Neural Coding of Appetitive Food Experiences in the Amygdala.

Real-life experiences involve the consumption of various foods, yet it is unclear how the brain distinguishes and categorizes such food experiences. Despite the crucial roles of the basolateral amygdala (BLA) in appetitive behavior and emotion, how BLA pyramidal cells and interneurons encode food experiences has not yet been well characterized. Here we employ large-scale tetrode recording techniques to investigate the coding properties of pyramidal neurons vs. fast-spiking interneurons in the BLA as mice freely consumed a variety of foods, such as biscuits, rice, milk and water. We found that putative pyramidal cells conformed to the power-of-two-based permutation logic, as postulated by the Theory of Connectivity, to generate specific-to-general neural clique-coding patterns. Many pyramidal cells exhibited firing increases specific to a given food type, while some other pyramidal cells increased firings to various combinations of multiple foods. In contrast, fast-spiking interneurons can increase or decrease firings to given food types, and were more broadly tuned to various food experiences. We further show that a subset of pyramidal cells exhibited rapid desensitization to repeated eating of the same food, correlated with rapid behavioral habituation. Finally, we provide the intuitive visualization of BLA ensemble activation patterns using the dimensionality-reduction classification method to decode real-time appetitive stimulus identity on a moment-to-moment, single trial basis. Elucidation of the neural coding patterns in the BLA provides a key insight into how the brain's emotion and memory circuits performs the computational operation of pattern discrimination and categorization of natural food experiences.

Histone Deacetylase Inhibitor Alleviates the Neurodegenerative Phenotypes and Histone Dysregulation in Presenilins-Deficient Mice.

Histone acetylation has been shown to play a crucial role in memory formation, and histone deacetylase (HDAC) inhibitor sodium butyrate (NaB) has been demonstrated to improve memory performance and rescue the neurodegeneration of several Alzheimer's Disease (AD) mouse models. The forebrain presenilin-1 and presenilin-2 conditional double knockout (cDKO) mice showed memory impairment, forebrain degeneration, tau hyperphosphorylation and inflammation that closely mimics AD-like phenotypes. In this article, we have investigated the effects of systemic administration of NaB on neurodegenerative phenotypes in cDKO mice. We found that chronic NaB treatment significantly restored contextual memory but did not alter cued memory in cDKO mice while such an effect was not permanent after treatment withdrawal. We further revealed that NaB treatment did not rescue reduced synaptic numbers and cortical shrinkage in cDKO mice, but significantly increased the neurogenesis in subgranular zone of dentate gyrus (DG). We also observed that tau hyperphosphorylation and inflammation related protein glial fibrillary acidic protein (GFAP) level were decreased in cDKO mice by NaB. Furthermore, GO and pathway analysis for the RNA-Seq data demonstrated that NaB treatment induced enrichment of transcripts associated with inflammation/immune processes and cytokine-cytokine receptor interactions. RT-PCR confirmed that NaB treatment inhibited the expression of inflammation related genes such as S100a9 and Ccl4 found upregulated in the brain of cDKO mice. Surprisingly, the level of brain histone acetylation in cDKO mice was dramatically increased and was decreased by the administration of NaB, which may reflect dysregulation of histone acetylation underlying memory impairment in cDKO mice. These results shed some lights on the possible molecular mechanisms of HDAC inhibitor in alleviating the neurodegenerative phenotypes of cDKO mice and provide a promising target for treating AD.

Neural Coding of Cell Assemblies via Spike-Timing Self-Information.

Cracking brain's neural code is of general interest. In contrast to the traditional view that enormous spike variability in resting states and stimulus-triggered responses reflects noise, here, we examine the "Neural Self-Information Theory" that the interspike-interval (ISI), or the silence-duration between 2 adjoining spikes, carries self-information that is inversely proportional to its variability-probability. Specifically, higher-probability ISIs convey minimal information because they reflect the ground state, whereas lower-probability ISIs carry more information, in the form of "positive" or "negative surprisals," signifying the excitatory or inhibitory shifts from the ground state, respectively. These surprisals serve as the quanta of information to construct temporally coordinated cell-assembly ternary codes representing real-time cognitions. Accordingly, we devised a general decoding method and unbiasedly uncovered 15 cell assemblies underlying different sleep cycles, fear-memory experiences, spatial navigation, and 5-choice serial-reaction time (5CSRT) visual-discrimination behaviors. We further revealed that robust cell-assembly codes were generated by ISI surprisals constituted of ~20% of the skewed ISI gamma-distribution tails, conforming to the "Pareto Principle" that specifies, for many events-including communication-roughly 80% of the output or consequences come from 20% of the input or causes. These results demonstrate that real-time neural coding arises from the temporal assembly of neural-clique members via silence variability-based self-information codes.

