemispheres. Although highly-connected hub clusters had similar distribution between two hemispheres, average ranking values of homologous parcels of two hemispheres were significantly different in 71% of all cortical parcels on group-level.Conclusion: In this study, we provided experimental evidence for the robustness, limits and applicability of a novel group-level ranking-based hubness analysis technique. Graph-based analysis of large HCP dataset using this new technique revealed striking hemispheric asymmetry and intraparcel heterogeneities in the structural connectivity of the human brain.

scholarly journals Insights into the Connectivity of the Human Brain Using DTI

2012 ◽  
Vol 1 (1) ◽  
pp. 78-91 ◽  
Author(s):  
S Kollias
Keyword(s):  
Human Brain ◽  
Molecular Diffusion ◽  
Diffusion Tensor ◽  
Wide Spectrum ◽  
Diffusion Imaging ◽  
Structural Connectivity ◽  
Nerve Tissue ◽  
Mammalian Brain ◽  
Fibre Direction

Diffusion tensor imaging (DTI) is a neuroimaging MR technique, which allows in vivo and non-destructive visualization of myeloarchitectonics in the neural tissue and provides quantitative estimates of WM integrity by measuring molecular diffusion. It is based on the phenomenon of diffusion anisotropy in the nerve tissue, in that water molecules diffuse faster along the neural fibre direction and slower in the fibre-transverse direction. On the basis of their topographic location, trajectory, and areas that interconnect the various fibre systems of the mammalian brain are divided into commissural, projectional and association fibre systems. DTI has opened an entirely new window on the white matter anatomy with both clinical and scientific applications. Its utility is found in both the localization and the quantitative assessment of specific neuronal pathways. The potential of this technique to address connectivity in the human brain is not without a few methodological limitations. A wide spectrum of diffusion imaging paradigms and computational tractography algorithms has been explored in recent years, which established DTI as promising new avenue, for the non-invasive in vivo mapping of structural connectivity at the macroscale level. Further improvements in the spatial resolution of DTI may allow this technique to be applied in the near future for mapping connectivity also at the mesoscale level. DOI: http://dx.doi.org/10.3126/njr.v1i1.6330 Nepalese Journal of Radiology Vol.1(1): 78-91

scholarly journals Association between childhood trauma and risk for obesity: a putative neurocognitive developmental pathway

BMC Medicine ◽  
2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Qiang Luo ◽  
◽  
Lingli Zhang ◽  
Chu-Chung Huang ◽  
Yan Zheng ◽  
...  
Keyword(s):  
Weight Gain ◽  
Childhood Abuse ◽  
Childhood Trauma ◽  
Diffusion Tensor ◽  
Behavioral Control ◽  
Structural Connectivity ◽  
Population Based ◽  
Imaging Data ◽  
Human Connectome Project ◽  
Morphometry Analysis

Abstract Background Childhood trauma increases the risk for adult obesity through multiple complex pathways, and the neural substrates are yet to be determined. Methods Participants from three population-based neuroimaging cohorts, including the IMAGEN cohort, the UK Biobank (UKB), and the Human Connectome Project (HCP), were recruited. Voxel-based morphometry analysis of both childhood trauma and body mass index (BMI) was performed in the longitudinal IMAGEN cohort; validation of the findings was performed in the UKB. White-matter connectivity analysis was conducted to study the structural connectivity between the identified brain region and subdivisions of the hypothalamus in the HCP. Results In IMAGEN, a smaller frontopolar cortex (FPC) was associated with both childhood abuse (CA) (β = − .568, 95%CI − .942 to − .194; p = .003) and higher BMI (β = − .086, 95%CI − .128 to − .043; p < .001) in male participants, and these findings were validated in UKB. Across seven data collection sites, a stronger negative CA-FPC association was correlated with a higher positive CA-BMI association (β = − 1.033, 95%CI − 1.762 to − .305; p = .015). Using 7-T diffusion tensor imaging data (n = 156), we found that FPC was the third most connected cortical area with the hypothalamus, especially the lateral hypothalamus. A smaller FPC at age 14 contributed to higher BMI at age 19 in those male participants with a history of CA, and the CA-FPC interaction enabled a model at age 14 to account for some future weight gain during a 5-year follow-up (variance explained 5.8%). Conclusions The findings highlight that a malfunctioning, top-down cognitive or behavioral control system, independent of genetic predisposition, putatively contributes to excessive weight gain in a particularly vulnerable population, and may inform treatment approaches.

