Neural dynamics in the limbic system during male social behaviors.

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.

On the Role of Theory and Modeling in Neuroscience.

In recent years, the field of neuroscience has gone through rapid experimental advances and a significant increase in the use of quantitative and computational methods. This growth has created a need for clearer analyses of the theory and modeling approaches used in the field. This issue is particularly complex in neuroscience because the field studies phenomena that cross a wide range of scales and often require consideration at varying degrees of abstraction, from precise biophysical interactions to the computations they implement. We argue that a pragmatic perspective of science, in which descriptive, mechanistic, and normative models and theories each play a distinct role in defining and bridging levels of abstraction, will facilitate neuroscientific practice. This analysis leads to methodological suggestions, including selecting a level of abstraction that is appropriate for a given problem, identifying transfer functions to connect models and data, and the use of models themselves as a form of experiment.

A prototype closed-loop brain-machine interface for the study and treatment of pain.

Chronic pain is characterized by discrete pain episodes of unpredictable frequency and duration. This hinders the study of pain mechanisms and contributes to the use of pharmacological treatments associated with side effects, addiction and drug tolerance. Here, we show that a closed-loop brain-machine interface (BMI) can modulate sensory-affective experiences in real time in freely behaving rats by coupling neural codes for nociception directly with therapeutic cortical stimulation. The BMI decodes the onset of nociception via a state-space model on the basis of the analysis of online-sorted spikes recorded from the anterior cingulate cortex (which is critical for pain processing) and couples real-time pain detection with optogenetic activation of the prelimbic prefrontal cortex (which exerts top-down nociceptive regulation). In rats, the BMI effectively inhibited sensory and affective behaviours caused by acute mechanical or thermal pain, and by chronic inflammatory or neuropathic pain. The approach provides a blueprint for demand-based neuromodulation to treat sensory-affective disorders, and could be further leveraged for nociceptive control and to study pain mechanisms.

Uncovering spatial representations from spatiotemporal patterns of rodent hippocampal field potentials.

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.

Stimulus-Driven and Spontaneous Dynamics in Excitatory-Inhibitory Recurrent Neural Networks for Sequence Representation.

Recurrent neural networks (RNNs) have been widely used to model sequential neural dynamics ("neural sequences") of cortical circuits in cognitive and motor tasks. Efforts to incorporate biological constraints and Dale's principle will help elucidate the neural representations and mechanisms of underlying circuits. We trained an excitatory-inhibitory RNN to learn neural sequences in a supervised manner and studied the representations and dynamic attractors of the trained network. The trained RNN was robust to trigger the sequence in response to various input signals and interpolated a time-warped input for sequence representation. Interestingly, a learned sequence can repeat periodically when the RNN evolved beyond the duration of a single sequence. The eigenspectrum of the learned recurrent connectivity matrix with growing or damping modes, together with the RNN's nonlinearity, were adequate to generate a limit cycle attractor. We further examined the stability of dynamic attractors while training the RNN to learn two sequences. Together, our results provide a general framework for understanding neural sequence representation in the excitatory-inhibitory RNN.

Predictive coding models for pain perception.

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.

Efficient Position Decoding Methods Based on Fluorescence Calcium Imaging in the Mouse Hippocampus.

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.

Mapping Cortical Integration of Sensory and Affective Pain Pathways.

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.

Deep Learning Based on Standard H&E Images of Primary Melanoma Tumors Identifies Patients at Risk for Visceral Recurrence and Death.

