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106


Fear Extinction Learning Modulates Large-scale Brain Connectivity

Wen, Zhenfu; Chen, Zhe Sage; Milad, Mohammed R
Exploring the neural circuits of the extinction of conditioned fear is critical to advance our understanding of fear- and anxiety-related disorders. The field has focused on examining the role of various regions of the medial prefrontal cortex, insular cortex, hippocampus, and amygdala in conditioned fear and its extinction. The contribution of this 'fear network' to the conscious awareness of fear has recently been questioned. And as such, there is a need to examine higher/multiple cortical systems that might contribute to the conscious feeling of fear and anxiety. Herein, we studied functional connectivity patterns across the entire brain to examine the contribution of multiple networks to the acquisition of fear extinction learning and its retrieval. We conducted trial-by-trial analyses on data from 137 healthy participants who underwent a two-day fear conditioning and extinction paradigm in a functional magnetic resonance imaging (fMRI) scanner. We found that functional connectivity across a broad range of brain regions, many of which are part of the default mode, frontoparietal, and ventral attention networks, increased from early to late extinction learning only to a conditioned cue. The increased connectivity during extinction learning predicted the magnitude of extinction memory tested 24 hours later. Together, these findings provide evidence supporting recent studies implicating distributed brain regions in learning, consolidation and expression of fear extinction memory in the human brain.
PMID: 34126211
ISSN: 1095-9572
CID: 4901082

Improving scalability in systems neuroscience

Chen, Zhe Sage; Pesaran, Bijan
Emerging technologies to acquire data at increasingly greater scales promise to transform discovery in systems neuroscience. However, current exponential growth in the scale of data acquisition is a double-edged sword. Scaling up data acquisition can speed up the cycle of discovery but can also misinterpret the results or possibly slow down the cycle because of challenges presented by the curse of high-dimensional data. Active, adaptive, closed-loop experimental paradigms use hardware and algorithms optimized to enable time-critical computation to provide feedback that interprets the observations and tests hypotheses to actively update the stimulus or stimulation parameters. In this perspective, we review important concepts of active and adaptive experiments and discuss how selectively constraining the dimensionality and optimizing strategies at different stages of discovery loop can help mitigate the curse of high-dimensional data. Active and adaptive closed-loop experimental paradigms can speed up discovery despite an exponentially increasing data scale, offering a road map to timely and iterative hypothesis revision and discovery in an era of exponential growth in neuroscience.
PMID: 33831347
ISSN: 1097-4199
CID: 4839702

Auxiliary classifier generative adversarial network for interictal epileptiform discharge modeling and EEG data augmentation

Chapter by: Geng, David; Chen, Zhe Sage
in: International IEEE/EMBS Conference on Neural Engineering, NER by
[S.l.] : IEEE Computer Society, 2021
pp. 1130-1133
ISBN: 9781728143378
CID: 4922262

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

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