On initial Brain Activity Mapping of episodic and semantic memory code in the hippocampus

The Role of Early Bioelectric Signals in the Regeneration of Planarian Anterior/Posterior Polarity

Axial patterning during planarian regeneration relies on a transcriptional circuit that confers distinct positional information on the two ends of an amputated fragment. The earliest known elements of this system begin demarcating differences between anterior and posterior wounds by 6 h postamputation. However, it is still unknown what upstream events break the axial symmetry, allowing a mutual repressor system to establish invariant, distinct biochemical states at the anterior and posterior ends. Here, we show that bioelectric signaling at 3 h is crucial for the formation of proper anterior-posterior polarity in planaria. Briefly manipulating the endogenous bioelectric state by depolarizing the injured tissue during the first 3 h of regeneration alters gene expression by 6 h postamputation and leads to a double-headed phenotype upon regeneration despite confirmed washout of ionophores from tissue. These data reveal a primary functional role for resting membrane potential taking place within the first 3 h after injury and kick-starting the downstream pattern of events that elaborate anatomy over the following 10 days. We propose a simple model of molecular-genetic mechanisms to explain how physiological events taking place immediately after injury regulate the spatial distribution of downstream gene expression and anatomy of regenerating planaria.

Optimization of macaque brain DMRI connectome by neuron tracing and myelin stain data

Accurate assessment of connectional anatomy of primate brains can be an important avenue to better understand the structural and functional organization of brains. To this end, numerous connectome projects have been initiated to create a comprehensive map of the connectional anatomy over a large spatial expanse. Tractography based on diffusion MRI (dMRI) data has been used as a tool by many connectome projects in that it is widely used to visualize axonal pathways and reveal microstructural features on living brains. However, the measures obtained from dMRI are indirect inference of microstructures. This intrinsic limitation reduces the reliability of dMRI in constructing connectomes for brains. In this work, we proposed a framework to increase the accuracy of constructing a dMRI-based connectome on macaque brains by integrating meso-scale connective information from tract-tracing data and micro-scale axonal orientation information from myelin stain data. Our results suggest that this integrative framework could advance the mapping accuracy of dMRI based connections and axonal pathways, and demonstrate the prospect of the proposed framework in constructing a large-scale connectome on living primate brains.

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.

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.

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 = 2ⁱ–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.

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.

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 = 2ⁱ−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.

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.

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.

Chlorovirus ATCV-1 is part of the human oropharyngeal virome and is associated with changes in cognitive functions in humans and mice

Chloroviruses (family Phycodnaviridae) are large DNA viruses known to infect certain eukaryotic green algae and have not been previously shown to infect humans or to be part of the human virome. We unexpectedly found sequences homologous to the chlorovirus Acanthocystis turfacea chlorella virus 1 (ATCV-1) in a metagenomic analysis of DNA extracted from human oropharyngeal samples. These samples were obtained by throat swabs of adults without a psychiatric disorder or serious physical illness who were participating in a study that included measures of cognitive functioning. The presence of ATCV-1 DNA was confirmed by quantitative PCR with ATCV-1 DNA being documented in oropharyngeal samples obtained from 40 (43.5%) of 92 individuals. The presence of ATCV-1 DNA was not associated with demographic variables but was associated with a modest but statistically significant decrease in the performance on cognitive assessments of visual processing and visual motor speed. We further explored the effects of ATCV-1 in a mouse model. The inoculation of ATCV-1 into the intestinal tract of 9-11-wk-old mice resulted in a subsequent decrease in performance in several cognitive domains, including ones involving recognition memory and sensory-motor gating. ATCV-1 exposure in mice also resulted in the altered expression of genes within the hippocampus. These genes comprised pathways related to synaptic plasticity, learning, memory formation, and the immune response to viral exposure.

Effects of diet on brain plasticity in animal and human studies: mind the gap

Dietary interventions have emerged as effective environmental inducers of brain plasticity. Among these dietary interventions, we here highlight the impact of caloric restriction (CR: a consistent reduction of total daily food intake), intermittent fasting (IF, every-other-day feeding), and diet supplementation with polyphenols and polyunsaturated fatty acids (PUFAs) on markers of brain plasticity in animal studies. Moreover, we also discuss epidemiological and intervention studies reporting the effects of CR, IF and dietary polyphenols and PUFAs on learning, memory, and mood. In particular, we evaluate the gap in mechanistic understanding between recent findings from animal studies and those human studies reporting that these dietary factors can benefit cognition, mood, and anxiety, aging, and Alzheimer's disease-with focus on the enhancement of structural and functional plasticity markers in the hippocampus, such as increased expression of neurotrophic factors, synaptic function and adult neurogenesis. Lastly, we discuss some of the obstacles to harnessing the promising effects of diet on brain plasticity in animal studies into effective recommendations and interventions to promote healthy brain function in humans. Together, these data reinforce the important translational concept that diet, a modifiable lifestyle factor, holds the ability to modulate brain health and function.

