scholarly journals Surface-based Single-subject Morphological Brain Networks: Effects of Morphological Index, Brain Parcellation and Similarity Measure, Sample Size-varying Stability and Test-retest Reliability

2021 ◽  
Author(s):  
Yinzhi Li ◽  
Ningkai Wang ◽  
Hao Wang ◽  
Yating Lv ◽  
Qihong Zou ◽  
...  
Keyword(s):  
Human Brain ◽  
Sample Size ◽  
Similarity Measure ◽  
Single Subject ◽  
Morphological Similarity ◽  
Brain Connectome ◽  
Analytical Strategies ◽  
Morphological Index ◽  
Similarity Networks ◽  
Test Retest Reliability

AbstractMorphological brain networks, in particular those at the individual level, have become an important approach for studying the human brain connectome; however, relevant methodology is far from being well-established in their formation, description and reproducibility. Here, we extended our previous study by constructing and characterizing single-subject morphological similarity networks from brain volume to surface space and systematically evaluated their reproducibility with respect to effects of different choices of morphological index, brain parcellation atlas and similarity measure, sample size-varying stability and test-retest reliability. Using the Human Connectome Project dataset, we found that surface-based single-subject morphological similarity networks shared common small-world organization, high parallel efficiency, modular architecture and bilaterally distributed hubs regardless of different analytical strategies. Nevertheless, quantitative values of all interregional similarities, global network measures and nodal centralities were significantly affected by choices of morphological index, brain parcellation atlas and similarity measure. Moreover, the morphological similarity networks varied along with the number of participants and approached stability until the sample size exceeded ∼70. Using an independent test-retest dataset, we found fair to good, even excellent, reliability for most interregional similarities and network measures, which were also modulated by different analytical strategies, in particular choices of morphological index. Specifically, fractal dimension and sulcal depth outperformed gyrification index and cortical thickness, higher-resolution atlases outperformed lower-resolution atlases, and Jensen-Shannon divergence-based similarity outperformed Kullback-Leibler divergence-based similarity. Altogether, our findings propose surface-based single-subject morphological similarity networks as a reliable method to characterize the human brain connectome and provide methodological recommendations and guidance for future research.

MicroVolt Variations of The Human Brain (Quantitative Electroencephalography) Display Differential Torque Effects During West-East versus North-South Orientation in the Geomagnetic Field

2016 ◽  
Vol 12 (2) ◽  
pp. 4255-4259
Author(s):  
Michael A Persinger ◽  
David A Vares ◽  
Paula L Corradini
Keyword(s):  
Human Brain ◽  
Geomagnetic Field ◽  
Neuronal Cell ◽  
Single Subject ◽  
Additional Energy ◽  
Actual Measurement ◽  
Neuronal Processes ◽  
Basic Physics ◽  
Order Of Magnitude ◽  
The Moment

                The human brain was assumed to be an elliptical electric dipole. Repeated quantitative electroencephalographic measurements over several weeks were completed for a single subject who sat in either a magnetic eastward or magnetic southward direction. The predicted potential difference equivalence for the torque while facing perpendicular (west-to-east) to the northward component of the geomagnetic field (relative to facing south) was 4 μV. The actual measurement was 10 μV. The oscillation frequency around the central equilibrium based upon the summed units of neuronal processes within the cerebral cortices for the moment of inertia was 1 to 2 ms which are the boundaries for the action potential of axons and the latencies for diffusion of neurotransmitters. The calculated additional energy available to each neuron within the human cerebrum during the torque condition was ~10-20 J which is the same order of magnitude as the energy associated with action potentials, resting membrane potentials, and ligand-receptor binding. It is also the basic energy at the level of the neuronal cell membrane that originates from gravitational forces upon a single cell and the local expression of the uniaxial magnetic anisotropic constant for ferritin which occurs in the brain. These results indicate that the more complex electrophysiological functions that are strongly correlated with cognitive and related human properties can be described by basic physics and may respond to specific geomagnetic spatial orientation.

scholarly journals Diffusion MRI microstructure models with in vivo human brain Connectome data: results from a multi-group comparison

2017 ◽  
Vol 30 (9) ◽  
pp. e3734 ◽  
Author(s):  
Uran Ferizi ◽  
Benoit Scherrer ◽  
Torben Schneider ◽  
Mohammad Alipoor ◽  
Odin Eufracio ◽  
...  
Keyword(s):  
Human Brain ◽  
Diffusion Mri ◽  
Group Comparison ◽  
Brain Connectome

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 Test-retest reliability and sample size estimates after MRI scanner relocation