Distinct retrosplenial cortex cell populations and their spike dynamics during ketamine-induced unconscious state.

Ketamine is known to induce psychotic-like symptoms, including delirium and visual hallucinations. It also causes neuronal damage and cell death in the retrosplenial cortex (RSC), an area that is thought to be a part of high visual cortical pathways and at least partially responsible for ketamine's psychotomimetic activities. However, the basic physiological properties of RSC cells as well as their response to ketamine in vivo remained largely unexplored. Here, we combine a computational method, the Inter-Spike Interval Classification Analysis (ISICA), and in vivo recordings to uncover and profile excitatory cell subtypes within layers 2&3 and 5&6 of the RSC in mice within both conscious, sleep, and ketamine-induced unconscious states. We demonstrate two distinct excitatory principal cell sub-populations, namely, high-bursting excitatory principal cells and low-bursting excitatory principal cells, within layers 2&3, and show that this classification is robust over the conscious states, namely quiet awake, and natural unconscious sleep periods. Similarly, we provide evidence of high-bursting and low-bursting excitatory principal cell sub-populations within layers 5&6 that remained distinct during quiet awake and sleep states. We further examined how these subtypes are dynamically altered by ketamine. During ketamine-induced unconscious state, these distinct excitatory principal cell subtypes in both layer 2&3 and layer 5&6 exhibited distinct dynamics. We also uncovered different dynamics of local field potential under various brain states in layer 2&3 and layer 5&6. Interestingly, ketamine administration induced high gamma oscillations in layer 2&3 of the RSC, but not layer 5&6. Our results show that excitatory principal cells within RSC layers 2&3 and 5&6 contain multiple physiologically distinct sub-populations, and they are differentially affected by ketamine.

Neural Code-Neural Self-information Theory on How Cell-Assembly Code Rises from Spike Time and Neuronal Variability.

A major stumbling block to cracking the real-time neural code is neuronal variability - neurons discharge spikes with enormous variability not only across trials within the same experiments but also in resting states. Such variability is widely regarded as a noise which is often deliberately averaged out during data analyses. In contrast to such a dogma, we put forth the Neural Self-Information Theory that neural coding is operated based on the self-information principle under which variability in the time durations of inter-spike-intervals (ISI), or neuronal silence durations, is self-tagged with discrete information. As the self-information processor, each ISI carries a certain amount of information based on its variability-probability distribution; higher-probability ISIs which reflect the balanced excitation-inhibition ground state convey minimal information, whereas lower-probability ISIs which signify rare-occurrence surprisals in the form of extremely transient or prolonged silence carry most information. These variable silence durations are naturally coupled with intracellular biochemical cascades, energy equilibrium and dynamic regulation of protein and gene expression levels. As such, this silence variability-based self-information code is completely intrinsic to the neurons themselves, with no need for outside observers to set any reference point as typically used in the rate code, population code and temporal code models. Moreover, temporally coordinated ISI surprisals across cell population can inherently give rise to robust real-time cell-assembly codes which can be readily sensed by the downstream neural clique assemblies. One immediate utility of this self-information code is a general decoding strategy to uncover a variety of cell-assembly patterns underlying external and internal categorical or continuous variables in an unbiased manner.

Discover mouse gene coexpression landscapes using dictionary learning and sparse coding.

Gene coexpression patterns carry rich information regarding enormously complex brain structures and functions. Characterization of these patterns in an unbiased, integrated, and anatomically comprehensive manner will illuminate the higher-order transcriptome organization and offer genetic foundations of functional circuitry. Here using dictionary learning and sparse coding, we derived coexpression networks from the space-resolved anatomical comprehensive in situ hybridization data from Allen Mouse Brain Atlas dataset. The key idea is that if two genes use the same dictionary to represent their original signals, then their gene expressions must share similar patterns, thereby considering them as "coexpressed." For each network, we have simultaneous knowledge of spatial distributions, the genes in the network and the extent a particular gene conforms to the coexpression pattern. Gene ontologies and the comparisons with published gene lists reveal biologically identified coexpression networks, some of which correspond to major cell types, biological pathways, and/or anatomical regions.

