Ensembles of change-point detectors: implications for real-time BMI applications

The decoder design and performance comparative analysis for closed-loop brain-machine interface system

Brain-machine interface (BMI) can convert electroencephalography signals (EEGs) into the control instructions of external devices, and the key of control performance is the accuracy and efficiency of decoder. However, the performance of different decoders obtaining control instructions from complex and variable EEG signals is very different and irregular in the different neural information transfer model. Aiming at this problem, the off-line and on-line performance of eight decoders based on the improved single-joint information transmission (SJIT) model is compared and analyzed in this paper, which can provide a theoretical guidance for decoder design. Firstly, in order to avoid the different types of neural activities in the decoding process on the decoder performance, eight decoders based on the improved SJIT model are designed. And then the off-line decoding performance of these decoders is tested and compared. Secondly, a closed-loop BMI system which combining by the designed decoder and the random forest encoder based on the improved SJIT model is constructed. Finally, based on the constructed closed-loop BMI system, the on-line decoding performance of decoders is compared and analyzed. The results show that the LSTM-based decoder has better on-line decoding performance than others in the improved SJIT model.

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

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 behaviours 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.

Closed-loop stimulation using a multiregion brain-machine interface has analgesic effects in rodents

Effective treatments for chronic pain remain limited. Conceptually, a closed-loop neural interface combining sensory signal detection with therapeutic delivery could produce timely and effective pain relief. Such systems are challenging to develop because of difficulties in accurate pain detection and ultrafast analgesic delivery. Pain has sensory and affective components, encoded in large part by neural activities in the primary somatosensory cortex (S1) and anterior cingulate cortex (ACC), respectively. Meanwhile, studies show that stimulation of the prefrontal cortex (PFC) produces descending pain control. Here, we designed and tested a brain-machine interface (BMI) combining an automated pain detection arm, based on simultaneously recorded local field potential (LFP) signals from the S1 and ACC, with a treatment arm, based on optogenetic activation or electrical deep brain stimulation (DBS) of the PFC in freely behaving rats. Our multiregion neural interface accurately detected and treated acute evoked pain and chronic pain. This neural interface is activated rapidly, and its efficacy remained stable over time. Given the clinical feasibility of LFP recordings and DBS, our findings suggest that BMI is a promising approach for pain treatment.

From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings

The meaning behind neural single unit activity has constantly been a challenge, so it will persist in the foreseeable future. As one of the most sourced strategies, detecting neural activity in high-resolution neural sensor recordings and then attributing them to their corresponding source neurons correctly, namely the process of spike sorting, has been prevailing so far. Support from ever-improving recording techniques and sophisticated algorithms for extracting worthwhile information and abundance in clustering procedures turned spike sorting into an indispensable tool in electrophysiological analysis. This review attempts to illustrate that in all stages of spike sorting algorithms, the past 5 years innovations' brought about concepts, results, and questions worth sharing with even the non-expert user community. By thoroughly inspecting latest innovations in the field of neural sensors, recording procedures, and various spike sorting strategies, a skeletonization of relevant knowledge lays here, with an initiative to get one step closer to the original objective: deciphering and building in the sense of neural transcript.

Decoding pain from brain activity

Pain is a dynamic, complex and multidimensional experience. The identification of pain from brain activity as neural readout may effectively provide a neural code for pain, and further provide useful information for pain diagnosis and treatment. Advances in neuroimaging and large-scale electrophysiology have enabled us to examine neural activity with improved spatial and temporal resolution, providing opportunities to decode pain in humans and freely behaving animals. This topical review provides a systematical overview of state-of-the-art methods for decoding pain from brain signals, with special emphasis on electrophysiological and neuroimaging modalities. We show how pain decoding analyses can help pain diagnosis and discovery of neurobiomarkers for chronic pain. Finally, we discuss the challenges in the research field and point to several important future research directions.

Pharmacological restoration of anti-nociceptive functions in the prefrontal cortex relieves chronic pain

Chronic pain affects one in four adults, and effective non-sedating and non-addictive treatments are urgently needed. Chronic pain causes maladaptive changes in the cerebral cortex, which can lead to impaired endogenous nociceptive processing. However, it is not yet clear if drugs that restore endogenous cortical regulation could provide an effective therapeutic strategy for chronic pain. Here, we studied the nociceptive response of neurons in the prelimbic region of the prefrontal cortex (PL-PFC) in freely behaving rats using a spared nerve injury (SNI) model of chronic pain, and the impact of AMPAkines, a class of drugs that increase central glutamate signaling, on such response. We found that neurons in the PL-PFC increase their firing rates in response to noxious stimulations; chronic neuropathic pain, however, suppressed this important cortical pain response. Meanwhile, CX546, a well-known AMPAkine, restored the anti-nociceptive response of PL-PFC neurons in the chronic pain condition. In addition, both systemic administration and direct delivery of CX546 into the PL-PFC inhibited symptoms of chronic pain, whereas optogenetic inactivation of the PFC neurons or administration of AMPA receptor antagonists in the PL-PFC blocked the anti-nociceptive effects of CX546. These results indicate that restoration of the endogenous anti-nociceptive functions in the PL-PFC by pharmacological agents such as AMPAkines constitutes a successful strategy to treat chronic neuropathic pain.