NeuroImage ◽  
2020 ◽  
Vol 211 ◽  
pp. 116608
Author(s):  
Tracy R. Melzer ◽  
Ross J. Keenan ◽  
Gareth J. Leeper ◽  
Stephen Kingston-Smith ◽  
Simon A. Felton ◽  
...  
Keyword(s):  
Sample Size ◽  
Retest Reliability ◽  
Test Retest Reliability ◽  
Size Estimates

scholarly journals Disrupted topological organization of human brain connectome in diabetic retinopathy patients

2019 ◽  
Vol Volume 15 ◽  
pp. 2487-2502 ◽  
Author(s):  
Xin Huang ◽  
Yan Tong ◽  
Chen-Xing Qi ◽  
Yang-Tao Xu ◽  
Han-Dong Dan ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Human Brain ◽  
Brain Connectome ◽  
Topological Organization

Hierarchical Density-Based Clustering of White Matter Tracts in the Human Brain

2010 ◽  
Vol 1 (4) ◽  
pp. 1-25 ◽  
Author(s):  
Junming Shao ◽  
Klaus Hahn ◽  
Qinli Yang ◽  
Afra Wohlschläeger ◽  
Christian Boehm ◽  
...  
Keyword(s):  
White Matter ◽  
Human Brain ◽  
Similarity Measure ◽  
Multiple Scales ◽  
Diffusion Tensor ◽  
Clustering Algorithms ◽  
Vast Number ◽  
Lower Bounding ◽  
Density Based Clustering ◽  
Neurosurgical Planning

Diffusion tensor magnetic resonance imaging (DTI) provides a promising way of estimating the neural fiber pathways in the human brain non-invasively via white matter tractography. However, it is difficult to analyze the vast number of resulting tracts quantitatively. Automatic tract clustering would be useful for the neuroscience community, as it can contribute to accurate neurosurgical planning, tract-based analysis, or white matter atlas creation. In this paper, the authors propose a new framework for automatic white matter tract clustering using a hierarchical density-based approach. A novel fiber similarity measure based on dynamic time warping allows for an effective and efficient evaluation of fiber similarity. A lower bounding technique is used to further speed up the computation. Then the algorithm OPTICS is applied, to sort the data into a reachability plot, visualizing the clustering structure of the data. Interactive and automatic clustering algorithms are finally introduced to obtain the clusters. Extensive experiments on synthetic data and real data demonstrate the effectiveness and efficiency of our fiber similarity measure and show that the hierarchical density-based clustering method can group these tracts into meaningful bundles on multiple scales as well as eliminating noisy fibers.

scholarly journals Mapping Short Association Fibers in the Early Cortical Visual Processing Stream Using In Vivo Diffusion Tractography

2020 ◽  
Vol 30 (8) ◽  
pp. 4496-4514 ◽  
Author(s):  
Fakhereh Movahedian Attar ◽  
Evgeniya Kirilina ◽  
Daniel Haenelt ◽  
Kerrin J Pine ◽  
Robert Trampel ◽  
...  
Keyword(s):  
Human Brain ◽  
Visual Processing ◽  
Repeated Measurements ◽  
Fiber Tractography ◽  
Cortical Areas ◽  
Brain Connectome ◽  
Cortical Visual Processing ◽  
Association Fibers ◽  
Fiber Connectivity

Abstract Short association fibers (U-fibers) connect proximal cortical areas and constitute the majority of white matter connections in the human brain. U-fibers play an important role in brain development, function, and pathology but are underrepresented in current descriptions of the human brain connectome, primarily due to methodological challenges in diffusion magnetic resonance imaging (dMRI) of these fibers. High spatial resolution and dedicated fiber and tractography models are required to reliably map the U-fibers. Moreover, limited quantitative knowledge of their geometry and distribution makes validation of U-fiber tractography challenging. Submillimeter resolution diffusion MRI—facilitated by a cutting-edge MRI scanner with 300 mT/m maximum gradient amplitude—was used to map U-fiber connectivity between primary and secondary visual cortical areas (V1 and V2, respectively) in vivo. V1 and V2 retinotopic maps were obtained using functional MRI at 7T. The mapped V1–V2 connectivity was retinotopically organized, demonstrating higher connectivity for retinotopically corresponding areas in V1 and V2 as expected. The results were highly reproducible, as demonstrated by repeated measurements in the same participants and by an independent replication group study. This study demonstrates a robust U-fiber connectivity mapping in vivo and is an important step toward construction of a more complete human brain connectome.

scholarly journals Single subject fMRI test–retest reliability metrics and confounding factors

NeuroImage ◽  
2013 ◽  
Vol 69 ◽  
pp. 231-243 ◽  
Author(s):  
Krzysztof J. Gorgolewski ◽  
Amos J. Storkey ◽  
Mark E. Bastin ◽  
Ian Whittle ◽  
Cyril Pernet
Keyword(s):  
Single Subject ◽  
Confounding Factors ◽  
Retest Reliability ◽  
Test Retest Reliability
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