Prefrontal cortical neurons play in important roles in performing rule-dependent tasks and workin... more Prefrontal cortical neurons play in important roles in performing rule-dependent tasks and working memory-based decision making. Motivated by experimental data, we develop an excitatory-inhibitory spiking recurrent neural network (SRNN) to perform a rule-dependent two-alternative forced choice (2AFC) task. We imposed several important biological constraints onto the SRNN, and adapted the spike frequency adaptation (SFA) and SuperSpike gradient methods to update the network parameters. These proposed strategies enabled us to train the SRNN efficiently and overcome the vanishing gradient problem during error back propagation through time. The trained SRNN produced rule-specific tuning in single-unit representations, showing rule-dependent population dynamics that strongly resemble experimentally observed data in rodent and monkey. Under varying test conditions, we further manipulated the parameters or configuration in computer simulation setups and investigated the impacts of rule-coding error, delay duration, weight connectivity and sparsity, and excitation/inhibition (E/I) balance on both task performance and neural representations. Overall, our modeling study provides a computational framework to understand neuronal representations at a fine timescale during working memory and cognitive control.
Computational models for state-dependent traveling waves in hippocampal formation
Hippocampal theta (4-10 Hz) oscillations have been identified as traveling waves in both rodents ... more Hippocampal theta (4-10 Hz) oscillations have been identified as traveling waves in both rodents and humans. In freely foraging rodents, the theta traveling wave is a planar wave propagating from the dorsal to ventral hippocampus along the septotemporal axis. Motivated from experimental findings, we develop a spiking neural network of excitatory and inhibitory neurons to generate state-dependent hippocampal traveling waves to improve current mechanistic understanding of propagating waves. Model simulations demonstrate the necessary conditions for generating wave propagation and characterize the traveling wave properties with respect to model parameters, running speed and brain state of the animal. Networks with long-range inhibitory connections are more suitable than networks with long-range excitatory connections. We further generalize the spiking neural network to model traveling waves in the medial entorhinal cortex (MEC) and predict that traveling theta waves in the hippocampus ...
Predictive coding is a computational theory on describing how the brain perceives and acts, which... more Predictive coding is a computational theory on describing how the brain perceives and acts, which has been widely adopted in sensory processing and motor control. Nociceptive and pain processing involves a large and distributed network of circuits. However, it is still unknown whether this distributed network is completely decentralized or requires networkwide coordination. Multiple lines of evidence from human and animal studies have suggested that the cingulate cortex and insula cortex (cingulate-insula network) are two major hubs in mediating information from sensory afferents and spinothalamic inputs, whereas subregions of cingulate and insula cortices have distinct projections and functional roles. In this mini-review, we propose an updated hierarchical predictive coding framework for pain perception and discuss its related computational, algorithmic, and implementation issues. We suggest active inference as a generalized predictive coding algorithm, and hierarchically organize...
Memory reactivations and replay, widely reported in the hippocampus and cortex across species, ha... more Memory reactivations and replay, widely reported in the hippocampus and cortex across species, have been implicated in memory consolidation, planning, and spatial and skill learning. Technological advances in electrophysiology, calcium imaging, and human neuroimaging techniques have enabled neuroscientists to measure large-scale neural activity with increasing spatiotemporal resolution and have provided opportunities for developing robust analytic methods to identify memory replay. In this article, we first review a large body of historically important and representative memory replay studies from the animal and human literature. We then discuss our current understanding of memory replay functions in learning, planning, and memory consolidation and further discuss the progress in computational modeling that has contributed to these improvements. Next, we review past and present analytic methods for replay analyses and discuss their limitations and challenges. Finally, looking ahead,...
The mediodorsal (MD) thalamus is a critical partner for the prefrontal cortex (PFC) in cognitive ... more The mediodorsal (MD) thalamus is a critical partner for the prefrontal cortex (PFC) in cognitive flexibility. Animal experiments have shown that the MD enhances prefrontal signal-to-noise ratio (SNR) in decision making under uncertainty. However, the computational mechanisms of this cognitive process remain unclear. Here we use performance-optimized computational models to dissect these mechanisms. We find that the inclusion of an MD-like feedforward module increases robustness to sensory noise and enhances working memory maintenance in the recurrent PFC network performing a context-dependent decision-making task. Incorporating genetically identified thalamocortical pathways that regulate signal amplification and noise reduction further improves performance and replicates key neurophysiological findings of neuronal tuning. Our model reveals a key computational mechanism of context-invariant, cell-type specific regulation of sensory uncertainty in a task-phase specific manner. Additi...
In light of the NIMH’s Research Domain Criteria (RDoC), the advent of functional neuroimaging, no... more In light of the NIMH’s Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of mental disorders. Machine learning (ML) and artificial intelligence (AI) technologies are playing an increasingly critical role in the new era of precision psychiatry. Combining ML/AI with neuromodulation technologies can potentially provide explainable solutions in clinical practice and effective therapeutic treatment. Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health. In this review, we provide a comprehensive review of the ML methodologies and applications by combining neuroimaging, neuromodulation and advanced mobile technologies in psychiatry practice. Additionally, we review the role of ML in molecular phenotyping, cross-species biomarker identification in precision psychiatry. We further di...