Heart rate and heart rate variability assessment identifies individual differences in fear response magnitudes to earthquake, free fall, and air puff in mice

Fear behaviors and fear memories in rodents have been traditionally assessed by the amount of freezing upon the presentation of conditioned cues or unconditioned stimuli. However, many experiences, such as encountering earthquakes or accidental fall from tree branches, may produce long-lasting fear memories but are behaviorally difficult to measure using freezing parameters. Here, we have examined changes in heartbeat interval dynamics as physiological readout for assessing fearful reactions as mice were subjected to sudden air puff, free-fall drop inside a small elevator, and a laboratory-version earthquake. We showed that these fearful events rapidly increased heart rate (HR) with simultaneous reduction of heart rate variability (HRV). Cardiac changes can be further analyzed in details by measuring three distinct phases: namely, the rapid rising phase in HR, the maximum plateau phase during which HRV is greatly decreased, and the recovery phase during which HR gradually recovers to baseline values. We showed that durations of the maximum plateau phase and HR recovery speed were quite sensitive to habituation over repeated trials. Moreover, we have developed the fear resistance index based on specific cardiac response features. We demonstrated that the fear resistance index remained largely consistent across distinct fearful events in a given animal, thereby enabling us to compare and rank individual mouse’s fear responsiveness among the group. Therefore, the fear resistance index described here can represent a useful parameter for measuring personality traits or individual differences in stress-susceptibility in both wild-type mice and post-traumatic stress disorder (PTSD) models.

Consequences of cancer treatments on adult hippocampal neurogenesis: implications for cognitive function and depressive symptoms

The human brain is capable of generating new functional neurons throughout life, a phenomenon known as adult neurogenesis. The generation of new neurons is sustained throughout adulthood due to the proliferation and differentiation of adult neural stem cells. This process in humans is uniquely located in the subgranular zone of the dentate gyrus in the hippocampus. Adult hippocampal neurogenesis (AHN) is thought to play a major role in hippocampus-dependent functions, such as spatial awareness, long-term memory, emotionality, and mood. The overall aim of current treatments for cancer (such as radiotherapy and chemotherapy) is to prevent aberrant cell division of cell populations associated with malignancy. However, the treatments in question are absolutist in nature and hence inhibit all cell division. An unintended consequence of this cessation of cell division is the impairment of adult neural stem cell proliferation and AHN. Patients undergoing treatment for cancerous malignancies often display specific forms of memory deficits, as well as depressive symptoms. This review aims to discuss the effects of cancer treatments on AHN and propose a link between the inhibition of the neurogenetic process in the hippocampus and the advent of the cognitive and mood-based deficits observed in patients and animal models undergoing cancer therapies. Possible evidence for coadjuvant interventions aiming to protect neural cells, and subsequently the mood and cognitive functions they regulate, from the ablative effects of cancer treatment are discussed as potential clinical tools to improve mental health among cancer patients.

Mapping and deciphering neural codes of NMDA receptor-dependent fear memory engrams in the hippocampus

Mapping and decoding brain activity patterns underlying learning and memory represents both great interest and immense challenge. At present, very little is known regarding many of the very basic questions regarding the neural codes of memory: are fear memories retrieved during the freezing state or non-freezing state of the animals? How do individual memory traces give arise to a holistic, real-time associative memory engram? How are memory codes regulated by synaptic plasticity? Here, by applying high-density electrode arrays and dimensionality-reduction decoding algorithms, we investigate hippocampal CA1 activity patterns of trace fear conditioning memory code in inducible NMDA receptor knockout mice and their control littermates. Our analyses showed that the conditioned tone (CS) and unconditioned foot-shock (US) can evoke hippocampal ensemble responses in control and mutant mice. Yet, temporal formats and contents of CA1 fear memory engrams differ significantly between the genotypes. The mutant mice with disabled NMDA receptor plasticity failed to generate CS-to-US or US-to-CS associative memory traces. Moreover, the mutant CA1 region lacked memory traces for “what at when” information that predicts the timing relationship between the conditioned tone and the foot shock. The degraded associative fear memory engram is further manifested in its lack of intertwined and alternating temporal association between CS and US memory traces that are characteristic to the holistic memory recall in the wild-type animals. Therefore, our study has decoded real-time memory contents, timing relationship between CS and US, and temporal organizing patterns of fear memory engrams and demonstrated how hippocampal memory codes are regulated by NMDA receptor synaptic plasticity.