A prototype closed-loop brain–machine interface for the study and treatment of pain

Abstract

Chronic pain is characterized by discrete pain episodes of unpredictable frequency and duration. This hinders the study of pain mechanisms, and contributes to the use of pharmacological treatments associated with side effects, addiction and drug tolerance. Here, we show that a closed-loop brain–machine interface (BMI) can modulate sensory-affective experiences in real time in freely behaving rats by coupling neural codes for nociception directly with therapeutic cortical stimulation. The BMI decodes the onset of nociception via a state-space model on the basis of the analysis of online-sorted spikes recorded from the anterior cingulate cortex (which is critical for pain processing), and couples real-time pain detection with optogenetic activation of the prelimbic prefrontal cortex (which exerts top–down nociceptive regulation). In rats, the BMI effectively inhibited sensory and affective behaviors caused by acute mechanical or thermal pain, and by chronic inflammatory or neuropathic pain. The approach provides a blueprint for demand-based neuromodulation to treat sensory-affective disorders, and could be further leveraged for nociceptive control and to study pain mechanisms.

Multimodal neural probes for combined optogenetics and electrophysiology

“…In addition, significant progress has been made in translational application of combined optogenetics/electrophysiology techniques. For example, closed-loop BCIs based on combined optogenetics/electrophysiology can record neural activity and process it in real time for optogenetic stimulation of neural activity and has shown great promise for seizure control and peripheral neuromodulation ( Grosenick et al., 2015 ; Kuo et al., 2018 ; Mickle et al., 2019 ; Zhang et al., 2021 ). Wireless implantable devices have enabled long-lasting and high-fidelity interfaces for electrical recording and optogenetic stimulation in free-moving animals ( Gutruf and Rogers., 2018 ; Jeong et al., 2015 ; McCall et al., 2013 , 2017 ; Noh et al., 2018 ; Qazi et al., 2019 ; Shin et al., 2017 ; Zhang et al., 2019 ).…”

Novel Non-invasive Transcranial Electrical Stimulation for Parkinson’s Disease

“…In addition, using a brain machine interface (BMI) can provide a quicker stimulation response. For example, in a chronic pain study that used BMI in mice to monitor and stimulate the brain, the time delay between characteristics detected and stimulation response was minimized; thus, the pain could be released even when recognizing acute pain signals ( Zhang et al, 2021 ).…”

Improving scalability in systems neuroscience

“…operation of the interface. The BMI seeks to disrupt ongoing network excitation or inhibition (e.g., seizure control or optogenetic control; Bere ´nyi et al, 2012;Paz et al, 2013;Grosenick et al, 2015) and/or shape neural plasticity (e.g., mood regulation; Zhang et al, 2021;Shanechi, 2019). In contrast, user feedback BMIs (e.g., visual and motor prostheses) depend on how the user learns to use the interface (Carmena et al, 2003;Koralek et al, 2012;Shenoy and Carmena, 2014).…”

“…Development of scalable methods for decoding arm or hand movement or assessing neural population dynamics can greatly advance the research field in motor control (Trautmann et al, 2019;Sussillo et al, 2016). The key component of BMIs is the feedback, as a form of neurostimulations (Bere ´nyi et al, 2012;Paz et al, 2013;Grosenick et al, 2015;Zhang et al, 2021), user-defined feedback control (Figure 2G; Carmena et al, 2003;Dangi et al, 2013;Shanechi et al, 2016), or the prediction error of neural responses (Figure 2H; Tafazoli et al, 2020), which can be further used to perturb the circuit or causally change the behavior. Finally, the time window of closed-loop feedback is critical, as it allows interaction with neurons and circuits differently.…”

“…In this perspective, we discuss how jointly scaling up data acquisition and data analysis in an active and adaptive manner can speed up the cycle and enable AACL experiments. We first review scalability in neurotechnology and instrumentation, (Lewi et al, 2009(Lewi et al, , 2011, adaptive stimulus optimization (Walker et al, 2019;Ponce et al, 2019), neurally defined stimulation (Bere ´nyi et al, 2012;Paz et al, 2013;Grosenick et al, 2015;Zhang et al, 2021), closed-loop decoder adaptation (Dangi et al, 2013), user-defined control or prosthetics (Carmena et al, 2003;Shanechi et al, 2016), or adaptive closed-loop neurostimulation (Tafazoli et al, 2020). (B) Schematic of adaptive stimulus optimization on the basis of BMI with neural decoder adaptation.…”