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109


Detecting acute pain signals from human EEG

Sun, Guanghao; Wen, Zhenfu; Ok, Deborah; Doan, Lisa; Wang, Jing; Chen, Zhe Sage
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/UNASSIGNED: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/UNASSIGNED: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.
PMID: 33010301
ISSN: 1872-678x
CID: 4684482

Efficient Position Decoding Methods Based on Fluorescence Calcium Imaging in the Mouse Hippocampus

Tu, Mengyu; Zhao, Ruohe; Adler, Avital; Gan, Wen-Biao; Chen, Zhe S
Large-scale fluorescence calcium imaging methods have become widely adopted for studies of long-term hippocampal and cortical neuronal dynamics. Pyramidal neurons of the rodent hippocampus show spatial tuning in freely foraging or head-fixed navigation tasks. Development of efficient neural decoding methods for reconstructing the animal's position in real or virtual environments can provide a fast readout of spatial representations in closed-loop neuroscience experiments. Here, we develop an efficient strategy to extract features from fluorescence calcium imaging traces and further decode the animal's position. We validate our spike inference-free decoding methods in multiple in vivo calcium imaging recordings of the mouse hippocampus based on both supervised and unsupervised decoding analyses. We systematically investigate the decoding performance of our proposed methods with respect to the number of neurons, imaging frame rate, and signal-to-noise ratio. Our proposed supervised decoding analysis is ultrafast and robust, and thereby appealing for real-time position decoding applications based on calcium imaging.
PMID: 32343646
ISSN: 1530-888x
CID: 4436862

Mapping Cortical Integration of Sensory and Affective Pain Pathways

Singh, Amrita; Patel, Divya; Li, Anna; Hu, Lizbeth; Zhang, Qiaosheng; Liu, Yaling; Guo, Xinling; Robinson, Eric; Martinez, Erik; Doan, Lisa; Rudy, Bernardo; Chen, Zhe S; Wang, Jing
Pain is an integrated sensory and affective experience. Cortical mechanisms of sensory and affective integration, however, remain poorly defined. Here, we investigate the projection from the primary somatosensory cortex (S1), which encodes the sensory pain information, to the anterior cingulate cortex (ACC), a key area for processing pain affect, in freely behaving rats. By using a combination of optogenetics, in vivo electrophysiology, and machine learning analysis, we find that a subset of neurons in the ACC receives S1 inputs, and activation of the S1 axon terminals increases the response to noxious stimuli in ACC neurons. Chronic pain enhances this cortico-cortical connection, as manifested by an increased number of ACC neurons that respond to S1 inputs and the magnified contribution of these neurons to the nociceptive response in the ACC. Furthermore, modulation of this S1→ACC projection regulates aversive responses to pain. Our results thus define a cortical circuit that plays a potentially important role in integrating sensory and affective pain signals.
PMID: 32220320
ISSN: 1879-0445
CID: 4368562

Deep learning based on standard H&E images of primary melanoma tumors identifies patients at risk for visceral recurrence and death

Kulkarni, Prathamesh M; Robinson, Eric J; Sarin Pradhan, Jaya; Gartrell-Corrado, Robyn D; Rohr, Bethany R; Trager, Megan H; Geskin, Larisa J; Kluger, Harriet M; Wong, Pok Fai; Acs, Balazs; Rizk, Emanuelle M; Yang, Chen; Mondal, Manas; Moore, Michael R; Osman, Iman; Phelps, Robert; Horst, Basil A; Chen, Zhe S; Ferringer, Tammie; Rimm, David L; Wang, Jing; Saenger, Yvonne M
PURPOSE/OBJECTIVE:Biomarkers for disease specific survival (DSS) in early stage melanoma are needed to select patients for adjuvant immunotherapy and accelerate clinical trial design. We present a pathology-based computational method using a deep neural network architecture for DSS prediction. EXPERIMENTAL DESIGN/METHODS:The model was trained on 108 patients from four institutions and tested on 104 patients from Yale School of Medicine (YSM). A receiver operating characteristic (ROC) curve was generated based on vote aggregation of individual image sequences, an optimized cutoff was selected, and the computational model was tested on a third independent population of 51 patients from Geisinger Health Systems (GHS). RESULTS:Area under the curve (AUC) in the YSM patients was 0.905 (p<0.0001). AUC in the GHS patients was 0.880 (p<0.0001). Using the cutoff selected in the YSM cohort, the computational model predicted DSS in the GHS cohort based on Kaplan-Meier (KM) analysis (p<0.0001). CONCLUSIONS:The novel method presented is applicable to digital images, obviating the need for sample shipment and manipulation and representing a practical advance over current genetic and IHC-based methods.
PMID: 31636101
ISSN: 1078-0432
CID: 4169052

Granger causality analysis of rat cortical functional connectivity in pain

Guo, Xinling; Zhang, Qiaosheng; Singh, Amrita; Wang, Jing; Chen, Zhe Sage
OBJECTIVE:The primary somatosensory cortex (S1) and the anterior cingulate cortex (ACC) are two most important brain regions encoding the sensory-discriminative and affective-emotional aspects of pain, respectively. However, the functional connectivity of these two areas during cortical 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/METHODS:We recorded multichannel local field potentials (LFPs) from the S1 and ACC from freely behaving rats under various conditions of pain stimulus (thermal vs. mechanical) and pain state (naive vs. chronic pain). We applied Granger causality (GC) analysis to the LFP recordings and inferred frequency-dependent GC statistics and directed information flow. MAIN RESULTS/RESULTS:We found increased information flow during noxious pain stimulus presentation in both S1-->ACC and ACC-->S1 directions, especially at theta and gamma frequency bands. Similar results were found between thermal and mechanical pain stimuli. The chronic pain state shares common observations, but with elevated GC statistics especially in the gamma band. Furthermore, time-varying GC analysis revealed negative correlation between 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/CONCLUSIONS:Our results reveal functional connectivity and directed information flow between the S1 and ACC during various pain conditions. The time-varying GC analysis support the cortico-cortical information loop consistent with the computational predictive coding paradigm.
PMID: 31945754
ISSN: 1741-2552
CID: 4261892

