Characterizing Attention with Predictive Network Models.

Recent work shows that models based on functional connectivity in large-scale brain networks can predict individuals' attentional abilities. While being some of the first generalizable neuromarkers of cognitive function, these models also inform our basic understanding of attention, providing empirical evidence that: (i) attention is a network property of brain computation; (ii) the functional architecture that underlies attention can be measured while people are not engaged in any explicit task; and (iii) this architecture supports a general attentional ability that is common to several laboratory-based tasks and is impaired in attention deficit hyperactivity disorder (ADHD). Looking ahead, connectivity-based predictive models of attention and other cognitive abilities and behaviors may potentially improve the assessment, diagnosis, and treatment of clinical dysfunction.

Optimizing real time fMRI neurofeedback for therapeutic discovery and development.

While reducing the burden of brain disorders remains a top priority of organizations like the World Health Organization and National Institutes of Health, the development of novel, safe and effective treatments for brain disorders has been slow. In this paper, we describe the state of the science for an emerging technology, real time functional magnetic resonance imaging (rtfMRI) neurofeedback, in clinical neurotherapeutics. We review the scientific potential of rtfMRI and outline research strategies to optimize the development and application of rtfMRI neurofeedback as a next generation therapeutic tool. We propose that rtfMRI can be used to address a broad range of clinical problems by improving our understanding of brain-behavior relationships in order to develop more specific and effective interventions for individuals with brain disorders. We focus on the use of rtfMRI neurofeedback as a clinical neurotherapeutic tool to drive plasticity in brain function, cognition, and behavior. Our overall goal is for rtfMRI to advance personalized assessment and intervention approaches to enhance resilience and reduce morbidity by correcting maladaptive patterns of brain function in those with brain disorders.

Orbitofrontal cortex neurofeedback produces lasting changes in contamination anxiety and resting-state connectivity.

Anxiety is a core human emotion but can become pathologically dysregulated. We used functional magnetic resonance imaging (fMRI) neurofeedback (NF) to noninvasively alter patterns of brain connectivity, as measured by resting-state fMRI, and to reduce contamination anxiety. Activity of a region of the orbitofrontal cortex associated with contamination anxiety was measured in real time and provided to subjects with significant but subclinical anxiety as a NF signal, permitting them to learn to modulate the target brain region. NF altered network connectivity of brain regions involved in anxiety regulation: subjects exhibited reduced resting-state connectivity in limbic circuitry and increased connectivity in the dorsolateral prefrontal cortex. NF has been shown to alter brain connectivity in other contexts, but it has been unclear whether these changes persist; critically, we observed changes in connectivity several days after the completion of NF training, demonstrating that such training can lead to lasting modifications of brain functional architecture. Training also increased subjects' control over contamination anxiety several days after the completion of NF training. Changes in resting-state connectivity in the target orbitofrontal region correlated with these improvements in anxiety. Matched subjects undergoing a sham feedback control task showed neither a reorganization of resting-state functional connectivity nor an improvement in anxiety. These data suggest that NF can enable enhanced control over anxiety by persistently reorganizing relevant brain networks and thus support the potential of NF as a clinically useful therapy.

The intrinsic connectivity distribution: a novel contrast measure reflecting voxel level functional connectivity.

Resting-state fMRI (rs-fMRI) holds promise as a clinical tool to characterize and monitor the phenotype of different neurological and psychiatric disorders. The most common analysis approach requires the definition of one or more regions-of-interest (ROIs). However this need for a priori ROI information makes rs-fMRI inadequate to survey functional connectivity differences associated with a range of neurological disorders where the ROI information may not be available. A second problem encountered in fMRI measures of connectivity is the need for an arbitrary correlation threshold to determine whether or not two areas are connected. This is problematic because in many cases the differences in tissue connectivity between disease groups and/or control subjects are threshold dependent. In this work we propose a novel voxel-based contrast mechanism for rs-fMRI, the intrinsic connectivity distribution (ICD), that neither requires a priori information to define a ROI, nor an arbitrary threshold to define a connection. We show the sensitivity of previous methods to the choice of connection thresholds and evaluate ICD using a survey study comparing young adults born prematurely to healthy term control subjects. Functional connectivity differences were found in hypothesized language processing areas in the left temporal-parietal areas. In addition, significant clinically-relevant differences were found between preterm and term control subjects, highlighting the importance of whole brain surveys independent of a priori information.

