Cortical Network Dynamics Research Articles

Hemodynamic cortical ripples through cyclicity analysis

Abstract A fine-grained understanding of dynamics in cortical networks is crucial to unpacking brain function. Resting-state functional magnetic resonance imaging (fMRI) gives rise to time series recordings of the activity of different brain regions, which are aperiodic and lack a base frequency. Cyclicity analysis, a novel technique robust under time reparametrizations, is effective in recovering the temporal ordering of such time series, collectively considered components of a multidimensional trajectory. Here, we extend this analytical method for characterizing the dynamic interaction between distant brain regions and apply it to the data from the Human Connectome Project. Our analysis detected cortical traveling waves of activity propagating along a spatial axis, resembling cortical hierarchical organization with consistent lead-lag relationships between specific brain regions in resting-state scans. In fMRI scans involving tasks, we observed short bursts of task-modulated strong temporal ordering that dominate overall lead-lag relationships between pairs of regions in the brain that align temporally with stimuli from the tasks. Our results suggest a possible role played by waves of excitation sweeping through brain regions that underlie emergent cognitive functions.

Cortical networks of parkinsonian gait: a metabolic and functional connectivity study.

Locomotion is an automated voluntary movement sustained by coordinated neural synchronization across a distributed brain network. The cerebral cortex is central for adapting the locomotion pattern to the environment and alterations of cortical network dynamics can lead to gait impairments. Gait problems are a common symptom with a still unclear pathophysiology and represent an unmet therapeutical need in Parkinson's disease. Little is known about the cortical network dynamics of locomotor control in these patients. We studied the cortical basis of parkinsonian gait by combining metabolic brain imaging with high-density EEG recordings and kinematic measurements performed at rest and during unperturbed overground walking. We found significant changes in functional connectivity between frontal, sensorimotor, and visuomotor cortical areas during walking as compared to resting. Specifically, hypokinetic gait was associated with poor information flow from the supplementary motor area (SMA) to precuneus and from calcarine to lingual gyrus, as well as high information flow from calcarine to cuneus. Our findings support a role for visuomotor integration processes in PD-related hypokinetic gait and suggest that reinforcing visual information may act as a compensatory strategy to allow SMA-mediated feedforward locomotor control in PD.

Investigating the Effects of Transcranial Alternating Current Stimulation on Cortical Oscillations and Network Dynamics.

Transcranial electrical brain stimulation techniques like transcranial direct current (tDCS) and transcranial alternating current (tACS) have emerged as potential tools for treating neurological diseases by modulating cortical excitability. These techniques deliver small electric currents to the brain non-invasively through electrodes on the scalp. tDCS uses constant direct current which weakly alters the membrane voltage of cortical neurons, while tACS utilizes alternating current to target and enhance cortical oscillations, though the underlying mechanisms are not fully understood more specifically. To elucidate how tACS perturbs endogenous network dynamics, we simulated spiking neuron network models. We identified distinct roles of the depolarizing and hyperpolarizing phases in driving network activity towards and away from the strong nonlinearity provided by pyramidal neurons. Exploring resonance effects, we found matching tACS frequency to the network's endogenous resonance frequency creates greater entrainment. Based on this, we developed an algorithm to determine the network's endogenous frequency, phase, and amplitude, then deliver optimized tACS to entrain network oscillations. Together, these computational results provide mechanistic insight into the effects of tACS on network dynamics and could inform future closed-loop tACS systems that dynamically tune stimulation parameters to ongoing brain activity.

Developmental dynamics of the prefrontal cortical SST and PV interneuron networks: Insights from the monkey highlight human-specific features.

