Disrupting the ventral premotor cortex interferes with the contribution of action observation to use-dependent plasticity.

Action observation (AO), observing another individual perform an action, has been implicated in several higher cognitive processes including forming basic motor memories. Previous work has shown that physical practice (PP) results in cortical motor representational changes, referred to as use-dependent plasticity (UDP), and that AO combined with PP potentiates UDP in both healthy adults and stroke patients. In humans, AO results in activation of the ventral premotor cortex (PMv), however, whether this PMv activation has a functional contribution to UDP is not known. Here, we studied the effects disruption of PMv has on UDP when subjects performed PP combined with AO (PP + AO). Subjects participated in two randomized crossover sessions measuring the amount of UDP resulting from PP + AO while receiving disruptive (1 Hz) TMS over the fMRI-activated PMv or over frontal cortex (Sham). We found that, unlike the sham session, disruptive TMS over PMv reduced the beneficial contribution of AO to UDP. To ensure that disruption of PMv was specifically interfering with the contribution of AO and not PP, subjects completed two more control sessions where they performed only PP while receiving disruptive TMS over PMv or frontal cortex. We found that the magnitude of UDP for both control sessions was similar to PP + AO with TMS over PMv. These findings suggest that the fMRI activation found in PMv during AO studies is functionally relevant to task performance, at least for the beneficial effects that AO exerts over motor training.

Author Correction: Deep learning algorithm predicts diabetic retinopathy progression in individual patients.

A Correction to this paper has been published: https://doi.org/10.1038/s41746-020-00365-5.

Deep learning algorithm predicts diabetic retinopathy progression in individual patients.

The global burden of diabetic retinopathy (DR) continues to worsen and DR remains a leading cause of vision loss worldwide. Here, we describe an algorithm to predict DR progression by means of deep learning (DL), using as input color fundus photographs (CFPs) acquired at a single visit from a patient with DR. The proposed DL models were designed to predict future DR progression, defined as 2-step worsening on the Early Treatment Diabetic Retinopathy Diabetic Retinopathy Severity Scale, and were trained against DR severity scores assessed after 6, 12, and 24 months from the baseline visit by masked, well-trained, human reading center graders. The performance of one of these models (prediction at month 12) resulted in an area under the curve equal to 0.79. Interestingly, our results highlight the importance of the predictive signal located in the peripheral retinal fields, not routinely collected for DR assessments, and the importance of microvascular abnormalities. Our findings show the feasibility of predicting future DR progression by leveraging CFPs of a patient acquired at a single visit. Upon further development on larger and more diverse datasets, such an algorithm could enable early diagnosis and referral to a retina specialist for more frequent monitoring and even consideration of early intervention. Moreover, it could also improve patient recruitment for clinical trials targeting DR.

The cost-effectiveness of a child nutrition education programme in Peru.

This article reports impact and cost results from a health facility-based nutrition education programme targeting children less than 2 years of age in Trujillo, Peru. Key elements of the programme included participative complementary feeding demonstrations, growth monitoring sessions and an accreditation process. Data were collected from six intervention and six control health facilities to measure utilization and costs associated with the intervention. To calculate the unit costs of services, these costs are allocated using activity-based costing. To measure the effects of the intervention, 338 children were followed through household surveys at regular intervals from birth until the age of 18 months. The intervention had a clear positive impact both on the use of nutrition-related services and on children's growth outcomes. Children in the intervention areas made 17.6 visits to health facilities in the first 18 months of life, compared with 14.1 visits for children in the control areas (P < 0.001). This pattern holds true for all socioeconomic groups. The intervention prevented 11.1 cases of stunting per 100 children. In multivariate logistic regression analysis, children in the intervention were 0.33 times as likely to be stunted as the controls (P = 0.002). The marginal cost of the intervention - including external costs, training, health education materials and extra travel and equipment - is 6.12 US dollars per child reached and 55.16 US dollars per case of stunting prevented. The estimated marginal cost of the intervention per death averted is 1952 US dollars.