Structural Disconnection of the Tool Use Network after Left Hemisphere Stroke Predicts Limb Apraxia Severity.

Producing a tool use gesture is a complex process drawing upon the integration of stored knowledge of tools and their associated actions with sensory-motor mechanisms supporting the planning and control of hand and arm actions. Understanding how sensory-motor systems in parietal cortex interface with semantic representations of actions and objects in the temporal lobe remains a critical issue and is hypothesized to be a key determinant of the severity of limb apraxia, a deficit in producing skilled action after left hemisphere stroke. We used voxel-based and connectome-based lesion-symptom mapping with data from 57 left hemisphere stroke participants to assess the lesion sites and structural disconnection patterns associated with poor tool use gesturing. We found that structural disconnection among the left inferior parietal lobule, lateral and ventral temporal cortices, and middle and superior frontal gyri predicted the severity of tool use gesturing performance. Control analyses demonstrated that reductions in right-hand grip strength were associated with motor system disconnection, largely bypassing regions supporting tool use gesturing. Our findings provide evidence that limb apraxia may arise, in part, from a disconnection between conceptual representations in the temporal lobe and mechanisms enabling skilled action production in the inferior parietal lobule.

Finding maximally disconnected subnetworks with shortest path tractography.

Connectome-based lesion symptom mapping (CLSM) can be used to relate disruptions of brain network connectivity with clinical measures. We present a novel method that extends current CLSM approaches by introducing a fast reliable and accurate way for computing disconnectomes, i.e. identifying damaged or lesioned connections. We introduce a new algorithm that finds the maximally disconnected subgraph containing regions and region pairs with the greatest shared connectivity loss. After normalizing a stroke patient's segmented MRI lesion into template space, probability weighted structural connectivity matrices are constructed from shortest paths found in white matter voxel graphs of 210 subjects from the Human Connectome Project. Percent connectivity loss matrices are constructed by measuring the proportion of shortest-path probability weighted connections that are lost because of an intersection with the patient's lesion. Maximally disconnected subgraphs of the overall connectivity loss matrix are then derived using a computationally fast greedy algorithm that closely approximates the exact solution. We illustrate the approach in eleven stroke patients with hemiparesis by identifying expected disconnections of the corticospinal tract (CST) with cortical sensorimotor regions. Major disconnections are found in the thalamus, basal ganglia, and inferior parietal cortex. Moreover, the size of the maximally disconnected subgraph quantifies the extent of cortical disconnection and strongly correlates with multiple clinical measures. The methods provide a fast, reliable approach for both visualizing and quantifying the disconnected portion of a patient's structural connectome based on their routine clinical MRI, without reliance on concomitant diffusion weighted imaging. The method can be extended to large databases of stroke patients, multiple sclerosis or other diseases causing focal white matter injuries helping to better characterize clinically relevant white matter lesions and to identify biomarkers for the recovery potential of individual patients.

Effect of different spatial normalization approaches on tractography and structural brain networks.

To facilitate the comparison of white matter morphologic connectivity across target populations, it is invaluable to map the data to a standardized neuroanatomical space. Here, we evaluated direct streamline normalization (DSN), where the warping was applied directly to the streamlines, with two publically available approaches that spatially normalize the diffusion data and then reconstruct the streamlines. Prior work has shown that streamlines generated after normalization from reoriented diffusion data do not reliably match the streamlines generated in native space. To test the impact of these different normalization methods on quantitative tractography measures, we compared the reproducibility of the resulting normalized connectivity matrices and network metrics with those originally obtained in native space. The two methods that reconstruct streamlines after normalization led to significant differences in network metrics with large to huge standardized effect sizes, reflecting a dramatic alteration of the same subject's native connectivity. In contrast, after normalizing with DSN we found no significant difference in network metrics compared with native space with only very small-to-small standardized effect sizes. DSN readily outperformed the other methods at preserving native space connectivity and introduced novel opportunities to define connectome networks without relying on gray matter parcellations.

Sensitivity analysis of human brain structural network construction.

