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The brain must dynamically integrate, coordinate, and respond to internal and external stimuli across multiple time scales. Non-invasive measurements of brain activity with fMRI have greatly advanced our understanding of the large-scale functional organization supporting these fundamental features of brain function. Conclusions from previous resting-state fMRI investigations were based upon static descriptions of functional connectivity (FC), and only recently studies have begun to capitalize on the wealth of information contained within the temporal features of spontaneous BOLD FC. Emerging evidence suggests that dynamic FC metrics may index changes in macroscopic neural activity patterns underlying critical aspects of cognition and behavior, though limitations with regard to analysis and interpretation remain. Here, we review recent findings, methodological considerations, neural and behavioral correlates, and future directions in the emerging field of dynamic FC investigations. •Imaging studies have recently begun to examine dynamic properties of FC.•Dynamic FC may yield novel insights into brain function and dysfunction.•We review results, methods, interpretations, and limitations in this emerging field.

Transient ischemia in the whole brain leads to neuronal loss/death in vulnerable brain regions. The striatum, neocortex and hippocampus selectively loose specific neurons after transient ischemia. Just 5 minutes of transient ischemia can cause pyramidal neuronal death in the hippocampal cornu ammonis (CA) 1 field at 4 days after transient ischemia. In this study, we investigated the effects of 5-minute (mild), 15-minute (severe), and 20-minute (lethal) transient ischemia by bilateral common carotid artery occlusion (BCCAO) on behavioral change and neuronal death and gliosis (astrocytosis and microgliosis) in gerbil hippocampal subregions (CA1-3 region and dentate gyrus). We performed spontaneous motor activity test to evaluate gerbil locomotor activity, cresyl violet staining to detect cellular distribution, neuronal nuclei immunohistochemistry to detect neuronal distribution, and Fluoro-Jade B histofluorescence to evaluate neuronal death. We also conducted immunohistochemical staining for glial fibrillary acidic protein and ionized calcium-binding adapter molecule 1 (Iba1) to evaluate astrocytosis and microgliosis, respectively. Animals subjected to 20-minute BCCAO died in at least 2 days. BCCAO for 15 minutes led to pyramidal cell death in hippocampal CA1-3 region 2 days later and granule cell death in hippocampal dentate gyrus 5 days later. Similar results were not found in animals subjected to 5-minute BCCAO. Gliosis was much more rapidly and severely progressed in animals subjected to 15-minute BCCAO than in those subjected to 5-minute BCCAO. Our results indicate that neuronal loss in the hippocampal formation following transient ischemia is significantly different according to regions and severity of transient ischemia. The experimental protocol was approved by Institutional Animal Care and Use Committee (AICUC) of Kangwon National University (approval No. KW-180124-1) on May 22, 2018.

Protein degradation is critical for brain function through processes that remain incompletely understood. Here, we investigated the in vivo function of the 20S neuronal membrane proteasome (NMP) in the brain of Xenopus laevis tadpoles. With biochemistry, immunohistochemistry, and electron microscopy, we demonstrated that NMPs are conserved in the tadpole brain and preferentially degrade neuronal activity—induced newly synthesized proteins in vivo. Using in vivo calcium imaging in the optic tectum, we showed that acute NMP inhibition rapidly increased spontaneous neuronal activity, resulting in hypersynchronization across tectal neurons. At the circuit level, inhibiting NMPs abolished learning-dependent improvement in visuomotor behavior in live animals and caused a significant deterioration in basal behavioral performance following visual training with enhanced visual experience. Our data provide in vivo characterization of NMP functions in the vertebrate nervous system and suggest that NMPmediated degradation of activity-induced nascent proteins may serve as a homeostatic modulatory mechanism in neurons that is critical for regulating neuronal activity and experience-dependent circuit plasticity.

