scholarly journals Bayesian non-central chi regression for neuroimaging

2016 ◽  
Author(s):  
Bertil Wegmann ◽  
Anders Eklund ◽  
Mattias Villani
Keyword(s):  
Regression Model ◽  
Diffusion Tensor ◽  
Brain Activity ◽  
Gaussian Model ◽  
Mean Diffusivity ◽  
Bayesian Variable Selection ◽  
Noise Model ◽  
Model Parameters ◽  
Tensor Model ◽  
Human Connectome Project

AbstractWe propose a regression model for non-central χ (NC-χ) distributed functional magnetic resonance imaging (fMRI) and diffusion weighted imaging (DWI) data, with the heteroscedastic Rician regression model as a prominent special case. The model allows both parameters in the NC-χ distribution to be linked to explanatory variables, with the relevant covariates automatically chosen by Bayesian variable selection. A highly efficient Markov chain Monte Carlo (MCMC) algorithm is proposed for simulating from the joint Bayesian posterior distribution of all model parameters and the binary covariate selection indicators. Simulated fMRI data is used to demonstrate that the Rician model is able to localize brain activity much more accurately than the traditionally used Gaussian model at low signal-to-noise ratios. Using a diffusion dataset from the Human Connectome Project, it is also shown that the commonly used approximate Gaussian noise model underestimates the mean diffusivity (MD) and the fractional anisotropy (FA) in the single-diffusion tensor model compared to the theoretically correct Rician model.

scholarly journals DACO: Distortion/artefact correction for diffusion MRI data in an integrated framework

2021 ◽  
Author(s):  
Yung-Chin Hsu ◽  
Wen-Yih Isaac Tseng
Keyword(s):  
Diffusion Mri ◽  
Diffusion Tensor ◽  
Mathematical Formulation ◽  
Magnetic Resonance Images ◽  
Model Parameters ◽  
Weighted Image ◽  
The Real ◽  
Integrated Framework ◽  
Human Connectome Project ◽  
Anatomical Images

In this paper we propose a registration-based algorithm to correct various distortions or artefacts (DACO) commonly observed in diffusion weighted (DW) magnetic resonance images (MRI). The registration in DACO is proceeded on the basis of a pseudo b_0 image, which is synthesized from the anatomical images such as T1-weighted image or T2-weighted image, and a pseudo diffusion MRI (dMRI) data, which is derived from the Gaussian model of diffusion tensor imaging (DTI) or the Hermite model of MAP-MRI. DACO corrects (1) the susceptibility-induced distortions, (2) the intensity inhomogeneity, and (3) the misalignment between the dMRI data and anatomical images by registering the real b_0 image to the pseudo b_0 image, and corrects (4) the eddy current (EC)-induced distortions and (5) the head motions by registering each of the DW images in the real dMRI data to the corresponding image in the pseudo dMRI data. As the above artefacts interact with each other, DACO models each type of artefact in an integrated framework and estimates these models in an interleaved and iterative manner. The mathematical formulation of the models and the comprehensive estimation procedures are detailed in this paper. The evaluation using the human connectome project data shows that DACO could estimate the model parameters accurately. Furthermore, the evaluation conducted on the real human data acquired from clinical MRI scanners reveals that the method could reduce the artefacts effectively. The DACO method leverages the anatomical image, which is routinely acquired in clinical practice, to correct the artefacts, minimizing the additional acquisitions needed to conduct the algorithm. Therefore, our method is beneficial to most dMRI data, particularly to those without acquiring the field map or blip-up and blip-down images.

scholarly journals Sensory processing sensitivity and axonal microarchitecture: Identifying brain structural characteristics for behavior

