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The real-time monitoring of neurochemical release in vivo plays a critical role in understanding the biochemical process of the complex nervous system. Current technologies for such applications, including microdialysis and fast-scan cyclic voltammetry, suffer from limited spatiotemporal resolution or poor selectivity. Here, we report a soft implantable aptamer-graphene microtransistor probe for real-time monitoring of neurochemical release. As a demonstration, we show the monitoring of dopamine with nearly cellular-scale spatial resolution, high selectivity (dopamine sensor > 19-fold over norepinephrine), and picomolar sensitivity, simultaneously. Systematic benchtop evaluations, ex vivo experiments, and in vivo studies in mice models highlight the key features and demonstrate the capability of capturing the dopamine release dynamics evoked by pharmacological stimulation, suggesting the potential applications in basic neuroscience studies and studying neurological disease-related processes. The developed system can be easily adapted for monitoring other neurochemicals and drugs by simply replacing the aptamers functionalized on the graphene microtransistors.

Objective In stroke survivors, a treatment-resistant problem is inability to volitionally differentiate upper limb wrist extension versus flexion. When one intends to extend the wrist, the opposite occurs, wrist flexion, rendering the limb non-functional. Conventional therapeutic approaches have had limited success in achieving functional recovery of patients with chronic and severe upper extremity impairments. Functional magnetic resonance imaging (fMRI) neurofeedback is an emerging strategy that has shown potential for stroke rehabilitation. There is a lack of information regarding unique blood-oxygenation-level dependent (BOLD) cortical activations uniquely controlling execution of wrist extension versus uniquely controlling wrist flexion. Therefore, a first step in providing accurate neural feedback and training to the stroke survivor is to determine the feasibility of classifying (or differentiating) brain activity uniquely associated with wrist extension from that of wrist flexion, first in healthy adults. Approach We studied brain signal of 10 healthy adults, who performed wrist extension and wrist flexion during fMRI data acquisition. We selected four types of analyses to study the feasibility of differentiating brain signal driving wrist extension versus wrist flexion, as follows: 1) general linear model (GLM) analysis; 2) support vector machine (SVM) classification; 3) ‘Winner Take All’; and 4) Relative Dominance. Results With these four methods and our data, we found that few voxels were uniquely active during either wrist extension or wrist flexion. SVM resulted in only minimal classification accuracies. There was no significant difference in activation magnitude between wrist extension versus flexion; however, clusters of voxels showed extension signal > flexion signal and other clusters vice versa. Spatial patterns of activation differed among subjects. Significance We encountered a number of obstacles to obtaining clear group results in healthy adults. These obstacles included the following: high variability across healthy adults in all measures studied; close proximity of uniquely active voxels to voxels that were common to both the extension and flexion movements; in general, higher magnitude of signal for the voxels common to both movements versus the magnitude of any given uniquely active voxel for one type of movement. Our results indicate that greater precision in imaging will be required to develop a truly effective method for differentiating wrist extension versus wrist flexion from fMRI data.