Patterns Of Neuronal Activity Research Articles

Impairment of Neuronal Activity in the Dorsolateral Prefrontal Cortex Occurs Early in Parkinsonism.

Parkinson's disease (PD) is often characterized by altered rates and patterns of neuronal activity in the sensorimotor regions of the basal ganglia thalamocortical network. Little is known, however, regarding how neuronal activity in the executive control network of the brain changes in the parkinsonian condition. Investigate the impact of parkinsonism on neuronal activity in the dorsolateral prefrontal cortex (DLPFC), a key region in executive control, during a go/nogo reaching task. Using a within-subject design, single and multi-unit neuronal activity was recorded in the DLPFC of a nonhuman primate before and after the induction of mild parkinsonism using the neurotoxin 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP). Coincident with development of mild parkinsonian motor signs, there was a marked reduction in the percentage of DLPFC cells with significant task-related firing rate modulation during go and nogo conditions. These results suggest that DLPFC dysfunction may occur early in parkinsonism and contribute to cognitive impairments and disrupted executive function often observed in PD patients.

Representations of stimulus meaning in the hippocampus.

The ability to discriminate and categorize the meaning of environmental stimuli and respond accordingly is essential for survival. The ventral hippocampus (vHPC) controls emotional and motivated behaviors in response to environmental cues and is hypothesized to do so in part by deciphering the positive or negative quality of these cues. Yet, what features of the environment are represented in the activity patterns of vCA1 neurons, and whether the positive or negative meaning of a stimulus is present at this stage, remains unclear. Here, using 2-photon calcium imaging across six different experimental paradigms, we consistently found that vCA1 ensembles encode the identity, sensory features, and intensity of learned and innately salient stimuli, but not their overall valence. These results offer a reappraisal of vCA1 function, wherein information corresponding to individual stimulus features and their behavioral saliency predominates, while valence-related information is attached elsewhere.

Transformer-based classification of visceral pain-related local field potential patterns in the brain

Neuronal ensemble activity entrained by local field potential (LFP) patterns underlies a variety of brain functions, including emotion, cognition, and pain perception. Recent advances in machine learning approaches may enable more effective methods for analyzing LFP patterns across multiple brain areas than conventional time-frequency analysis. In this study, we tested the performance of two machine learning algorithms, AlexNet and the Transformer models, to classify LFP patterns in eight pain-related brain regions before and during acetic acid-induced visceral pain behaviors. Over short time windows lasting several seconds, applying AlexNet to LFP power datasets, but not to raw time-series LFP traces from multiple brain areas, successfully achieved superior classification performance compared with simple LFP power analysis. Furthermore, applying the Transformer directly to the raw LFP traces achieved significantly superior classification performance than AlexNet when using LFP power datasets. These results demonstrate the utility of the Transformer in the analysis of neurophysiological signals, and pave the way for its future applications in the decoding of more complex neuronal activity patterns.

Assembloid model to study loop circuits of the human nervous system.

Neural circuits connecting the cerebral cortex, the basal ganglia and the thalamus are fundamental networks for sensorimotor processing and their dysfunction has been consistently implicated in neuropsychiatric disorders 1-9 . These recursive, loop circuits have been investigated in animal models and by clinical neuroimaging, however, direct functional access to developing human neurons forming these networks has been limited. Here, we use human pluripotent stem cells to reconstruct an in vitro cortico-striatal-thalamic-cortical circuit by creating a four-part loop assembloid. More specifically, we generate regionalized neural organoids that resemble the key elements of the cortico-striatal-thalamic-cortical circuit, and functionally integrate them into loop assembloids using custom 3D-printed biocompatible wells. Volumetric and mesoscale calcium imaging, as well as extracellular recordings from individual parts of these assembloids reveal the emergence of synchronized patterns of neuronal activity. In addition, a multi-step rabies retrograde tracing approach demonstrate the formation of neuronal connectivity across the network in loop assembloids. Lastly, we apply this system to study heterozygous loss of ASH1L gene associated with autism spectrum disorder and Tourette syndrome and discover aberrant synchronized activity in disease model assembloids. Taken together, this human multi-cellular platform will facilitate functional investigations of the cortico-striatal-thalamic-cortical circuit in the context of early human development and in disease conditions.