2021 ◽  
Author(s):  
Szabolcs David ◽  
Lucy L Brown ◽  
Anneriet M Heemskerk ◽  
Elaine Aron ◽  
Alexander Leemans ◽  
...  
Keyword(s):  
Diffusion Tensor Imaging ◽  
White Matter ◽  
Sensory Processing ◽  
Diffusion Tensor ◽  
Structural Characteristics ◽  
Inferior Frontal Gyrus ◽  
Mean Diffusivity ◽  
Human Connectome Project ◽  
Sensory Processing Sensitivity ◽  
The Right

Previously, researchers used functional MRI to identify regional brain activations associated with sensory processing sensitivity (SPS), a proposed normal phenotype trait. To further validate SPS as a behavioral entity, to characterize it anatomically, and to test the usefulness in psychology of methodologies that assess axonal properties, the present study correlated SPS proxy questionnaire scores (adjusted for neuroticism) with diffusion tensor imaging measures. Participants (n=408) from the Young Adult Human Connectome Project that are free of neurologic and psychiatric disorders were investigated. We computed mean diffusivity (MD), radial diffusivity (RD), axial diffusivity (AD) and fractional anisotropy (FA). A voxelwise, exploratory analysis showed that MD and RD correlated positively with SPS proxy scores in the right and left subcallosal and anterior ventral cingulum bundle, and the right forceps minor of the corpus callosum (peak Cohens D effect size = 0.269). Further analyses showed correlations throughout the entire right and left ventromedial prefrontal cortex, including the superior longitudinal fasciculus, inferior fronto-occipital fasciculus, uncinate and arcuate fasciculus. These prefrontal regions are generally involved in emotion, reward and social processing. FA was negatively correlated with SPS proxy scores in white matter of the right premotor/motor/somatosensory/supramarginal gyrus regions, which are associated with empathy, theory of mind, primary and secondary somatosensory processing. Region of interest (ROI) analysis, based-on previous fMRI results and Freesurfer atlas-defined areas, showed small effect sizes, (+0.151 to -0.165) in white matter of the precuneus and inferior frontal gyrus. Other ROI effects were found in regions of the dorsal and ventral visual pathways and primary auditory cortex. The results reveal that in a large, diverse group of participants axonal microarchitectural differences can be identified with SPS traits that are subtle and in the range of typical behavior. The results suggest that the heightened sensory processing in people who show SPS may be influenced by the microstructure of white matter in specific neocortical regions. Although previous fMRI studies had identified most of these general neocortical regions, the DTI results put a new focus on brain areas related to attention and cognitive flexibility, empathy, emotion and low-level sensory processing, as in the primary sensory cortex. Psychological trait characterization may benefit from diffusion tensor imaging methodology by identifying influential brain systems for traits.

scholarly journals A dynamical systems framework to uncover the drivers of large-scale cortical activity

2019 ◽  
Author(s):  
Arian Ashourvan ◽  
Sérgio Pequito ◽  
Maxwell Bertolero ◽  
Jason Z. Kim ◽  
Danielle S. Bassett ◽  
...  
Keyword(s):  
Large Scale ◽  
Computational Models ◽  
Functional Neuroimaging ◽  
Brain Activity ◽  
Intrinsic Activity ◽  
Model Parameters ◽  
Sensory Stimuli ◽  
Human Connectome Project ◽  
External Stimuli ◽  
And Task

ABSTRACTA fundamental challenge in neuroscience is to uncover the principles governing complex interactions between the brain and its external environment. Over the past few decades, the development of functional neuroimaging techniques and tools from graph theory, network science, and computational neuroscience have markedly expanded opportunities to study the intrinsic organization of brain activity. However, many current computational models are fundamentally limited by little to no explicit assessment of the brain’s interactions with external stimuli. To address this limitation, we propose a simple scheme that jointly estimates the intrinsic organization of brain activity and extrinsic stimuli. Specifically, we adopt a linear dynamical model (intrinsic activity) under unknown exogenous inputs (e.g., sensory stimuli), and jointly estimate the model parameters and exogenous inputs. First, we demonstrate the utility of this scheme by accurately estimating unknown external stimuli in a synthetic example. Next, we examine brain activity at rest and task for 99 subjects from the Human Connectome Project, and find significant task-related changes in the identified system, and task-related increases in the estimated external inputs showing high similarity to known task regressors. Finally, through detailed examination of fluctuations in the spatial distribution of the oscillatory modes of the estimated system during the resting state, we find an apparent non-stationarity in the profile of modes that span several brain regions including the visual and the dorsal attention systems. The results suggest that these brain structures display a time-varying relationship, or alternatively, receive non-stationary exogenous inputs that can lead to apparent system non-stationarities. Together, our embodied model of brain activity provides an avenue to gain deeper insight into the relationship between cortical functional dynamics and their drivers.

