scholarly journals Diffantom: Whole-Brain Diffusion MRI Phantoms Derived from Real Datasets of the Human Connectome Project

2016 ◽  
Vol 10 ◽  
Author(s):  
Oscar Esteban ◽  
Emmanuel Caruyer ◽  
Alessandro Daducci ◽  
Meritxell Bach-Cuadra ◽  
María J. Ledesma-Carbayo ◽  
...  
Keyword(s):  
Diffusion Mri ◽  
Whole Brain ◽  
Human Connectome Project

scholarly journals Diffantom: whole-brain diffusion MRI phantoms derived from real datasets of the Human Connectome Project

2015 ◽  
Author(s):  
Oscar Esteban ◽  
Emmanuel Caruyer ◽  
Alessandro Daducci ◽  
Meritxell Bach-Cuadra ◽  
Maria-J. Ledesma-Carbayo ◽  
...  
Keyword(s):  
Diffusion Mri ◽  
Whole Brain ◽  
Human Connectome Project ◽  
Digital Phantom

Diffantom is a whole-brain digital phantom generated from a dataset from the Human Connectome Project. Diffantom is presented here to be openly and freely distributed along with the diffantomizer workflow to generate new diffantoms. We encourage the neuroimage community to contribute with their own diffantoms and share them openly.

scholarly journals A Measure of Whole-Brain White Matter Topography

2019 ◽  
Author(s):  
Junyan Wang ◽  
Yonggang Shi
Keyword(s):  
White Matter ◽  
Diffusion Mri ◽  
Large Scale ◽  
Uk Biobank ◽  
Reliable Measure ◽  
Whole Brain ◽  
Brain White Matter ◽  
Human Connectome Project ◽  
Natural Coordinate System ◽  
Natural Coordinate

The unprecedentedly high-quality large-scale brain imaging datasets, from such as the Human Connectome Project (HCP) and UK-Biobank, provide a unique opportunity for measuring the white matter topography of the human brain. By leveraging the multi-shell diffusion MRI data from the original young adult HCP, we systematically develop a reliable measure of the whole-brain white matter topography, and we coin it topographic vector. As the main result, we find that the three most dominant dimensions of the topographic vectors strongly and linearly correlate with the coordinates of the corresponding streamlines of the whole-brain tractograms. Our results support the earlier prescient hypothesis that brain development follows a “base-plan” established by three (main) chemotactic gradients of early embryogenesis, and they implicate that the whole brain white matter tracts can be represented by vectors of a natural coordinate system.

scholarly journals Functional and diffusion MRI reveal the neurophysiological basis of neonates’ noxious-stimulus evoked brain activity

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Luke Baxter ◽  
Fiona Moultrie ◽  
Sean Fitzgibbon ◽  
Marianne Aspbury ◽  
Roshni Mansfield ◽  
...  
Keyword(s):  
White Matter ◽  
Prediction Model ◽  
Resting State ◽  
Early Life ◽  
Diffusion Mri ◽  
Brain Activity ◽  
Noxious Stimulus ◽  
Noxious Stimulation ◽  
Human Connectome Project ◽  
And Diffusion

AbstractUnderstanding the neurophysiology underlying neonatal responses to noxious stimulation is central to improving early life pain management. In this neonatal multimodal MRI study, we use resting-state and diffusion MRI to investigate inter-individual variability in noxious-stimulus evoked brain activity. We observe that cerebral haemodynamic responses to experimental noxious stimulation can be predicted from separately acquired resting-state brain activity (n = 18). Applying this prediction model to independent Developing Human Connectome Project data (n = 215), we identify negative associations between predicted noxious-stimulus evoked responses and white matter mean diffusivity. These associations are subsequently confirmed in the original noxious stimulation paradigm dataset, validating the prediction model. Here, we observe that noxious-stimulus evoked brain activity in healthy neonates is coupled to resting-state activity and white matter microstructure, that neural features can be used to predict responses to noxious stimulation, and that the dHCP dataset could be utilised for future exploratory research of early life pain system neurophysiology.