Spatially modulated grid cells has been recently found in the rat secondary visual cortex (V2) du... more Spatially modulated grid cells has been recently found in the rat secondary visual cortex (V2) during activation navigation. However, the computational mechanism and functional significance of V2 grid cells remain unknown, and a theory-driven conceptual model for experimentally observed visual grids is missing. To address the knowledge gap and make experimentally testable predictions, here we trained a biologically-inspired excitatory-inhibitory recurrent neural network (E/I-RNN) to perform a two-dimensional spatial navigation task with multisensory (e.g., velocity, acceleration, and visual) input. We found grid-like responses in both excitatory and inhibitory RNN units, and these grid responses were robust with respect to the choices of spatial cues, dimensionality of visual input, activation function, and network connectivity. Dimensionality reduction analysis of population responses revealed a low-dimensional torus-like manifold and attractor, showing the stability of grid patter...
Prefrontal cortex plays a prominent role in performing flexible cognitive functions and working m... more Prefrontal cortex plays a prominent role in performing flexible cognitive functions and working memory, yet the underlying computational principle remains poorly understood. Here we trained a rate-based recurrent neural network (RNN) to explore how the context rules are encoded, maintained across seconds-long mnemonic delay, and subsequently used in a context-dependent decision-making task. The trained networks emerged key experimentally observed features in the prefrontal cortex (PFC) of rodent and monkey experiments, such as mixed-selectivity, sparse representations, neuronal sequential activity and rotation dynamics. To uncover the high-dimensional neural dynamical system, we further proposed a geometric framework to quantify and visualize population coding and sensory integration in a temporally-defined manner. We employed dynamic epoch-wise principal component analysis (PCA) to define multiple task-specific subspaces and task-related axes, and computed the angles between task-r...
Objective. The orofacial primary motor cortex (MIo) plays a critical role in controlling tongue a... more 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. 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 the 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 the MIo and identify the complex mapping between MIo ensemble spike activity and high-dimensional kinematics. Main 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. 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 stagedependent manner. Unsupervised learning and decoding analysis may reveal the link between complex MIo spatiotemporal patterns and chewing kinematics.
Glucocorticoid (GC) is widely used for therapeutic purposes in immunological and hematological di... more Glucocorticoid (GC) is widely used for therapeutic purposes in immunological and hematological disorders. Annexin A1 (ANXA1/lipocortin-1/lipomodulin), a GC-inducible molecule, was regarded as a vital anti-inflammatory mediator of GC. Thioredoxin binding protein-2 (TBP-2/VDUP1/TXNIP), a regulator of redox reactions, cell growth and lipid metabolism, was also reportedly induced by GC. HTLV-I infected T cells undergo the transition from the IL-2 dependent to IL-2 independent growth during the long-term culture in vitro. We found that these T cells responded to GC with growth arrest and apoptosis in the IL-2 dependent growth stage, whereas they failed to respond to GC after their growth had shifted into the IL-2 independent stage. Here we employed these T cell lines and studied the roles of ANXA1 and TBP-2 in mediating GC-induced apoptosis. In GC-sensitive T cells, ANXA1 expression was negligible and unaffected by GC treatment, whereas TBP-2 was expressed and induced by GC treatment. In GC-resistant T cells, however, ANXA1 was highly expressed regardless of GC treatment and promoted cellular proliferation. In contrast, TBP-2 expression was lost and could not mediate the GC-induced apoptosis. In conclusion, these results suggest that TBP-2, but not ANXA1, is directly involved in the switching of GC sensitivity and GC resistance in HTLV-I infected T cell lines, whereas ANXA1 may be 3 a biomarker indicative of the advanced stage of the transformation.
Pain is known to have sensory and affective components. The sensory pain component is encoded by ... more Pain is known to have sensory and affective components. The sensory pain component is encoded by neurons in the primary somatosensory cortex (S1), whereas the emotional or affective pain experience is in large part processed by neural activities in the anterior cingulate cortex (ACC). The timing of how a mechanical or thermal noxious stimulus triggers activation of peripheral pain fibers is well-known. However, the temporal processing of nociceptive inputs in the cortex remains little studied. Here, we took two approaches to examine how nociceptive inputs are processed by the S1 and ACC. We simultaneously recorded local field potentials in both regions, during the application of a brain-computer interface (BCI). First, we compared event related potentials in the S1 and ACC. Next, we used an algorithmic pain decoder enabled by machine-learning to detect the onset of pain which was used during the implementation of the BCI to automatically treat pain. We found that whereas mechanical ...
ObjectiveSudden unexpected death in epilepsy (SUDEP) is the leading cause of epilepsy-related mor... more ObjectiveSudden unexpected death in epilepsy (SUDEP) is the leading cause of epilepsy-related mortality. Although lots of effort has been made in identifying clinical risk factors for SUDEP in the literature, there are few validated methods to predict individual SUDEP risk. Prolonged postictal EEG suppression (PGES) is a potential SUDEP biomarker, but its occurrence is infrequent and requires epilepsy monitoring unit admission. We use machine learning methods to examine SUDEP risk using interictal EEG and ECG recordings from SUDEP cases and matched living epilepsy controls.MethodsThis multicenter, retrospective, cohort study examined interictal EEG and ECG recordings from 30 SUDEP cases and 58 age-matched living epilepsy patient controls. We trained machine learning models with interictal EEG and ECG features to predict the retrospective SUDEP risk for each patient. We assessed cross-validated classification accuracy and the area under the receiver operating characteristic (AUC) cur...
Background: Advances in human neuroimaging has enabled us to study functional connections among v... more 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.
Chronic pain alters cortical and subcortical plasticity, causing enhanced sensory and affective r... more 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-gamm...
Objective. The primary somatosensory cortex (S1) and the anterior cingulate cortex (ACC) are two ... more Objective. The primary somatosensory cortex (S1) and the anterior cingulate cortex (ACC) are two of the most important cortical brain regions encoding the sensorydiscriminative 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 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 between the S1 and ACC. Main results. We found an 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 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.
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Papers by Sage Chen