Faculty Bibliography

Distributed hypothalamic-midbrain neural circuits orchestrate complex behavioral responses during social interactions. How population-averaged neural activity measured by multi-fiber photometry (MFP) for calcium fluorescence signals correlates with social behaviors is a fundamental question. We propose a state-space analysis framework to characterize mouse MFP data based on dynamic latent variable models, which include continuous-state linear dynamical system (LDS) and discrete-state hidden semi-Markov model (HSMM). We validate these models on extensive MFP recordings during aggressive and mating behaviors in male-male and male-female interactions, respectively. Our results show that these models are capable of capturing both temporal behavioral structure and associated neural states. Overall, these analysis approaches provide an unbiased strategy to examine neural dynamics underlying social behaviors and reveals mechanistic insights into the relevant networks.

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 and entorhinal cortex are in sink.

BACKGROUND/UNASSIGNED:Prefrontal cortical neurons play essential roles in performing rule-dependent tasks and working memory-based decision making. METHODS/UNASSIGNED:Motivated by PFG recordings of task-performing mice, we developed 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 spike frequency adaptation (SFA) and SuperSpike gradient methods to train the SRNN efficiently. RESULTS/UNASSIGNED:The trained SRNN produced emergent rule-specific tunings in single-unit representations, showing rule-dependent population dynamics that resembled experimentally observed data. Under varying test conditions, we manipulated the SRNN parameters or configuration in computer simulations, and we investigated the impacts of rule-coding error, delay duration, recurrent weight connectivity and sparsity, and excitation/inhibition (E/I) balance on both task performance and neural representations. CONCLUSIONS/UNASSIGNED:Overall, our modeling study provides a computational framework to understand neuronal representations at a fine timescale during working memory and cognitive control, and provides new experimentally testable hypotheses in future experiments.

INTRODUCTION/UNASSIGNED:Chronic pain negatively impacts a range of sensory and affective behaviors. Previous studies have shown that the presence of chronic pain not only causes hypersensitivity at the site of injury but may also be associated with pain-aversive experiences at anatomically unrelated sites. While animal studies have indicated that the cingulate and prefrontal cortices are involved in this generalized hyperalgesia, the mechanisms distinguishing increased sensitivity at the site of injury from a generalized site-nonspecific enhancement in the aversive response to nociceptive inputs are not well known. METHODS/UNASSIGNED: = 15) by analyzing behavioral and electroencephalographic (EEG) data. RESULTS/UNASSIGNED:As expected, participants with chronic pain endorsed enhanced pain with mechanical stimuli in both back and hand. We further analyzed electroencephalographic (EEG) recordings during these evoked pain episodes. Brain oscillations in theta and alpha bands in the medial orbitofrontal cortex (mOFC) were associated with localized hypersensitivity, while increased gamma oscillations in the anterior cingulate cortex (ACC) and increased theta oscillations in the dorsolateral prefrontal cortex (dlPFC) were associated with generalized hyperalgesia. DISCUSSION/UNASSIGNED:These findings indicate that chronic pain may disrupt multiple cortical circuits to impact nociceptive processing.

Sexual and aggressive behaviors are vital for species survival and individual reproductive success. Although many limbic regions have been found relevant to these behaviors, how social cues are represented across regions and how the network activity generates each behavior remains elusive. To answer these questions, we utilize multi-fiber photometry (MFP) to simultaneously record Ca²⁺ signals of estrogen receptor alpha (Esr1)-expressing cells from 13 limbic regions in male mice during mating and fighting. We find that conspecific sensory information and social action signals are widely distributed in the limbic system and can be decoded from the network activity. Cross-region correlation analysis reveals striking increases in the network functional connectivity during the social action initiation phase, whereas late copulation is accompanied by a "dissociated" network state. Based on the response patterns, we propose a mating-biased network (MBN) and an aggression-biased network (ABN) for mediating male sexual and aggressive behaviors, respectively.

BACKGROUND:The striatal-pallidal pathway plays an important role in cognitive control and modulation of behaviors. Globus pallidus interna (GPi), as a primary output structure, is crucial in modulating excitation and inhibition. Studies of GPi in psychiatric illnesses are lacking given the technical challenges of examining this small and functionally diverse subcortical structure. METHODS:71 medication-naïve first episode schizophrenia (FES) participants and 73 healthy controls (HC) were recruited at the Shanghai Mental Health Center. Clinical symptoms and imaging data were collected at baseline and, in a subset of patients, 8 weeks after initiating treatment. Resting-state functional connectivity of sub-regions of the GP were assessed using a novel mask that combines two atlases to create 8 ROIs in the GP. RESULTS: = 0.486, p < 0.001). CONCLUSIONS:Our results implicate striatal-pallidal-thalamic pathways in antipsychotic efficacy. If replicated, these findings may reflect failure of neurodevelopmental processes in adolescence and early adulthood that decrease functional connectivity as an index of failure of the limbic/associative GPi to appropriately inhibit irrelevant signals in psychosis.