scholarly journals Diffusion Tensor Model links to Neurite Orientation Dispersion and Density Imaging at high b-value in Cerebral Cortical Gray Matter

2018 ◽  
Author(s):  
Hikaru Fukutomi ◽  
Matthew F. Glasser ◽  
Katsutoshi Murata ◽  
Thai Akasaka ◽  
Koji Fujimoto ◽  
...  
Keyword(s):  
Simulation Analysis ◽  
Diffusion Tensor ◽  
B Value ◽  
Tensor Model ◽  
Diffusion Weighted Mri ◽  
High B Value ◽  
Diffusion Weighted ◽  
Human Connectome Project ◽  
Fluid Fraction ◽  
Cortical Gray Matter

AbstractDiffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) are widely used models to infer microstructural features in the brain from diffusion-weighted MRI. Several studies have recently applied both models to increase sensitivity to biological changes, however, it remains uncertain how these measures are associated. Here we show that cortical distributions of DTI and NODDI are associated depending on the choice of b-value, a factor reflecting strength of diffusion weighting gradient. We analyzed a combination of high, intermediate and low b-value data of multi-shell diffusion-weighted MRI (dMRI) in healthy 456 subjects of the Human Connectome Project using NODDI, DTI and a mathematical conversion from DTI to NODDI. Cortical distributions of DTI and DTI-derived NODDI metrics were remarkably associated with those in NODDI, particularly when applied highly diffusion-weighted data (b-value =3000 sec/mm2). This was supported by simulation analysis, which revealed that DTI-derived parameters with lower b-value datasets suffered from errors due to heterogeneity of cerebrospinal fluid fraction and partial volume. These findings suggest that high b-value DTI redundantly parallels with NODDI-based cortical neurite measures, but the conventional low b-value DTI does not reasonably characterize cortical microarchitecture.

scholarly journals A Bayesian Heteroscedastic GLM with Application to fMRI Data with Motion Spikes

2016 ◽  
Author(s):  
Anders Eklund ◽  
Martin A. Lindquist ◽  
Mattias Villani
Keyword(s):  
Variable Selection ◽  
Brain Activity ◽  
Simulated Data ◽  
Bayesian Variable Selection ◽  
Fmri Data ◽  
Head Motion ◽  
Model Parameters ◽  
Design Matrix ◽  
Explanatory Variables ◽  
The Mean

AbstractWe propose a voxel-wise general linear model with autoregressive noise and heteroscedastic noise innovations (GLMH) for analyzing functional magnetic resonance imaging (fMRI) data. The model is analyzed from a Bayesian perspective and has the benefit of automatically down-weighting time points close to motion spikes in a data-driven manner. We develop a highly efficient Markov Chain Monte Carlo (MCMC) algorithm that allows for Bayesian variable selection among the regressors to model both the mean (i.e., the design matrix) and variance. This makes it possible to include a broad range of explanatory variables in both the mean and variance (e.g., time trends, activation stimuli, head motion parameters and their temporal derivatives), and to compute the posterior probability of inclusion from the MCMC output. Variable selection is also applied to the lags in the autoregressive noise process, making it possible to infer the lag order from the data simultaneously with all other model parameters. We use both simulated data and real fMRI data from OpenfMRI to illustrate the importance of proper modeling of heteroscedasticity in fMRI data analysis. Our results show that the GLMH tends to detect more brain activity, compared to its homoscedastic counterpart, by allowing the variance to change over time depending on the degree of head motion.