Network neuroscience leverages diffusion-weighted magnetic resonance imaging and tractography to quantify structural connectivity of the human brain. However, scientists and practitioners lack a clear understanding of the effects of varying tractography parameters on the constructed structural networks. With diffusion images from the Human Connectome Project (HCP), we characterize how structural networks are impacted by the spatial resolution of brain atlases, total number of tractography streamlines, and grey matter dilation with various graph metrics. We demonstrate how injudicious combinations of highly refined brain parcellations and low numbers of streamlines may inadvertently lead to disconnected network models with isolated nodes. Furthermore, we provide solutions to significantly reduce the likelihood of generating disconnected networks. In addition, for different tractography parameters, we investigate the distributions of values taken by various graph metrics across the population of HCP subjects. Analyzing the ranks of individual subjects within the graph metric distributions, we find that the ranks of individuals are affected differently by atlas scale changes. Our work serves as a guideline for researchers to optimize the selection of tractography parameters and illustrates how biological characteristics of the brain derived in network neuroscience studies can be affected by the choice of atlas parcellation schemes.

Analytic tractography: A closed-form solution for estimating local white matter connectivity with diffusion MRI.

White matter structures composed of myelinated axons in the living human brain are primarily studied by diffusion-weighted MRI (dMRI). These long-range projections are typically characterized in a two-step process: dMRI signal is used to estimate the orientation of axon segments within each voxel, then these local orientations are linked together to estimate the spatial extent of putative white matter bundles. Tractography, the process of tracing bundles across voxels, either requires computationally expensive (probabilistic) simulations to model uncertainty in fiber orientation or ignores it completely (deterministic). Furthermore, simulation necessarily generates a finite number of trajectories, introducing "simulation error" to trajectory estimates. Here we introduce a method to analytically (via a closed-form solution) take an orientation distribution function (ODF) from each voxel and calculate the probabilities that a trajectory projects from a voxel into each directly adjacent voxels. We validate our method by demonstrating experimentally that probabilistic simulations converge to our analytically computed transition probabilities at the voxel level as the number of simulated seeds increases. We then show that our method accurately calculates the ground-truth transition probabilities from a publicly available phantom dataset. As a demonstration, we incorporate our analytic method for voxel transition probabilities into the Voxel Graph framework, creating a quantitative framework for assessing white matter structure, which we call "analytic tractography". The long-range connectivity problem is reduced to finding paths in a graph whose adjacency structure reflects voxel-to-voxel analytic transition probabilities. We demonstrate that this approach performs comparably to the current most widely-used probabilistic and deterministic approaches at a fraction of the computational cost. We also demonstrate that analytic tractography works on multiple diffusion sampling schemes, reconstruction method or parameters used to define paths. Open source software compatible with popular dMRI reconstruction software is provided.

Measurement of population receptive fields in human early visual cortex using back-projection tomography.

Properties of human visual population receptive fields (pRFs) are currently estimated by performing measurements of visual stimulation using functional magnetic resonance imaging (fMRI), and then fitting the results using a predefined model shape for the pRF. Various models exist and different models may be appropriate under different circumstances, but the validity of the models has never been verified, suggesting the need for a model-free approach. Here, we demonstrate that pRFs can be directly reconstructed using a back-projection-tomography approach that requires no a priori model. The back-projection method involves sweeping thin contrast-defined bars across the visual field whose orientation and direction is rotated through 0°-180° in discrete increments. The measured fMRI time series within a cortical location can be approximated as a projection of the pRF along the long axis of the bar. The signals produced by a set of bar sweeps encircling the visual field form a sinogram. pRFs were reconstructed from these sinograms with a novel scheme that corrects for the blur introduced by the hemodynamic response and the stimulus-bar width. pRF positions agree well with the conventional model-based approach. Notably, a subset of the reconstructed pRFs shows significant asymmetry for both their excitatory and suppressive regions. Reconstructing pRFs using the tomographic approach is a fast, reliable, and accurate way to noninvasively estimate human pRF parameters and visual-field maps without the need for any a priori shape assumption.

