Vascular physiology drives functional brain networks

We present the first evidence for vascular regulation driving fMRI signals in specific functional brain networks. Using concurrent neuronal and vascular stimuli, we collected 30 BOLD fMRI datasets in 10 healthy individuals: a working memory task, flashing checkerboard stimulus, and CO2 inhalation challenge were delivered in concurrent but orthogonal paradigms. The resulting imaging data were averaged together and decomposed using independent component analysis, and three “neuronal networks” were identified as demonstrating maximum temporal correlation with the neuronal stimulus paradigms: Default Mode Network, Task Positive Network, and Visual Network. For each of these, we observed a second network component with high spatial overlap. Using dual regression in the original 30 datasets, we extracted the time-series associated with these network pairs and calculated the percent of variance explained by the neuronal or vascular stimuli using a normalized R2 parameter. In each pairing, one network was dominated by the appropriate neuronal stimulus, and the other was dominated by the vascular stimulus as represented by the end-tidal CO2 time-series recorded in each scan. We acquired a second dataset in 8 of the original participants, where no CO2 challenge was delivered and CO2 levels fluctuated naturally with breathing variations. Although splitting of functional networks was not robust in these data, performing dual regression with the network maps from the original analysis in this new dataset successfully replicated our observations. Thus, in addition to responding to localized metabolic changes, the brain’s vasculature may be regulated in a coordinated manner that mimics (and potentially supports) specific functional brain networks. Multi-modal imaging and advances in fMRI acquisition and analysis could facilitate further study of the dual nature of functional brain networks. It will be critical to understand network-specific vascular function, and the behavior of a coupled vascular-neural network, in future studies of brain pathology

fMRI activation detection with EEG priors

The purpose of brain mapping techniques is to advance the understanding of the relationship between structure and function in the human brain in so-called activation studies. In this work, an advanced statistical model for combining functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) recordings is developed to fuse complementary information about the location of neuronal activity. More precisely, a new Bayesian method is proposed for enhancing fMRI activation detection by the use of EEG-based spatial prior information in stimulus based experimental paradigms. I.e., we model and analyse stimulus influence by a spatial Bayesian variable selection scheme, and extend existing high-dimensional regression methods by incorporating prior information on binary selection indicators via a latent probit regression with either a spatially-varying or constant EEG effect. Spatially-varying effects are regularized by intrinsic Markov random field priors. Inference is based on a full Bayesian Markov Chain Monte Carlo (MCMC) approach. Whether the proposed algorithm is able to increase the sensitivity of mere fMRI models is examined in both a real-world application and a simulation study. We observed, that carefully selected EEG--prior information additionally increases sensitivity in activation regions that have been distorted by a low signal-to-noise ratio

Self-Ordered Search: A Novel fMRI Task to Study Working Memory in Children with Catastrophic Disease

Children treated for brain tumors are at increased risk for developing cognitive deficits. The self-ordered search (SOS) is a computerized neuropsychological test used to investigate working memory, a cognitive system whose function is integral to many high level cognitive processes. Functional-MRI (fMRI) provides important opportunities to characterize neural correlates of SOSperformance non-invasively. Implementation of the SOStask presents challenges in the unique environment of the MRI scanner. First, SOSrequires participants to select a single stimulus from a set. Second, SOSis a behaviorally driven task that entails variable event timing among participants which complicates group analysis of fMRI data. The work presented here consists of the implementation, validation and application of the SOSfor fMRI and associated analysis techniques. Eye-tracking with a MRI-safe response device was used as an interface for the fMRI task, allowing the participant to select an individual stimulus from a two-dimensional array. Performance information was used to generate individual subject design matrices for fMRI analysis, preserving important behaviorally measures (time to completion). Healthy volunteers and patients treated for childhood brain tumors performed the SOS task and N-back task, a commonly used working memory task for fMRI. The eye-tracking interface performed well after initial problems with equipment and calibration routine were solved. Activation patterns identified by general linear model (GLM) analysis were similar between SOS and N-back tasks and included dorsolateral prefrontal cortex, ventral prefrontal cortex, dorsal cingulate, bilateral premotor, and parietal areas. Independent component analysis identified task-correlated components that were consistent with the GLM. Increasing activation across the general network was associated with fewer errors during the N-back task. Differences in activation between patient group and healthy group were identified in the parietal and retrosplenial cortex. Analysis of the performance data suggests differences between the healthy and patient groups. Our novel eye-tracking interface provides a natural interface that controls for movement and motor planning associated with complex response devices. The SOS for fMRI provides a new tool that will allow us to investigate deficits of working memory in children treated for brain tumors

