Neuronal architectures comprise synaptically connected neurons distributed throughout the central nervous system, the coordinated activities of which orchestrate neurological functions ranging from breathing to movement and cognition. Disentangling these neuronal architectures and how they are disrupted in disease is a fundamental goal of neuroscience. Historically, this challenge has been addressed with a reductionist framework that translated hypotheses into the interrogation of discrete neuronal subpopulations based on a priori expectations. The advent of high-throughput methodologies, including whole–central nervous system imaging in rodent models and single-cell transcriptomic readouts, now enable the visualization and characterization of neuronal subpopulations throughout the central nervous system. Increases in scale further enable comparative experimental designs that can be navigated with computational frameworks. These advances augur a new era wherein neuronal architectures implicated in diverse neurological functions, yet obscured by the complexity of the central nervous system, can be exposed without bias and interrogated with genetically guided experimental manipulations.

Neurological diseases, such as cerebrovascular disease, Parkinson's disease (PD), Alzheimer's disease, have become the leading cause of death in China. Neurological function evaluation is crucial for the diagnosis and intervention of neurological diseases. Clinically, neurological function is evaluated by various scales, tests, and questionnaires. However, these methods rely on costly professional equipment and medical personnel. They cannot be used as a means of daily evaluation of neurological diseases.

In work that combines a deep understanding of the biology of soft-bodied animals such as earthworms with advances in materials and electronic technologies, researchers from the United States and China have developed a robotic device containing a stretchable transistor that allows neurological function. Cunjiang Yu, Bill D. Cook Associate Professor of Mechanical Engineering at the University of Houston, said the work represents a significant step toward the development of prosthetics that could directly connect with the peripheral nerves in biological tissues, offering neurological function to artificial limbs, as well as toward advances in soft neurorobots capable of thinking and making judgments. Yu is corresponding author for a paper describing the work, published in Science Advances. He is also a principal investigator with the Texas Center for Superconductivity at the University of Houston. "When human skin is touched, you feel it," Yu said to describe the human capabilities the new device can mimic.

Cunjiang Yu, Bill D. Cook Associate Professor of Mechanical Engineering at the University of Houston, said the work represents a significant step toward the development of prosthetics that could directly connect with the peripheral nerves in biological tissues, offering neurological function to artificial limbs, as well as toward advances in soft neurorobots capable of thinking and making judgments. Yu is corresponding author for a paper describing the work, published in Science Advances. He is also a principal investigator with the Texas Center for Superconductivity at the University of Houston. "When human skin is touched, you feel it," Yu said to describe the human capabilities the new device can mimic. "The feeling originates in your brain, through neural pathways from your skin to the brain."

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder. Finding the biomarkers associated with ASD is extremely helpful to understand the underlying roots of the disorder and can lead to earlier diagnosis and more targeted treatment. Although Deep Neural Networks (DNNs) have been applied in functional magnetic resonance imaging (fMRI) to identify ASD, understanding the data-driven computational decision making procedure has not been previously explored. Therefore, in this work, we address the problem of interpreting reliable biomarkers associated with identifying ASD; specifically, we propose a 2-stage method that classifies ASD and control subjects using fMRI images and interprets the saliency features activated by the classifier. First, we trained an accurate DNN classifier. Then, for detecting the biomarkers, different from the DNN visualization works in computer vision, we take advantage of the anatomical structure of brain fMRI and develop a frequency-normalized sampling method to corrupt images. Furthermore, in the ASD vs. control subjects classification scenario, we provide a new approach to detect and characterize important brain features into three categories. The biomarkers we found by the proposed method are robust and consistent with previous findings in the literature. We also validate the detected biomarkers by neurological function decoding and comparing with the DNN activation maps.