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.

scholarly journals White-matter free-water diffusion MRI in schizophrenia: a systematic review and meta-analysis

2022 ◽  
Author(s):  
Inês Carreira Figueiredo ◽  
Faith Borgan ◽  
Ofer Pasternak ◽  
Federico E. Turkheimer ◽  
Oliver D. Howes
Keyword(s):  
White Matter ◽  
Healthy Volunteers ◽  
Diffusion Mri ◽  
Clinical Symptoms ◽  
Free Water ◽  
Brain Regions ◽  
Water Levels ◽  
First Episode ◽  
Whole Brain ◽  
Illness Duration

AbstractWhite-matter abnormalities, including increases in extracellular free-water, are implicated in the pathophysiology of schizophrenia. Recent advances in diffusion magnetic resonance imaging (MRI) enable free-water levels to be indexed. However, the brain levels in patients with schizophrenia have not yet been systematically investigated. We aimed to meta-analyse white-matter free-water levels in patients with schizophrenia compared to healthy volunteers. We performed a literature search in EMBASE, MEDLINE, and PsycINFO databases. Diffusion MRI studies reporting free-water in patients with schizophrenia compared to healthy controls were included. We investigated the effect of demographic variables, illness duration, chlorpromazine equivalents of antipsychotic medication, type of scanner, and clinical symptoms severity on free-water measures. Ten studies, including five of first episode of psychosis have investigated free-water levels in schizophrenia, with significantly higher levels reported in whole-brain and specific brain regions (including corona radiata, internal capsule, superior and inferior longitudinal fasciculus, cingulum bundle, and corpus callosum). Six studies, including a total of 614 participants met the inclusion criteria for quantitative analysis. Whole-brain free-water levels were significantly higher in patients relative to healthy volunteers (Hedge’s g = 0.38, 95% confidence interval (CI) 0.07–0.69, p = 0.02). Sex moderated this effect, such that smaller effects were seen in samples with more females (z = −2.54, p < 0.05), but antipsychotic dose, illness duration and symptom severity did not. Patients with schizophrenia have increased free-water compared to healthy volunteers. Future studies are necessary to determine the pathological sources of increased free-water, and its relationship with illness duration and severity.

scholarly journals Leveling up: improving power in fMRI by moving beyond cluster-level inference

2021 ◽  
Author(s):  
Stephanie Noble ◽  
Mandy Mejia ◽  
Andrew Zalesky ◽  
Dustin Scheinost
Keyword(s):  
Average Effect ◽  
Brain Areas ◽  
Whole Brain ◽  
Typical Sample ◽  
Human Connectome Project ◽  
Inference Network ◽  
The Brain ◽  
Cluster Level ◽  
Human Neuroimaging ◽  
Adequate Power

Inference in neuroimaging commonly occurs at the level of "clusters" of neighboring voxels or connections, thought to reflect functionally specific brain areas. Yet increasingly large studies reveal effects that are shared throughout the brain, suggesting that reported clusters may only reflect the "tip of the iceberg" of underlying effects. Here, we empirically compare power of traditional levels of inference (edge and cluster) with broader levels of inference (network and whole-brain) by resampling functional connectivity data from the Human Connectome Project (n=40, 80, 120). Only network- and whole brain-level inference attained or surpassed "adequate" power (β =80%) to detect an average effect, with almost double the power for network- compared with cluster-level procedures at more typical sample sizes. Likewise, effects tended to be widespread, and more widespread pooling resulted in stronger magnitude effects. Power also substantially increased when controlling FDR rather than FWER. Importantly, there may be similar implications for task-based activation analyses where effects are also increasingly understood to be widespread. However, increased power with broader levels of inference may diminish the specificity to localize effects, especially for non-task contexts. These findings underscore the benefit of shifting the scale of inference to better capture the underlying signal, which may unlock opportunities for discovery in human neuroimaging.

