Behavioral Health, Telemedicine, and Opportunities for Improving Access.

PURPOSE OF REVIEW: The purpose of this review is to summarize advances in behavioral treatments for pain and headache disorders, as well as recent innovations in telemedicine for behavioral treatments. RECENT FINDINGS: Research for behavioral treatments continues to support their use as part of a multidisciplinary approach to comprehensive management for pain and headache conditions. Behavioral treatments incorporate both behavioral change and cognitive interventions and have been shown to improve outcomes beyond that of medical management alone. The onset of the COVID-19 public health emergency necessitated the rapid uptake of nontraditional modalities for behavioral treatments, particularly telemedicine. Telemedicine has long been considered the answer to several barriers to accessing behavioral treatments, and as a result of COVID-19 significant progress has been made evaluating a variety of telemedicine modalities including synchronous, asynchronous, and mobile health applications. Researchers are encouraged to continue investigating how best to leverage these modalities to improve access to behavioral treatments and to continue evaluating the efficacy of telemedicine compared to traditional in-person care. Comprehensive pain and headache management should include behavioral treatments to address a variety of behavior change and cognitive targets. Policy changes and advances in telemedicine for behavioral treatments provide the opportunity to address historical barriers limiting access.

ID-Seg: an infant deep learning-based segmentation framework to improve limbic structure estimates.

Infant brain magnetic resonance imaging (MRI) is a promising approach for studying early neurodevelopment. However, segmenting small regions such as limbic structures is challenging due to their low inter-regional contrast and high curvature. MRI studies of the adult brain have successfully applied deep learning techniques to segment limbic structures, and similar deep learning models are being leveraged for infant studies. However, these deep learning-based infant MRI segmentation models have generally been derived from small datasets, and may suffer from generalization problems. Moreover, the accuracy of segmentations derived from these deep learning models relative to more standard Expectation-Maximization approaches has not been characterized. To address these challenges, we leveraged a large, public infant MRI dataset (n = 473) and the transfer-learning technique to first pre-train a deep convolutional neural network model on two limbic structures: amygdala and hippocampus. Then we used a leave-one-out cross-validation strategy to fine-tune the pre-trained model and evaluated it separately on two independent datasets with manual labels. We term this new approach the Infant Deep learning SEGmentation Framework (ID-Seg). ID-Seg performed well on both datasets with a mean dice similarity score (DSC) of 0.87, a mean intra-class correlation (ICC) of 0.93, and a mean average surface distance (ASD) of 0.31 mm. Compared to the Developmental Human Connectome pipeline (dHCP) pipeline, ID-Seg significantly improved segmentation accuracy. In a third infant MRI dataset (n = 50), we used ID-Seg and dHCP separately to estimate amygdala and hippocampus volumes and shapes. The estimates derived from ID-seg, relative to those from the dHCP, showed stronger associations with behavioral problems assessed in these infants at age 2. In sum, ID-Seg consistently performed well on two different datasets with an 0.87 DSC, however, multi-site testing and extension for brain regions beyond the amygdala and hippocampus are still needed.