[EXPRESS] Sleep spindles as a diagnostic and therapeutic target for chronic pain

Caravan, Bassir; Hu, Lizabeth; Veyg, Daniel; Kulkarni, Prathamesh; Zhang, Qiaosheng; Chen, Zhe; Wang, Jing
Pain is known to disrupt sleep patterns, and disturbances in sleep can further worsen pain symptoms. Sleep spindles occur during slow wave sleep and have established effects on sensory and affective processing in mammals. A number of chronic neuropsychiatric conditions, meanwhile, are known to alter sleep spindle density. The effect of persistent pain on sleep spindle waves, however, remains unknown, and studies of sleep spindles are challenging due to long period of monitoring and data analysis. Utilizing automated sleep spindle detection algorithms built on deep learning, we can monitor the effect of pain states on sleep spindle activity. In this study, we show that in a chronic pain model in rodents, there is a significant decrease in sleep spindle activity compared to controls. Meanwhile, methods to restore sleep spindles are associated with decreased pain symptoms. These results suggest that sleep spindle density correlates with chronic pain and may be both a potential biomarker for chronic pain and a target for neuromodulaton therapy.
PMID: 31912761
ISSN: 1744-8069
CID: 4257342

Analysis of nutritional support in a case of heat stroke complicated with multiple organ dysfunction syndrome. [Chinese]

Fei, Y; Zhang, Y; Chen, Z; Dai, C; Gan, H; Wang, J
Objective: To analyze the nutritional support regimen for a patient with heat stroke complicated with multiple organ dysfunction syndrome.
Method(s): The clinical pharmacists participated in the treatment of a patient with heat stroke and multiple organ dysfunction, and assisted the physicians to develop the nutrition support regimen. Results; After 12 days of nu-tritional support and comprehensive treatment, the liver and kidney functions of the patients gradually returned to normal, without obvious related complications. The gastrointestinal tract function recovered, displaying good nutritional support effect. Conclusion; On the basis of the standardized treatment regimen for heat stroke, reasonable nutritional support and effective pharmaceutical care will help heat stroke patients to have good recovery.
Copyright
EMBASE:630975703
ISSN: 1671-2838
CID: 4974502

Tracking Changes in Brain Network Connectivity under Transcranial Current Stimulation

Jami, Apoorva Sagarwal; Guo, Xinling; Kulkarni, Prathamesh; Henin, Simon E; Liu, Anli; Chen, Zhe
Noninvasive transcranial brain stimulation has been widely used in experimental and clinical applications to perturb the brain activity, aiming at promoting synaptic plasticity or enhancing functional connectivity within targeted brain regions. However, there are different types of neurostimulations and various choices of stimulation parameters; how these choices influence the intermediate neurophysiological effects and brain connectivity remain incompletely understood. We propose several quantitative methods to investigate the brain connectivity of an epileptic patient before and after transcranial alternating/direct current stimulation (tACS/tDCS). The neuro-feedback derived from our analyses may provide useful cues for the effectiveness of neurostimulation.
PMID: 31947314
ISSN: 1557-170x
CID: 4271622

A Predictive Coding Model for Evoked and Spontaneous Pain Perception

Song, Yuru; Kemprecos, Helen; Wang, Jing; Chen, Zhe
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.
PMID: 31946512
ISSN: 1557-170x
CID: 4271612

Dynamics of motor cortical activity during naturalistic feeding behavior

Liu, Shizhao; Iriarte-Diaz, Jose; Hatsopoulos, Nicholas; Ross, Callum F; Takahashi, Kazutaka; Chen, Zhe Sage
OBJECTIVE:The orofacial primary motor cortex (MIo) plays a critical role in controlling tongue and jaw movements during oral motor functions, such as chewing, swallowing and speech. However, the neural mechanisms of MIo during naturalistic feeding are still poorly understood. There is a strong need for a systematic study of motor cortical dynamics during feeding behavior. APPROACH/METHODS:To investigate the neural dynamics and variability of MIo neuronal activity during naturalistic feeding, we used chronically implanted micro-electrode arrays to simultaneously recorded ensembles of neuronal activity in MIo of two monkeys (Macaca mulatta) while eating various types of food. We developed a Bayesian nonparametric latent variable model to reveal latent structures of neuronal population activity of MIo and identify the complex mapping between MIo ensemble spike activity and high-dimensional kinematics. MAIN RESULTS/RESULTS:Rhythmic neuronal firing patterns and oscillatory dynamics are evident in single-unit activity. At the population level, we uncovered the neural dynamics of rhythmic chewing, and quantified the neural variability at multiple timescales (complete feeding sequences, chewing sequence stages, chewing gape cycle phases) across food types. Our approach accommodates time-warping of chewing sequences and automatic model selection, and maps the latent states to chewing behaviors at fine timescales. SIGNIFICANCE/CONCLUSIONS:Our work shows that neural representations of MIo ensembles display spatiotemporal patterns in chewing gape cycles at different chew sequence stages, and these patterns vary in a stage-dependent manner. Unsupervised learning and decoding analysis may reveal the link between complex MIo spatiotemporal patterns and chewing kinematics.
PMID: 30721881
ISSN: 1741-2552
CID: 3631362