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 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 Euclidean distance as a measure to distinguish ventral and dorsal white matter connectivity in the human brain

2016 ◽  
Author(s):  
Philipp Kellmeyer ◽  
Magnus-Sebastian Vry
Keyword(s):  
White Matter ◽  
Human Brain ◽  
Cognitive Processing ◽  
Euclidean Distance ◽  
Large Scale ◽  
Diffusion Tensor ◽  
Large Body ◽  
Coordinate Space ◽  
Fiber Tractography

AbstractFiber tractography based on diffusion tensor imaging (DTI) has become an important research tool for investigating the anatomical connectivity between brain regions in vivo. Combining DTI with functional magnetic resonance imaging (fMRI) allows for the mapping of structural and functional architecture of large-scale networks for cognitive processing. This line of research has shown that ventral and dorsal fiber pathways subserve different aspects of bottom-up- and top-down processing in the human brain.Here, we investigate the feasibility and applicability of Euclidean distance as a simple geometric measure to differentiate ventral and dorsal long-range white matter fiber pathways tween parietal and inferior frontal cortical regions, employing a body of studies that used probabilistic tractography.We show that ventral pathways between parietal and inferior frontal cortex have on average a significantly longer Euclidean distance in 3D-coordinate space than dorsal pathways. We argue that Euclidean distance could provide a simple measure and potentially a boundary value to assess patterns of connectivity in fMRI studies. This would allow for a much broader assessment of general patterns of ventral and dorsal large-scale fiber connectivity for different cognitive operations in the large body of existing fMRI studies lacking additional DTI data.

O4-03-01: Structural connectivity in three mouse models of Alzheimer's disease: An in vivo diffusion tensor imaging study

2013 ◽  
Vol 9 ◽  
pp. P683-P684
Author(s):  
Moira Marizzoni ◽  
Edoardo Micotti ◽  
Alessandra Paladini ◽  
Claudia Balducci ◽  
Anna Caroli ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Diffusion Tensor Imaging ◽  
Mouse Models ◽  
Diffusion Tensor ◽  
Structural Connectivity ◽  
Imaging Study ◽  
Diffusion Tensor Imaging Study

scholarly journals ReAl-LiFE: Accelerating the Discovery of Individualized Brain Connectomes on GPUs

2019 ◽  
Vol 33 ◽  
pp. 630-638 ◽  
Author(s):  
Sawan Kumar ◽  
Varsha Sreenivasan ◽  
Partha Talukdar ◽  
Franco Pestilli ◽  
Devarajan Sridharan
Keyword(s):  
Diffusion Imaging ◽  
Computing Time ◽  
Structural Connectivity ◽  
Real Life ◽  
Support Vector ◽  
Healthy Controls ◽  
Linear Classifiers ◽  
Generalized Matrix ◽  
Access Patterns

Diffusion imaging and tractography enable mapping structural connections in the human brain, in-vivo. Linear Fascicle Evaluation (LiFE) is a state-of-the-art approach for pruning spurious connections in the estimated structural connectome, by optimizing its fit to the measured diffusion data. Yet, LiFE imposes heavy demands on computing time, precluding its use in analyses of large connectome databases. Here, we introduce a GPU-based implementation of LiFE that achieves 50-100x speedups over conventional CPU-based implementations for connectome sizes of up to several million fibers. Briefly, the algorithm accelerates generalized matrix multiplications on a compressed tensor through efficient GPU kernels, while ensuring favorable memory access patterns. Leveraging these speedups, we advance LiFE’s algorithm by imposing a regularization constraint on estimated fiber weights during connectome pruning. Our regularized, accelerated, LiFE algorithm (“ReAl-LiFE”) estimates sparser connectomes that also provide more accurate fits to the underlying diffusion signal. We demonstrate the utility of our approach by classifying pathological signatures of structural connectivity in patients with Alzheimer’s Disease (AD). We estimated million fiber whole-brain connectomes, followed by pruning with ReAl-LiFE, for 90 individuals (45 AD patients and 45 healthy controls). Linear classifiers, based on support vector machines, achieved over 80% accuracy in classifying AD patients from healthy controls based on their ReAl-LiFE pruned structural connectomes alone. Moreover, classification based on the ReAl-LiFE pruned connectome outperformed both the unpruned connectome, as well as the LiFE pruned connectome, in terms of accuracy. We propose our GPU-accelerated approach as a widely relevant tool for non-negative least squares optimization, across many domains.

scholarly journals High-resolution in vivo diffusion imaging of the human brain with generalized slice dithered enhanced resolution: Simultaneous multislice (gSlider-SMS)

