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.

scholarly journals Local Connectome Phenotypes Predict Social, Health, and Cognitive Factors

2017 ◽  
Author(s):  
Michael A. Powell ◽  
Javier O. Garcia ◽  
Fang-Cheng Yeh ◽  
Jean M. Vettel ◽  
Timothy Verstynen
Keyword(s):  
White Matter ◽  
Social Health ◽  
Cognitive Factors ◽  
Cognitive Measures ◽  
Human Connectome Project ◽  
Subject Variability ◽  
Fibers Orientation ◽  
Low Dimensional ◽  
First Time ◽  
The Brain

AbstractThe 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.Author SummaryThe local connectome is the pattern of fiber systems (i.e., number of fibers, orientation, and size) within a voxel and reflects the proximal characteristics of white matter fascicles distributed throughout the brain. Here we show how variability in the local connectome is correlated in a principled way across individuals. This inter-subject correlation is reliable enough that unique phenotype maps can be learned to predict between-subject variability in a range of social, health, and cognitive attributes. This work shows, for the first time, how shared variability across individuals is reflected in the local connectome.

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.

P3-196: WHITE MATTER DEGENERATES OVER TIME: A LONGITUDINAL DIFFUSION MRI ANALYSIS

2014 ◽  
Vol 10 ◽  
pp. P701-P702
Author(s):  
Lotte Cremers ◽  
Marius de Groot ◽  
Albert Hofman ◽  
Gabriel Krestin ◽  
Aad van der Lugt ◽  
...  
Keyword(s):  
White Matter ◽  
Diffusion Mri ◽  
Longitudinal Diffusion ◽  
Over Time

scholarly journals White Matter Integrity Changes and Neurocognitive Functioning in Adult-Late Onset DM1: A Follow-Up DTI Study

2021 ◽  
Author(s):  
Garazi Labayru ◽  
Borja Camino-Pontes ◽  
Antonio Jimenez-Marin ◽  
Joana Garmendia ◽  
Jorge Villanua ◽  
...  
Keyword(s):  
White Matter ◽  
Late Onset ◽  
Neurocognitive Functioning ◽  
Frontal Part ◽  
Multisystemic Disease ◽  
The Brain ◽  
Over Time ◽  
Anterior Posterior

Abstract Background: Myotonic Dystrophy Type 1 (DM1) is a multisystemic disease that affects gray and white matter (WM) tissues. WM changes in DM1 include increased hyperintensities and altered tract integrity distributed in a widespread manner. However, the precise spatiotemporal changes are yet undetermined. Methods: MRI data were acquired from 8 adult- and late-onset DM1 patients and 10 healthy controls (HC) at two different timepoints over 9.06 years. Fractional anisotropy (FA) variations were assessed with Tract-Based Spatial Statistics. Transversal and longitudinal intra- and intergroup analyses were conducted, along with correlation analyses with clinical and neuropsychological data.Results: At baseline, reduced FA values were found in patients in the uncinate, anterior-thalamic, fronto-occipital, and longitudinal tracts. At follow-up, the WM disconnection was shown to have spread from the frontal part to the rest of the tracts in the brain. Furthermore, WM lesion burden was negatively correlated with FA values, while visuo-construction and intellectual functioning were positively correlated with global and regional FA values at follow-up.Conclusion: DM1 patients showed a pronounced WM integrity loss over time compared to HC, with a neurodegeneration pattern that suggests a progressive anterior-posterior disconnection. The visuo-construction domain stands out as the most sensitive neuropsychological measure for WM microstructural impairment.

scholarly journals Pandora: 4-D white matter bundle population-based atlases derived from diffusion MRI fiber tractography

2020 ◽  
Author(s):  
Colin B Hansen ◽  
Qi Yang ◽  
Ilwoo Lyu ◽  
Francois Rheault ◽  
Cailey Kerley ◽  
...  
Keyword(s):  
White Matter ◽  
Diffusion Mri ◽  
State Of The Art ◽  
Population Based ◽  
Small Sample ◽  
Fiber Tractography ◽  
Statistical Inferences ◽  
Small Sample Sizes ◽  
The Brain ◽  
Brain Atlases