Transcriptome Architecture of Adult Mouse Brain Revealed by Sparse Coding of Genome-Wide In Situ Hybridization Images.

Highly differentiated brain structures with distinctly different phenotypes are closely correlated with the unique combination of gene expression patterns. Using a genome-wide in situ hybridization image dataset released by Allen Mouse Brain Atlas, we present a data-driven method of dictionary learning and sparse coding. Our results show that sparse coding can elucidate patterns of transcriptome organization of mouse brain. A collection of components obtained from sparse coding display robust region-specific molecular signatures corresponding to the canonical neuroanatomical subdivisions including fiber tracts and ventricular systems. Other components revealed finer anatomical delineation of domains previously considered homogeneous. We also build an open-access informatics portal that contains the detail of each component along with its ontology and expressed genes. This portal allows intuitive visualization, interpretation and explorations of the transcriptome architecture of a mouse brain.

Adult forebrain NMDA receptors gate social motivation and social memory.

Motivation to engage in social interaction is critical to ensure normal social behaviors, whereas dysregulation in social motivation can contribute to psychiatric diseases such as schizophrenia, autism, social anxiety disorders and post-traumatic stress disorder (PTSD). While dopamine is well known to regulate motivation, its downstream targets are poorly understood. Given the fact that the dopamine 1 (D1) receptors are often physically coupled with the NMDA receptors, we hypothesize that the NMDA receptor activity in the adult forebrain principal neurons are crucial not only for learning and memory, but also for the proper gating of social motivation. Here, we tested this hypothesis by examining sociability and social memory in inducible forebrain-specific NR1 knockout mice. These mice are ideal for exploring the role of the NR1 subunit in social behavior because the NR1 subunit can be selectively knocked out after the critical developmental period, in which NR1 is required for normal development. We found that the inducible deletion of the NMDA receptors prior to behavioral assays impaired, not only object and social recognition memory tests, but also resulted in profound deficits in social motivation. Mice with ablated NR1 subunits in the forebrain demonstrated significant decreases in sociability compared to their wild type counterparts. These results suggest that in addition to its crucial role in learning and memory, the NMDA receptors in the adult forebrain principal neurons gate social motivation, independent of neuronal development.

Brain Computation Is Organized via Power-of-Two-Based Permutation Logic.

There is considerable scientific interest in understanding how cell assemblies-the long-presumed computational motif-are organized so that the brain can generate intelligent cognition and flexible behavior. The Theory of Connectivity proposes that the origin of intelligence is rooted in a power-of-two-based permutation logic (N = 2 i -1), producing specific-to-general cell-assembly architecture capable of generating specific perceptions and memories, as well as generalized knowledge and flexible actions. We show that this power-of-two-based permutation logic is widely used in cortical and subcortical circuits across animal species and is conserved for the processing of a variety of cognitive modalities including appetitive, emotional and social information. However, modulatory neurons, such as dopaminergic (DA) neurons, use a simpler logic despite their distinct subtypes. Interestingly, this specific-to-general permutation logic remained largely intact although NMDA receptors-the synaptic switch for learning and memory-were deleted throughout adulthood, suggesting that the logic is developmentally pre-configured. Moreover, this computational logic is implemented in the cortex via combining a random-connectivity strategy in superficial layers 2/3 with nonrandom organizations in deep layers 5/6. This randomness of layers 2/3 cliques-which preferentially encode specific and low-combinatorial features and project inter-cortically-is ideal for maximizing cross-modality novel pattern-extraction, pattern-discrimination and pattern-categorization using sparse code, consequently explaining why it requires hippocampal offline-consolidation. In contrast, the nonrandomness in layers 5/6-which consists of few specific cliques but a higher portion of more general cliques projecting mostly to subcortical systems-is ideal for feedback-control of motivation, emotion, consciousness and behaviors. These observations suggest that the brain's basic computational algorithm is indeed organized by the power-of-two-based permutation logic. This simple mathematical logic can account for brain computation across the entire evolutionary spectrum, ranging from the simplest neural networks to the most complex.