High-resolution functional magnetic resonance imaging methods for human midbrain.

Functional MRI (fMRI) is a widely used tool for non-invasively measuring correlates of human brain activity. However, its use has mostly been focused upon measuring activity on the surface of cerebral cortex rather than in subcortical regions such as midbrain and brainstem. Subcortical fMRI must overcome two challenges: spatial resolution and physiological noise. Here we describe an optimized set of techniques developed to perform high-resolution fMRI in human SC, a structure on the dorsal surface of the midbrain; the methods can also be used to image other brainstem and subcortical structures. High-resolution (1.2 mm voxels) fMRI of the SC requires a non-conventional approach. The desired spatial sampling is obtained using a multi-shot (interleaved) spiral acquisition (1). Since, T(2)* of SC tissue is longer than in cortex, a correspondingly longer echo time (T(E) ~ 40 msec) is used to maximize functional contrast. To cover the full extent of the SC, 8-10 slices are obtained. For each session a structural anatomy with the same slice prescription as the fMRI is also obtained, which is used to align the functional data to a high-resolution reference volume. In a separate session, for each subject, we create a high-resolution (0.7 mm sampling) reference volume using a T(1)-weighted sequence that gives good tissue contrast. In the reference volume, the midbrain region is segmented using the ITK-SNAP software application (2). This segmentation is used to create a 3D surface representation of the midbrain that is both smooth and accurate (3). The surface vertices and normals are used to create a map of depth from the midbrain surface within the tissue (4). Functional data is transformed into the coordinate system of the segmented reference volume. Depth associations of the voxels enable the averaging of fMRI time series data within specified depth ranges to improve signal quality. Data is rendered on the 3D surface for visualization. In our lab we use this technique for measuring topographic maps of visual stimulation and covert and overt visual attention within the SC (1). As an example, we demonstrate the topographic representation of polar angle to visual stimulation in SC.

Surface-based analysis methods for high-resolution functional magnetic resonance imaging.

Functional magnetic resonance imaging (fMRI) has become a popular technique for studies of human brain activity. Typically, fMRI is performed with >3-mm sampling, so that the imaging data can be regarded as two-dimensional samples that average through the 1.5-4-mm thickness of cerebral cortex. The increasing use of higher spatial resolutions, <1.5-mm sampling, complicates the analysis of fMRI, as one must now consider activity variations within the depth of the brain tissue. We present a set of surface-based methods to exploit the use of high-resolution fMRI for depth analysis. These methods utilize white-matter segmentations coupled with deformable-surface algorithms to create a smooth surface representation at the gray-white interface and pial membrane. These surfaces provide vertex positions and normals for depth calculations, enabling averaging schemes that can increase contrast-to-noise ratio, as well as permitting the direct analysis of depth profiles of functional activity in the human brain.

Topography of covert visual attention in human superior colliculus.

Experiments were performed to examine the topography of covert visual attention signals in human superior colliculus (SC), both across its surface and in its depth. We measured the retinotopic organization of SC to direct visual stimulation using a 90° wedge of moving dots that slowly rotated around fixation. Subjects (n = 5) were cued to perform a difficult speed-discrimination task in the rotating region. To measure the retinotopy of covert attention, we used a full-field array of similarly moving dots. Subjects were cued to perform the same speed-discrimination task within a 90° wedge-shaped region, and only the cue rotated around fixation. High-resolution functional magnetic resonance imaging (fMRI, 1.2 mm voxels) data were acquired throughout SC. These data were then aligned to a high-resolution T1-weighted reference volume. The SC was segmented in this volume so that the surface of the SC could be computationally modeled and to permit calculation of a depth map for laminar analysis. Retinotopic maps were obtained for both direct visual stimulation and covert attention. These maps showed a similar spatial distribution to visual stimulation maps observed in rhesus macaque and were in registration with each other. Within the depth of SC, both visual attention and stimulation produced activity primarily in the superficial and intermediate layers, but stimulation activity extended significantly more deeply than attention.