Spatiotemporal mixed modeling of multi-subject task fMRI via method of moments

Estimating spatiotemporal models for multi-subject fMRI is computationally challenging. We propose a mixed model for localization studies with spatial random effects and time-series errors. We develop method-of-moment estimators that leverage population and spatial information and are scalable to massive datasets. In simulations, subject-specific estimates of activation are considerably more accurate than the standard voxel-wise general linear model. Our mixed model also allows for valid population inference. We apply our model to cortical data from motor and theory of mind tasks from the Human Connectome Project (HCP). The proposed method results in subject-specific predictions that appear smoother and less noisy than those from the popular single-subject univariate approach. In particular, the regions of motor cortex associated with a left-hand finger-tapping task appear to be more clearly delineated. Subject-specific maps of activation from task fMRI are increasingly used in pre-surgical planning for tumor removal and in locating targets for transcranial magnetic stimulation. Our findings suggest that using spatial and population information is a promising avenue for improving clinical neuroimaging

Impacts of Simultaneous Multislice Acquisition on Sensitivity and Specificity in fMRI

Simultaneous multislice (SMS) imaging can be used to decrease the time between acquisition of fMRI volumes, which can increase sensitivity by facilitating the removal of higher-frequency artifacts and boosting effective sample size. The technique requires an additional processing step in which the slices are separated, or unaliased, to recover the whole brain volume. However, this may result in signal “leakage” between aliased locations, i.e., slice “leakage,” and lead to spurious activation (decreased specificity). SMS can also lead to noise amplification, which can reduce the benefits of decreased repetition time. In this study, we evaluate the original slice-GRAPPA (no leak block) reconstruction algorithmand acceleration factor (AF = 8) used in the fMRI data in the young adult Human Connectome Project (HCP). We also evaluate split slice-GRAPPA (leak block), which can reduce slice leakage. We use simulations to disentangle higher test statistics into true positives (sensitivity) and false positives (decreased specificity). Slice leakage was greatly decreased by split slice-GRAPPA. Noise amplification was decreased by using moderate acceleration factors (AF = 4). We examined slice leakage in unprocessed fMRI motor task data from the HCP. When data were smoothed, we found evidence of slice leakage in some, but not all, subjects. We also found evidence of SMS noise amplification in unprocessed task and processed resting-state HCP data

Improving the accuracy of brain activation maps in the group-level analysis of fMRI data utilizing spatiotemporal Gaussian process model

OBJECTIVE: Accuracy and precision of the statistical analysis methods used for brain activation maps are essential. Adjusting models to consider spatiotemporal correlation embedded in fMRI data may increase their accuracy, but it also introduces a high computational cost. The present study aimed to apply and assess the spatiotemporal Gaussian process (STGP) model to improve accuracy and reduce cost. METHODS: We applied the spatiotemporal Gaussian process (STGP) model for both simulated and experimental memory tfMRI data and compared the findings with fast, fully Bayesian, and General Linear Models (GLM). To assess their accuracy and precision, the models were fitted to the simulated data (1000 voxels,100 times point for 50 people), and an average of accuracy indexes of 100 repetitions was computed. Functional and activation maps for all models were calculated in experimental data analysis. RESULTS: STGP model resulted in a higher Z-score in the whole brain, in the 1000 most activated voxels, and in the frontal lobe as the approved memory area. Based on the simulated data, the STGP model showed more accuracy and precision than the other two models. However, its computational time was more than the GLM, as the price of model correction, but much less than that of the fast, fully Bayesian model. CONCLUSION: Spatiotemporal correlation further improved the accuracy of the STGP compared to the GLM and fast, fully Bayesian model. This can result in more accurate activation maps. Moreover, the STGP model’s computational speed appears to be reasonable for model application

Disentangling causal webs in the brain using functional Magnetic Resonance Imaging: A review of current approaches

In the past two decades, functional Magnetic Resonance Imaging has been used to relate neuronal network activity to cognitive processing and behaviour. Recently this approach has been augmented by algorithms that allow us to infer causal links between component populations of neuronal networks. Multiple inference procedures have been proposed to approach this research question but so far, each method has limitations when it comes to establishing whole-brain connectivity patterns. In this work, we discuss eight ways to infer causality in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality, Likelihood Ratios, LiNGAM, Patel's Tau, Structural Equation Modelling, and Transfer Entropy. We finish with formulating some recommendations for the future directions in this area