The primate prefrontal cortex (PFC) is a quintessential hub of cognitive functions. Amidst its intricate neural architecture, the interplay of distinct neuronal subtypes, notably parvalbumin (PV) and somatostatin (SST) interneurons (INs), emerge as a cornerstone in sculpting cortical circuitry and governing cognitive processes. While considerable strides have been made in elucidating the developmental trajectory of these neurons in rodent models, our understanding of their postmigration developmental dynamics in primates still needs to be studied. Disruptions to this developmental trajectory can compromise IN function, impairing signal gating and circuit modulation within cortical networks. This study examined the expression patterns of PV and SST, ion transporter KCC2, and ion channel subtypes Kv3.1b, and Nav1.1 - associated with morphophysiological stages of development in the postnatal marmoset monkey in different frontal cortical regions (granular areas 8aD, 8aV, 9, 46; agranular areas 11, 47L). Our results demonstrate that the maturation of PV+ INs extends into adolescence, characterized by discrete epochs associated with specific expression dynamics of ion channel subtypes. Interestingly, we observed a postnatal decrease in SST interneurons, contrasting with studies in rodents. This endeavor broadens our comprehension of primate cortical development and furnishes invaluable insights into the etiology and pathophysiology of neurodevelopmental disorders characterized by perturbations in PV and SST IN function.

Intentional binding for noninvasive BCI control.

Noninvasive brain-computer interfaces (BCIs) allow to interact with the external environment by naturally bypassing the musculoskeletal system.
Making BCIs efficient and accurate is paramount to improve the reliability of real-life and clinical applications, from open-loop device control to closed-loop neurorehabilitation. By promoting sense of agency and embodiment, realistic setups including multimodal channels of communication, such as eye-gaze, and robotic prostheses aim to improve BCI performance. However, how the mental imagery command should be integrated in those hybrid systems so as to ensure the best interaction is still poorly understood.
To address this question, we performed a hybrid EEG-based BCI training involving healthy volunteers enrolled in a reach-and-grasp action operated by a robotic arm. Main results showed that the hand grasping motor imagery timing significantly affects the BCI accuracy evolution as well as the spatiotemporal brain dynamics. Larger accuracy improvement was obtained when motor imagery is performed just after the robot reaching, as compared to before or during the movement. The proximity with the subsequent robot grasping favored intentional binding, led to stronger motor-related brain activity, and primed the ability of sensorimotor areas to integrate information from regions implicated in higher-order cognitive functions.
Taken together, these findings provided fresh evidence about the effects of intentional binding on human behavior and cortical network dynamics that can be exploited to design a new generation of efficient brain-machine interfaces.

Brain Network Interconnectivity Dynamics Explain Metacognitive Differences in Listening Behavior.

Complex auditory scenes pose a challenge to attentive listening, rendering listeners slower and more uncertain in their perceptual decisions. How can we explain such behaviors from the dynamics of cortical networks that pertain to the control of listening behavior? We here follow up on the hypothesis that human adaptive perception in challenging listening situations is supported by modular reconfiguration of auditory-control networks in a sample of N = 40 participants (13 males) who underwent resting-state and task functional magnetic resonance imaging (fMRI). Individual titration of a spatial selective auditory attention task maintained an average accuracy of ∼70% but yielded considerable interindividual differences in listeners' response speed and reported confidence in their own perceptual decisions. Whole-brain network modularity increased from rest to task by reconfiguring auditory, cinguloopercular, and dorsal attention networks. Specifically, interconnectivity between the auditory network and cinguloopercular network decreased during the task relative to the resting state. Additionally, interconnectivity between the dorsal attention network and cinguloopercular network increased. These interconnectivity dynamics were predictive of individual differences in response confidence, the degree of which was more pronounced after incorrect judgments. Our findings uncover the behavioral relevance of functional cross talk between auditory and attentional-control networks during metacognitive assessment of one's own perception in challenging listening situations and suggest two functionally dissociable cortical networked systems that shape the considerable metacognitive differences between individuals in adaptive listening behavior.

