| 1. |
- Abramian, David, 1992-, et al.
(författare)
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Diffusion-Informed Spatial Smoothing of fMRI Data in White Matter Using Spectral Graph Filters
- 2021
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Ingår i: NeuroImage. - : Elsevier. - 1053-8119 .- 1095-9572. ; 237
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Tidskriftsartikel (refereegranskat)abstract
- Brain activation mapping using functional magnetic resonance imaging (fMRI) has been extensively studied in brain gray matter (GM), whereas in large disregarded for probing white matter (WM). This unbalanced treatment has been in part due to controversies in relation to the nature of the blood oxygenation level-dependent (BOLD) contrast in WM and its detachability. However, an accumulating body of studies has provided solid evidence of the functional significance of the BOLD signal in WM and has revealed that it exhibits anisotropic spatio-temporal correlations and structure-specific fluctuations concomitant with those of the cortical BOLD signal. In this work, we present an anisotropic spatial filtering scheme for smoothing fMRI data in WM that accounts for known spatial constraints on the BOLD signal in WM. In particular, the spatial correlation structure of the BOLD signal in WM is highly anisotropic and closely linked to local axonal structure in terms of shape and orientation, suggesting that isotropic Gaussian filters conventionally used for smoothing fMRI data are inadequate for denoising the BOLD signal in WM. The fundamental element in the proposed method is a graph-based description of WM that encodes the underlying anisotropy observed across WM, derived from diffusion-weighted MRI data. Based on this representation, and leveraging graph signal processing principles, we design subject-specific spatial filters that adapt to a subject’s unique WM structure at each position in the WM that they are applied at. We use the proposed filters to spatially smooth fMRI data in WM, as an alternative to the conventional practice of using isotropic Gaussian filters. We test the proposed filtering approach on two sets of simulated phantoms, showcasing its greater sensitivity and specificity for the detection of slender anisotropic activations, compared to that achieved with isotropic Gaussian filters. We also present WM activation mapping results on the Human Connectome Project’s 100-unrelated subject dataset, across seven functional tasks, showing that the proposed method enables the detection of streamline-like activations within axonal bundles.
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| 2. |
- Björnsdotter, Malin, et al.
(författare)
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A Monte Carlo method for locally multivariate brain mapping.
- 2011
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Ingår i: NeuroImage. - : Elsevier BV. - 1095-9572 .- 1053-8119.
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Tidskriftsartikel (refereegranskat)abstract
- Locally multivariate approaches to functional brain mapping offer a highly appealing complement to conventional statistics, but require restrictive region-of-interest hypotheses, or, in exhaustive search forms (such as the "searchlight" algorithm; Kriegeskorte et al., 2006), are excessively computer intensive. We therefore propose a non-restrictive, comparatively fast yet highly sensitive method based on Monte Carlo approximation principles where locally multivariate maps are computed by averaging across voxelwise condition-discriminative information obtained from repeated stochastic sampling of fixed-size search volumes. On simulated data containing discriminative regions of varying size and contrast-to-noise ratio (CNR), the Monte Carlo method reduced the required computer resources by as much as 75% compared to the searchlight with no reduction in mapping performance. Notably, the Monte Carlo mapping approach not only outperformed the general linear method (GLM), but also produced higher discriminative voxel detection scores than the searchlight irrespective of classifier (linear or nonlinear support vector machine), discriminative region size or CNR. The improved performance was explained by the information-average procedure, and the Monte Carlo approach yielded mapping sensitivities of a few percent lower than an information-average exhaustive search. Finally, we demonstrate the utility of the algorithm on whole-brain, multi-subject functional magnetic resonance imaging (fMRI) data from a tactile study, revealing that the central representation of gentle touch is spatially distributed in somatosensory, insular and visual regions.
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| 3. |
- Cardin, Velia, et al.
