scholarly journals Clinically-Feasible Brain Morphometric Similarity Network Construction Approaches with Restricted Magnetic Resonance Imaging Acquisitions and their Relationship with Cognition

2019 ◽  
Author(s):  
Daniel J. King ◽  
Amanda Wood
Keyword(s):  
Developmentally Appropriate ◽  
Structural Connectivity ◽  
Developmental Neuroscience ◽  
Architectural Features ◽  
Human Connectome Project ◽  
Valid Estimate ◽  
Mri Sequences ◽  
Project Data ◽  
Potential Tool

Morphometric Similarity Networks (MSNs) estimate structural 'connectivity' as a biologically meaningful set of statistical similarities between cyto-architectural features derived in-vivo from multiple MRI sequences. These networks have shown to be clinically relevant, predicting 40% variance in IQ. However, the sequences required (T1w and T2w 3D anatomical, DWI) to produce these networks typically have long acquisition times, which are less feasible in some populations. Thus, estimating MSNs using features from only a T1w MRI is attractive to both clinical and developmental neuroscience. We aimed to determine whether reduced-feature approaches approximate the original MSN model as a potential tool to investigate brain structure. Using Human Connectome Project data, we extended previous investigations of reduced-feature MSNs by comparing not only T1w-derived networks but additional MSNs generated with fewer MR sequences to their full acquisition counterparts. We produce MSNs which are highly similar at the edge-level, to those generated with multi-modal imaging. We also find that, regardless of the number of features, these networks have limited predictive validity of generalised cognitive ability scores in contrast to previous research. Overall, settings in which multi-modal imaging is not available or clinically/developmentally appropriate, T1w-restricted MSN construction provides a valid estimate of the MSN.

scholarly journals Anatomical Characterization of the Human Structural Connectivity between the Pedunculopontine Nucleus and Globus Pallidus via Multi-Shell Multi-Tissue Tractography

Medicina ◽  
2020 ◽  
Vol 56 (9) ◽  
pp. 452
Author(s):  
Salvatore Bertino ◽  
Gianpaolo Antonio Basile ◽  
Giuseppe Anastasi ◽  
Alessia Bramanti ◽  
Bartolo Fonti ◽  
...  
Keyword(s):  
Globus Pallidus ◽  
Spatial Organization ◽  
Structural Connectivity ◽  
Spatial Relations ◽  
Pedunculopontine Nucleus ◽  
Anterior Portion ◽  
Therapeutic Implications ◽  
Efferent Connections ◽  
Human Connectome Project

Background and objectives: The internal (GPi) and external segments (GPe) of the globus pallidus represent key nodes in the basal ganglia system. Connections to and from pallidal segments are topographically organized, delineating limbic, associative and sensorimotor territories. The topography of pallidal afferent and efferent connections with brainstem structures has been poorly investigated. In this study we sought to characterize in-vivo connections between the globus pallidus and the pedunculopontine nucleus (PPN) via diffusion tractography. Materials and Methods: We employed structural and diffusion data of 100 subjects from the Human Connectome Project repository in order to reconstruct the connections between the PPN and the globus pallidus, employing higher order tractography techniques. We assessed streamline count of the reconstructed bundles and investigated spatial relations between pallidal voxels connected to the PPN and pallidal limbic, associative and sensorimotor functional territories. Results: We successfully reconstructed pallidotegmental tracts for the GPi and GPe in all subjects. The number of streamlines connecting the PPN with the GPi was greater than the number of those joining it with the GPe. PPN maps within pallidal segments exhibited a distinctive spatial organization, being localized in the ventromedial portion of the GPi and in the ventral-anterior portion in the GPe. Regarding their spatial relations with tractography-derived maps of pallidal functional territories, the highest value of percentage overlap was noticed between PPN maps and the associative territory. Conclusions: We successfully reconstructed the anatomical course of the pallidotegmental pathways and comprehensively characterized their topographical arrangement within both pallidal segments. PPM maps were localized in the ventromedial aspect of the GPi, while they occupied the anterior pole and the most ventral portion of the GPe. A better understanding of the spatial and topographical arrangement of the pallidotegmental pathways may have pathophysiological and therapeutic implications in movement disorders.

scholarly journals Ventralis intermedius nucleus anatomical variability assessment by MRI structural connectivity

2020 ◽  
Author(s):  
Francisca Ferreira ◽  
Harith Akram ◽  
John Ashburner ◽  
Ludvic Zrinzo ◽  
Hui Zhang ◽  
...  
Keyword(s):  
Structural Connectivity ◽  
List Type ◽  
Superior Cerebellar Peduncle ◽  
Probabilistic Tractography ◽  
Thalamic Nuclei ◽  
Individual Subject ◽  
Neurosurgical Planning ◽  
Human Connectome Project ◽  
The Right