Co-activation patterns during viewing of different video game genres

Past research has revealed cognitive improvements resulting from engagement with both traditional action video games and newer action-like video games, such as action real-time strategy games (ARSG). However, the cortical dynamics elicited by different video gaming genres remain unclear. This study explored the temporal dynamics of cortical networks in response to different gaming genres. Functional magnetic resonance imaging (fMRI) data were obtained during eye-closed resting and passive viewing of gameplay videos of three genres: life simulation games (LSG), first-person shooter games (FPS), and ARSG. Data analysis used a seed-free Co-Activation Pattern (CAP) based on Regions of Interest (ROIs). When comparing the viewing of action-like video games (FPS and ARSG) to LSG viewing, significant dynamic distinctions were observed in both primary and higher-order networks. Within action-like video games, compared to FPS viewing, ARSG viewing elicited a more pronounced increase in the Fraction of Time and Counts of attentional control-related CAPs, along with an increased Transition Probability from sensorimotor-related CAPs to attentional control-related CAPs. Compared to ARSG viewing, FPS viewing elicited a significant increase in the Fraction of Time of sensorimotor-related CAPs, when gaming experience was considered as a covariate. Thus, different video gaming genres, including distinct action-like video gaming genres, elicited unique dynamic patterns in whole-brain CAPs, potentially influencing the development of various cognitive processes.

Biophysical modulation and robustness of itinerant complexity in neuronal networks.

Transient synchronization of bursting activity in neuronal networks, which occurs in patterns of metastable itinerant phase relationships between neurons, is a notable feature of network dynamics observed in vivo. However, the mechanisms that contribute to this dynamical complexity in neuronal circuits are not well understood. Local circuits in cortical regions consist of populations of neurons with diverse intrinsic oscillatory features. In this study, we numerically show that the phenomenon of transient synchronization, also referred to as metastability, can emerge in an inhibitory neuronal population when the neurons' intrinsic fast-spiking dynamics are appropriately modulated by slower inputs from an excitatory neuronal population. Using a compact model of a mesoscopic-scale network consisting of excitatory pyramidal and inhibitory fast-spiking neurons, our work demonstrates a relationship between the frequency of pyramidal population oscillations and the features of emergent metastability in the inhibitory population. In addition, we introduce a method to characterize collective transitions in metastable networks. Finally, we discuss potential applications of this study in mechanistically understanding cortical network dynamics.

Dynamic fading memory and expectancy effects in the monkey primary visual cortex

In order to investigate the involvement of the primary visual cortex (V1) in working memory (WM), parallel, multisite recordings of multi-unit activity were obtained from monkey V1 while the animals performed a delayed match-to-sample (DMS) task. During the delay period, V1 population firing rate vectors maintained a lingering trace of the sample stimulus that could be reactivated by intervening impulse stimuli that enhanced neuronal firing. This fading trace of the sample did not require active engagement of the monkeys in the DMS task and likely reflects the intrinsic dynamics of recurrent cortical networks in lower visual areas. This renders an active, attention-dependent involvement of V1 in the maintenance of WM contents unlikely. By contrast, population responses to the test stimulus depended on the probabilistic contingencies between sample and test stimuli. Responses to tests that matched expectations were reduced which agrees with concepts of predictive coding.