Abstract WP45: Comparison of Automated Whole Brain CT Perfusion Analysis with Perfusion-Diffusion MRI in Ischemic Stroke

Stroke ◽  
2013 ◽  
Vol 44 (suppl_1) ◽  
Author(s):  
Bruce C Campbell ◽  
Søren Christensen ◽  
Nawaf Yassi ◽  
Gagan Sharma ◽  
Andrew Bivard ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Diffusion Mri ◽  
Ct Perfusion ◽  
Diffusion Imaging ◽  
Residue Function ◽  
Accurate Estimate ◽  
Mr Perfusion ◽  
Infarct Core ◽  
Whole Brain ◽  
Brain Ct

Background and purpose: CT perfusion (CTP) provides rapid and accessible imaging of ischemic stroke pathophysiology. Studies with limited brain coverage CTP have suggested that relative cerebral blood flow (relCBF) is the optimal CTP parameter to define irreversible infarction. We analyzed patients with whole brain CT perfusion and contemporaneous MR perfusion-diffusion imaging to confirm the optimal CTP parameter for infarct core and compare mismatch classification between MR and CT. Methods: Acute ischemic stroke patients <6hr after onset had whole brain CTP (320slice) closely followed by perfusion-diffusion MRI. Maps of CBF, CBV and time-to-peak of the deconvolved tissue residue function (Tmax) were generated by RAPID automated perfusion analysis software (Stanford University) using delay insensitive deconvolution. The optimal CTP map to identify infarct core was selected by maximizing the average Dice co-efficient across the same threshold range for all patients using co-registered diffusion lesion (manually outlined to its maximal visual extent) as reference region. Mismatch classification agreement between CT and MRI was then assessed using 2 definitions: mismatch ratio a) >1.2 or b) >1.8, absolute mismatch a) >10mL or b) >15mL, infarct core<70mL. Results: In 28 patients imaged <6hr from stroke onset (median age 69, median onset to CT 180min, median CT to MR 69min), relCBF provided the most accurate estimate for infarct core, significantly better than absolute or relative CBV (both p<0.001). Using relCBF to generate acute CTP infarct core volumes, the median magnitude of volume difference versus diffusion MR was 6.9mL, interquartile range 1.6-27.4mL. CTP mismatch between relCBF core and Tmax>6sec perfusion lesion was assessed in 25 patients (3/28 had no MR perfusion). CTP and MR perfusion-diffusion mismatch classification agreed in 23/25 (92%) patients (kappa 0.84) using either definition. Conclusions: This study using whole brain CTP confirms the greater accuracy of CBF over CBV for estimation of the infarct core. The >90% agreement in mismatch classification between CTP and MRI supports the concept that both modalities can identify similar patient populations for clinical trials of reperfusion therapies.

scholarly journals A Multichannel 2D Convolutional Neural Network Model for Task-Evoked fMRI Data Classification

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Jinlong Hu ◽  
Yuezhen Kuang ◽  
Bin Liao ◽  
Lijie Cao ◽  
Shoubin Dong ◽  
...  
Keyword(s):  
Neural Networks ◽  
Brain Imaging ◽  
Motor Task ◽  
Data Classification ◽  
Fmri Data ◽  
Imaging Data ◽  
Whole Brain ◽  
Human Connectome Project ◽  
Brain Imaging Data ◽  
3D Cnn

Deep learning models have been successfully applied to the analysis of various functional MRI data. Convolutional neural networks (CNN), a class of deep neural networks, have been found to excel at extracting local meaningful features based on their shared-weights architecture and space invariance characteristics. In this study, we propose M2D CNN, a novel multichannel 2D CNN model, to classify 3D fMRI data. The model uses sliced 2D fMRI data as input and integrates multichannel information learned from 2D CNN networks. We experimentally compared the proposed M2D CNN against several widely used models including SVM, 1D CNN, 2D CNN, 3D CNN, and 3D separable CNN with respect to their performance in classifying task-based fMRI data. We tested M2D CNN against six models as benchmarks to classify a large number of time-series whole-brain imaging data based on a motor task in the Human Connectome Project (HCP). The results of our experiments demonstrate the following: (i) convolution operations in the CNN models are advantageous for high-dimensional whole-brain imaging data classification, as all CNN models outperform SVM; (ii) 3D CNN models achieve higher accuracy than 2D CNN and 1D CNN model, but 3D CNN models are computationally costly as any extra dimension is added in the input; (iii) the M2D CNN model proposed in this study achieves the highest accuracy and alleviates data overfitting given its smaller number of parameters as compared with 3D CNN.

