Predictive sparse modeling of fMRI data for improved classification, regression, and visualization using the k -support norm

Interaction between the prefrontal and visual cortices supports subjective fear

It has been reported that threatening and non-threatening visual stimuli can be distinguished based on the multi-voxel patterns of haemodynamic activity in the human ventral visual stream. Do these findings mean that there may be evolutionarily hardwired mechanisms within early perception, for the fast and automatic detection of threat, and maybe even for the generation of the subjective experience of fear? In this human neuroimaging study, we presented participants ('fear' group: N = 30; 'no fear' group: N = 30) with 2700 images of animals that could trigger subjective fear or not as a function of the individual's idiosyncratic 'fear profiles' (i.e. fear ratings of animals reported by a given participant). We provide evidence that the ventral visual stream may represent affectively neutral visual features that are statistically associated with fear ratings of participants, without representing the subjective experience of fear itself. More specifically, we show that patterns of haemodynamic activity predictive of a specific 'fear profile' can be observed in the ventral visual stream whether a participant reports being afraid of the stimuli or not. Further, we found that the multivariate information synchronization between ventral visual areas and prefrontal regions distinguished participants who reported being subjectively afraid of the stimuli from those who did not. Together, these findings support the view that the subjective experience of fear may depend on the relevant visual information triggering implicit metacognitive mechanisms in the prefrontal cortex. This article is part of the theme issue 'Sensing and feeling: an integrative approach to sensory processing and emotional experience'.

Real-Time Passive Acoustic Mapping Using Sparse Matrix Multiplication

Passive acoustic mapping enables the spatiotemporal monitoring of cavitation with circulating microbubbles during focused ultrasound (FUS)-mediated blood-brain barrier opening. However, the computational load for processing large data sets of cavitation maps or more complex algorithms limit the visualization in real-time for treatment monitoring and adjustment. In this study, we implemented a graphical processing unit (GPU)-accelerated sparse matrix-based beamforming and time exposure acoustics in a neuronavigation-guided ultrasound system for real-time spatiotemporal monitoring of cavitation. The system performance was tested in silico through benchmarking, in vitro using nonhuman primate (NHP) and human skull specimens, and demonstrated in vivo in NHPs. We demonstrated the stability of the cavitation map for integration times longer than 62.5 [Formula: see text]. A compromise between real-time displaying and cavitation map quality obtained from beamformed RF data sets with a size of 2000 ×128 ×30 (axial [Formula: see text]) was achieved for an integration time of [Formula: see text], which required a computational time of 0.27 s (frame rate of 3.7 Hz) and could be displayed in real-time between pulses at PRF = 2 Hz. Our benchmarking tests show that the GPU sparse-matrix algorithm processed the RF data set at a computational rate of [Formula: see text]/pixel/sample, which enables adjusting the frame rate and the integration time as needed. The neuronavigation system with real-time implementation of cavitation mapping facilitated the localization of the cavitation activity and helped to identify distortions due to FUS phase aberration. The in vivo test of the method demonstrated the feasibility of GPU-accelerated sparse matrix computing in a close to a clinical condition, where focus distortions exemplify problems during treatment. These experimental conditions show the need for spatiotemporal monitoring of cavitation with real-time capability that enables the operator to correct or halt the sonication in case substantial aberrations are observed.

Abnormal Connectivity Within Anterior Cortical Midline Structures in Bipolar Disorder: Evidence From Integrated MRI and Functional MRI

Background: Aberrant functional and structural connectivity across multiple brain networks have been reported in bipolar disorder (BD). However, most previous studies consider the functional and structural alterations in isolation regardless of their possible integrative relationship. The present study aimed to identify the brain connectivity alterations in BD by capturing the latent nexus in multimodal neuroimaging data. Methods: Structural and resting-state images were acquired from 83 patients with BD and 94 healthy controls (HCs). Combined with univariate methods conducted to detect the dysconnectivity in BD, we also employed a semi-multimodal fusion framework fully utilizing the interrelationship between the two modalities to distinguish patients from HCs. Moreover, one-way analysis of variance was adopted to explore whether the detected dysconnectivity has differences across stages of patients with BD. Results: The semi-multimodal fusion framework distinguished patients from HCs with 81.47% accuracy, 85.42% specificity, and 74.75% sensitivity. The connection between the anterior cingulate cortex (ACC) and superior medial prefrontal cortex (sMPFC) contributed the most to BD diagnosis. Consistently, the univariate method also identified that this ACC-sMPFC functional connection significantly decreased in BD patients compared to HCs, and the significant order of the dysconnectivity is: depressive episode < HCs and remission episode < HCs. Conclusions: Our findings, by adopting univariate and multivariate analysis methods, shed light on the decoupling within the anterior midline brain in the pathophysiology of BD, and this decoupling may serve as a trait marker for this disease.

Identification of Brain Functional Networks Using a Model-Based Approach

Functional MRI (fMRI) is a valuable brain imaging technique. A significant problem, when analyzing fMRI time series, is the finding of functional brain networks when the brain is at rest, i.e. no external stimulus is applied to the subject. In this work, we present a probabilistic method to estimate the Resting State Networks (RSNs) using a model-based approach. More specifically, RSNs are assumed to be the result of a clustering procedure. In order to perform the clustering, a mixture of regression models are used. Furthermore, special care has been given in order to incorporate important functionalities, such as spatial and embedded sparsity enforcing properties, through the use of informative priors over the model parameters. Another interesting feature of the proposed scheme is the flexibility to handle all the brain time series at once, allowing more robust solutions. We provide comparative experimental results, using an artificial fMRI dataset and two real resting state fMRI datasets, that empirically illustrate the efficiency of the proposed regression mixture model.