Faculty Bibliography

The capacity of hippocampal circuits to transform inputs into downstream outputs is fundamental to navigation and memory, yet the circuit-level mechanisms that enable this flexibility in adapting to experience remain unclear. Here we approach this problem by performing large-scale (up to 1,024 channel) recordings across the hippocampal-retrosplenial cortex (RSC) circuit in behaving mice, enabling simultaneous access to spiking activity in dentate gyrus (DG), CA3, CA2, CA1 and RSC. On the basis of a linear dimensionality-reduction technique known as partial canonical correlation analysis, we identify low-dimensional communication subspaces¹ between two regions while accounting for influences from a third area. These subspaces captured distinct input-output transformations in the CA1 region, linking upstream hippocampal activity (DG, CA3 and CA2) to downstream cortical targets (RSC). Intrinsic firing properties and anatomical location constrained subspace memberships-members were mapped to deep sublayers of the CA3-CA1-RSC axis during both spatial and non-spatial tasks. These subspaces could recombine overlapping neuronal pools to support distinct interareal interactions across changing experiences and brain states. Reactivation patterns of CA1-CA3 subspaces, but not those of CA1-RSC, during post-experience sleep correlated with replay, reflecting a plasticity-stability balance in the input-output transformation along the hippocampal-retrosplenial axis. Our findings suggest a model in which hippocampal-neocortical communication reconfigures predetermined circuit motifs to flexibly encode experiences.

Artificial intelligence (AI) and machine learning (ML) have seen remarkable growth in mental health applications over the past few decades, demonstrating significant potential to transform psychiatric care. Despite these advancements, the translation of AI systems into clinical practice remains fraught with challenges. This Perspective examines critical hurdles in psychiatric AI research, emphasizing limitations in research rigor, model reliability, interpretability, clinical utility, and ethical considerations. We argue that a human-assisted AI framework-incorporating incremental feedback, self-adaptation, and dynamic collaboration-can address biases, enhance transparency, and build trust in AI systems. Moreover, initiatives in clinical education, cultural adaptation, and data/software sharing are essential to fostering public engagement, data transparency, and research reproducibility. By focusing on these areas, we aim to bridge the gap between AI potential and its successful, ethical implementation in mental health care, guiding the development of trustworthy, effective, and culturally adaptive AI-powered psychiatric tools.

The autonomic nervous system (ANS) plays a vital role in health care for both acute care and chronic diseases. The traditional view of the ANS is to divide it into individual organ systems and study the separate components with a reductionist approach, which has been proven insufficient. Here, we argue that a holistic network-level view of the ANS is critical for generating new insights and deepening our understanding of its complex and dynamic functions. In this review, we treat the ANS as such a coordinated and dynamic network. We advocate for studying its interactions with major organ systems and the central nervous system using continuous and longitudinal monitoring in ambulatory and at-home settings rather than clinic-based snapshots. We first briefly review ANS physiology, then outline our network perspective, and finally highlight cutting-edge research directions and emerging engineering innovations in ANS monitoring, modeling, and modulation that benefit from this network-level view.

The mammalian forebrain is the seat of higher cognition with architectural parallels to modern machine learning systems. Specifically, the cortex resembles recurrent neural networks (RNNs) while the thalamus resembles feedforward neural networks (FNNs). How such architectural features endow the forebrain with its learning capacity, is unknown. Here we take inspiration from empirical thalamocortical discovery and develop a multiplicative coupling mechanism between RNN-FNN architectures that collectively enhance their computational strengths and learning. The multiplicative interaction imposes a Hebbian-weight amplification onto synaptic-neuronal coupling, enabling context-dependent gating and rapid switching. We demonstrate that multiplicative feedback-driven synaptic plasticity achieves 2-100 folds of speed improvement in supervised, reinforcement and unsupervised learning settings, boosting memory capacity, model robustness and generalization of RNNs. We further demonstrate the efficacy and biological plausibility of multiplicative gating in modeling multiregional circuits, including a prefrontal cortex-mediodorsal thalamus network for context-dependent decision making, a cortico-thalamic-cortical network for working memory and attention, and an entorhinal cortex-hippocampus network for visuospatial navigation and sequence replay. Taken together, our results demonstrate the profound insights into neuroscience-inspired computation that enable multi-plastic attractor dynamics and computation in recurrent neural circuits.