Synaptic Marker NPTX2 Predicts Risk of MCI Clinical Symptom Onset

AbstractBackgroundNeuronal pentraxin 2 (NPTX2) is a synaptic protein involved in the homeostatic regulation of cortical network dynamics and synaptic plasticity. Prior cross‐sectional studies suggest that NPTX2 levels decline across the spectrum of Alzheimer’s disease (AD), and that NPTX2 levels can distinguish individuals with normal cognition vs. mild cognitive impairment (MCI) and AD dementia. Less is known about how levels of NPTX2 among individuals with normal cognition relate to future clinical outcomes. To address this, we examined whether baseline levels of NPTX2 predicted time to onset of clinical symptoms of MCI, both alone and in combination with AD biomarkers, among individuals who were cognitively normal at baseline and have undergone 16.3y of follow‐up, on average.MethodCerebrospinal fluid (CSF) was collected at baseline from 269 cognitively normal BIOCARD Study participants (M baseline age = 57.7y), including 77 (29%) who subsequently progressed to MCI or dementia (Table 1). Baseline NPTX2 levels were measured from three highly correlated peptides using quantitative parallel reaction monitoring mass spectrometry. NPTX2 peptide levels were standardized and averaged to create an NPTX2 composite score. Baseline levels of Aβ42/Aβ40, p‐tau181, and t‐tau were measured using Lumipulse automated electrochemiluminescence assays.ResultIn Cox regression models, lower NPTX2 levels were significantly associated with earlier time to MCI clinical symptom onset (HR = 0.76, 95% CI (0.601, 0.962), p = 0.02; Figure 1a). After accounting for CSF AD biomarkers (i.e., Aβ42/Aβ40 and tau), the association between NPTX2 levels and MCI symptom onset appeared stronger (Table 2; Figure 1b). Importantly, NPTX2 did not interact with AD biomarker levels (all p > 0.11) or APOE‐ε4 genetic status (p = 0.71) in relation to MCI symptom onset.ConclusionCSF levels of the synaptic marker NPTX2 predict time to progress to MCI among individuals with normal cognition, suggesting that NPTX2 may be an early prognostic biomarker of synaptic dysfunction. Moreover, this association appears to be additive and independent of AD biomarker levels. Future work is underway to determine if NPTX2 provides resilience to cognitive impairment in the presence of AD pathology, and if there is a differential relationship with markers of amyloid vs. tau.

Analysis of corticomuscular-cortical functional network based on time-delayed maximal information spectral coefficient

Objective. The study of brain networks has become an influential tool for investigating post-stroke brain function. However, studies on the dynamics of cortical networks associated with muscle activity are limited. This is crucial for elucidating the altered coordination patterns in the post-stroke motor control system. Approach. In this study, we introduced the time-delayed maximal information spectral coefficient (TDMISC) method to assess the local frequency band characteristics (alpha, beta, and gamma bands) of functional corticomuscular coupling (FCMC) and cortico-cortical network parameters. We validated the effectiveness of TDMISC using a unidirectionally coupled Hénon maps model and a neural mass model. Main result. A grip task with 25% of maximum voluntary contraction was designed, and simulation results demonstrated that TDMISC accurately characterizes signals’ local frequency band and directional properties. In the gamma band, the affected side showed significantly strong FCMC in the ascending direction. However, in the beta band, the affected side exhibited significantly weak FCMC in all directions. For the cortico-cortical network parameters, the affected side showed a lower clustering coefficient than the unaffected side in all frequency bands. Additionally, the affected side exhibited a longer shortest path length than the unaffected side in all frequency bands. In all frequency bands, the unaffected motor cortex in the stroke group exerted inhibitory effects on the affected motor cortex, the parietal associative areas, and the somatosensory cortices. Significance. These results provide meaningful insights into neural mechanisms underlying motor dysfunction.

Spatiotemporal Dynamics of Memory Encoding and Memory Retrieval States.

Memory encoding and memory retrieval are neurally distinct brain states that can be differentiated on the basis of cortical network activity. However, it is unclear whether sustained engagement of one network or fluctuations between multiple networks give rise to these memory states. The spatiotemporal dynamics of memory states may have important implications for memory behavior and cognition; however, measuring temporally resolved signals of cortical networks poses a challenge. Here, we recorded scalp electroencephalography from participants performing a mnemonic state task in which they were biased toward memory encoding or retrieval. We performed a microstate analysis to measure the temporal dynamics of cortical networks throughout this mnemonic state task. We find that Microstate E, a putative analog of the default mode network, shows temporally sustained dissociations between memory encoding and retrieval, with greater engagement during retrieve compared with encode trials. We further show that decreased engagement of Microstate E is a general property of encoding, rather than a reflection of retrieval suppression. Thus, memory success, as well as cognition more broadly, may be influenced by the ability to engage or disengage Microstate E in a goal-dependent manner.

Sleep disrupts complex spiking dynamics in the neocortex and hippocampus.