Comparison of extracellular Giant depolarizing potentials in vitro and early sharp waves in vivo in the CA1 hippocampus of neonatal rats

Giant Depolarizing Potentials (GDPs) and Early Sharp Waves (eSPWs) are the major patterns of neuronal network activity in the developing hippocampus of neonatal rodents in vitro and in vivo, respectively. Because of certain similarities in their electrographic traits, GDPs and eSPWs were originally considered as homologous patterns. Here, we compared electrographic features and current density profiles of field GDPs (fGDPs) and eSPWs using extracellular multisite silicon probe recordings from neonatal rat CA1 hippocampus. We found that fGDPs in hippocampal slices were much less in amplitude than eSPWs, and were characterized by electronegativity and current sinks in CA1 pyramidal cell layer and stratum radiatum, and positive waves/sources in stratum lacunosum-moleculare. eSPWs in vivo showed a remarkably different depth profile, with positivity and current source in the CA1 pyramidal cell layer, and negativity/sinks in stratum radiatum and stratum lacunosum-moleculare. Current sinks of CA3-evoked responses corresponded to sinks of fGDPs and eSPWs in the stratum radiatum. However, current sinks of entorhinal inputs - evoked responses in stratum lacunosum-moleculare, which were characteristic of eSPWs, were absent in fGDPs. In addition, fGDPs more strongly modulated neuronal firing in CA1 compared to eSPWs. Thus, we show important differences in the electrographic properties of GDPs and eSPWs that challenge the homology of these two activity patterns.

Cell Type-Specific Modulation of Acute Itch Processing in the Anterior Cingulate Cortex.

Despite remarkable progress in understanding the fundamental bases of itching, its cortical mechanisms remain poorly understood. Herein, the causal contributions of defined anterior cingulate cortex (ACC) neuronal populations to acute itch modulation in mice are established. Using cell type-specific manipulations, the opposing functions of ACC glutamatergic and GABAergic neurons in regulating acute itching are demonstrated. Photometry studies indicated that ACC glutamatergic neurons are activated during scratching induced by both histamine and chloroquine, whereas the activation pattern of GABAergic neurons is complicated by GABAergic subpopulations and acute itch modalities. By combining cell type- and projection-specific techniques, a thalamocortical circuit is further identified from the mediodorsal thalamus driving the itch-scratching cycle related to histaminergic and non-histaminergic itching, which is contingent on the activation of postsynaptic parvalbumin-expressing neurons in the ACC. These findings reveal a cellular and circuit signature of ACC neurons orchestrating behavioral responses to itching and may provide insights into therapies for itch-related diseases.

Electrophysiological and Behavioral Markers of Hyperdopaminergia in DAT-KO Rats.

Background/Objectives: Dopamine dysfunction (DA) is a hallmark of many neurological disorders. In this case, the mechanism of changes in dopamine transmission on behavior remains unclear. This study is a look into the intricate link between disrupted DA signaling, neuronal activity patterns, and behavioral abnormalities in a hyperdopaminergic animal model. Methods: To study the relationship between altered DA levels, neuronal activity, and behavioral deficits, local field potentials (LFPs) were recorded during four different behaviors in dopamine transporter knockout rats (DAT-KO). At the same time, local field potentials were recorded in the striatum and prefrontal cortex. Correlates of LFP and accompanying behavioral patterns in genetically modified (DAT-KO) and control animals were studied. Results: DAT-KO rats exhibited desynchronization between LFPs of the striatum and prefrontal cortex, particularly during exploratory behavior. A suppressive effect of high dopamine levels on the striatum was also observed. Wild-type rats showed greater variability in LFP patterns across certain behaviors, while DAT-KO rats showed more uniform patterns. Conclusions: The decisive role of the synchrony of STR and PFC neurons in the organization of motor acts has been revealed. The greater variability of control animals in certain forms of behavior probably suggests greater adaptability. More uniform patterns in DAT-KO rats, indicating a loss of striatal flexibility when adapting to specific motor tasks. It is likely that hyperdopaminergy in the DAT-KO rat reduces the efficiency of information processing due to less synchronized activity during active behavior.