Electroencephalography (EEG) connectomes offer powerful tools for studying brain connectivity and advancing our understanding of brain function and dysfunction in both healthy and pathological conditions. Celebrating the 100th anniversary of EEG discovery, this Perspective explores the frontiers of EEG-based brain connectivity in basic and translational neuroscience research. We review new concepts, emerging analysis frameworks and significant advances in harnessing EEG connectomes. We suggest that leveraging machine learning approaches may offer promising paths to maximize the strengths of EEG connectomes. We also discuss how combined EEG connectome and neuromodulation provide a personalized and adaptive closed-loop paradigm to promote neuroplasticity and treat dysfunctional brains. We further address the limitations and challenges of the current methodology and touch on important issues regarding research rigour and clinical viability for translational impact.

INTRODUCTION/UNASSIGNED:This study aims to investigate the interregional functional connectivity in chronic back pain patients with widespread hyperalgesia, patients with localized back pain, and pain-free controls using stimulus-evoked high-density EEG recordings. METHODS/UNASSIGNED:We conducted high-density EEG recordings to compare the functional connectivity and betweenness centrality between these groups. RESULTS/UNASSIGNED:Compared with controls, chronic pain patients showed altered functional connectivity between regions that process cognitive information and regions that process sensory or affective information. Widespread hyperalgesia, however, is further differentiated from localized pain by decreased inter-hemispheric connectivity of sensory and affective areas and increased intra-hemispheric connectivity between sensory and cognitive cortices. Graph-theoretic analysis showed that whereas chronic pain is associated with decreased centrality of prefrontal, orbitofrontal, and cingulate areas, widespread hyperalgesia is distinguished by increased centrality of prefrontal and insular areas. DISCUSSION/UNASSIGNED:Together, our results show that although widespread hyperalgesia shares certain features with localized pain, it is further characterized by distinct cortical mechanisms.

Widespread hyperalgesia, characterized by pain sensitivity beyond the primary pain site, is a common yet under-characterized feature across chronic pain conditions, including chronic pancreatitis (CP). In this exploratory study, we identified a candidate neural biosignature of widespread hyperalgesia using high-density electroencephalography (EEG) in patients with chronic low back pain (cLBP). Specifically, stimulus-evoked delta, theta, and alpha oscillatory activity in the bilateral medial orbitofrontal cortex (mOFC) differentiated cLBP patients with widespread hyperalgesia from healthy controls. To examine cross-condition generalizability and advance predictive biomarker development for CP, we applied this mOFC-derived EEG biosignature to an independent cohort of patients with CP. The biosignature distinguished CP patients with widespread hyperalgesia and predicted individual treatment responses to peripherally targeted endoscopic therapy. These preliminary findings provide early support for a shared cortical signature of central sensitization across pain conditions and offer translational potential for developing EEG-based predictive tools for treatment response in CP.

Identifying directed spectral information flow between multivariate time series is important for many applications in finance, climate, geophysics and neuroscience. Spectral Granger causality (SGC) is a prediction-based measure characterizing directed information flow at specific oscillatory frequencies. However, traditional vector autoregressive (VAR) approaches are insufficient to assess SGC when time series have mixed frequencies (MF) or are coupled by nonlinearity. Here we propose a time-frequency canonical correlation analysis approach ("MF-TFCCA") to assess the strength and driving frequency of spectral information flow. We validate the approach with extensive computer simulations on MF time series under various interaction conditions and further assess statistical significance of the estimate with surrogate data. In various benchmark comparisons, MF-TFCCA consistently outperforms the traditional parametric MF-VAR model in both computational efficiency and detection accuracy, and recovers the dominant driving frequencies. We further apply MF-TFCCA to real-life finance, climate and neuroscience data. Our analysis framework provides an exploratory and computationally efficient nonparametric approach to quantify directed information flow between MF time series in the presence of complex and nonlinear interactions.

The mediodorsal (MD) thalamus is a critical partner for the prefrontal cortex (PFC) in cognitive control. Accumulating evidence has shown that the MD regulates task uncertainty in decision making and enhance cognitive flexibility. However, the computational mechanism of this cognitive process remains unclear. Here we trained biologically-constrained computational models to delineate the mechanistic role of MD in context-dependent decision making. We show that the addition of a feedforward MD structure to the recurrent PFC increases robustness to low cueing signal-to-noise ratio, enhances working memory, and enables rapid context switching. Incorporating genetically identified thalamocortical connectivity and interneuron cell types into the model replicates key neurophysiological findings in task-performing animals. Our model reveals computational mechanisms and geometric interpretations of MD in regulating cue uncertainty and context switching to enable cognitive flexibility. Our model makes experimentally testable predictions linking cognitive deficits with disrupted thalamocortical connectivity, prefrontal excitation-inhibition imbalance and dysfunctional inhibitory cell types.