High-Definition Fiber Tractography of the Human Brain

Neurosurgery ◽  
2012 ◽  
Vol 71 (2) ◽  
pp. 430-453 ◽  
Author(s):  
Juan C. Fernandez-Miranda ◽  
Sudhir Pathak ◽  
Johnathan Engh ◽  
Kevin Jarbo ◽  
Timothy Verstynen ◽  
...  
Keyword(s):  
White Matter ◽  
Human Brain ◽  
Structural Connectivity ◽  
Low Grade ◽  
Superior Cerebellar Peduncle ◽  
Fiber Tractography ◽  
Imaging Data ◽  
Thalamic Nuclei ◽  
High Definition ◽  
Diffusion Spectrum Imaging

Abstract BACKGROUND: High-definition fiber tracking (HDFT) is a novel combination of processing, reconstruction, and tractography methods that can track white matter fibers from cortex, through complex fiber crossings, to cortical and subcortical targets with subvoxel resolution. OBJECTIVE: To perform neuroanatomical validation of HDFT and to investigate its neurosurgical applications. METHODS: Six neurologically healthy adults and 36 patients with brain lesions were studied. Diffusion spectrum imaging data were reconstructed with a Generalized Q-Ball Imaging approach. Fiber dissection studies were performed in 20 human brains, and selected dissection results were compared with tractography. RESULTS: HDFT provides accurate replication of known neuroanatomical features such as the gyral and sulcal folding patterns, the characteristic shape of the claustrum, the segmentation of the thalamic nuclei, the decussation of the superior cerebellar peduncle, the multiple fiber crossing at the centrum semiovale, the complex angulation of the optic radiations, the terminal arborization of the arcuate tract, and the cortical segmentation of the dorsal Broca area. From a clinical perspective, we show that HDFT provides accurate structural connectivity studies in patients with intracerebral lesions, allowing qualitative and quantitative white matter damage assessment, aiding in understanding lesional patterns of white matter structural injury, and facilitating innovative neurosurgical applications. High-grade gliomas produce significant disruption of fibers, and low-grade gliomas cause fiber displacement. Cavernomas cause both displacement and disruption of fibers. CONCLUSION: Our HDFT approach provides an accurate reconstruction of white matter fiber tracts with unprecedented detail in both the normal and pathological human brain. Further studies to validate the clinical findings are needed.

scholarly journals Large-Scale Phenomic and Genomic Analysis of Brain Asymmetrical Skew

2021 ◽  
Author(s):  
Xiang-Zhen Kong ◽  
Merel Postema ◽  
Dick Schijven ◽  
Amaia Carrión Castillo ◽  
Antonietta Pepe ◽  
...  
Keyword(s):  
Large Scale ◽  
Right Hemisphere ◽  
Association Studies ◽  
Genomic Analysis ◽  
Population Level ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Imaging Data ◽  
Physical And Mental Health ◽  
Human Connectome Project

Abstract The human cerebral hemispheres show a left–right asymmetrical torque pattern, which has been claimed to be absent in chimpanzees. The functional significance and developmental mechanisms are unknown. Here, we carried out the largest-ever analysis of global brain shape asymmetry in magnetic resonance imaging data. Three population datasets were used, UK Biobank (N = 39 678), Human Connectome Project (N = 1113), and BIL&GIN (N = 453). At the population level, there was an anterior and dorsal skew of the right hemisphere, relative to the left. Both skews were associated independently with handedness, and various regional gray and white matter metrics oppositely in the two hemispheres, as well as other variables related to cognitive functions, sociodemographic factors, and physical and mental health. The two skews showed single nucleotide polymorphisms-based heritabilities of 4–13%, but also substantial polygenicity in causal mixture model analysis, and no individually significant loci were found in genome-wide association studies for either skew. There was evidence for a significant genetic correlation between horizontal brain skew and autism, which requires future replication. These results provide the first large-scale description of population-average brain skews and their inter-individual variations, their replicable associations with handedness, and insights into biological and other factors which associate with human brain asymmetry.

scholarly journals A large-scale diffusion imaging study of tinnitus and hearing loss

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rafay A. Khan ◽  
Bradley P. Sutton ◽  
Yihsin Tai ◽  
Sara A. Schmidt ◽  
Somayeh Shahsavarani ◽  
...  
Keyword(s):  
Hearing Loss ◽  
Neural Plasticity ◽  
Large Scale ◽  
Diffusion Imaging ◽  
Structural Connectivity ◽  
Mean Diffusivity ◽  
Imaging Study ◽  
Normal Hearing ◽  
Imaging Data ◽  
Difficult Condition