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

2020 ◽  
Author(s):  
Luke Baxter ◽  
Fiona Moultrie ◽  
Sean Fitzgibbon ◽  
Marianne Aspbury ◽  
Roshni Mansfield ◽  
...  
Keyword(s):  
White Matter ◽  
Pain Management ◽  
Resting State ◽  
Diffusion Mri ◽  
Brain Activity ◽  
Mean Diffusivity ◽  
Structural Basis ◽  
Noxious Stimulation ◽  
State Activity ◽  
Human Connectome Project

AbstractUnderstanding the neurophysiology underlying pain perception in infants is central to improving early life pain management. In this multimodal MRI study, we use resting-state functional and white matter diffusion MRI to investigate individual variability in infants’ noxious-evoked brain activity. In an 18-infant nociception-paradigm dataset, we show it is possible to predict infants’ cerebral haemodynamic responses to experimental noxious stimulation using their resting-state activity across nine networks from a separate stimulus-free scan. In an independent 215-infant Developing Human Connectome Project dataset, we use this resting-state-based prediction model to generate noxious responses. We identify a significant correlation between these predicted noxious responses and infants’ white matter mean diffusivity, and this relationship is subsequently confirmed within our nociception-paradigm dataset. These findings reveal that a newborn infant’s pain-related brain activity is tightly coupled to both their spontaneous resting-state activity and underlying white matter microstructure. This work provides proof-of-concept that knowledge of an infant’s functional and structural brain architecture could be used to predict pain responses, informing infant pain management strategies and facilitating evidence-based personalisation of care.

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

2020 ◽  
Author(s):  
Luke Baxter ◽  
Fiona Moultrie ◽  
Sean Fitzgibbon ◽  
Marianne Aspbury ◽  
Roshni Mansfield ◽  
...  
Keyword(s):  
White Matter ◽  
Pain Management ◽  
Resting State ◽  
Diffusion Mri ◽  
Brain Activity ◽  
Mean Diffusivity ◽  
Structural Basis ◽  
Noxious Stimulation ◽  
State Activity ◽  
Human Connectome Project

Abstract Understanding the neurophysiology underlying pain perception in infants is central to improving early life pain management. In this multimodal MRI study, we use resting-state functional and white matter diffusion MRI to investigate individual variability in infants’ noxious-evoked brain activity. In an 18-infant nociception-paradigm dataset, we show it is possible to predict infants’ cerebral haemodynamic responses to experimental noxious stimulation using their resting-state activity across nine networks from a separate stimulus-free scan. In an independent 215-infant Developing Human Connectome Project dataset, we use this resting-state-based prediction model to generate noxious responses. We identify a significant correlation between these predicted noxious responses and infants’ white matter mean diffusivity, and this relationship is subsequently confirmed within our nociception-paradigm dataset. These findings reveal that a newborn infant’s pain-related brain activity is tightly coupled to both their spontaneous resting-state activity and underlying white matter microstructure. This work provides proof-of-concept that knowledge of an infant’s functional and structural brain architecture could be used to predict pain responses, informing infant pain management strategies and facilitating evidence-based personalisation of care.