(författare)
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Differential activity in Heschl's gyrus between deaf and hearing individuals is due to auditory deprivation rather than language modality
- 2016
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Ingår i: NeuroImage. - : Elsevier BV. - 1053-8119 .- 1095-9572. ; 124, s. 96-106
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Tidskriftsartikel (refereegranskat)abstract
- Sensory cortices undergo crossmodal reorganisation as a consequence of sensory deprivation. Congenital deafness in humans represents a particular case with respect to other types of sensory deprivation, because cortical reorganisation is not only a consequence of auditory deprivation, but also of language-driven mechanisms. Visual crossmodal plasticity has been found in secondary auditory cortices of deaf individuals, but it is still unclear if reorganisation also takes place in primary auditory areas, and how this relates to language modality and auditory deprivation.Here, we dissociated the effects of language modality and auditory deprivation on crossmodal plasticity in Heschl's gyrus as a whole, and in cytoarchitectonic region Te1.0 (likely to contain the core auditory cortex). Using fMRI, we measured the BOLD response to viewing sign language in congenitally or early deaf individuals with and without sign language knowledge, and in hearing controls.Results show that differences between hearing and deaf individuals are due to a reduction in activation caused by visual stimulation in the hearing group, which is more significant in Te1.0 than in Heschl's gyrus as a whole. Furthermore, differences between deaf and hearing groups are due to auditory deprivation, and there is no evidence that the modality of language used by deaf individuals contributes to crossmodal plasticity in Heschl's gyrus.
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| 4. |
- Croy, Ilona, et al.
(författare)
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Olfactory modulation of affective touch processing - A neurophysiological investigation
- 2016
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Ingår i: Neuroimage. - : Elsevier BV. - 1053-8119 .- 1095-9572. ; 135, s. 135-141
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Tidskriftsartikel (refereegranskat)abstract
- Touch can be highly emotional, and depending on the environment, it can be perceived as pleasant and comforting or disgusting and dangerous. Here, we studied the impact of context on the processing of tactile stimuli using a functional magnetic resonance imaging (fMRI) paradigm. This was achieved by embedding tactile stimulation in a variable olfactory environment. Twenty people were scanned with BOLD fMRI while receiving the following stimulus blocks: Slow stroking Touch, Civette odor (feces like), Rose odor, Touch + Civette, and Touch + Rose. Ratings of pleasantness and intensity of tactile stimuli and ratings of disgust and intensity of olfactory stimuli were collected. The impact of the olfactory context on the processing of touch was studied using covariance analyses. Coupling between olfactory processing and somatosensory processing areas was assessed with psychophysiological interaction analysis (PPI). A subjectively disgusting olfactory environment significantly reduced the perceived pleasantness of touch. The touch fMRI activation in the secondary somatosensory cortex, operculum 1 (OP1), was positively correlated with the disgust towards the odors. Decreased pleasantness of touch was related to decreased posterior insula activity. PPI analysis revealed a significant interaction between the OP1, posterior insula, and regions processing the disgust of odors (orbitofrontal cortex and amygdala). We conclude that the disgust evaluation of the olfactory environment moderates neural reactivity in somatosensory regions by upregulation of the OP1 and downregulation of the posterior insula. This adaptive regulation of affective touch processing may facilitate adaptive reaction to a potentially harmful stimulus.
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| 5. |
- De Luca, Alberto, et al.