Machine learning is becoming an increasingly common component of routine data analyses in clinical research. The past decade in pain research has witnessed great advances in human neuroimaging and machine learning. With each finding, the pain research community takes one step closer to uncovering fundamental mechanisms underlying chronic pain and at the same time proposing neurophysiological biomarkers. However, it remains challenging to fully understand chronic pain due to its multidimensional representations within the brain. By utilizing cost-effective and non-invasive imaging techniques such as electroencephalography (EEG) and analyzing the resulting data with advanced analytic methods, we have the opportunity to better understand and identify specific neural mechanisms associated with the processing and perception of chronic pain. This narrative literature review summarizes studies from the last decade describing the utility of EEG as a potential biomarker for chronic pain by synergizing clinical and computational perspectives.

Injuries of various types occur commonly in the lives of humans and other animals and lead to a pattern of persistent pain and recuperative behaviour that allows safe and effective recovery. In this Perspective, we propose a control-theoretic framework to explain the adaptive processes in the brain that drive physiological post-injury behaviour. We set out an evolutionary and ethological view on how animals respond to injury, illustrating how the behavioural state associated with persistent pain and recuperation may be just as important as phasic pain in ensuring survival. Adopting a normative approach, we suggest that the brain implements a continuous optimal inference of the current state of injury from diverse sensory and physiological signals. This drives the various effector control mechanisms of behavioural homeostasis, which span the modulation of ongoing motivation and perception to drive rest and hyper-protective behaviours. However, an inherent problem with this is that these protective behaviours may partially obscure information about whether injury has resolved. Such information restriction may seed a tendency to aberrantly or persistently infer injury, and may thus promote the transition to pathological chronic pain states.

IMPORTANCE/UNASSIGNED:Sleep disorders are one of the most frequent comorbidities in children with autism spectrum disorder (ASD). However, the link between neurodevelopmental effects in ASD children with their underlying sleep microarchitecture is not well understood. An improved understanding of etiology of sleep difficulties and identification of sleep-associated biomarkers for children with ASD can improve the accuracy of clinical diagnosis. OBJECTIVES/UNASSIGNED:To investigate whether machine learning models can identify biomarkers for children with ASD based on sleep EEG recordings. DESIGN SETTING AND PARTICIPANTS/UNASSIGNED: = 79) selected from the Childhood Adenotonsillectomy Trial (CHAT) was also used to validate the models. Furthermore, an independent smaller NCH cohort of younger infants and toddlers (age: 0.5-3 yr.; 38 autism and 75 controls) was used for additional validation. MAIN OUTCOMES AND MEASURES/UNASSIGNED:We computed periodic and non-periodic characteristics from sleep EEG recordings: sleep stages, spectral power, sleep spindle characteristics, and aperiodic signals. Machine learning models including the Logistic Regression (LR) classifier, Support Vector Machine (SVM), and Random Forest (RF) model were trained using these features. We determined the autism class based on the prediction score of the classifier. The area under the receiver operating characteristics curve (AUC), accuracy, sensitivity, and specificity were used to evaluate the model performance. RESULTS/UNASSIGNED:In the NCH study, RF outperformed two other models with a 10-fold cross-validated median AUC of 0.95 (interquartile range [IQR], [0.93, 0.98]). The LR and SVM models performed comparably across multiple metrics, with median AUC 0.80 [0.78, 0.85] and 0.83 [0.79, 0.87], respectively. In the CHAT study, three tested models have comparable AUC results: LR: 0.83 [0.76, 0.92], SVM: 0.87 [0.75, 1.00], and RF: 0.85 [0.75, 1.00]. Sleep spindle density, amplitude, spindle-slow oscillation (SSO) coupling, aperiodic signal's spectral slope and intercept, as well as the percentage of REM sleep were found to be key discriminative features in the predictive models. CONCLUSION AND RELEVANCE/UNASSIGNED:Our results suggest that integration of EEG feature engineering and machine learning can identify sleep-based biomarkers for ASD children and produce good generalization in independent validation datasets. Microstructural EEG alterations may help reveal underlying pathophysiological mechanisms of autism that alter sleep quality and behaviors. Machine learning analysis may reveal new insight into the etiology and treatment of sleep difficulties in autism.

Neurons in the prefrontal cortex (PFC) can provide top-down regulation of sensory-affective experiences such as pain. Bottom-up modulation of sensory coding in the PFC, however, remains poorly understood. Here, we examined how oxytocin (OT) signaling from the hypothalamus regulates nociceptive coding in the PFC. In vivo time-lapse endoscopic calcium imaging in freely behaving rats showed that OT selectively enhanced population activity in the prelimbic PFC in response to nociceptive inputs. This population response resulted from the reduction of evoked GABAergic inhibition and manifested as elevated functional connectivity involving pain-responsive neurons. Direct inputs from OT-releasing neurons in the paraventricular nucleus (PVN) of the hypothalamus are crucial to maintaining this prefrontal nociceptive response. Activation of the prelimbic PFC by OT or direct optogenetic stimulation of oxytocinergic PVN projections reduced acute and chronic pain. These results suggest that oxytocinergic signaling in the PVN-PFC circuit constitutes a key mechanism to regulate cortical sensory processing.