Arterial Stiffness Is Associated with White Matter Disruption and Cognitive Impairment: A Community-Based Cohort Study

2021 ◽  
Vol 80 (2) ◽  
pp. 567-576
Author(s):  
Fei Han ◽  
Fei-Fei Zhai ◽  
Ming-Li Li ◽  
Li-Xin Zhou ◽  
Jun Ni ◽  
...  
Keyword(s):  
Cognitive Function ◽  
White Matter ◽  
Arterial Stiffness ◽  
Cognitive Decline ◽  
Sex Education ◽  
Diffusion Tensor ◽  
Mean Diffusivity ◽  
White Matter Injury ◽  
White Matter Integrity ◽  
Cross Sectional

Background: Mechanisms through which arterial stiffness impacts cognitive function are crucial for devising better strategies to prevent cognitive decline. Objective: To examine the associations of arterial stiffness with white matter integrity and cognition in community dwellings, and to investigate whether white matter injury was the intermediate of the associations between arterial stiffness and cognition. Methods: This study was a cross-sectional analysis on 952 subjects (aged 55.5±9.1 years) who underwent diffusion tensor imaging and measurement of brachial-ankle pulse wave velocity (baPWV). Both linear regression and tract-based spatial statistics were used to investigate the association between baPWV and white matter integrity. The association between baPWV and global cognitive function, measured as the mini-mental state examination (MMSE) was evaluated. Mediation analysis was performed to assess the influence of white matter integrity on the association of baPWV with MMSE. Results: Increased baPWV was significantly associated with lower mean global fractional anisotropy (β= –0.118, p < 0.001), higher mean diffusivity (β= 0.161, p < 0.001), axial diffusivity (β= 0.160, p < 0.001), and radial diffusivity (β= 0.147, p < 0.001) after adjustment of age, sex, and hypertension, which were measures having a direct effect on arterial stiffness and white matter integrity. After adjustment of age, sex, education, apolipoprotein E ɛ4, cardiovascular risk factors, and brain atrophy, we found an association of increased baPWV with worse performance on MMSE (β= –0.093, p = 0.011). White matter disruption partially mediated the effect of baPWV on MMSE. Conclusion: Arterial stiffness is associated with white matter disruption and cognitive decline. Reduced white matter integrity partially explained the effect of arterial stiffness on cognition.

The effect of microvascular obstruction on the myocardial microstructure: a diffusion tensor imaging study

2021 ◽  
Vol 22 (Supplement_1) ◽  
Author(s):  
A Das ◽  
K Kelly ◽  
M Aldred ◽  
I Teh ◽  
CK Stoeck ◽  
...  
Keyword(s):  
Microvascular Obstruction ◽  
Diffusion Tensor ◽  
Spin Echo ◽  
Mean Diffusivity ◽  
Imaging Study ◽  
Free Breathing ◽  
Helix Angle ◽  
Acquisition Time ◽  
Heart Research

