Functional network properties derived from wide-field calcium imaging differ with wakefulness and across cell type.

To improve 'bench-to-bedside' translation, it is integral that knowledge flows bidirectionally-from animal models to humans, and vice versa. This requires common analytical frameworks, as well as open software and data sharing practices. We share a new pipeline (and test dataset) for the preprocessing of wide-field optical fluorescence imaging data-an emerging mode applicable in animal models-as well as results from a functional connectivity and graph theory analysis inspired by recent work in the human neuroimaging field. The approach is demonstrated using a dataset comprised of two test-cases: (1) data from animals imaged during awake and anesthetized conditions with excitatory neurons labeled, and (2) data from awake animals with different genetically encoded fluorescent labels that target either excitatory neurons or inhibitory interneuron subtypes. Both seed-based connectivity and graph theory measures (global efficiency, transitivity, modularity, and characteristic path-length) are shown to be useful in quantifying differences between wakefulness states and cell populations. Wakefulness state and cell type show widespread effects on canonical network connectivity with variable frequency band dependence. Differences between excitatory neurons and inhibitory interneurons are observed, with somatostatin expressing inhibitory interneurons emerging as notably dissimilar from parvalbumin and vasoactive polypeptide expressing cells. In sum, we demonstrate that our pipeline can be used to examine brain state and cell-type differences in mesoscale imaging data, aiding translational neuroscience efforts. In line with open science practices, we freely release the pipeline and data to encourage other efforts in the community.

CineCT platform for in vivo and ex vivo measurement of 3D high resolution Lagrangian strains in the left ventricle following myocardial infarction and intramyocardial delivery of theranostic hydrogel.

Myocardial infarction (MI) produces acute changes in strain and stiffness within the infarct that can affect remote areas of the left ventricle (LV) and drive pathological remodeling. We hypothesized that intramyocardial delivery of a hydrogel within the MI region would lower wall stress and reduce adverse remodeling in Yorkshire pigs (n = 5). 99mTc-Tetrofosmin SPECT imaging defined the location and geometry of induced MI and border regions in pigs, and in vivo and ex vivo contrast cine computed tomography (cineCT) quantified deformations of the LV myocardium. Serial in vivo cineCT imaging provided data in hearts from control pigs (n = 3) and data from pigs (n = 5) under baseline conditions before MI induction, post-MI day 3, post-MI day 7, and one hour after intramyocardial delivery of a hyaluronic acid (HA)-based hydrogel with shear-thinning and self-healing properties to the central infarct area. Isolated, excised hearts underwent similar cineCT imaging using an ex vivo perfused heart preparation with cyclic LV pressurization. Deformations were evaluated using nonlinear image registration of cineCT volumes between end-diastole (ED) and end-systole (ES), and 3D Lagrangian strains were calculated from the displacement gradients. Post-MI day 3, radial, circumferential, maximum principal, and shear strains were reduced within the MI region (p < 0.04) but were unchanged in normal regions (p > 0.6), and LV end diastolic volume (LV EDV) increased (p = 0.004), while ejection fraction (EF) and stroke volume (SV) decreased (p < 0.02). Post-MI day 7, radial strains in MI border zones increased (p = 0.04) and dilation of LV EDV continued (p = 0.052). There was a significant negative linear correlation between regional radial and maximum principal/shear strains and percent infarcted tissue in all hearts (R2 > 0.47, p < 0.004), indicating that cineCT strain measures could predict MI location and degree of injury. Post-hydrogel day 7 post-MI, LV EDV was significantly reduced (p = 0.009), EF increased (p = 0.048), and radial (p = 0.021), maximum principal (p = 0.051), and shear strain (p = 0.047) increased within regions bordering the infarct. A smaller strain improvement within the infarct and normal regions was also noted on average along with an improvement in SV in 4 out of 5 hearts. CineCT provides a reliable method to assess regional changes in strains post-MI and the therapeutic effects of intramyocardial hydrogel delivery.

Application of BOLD Magnetic Resonance Imaging for Evaluating Regional Volumetric Foot Tissue Oxygenation: A Feasibility Study in Healthy Volunteers.