AbstractSubjective, chronic tinnitus, the perception of sound in the absence of an external source, commonly occurs with many comorbidities, making it a difficult condition to study. Hearing loss, often believed to be the driver for tinnitus, is perhaps one of the most significant comorbidities. In the present study, white matter correlates of tinnitus and hearing loss were examined. Diffusion imaging data were collected from 96 participants—43 with tinnitus and hearing loss (TINHL), 17 with tinnitus and normal hearing thresholds (TINNH), 17 controls with hearing loss (CONHL) and 19 controls with normal hearing (CONNH). Fractional anisotropy (FA), mean diffusivity and probabilistic tractography analyses were conducted on the diffusion imaging data. Analyses revealed differences in FA and structural connectivity specific to tinnitus, hearing loss, and both conditions when comorbid, suggesting the existence of tinnitus-specific neural networks. These findings also suggest that age plays an important role in neural plasticity, and thus may account for some of the variability of results in the literature. However, this effect is not seen in tractography results, where a sensitivity analysis revealed that age did not impact measures of network integration or segregation. Based on these results and previously reported findings, we propose an updated model of tinnitus, wherein the internal capsule and corpus callosum play important roles in the evaluation of, and neural plasticity in response to tinnitus.

scholarly journals A cognitive state transformation model for task-general and task-specific subsystems of the brain connectome

2020 ◽  
Author(s):  
Kwangsun Yoo ◽  
Monica D. Rosenberg ◽  
Young Hye Kwon ◽  
Dustin Scheinost ◽  
Robert T Constable ◽  
...  
Keyword(s):  
Functional Organization ◽  
Transformation Model ◽  
Cognitive State ◽  
Imaging Data ◽  
State Transformation ◽  
Cognitive States ◽  
Brain Connectome ◽  
Human Connectome Project ◽  
Task Goals ◽  
The Brain

The human brain flexibly controls different cognitive behaviors, such as memory and attention, to satisfy contextual demands. Much progress has been made to reveal task-induced modulations in the whole-brain functional connectome, but we still lack a way to model changes in the brain's functional organization. Here, we present a novel connectome-to-connectome (C2C) state transformation framework that enables us to model the brain's functional reorganization in response to specific task goals. Using functional magnetic resonance imaging data from the Human Connectome Project, we demonstrate that the C2C model accurately generates an individual's task-specific connectomes from their task-free connectome with a high degree of specificity across seven different cognitive states. Moreover, the C2C model amplifies behaviorally relevant individual differences in the task-free connectome, thereby improving behavioral predictions. Finally, the C2C model reveals how the connectome reorganizes between cognitive states. Previous studies have reported that task-induced modulation of the brain connectome is domain-specific as well as domain-general, but did not specify how brain systems reconfigure to specific cognitive states. Our observations support the existence of reliable state-specific systems in the brain and indicate that we can quantitatively describe patterns of brain reorganization, common across individuals, in a computational model.

scholarly journals Large-scale Phenomic and Genomic Analysis of Brain Asymmetrical Skew

2019 ◽  
Author(s):  
Xiang-Zhen Kong ◽  
Merel Postema ◽  
Amaia Carrión Castillo ◽  
Antonietta Pepe ◽  
Fabrice Crivello ◽  
...  
Keyword(s):  
Large Scale ◽  
Right Hemisphere ◽  
Genomic Analysis ◽  
Population Level ◽  
Sociodemographic Factors ◽  
Autism Spectrum ◽  
Imaging Data ◽  
Physical And Mental Health ◽  
Human Connectome Project ◽  
The Uk

AbstractThe human cerebral hemispheres show a left-right asymmetrical torque pattern, which has been claimed to be absent in chimpanzees. The functional significance and developmental mechanisms are unknown. Here we carried out the largest-ever analysis of global brain shape asymmetry in magnetic resonance imaging data. Three population datasets were used, the UK Biobank (N = 39,678), Human Connectome Project (N = 1,113) and BIL&GIN (N = 453). At the population level, there was an anterior and dorsal skew of the right hemisphere, relative to the left. Both skews were associated independently with handedness, and various regional grey and white matter metrics oppositely in the two hemispheres, as well as other variables related to cognitive functions, sociodemographic factors, and physical and mental health. The two skews showed SNP-based heritabilities of 4-13%, but also substantial polygenicity in causal mixture model analysis, and no individually significant loci were found in GWAS for either skew. There was evidence for a significant genetic correlation (rg=−0.40, p=0.0075) between horizontal brain skew and Autism Spectrum Disorder. These results provide the first large-scale description of population-average brain skews and their inter-individual variations, their replicable associations with handedness, and insights into biological and other factors which associate with human brain asymmetry.

scholarly journals Sensitivity analysis of human brain structural network construction