Distinct brain-wide presynaptic networks underlie the functional identity of individual cortical neurons.

Neuronal connections provide the scaffolding for neuronal function. Revealing the connectivity of functionally identified individual neurons is necessary to understand how activity patterns emerge and support behavior. Yet, the brain-wide presynaptic wiring rules that lay the foundation for the functional selectivity of individual neurons remain largely unexplored. Cortical neurons, even in primary sensory cortex, are heterogeneous in their selectivity, not only to sensory stimuli but also to multiple aspects of behavior. Here, to investigate presynaptic connectivity rules underlying the selectivity of pyramidal neurons to behavioral state 1-12 in primary somatosensory cortex (S1), we used two-photon calcium imaging, neuropharmacology, single-cell based monosynaptic input tracing, and optogenetics. We show that behavioral state-dependent neuronal activity patterns are stable over time. These are minimally affected by neuromodulatory inputs and are instead driven by glutamatergic inputs. Analysis of brain-wide presynaptic networks of individual neurons with distinct behavioral state-dependent activity profiles revealed characteristic patterns of anatomical input. While both behavioral state-related and unrelated neurons had a similar pattern of local inputs within S1, their long-range glutamatergic inputs differed. Individual cortical neurons, irrespective of their functional properties, received converging inputs from the main S1-projecting areas. Yet, neurons that tracked behavioral state received a smaller proportion of motor cortical inputs and a larger proportion of thalamic inputs. Optogenetic suppression of thalamic inputs reduced behavioral state-dependent activity in S1, but this activity was not externally driven. Our results revealed distinct long-range glutamatergic inputs as a substrate for preconfigured network dynamics associated with behavioral state.

Validation of neuron activation patterns for artificial intelligence models in oculomics

Recent advancements in artificial intelligence (AI) have prompted researchers to expand into the field of oculomics; the association between the retina and systemic health. Unlike conventional AI models trained on well-recognized retinal features, the retinal phenotypes that most oculomics models use are more subtle. Consequently, applying conventional tools, such as saliency maps, to understand how oculomics models arrive at their inference is problematic and open to bias. We hypothesized that neuron activation patterns (NAPs) could be an alternative way to interpret oculomics models, but currently, most existing implementations focus on failure diagnosis. In this study, we designed a novel NAP framework to interpret an oculomics model. We then applied our framework to an AI model predicting systolic blood pressure from fundus images in the United Kingdom Biobank dataset. We found that the NAP generated from our framework was correlated to the clinically relevant endpoint of cardiovascular risk. Our NAP was also able to discern two biologically distinct groups among participants who were assigned the same predicted systolic blood pressure. These results demonstrate the feasibility of our proposed NAP framework for gaining deeper insights into the functioning of oculomics models. Further work is required to validate these results on external datasets.

Evolution of lateralized gustation in nematodes.

Animals with small nervous systems have a limited number of sensory neurons that must encode information from a changing environment. This problem is particularly exacerbated in nematodes that populate a wide variety of distinct ecological niches but only have a few sensory neurons available to encode multiple modalities. How does sensory diversity prevail within this neuronal constraint? To identify the genetic basis for patterning different nervous systems, we demonstrate that sensory neurons in the Pristionchus pacificus respond to various salt sensory cues in a manner that is partially distinct from that of the distantly related nematode C. elegans . By visualizing neuronal activity patterns, we show that contrary to previous expectations based on its genome sequence, the salt responses of P. pacificus are encoded in a left/right asymmetric manner in the bilateral ASE neuron pair. Our study illustrates patterns of evolutionary stability and change in the gustatory system of nematodes.

Visualizing synaptic pruning in the mammalian brain.

Patterns of spontaneous neuronal activity instruct the refinement of developing brain circuits.

