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Uncovering spatial representations from spatiotemporal patterns of rodent hippocampal field potentials
Cao, Liang; Varga, Viktor; Chen, Zhe S
Spatiotemporal patterns of large-scale spiking and field potentials of the rodent hippocampus encode spatial representations during maze runs, immobility, and sleep. Here, we show that multisite hippocampal field potential amplitude at ultra-high-frequency band (FPAuhf), a generalized form of multiunit activity, provides not only a fast and reliable reconstruction of the rodent's position when awake, but also a readout of replay content during sharp-wave ripples. This FPAuhf feature may serve as a robust real-time decoding strategy from large-scale recordings in closed-loop experiments. Furthermore, we develop unsupervised learning approaches to extract low-dimensional spatiotemporal FPAuhf features during run and ripple periods and to infer latent dynamical structures from lower-rank FPAuhf features. We also develop an optical flow-based method to identify propagating spatiotemporal LFP patterns from multisite array recordings, which can be used as a decoding application. Finally, we develop a prospective decoding strategy to predict an animal's future decision in goal-directed navigation.
PMCID:8654278
PMID: 34888543
ISSN: 2667-2375
CID: 5110442
Decoding pain from brain activity
Chen, Zhe Sage
Pain is a dynamic, complex and multidimensional experience. The identification of pain from brain activity as neural readout may effectively provide a neural code for pain, and further provide useful information for pain diagnosis and treatment. Advances in neuroimaging and large-scale electrophysiology have enabled us to examine neural activity with improved spatial and temporal resolution, providing opportunities to decode pain in humans and freely behaving animals. This topical review provides a systematical overview of state-of-the-art methods for decoding pain from brain signals, with special emphasis on electrophysiological and neuroimaging modalities. We show how pain decoding analyses can help pain diagnosis and discovery of neurobiomarkers for chronic pain. Finally, we discuss the challenges in the research field and point to several important future research directions.
PMID: 34608868
ISSN: 1741-2552
CID: 5039502
Sharp Tuning of Head Direction and Angular Head Velocity Cells in the Somatosensory Cortex
Long, Xiaoyang; Deng, Bin; Young, Calvin K; Liu, Guo-Long; Zhong, Zeqi; Chen, Qian; Yang, Hui; Lv, Sheng-Qing; Chen, Zhe Sage; Zhang, Sheng-Jia
Head direction (HD) cells form a fundamental component in the brain's spatial navigation system and are intricately linked to spatial memory and cognition. Although HD cells have been shown to act as an internal neuronal compass in various cortical and subcortical regions, the neural substrate of HD cells is incompletely understood. It is reported that HD cells in the somatosensory cortex comprise regular-spiking (RS, putative excitatory) and fast-spiking (FS, putative inhibitory) neurons. Surprisingly, somatosensory FS HD cells fire in bursts and display much sharper head-directionality than RS HD cells. These FS HD cells are nonconjunctive, rarely theta rhythmic, sparsely connected and enriched in layer 5. Moreover, sharply tuned FS HD cells, in contrast with RS HD cells, maintain stable tuning in darkness; FS HD cells' coexistence with RS HD cells and angular head velocity (AHV) cells in a layer-specific fashion through the somatosensory cortex presents a previously unreported configuration of spatial representation in the neocortex. Together, these findings challenge the notion that FS interneurons are weakly tuned to sensory stimuli, and offer a local circuit organization relevant to the generation and transmission of HD signaling in the brain.
PMID: 35297541
ISSN: 2198-3844
CID: 5182432
Prefrontal transthalamic uncertainty processing drives flexible switching
Lam, Norman H; Mukherjee, Arghya; Wimmer, Ralf D; Nassar, Matthew R; Chen, Zhe Sage; Halassa, Michael M
Making adaptive decisions in complex environments requires appropriately identifying sources of error1,2. The frontal cortex is critical for adaptive decisions, but its neurons show mixed selectivity to task features3 and their uncertainty estimates4, raising the question of how errors are attributed to their most likely causes. Here, by recording neural responses from tree shrews (Tupaia belangeri) performing a hierarchical decision task with rule reversals, we find that the mediodorsal thalamus independently represents cueing and rule uncertainty. This enables the relevant thalamic population to drive prefrontal reconfiguration following a reversal by appropriately attributing errors to an environmental change. Mechanistic dissection of behavioural switching revealed a transthalamic pathway for cingulate cortical error monitoring5,6 to reconfigure prefrontal executive control7. Overall, our work highlights a potential role for the thalamus in demixing cortical signals while providing a low-dimensional pathway for cortico-cortical communication.