AbstractBrain atlases have proven to be valuable neuroscience tools for localizing regions of interest and performing statistical inferences on populations. Although many human brain atlases exist, most do not contain information about white matter structures, often neglecting them completely or labelling all white matter as a single homogenous substrate. While few white matter atlases do exist based on diffusion MRI fiber tractography, they are often limited to descriptions of white matter as spatially separate “regions” rather than as white matter “bundles” or fascicles, which are well-known to overlap throughout the brain. Additional limitations include small sample sizes, few white matter pathways, and the use of outdated diffusion models and techniques. Here, we present a new population-based collection of white matter atlases represented in both volumetric and surface coordinates in a standard space. These atlases are based on 2443 subjects, and include 216 white matter bundles derived from 6 different state-of-the-art tractography techniques. This atlas is freely available and will be a useful resource for parcellation and segmentation.

scholarly journals FiberNeat: unsupervised streamline clustering and white matter tract filtering in latent space

2021 ◽  
Author(s):  
Bramsh Qamar Chandio ◽  
Tamoghna Chattopadhyay ◽  
Conor Owens-Walton ◽  
Julio E Villalon Reina ◽  
Leila Nabulsi ◽  
...  
Keyword(s):  
White Matter ◽  
False Positive ◽  
Diffusion Mri ◽  
Nonlinear Dimensionality Reduction ◽  
White Matter Tract ◽  
Filtering Method ◽  
Reduction Techniques ◽  
Latent Space ◽  
Dimensionality Reduction Techniques ◽  
The Brain

Whole-brain tractograms generated from diffusion MRI digitally represent the white matter structure of the brain and are composed of millions of streamlines. Such tractograms can have false positive and anatomically implausible streamlines. To obtain anatomically relevant streamlines and tracts, supervised and unsupervised methods can be used for tractogram clustering and tract extraction. Here we propose FiberNeat, an unsupervised streamline clustering and tract filtering method. FiberNeat takes an input set of streamlines that could either be unlabeled clusters or labeled tracts. Individual clusters/tracts are projected into a latent space using nonlinear dimensionality reduction techniques, such as t-SNE and UMAP, to find spurious and outlier streamlines. In addition, outlier streamline clusters are detected using DBSCAN and then removed from the data in streamline space. Quantitative comparisons with expertly delineated tracts show the promise of the approach. This approach can be deployed as a filtering step after tracts are extracted.

scholarly journals White Matter Hyperintensity Regression: Comparison of Brain Atrophy and Cognitive Profiles with Progression and Stable Groups

2019 ◽  
Vol 9 (7) ◽  
pp. 170 ◽  
Author(s):  
Omar M. Al-Janabi ◽  
Christopher E. Bauer ◽  
Larry B. Goldstein ◽  
Richard R. Murphy ◽  
Ahmed A. Bahrani ◽  
...  
Keyword(s):  
White Matter ◽  
Brain Atrophy ◽  
Memory Performance ◽  
Subcortical White Matter ◽  
Cognitive Status ◽  
White Matter Hyperintensity ◽  
Cognitive Profiles ◽  
Cognitive Measures ◽  
Volumetric Quantification ◽  
Over Time

Subcortical white matter hyperintensities (WMHs) in the aging population frequently represent vascular injury that may lead to cognitive impairment. WMH progression is well described, but the factors underlying WMH regression remain poorly understood. A sample of 351 participants from the Alzheimer’s Disease Neuroimaging Initiative 2 (ADNI2) was explored who had WMH volumetric quantification, structural brain measures, and cognitive measures (memory and executive function) at baseline and after approximately 2 years. Selected participants were categorized into three groups based on WMH change over time, including those that demonstrated regression (n = 96; 25.5%), stability (n = 72; 19.1%), and progression (n = 209; 55.4%). There were no significant differences in age, education, sex, or cognitive status between groups. Analysis of variance demonstrated significant differences in atrophy between the progression and both regression (p = 0.004) and stable groups (p = 0.012). Memory assessments improved over time in the regression and stable groups but declined in the progression group (p = 0.003; p = 0.018). WMH regression is associated with decreased brain atrophy and improvement in memory performance over two years compared to those with WMH progression, in whom memory and brain atrophy worsened. These data suggest that WMHs are dynamic and associated with changes in atrophy and cognition.

