Building a Precision Medicine Delivery Platform for Clinics: The University of California, San Francisco, BRIDGE Experience.

Despite an ever-expanding number of analytics with the potential to impact clinical care, the field currently lacks point-of-care technological tools that allow clinicians to efficiently select disease-relevant data about their patients, algorithmically derive clinical indices (eg, risk scores), and view these data in straightforward graphical formats to inform real-time clinical decisions. Thus far, solutions to this problem have relied on either bottom-up approaches that are limited to a single clinic or generic top-down approaches that do not address clinical users' specific setting-relevant or disease-relevant needs. As a road map for developing similar platforms, we describe our experience with building a custom but institution-wide platform that enables economies of time, cost, and expertise. The BRIDGE platform was designed to be modular and scalable and was customized to data types relevant to given clinical contexts within a major university medical center. The development process occurred by using a series of human-centered design phases with extensive, consistent stakeholder input. This institution-wide approach yielded a unified, carefully regulated, cross-specialty clinical research platform that can be launched during a patient's electronic health record encounter. The platform pulls clinical data from the electronic health record (Epic; Epic Systems) as well as other clinical and research sources in real time; analyzes the combined data to derive clinical indices; and displays them in simple, clinician-designed visual formats specific to each disorder and clinic. By integrating an application into the clinical workflow and allowing clinicians to access data sources that would otherwise be cumbersome to assemble, view, and manipulate, institution-wide platforms represent an alternative approach to achieving the vision of true personalized medicine.

Clinical, neuroimaging, and neuropathological characterization of a patient with Alzheimer's disease syndrome due to Pick's pathology.

The most common neurodegenerative syndrome associated with Pick's disease pathology (PiD) is behavioral variant frontotemporal dementia (bvFTD), which features profound social behavioral changes. Rarely, PiD can manifest as an Alzheimer's disease (AD)-type dementia with early memory impairment. We describe a patient with AD-type dementia and pure PiD pathology who showed slowly progressive memory impairment, early social changes, and paucity of motor symptoms. Atrophy and PiD were found mainly in frontotemporal regions underlying social behavior. This report may help predict the pathology of patients with atypical AD, which will ultimately be critical for enrolling suitable subjects into disease-modifying clinical trials.

Individual differences in socioemotional sensitivity are an index of salience network function.

Connectivity in intrinsically connected networks (ICNs) may predict individual differences in cognition and behavior. The drastic alterations in socioemotional awareness of patients with behavioral variant frontotemporal dementia (bvFTD) are presumed to arise from changes in one such ICN, the salience network (SN). We examined how individual differences in SN connectivity are reflected in overt social behavior in healthy individuals and patients, both to provide neuroscientific insight into this key brain-behavior relationship, and to provide a practical tool to diagnose patients with early bvFTD. We measured SN functional connectivity and socioemotional sensitivity in 65 healthy older adults and 103 patients in the earliest stage [Clinical Dementia Rating (CDR) Scale score ≤1] of five neurodegenerative diseases [14 bvFTD, 29 Alzheimer's disease (AD), 20 progressive supranuclear palsy (PSP), 21 semantic variant primary progressive aphasia (svPPA), and 19 non-fluent variant primary progressive aphasia (nfvPPA)]. All participants underwent resting-state functional imaging and an informant described their responsiveness to subtle emotional expressions using the Revised Self-Monitoring Scale (RSMS). Higher functional connectivity in the SN, predominantly between the right anterior insula (AI) and both "hub" cortical and "interoceptive" subcortical nodes, predicted socioemotional sensitivity among healthy individuals, showing that socioemotional sensitivity is a behavioral marker of SN function, and particularly of right AI functional connectivity. The continuity of this relationship in both healthy and neurologically affected individuals highlights the role of socioemotional sensitivity as an early diagnostic marker of SN connectivity. Clinically, this is particularly important for identification of patients in the earliest stage of bvFTD, where the SN is selectively vulnerable.

Deconstructing empathy: Neuroanatomical dissociations between affect sharing and prosocial motivation using a patient lesion model.