AbstractThe ventralis intermedius nucleus (Vim) is centrally placed in the dentato-thalamo-cortical pathway (DTCp) and is a key surgical target in the treatment of severe medically refractory tremor. It is not visible on conventional MRI sequences; consequently, stereotactic targeting currently relies on atlas-based coordinates. This fails to capture individual anatomical variability, which may lead to poor long-term clinical efficacy. Probabilistic tractography, combined with known anatomical connectivity, enables localisation of thalamic nuclei at an individual subject level. There are, however, a number of confounds associated with this technique that may influence results.Here we focused on an established method, using probabilistic tractography to reconstruct the DTCp, to identify the connectivity-defined Vim (cd-Vim) in vivo. Using 100 healthy individuals from the Human Connectome Project, our aim was to quantify cd-Vim variability across this population, measure the discrepancy with atlas-defined Vim (ad-Vim), and assess the influence of potential methodological confounds.We found no significant effect of any of the confounds. The mean cd-Vim coordinate was located within 1.9 mm (left) and 2.1 mm (right) of the average midpoint and 4.9 mm (left) and 5.4 mm (right) from the ad-Vim coordinates. cd-Vim location was more variable on the right, which reflects hemispheric asymmetries in the probabilistic DTCp reconstructed. The superior cerebellar peduncle was identified as a potential source of artificial variance.This work demonstrates significant individual anatomical variability of the cd-Vim that atlas-based approaches fail to capture. This variability was not related to any methodological confound tested. Lateralisation of cerebellar functions, such as speech, may contribute to the observed asymmetry. Tractography-based methods seem sensitive to individual anatomical variability that is missed by conventional neurosurgical targeting; These findings may form the basis for translational tools to improve efficacy and reduce side-effects of thalamic surgery for tremor.HighlightsConnectivity-based Vim position varied markedly between subjects and from atlas-defined coordinates.This positional variability was not related to any methodological confound tested.Hemispheric asymmetry was observed in connectivity-based Vim position.We hypothesise lateralization of cerebellar functions, such as language, may contribute to asymmetry.Knowledge of Vim position variability could help inform neurosurgical planning in the management of tremor.

scholarly journals Modelling white matter in gyral blades as a continuous vector field

2020 ◽  
Author(s):  
Michiel Cottaar ◽  
Matteo Bastiani ◽  
Nikhil Boddu ◽  
Matthew Glasser ◽  
Suzanne Haber ◽  
...  
Keyword(s):  
Vector Field ◽  
White Matter ◽  
Grey Matter ◽  
Structural Connectivity ◽  
Cortical Surface ◽  
Divergence Free Vector ◽  
Human Connectome Project ◽  
Cortical Grey Matter ◽  
Tractography Data

1AbstractMany brain imaging studies aim to measure structural connectivity with diffusion tractography. However, biases in tractography data, particularly near the boundary between white matter and cortical grey matter can limit the accuracy of such studies. When seeding from the white matter, streamlines tend to travel parallel to the convoluted cortical surface, largely avoiding sulcal fundi and terminating preferentially on gyral crowns. When seeding from the cortical grey matter, streamlines generally run near the cortical surface until reaching deep white matter. These so-called “gyral biases” limit the accuracy and effective resolution of cortical structural connectivity profiles estimated by tractography algorithms, and they do not reflect the expected distributions of axonal densities seen in invasive tracer studies or stains of myelinated fibres. We propose an algorithm that concurrently models fibre density and orientation using a divergence-free vector field within gyral blades to encourage an anatomically-justified streamline density distribution along the cortical white/grey-matter boundary while maintaining alignment with the diffusion MRI estimated fibre orientations. Using in vivo data from the Human Connectome Project, we show that this algorithm reduces tractography biases. We compare the structural connectomes to functional connectomes from resting-state fMRI, showing that our model improves cross-modal agreement. Finally, we find that after parcellation the changes in the structural connectome are very minor with slightly improved interhemispheric connections (i.e, more homotopic connectivity) and slightly worse intrahemispheric connections when compared to tracers.

scholarly journals Clinically feasible brain morphometric similarity network construction approaches with restricted magnetic resonance imaging acquisitions