Utilising activity patterns of a complex biophysical network model to optimise intra-striatal deep brain stimulation

A large-scale biophysical network model for the isolated striatal body is developed to optimise potential intrastriatal deep brain stimulation applied to, e.g. obsessive-compulsive disorder. The model is based on modified Hodgkin–Huxley equations with small-world connectivity, while the spatial information about the positions of the neurons is taken from a detailed human atlas. The model produces neuronal spatiotemporal activity patterns segregating healthy from pathological conditions. Three biomarkers were used for the optimisation of stimulation protocols regarding stimulation frequency, amplitude and localisation: the mean activity of the entire network, the frequency spectrum of the entire network (rhythmicity) and a combination of the above two. By minimising the deviation of the aforementioned biomarkers from the normal state, we compute the optimal deep brain stimulation parameters, regarding position, amplitude and frequency. Our results suggest that in the DBS optimisation process, there is a clear trade-off between frequency synchronisation and overall network activity, which has also been observed during in vivo studies.

A multi-symptomatic model of heroin use disorder in rats reveals distinct behavioral profiles and neuronal correlates of heroin vulnerability versus resiliency.

The behavioral and diagnostic heterogeneity within human opioid use disorder (OUD) diagnosis is not readily captured in current animal models, limiting translational relevance of the mechanistic research that is conducted in experimental animals. We hypothesize that a non-linear clustering of OUD-like behavioral traits will capture population heterogeneity and yield subpopulations of OUD vulnerable rats with distinct behavioral and neurocircuit profiles. Over 900 male and female heterogeneous stock rats, a line capturing genetic and behavioral heterogeneity present in humans, were assessed for several measures of heroin use and rewarded and non-rewarded seeking behaviors. Using a non-linear stochastic block model clustering analysis, rats were assigned to OUD vulnerable, intermediate and resilient clusters. Additional behavioral tests and circuit analyses using c-fos protein activation were conducted on the vulnerable and resilient subpopulations. OUD vulnerable rats exhibited greater heroin taking and seeking behaviors relative to those in the intermediate and resilient clusters. Akin to human OUD diagnosis, further vulnerable rat sub-clustering revealed subpopulations with different combinations of behavioral traits, including sex differences. Lastly, heroin cue-induced neuronal patterns of circuit activation differed between resilient and vulnerable phenotypes. Behavioral sex differences were recapitulated in patterns of circuitry activation, including males preferentially engaging extended amygdala stress circuitry, and females cortico-striatal drug cue-seeking circuitry. Using a non-linear clustering approach in rats, we captured behavioral diagnostic heterogeneity reflective of human OUD diagnosis. OUD vulnerability and resiliency were associated with distinct neuronal activation patterns, posing this approach as a translational tool in assessing neurobiological mechanisms underpinning OUD.

Decoding Arc transcription: a live-cell study of stimulation patterns and transcriptional output.

Activity-regulated cytoskeleton-associated protein (Arc) plays a crucial role in synaptic plasticity, a process integral to learning and memory. Arc transcription is induced within a few minutes of stimulation, making it a useful marker for neuronal activity. However, the specific neuronal activity patterns that initiate Arc transcription have remained elusive due to the inability to observe mRNA transcription in live cells in real time. Using a genetically encoded RNA indicator (GERI) mouse model that expresses endogenous Arc mRNA tagged with multiple GFPs, we investigated Arc transcriptional activity in response to various electrical field stimulation patterns. The GERI mouse model was generated by crossing the Arc-PBS knock-in mouse, engineered with binding sites in the 3' untranslated region (UTR) of Arc mRNA, and the transgenic mouse expressing the cognate binding protein fused to GFP. In dissociated hippocampal neurons, we found that the pattern of stimulation significantly affects Arc transcription. Specifically, theta-burst stimulation consisting of high-frequency (100 Hz) bursts delivered at 10 Hz frequency induced the highest rate of Arc transcription. Concurrently, the amplitudes of nuclear calcium transients also reached their peak with 10 Hz burst stimulation, indicating a correlation between calcium concentration and transcription. However, our dual-color single-cell imaging revealed that there were no significant differences in calcium amplitudes between Arc-positive and Arc-negative neurons upon 10 Hz burst stimulation, suggesting the involvement of other factors in the induction of Arc transcription. Our live-cell RNA imaging provides a deeper insight into the complex regulation of transcription by activity patterns and calcium signaling pathways.

Patterns of neural activity in prelimbic cortex neurons correlate with attentional behavior in the rodent continuous performance test.