Abstract Funding Acknowledgements Type of funding sources: Foundation. Main funding source(s): Heart Research UK Background Diffusion tensor cardiac magnetic resonance (DT-CMR) imaging allows for characterising myocardial microstructure in-vivo using mean diffusivity (MD), fractional anisotropy (FA), secondary eigenvector angle (E2A) and helix angle (HA) maps. Following myocardial infarction (MI), alterations in MD, FA and HA proportions have previously been reported. E2A depicts the contractile state of myocardial sheetlets, however the behaviour of E2A in infarct segments, and all DTI markers in areas of microvascular obstruction (MVO) is also not fully understood.  Purpose We performed spin echo DTI in patients following ST-elevation MI (STEMI) in order to investigate acute changes in DTI parameters in remote and infarct segments both with and without MVO. Method Twenty STEMI patients (16 men, 4 women, mean age 59) had acute (5 ± 2d) 3T CMR scans. CMR protocol included: second order motion compensated (M012) free-breathing spin echo DTI (3 slices, 18 diffusion directions at b-values 100s/mm2[3], 200s/mm2[3] and 500s/mm2[12], reconstructed resolution was 1.66x1.66x8mm); cine and late gadolinium enhancement (LGE) imaging. Average MD, FA, E2A HA parameters were calculated on a  16 AHA segmental level. HA maps were described by dividing values into left-handed HA (LHM, -90° &lt; HA &lt; -30°), circumferential HA (CM, -30° &lt; HA &lt; 30°), and right-handed HA (RHM, 30° &lt; HA &lt; 90°) and reported as relative proportions. Segments were defined as infarct (positive for LGE) and remote (opposite to the infarct).  Results DTI acquisition was successful in all patients (acquisition time 13 ± 5mins). Ten patients had evidence of MVO on LGE images. MD was significantly higher in infarct regions in comparison to remote; MVO-ve infarct segments had significantly higher MD than MVO + ve infarct segments (MD remote= 1.46 ± 0.12x10-3mm2/s, MD MVO + ve = 1.59 ± 0.12x10-3mm2/s, MD MVO-ve  = 1.75 ± 0.12x10-3mm2/s, ANOVA p &lt; 0.01). FA was reduced in infarct segments in comparison to remote; MVO-ve infarct segments had significantly lower FA than MVO + ve infarct segments (FAremote= 0.37 ± 0.02, FA MVO + ve = 0.31 ± 0.02 x 10-3mm2/s, MD MVO-ve =0.25 ± 0.02, ANOVA p &lt; 0.01). E2A values were significantly lower in infarct segments compared to remote; MVO + ve infarct segments had significantly lower values than MVO-ve. (E2A remote= 57.4 ± 5.2°, E2A MVO-ve = 46.8 ± 2.5°, E2A MVO + ve = 36.8 ± 3.1°, ANOVA p &lt; 0.001). RHM% (corresponding to subendocardium) was significantly lower in infarct segments compared to remote; MVO + ve infarct segments had significantly lower RHM% than MVO-ve. (RHM remote= 37 ± 3%, RHM RHM MVO-ve= 28 ± 7%, MVO + ve= 8 ± 5%, ANOVA p &lt; 0.001). Conclusion The presence of MVO results in a decrease in MD and increase in FA in comparison to surrounding infarct segments. However, the reduction in E2A and right-handed myocytes on HA in infarct segments is further exacerbated by the presence of MVO. Further study is required to investigate the underlying mechanisms for such alterations in signal intensity. Abstract Figure. A case of transmural septal MI with MVO

scholarly journals Demyelination and remyelination detected in an alternative cuprizone mouse model of multiple sclerosis with 7.0 T multiparameter magnetic resonance imaging

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shuang Ding ◽  
Yu Guo ◽  
Xiaoya Chen ◽  
Silin Du ◽  
Yongliang Han ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Multiple Sclerosis ◽  
Magnetic Resonance ◽  
Mouse Model ◽  
Diffusion Tensor ◽  
Mean Diffusivity ◽  
Blue Staining ◽  
Resonance Imaging ◽  
The Mean ◽  
Underlying Mechanisms

AbstractThe aim of this study was to investigate the mechanisms underlying demyelination and remyelination with 7.0 T multiparameter magnetic resonance imaging (MRI) in an alternative cuprizone (CPZ) mouse model of multiple sclerosis (MS). Sixty mice were divided into six groups (n = 10, each), and these groups were imaged with 7.0 T multiparameter MRI and treated with an alternative CPZ administration schedule. T2-weighted imaging (T2WI), susceptibility-weighted imaging (SWI), and diffusion tensor imaging (DTI) were used to compare the splenium of the corpus callosum (sCC) among the groups. Prussian blue and Luxol fast blue staining were performed to assess pathology. The correlations of the mean grayscale value (mGSV) of the pathology results and the MRI metrics were analyzed to evaluate the multiparameter MRI results. One-way ANOVA and post hoc comparison showed that the normalized T2WI (T2-nor), fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) values were significantly different among the six groups, while the mean phase (Φ) value of SWI was not significantly different among the groups. Correlation analysis showed that the correlation between the T2-nor and mGSV was higher than that among the other values. The correlations among the FA, RD, MD, and mGSV remained instructive. In conclusion, ultrahigh-field multiparameter MRI can reflect the pathological changes associated with and the underlying mechanisms of demyelination and remyelination in MS after the successful establishment of an acute CPZ-induced model.

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