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

The alpha2 nicotinic acetylcholine receptor, a subunit with unique and selective expression in inhibitory interneurons associated with principal cells

Nicotinic acetylcholine receptors (nAChRs) play crucial roles in various human disorders, with the α7, α4, α6, and α3-containing nAChR subtypes extensively studied in relation to conditions such as Alzheimer's disease, Parkinson's disease, nicotine dependence, mood disorders, and stress disorders. In contrast, the α2-nAChR subunit has received less attention due to its more restricted expression and the scarcity of specific agonists and antagonists for studying its function. Nevertheless, recent research has shed light on the unique expression pattern of the Chrna2 gene, which encodes the α2-nAChR subunit, and its involvement in distinct populations of inhibitory interneurons. This review highlights the structure, pharmacology, localization, function, and disease associations of α2-containing nAChRs and points to the unique expression pattern of the Chrna2 gene and its role in different inhibitory interneuron populations. These populations, including the oriens lacunosum moleculare (OLM) cells in the hippocampus, Martinotti cells in the neocortex, and Renshaw cells in the spinal cord, share common features and contribute to recurrent inhibitory microcircuits. Thus, the α2-nAChR subunit's unique expression pattern in specific interneuron populations and its role in recurrent inhibitory microcircuits highlight its importance in various physiological processes. Further research is necessary to uncover the comprehensive functionality of α2-containing nAChRs, delineate their specific contributions to neuronal circuits, and investigate their potential as therapeutic targets for related disorders.

Spikebench: An open benchmark for spike train time-series classification

Modern well-performing approaches to neural decoding are based on machine learning models such as decision tree ensembles and deep neural networks. The wide range of algorithms that can be utilized to learn from neural spike trains, which are essentially time-series data, results in the need for diverse and challenging benchmarks for neural decoding, similar to the ones in the fields of computer vision and natural language processing. In this work, we propose a spike train classification benchmark, based on open-access neural activity datasets and consisting of several learning tasks such as stimulus type classification, animal's behavioral state prediction, and neuron type identification. We demonstrate that an approach based on hand-crafted time-series feature engineering establishes a strong baseline performing on par with state-of-the-art deep learning-based models for neural decoding. We release the code allowing to reproduce the reported results.

Distinct thalamocortical circuits underlie allodynia induced by tissue injury and by depression-like states

In humans, tissue injury and depression can both cause pain hypersensitivity, but whether this involves distinct circuits remains unknown. Here, we identify two discrete glutamatergic neuronal circuits in male mice: a projection from the posterior thalamic nucleus (PO^Glu) to primary somatosensory cortex glutamatergic neurons (S1^Glu) mediates allodynia from tissue injury, whereas a pathway from the parafascicular thalamic nucleus (PF^Glu) to anterior cingulate cortex GABA-containing neurons to glutamatergic neurons (ACC^GABA→Glu) mediates allodynia associated with a depression-like state. In vivo calcium imaging and multi-tetrode electrophysiological recordings reveal that PO^Glu and PF^Glu populations undergo different adaptations in the two conditions. Artificial manipulation of each circuit affects allodynia resulting from either tissue injury or depression-like states, but not both. Our study demonstrates that the distinct thalamocortical circuits PO^Glu→S1^Glu and PF^Glu→ACC^GABA→Glu subserve allodynia associated with tissue injury and depression-like states, respectively, thus providing insights into the circuit basis of pathological pain resulting from different etiologies.

Alternative classifications of neurons based on physiological properties and synaptic responses, a computational study

One of the central goals of today's neuroscience is to achieve the conceivably most accurate classification of neuron types in the mammalian brain. As part of this research effort, electrophysiologists commonly utilize current clamp techniques to gain a detailed characterization of the neurons' physiological properties. While this approach has been useful, it is not well understood whether neurons that share physiological properties of a particular phenotype would also operate consistently under the action of natural synaptic inputs. We approached this problem by simulating a biophysically diverse population of model neurons based on 3 generic phenotypes. We exposed the model neurons to two types of stimulation to investigate their voltage responses under conventional current step protocols and under simulated synaptic bombardment. We extracted standard physiological parameters from the voltage responses elicited by current step stimulation and spike arrival times descriptive of the model's firing behavior under synaptic inputs. The biophysical phenotypes could be reliably identified using classification based on the 'static' physiological properties, but not the interspike interval-based parameters. However, the model neurons associated with the biophysically different phenotypes retained cell type specific features in the fine structure of their spike responses that allowed their accurate classification.