Human Connectome-Based Tractographic Atlas of the Brainstem Connections and Surgical Approaches

Neurosurgery ◽  
2016 ◽  
Vol 79 (3) ◽  
pp. 437-455 ◽  
Author(s):  
Antonio Meola ◽  
Fang-Cheng Yeh ◽  
Wendy Fellows-Mayle ◽  
Jared Weed ◽  
Juan C. Fernandez-Miranda
Keyword(s):  
Spatial Resolution ◽  
Diffusion Mri ◽  
Diffusion Tensor ◽  
Population Based ◽  
Medial Longitudinal Fasciculus ◽  
Surgical Approaches ◽  
Medial Lemniscus ◽  
Spinothalamic Tract ◽  
Rubrospinal Tract ◽  
Human Connectome Project

Abstract BACKGROUND The brainstem is one of the most challenging areas for the neurosurgeon because of the limited space between gray matter nuclei and white matter pathways. Diffusion tensor imaging-based tractography has been used to study the brainstem structure, but the angular and spatial resolution could be improved further with advanced diffusion magnetic resonance imaging (MRI). OBJECTIVE To construct a high-angular/spatial resolution, wide-population-based, comprehensive tractography atlas that presents an anatomical review of the surgical approaches to the brainstem. METHODS We applied advanced diffusion MRI fiber tractography to a population-based atlas constructed with data from a total of 488 subjects from the Human Connectome Project-488. Five formalin-fixed brains were studied for surgical landmarks. Luxol Fast Blue-stained histological sections were used to validate the results of tractography RESULTS We acquired the tractography of the major brainstem pathways and validated them with histological analysis. The pathways included the cerebellar peduncles, corticospinal tract, corticopontine tracts, medial lemniscus, lateral lemniscus, spinothalamic tract, rubrospinal tract, central tegmental tract, medial longitudinal fasciculus, and dorsal longitudinal fasciculus. Then, the reconstructed 3-dimensional brainstem structure was sectioned at the level of classic surgical approaches, namely supracollicular, infracollicular, lateral mesencephalic, perioculomotor, peritrigeminal, anterolateral (to the medulla), and retro-olivary approaches. CONCLUSION The advanced diffusion MRI fiber tracking is a powerful tool to explore the brainstem neuroanatomy and to achieve a better understanding of surgical approaches.

scholarly journals Local connectome phenotypes predict social, health, and cognitive factors

2018 ◽  
Vol 2 (1) ◽  
pp. 86-105 ◽  
Author(s):  
Michael A. Powell ◽  
Javier O. Garcia ◽  
Fang-Cheng Yeh ◽  
Jean M. Vettel ◽  
Timothy Verstynen
Keyword(s):  
White Matter ◽  
Diffusion Mri ◽  
Social Health ◽  
Cognitive Factors ◽  
High Dimensional ◽  
Cognitive Measures ◽  
Human Connectome Project ◽  
Low Dimensional ◽  
The Brain ◽  
Over Time

The unique architecture of the human connectome is defined initially by genetics and subsequently sculpted over time with experience. Thus, similarities in predisposition and experience that lead to similarities in social, biological, and cognitive attributes should also be reflected in the local architecture of white matter fascicles. Here we employ a method known as local connectome fingerprinting that uses diffusion MRI to measure the fiber-wise characteristics of macroscopic white matter pathways throughout the brain. This fingerprinting approach was applied to a large sample ( N = 841) of subjects from the Human Connectome Project, revealing a reliable degree of between-subject correlation in the local connectome fingerprints, with a relatively complex, low-dimensional substructure. Using a cross-validated, high-dimensional regression analysis approach, we derived local connectome phenotype (LCP) maps that could reliably predict a subset of subject attributes measured, including demographic, health, and cognitive measures. These LCP maps were highly specific to the attribute being predicted but also sensitive to correlations between attributes. Collectively, these results indicate that the local architecture of white matter fascicles reflects a meaningful portion of the variability shared between subjects along several dimensions.

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