OBJECTIVE/BACKGROUND: To evaluate the feasibility and repeatability of applying blood oxygen level-dependent (BOLD) magnetic resonance imaging (MRI) in the feet to quantify regional dynamic changes in tissue oxygenation during proximal cuff occlusion and reactive hyperemia. METHODS: Ten healthy male subjects underwent BOLD and T1-weighted imaging of the feet on two separate occasions, using a 3-T scanner. Dynamic changes in BOLD signal intensity were assessed before and during proximal cuff occlusion of the thigh and during reactive hyperemia, and BOLD time course data were evaluated for the time-to-half ischemic minimum, minimum ischemic value, peak hyperemic value, time-to-peak hyperemia, time-to-half peak hyperemia, and end value. T1-weighted images were used for segmentation of volumes of interest (VOI) in anatomical regions of the foot (heel, toes, dorsal foot, medial and lateral plantar foot). Repeatability of vascular responses was assessed for each foot VOI using semiautomated image registration and quantification of serial BOLD images. RESULTS: The heel VOI demonstrated a significantly higher peak hyperemic response, expressed as percent change from baseline BOLD signal intensity, compared with all other VOIs of the foot (heel, 7.4 ± 1.2%; toes, 5.6 ± 0.8%; dorsal foot, 5.7 ± 1.6%; medial plantar, 5.6 ± 1.7%; lateral plantar, 5.6 ± 1.5% [p < .05]). Additionally, the lateral plantar VOI had a significantly lower terminal signal intensity value (i.e., end value) when compared with all foot VOIs (p < .05). BOLD MRI was repeatable between visits in all foot VOIs, with no significant differences between study visits for any of the evaluated functional indices. CONCLUSION: BOLD MRI offers a repeatable technique for volumetric assessment of regional foot tissue oxygenation. Future application of BOLD imaging in the feet of patients with peripheral vascular disease may permit serial evaluation of regional tissue oxygenation and allow for improved assessment of therapeutic interventions targeting specific sites of the foot.

Groupwise whole-brain parcellation from resting-state fMRI data for network node identification.

In this paper, we present a groupwise graph-theory-based parcellation approach to define nodes for network analysis. The application of network-theory-based analysis to extend the utility of functional MRI has recently received increased attention. Such analyses require first and foremost a reasonable definition of a set of nodes as input to the network analysis. To date many applications have used existing atlases based on cytoarchitecture, task-based fMRI activations, or anatomic delineations. A potential pitfall in using such atlases is that the mean timecourse of a node may not represent any of the constituent timecourses if different functional areas are included within a single node. The proposed approach involves a groupwise optimization that ensures functional homogeneity within each subunit and that these definitions are consistent at the group level. Parcellation reproducibility of each subunit is computed across multiple groups of healthy volunteers and is demonstrated to be high. Issues related to the selection of appropriate number of nodes in the brain are considered. Within typical parameters of fMRI resolution, parcellation results are shown for a total of 100, 200, and 300 subunits. Such parcellations may ultimately serve as a functional atlas for fMRI and as such three atlases at the 100-, 200- and 300-parcellation levels derived from 79 healthy normal volunteers are made freely available online along with tools to interface this atlas with SPM, BioImage Suite and other analysis packages.

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.

Graph-theory based parcellation of functional subunits in the brain from resting-state fMRI data.

Resting-state fMRI provides a method to examine the functional network of the brain under spontaneous fluctuations. A number of studies have proposed using resting-state BOLD data to parcellate the brain into functional subunits. In this work, we present two state-of-the-art graph-based partitioning approaches, and investigate their application to the problem of brain network segmentation using resting-state fMRI. The two approaches, the normalized cut (Ncut) and the modularity detection algorithm, are also compared to the Gaussian mixture model (GMM) approach. We show that the Ncut approach performs consistently better than the modularity detection approach, and it also outperforms the GMM approach for in vivo fMRI data. Resting-state fMRI data were acquired from 43 healthy subjects, and the Ncut algorithm was used to parcellate several different cortical regions of interest. The group-wise delineation of the functional subunits based on resting-state fMRI was highly consistent with the parcellation results from two task-based fMRI studies (one with 18 subjects and the other with 20 subjects). The findings suggest that whole-brain parcellation of the cortex using resting-state fMRI is feasible, and that the Ncut algorithm provides the appropriate technique for this task.