AUGMENTED INLINE-BASED NAVIGATION FOR STEREOTACTIC IMAGE GUIDED NEUROSURGERY.

Image-guided neurosurgery requires navigation in 3D using a computer-assisted surgery system that tracks surgical tools in realtime and displays their positions with respect to the preoperatively acquired images (e.g. CT, MRI, fMRI etc.) A key problem in image guided procedures is the need to navigate to specific locations highlighted in the images, such as image-derived functional areas, that have no obvious corresponding anatomical landmarks - we refer to such locations as virtual landmarks. To address these issues, we contribute a novel interactive visualization technique to provide improved feedback to surgeons - Augmented inline visualization. Based on the results of an expert evaluation, we found neurosurgeons to be 30% more accurate when using our augmented inline representation.

WHOLE BODY NONRIGID CT-PET REGISTRATION USING WEIGHTED DEMONS.

We present a new registration method for whole-body rat computed tomography (CT) image and positron emission tomography (PET) images using a weighted demons algorithm. The CT and PET images are acquired in separate scanners at different times and the inherent differences in the imaging protocols produced significant nonrigid changes between the two acquisitions in addition to heterogeneous image characteristics. In this situation, we utilized both the transmission-PET and the emission-PET images in the deformable registration process emphasizing particular regions of the moving transmission-PET image using the emission-PET image. We validated our results with nine rat image sets using M-Hausdorff distance similarity measure. We demonstrate improved performance compared to standard methods such as Demons and normalized mutual information-based non-rigid FFD registration.

SERIAL NONRIGID VASCULAR REGISTRATION USING WEIGHTED NORMALIZED MUTUAL INFORMATION.

Vascular registration is a challenging problem with many potential applications. However, registering vessels accurately is difficult as they often occupy a small portion of the image and their relative motion/deformation is swamped by the displacements seen in large organs such as the heart and the liver. Our registration method uses a vessel detection algorithm to generate a vesselness image (probability of having a vessel at any given voxel) which is used to construct a weighting factor that is used to modify the intensity metric to give preference to vascular structures while maintaining the larger context. Therefore, our proposing method uses fully data-driven calculated weights and needs no prior knowledge for the weight calculation. We applied our method to the registration of serial MRI lamb images obtained from studies on tissue engineered vascular grafts and demonstrate encouraging performance as compared to non-weighted registration methods.

DTI abnormalities in anterior corpus callosum of rats with spike-wave epilepsy.

OBJECTIVE: Absence epilepsy is a common seizure disorder in children which can produce chronic psychosocial sequelae. Human patients and rat absence models show bilateral spike-wave discharges (SWD) in cortical regions. We employed diffusion tensor imaging (DTI) in rat absence models to detect abnormalities in white matter pathways connecting regions of seizure activity. METHODS: We studied Wistar albino Glaxo rats of Rijswijk (WAG/Rij), genetic absence epilepsy rats of Strasbourg (GAERS), and corresponding nonepileptic control strains. Ex vivo DTI was performed at 9.4 T with diffusion gradients applied in 16 orientations. We compared fractional anisotropy (FA), perpendicular (lambda(perpendicular)) and parallel (lambda(||)) diffusivity between groups using t-maps and region of interest (ROI) measurements. RESULTS: Adult epileptic WAG/Rij rats exhibited a localized decrease in FA in the anterior corpus callosum. This area was confirmed by tractography to interconnect somatosensory cortex regions most intensely involved in seizures. This FA decrease was not present in young WAG/Rij rats before onset of SWD. GAERS, which have more severe SWD than WAG/Rij, exhibited even more pronounced callosal FA decreases. Reduced FA in the epileptic animals originated from an increased lambda(perpendicular) with no significant changes in lambda(||). INTERPRETATION: Reduced FA with increased lambda(perpendicular) suggests that chronic seizures cause reduction in myelin or decreased axon fiber density in white matter pathways connecting regions of seizure activity. These DTI abnormalities may improve the understanding of chronic neurological difficulties in children suffering with absence epilepsy, and may also serve as a noninvasive biomarker for monitoring beneficial effects of treatment.