Dopamine Rebound-Excitation Theory: Putting Brakes on PTSD.

It is not uncommon for humans or animals to experience traumatic events in their lifetimes. However, the majority of individuals are resilient to long-term detrimental changes turning into anxiety and depression, such as post-traumatic stress disorder (PTSD). What underlying neural mechanism accounts for individual variability in stress resilience? Hyperactivity in fear circuits, such as the amygdalar system, is well-known to be the major pathophysiological basis for PTSD, much like a "stuck accelerator." Interestingly, increasing evidence demonstrates that dopamine (DA) - traditionally known for its role in motivation, reward prediction, and addiction - is also crucial in regulating fear learning and anxiety. Yet, how dopaminergic (DAergic) neurons control stress resilience is unclear, especially given that DAergic neurons have multiple subtypes with distinct temporal dynamics. Here, we propose the Rebound-Excitation Theory, which posits that DAergic neurons' rebound-excitation at the termination of fearful experiences serves as an important "brake" by providing intrinsic safety-signals to fear-processing neural circuits in a spatially and temporally controlled manner. We discuss how DAergic neuron rebound-excitation may be regulated by genetics and experiences, and how such physiological properties may be used as a brain-activity biomarker to predict and confer individual resilience to stress and anxiety.

512-Channel and 13-Region Simultaneous Recordings Coupled with Optogenetic Manipulation in Freely Behaving Mice.

The development of technologies capable of recording both single-unit activity and local field potentials (LFPs) over a wide range of brain circuits in freely behaving animals is the key to constructing brain activity maps. Although mice are the most popular mammalian genetic model, in vivo neural recording has been traditionally limited to smaller channel count and fewer brain structures because of the mouse's small size and thin skull. Here, we describe a 512-channel tetrode system that allows us to record simultaneously over a dozen cortical and subcortical structures in behaving mice. This new technique offers two major advantages - namely, the ultra-low cost and the do-it-yourself flexibility for targeting any combination of many brain areas. We show the successful recordings of both single units and LFPs from 13 distinct neural circuits of the mouse brain, including subregions of the anterior cingulate cortices, retrosplenial cortices, somatosensory cortices, secondary auditory cortex, hippocampal CA1, dentate gyrus, subiculum, lateral entorhinal cortex, perirhinal cortex, and prelimbic cortex. This 512-channel system can also be combined with Cre-lox neurogenetics and optogenetics to further examine interactions between genes, cell types, and circuit dynamics across a wide range of brain structures. Finally, we demonstrate that complex stimuli - such as an earthquake and fear-inducing foot-shock - trigger firing changes in all of the 13 brain regions recorded, supporting the notion that neural code is highly distributed. In addition, we show that localized optogenetic manipulation in any given brain region could disrupt network oscillations and caused changes in single-unit firing patterns in a brain-wide manner, thereby raising the cautionary note of the interpretation of optogenetically manipulated behaviors.

Theory of Connectivity: Nature and Nurture of Cell Assemblies and Cognitive Computation.

Richard Semon and Donald Hebb are among the firsts to put forth the notion of cell assembly-a group of coherently or sequentially-activated neurons-to represent percept, memory, or concept. Despite the rekindled interest in this century-old idea, the concept of cell assembly still remains ill-defined and its operational principle is poorly understood. What is the size of a cell assembly? How should a cell assembly be organized? What is the computational logic underlying Hebbian cell assemblies? How might Nature vs. Nurture interact at the level of a cell assembly? In contrast to the widely assumed randomness within the mature but naïve cell assembly, the Theory of Connectivity postulates that the brain consists of the developmentally pre-programmed cell assemblies known as the functional connectivity motif (FCM). Principal cells within such FCM is organized by the power-of-two-based mathematical principle that guides the construction of specific-to-general combinatorial connectivity patterns in neuronal circuits, giving rise to a full range of specific features, various relational patterns, and generalized knowledge. This pre-configured canonical computation is predicted to be evolutionarily conserved across many circuits, ranging from these encoding memory engrams and imagination to decision-making and motor control. Although the power-of-two-based wiring and computational logic places a mathematical boundary on an individual's cognitive capacity, the fullest intellectual potential can be brought about by optimized nature and nurture. This theory may also open up a new avenue to examining how genetic mutations and various drugs might impair or improve the computational logic of brain circuits.