Detecting acute pain signals from human EEG

BACKGROUND

Advances in human neuroimaging has enabled us to study functional connections among various brain regions in pain states. Despite a wealth of studies at high anatomic resolution, the exact neural signals for the timing of pain remain little known. Identifying the onset of pain signals from distributed cortical circuits may reveal the temporal dynamics of pain responses and subsequently provide important feedback for closed-loop neuromodulation for pain.

NEW METHOD

Here we developed an unsupervised learning method for sequential detection of acute pain signals based on multichannel human EEG recordings. Following EEG source localization, we used a state-space model (SSM) to detect the onset of acute pain signals based on the localized regions of interest (ROIs).

RESULTS

We validated the SSM-based detection strategy using two human EEG datasets, including one public EEG recordings of 50 subjects. We found that the detection accuracy varied across tested subjects and detection methods. We also demonstrated the feasibility for cross-subject and cross-modality prediction of detecting the acute pain signals.

COMPARISON WITH EXISTING METHODS

In contrast to the batch supervised learning analysis based on a support vector machine (SVM) classifier, the unsupervised learning method requires fewer number of training trials in the online experiment, and shows comparable or improved performance than the supervised method.

CONCLUSIONS

Our unsupervised SSM-based method combined with EEG source localization showed robust performance in detecting the onset of acute pain signals.

A Predictive Coding Model for Evoked and Spontaneous Pain Perception

Pain is a complex multidimensional experience, and pain perception is still incompletely understood. Here we combine animal behavior, electrophysiology, and computer modeling to dissect mechanisms of evoked and spontaneous pain. We record the local field potentials (LFPs) from the primary somatosensory cortex (S1) and anterior cingulate cortex (ACC) of freely behaving rats during pain episodes, and develop a predictive coding model to investigate the temporal coordination of oscillatory activity between the S1 and ACC. Our preliminary results from computational simulations support the experimental findings and provide new predictions.

Granger causality analysis of rat cortical functional connectivity in pain

OBJECTIVE

The primary somatosensory cortex (S1) and the anterior cingulate cortex (ACC) are two of the most important cortical brain regions encoding the sensory-discriminative and affective-emotional aspects of pain, respectively. However, the functional connectivity of these two areas during pain processing remains unclear. Developing methods to dissect the functional connectivity and directed information flow between cortical pain circuits can reveal insight into neural mechanisms of pain perception.

APPROACH

We recorded multichannel local field potentials (LFPs) from the S1 and ACC in freely behaving rats under various conditions of pain stimulus (thermal versus mechanical) and pain state (naive versus chronic pain). We applied Granger causality (GC) analysis to the LFP recordings and inferred frequency-dependent GC statistics between the S1 and ACC.

MAIN RESULTS

We found an increased information flow during noxious pain stimulus presentation in both S1[Formula: see text]ACC and ACC[Formula: see text]S1 directions, especially at theta and gamma frequency bands. Similar results were found for thermal and mechanical pain stimuli. The chronic pain state shares common observations, except for further elevated GC measures especially in the gamma band. Furthermore, time-varying GC analysis revealed a negative correlation between the direction-specific and frequency-dependent GC and animal's paw withdrawal latency. In addition, we used computer simulations to investigate the impact of model mismatch, noise, missing variables, and common input on the conditional GC estimate. We also compared the GC results with the transfer entropy (TE) estimates.

SIGNIFICANCE

Our results reveal functional connectivity and directed information flow between the S1 and ACC during various pain conditions. The dynamic GC analysis support the hypothesis of cortico-cortical information loop in pain perception, consistent with the computational predictive coding paradigm.

Cortical Pain Processing in the Rat Anterior Cingulate Cortex and Primary Somatosensory Cortex

Pain is a complex multidimensional experience encompassing sensory-discriminative, affective-motivational and cognitive-emotional components mediated by different neural mechanisms. Investigations of neurophysiological signals from simultaneous recordings of two or more cortical circuits may reveal important circuit mechanisms on cortical pain processing. The anterior cingulate cortex (ACC) and primary somatosensory cortex (S1) represent two most important cortical circuits related to sensory and affective processing of pain. Here, we recorded in vivo extracellular activity of the ACC and S1 simultaneously from male adult Sprague-Dale rats (n = 5), while repetitive noxious laser stimulations were delivered to animalÕs hindpaw during pain experiments. We identified spontaneous pain-like events based on stereotyped pain behaviors in rats. We further conducted systematic analyses of spike and local field potential (LFP) recordings from both ACC and S1 during evoked and spontaneous pain episodes. From LFP recordings, we found stronger phase-amplitude coupling (theta phase vs. gamma amplitude) in the S1 than the ACC (n = 10 sessions), in both evoked (p = 0.058) and spontaneous pain-like behaviors (p = 0.017, paired signed rank test). In addition, pain-modulated ACC and S1 neuronal firing correlated with the amplitude of stimulus-induced event-related potentials (ERPs) during evoked pain episodes. We further designed statistical and machine learning methods to detect pain signals by integrating ACC and S1 ensemble spikes and LFPs. Together, these results reveal differential coding roles between the ACC and S1 in cortical pain processing, as well as point to distinct neural mechanisms between evoked and putative spontaneous pain at both LFP and cellular levels.