Neuronal interactions give rise to complex dynamics in cortical networks, often described in terms of the diversity of activity patterns observed in a neural signal. Interestingly, the complexity of spontaneous electroencephalographic signals decreases during slow-wave sleep (SWS); however, the underlying neural mechanisms remain elusive. Here, we analyse in-vivo recordings from neocortical and hippocampal neuronal populations in rats and show that the complexity decrease is due to the emergence of synchronous neuronal DOWN states. Namely, we find that DOWN states during SWS force the population activity to be more recurrent, deterministic, and less random than during REM sleep or wakefulness, which, in turn, leads to less complex field recordings. Importantly, when we exclude DOWN states from the analysis, the recordings during wakefulness and sleep become indistinguishable: the spiking activity in all the states collapses to a common scaling. We complement these results by implementing a critical branching model of the cortex, which shows that inducing DOWN states to only a percentage of neurons is enough to generate a decrease in complexity that replicates SWS.

The interaction between changes of muscle activation and cortical network dynamics during isometric elbow contraction: a sEMG and fNIRS study.

Objective: The relationship between muscle activation during motor tasks and cerebral cortical activity remains poorly understood. The aim of this study was to investigate the correlation between brain network connectivity and the non-linear characteristics of muscle activation changes during different levels of isometric contractions. Methods: Twenty-one healthy subjects were recruited and were asked to perform isometric elbow contractions in both dominant and non-dominant sides. Blood oxygen concentrations in brain from functional Near-infrared Spectroscopy (fNIRS) and surface electromyography (sEMG) signals in the biceps brachii (BIC) and triceps brachii (TRI) muscles were recorded simultaneously and compared during 80% and 20% of maximum voluntary contraction (MVC). Functional connectivity, effective connectivity, and graph theory indicators were used to measure information interaction in brain activity during motor tasks. The non-linear characteristics of sEMG signals, fuzzy approximate entropy (fApEn), were used to evaluate the signal complexity changes in motor tasks. Pearson correlation analysis was used to examine the correlation between brain network characteristic values and sEMG parameters under different task conditions. Results: The effective connectivity between brain regions in motor tasks in dominant side was significantly higher than that in non-dominant side under different contractions (p < 0.05). The results of graph theory analysis showed that the clustering coefficient and node-local efficiency of the contralateral motor cortex were significantly varied under different contractions (p < 0.01). fApEn and co-contraction index (CCI) of sEMG under 80% MVC condition were significantly higher than that under 20% MVC condition (p < 0.05). There was a significant positive correlation between the fApEn and the blood oxygen value in the contralateral brain regions in both dominant or non-dominant sides (p < 0.001). The node-local efficiency of the contralateral motor cortex in the dominant side was positively correlated with the fApEn of the EMG signals (p < 0.05). Conclusion: In this study, the mapping relationship between brain network related indicators and non-linear characteristic of sEMG in different motor tasks was verified. These findings provide evidence for further exploration of the interaction between the brain activity and the execution of motor tasks, and the parameters might be useful in evaluation of rehabilitation intervention.

Synapse-Specific Modulation of Synaptic Responses by Brain States in Hippocampal Pathways.

Synaptic changes play a major role in memory processes. Modulation of synaptic responses by brain states remains, however, poorly understood in hippocampal networks, even in basal conditions. We recorded evoked synaptic responses at five hippocampal pathways in freely moving male rats. We showed that, at the perforant path to dentate gyrus (PP-DG) synapse, responses increase during wakefulness compared with sleep. At the Schaffer collaterals to CA1 (SC-CA1) synapse, responses increase during non-REM sleep (NREM) compared with the other states. During REM sleep (REM), responses decreased at the PP-DG and SC-CA1 synapses compared with NREM, while they increased at the fornix to nucleus accumbens synapse (Fx-NAc) during REM compared with the other states. In contrast, responses at the fornix to medial PFC synapse (Fx-PFC) and at the fornix to amygdala synapse (Fx-Amy) were weakly modulated by vigilance states. Extended sleep periods led to synaptic changes at PP-DG and Fx-Amy synapses but not at the other synapses. Synaptic responses were also linked to local oscillations and were highly correlated between Fx-PFC and Fx-NAc but not between Fx-Amy and these synapses. These results reveal synapse-specific modulations that may contribute to memory consolidation during the sleep-wake cycle.SIGNIFICANCE STATEMENT Surprisingly, the cortical network dynamics remains poorly known at the synaptic level. We tested the hypothesis that brain states would modulate synaptic changes in the same way at different cortical connections. To tackle this issue, we implemented an approach to explore the synaptic behavior of five connections upstream and downstream the rat hippocampus. Our study reveals that synaptic responses are modulated in a highly synapse-specific manner by wakefulness and sleep states as well as by local oscillations at these connections. Moreover, we found rapid synaptic changes during wake and sleep transitions as well as synaptic down and upregulations after extended periods of sleep. These synaptic changes are likely related to the mechanisms of sleep-dependent memory consolidation.