Cre-Lox Neurogenetics: 20 Years of Versatile Applications in Brain Research and Counting .

Defining and manipulating specific neurons in the brain has garnered enormous interest in recent years, because such an approach is now widely recognized as crucial for deepening our understanding of how the brain works. When I started exploring the Cre-loxP recombination for brain research in the early 1990s, it was written off as a dead-end project by a young fool. Yet over the past 20 years, Cre-lox recombination-mediated neurogenetics has emerged as one of the most powerful and versatile technology platforms for cell-specific gene knockouts, transgenic overexpression, Brainbow imaging, neural pathway tracing with retrovirus and CLARITY, chemical genetics, and optogenetics. Its popularity and greater utility in neuroscience research is also largely thanks to the NIH's bold Blueprint for Neuroscience Research Initiative to launch several Cre-driver resource projects, as well as individual laboratories and private research organizations. With newly-discovered, genetically-encoded molecules that are capable of responding to sonar and magnetic stimulation, for sonogenetics or magnetogenetics, respectively, or detecting rapid voltage changes in neurons, Cre-lox neurogenetics will continue to aid brain research for years to come.

Camera-Based, Non-Contact, Vital-Signs Monitoring Technology May Provide a Way for the Early Prevention of SIDS in Infants.

Sudden infant death syndrome (SIDS) is the unexplained death, usually during sleep, of a baby younger than 1-year-old. Even though researchers have discovered some factors that may put babies at extra risk, SIDS remains unpredictable up until now. One hypothesis is that impaired cardiovascular control may play a role in the underlying mechanism of SIDS. A reduction of heart rate variability (HRV) and progressive decrease in heart rate (HR) have been observed in infants who have later succumbed to SIDS. Many clues indicated the heart could be the final weakness in SIDS. Therefore, continuous monitoring of the dynamic changes within the heart may provide a possible preventive strategy of SIDS. Camera-based photoplethysmography was recently demonstrated as a contactless method to determine HR and HRV. This perspective presents a hypothesis that a camera-based, non-contact, vital-sign monitoring technology, which can indicate abnormal changes or a sudden loss of vital signs in a timely manner, may enable a crucial and low-cost means for the early prevention of SIDS in newborn infants.

A Postulate on the Brain's Basic Wiring Logic.

How should evolution and development build the brain to be capable of flexible and generative cognition? I wish to put forth a 'power-of-two'-based wiring logic that provides the basic computational principle in organizing the microarchitecture of cell assemblies that would readily enable knowledge and adaptive behaviors to emerge upon learning.

Importance of the GluN2B carboxy-terminal domain for enhancement of social memories.

The N-methyl-D-aspartate (NMDA) receptor is known to be necessary for many forms of learning and memory, including social recognition memory. Additionally, the GluN2 subunits are known to modulate multiple forms of memory, with a high GluN2A:GluN2B ratio leading to impairments in long-term memory, while a low GluN2A:GluN2B ratio enhances some forms of long-term memory. Here, we investigate the molecular motif responsible for the differences in social recognition memory and olfactory memory in the forebrain-specific transgenic GluN2A overexpression mice and the forebrain-specific transgenic GluN2B overexpression mice by using two transgenic mouse lines that overexpress chimeric GluN2 subunits. The transgenic chimeric GluN2 subunit mice were tested for their ability to learn and remember fruit scents, male juveniles of the same strain, females of the same strain, male juveniles of another strain, and rodents of another species. The data presented here demonstrate that the GluN2B carboxy-terminal domain is necessary for enhanced social recognition memory in GluN2B transgenic overexpression mice. Furthermore, the GluN2A carboxy-terminal domain is responsible for the impaired long-term olfactory and social memory observed in the GluN2A overexpression mice.

Computational Classification Approach to Profile Neuron Subtypes from Brain Activity Mapping Data.