Tractography of White Matter Fibers Using Diffusion MRI

2022 ◽  
pp. 98-112
Author(s):  
Strivathsav Ashwin Ramamoorthy
Keyword(s):  
White Matter ◽  
Human Brain ◽  
Diffusion Mri ◽  
Neural Circuits ◽  
Water Molecules ◽  
Neural Connectivity ◽  
Non Invasive ◽  
Diffusion Weighted Images ◽  
Different Types ◽  
The Brain

To understand more about the human brain and how it works, it is vital to understand how the neural circuits connect different regions of the brain. The human brain is filled predominantly with water and the majority of the water molecules undergo diffusion which can be captured with the help of diffusion MRI. Diffusion weighted images enable us to reconstruct the neural circuits in a non-invasive manner, and this procedure is referred to as tractography. Tractography aids neurosurgeons to understand the neural connectivity of the patient. This chapter attempts to explain the procedure of tractography and different types of algorithms.

scholarly journals A functional-structural connectivity metric detects ipsilateral connections with distinct functional specialisation in each hemisphere

2020 ◽  
Author(s):  
Oren Civier ◽  
Marion Sourty ◽  
Fernando Calamante
Keyword(s):  
Functional Connectivity ◽  
Functional Mri ◽  
Measurement Errors ◽  
Diffusion Mri ◽  
Right Hemisphere ◽  
Structural Connectivity ◽  
Joint Analysis ◽  
Fibre Bundles ◽  
Human Connectome Project ◽  
The Brain

AbstractWe introduce a connectomics metric that integrates information on structural connectivity (SC) from diffusion MRI tractography and functional connectivity (FC) from resting-state functional MRI, at individual subject level. The metric is based on the ability of SC to broadly predict FC using a simple linear predictive model; for each connection in the brain, the metric quantifies the deviation from that model. For the metric to capture underlying physiological properties, we minimise systematic measurement errors and processing biases in both SC and FC, and address several challenges with the joint analysis. This also includes a data-driven normalisation approach. The combined metric may provide new information by indirectly assessing white matter structural properties that cannot be inferred from diffusion MRI alone, and/or complex interregional neural interactions that cannot be inferred from functional MRI alone. To demonstrate the utility of the metric, we used young adult data from the Human Connectome Project to examine all bilateral pairs of ipsilateral connections, i.e. each left-hemisphere connection in the brain was paired with its right-hemisphere homologue. We detected a minority of bilateral pairs where the metric value is significantly different across hemispheres, which we suggest reflects cases of ipsilateral connections that have distinct functional specialisation in each hemisphere. The pairs with significant effects spanned all cortical lobes, and also included several cortico-subcortical connections. Our findings highlight the potential in a joint analysis of structural and functional measures of connectivity, both for clinical applications and to help in the interpretation of results from standard functional connectivity analysis.Significance StatementBased on the notion that structure predicts function, the scientific community sought to demonstrate that structural information on fibre bundles that connect brain regions is sufficient to estimate the strength of interregional interactions. However, an accurate prediction using MRI has proved elusive. This paper posits that the failure to predict function from structure originates from limitations in measurement or interpretation of either diffusion MRI (to assess fibre bundles), fMRI (to assess functional interactions), or both. We show that these limitations can be nevertheless beneficial, as the extent of divergence between the two modalities may reflect hard-to-measure properties of interregional connections, such as their functional role in the brain. This provides many insights, including into the division of labour between hemispheres.

scholarly journals Functional and diffusion MRI reveal the functional and structural basis of infants’ noxious-evoked brain activity

2021 ◽  
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 ◽  
Structural Basis ◽  
Noxious Stimulation ◽  
Human Connectome Project ◽  
And Diffusion

Abstract Understanding 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-evoked brain activity. We demonstrate 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-evoked responses and white matter mean diffusivity. These associations are subsequently confirmed in the original noxious stimulation paradigm dataset, validating the prediction model. This study in healthy neonates demonstrates that noxious-evoked brain activity is tightly coupled to both 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.

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