Source-sink connectivity: a novel interictal EEG marker for seizure localization.

Over 15 million epilepsy patients worldwide have drug-resistant epilepsy. Successful surgery is a standard of care treatment but can only be achieved through complete resection or disconnection of the epileptogenic zone, the brain region(s) where seizures originate. Surgical success rates vary between 20% and 80%, because no clinically validated biological markers of the epileptogenic zone exist. Localizing the epileptogenic zone is a costly and time-consuming process, which often requires days to weeks of intracranial EEG (iEEG) monitoring. Clinicians visually inspect iEEG data to identify abnormal activity on individual channels occurring immediately before seizures or spikes that occur interictally (i.e. between seizures). In the end, the clinical standard mainly relies on a small proportion of the iEEG data captured to assist in epileptogenic zone localization (minutes of seizure data versus days of recordings), missing opportunities to leverage these largely ignored interictal data to better diagnose and treat patients. IEEG offers a unique opportunity to observe epileptic cortical network dynamics but waiting for seizures increases patient risks associated with invasive monitoring. In this study, we aimed to leverage interictal iEEG data by developing a new network-based interictal iEEG marker of the epileptogenic zone. We hypothesized that when a patient is not clinically seizing, it is because the epileptogenic zone is inhibited by other regions. We developed an algorithm that identifies two groups of nodes from the interictal iEEG network: those that are continuously inhibiting a set of neighbouring nodes ('sources') and the inhibited nodes themselves ('sinks'). Specifically, patient-specific dynamical network models were estimated from minutes of iEEG and their connectivity properties revealed top sources and sinks in the network, with each node being quantified by source-sink metrics. We validated the algorithm in a retrospective analysis of 65 patients. The source-sink metrics identified epileptogenic regions with 73% accuracy and clinicians agreed with the algorithm in 93% of seizure-free patients. The algorithm was further validated by using the metrics of the annotated epileptogenic zone to predict surgical outcomes. The source-sink metrics predicted outcomes with an accuracy of 79% compared to an accuracy of 43% for clinicians' predictions (surgical success rate of this dataset). In failed outcomes, we identified brain regions with high metrics that were untreated. When compared with high frequency oscillations, the most commonly proposed interictal iEEG feature for epileptogenic zone localization, source-sink metrics outperformed in predictive power (by a factor of 1.2), suggesting they may be an interictal iEEG fingerprint of the epileptogenic zone.

Saccades and presaccadic stimulus repetition alter cortical network topology and dynamics: evidence from EEG and graph theoretical analysis.