The analysis of cell type-specific activity patterns during behaviors is important for better understanding of how neural circuits generate cognition, but has not been well explored from in vivo neurophysiological datasets. Here, we describe a computational approach to uncover distinct cell subpopulations from in vivo neural spike datasets. This method, termed "inter-spike-interval classification-analysis" (ISICA), is comprised of four major steps: spike pattern feature-extraction, pre-clustering analysis, clustering classification, and unbiased classification-dimensionality selection. By using two key features of spike dynamic - namely, gamma distribution shape factors and a coefficient of variation of inter-spike interval - we show that this ISICA method provides invariant classification for dopaminergic neurons or CA1 pyramidal cell subtypes regardless of the brain states from which spike data were collected. Moreover, we show that these ISICA-classified neuron subtypes underlie distinct physiological functions. We demonstrate that the uncovered dopaminergic neuron subtypes encoded distinct aspects of fearful experiences such as valence or value, whereas distinct hippocampal CA1 pyramidal cells responded differentially to ketamine-induced anesthesia. This ISICA method should be useful to better data mining of large-scale in vivo neural datasets, leading to novel insights into circuit dynamics associated with cognitions.

Technology platforms for remote monitoring of vital signs in the new era of telemedicine.

Driven by healthcare cost and home healthcare need, the development of remote monitoring technologies is poised to improve and revolutionize healthcare delivery and accessibility. This paper reviews the recent progress in the field of remote monitoring technologies that may have the potential to become the basic platforms for telemedicine. In particular, key techniques and devices for monitoring cardiorespiratory activity, blood pressure and blood glucose concentration are summarized and discussed. In addition, the US FDA approved remote vital signs monitoring devices currently available on the market are presented.

Optimization of large-scale mouse brain connectome via joint evaluation of DTI and neuron tracing data.

Tractography based on diffusion tensor imaging (DTI) data has been used as a tool by a large number of recent studies to investigate structural connectome. Despite its great success in offering unique 3D neuroanatomy information, DTI is an indirect observation with limited resolution and accuracy and its reliability is still unclear. Thus, it is essential to answer this fundamental question: how reliable is DTI tractography in constructing large-scale connectome? To answer this question, we employed neuron tracing data of 1772 experiments on the mouse brain released by the Allen Mouse Brain Connectivity Atlas (AMCA) as the ground-truth to assess the performance of DTI tractography in inferring white matter fiber pathways and inter-regional connections. For the first time in the neuroimaging field, the performance of whole brain DTI tractography in constructing a large-scale connectome has been evaluated by comparison with tracing data. Our results suggested that only with the optimized tractography parameters and the appropriate scale of brain parcellation scheme, can DTI produce relatively reliable fiber pathways and a large-scale connectome. Meanwhile, a considerable amount of errors were also identified in optimized DTI tractography results, which we believe could be potentially alleviated by efforts in developing better DTI tractography approaches. In this scenario, our framework could serve as a reliable and quantitative test bed to identify errors in tractography results which will facilitate the development of such novel tractography algorithms and the selection of optimal parameters.

Principles of Intelligence: On Evolutionary Logic of the Brain.

Humans and animals may encounter numerous events, objects, scenes, foods and countless social interactions in a lifetime. This means that the brain is constructed by evolution to deal with uncertainties and various possibilities. What is the architectural abstraction of intelligence that enables the brain to discover various possible patterns and knowledge about complex, evolving worlds? Here, I discuss the Theory of Connectivity-a "power-of-two" based, operational principle that can serve as a unified wiring and computational logic for organizing and constructing cell assemblies into the microcircuit-level building block, termed as functional connectivity motif (FCM). Defined by the power-of-two based equation, N = 2 (i) -1, each FCM consists of the principal projection neuron cliques (N), ranging from those specific cliques receiving specific information inputs (i) to those general and sub-general cliques receiving various combinatorial convergent inputs. As the evolutionarily conserved logic, its validation requires experimental demonstrations of the following three major properties: (1) Anatomical prevalence-FCMs are prevalent across neural circuits, regardless of gross anatomical shapes; (2) Species conservancy-FCMs are conserved across different animal species; and (3) Cognitive universality-FCMs serve as a universal computational logic at the cell assembly level for processing a variety of cognitive experiences and flexible behaviors. More importantly, this Theory of Connectivity further predicts that the specific-to-general combinatorial connectivity pattern within FCMs should be preconfigured by evolution, and emerge innately from development as the brain's computational primitives. This proposed design-principle can also explain the general purpose of the layered cortex and serves as its core computational algorithm.