Sustained attention, the ability to focus on a stimulus or task over extended periods, is crucial for higher level cognition, and is impaired in individuals diagnosed with neuropsychiatric and neurodevelopmental disorders, including attention-deficit/hyperactivity disorder, schizophrenia, and depression. Translational tasks like the rodent continuous performance test (rCPT) can be used to study the cellular mechanisms underlying sustained attention. Accumulating evidence points to a role for the prelimbic cortex (PrL) in sustained attention, as electrophysiological single unit and local field (LFPs) recordings reflect changes in neural activity in the PrL in mice performing sustained attention tasks. While the evidence correlating PrL electrical activity with sustained attention is compelling, limitations inherent to electrophysiological recording techniques, including low sampling in single unit recordings and source ambivalence for LFPs, impede the ability to fully resolve the cellular mechanisms in the PrL that contribute to sustained attention. In vivo endoscopic calcium imaging using genetically encoded calcium sensors in behaving animals can address these questions by simultaneously recording up to hundreds of neurons at single cell resolution. Here, we used in vivo endoscopic calcium imaging to record patterns of neuronal activity in PrL neurons using the genetically encoded calcium sensor GCaMP6f in mice performing the rCPT at three timepoints requiring differing levels of cognitive demand and task proficiency. A higher proportion of PrL neurons were recruited during correct responses in sessions requiring high cognitive demand and task proficiency, and mice intercalated non-responsive-disengaged periods with responsive-engaged periods that resemble attention lapses. During disengaged periods, the correlation of calcium activity between PrL neurons was higher compared to engaged periods, suggesting a neuronal network state change during attention lapses in the PrL. Overall, these findings illustrate that cognitive demand, task proficiency, and task engagement differentially recruit activity in a subset of PrL neurons during sustained attention.

Regulation of GABAergic neurotransmission by purinergic receptors in brain physiology and disease.

Purinergic receptors regulate the processing of neural information in the hippocampus and cerebral cortex, structures related to cognitive functions. These receptors are activated when astrocytic and neuronal populations release adenosine triphosphate (ATP) in an autocrine and paracrine manner, following sustained patterns of neuronal activity. The modulation by these receptors of GABAergic transmission has only recently been studied. Through their ramifications, astrocytes and GABAergic interneurons reach large groups of excitatory pyramidal neurons. Their inhibitory effect establishes different synchronization patterns that determine gamma frequency rhythms, which characterize neural activities related to cognitive processes. During early life, GABAergic-mediated synchronization of excitatory signals directs the experience-driven maturation of cognitive development, and dysfunctions concerning this process have been associated with neurological and neuropsychiatric diseases. Purinergic receptors timely modulate GABAergic control over ongoing neural activity and deeply affect neural processing in the hippocampal and neocortical circuitry. Stimulation of A2 receptors increases GABA release from presynaptic terminals, leading to a considerable reduction in neuronal firing of pyramidal neurons. A1 receptors inhibit GABAergic activity but only act in the early postnatal period when GABA produces excitatory signals. P2X and P2Y receptors expressed in pyramidal neurons reduce the inhibitory tone by blocking GABAA receptors. Finally, P2Y receptor activation elicits depolarization of GABAergic neurons and increases GABA release, thus favoring the emergence of gamma oscillations. The present review provides an overall picture of purinergic influence on GABAergic transmission and its consequences on neural processing, extending the discussion to receptor subtypes and their involvement in the onset of brain disorders, including epilepsy and Alzheimer's disease.

Aberrant neural oscillations in poststroke aphasia.

Neural oscillations are electrophysiological indicators of synchronous neuronal activity in the brain. Recent work suggests aberrant patterns of neuronal activity in patients with poststroke aphasia. Yet, there is a lack of systematic explorations of neural oscillations in poststroke aphasia. Investigating changes in the dynamics of neuronal activity after stroke may be helpful to identify neural markers of aphasia and language recovery and increase the current understanding of successful language rehabilitation. This review summarizes research on neural oscillations in poststroke aphasia and evaluates their potential as biomarkers for specific linguistic processes. We searched the literature through PubMed, Web of Science, and EBSCO, and selected 31 studies that met the inclusion criteria. Our analyses focused on neural oscillation activity in each frequency band, brain connectivity, and therapy-induced changes during language recovery. Our review highlights potential neurophysiological markers; however, the literature remains confounded, casting doubt on the reliability of these findings. Future research must address these confounds to confirm the robustness of cross-study findings on neural oscillations in poststroke aphasia.

