A set of software tools for building and distributing models of macromolecular assemblies uses an integrative structure modeling approach, which casts the building of models as a computational optimization problem where information is encoded into a scoring function used to evaluate candidate models.

Adult owl and squirrel monkeys were trained to master a small-object retrieval sensorimotor skill. Behavioral observations along with positive changes in the cortical area 3b representations of specific skin surfaces implicated specific glabrous finger inputs as important contributors to skill acquisition. The area 3b zones over which behaviorally important surfaces were represented were destroyed by microlesions, which resulted in a degradation of movements that had been developed in the earlier skill acquisition. Monkeys were then retrained at the same behavioral task. They could initially perform it reasonably well using the stereotyped movements that they had learned in prelesion training, although they acted as if key finger surfaces were insensate. However, monkeys soon initiated alternative strategies for small object retrieval that resulted in a performance drop. Over several- to many-week-long period, monkeys again used the fingers for object retrieval that had been used successfully before the lesion, and reacquired the sensorimotor skill. Detailed maps of the representations of the hands in SI somatosensory cortical fields 3b, 3a, and 1 were derived after postlesion functional recovery. Control maps were derived in the same hemispheres before lesions, and in opposite hemispheres. Among other findings, these studies revealed the following 1) there was a postlesion reemergence of the representation of the fingertips engaged in the behavior in novel locations in area 3b in two of five monkeys and a less substantial change in the representation of the hand in the intact parts of area 3b in three of five monkeys. 2) There was a striking emergence of a new representation of the cutaneous fingertips in area 3a in four of five monkeys, predominantly within zones that had formerly been excited only by proprioceptive inputs. This new cutaneous fingertip representation disproportionately represented behaviorally crucial fingertips. 3) There was an approximately two times enlargement of the representation of the fingers recorded in cortical area 1 in postlesion monkeys. The specific finger surfaces employed in small-object retrieval were differentially enlarged in representation. 4) Multiple-digit receptive fields were recorded at a majority of emergent, cutaneous area 3a sites in all monkeys and at a substantial number of area 1 sites in three of five postlesion monkeys. Such fields were uncommon in area 1 in control maps. 5) Single receptive fields and the component fields of multiple-digit fields in postlesion representations were within normal receptive field size ranges. 6) No significant changes were recorded in the SI hand representations in the opposite (untrained, intact) control hemisphere. These findings are consistent with "substitution" and "vicariation" (adaptive plasticity) models of recovery from brain damage and stroke.

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Background Although effective mental health treatments exist, the ability to match individuals to optimal treatments is poor, and timely assessment of response is difficult. One reason for these challenges is the lack of objective measurement of psychiatric symptoms. Sensors and active tasks recorded by smartphones provide a low-burden, low-cost, and scalable way to capture real-world data from patients that could augment clinical decision-making and move the field of mental health closer to measurement-based care. Objective This study tests the feasibility of a fully remote study on individuals with self-reported depression using an Android-based smartphone app to collect subjective and objective measures associated with depression severity. The goals of this pilot study are to develop an engaging user interface for high task adherence through user-centered design; test the quality of collected data from passive sensors; start building clinically relevant behavioral measures (features) from passive sensors and active inputs; and preliminarily explore connections between these features and depression severity. Methods A total of 600 participants were asked to download the study app to join this fully remote, observational 12-week study. The app passively collected 20 sensor data streams (eg, ambient audio level, location, and inertial measurement units), and participants were asked to complete daily survey tasks, weekly voice diaries, and the clinically validated Patient Health Questionnaire (PHQ-9) self-survey. Pairwise correlations between derived behavioral features (eg, weekly minutes spent at home) and PHQ-9 were computed. Using these behavioral features, we also constructed an elastic net penalized multivariate logistic regression model predicting depressed versus nondepressed PHQ-9 scores (ie, dichotomized PHQ-9). Results A total of 415 individuals logged into the app. Over the course of the 12-week study, these participants completed 83.35% (4151/4980) of the PHQ-9s. Applying data sufficiency rules for minimally necessary daily and weekly data resulted in 3779 participant-weeks of data across 384 participants. Using a subset of 34 behavioral features, we found that 11 features showed a significant (P<.001 Benjamini-Hochberg adjusted) Spearman correlation with weekly PHQ-9, including voice diary–derived word sentiment and ambient audio levels. Restricting the data to those cases in which all 34 behavioral features were present, we had available 1013 participant-weeks from 186 participants. The logistic regression model predicting depression status resulted in a 10-fold cross-validated mean area under the curve of 0.656 (SD 0.079). Conclusions This study finds a strong proof of concept for the use of a smartphone-based assessment of depression outcomes. Behavioral features derived from passive sensors and active tasks show promising correlations with a validated clinical measure of depression (PHQ-9). Future work is needed to increase scale that may permit the construction of more complex (eg, nonlinear) predictive models and better handle data missingness.

Psychiatry has been limited by historically rooted practices centered primarily on subjective observation. Fields such as oncology have progressed toward data-driven clinical decision-making that combines subjective clinical assessment of symptoms and preferences with biological measures such as genetics, biomarkers, imaging, and integrative physiology to derive quantitative risk scores and decision support. In contrast, psychiatry has just begun to scratch the surface of measurement-based care with validated clinical questionnaires. An opportunity exists to improve modern psychiatric care with novel data streams from digital sensors combined with clinical observation and subjective self-report. The prospect of integrating this complex information with modern computational and analytical methods could advance the field, both in research and clinical practice. Here we discuss this possibility and propose some key priorities to enable these innovations toward improving clinical outcomes in the future.