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Predictive coding models for pain perception

Song, Yuru; Yao, Mingchen; Kemprecos, Helen; Byrne, Aine; Xiao, Zhengdong; Zhang, Qiaosheng; Singh, Amrita; Wang, Jing; Chen, Zhe S
Pain is a complex, multidimensional experience that involves dynamic interactions between sensory-discriminative and affective-emotional processes. Pain experiences have a high degree of variability depending on their context and prior anticipation. Viewing pain perception as a perceptual inference problem, we propose a predictive coding paradigm to characterize evoked and non-evoked pain. We record the local field potentials (LFPs) from the primary somatosensory cortex (S1) and the anterior cingulate cortex (ACC) of freely behaving rats-two regions known to encode the sensory-discriminative and affective-emotional aspects of pain, respectively. We further use predictive coding to investigate the temporal coordination of oscillatory activity between the S1 and ACC. Specifically, we develop a phenomenological predictive coding model to describe the macroscopic dynamics of bottom-up and top-down activity. Supported by recent experimental data, we also develop a biophysical neural mass model to describe the mesoscopic neural dynamics in the S1 and ACC populations, in both naive and chronic pain-treated animals. Our proposed predictive coding models not only replicate important experimental findings, but also provide new prediction about the impact of the model parameters on the physiological or behavioral read-out-thereby yielding mechanistic insight into the uncertainty of expectation, placebo or nocebo effect, and chronic pain.
PMID: 33595765
ISSN: 1573-6873
CID: 4781012

Deep learning for robust detection of interictal epileptiform discharges

Geng, David; Alkhachroum, Ayham; Melo Bicchi, Manuel; Jagid, Jonathan; Cajigas, Iahn; Chen, Zhe Sage
OBJECTIVE:Automatic detection of interictal epileptiform discharges (IEDs, short as ``spikes'') from an epileptic brain can help predict seizure recurrence and support the diagnosis of epilepsy. Developing fast, reliable and robust detection methods for IEDs based on scalp or intracortical EEG may facilitate online seizure monitoring and closed-loop neurostimulation. APPROACH/METHODS:We developed a new deep learning approach, which employs a long short-term memory (LSTM) network architecture (``IEDnet'') and an auxiliary classifier generative adversarial network (AC-GAN), to train on both expert-annotated and augmented spike events from intracranial electroencephalography (iEEG) recordings of epilepsy patients. We validated our IEDnet with two real-world iEEG datasets, and compared IEDnet with the support vector machine (SVM) and random forest (RF) classifiers on their detection performances. MAIN RESULTS/RESULTS:IEDnet achieved excellent cross-validated detection performances in terms of both sensitivity and specificity, and outperformed SVM and RF. Synthetic spike samples augmented by AC-GAN further improved the detection performance. In addition, the performance of IEDnet was robust with respect to the sampling frequency and noise. Furthermore, we also demonstrated the cross-institutional generalization ability of IEDnet while testing between two datasets. SIGNIFICANCE/CONCLUSIONS:IEDnet achieves excellent detection performances in identifying interictal spikes. AC-GAN can produce augmented iEEG samples to improve supervised deep learning.
PMID: 33770777
ISSN: 1741-2552
CID: 4823682

Fear-induced brain activations distinguish anxious and trauma-exposed brains

Wen, Zhenfu; Marin, Marie-France; Blackford, Jennifer Urbano; Chen, Zhe Sage; Milad, Mohammed R
Translational models of fear conditioning and extinction have elucidated a core neural network involved in the learning, consolidation, and expression of conditioned fear and its extinction. Anxious or trauma-exposed brains are characterized by dysregulated neural activations within regions of this fear network. In this study, we examined how the functional MRI activations of 10 brain regions commonly activated during fear conditioning and extinction might distinguish anxious or trauma-exposed brains from controls. To achieve this, activations during four phases of a fear conditioning and extinction paradigm in 304 participants with or without a psychiatric diagnosis were studied. By training convolutional neural networks (CNNs) using task-specific brain activations, we reliably distinguished the anxious and trauma-exposed brains from controls. The performance of models decreased significantly when we trained our CNN using activations from task-irrelevant brain regions or from a brain network that is irrelevant to fear. Our results suggest that neuroimaging data analytics of task-induced brain activations within the fear network might provide novel prospects for development of brain-based psychiatric diagnosis.
PMID: 33441547
ISSN: 2158-3188
CID: 4747012

Spatiotemporal patterns of rodent hippocampal field potentials uncover spatial representations

Chen, Zhe S
ORIGINAL:0015305
ISSN: 2667-2375
CID: 5000172

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

Ketamine normalizes high-gamma power in the anterior cingulate cortex in a rat chronic pain model

Friesner, Isabel D; Martinez, Erik; Zhou, Haocheng; Gould, Jonathan Douglas; Li, Anna; Chen, Zhe Sage; Zhang, Qiaosheng; Wang, Jing
Chronic pain alters cortical and subcortical plasticity, causing enhanced sensory and affective responses to peripheral nociceptive inputs. Previous studies have shown that ketamine had the potential to inhibit abnormally amplified affective responses of single neurons by suppressing hyperactivity in the anterior cingulate cortex (ACC). However, the mechanism of this enduring effect has yet to be understood at the network level. In this study, we recorded local field potentials from the ACC of freely moving rats. Animals were injected with complete Freund's adjuvant (CFA) to induce persistent inflammatory pain. Mechanical stimulations were administered to the hind paw before and after CFA administration. We found a significant increase in the high-gamma band (60-100 Hz) power in response to evoked pain after CFA treatment. Ketamine, however, reduced the high-gamma band power in response to evoked pain in CFA-treated rats. In addition, ketamine had a sustained effect on the high-gamma band power lasting up to five days after a single dose administration. These results demonstrate that ketamine has the potential to alter maladaptive neural responses in the ACC induced by chronic pain.
PMCID:7513294
PMID: 32967695
ISSN: 1756-6606
CID: 4617632

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