Voxel selection in fMRI data analysis based on sparse representation

Multivariate pattern analysis approaches toward detection of brain regions from fMRI data have been gaining attention recently. In this study, we introduce an iterative sparse-representation-based algorithm for detection of voxels in functional MRI (fMRI) data with task relevant information. In each iteration of the algorithm, a linear programming problem is solved and a sparse weight vector is subsequently obtained. The final weight vector is the mean of those obtained in all iterations. The characteristics of our algorithm are as follows: 1) the weight vector (output) is sparse; 2) the magnitude of each entry of the weight vector represents the significance of its corresponding variable or feature in a classification or regression problem; and 3) due to the convergence of this algorithm, a stable weight vector is obtained. To demonstrate the validity of our algorithm and illustrate its application, we apply the algorithm to the Pittsburgh Brain Activity Interpretation Competition 2007 functional fMRI dataset for selecting the voxels, which are the most relevant to the tasks of the subjects. Based on this dataset, the aforementioned characteristics of our algorithm are analyzed, and a comparison between our method with the univariate general-linear-model-based statistical parametric mapping is performed. Using our method, a combination of voxels are selected based on the principle of effective/sparse representation of a task. Data analysis results in this paper show that this combination of voxels is suitable for decoding tasks and demonstrate the effectiveness of our method

Functional MRI during hippocampal deep brain stimulation in the healthy rat brain

Deep Brain Stimulation (DBS) is a promising treatment for neurological and psychiatric disorders. The mechanism of action and the effects of electrical fields administered to the brain by means of an electrode remain to be elucidated. The effects of DBS have been investigated primarily by electrophysiological and neurochemical studies, which lack the ability to investigate DBS-related responses on a whole-brain scale. Visualization of whole-brain effects of DBS requires functional imaging techniques such as functional Magnetic Resonance Imaging (fMRI), which reflects changes in blood oxygen level dependent (BOLD) responses throughout the entire brain volume. In order to visualize BOLD responses induced by DBS, we have developed an MRI-compatible electrode and an acquisition protocol to perform DBS during BOLD fMRI. In this study, we investigate whether DBS during fMRI is valuable to study local and whole-brain effects of hippocampal DBS and to investigate the changes induced by different stimulation intensities. Seven rats were stereotactically implanted with a custom-made MRI-compatible DBS-electrode in the right hippocampus. High frequency Poisson distributed stimulation was applied using a block-design paradigm. Data were processed by means of Independent Component Analysis. Clusters were considered significant when p-values were <0.05 after correction for multiple comparisons. Our data indicate that real-time hippocampal DBS evokes a bilateral BOLD response in hippocampal and other mesolimbic structures, depending on the applied stimulation intensity. We conclude that simultaneous DBS and fMRI can be used to detect local and whole-brain responses to circuit activation with different stimulation intensities, making this technique potentially powerful for exploration of cerebral changes in response to DBS for both preclinical and clinical DBS

fMRI correlates of subjective reversals in ambiguous structure-from-motion

We used fMRI to examine the neural correlates of subjective reversals for bistable structure-from-motion. We compared transparent random-dot kinematograms depicting either a cylinder rotating in depth or two flat surfaces translating in opposite directions at apparently different depths. For both such stimuli, the motion of dots on the different apparent depth planes typically appears to reverse direction periodically on prolonged viewing. Yet for cylindrical but not flat stimuli, such subjective reversals also coincide with apparent reversal of 3D rotation direction. We hypothesized that the lateral occipital complex (region LOC), sensitive to 3D form, might show greater event-related activity for subjective reversals of cylindrical than flat stimuli; conversely, motion-sensitive hMT+/V5 should respond in common to subjective reversals for either type of stimuli, as both are perceived as changes in planar motion. We obtained an event-related measure of neural activity associated with subjective reversals after first factoring out block-related differences between cylindrical versus flat stimuli (and thereby the associated low-level blocked stimulus differences). In support of our hypothesis, only the cylindrical stimuli produced reversal-related activity in contralateral human LOC. In contrast, the hMT+/V5 complex was activated alike by subjective reversals for both cylindrical and flat stimuli. Intriguingly, V1 also showed (contralateral) specificity for rotational reversals, suggesting a possible feedback influence from LOC. These results reveal specific neural correlates for subjective switches of 3D rotation versus translation, as distinct from subjective reversals in general