PMID: 39537928
ISSN: 1476-4687
CID: 5753342
Closed-loop neural interfaces for pain: Where do we stand?
Wang, Jing; Chen, Zhe Sage
Advances in closed-loop neural interfaces and neuromodulation have offered a potentially effective and non-addictive treatment for chronic pain. These interfaces link neural sensors with device outputs to provide temporally precise stimulation. We discuss challenges and trends of state-of-the-art neural interfaces for treating pain in animal models and human pilot trials.
PMID: 39413730
ISSN: 2666-3791
CID: 5711692
Identifying behavioral links to neural dynamics of multifiber photometry recordings in a mouse social behavior network
Chen, Yibo; Chien, Jonathan; Dai, Bing; Lin, Dayu; Chen, Zhe Sage
Distributed hypothalamic-midbrain neural circuits help orchestrate complex behavioral responses during social interactions. Given rapid advances in optical imaging, it is a fundamental question how population-averaged neural activity measured by multi-fiber photometry (MFP) for calcium fluorescence signals correlates with social behaviors is a fundamental question. This paper aims to investigate the correspondence between MFP data and social behaviors. 
Approach: We propose a state-space analysis framework to characterize mouse MFP data based on dynamic latent variable models, which include a continuous-state linear dynamical system (LDS) and a 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. 
Main Results: Our results show that these models are capable of capturing both temporal behavioral structure and associated neural states, and produce interpretable latent states. Our approach is also validated in computer simulations in the presence of known ground truth.
Significance: Overall, these analysis approaches provide a state-space framework to examine neural dynamics underlying social behaviors and reveals mechanistic insights into the relevant networks. 

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PMID: 38861996
ISSN: 1741-2552
CID: 5668992
Large-scale foundation models and generative AI for BigData neuroscience
Wang, Ran; Chen, Zhe Sage
Recent advances in machine learning have led to revolutionary breakthroughs in computer games, image and natural language understanding, and scientific discovery. Foundation models and large-scale language models (LLMs) have recently achieved human-like intelligence thanks to BigData. With the help of self-supervised learning (SSL) and transfer learning, these models may potentially reshape the landscapes of neuroscience research and make a significant impact on the future. Here we present a mini-review on recent advances in foundation models and generative AI models as well as their applications in neuroscience, including natural language and speech, semantic memory, brain-machine interfaces (BMIs), and data augmentation. We argue that this paradigm-shift framework will open new avenues for many neuroscience research directions and discuss the accompanying challenges and opportunities.
PMID: 38897235
ISSN: 1872-8111
CID: 5672162
Pixel-wise programmability enables dynamic high-SNR cameras for high-speed microscopy
Zhang, Jie; Newman, Jonathan; Wang, Zeguan; Qian, Yong; Feliciano-Ramos, Pedro; Guo, Wei; Honda, Takato; Chen, Zhe Sage; Linghu, Changyang; Etienne-Cummings, Ralph; Fossum, Eric; Boyden, Edward; Wilson, Matthew
High-speed wide-field fluorescence microscopy has the potential to capture biological processes with exceptional spatiotemporal resolution. However, conventional cameras suffer from low signal-to-noise ratio at high frame rates, limiting their ability to detect faint fluorescent events. Here, we introduce an image sensor where each pixel has individually programmable sampling speed and phase, so that pixels can be arranged to simultaneously sample at high speed with a high signal-to-noise ratio. In high-speed voltage imaging experiments, our image sensor significantly increases the output signal-to-noise ratio compared to a low-noise scientific CMOS camera (~2-3 folds). This signal-to-noise ratio gain enables the detection of weak neuronal action potentials and subthreshold activities missed by the standard scientific CMOS cameras. Our camera with flexible pixel exposure configurations offers versatile sampling strategies to improve signal quality in various experimental conditions.
PMID: 38802338
ISSN: 2041-1723
CID: 5663342
Identifying behavioral links to neural dynamics of multifiber photometry recordings in a mouse social behavior network
Chen, Yibo; Chien, Jonathan; Dai, Bing; Lin, Dayu; Chen, Zhe Sage
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.
PMCID:10793434
PMID: 38234793
CID: 5631482
Aberrant resting-state functional connectivity of the globus pallidus interna in first-episode schizophrenia
Qi, Wei; Wen, Zhenfu; Chen, Jingyun; Capichioni, Gillian; Ando, Fumika; Chen, Zhe Sage; Wang, Jijun; Yoncheva, Yuliya; Castellanos, Francisco X; Milad, Mohammed; Goff, Donald C
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.
PMID: 37716202
ISSN: 1573-2509
CID: 5593342