(författare)
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On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types : Chronicles of the MEMENTO challenge
- 2021
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Ingår i: NeuroImage. - : Elsevier BV. - 1053-8119 .- 1095-9572. ; 240
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Tidskriftsartikel (refereegranskat)abstract
- Diffusion MRI (dMRI) has become an invaluable tool to assess the microstructural organization of brain tissue. Depending on the specific acquisition settings, the dMRI signal encodes specific properties of the underlying diffusion process. In the last two decades, several signal representations have been proposed to fit the dMRI signal and decode such properties. Most methods, however, are tested and developed on a limited amount of data, and their applicability to other acquisition schemes remains unknown. With this work, we aimed to shed light on the generalizability of existing dMRI signal representations to different diffusion encoding parameters and brain tissue types. To this end, we organized a community challenge - named MEMENTO, making available the same datasets for fair comparisons across algorithms and techniques. We considered two state-of-the-art diffusion datasets, including single-diffusion-encoding (SDE) spin-echo data from a human brain with over 3820 unique diffusion weightings (the MASSIVE dataset), and double (oscillating) diffusion encoding data (DDE/DODE) of a mouse brain including over 2520 unique data points. A subset of the data sampled in 5 different voxels was openly distributed, and the challenge participants were asked to predict the remaining part of the data. After one year, eight participant teams submitted a total of 80 signal fits. For each submission, we evaluated the mean squared error, the variance of the prediction error and the Bayesian information criteria. The received submissions predicted either multi-shell SDE data (37%) or DODE data (22%), followed by cartesian SDE data (19%) and DDE (18%). Most submissions predicted the signals measured with SDE remarkably well, with the exception of low and very strong diffusion weightings. The prediction of DDE and DODE data seemed more challenging, likely because none of the submissions explicitly accounted for diffusion time and frequency. Next to the choice of the model, decisions on fit procedure and hyperparameters play a major role in the prediction performance, highlighting the importance of optimizing and reporting such choices. This work is a community effort to highlight strength and limitations of the field at representing dMRI acquired with trending encoding schemes, gaining insights into how different models generalize to different tissue types and fiber configurations over a large range of diffusion encodings.
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| 6. |
- Dela Haije, Tom, et al.
(författare)
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Enforcing necessary non-negativity constraints for common diffusion MRI models using sum of squares programming
- 2020
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Ingår i: NeuroImage. - : ACADEMIC PRESS INC ELSEVIER SCIENCE. - 1053-8119 .- 1095-9572. ; 209
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Tidskriftsartikel (refereegranskat)abstract
- In this work we investigate the use of sum of squares constraints for various diffusion-weighted MRI models, with a goal of enforcing strict, global non-negativity of the diffusion propagator. We formulate such constraints for the mean apparent propagator model and for spherical deconvolution, guaranteeing strict non-negativity of the corresponding diffusion propagators. For the cumulant expansion similar constraints cannot exist, and we instead derive a set of auxiliary constraints that are necessary but not sufficient to guarantee non-negativity. These constraints can all be verified and enforced at reasonable computational costs using semidefinite programming. By verifying our constraints on standard reconstructions of the different models, we show that currently used weak constraints are largely ineffective at ensuring non-negativity. We further show that if strict non-negativity is not enforced then estimated model parameters may suffer from significant errors, leading to serious inaccuracies in important derived quantities such as the main fiber orientations, mean kurtosis, etc. Finally, our experiments confirm that the observed constraint violations are mostly due to measurement noise, which is difficult to mitigate and suggests that properly constrained optimization should currently be considered the norm in many cases.
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| 7. |
- Eklund, Anders, et al.
(författare)
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A Bayesian Heteroscedastic GLM with Application to fMRI Data with Motion Spikes
- 2017
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Ingår i: NeuroImage. - : Elsevier. - 1053-8119 .- 1095-9572. ; 155, s. 354-369
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Tidskriftsartikel (refereegranskat)abstract
- We 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.
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| 8. |
- Eklund, Anders, et al.
(författare)
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Does Parametric fMRI Analysis with SPM Yield Valid Results? - An Empirical Study of 1484 Rest Datasets
- 2012
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Ingår i: NeuroImage. - : Elsevier. - 1053-8119 .- 1095-9572. ; 61:3, s. 565-578
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Tidskriftsartikel (refereegranskat)abstract
- The validity of parametric functional magnetic resonance imaging (fMRI) analysis has only been reported for simulated data.Recent advances in computer science and data sharing make it possible to analyze large amounts of real fMRI data. In this study,1484 rest datasets have been analyzed in SPM8, to estimate true familywise error rates. For a familywise significance threshold of5%, significant activity was found in 1% - 70% of the 1484 rest datasets, depending on repetition time, paradigm and parametersettings. This means that parametric significance thresholds in SPM both can be conservative or very liberal. The main reason forthe high familywise error rates seems to be that the global AR(1) auto correlation correction in SPM fails to model the spectra ofthe residuals, especially for short repetition times. The findings that are reported in this study cannot be generalized to parametricfMRI analysis in general, other software packages may give different results. By using the computational power of the graphicsprocessing unit (GPU), the 1484 rest datasets were also analyzed with a random permutation test. Significant activity was thenfound in 1% - 19% of the datasets. These findings speak to the need for a better model of temporal correlations in fMRI timeseries.