Score-Driven Modeling of Spatio-Temporal Data

A simultaneous autoregressive score-driven model with autoregressive disturbances is developed for spatio-temporal data that may exhibit heavy tails. The model specification rests on a signal plus noise decomposition of a spatially filtered process, where the signal can be approximated by a nonlinear function of the past variables and a set of explanatory variables, while the noise follows a multivariate Student-t distribution. The key feature of the model is that the dynamics of the space-time varying signal are driven by the score of the conditional likelihood function. When the distribution is heavy-tailed, the score provides a robust update of the space-time varying location. Consistency and asymptotic normality of maximum likelihood estimators are derived along with the stochastic properties of the model. The motivating application of the proposed model comes from brain scans recorded through functional magnetic resonance imaging when subjects are at rest and not expected to react to any controlled stimulus. We identify spontaneous activations in brain regions as extreme values of a possibly heavy-tailed distribution, by accounting for spatial and temporal dependence

Decoding the consumer’s brain: Neural representations of consumer experience

Understanding consumer experience – what consumers think about brands, how they feel about services, whether they like certain products – is crucial to marketing practitioners. ‘Neuromarketing’, as the application of neuroscience in marketing research is called, has generated excitement with the promise of understanding consumers’ minds by probing their brains directly. Recent advances in neuroimaging analysis leverage machine learning and pattern classification techniques to uncover patterns from neuroimaging data that can be associated with thoughts and feelings. In this dissertation, I measure brain responses of consumers by functional magnetic resonance imaging (fMRI) in order to ‘decode’ their mind. In three different studies, I have demonstrated how different aspects of consumer experience can be studied with fMRI recordings. First, I study how consumers think about brand image by comparing their brain responses during passive viewing of visual templates (photos depicting various social scenarios) to those during active visualizing of a brand’s image. Second, I use brain responses during viewing of affective pictures to decode emotional responses during watching of movie-trailers. Lastly, I examine whether marketing videos that evoke s

Bayesian Spatial Binary Regression for Label Fusion in Structural Neuroimaging

Many analyses of neuroimaging data involve studying one or more regions of interest (ROIs) in a brain image. In order to do so, each ROI must first be identified. Since every brain is unique, the location, size, and shape of each ROI varies across subjects. Thus, each ROI in a brain image must either be manually identified or (semi-) automatically delineated, a task referred to as segmentation. Automatic segmentation often involves mapping a previously manually segmented image to a new brain image and propagating the labels to obtain an estimate of where each ROI is located in the new image. A more recent approach to this problem is to propagate labels from multiple manually segmented atlases and combine the results using a process known as label fusion. To date, most label fusion algorithms either employ voting procedures or impose prior structure and subsequently find the maximum a posteriori estimator (i.e., the posterior mode) through optimization. We propose using a fully Bayesian spatial regression model for label fusion that facilitates direct incorporation of covariate information while making accessible the entire posterior distribution. We discuss the implementation of our model via Markov chain Monte Carlo and illustrate the procedure through both simulation and application to segmentation of the hippocampus, an anatomical structure known to be associated with Alzheimer's disease.Comment: 24 pages, 10 figure

Pedaling-related brain activation in people post-stroke: an fMRI study

This study aimed to enhance our understanding of supraspinal control of locomotion in stroke survivors and its relationship to locomotor impairment. We focused mainly on the locomotor component of walking, which involves rhythmic, reciprocal, flexion and extension movements of multiple joints in both legs. Functional magnetic resonance imaging (fMRI) was used to record human brain activity while pedaling was used as a model of locomotion. First, we examined the spatiotemporal characteristics of hemodynamic responses recorded with fMRI and found that they were different in stroke survivors and control subjects. However, these differences were not substantial enough to require altering the normal canonical hemodynamic response function to obtain valid measurements of pedaling-related brain activity. During pedaling, stroke survivors and control subjects showed activity in the sensorimotor cortex and cerebellum. Stroke survivors had reduced volume of activation in those regions, however the signal intensity was similar between the groups. In stroke survivors, sensorimotor cortex activity was symmetrically distributed across the damaged and undamaged hemispheres; while cerebellum activity was lateralized to the damaged hemisphere. These brain activation patterns were different from those observed during non-locomotor movements, where volume of activation was unchanged but signal intensity was reduced in stroke survivors. We conclude that neural adaptations for producing locomotor and non-locomotor movements post-stroke are not the same and that the spinal cord and cerebellum might have a compensatory role in producing hemiparetic locomotion. Finally, we examined the relationship between locomotor performance and pedaling-related brain activity measured with fMRI. We found no relationship between the brain activation symmetry and locomotor symmetry, suggesting that the brain activation from each hemisphere was not directly responsible for control of the contralateral leg. However, our stroke survivors demonstrated poor locomotor performance and decreased volume of activation measured during pedaling, suggesting that impaired locomotion was associated with reduced volume of activation. Signal intensity of brain activity was associated with rate of pedaling in stroke survivors, suggesting that increased signal intensity in the active brain areas may compensate for reduced volume of activation in the production of hemiparetic locomotion