PURPOSE: Biomarkers for disease-specific survival (DSS) in early-stage melanoma are needed to select patients for adjuvant immunotherapy and accelerate clinical trial design. We present a pathology-based computational method using a deep neural network architecture for DSS prediction. EXPERIMENTAL DESIGN: The model was trained on 108 patients from four institutions and tested on 104 patients from Yale School of Medicine (YSM, New Haven, CT). A receiver operating characteristic (ROC) curve was generated on the basis of vote aggregation of individual image sequences, an optimized cutoff was selected, and the computational model was tested on a third independent population of 51 patients from Geisinger Health Systems (GHS). RESULTS: Area under the curve (AUC) in the YSM patients was 0.905 (P < 0.0001). AUC in the GHS patients was 0.880 (P < 0.0001). Using the cutoff selected in the YSM cohort, the computational model predicted DSS in the GHS cohort based on Kaplan-Meier (KM) analysis (P < 0.0001). CONCLUSIONS: The novel method presented is applicable to digital images, obviating the need for sample shipment and manipulation and representing a practical advance over current genetic and IHC-based methods.

Enzyme and Transporter Kinetics for CPT-11 (Irinotecan) and SN-38: An Insight on Tumor Tissue Compartment Pharmacokinetics Using PBPK.

BACKGROUND: Computational tools are becoming more and more powerful and comprehensive as compared to past decades in facilitating pharmaceutical, pharmacological and clinical practice. Anticancer agents are used either as monotherapy or in combination therapy to treat malignant conditions of the body. A single antineoplastic agent may be used in different types of malignancies at different doses according to the stage of the disease. OBJECTIVE: To study the behavior of CPT-11 (Irinotecan) and its metabolite SN-38 in tumor tissue compartment through the Whole Body-Physiologically Pharmacokinetics (WB-PBPK) and to determine the activity of metabolic enzymes and transporters participating in the disposition of CPT-11 and SN-38 working in their physiological environment inside the human body. METHODS: Whole body PBPK approach is used to determine the activity of different metabolic enzymes and transporters involved in the disposition of CPT-11 and its active metabolite, SN-38. The concentrations and pharmacokinetic parameters of the parent compound and its metabolite administered at clinically applicable dose via the intravenous route in the tumor tissue are predicted using this approach. RESULTS: The activity rate constants of metabolic enzymes and transporters of CPT-11 are derived at their natural anatomic locations. Concentration-time curves of CPT-11 and SN-38 with their 5th to 95th percentage range are achieved at the tumor tissue level. Mean tumor tissue pharmacokinetics of both compounds are determined in a population of 100 individuals. CONCLUSION: Tumor tissue concentration-time curves of CPT-11 and SN-38 can be determined via PBPK modeling. Rate constants of enzymes and transporters can be shown for healthy and tumor bearing individuals. The results will throw light on the effective concentration of active compound at its target tissue at the clinically applied IV dose.

From Antimicrobial to Anticancer Peptides: The Transformation of Peptides.

BACKGROUND: Antimicrobial peptides play an important role in the innate immune system. Possessing broad-spectrum antibacterial activity, antimicrobial peptides can quickly treat and kill various targets, including gram-negative bacteria, gram-positive bacteria, fungi, and tumor cells. OBJECTIVE: An overview of the state of play with regard to the research trend of antimicrobial peptides in recent years and the situation of targeting tumor cells, and to make statistical analysis of the patents related to anticancer peptides published in recent years, is important both from toxicological and medical tumor therapy point of view. METHODS: Based on the Science Citation Index Expanded version, the Derwent Innovation Index and Innography as data sources, the relevant literature and patents concerning antimicrobial peptides and anticancer peptides were analyzed through the Thomson Data Analyzer. Results of toxicologic and pharmacologic studies that brought to the development of patents for methods to novel tumor drugs were analyzed and sub-divided according to the specific synthesis of anticancer peptides. RESULTS: The literature and patent search data show that the research and development of global antimicrobial peptides and anticancer peptides has been in an incremental mode. Growing patent evidence indicate that bioinformatics technology is a valuable strategy to modify, synthesize or recombine existing antimicrobial peptides to obtain tumor drugs with high activity, low toxicity and multiple targets. CONCLUSION: These findings may have important clinical implications for cancer treatment, especially in patients with conditions that are not currently treatable by other drugs, or that are resistant to existing cancer drugs.