LIGHT-SENSITIVE VISUALIZATION OF MULTIMODAL DATA FOR NEUROSURGICAL APPLICATIONS.

We present a technique for enhancing multimodal visualizations for image-guided neurosurgery in the presence of adverse lighting conditions. In the surgical environment, images used for real time navigation are displayed in suboptimal conditions due to the varying lighting conditions. Our approach actively monitors the incoming light on the display and appropriately enhances the visualization based on the change in light. Based on the results of a user study to evaluate our approach, we found that our enhanced visualization techniques were mostly preferred over regular visualizations.

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.

Constrained non-rigid registration for use in image-guided adaptive radiotherapy.

A constrained non-rigid registration (CNRR) algorithm for use in prostate image-guided adaptive radiotherapy is presented in a coherent mathematical framework. The registration algorithm is based on a global rigid transformation combined with a series of local injective non-rigid multi-resolution cubic B-spline Free Form Deformation (FFD) transformations. The control points of the FFD are used to non-rigidly constrain the transformation to the prostate, rectum, and bladder. As well, the control points are used to rigidly constrain the transformation to the estimated position of the pelvis, left femur, and right femur. The algorithm was tested with both 3D conformal radiotherapy (3DCRT) and intensity-modulated radiotherapy (IMRT) dose plan data sets. The 3DCRT dose plan set consisted of 10 fan-beam CT (FBCT) treatment-day images acquired from four different patients. The IMRT dose plan set consisted of 32 cone-beam CT (CBCT) treatment-day images acquired from 4 different patients. The CNRR was tested with different combinations of anatomical constraints and each test significantly outperformed both rigid and non-rigid registration at aligning constrained bones and critical organs. The CNRR results were used to adapt the dose plans to account for patient positioning errors as well as inter-day bone motion and intrinsic organ deformation. Each adapted dose plan improved performance by lowering radiation distribution to the rectum and bladder while increasing or maintaining radiation distribution to the prostate.

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.

Improving the Reliability of Shape Comparison by Perturbation.

Shape comparison is a key scenario in morphometric study, where registration is often involved and found to be unreliable: different registrations can lead to different shape differences. This paper proposes a generic scheme applicable to most registration methods, to reduce this unreliability. It perturbs the registration processes by feeding them with resampled shape groups, and then aggregates the results to yield the final result. This scheme can be simplified for pair-wise registration methods to reduce the computation. Experiments are conducted on both synthetic and biomedical shapes using different registration methods, which demonstrate its effectiveness.

A constrained non-rigid registration algorithm for use in prostate image-guided radiotherapy.

A constrained non-rigid registration (CNRR) algorithm for use in updating prostate external beam image-guided radiotherapy treatment plans is presented in this paper. The developed algorithm is based on a multi-resolution cubic B-spline FFD transformation and has been tested and verified using 3D CT images from 10 sets of real patient data acquired from 4 different patients on different treatment days. The registration can be constrained to any combination of the prostate, rectum, bladder, pelvis, left femur, and right femur. The CNRR was tested with 5 different combinations of constraints and each test significantly outperformed both rigid and non-rigid registration at aligning constrained bones and critical organs. The CNRR was then used to update the treatment plans to account for articulated, rigid bone motion and non-rigid organ deformation. Each updated treatment plan outperformed the original treatment plan by increasing radiation dosage to the prostate and lowering radiation dosage to the rectum and bladder.

Initial evaluation of the use of USPIO cell labeling and noninvasive MR monitoring of human tissue-engineered vascular grafts in vivo.