2020 ◽  
Vol 4 (1) ◽  
pp. 274-291
Author(s):  
Daniel J. King ◽  
Amanda G. Wood
Keyword(s):  
Multimodal Imaging ◽  
Structural Mri ◽  
Resonance Imaging ◽  
Network Construction ◽  
Future Studies ◽  
Anatomical Features ◽  
Developmental Neuroscience ◽  
Human Connectome Project ◽  
Similarity Networks ◽  
Potential Tool

Morphometric similarity networks (MSNs) estimate organization of the cortex as a biologically meaningful set of similarities between anatomical features at the macro- and microstructural level, derived from multiple structural MRI (sMRI) sequences. These networks are clinically relevant, predicting 40% variance in IQ. However, the sequences required (T1w, T2w, DWI) to produce these networks are longer acquisitions, less feasible in some populations. Thus, estimating MSNs using features from T1w sMRI is attractive to clinical and developmental neuroscience. We studied whether reduced-feature approaches approximate the original MSN model as a potential tool to investigate brain structure. In a large, homogenous dataset of healthy young adults (from the Human Connectome Project, HCP), we extended previous investigations of reduced-feature MSNs by comparing not only T1w-derived networks, but also additional MSNs generated with fewer MR sequences, to their full acquisition counterparts. We produce MSNs that are highly similar at the edge level to those generated with multimodal imaging; however, the nodal topology of the networks differed. These networks had limited predictive validity of generalized cognitive ability. Overall, when multimodal imaging is not available or appropriate, T1w-restricted MSN construction is feasible, provides an appropriate estimate of the MSN, and could be a useful approach to examine outcomes in future studies.

Perceived friendship and binge drinking in young adults: A study of the Human Connectome Project data

2021 ◽  
Vol 224 ◽  
pp. 108731
Author(s):  
Guangfei Li ◽  
Yu Chen ◽  
Thang M. Le ◽  
Simon Zhornitsky ◽  
Wuyi Wang ◽  
...  
Keyword(s):  
Young Adults ◽  
Binge Drinking ◽  
Human Connectome Project ◽  
Project Data

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 Proteomic analysis of silica hybrid sol-gel coatings: a potential tool for predicting the biocompatibility of implants in vivo

Biofouling ◽  
2017 ◽  
Vol 33 (8) ◽  
pp. 676-689 ◽  
Author(s):  
F. Romero-Gavilan ◽  
A. M. Sánchez-Pérez ◽  
N. Araújo-Gomes ◽  
M. Azkargorta ◽  
I. Iloro ◽  
...  
Keyword(s):  
Proteomic Analysis ◽  
Sol Gel ◽  
Potential Tool ◽  
Silica Hybrid

scholarly journals Using diffusion MRI data acquired with ultra-high gradients to improve tractography in routine-quality data

2021 ◽  
Author(s):  
Chiara Maffei ◽  
Christine Lee ◽  
Michael Planich ◽  
Manisha Ramprasad ◽  
Nivedita Ravi ◽  
...  
Keyword(s):  
Diffusion Mri ◽  
Region Of Interest ◽  
Training Data ◽  
Quality Data ◽  
High Quality ◽  
Scanning Time ◽  
Human Connectome Project ◽  
High Gradients ◽  
Comprehensive Comparison

The development of scanners with ultra-high gradients, spearheaded by the Human Connectome Project, has led to dramatic improvements in the spatial, angular, and diffusion resolution that is feasible for in vivo diffusion MRI acquisitions. The improved quality of the data can be exploited to achieve higher accuracy in the inference of both microstructural and macrostructural anatomy. However, such high-quality data can only be acquired on a handful of Connectom MRI scanners worldwide, while remaining prohibitive in clinical settings because of the constraints imposed by hardware and scanning time. In this study, we first update the classical protocols for tractography-based, manual annotation of major white-matter pathways, to adapt them to the much greater volume and variability of the streamlines that can be produced from today's state-of-the-art diffusion MRI data. We then use these protocols to annotate 42 major pathways manually in data from a Connectom scanner. Finally, we show that, when we use these manually annotated pathways as training data for global probabilistic tractography with anatomical neighborhood priors, we can perform highly accurate, automated reconstruction of the same pathways in much lower-quality, more widely available diffusion MRI data. The outcomes of this work include both a new, comprehensive atlas of WM pathways from Connectom data, and an updated version of our tractography toolbox, TRActs Constrained by UnderLying Anatomy (TRACULA), which is trained on data from this atlas. Both the atlas and TRACULA are distributed publicly as part of FreeSurfer. We present the first comprehensive comparison of TRACULA to the more conventional, multi-region-of-interest approach to automated tractography, and the first demonstration of training TRACULA on high-quality, Connectom data to benefit studies that use more modest acquisition protocols.

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