2017 ◽  
Vol 1 (4) ◽  
pp. 446-467 ◽  
Author(s):  
Kuang Wei ◽  
Matthew Cieslak ◽  
Clint Greene ◽  
Scott T. Grafton ◽  
Jean M. Carlson
Keyword(s):  
Human Brain ◽  
Structural Connectivity ◽  
Network Models ◽  
Clear Understanding ◽  
Graph Metrics ◽  
Human Connectome Project ◽  
Graph Metric ◽  
Weighted Magnetic Resonance Imaging ◽  
Structural Networks ◽  
Network Neuroscience

Network neuroscience leverages diffusion-weighted magnetic resonance imaging and tractography to quantify structural connectivity of the human brain. However, scientists and practitioners lack a clear understanding of the effects of varying tractography parameters on the constructed structural networks. With diffusion images from the Human Connectome Project (HCP), we characterize how structural networks are impacted by the spatial resolution of brain atlases, total number of tractography streamlines, and grey matter dilation with various graph metrics. We demonstrate how injudicious combinations of highly refined brain parcellations and low numbers of streamlines may inadvertently lead to disconnected network models with isolated nodes. Furthermore, we provide solutions to significantly reduce the likelihood of generating disconnected networks. In addition, for different tractography parameters, we investigate the distributions of values taken by various graph metrics across the population of HCP subjects. Analyzing the ranks of individual subjects within the graph metric distributions, we find that the ranks of individuals are affected differently by atlas scale changes. Our work serves as a guideline for researchers to optimize the selection of tractography parameters and illustrates how biological characteristics of the brain derived in network neuroscience studies can be affected by the choice of atlas parcellation schemes.

scholarly journals Multifactorial Dynamics of White Matter Connectivity During Adolescence

2017 ◽  
Author(s):  
Birkan Tunç ◽  
Drew Parker ◽  
Russell T. Shinohara ◽  
Mark A. Elliott ◽  
Kosha Ruparel ◽  
...  
Keyword(s):  
White Matter ◽  
Expression Profiles ◽  
Community Sample ◽  
Diffusion Imaging ◽  
Structural Connectivity ◽  
Brain Network ◽  
Rapid Expansion ◽  
Imaging Data ◽  
Behavioral Repertoire ◽  
White Matter Connectivity

AbstractStudying developmental changes in white matter connectivity is critical for understanding neurobiological substrates of cognition, learning, and neuropsychiatric disorders. This becomes especially important during adolescence when a rapid expansion of the behavioral repertoire occurs. Several factors such as brain geometry, genetic expression profiles, and higher level architectural specifications such as the presence of segregated modules have been associated with the observed organization of white matter connections. However, we lack understanding of the extent to which such factors jointly describe the brain network organization, nor have insights into how their contribution changes developmentally. We constructed a multifactorial model of white matter connectivity using Bayesian network analysis and tested it with diffusion imaging data from a large community sample. We investigated contributions of multiple factors in explaining observed connectivity, including architectural specifications, which promote a modular yet integrative organization, and brain’s geometric and genetic features. Our results demonstrated that the initially dominant geometric and genetic factors become less influential with age, whereas the effect of architectural specifications increases. The identified structural modules are associated with well-known functional systems, and the level of association increases with age. This integrative analysis provides a computational characterization of the normative evolution of structural connectivity during adolescence.

scholarly journals The visual word form area (VWFA) is part of both language and attention circuitry

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Lang Chen ◽  
Demian Wassermann ◽  
Daniel A. Abrams ◽  
John Kochalka ◽  
Guillermo Gallardo-Diez ◽  
...  
Keyword(s):  
Spatial Attention ◽  
Visual Processing ◽  
Structural Connectivity ◽  
Visual Word ◽  
Word Form ◽  
Imaging Data ◽  
Brain Organization ◽  
General Role ◽  
Visual Word Form Area ◽  
Human Connectome Project

AbstractWhile predominant models of visual word form area (VWFA) function argue for its specific role in decoding written language, other accounts propose a more general role of VWFA in complex visual processing. However, a comprehensive examination of structural and functional VWFA circuits and their relationship to behavior has been missing. Here, using high-resolution multimodal imaging data from a large Human Connectome Project cohort (N = 313), we demonstrate robust patterns of VWFA connectivity with both canonical language and attentional networks. Brain-behavior relationships revealed a striking pattern of double dissociation: structural connectivity of VWFA with lateral temporal language network predicted language, but not visuo-spatial attention abilities, while VWFA connectivity with dorsal fronto-parietal attention network predicted visuo-spatial attention, but not language abilities. Our findings support a multiplex model of VWFA function characterized by distinct circuits for integrating language and attention, and point to connectivity-constrained cognition as a key principle of human brain organization.

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