This pilot study examines noninvasive MR monitoring of tissue-engineered vascular grafts (TEVGs) in vivo using cells labeled with iron oxide nanoparticles. Human aortic smooth muscle cells (hASMCs) were labeled with ultrasmall superparamagnetic iron oxide (USPIO) nanoparticles. The labeled hASMCs, along with human aortic endothelial cells, were incorporated into eight TEVGs and were then surgically implanted as aortic interposition grafts in a C.B-17 SCID/bg mouse host. USPIO-labeled hASMCs persisted in the grafts throughout a 3 wk observation period and allowed noninvasive MR imaging of the human TEVGs for real-time, serial monitoring of hASMC retention. This study demonstrates the feasibility of applying noninvasive imaging techniques for evaluation of in vivo TEVG performance.

TRACKING ORGAN OVERLAP FOR A CONSTRAINED NON-RIGID REGISTRATION ALGORITHM.

This paper tracks organ (prostate, rectum, bladder) overlap in a constrained non-rigid registration (NRR) algorithm to register computed tomographic (CT) images used in external beam prostate radiotherapy. The local motion of the organs is described by a hierarchical multi-resolution FFD based on cubic B-splines. Registration is achieved by minimizing a cost function which is a combination of three functions representing the overlap of the critical organs, image similarity and smoothness of the transformation. The constrained NRR algorithm generated better registration results when compared to an unconstrained NRR algorithm.

A CONSTRAINED NON-RIGID REGISTRATION ALGORITHM FOR APPLICATION IN PROSTATE RADIOTHERAPY.

This paper presents a novel free-form deformation registration algorithm with non-rigid constraints to capture the transformation between the planning day and treatment day CT images used for external beam radiotherapy for prostate cancer. The algorithm is constrained to the predetermined motion of a segmented organ, which is described by an injective free-form deformation (FFD) based on B-splines. The end goal is for the injective transformation to be used to update the radiotherapy plan to take into account bone and soft tissue deformation. The results of the algorithm have been compared to those achieved using rigid and fully non-rigid registration. The results clearly indicate that the constrained non-rigid registration algorithm presented in this paper performed much better at capturing the motion of the constrained organ, the bladder in this case, than the rigid or fully non-rigid registration algorithms.

LEVEL SET BASED CLUSTERING FOR ANALYSIS OF FUNCTIONAL MRI DATA.

We present a level set based clustering technique to detect activation regions from functional brain images using contextual information. Earlier similar approaches have been primarily concerned with local spatial context. Our approach relies on the idea that voxels within a functional region have similar temporal behavior. Using a level set formulation, a two-dimensional curve is evolved with a speed proportional to a similarity measure between the fMRI signals of voxels lying on the curve and their neighbors in the direction of propagation. The correlation coefficient is used to quantify similarity in time series of adjacent voxels. Simulation results from synthetic images demonstrate that using spatio-temporal contextual information provides better segmentation than a context-free, voxel-wise technique. Results from a real fMRI experiment using auditory stimulation are also presented.

Spatial resolution, signal-to-noise ratio, and smoothing in multi-subject functional MRI studies.

Functional MRI is aimed at localizing cortical activity to understand the role of specific cortical regions, providing insight into the neurophysiological underpinnings of brain function. Scientists developing fMRI methodology seek to improve detection of subtle activations and to spatially localize these activations more precisely. Except for applications in the clinical environment, such as functional mapping in patients prior to neurosurgical intervention, most basic neuroscience studies involve group level random-effects analyses. Prior to grouping data, the data from each individual are typically smoothed. A wide range of motivations for smoothing have been given including to match the spatial scale of hemodynamic responses, to normalize the error distribution (by the Central Limit Theorem) to improve the validity of inferences based on parametric tests, and, in the context of inter-subject averaging smoothing has been shown necessary to project the data down to a scale where homologies in functional anatomy are expressed across subjects. This work demonstrates that, for single-subject studies, if smoothing is to be employed, the data should be acquired at lower resolutions to maximize SNR. The benefits of a low-resolution acquisition are limited by partial volume effects and by the weak impact of resolution-dependent noise on the overall group level statistics. Given that inter-subject noise dominates across a range of tasks, improvements in within-subject noise, through changes in acquisition strategy or even moving to higher field strength, may do little to improve group statistics. Such improvements however may greatly impact single-subject studies such as those used in neurosurgical planning.