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| 9. |
- Esteban-Cornejo, Irene, et al.
(författare)
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Fitness, cortical thickness and surface area in overweight/obese children: The mediating role of body composition and relationship with intelligence
- 2019
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Ingår i: NeuroImage. - : ACADEMIC PRESS INC ELSEVIER SCIENCE. - 1053-8119 .- 1095-9572. ; 186, s. 771-781
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Tidskriftsartikel (refereegranskat)abstract
- Cortical thickness and surface area are thought to be genetically unrelated and shaped by independent neurobiological events suggesting that they should be considered separately in morphometric analyses. Although the developmental trajectories of cortical thickness and surface area may differ across brain regions and ages, there is no consensus regarding the relationships of physical fitness with cortical thickness and surface area as well as for its subsequent influence on intelligence. Thus, this study examines: (i) the associations of physical fitness components (i.e., cardiorespiratory fitness, speed-agility and muscular fitness) with overall and regional cortical thickness and surface area; (ii) whether body composition indicators (i.e., body mass index, fat-free mass index and fat mass index) mediate these associations; and (iii) the association of physical fitness and cortical thickness with intelligence in overweight/obese children. A total of 101 overweight/obese children aged 8-11 years were recruited in Granada, Spain. The physical fitness components were assessed following the ALPHA health-related fitness test battery. T1-weighted images were acquired with a 3.0 Tesla Siemens Magnetom Tim Trio system. We used FreeSurfer software version 5.3.0 to assess cortical thickness (mm) and surface area (mm(2)). The main results showed that cardiorespiratory fitness and speed-agility were related to overall cortical thickness (beta = 0.321 and beta = 0.302, respectively; both P amp;lt; 0.05), and in turn, cortical thickness was associated with higher intelligence (beta = 0.198, P amp;lt; 0.05). Muscular fitness was not related to overall cortical thickness. None of the three physical fitness components were related to surface area (p amp;gt; 0.05). The associations of cardiorespiratory fitness and speed-agility with overall cortical thickness were mediated by fat mass index (56.86% amp; 62.28%, respectively). In conclusion, cardiorespiratory fitness and speed-agility, but not muscular fitness, are associated with overall cortical thickness, and in turn, thicker brain cortex is associated with higher intelligence in overweight/obese children. Yet, none of the three physical fitness components were related to surface area. Importantly, adiposity may hinder the benefits of cardiorespiratory fitness and speed-agility on cortical thickness. Understanding individual differences in brain morphology may have important implications for educators and policy makers who aim to determine policies and interventions to maximize academic learning and occupational success later in life.
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| 10. |
- Friman, Ola, et al.
(författare)
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Adaptive analysis of fMRI data
- 2003
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Ingår i: NeuroImage. - 1053-8119 .- 1095-9572. ; 19:3, s. 837-845
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Tidskriftsartikel (refereegranskat)abstract
- This article introduces novel and fundamental improvements of fMRI data analysis. Central is a technique termed constrained canonical correlation analysis, which can be viewed as a natural extension and generalization of the popular general linear model method. The concept of spatial basis filters is presented and shown to be a very successful way of adaptively filtering the fMRI data. A general method for designing suitable hemodynamic response models is also proposed and incorporated into the constrained canonical correlation approach. Results that demonstrate how each of these parts significantly improves the detection of brain activity, with a computation time well within limits for practical use, are provided.
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