Spatiotemporal Properties of Common Semantic Categories for Words and Pictures.

The timing of semantic processing during object recognition in the brain is a topic of ongoing discussion. One way of addressing this question is by applying multivariate pattern analysis to human electrophysiological responses to object images of different semantic categories. However, although multivariate pattern analysis can reveal whether neuronal activity patterns are distinct for different stimulus categories, concerns remain on whether low-level visual features also contribute to the classification results. To circumvent this issue, we applied a cross-decoding approach to magnetoencephalography data from stimuli from two different modalities: images and their corresponding written words. We employed items from three categories and presented them in a randomized order. We show that if the classifier is trained on words, pictures are classified between 150 and 430 msec after stimulus onset, and when training on pictures, words are classified between 225 and 430 msec. The topographical map, identified using a searchlight approach for cross-modal activation in both directions, showed left lateralization, confirming the involvement of linguistic representations. These results point to semantic activation of pictorial stimuli occurring at ≈150 msec, whereas for words, the semantic activation occurs at ≈230 msec.

Neuronal activation patterns during self-referential pain imagination

In clinical assessments and pain therapy, patients are asked to imagine themselves in pain. However, the underlying neuronal processes remain poorly understood. Prior research has focused on empathy for pain or reported small sample sizes. Thus, the present study aimed to promote the neurobiological understanding of self-referential pain imagination. We hypothesised to find activation contrasts (pain vs. no pain) across pain-related areas and expected two of the most prominent predictors of chronic pain, pain sensitivity (PS) and locus of control (LoC), to be moderators.In an fMRI study, N = 82 participants completed a pain imagination task, in which they were asked to imagine themselves in painful and non-painful situations presented in the form of pictures and texts. After each trial, they were instructed to give painfulness ratings. As a laboratory measure of PS, electrical pain thresholds were assessed. A questionnaire was completed to measure LoC.Across presentation modes we found activity contrasts in previously pain-related regions, such as the prefrontal, supplementary motor, primary motor, somatosensory and posterior parietal cortices, and the cerebellum. We found positive associations of PS and external LoC with painfulness ratings, and a negative correlation between PS and internal LoC. Despite our hypotheses, neither PS nor internal LoC were significant predictors of the BOLD-signal contrasts.Though future studies are needed to draw further conclusions, our results provide preliminary evidence of a potential neuronal imagination-perception overlap in pain.

A potentiation of REM sleep-active neurons in the lateral habenula may be responsible for the sleep disturbance in depression

Psychiatric disorders with dysfunction of the lateral habenula (LHb) show sleep disturbance, especially a disinhibition of rapid eye movement (REM) sleep in major depression. However, the role of LHb in physiological sleep control and how LHb contributes to sleep disturbance in major depression remain elusive. Here, we found that functional manipulations of LHb glutamatergic neurons bidirectionally modulated both non-REM (NREM) sleep and REM sleep. Activity recording revealed heterogeneous activity patterns of LHb neurons across sleep/wakefulness cycles, but LHb neurons were preferentially active during REM sleep. Using an activity-dependent tagging method, we selectively labeled a population of REM sleep-active LHb neurons and demonstrated that these neurons specifically promoted REM sleep. Neural circuit studies showed that LHb neurons regulated REM sleep via projections to the ventral tegmental area but not to the rostromedial tegmental nucleus. Furthermore, we found that the increased REM sleep in a depression mouse model was associated with a potentiation of REM sleep-active LHb neurons, including an increased proportion, elevated spike firing, and altered activity mode. Importantly, inhibition of REM sleep-active LHb neurons not only attenuated the increased REM sleep but also alleviated depressive-like behaviors in a depression mouse model. Thus, our results demonstrated that REM sleep-active LHb neurons selectively promoted REM sleep, and a potentiation of these neurons contributed to depression-associated sleep disturbance.