2017 ◽  
Vol 79 (1) ◽  
pp. 141-151 ◽  
Author(s):  
Kawin Setsompop ◽  
Qiuyun Fan ◽  
Jason Stockmann ◽  
Berkin Bilgic ◽  
Susie Huang ◽  
...  
Keyword(s):  
High Resolution ◽  
Human Brain ◽  
Diffusion Imaging ◽  
Enhanced Resolution ◽  
Simultaneous Multislice

scholarly journals Assessing HD-EEG functional connectivity states using a human brain computational model

2021 ◽  
Author(s):  
Judie Tabbal ◽  
Aya Kabbara ◽  
Maxime Yochum ◽  
Mohamad Khalil ◽  
Mahmoud HASSAN ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Human Brain ◽  
Computational Model ◽  
Diffusion Tensor ◽  
Clustering Algorithms ◽  
Structural Connectivity ◽  
Brain Network ◽  
Systematic Evaluation ◽  
Fast Time ◽  
Network States

Electro/Magnetoencephalography (EEG/MEG) source-space network analysis is increasingly recognized as a powerful tool to track fast electrophysiological brain dynamics. However, an objective and quantitative evaluation of the various steps, from source localization and functional connectivity to clustering algorithms, is challenging, due to the lack of realistic controlled data. Here, we used a human brain computational model containing both physiologically-based cellular GABAergic and Glutamatergic circuits coupled through Diffusion Tensor Imaging -based structural connectivity, to generate realistic High Density-EEG (256 channels) recordings. We designed a scenario of successive gamma-band oscillations in distinct cortical areas in order to emulate a virtual picture naming task. We identified the fast time-varying network states and quantified the performance of the key steps involved in the pipeline: (1) inverse models to reconstruct cortical-level sources, (2) functional connectivity measures to compute statistical interdependency between regional time series, and (3) dimensionality reduction methods to derive dominant brain network states (BNS). Using a systematic evaluation of the different independent/principal/non-negative decomposition techniques along with a clustering approach, results show significant variability among the tested algorithms in terms of spatial and temporal accuracy. We outlined the spatial precision, the temporal sensitivity, as well as the global accuracy of the extracted BNS relative to each method. Our findings suggest a good performance of wMNE/PLV combination to elucidate the appropriate functional networks and ICA techniques to derive relevant dynamic brain network states. Our aim here is twofold: 1) to provide quantitative assessment on the advantages and the limitations of each of the analyzed techniques and 2) to introduce (and share) a complete framework that can be used to optimize the entire pipeline of EEG/MEG source connectivity. With such framework, other tasks can be generated and used for validation and other methodological points can be also addressed.

scholarly journals Visual Pathway Study Using In Vivo Diffusion Tensor Imaging Tractography to Complement Classic Anatomy

2011 ◽  
Vol 70 (suppl_1) ◽  
pp. ons145-ons156 ◽  
Author(s):  
Wentao Wu ◽  
Laura Rigolo ◽  
Lauren J. O'Donnell ◽  
Isaiah Norton ◽  
Sargent Shriver ◽  
...  
Keyword(s):  
Diffusion Tensor Imaging ◽  
Diffusion Tensor ◽  
Diffusion Imaging ◽  
Visual Pathways ◽  
Brain Anatomy ◽  
3 Dimensional ◽  
Complementary Method ◽  
The Individual ◽  
Diffusion Tensor Imaging Tractography

Abstract BACKGROUND: Knowledge of the individual course of the optic radiations (ORs) is important to avoid postoperative visual deficits. Cadaveric studies of the visual pathways are limited because it has not been possible to separate the OR from neighboring tracts accurately and results may not apply to individual patients. Diffusion tensor imaging studies may be able to demonstrate the relationships between the OR and neighboring fibers in vivo in individual subjects. OBJECTIVE: To use diffusion tensor imaging tractography to study the OR and the Meyer loop (ML) anatomy in vivo. METHODS: Ten healthy subjects underwent magnetic resonance imaging with diffusion imaging at 3 T. With the use of a fiducial-based diffusion tensor imaging tractography tool (Slicer 3.3), seeds were placed near the lateral geniculate nucleus to reconstruct individual visual pathways and neighboring tracts. Projections of the ORs onto 3-dimensional brain models were shown individually to quantify relationships to key landmarks. RESULTS: Two patterns of visual pathways were found. The OR ran more commonly deep in the whole superior and middle temporal gyri and superior temporal sulcus. The OR was closely surrounded in all cases by an inferior longitudinal fascicle and a parieto/occipito/temporo-pontine fascicle. The mean left and right distances between the tip of the OR and temporal pole were 39.8 ± 3.8 and 40.6 ± 5.7 mm, respectively. CONCLUSION: Diffusion tensor imaging tractography provides a practical complementary method to study the OR and the Meyer loop anatomy in vivo with reference to individual 3-dimensional brain anatomy.

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