Affect sharing and prosocial motivation are integral parts of empathy that are conceptually and mechanistically distinct. We used a neurodegenerative disease (NDG) lesion model to examine the neural correlates of these two aspects of real-world empathic responding. The study enrolled 275 participants, including 44 healthy older controls and 231 patients diagnosed with one of five neurodegenerative diseases (75 Alzheimer's disease, 58 behavioral variant frontotemporal dementia (bvFTD), 42 semantic variant primary progressive aphasia (svPPA), 28 progressive supranuclear palsy, and 28 non-fluent variant primary progressive aphasia (nfvPPA). Informants completed the Revised Self-Monitoring Scale's Sensitivity to the Expressive Behavior of Others (RSMS-EX) subscale and the Interpersonal Reactivity Index's Empathic Concern (IRI-EC) subscale describing the typical empathic behavior of the participants in daily life. Using regression modeling of the voxel based morphometry of T1 brain scans prepared using SPM8 DARTEL-based preprocessing, we isolated the variance independently contributed by the affect sharing and the prosocial motivation elements of empathy as differentially measured by the two scales. We found that the affect sharing component uniquely correlated with volume in right>left medial and lateral temporal lobe structures, including the amygdala and insula, that support emotion recognition, emotion generation, and emotional awareness. Prosocial motivation, in contrast, involved structures such as the nucleus accumbens (NaCC), caudate head, and inferior frontal gyrus (IFG), which suggests that an individual must maintain the capacity to experience reward, to resolve ambiguity, and to inhibit their own emotional experience in order to effectively engage in spontaneous altruism as a component of their empathic response to others.

Position Information Encoded by Population Activity in Hierarchical Visual Areas.

Neurons in high-level visual areas respond to more complex visual features with broader receptive fields (RFs) compared to those in low-level visual areas. Thus, high-level visual areas are generally considered to carry less information regarding the position of seen objects in the visual field. However, larger RFs may not imply loss of position information at the population level. Here, we evaluated how accurately the position of a seen object could be predicted (decoded) from activity patterns in each of six representative visual areas with different RF sizes [V1-V4, lateral occipital complex (LOC), and fusiform face area (FFA)]. We collected functional magnetic resonance imaging (fMRI) responses while human subjects viewed a ball randomly moving in a two-dimensional field. To estimate population RF sizes of individual fMRI voxels, RF models were fitted for individual voxels in each brain area. The voxels in higher visual areas showed larger estimated RFs than those in lower visual areas. Then, the ball's position in a separate session was predicted by maximum likelihood estimation using the RF models of individual voxels. We also tested a model-free multivoxel regression (support vector regression, SVR) to predict the position. We found that regardless of the difference in RF size, all visual areas showed similar prediction accuracies, especially on the horizontal dimension. Higher areas showed slightly lower accuracies on the vertical dimension, which appears to be attributed to the narrower spatial distributions of the RF centers. The results suggest that much position information is preserved in population activity through the hierarchical visual pathway regardless of RF sizes and is potentially available in later processing for recognition and behavior.

Near-native protein loop sampling using nonparametric density estimation accommodating sparcity.

Unlike the core structural elements of a protein like regular secondary structure, template based modeling (TBM) has difficulty with loop regions due to their variability in sequence and structure as well as the sparse sampling from a limited number of homologous templates. We present a novel, knowledge-based method for loop sampling that leverages homologous torsion angle information to estimate a continuous joint backbone dihedral angle density at each loop position. The φ,ψ distributions are estimated via a Dirichlet process mixture of hidden Markov models (DPM-HMM). Models are quickly generated based on samples from these distributions and were enriched using an end-to-end distance filter. The performance of the DPM-HMM method was evaluated against a diverse test set in a leave-one-out approach. Candidates as low as 0.45 Å RMSD and with a worst case of 3.66 Å were produced. For the canonical loops like the immunoglobulin complementarity-determining regions (mean RMSD <2.0 Å), the DPM-HMM method performs as well or better than the best templates, demonstrating that our automated method recaptures these canonical loops without inclusion of any IgG specific terms or manual intervention. In cases with poor or few good templates (mean RMSD >7.0 Å), this sampling method produces a population of loop structures to around 3.66 Å for loops up to 17 residues. In a direct test of sampling to the Loopy algorithm, our method demonstrates the ability to sample nearer native structures for both the canonical CDRH1 and non-canonical CDRH3 loops. Lastly, in the realistic test conditions of the CASP9 experiment, successful application of DPM-HMM for 90 loops from 45 TBM targets shows the general applicability of our sampling method in loop modeling problem. These results demonstrate that our DPM-HMM produces an advantage by consistently sampling near native loop structure. The software used in this analysis is available for download at http://www.stat.tamu.edu/~dahl/software/cortorgles/.