Silk-Enabled Conformal Multifunctional Bioelectronics for Investigation of Spatiotemporal Epileptiform Activities and Multimodal Neural Encoding/Decoding

Flexible electronics can serve as powerful tools for biomedical diagnosis and therapies of neurological disorders, particularly for application cases with brain-machine interfaces (BMIs). Existing conformal soft bioelectrodes are applicable for basic electrocorticogram (ECoG) collecting/monitoring. Nevertheless, as an emerging and promising approach, further multidisciplinary efforts are still demanded for in-depth exploitations with these conformal soft electronics toward their practical neurophysiological applications in both scientific research and real-world clinical operation. Here, clinically-friendly silk-supported/delivered soft bioelectronics are developed, and multiple functions and features valuable for customizable intracranial applications (e.g., biocompatible and spontaneously conformal coupling with cortical surface, spatiotemporal ECoG detecting/monitoring, electro-neurophysiological neural stimulating/decoding, controllable loading/delivery of therapeutic molecules, and parallel optical readouts of operating states) are integrated.

Electrophysiological Profiling of Neocortical Neural Subtypes: A Semi-Supervised Method Applied to in vivo Whole-Cell Patch-Clamp Data

A lot of efforts have been made to understand the structure and function of neocortical circuits. In fact, a promising way to understand the functions of cortical circuits is the classification of the neural types, based on their different properties. Recent studies focused on applying modern computational methods to classify neurons based on molecular, morphological, physiological, or mixed of these criteria. Although there are studies in the literature on in vitro/vivo extracellular or in vitro intracellular recordings, a study on the classification of neuronal types using in vivo whole-cell patch-clamp recordings is still lacking. We thus proposed a novel semi-supervised classification method based on waveform shape of neurons' spikes using in vivo whole-cell patch-clamp recordings. We, first, detected spike candidates. Then discriminative features were extracted from the time samples of the spikes using discrete cosine transform. We then extracted the center of clusters using fuzzy c-mean clustering and finally, the neurons were classified using the minimum distance classifier. We distinguished three types of neurons: excitatory pyramidal cells (Pyr) and two types of inhibitory neurons: GABAergic- parvalbumin positive (PV), and somatostatin positive (SST) non-pyramidal cells in layer II/III of the mice primary visual cortex. We used 10-fold cross validation in our study. The classification accuracy for PV, Pyr, and SST was 91.59 ± 1.69, 97.47 ± 0.67, and 89.06 ± 1.99, respectively. Overall, the algorithm correctly classified 92.67 ± 0.54% of the cells, confirming the relative robustness of the discriminant functions. The performance of the method was further assessed on in vitro recordings by using a pool of 50 neurons from Allen institute Cell Types Database (5 major subtypes of neurons: Pyr, PV, SST, 5HT3a, and vasoactive intestinal peptide (VIP) cells). Its overall accuracy was 84.13 ± 0.81% on this data set using cross validation framework. The proposed algorithm is thus a promising new tool in recognizing cell's type with high accuracy in laboratories using in vivo/vitro whole-cell patch-clamp recording technique. The developed programs and the entire dataset are available online to interested readers.

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.

Mining Big Neuron Morphological Data

The advent of automatic tracing and reconstruction technology has led to a surge in the number of neurons 3D reconstruction data and consequently the neuromorphology research. However, the lack of machine-driven annotation schema to automatically detect the types of the neurons based on their morphology still hinders the development of this branch of science. Neuromorphology is important because of the interplay between the shape and functionality of neurons and the far-reaching impact on the diagnostics and therapeutics in neurological disorders. This survey paper provides a comprehensive research in the field of automatic neurons classification and presents the existing challenges, methods, tools, and future directions for automatic neuromorphology analytics. We summarize the major automatic techniques applicable in the field and propose a systematic data processing pipeline for automatic neuron classification, covering data capturing, preprocessing, analyzing, classification, and retrieval. Various techniques and algorithms in machine learning are illustrated and compared to the same dataset to facilitate ongoing research in the field.

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.

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.

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.