Parietal and frontal cortex are involved in saccade generation, and their output signals modify visual signals throughout cortex. Local signals associated with these interactions are well described, but their large-scale progression and network dynamics are unknown. Here, we combined source localized electroencephalography (EEG) and graph theory analysis (GTA) to understand how saccades and presaccadic visual stimuli interactively alter cortical network dynamics in humans. Twenty-one participants viewed 1-3 vertical/horizontal grids, followed by grid with the opposite orientation just before a horizontal saccade or continued fixation. EEG signals from the presaccadic interval (or equivalent fixation period) were used for analysis. Source localization-through-time revealed a rapid frontoparietal progression of presaccadic motor signals and stimulus-motor interactions, with additional band-specific modulations in several frontoparietal regions. GTA analysis revealed a saccade-specific functional network with major hubs in inferior parietal cortex (alpha) and the frontal eye fields (beta), and major saccade-repetition interactions in left prefrontal (theta) and supramarginal gyrus (gamma). This network showed enhanced segregation, integration, synchronization, and complexity (compared with fixation), whereas stimulus repetition interactions reduced synchronization and complexity. These cortical results demonstrate a widespread influence of saccades on both regional and network dynamics, likely responsible for both the motor and perceptual aspects of saccades.

Effects of virtual lesions on temporal dynamics in cortical networks based on personalized dynamic models

The human brain dynamically shifts between a predominantly integrated state and a predominantly segregated state, each with different roles in supporting cognition and behavior. However, no studies to date have investigated lesions placed in different regions of the cerebral cortex to determine the effects on the temporal balance of integration and segregation. Here, we used the integrated state occurrence rate to measure the temporal balance of integration and segregation in the resting brain. Based on dynamic mean-field models coupled through the individual's structural white matter connections, neural activity was modeled. By lesioning different individual nodes from the model, our results implied that the impact of lesions reaches far beyond focal damage and could impair cognition by affecting system-level dynamics. Lesions in a portion of the nodes in the visual, somatomotor, limbic, and default networks significantly compromised the temporal balance of integration and segregation. Crucially, the effects of lesions in different regions could be predicted based on the hierarchical axis inferred from the T1w/T2w map and specific graph measures of structural brain networks. Taken together, our findings indicate the possibility of significant contributions of anatomical heterogeneity to the dynamics of functional network topology. Although still in its infancy, computational modeling may provide an entry point for understanding brain disorders at a causal mechanistic level, possibly leading to novel, more effective therapeutic interventions.

Principles of GABAergic signaling in developing cortical network dynamics.

GABAergic signaling provides inhibitory stabilization and spatiotemporally coordinates the firing of recurrently connected excitatory neurons in mature cortical circuits. Inhibition thus enables self-generated neuronal activity patterns that underlie various aspects of sensation and cognition. In this review, we aim to provide a conceptual framework describing how and when GABA-releasing interneurons acquire their network functions during development. Focusing on the developing visual neocortex and hippocampus in mice and rats invivo, we hypothesize that at the onset of patterned activity, glutamatergic neurons are stable by themselves and inhibitory stabilization is not yet functional. We review important milestones in the development of GABAergic signaling and illustrate how the cell-type-specific strengthening of synaptic inhibition toward eye opening shapes cortical network dynamics and allows the developing cortex to progressively disengage from extra-cortical synaptic drive. We translate this framework to human cortical development and discuss clinical implications for the treatment of neonatal seizures.

Contributions of Distinct Auditory Cortical Inhibitory Neuron Types to the Detection of Sounds in Background Noise.

The ability to separate background noise from relevant acoustic signals is essential for appropriate sound-driven behavior in natural environments. Examples of this separation are apparent in the auditory system, where neural responses to behaviorally relevant stimuli become increasingly noise invariant along the ascending auditory pathway. However, the mechanisms that underlie this reduction in responses to background noise are not well understood. To address this gap in knowledge, we first evaluated the effects of auditory cortical inactivation on mice of both sexes trained to perform a simple auditory signal-in-noise detection task and found that outputs from the auditory cortex are important for the detection of auditory stimuli in noisy environments. Next, we evaluated the contributions of the two most common cortical inhibitory cell types, parvalbumin-expressing (PV+) and somatostatin-expressing (SOM+) interneurons, to the perception of masked auditory stimuli. We found that inactivation of either PV+ or SOM+ cells resulted in a reduction in the ability of mice to determine the presence of auditory stimuli masked by noise. These results indicate that a disruption of auditory cortical network dynamics by either of these two types of inhibitory cells is sufficient to impair the ability to separate acoustic signals from noise.