Arousal levels perpetually rise and fall spontaneously. How markers of arousal—pupil size and frequency content of brain activity—relate to each other and influence behavior in humans is poorly understood. We simultaneously monitored magnetoencephalography and pupil in healthy volunteers at rest and during a visual perceptual decision-making task. Spontaneously varying pupil size correlates with power of brain activity in most frequency bands across large-scale resting state cortical networks. Pupil size recorded at prestimulus baseline correlates with subsequent shifts in detection bias ( c ) and sensitivity ( d ’). When dissociated from pupil-linked state, prestimulus spectral power of resting state networks still predicts perceptual behavior. Fast spontaneous pupil constriction and dilation correlate with large-scale brain activity as well but not perceptual behavior. Our results illuminate the relation between central and peripheral arousal markers and their respective roles in human perceptual decision-making.

Resting-state functional connectivity (FC), which measures the correlation of spontaneous hemodynamic signals (HemoS) between brain areas, is widely used to study brain networks noninvasively. It is commonly assumed that spatial patterns of HemoS-based FC (Hemo-FC) reflect large-scale dynamics of underlying neuronal activity. To date, studies of spontaneous neuronal activity cataloged heterogeneous types of events ranging from waves of activity spanning the entire neocortex to flash-like activations of a set of anatomically connected cortical areas. However, it remains unclear how these various types of large-scale dynamics are inter-related. More importantly, whether each type of large-scale dynamics contributes to Hemo-FC has not been explored. Here, we addressed these questions by simultaneously monitoring neuronal calcium signals (CaS) and HemoS in the entire neocortex of mice at high spatiotemporal resolution. We found a significant relationship between two seemingly different types of large-scale spontaneous neuronal activity—namely, global waves propagating across the neocortex and transient coactivations among cortical areas sharing high FC. Different sets of cortical areas, sharing high FC within each set, were coactivated at different timings of the propagating global waves, suggesting that spatial information of cortical network characterized by FC was embedded in the phase of the global waves. Furthermore, we confirmed that such transient coactivations in CaS were indeed converted into spatially similar coactivations in HemoS and were necessary to sustain the spatial structure of Hemo-FC. These results explain how global waves of spontaneous neuronal activity propagating across large-scale cortical network contribute to Hemo-FC in the resting state.

Neural responses to the same stimulus show significant variability over trials, with this variability typically reduced (quenched) after a stimulus is presented. This trial-to-trial variability (TTV) has been much studied, however how this neural variability quenching is influenced by the ongoing dynamics of the prestimulus period is unknown. Utilizing a human intracranial stereo-electroencephalography (sEEG) data set, we investigate how prestimulus dynamics, as operationalized by standard deviation (SD), shapes poststimulus activity through trial-to-trial variability (TTV). We first observed greater poststimulus variability quenching in those real trials exhibiting high prestimulus variability as observed in all frequency bands. Next, we found that the relative effect of the stimulus was higher in the later (300-600ms) than the earlier (0-300ms) poststimulus period. Lastly, we replicate our findings in a separate EEG dataset and extend them by finding that trials with high prestimulus variability in the theta and alpha bands had faster reaction times. Together, our results demonstrate that stimulus-related activity, including its variability, is a blend of two factors: 1) the effects of the external stimulus itself, and 2) the effects of the ongoing dynamics spilling over from the prestimulus period - the state at stimulus onset - with the second dwarfing the influence of the first.

Most studies of resting-state functional connectivity using fMRI employ methods that assume temporal stationarity, such as correlation and data-driven decompositions computed across the duration of the scan. However, evidence from both task-based fMRI studies and animal electrophysiology suggests that functional connectivity may exhibit dynamic changes within time scales of seconds to minutes. In the present study, we investigated the dynamic behavior of resting-state connectivity across the course of a single scan, performing a time–frequency coherence analysis based on the wavelet transform. We focused on the connectivity of the posterior cingulate cortex (PCC), a primary node of the default-mode network, examining its relationship with both the “anticorrelated” (“task-positive”) network as well as other nodes of the default-mode network. It was observed that coherence and phase between the PCC and the anticorrelated network was variable in time and frequency, and statistical testing based on Monte Carlo simulations revealed the presence of significant scale-dependent temporal variability. In addition, a sliding-window correlation procedure identified other regions across the brain that exhibited variable connectivity with the PCC across the scan, which included areas previously implicated in attention and salience processing. Although it is unclear whether the observed coherence and phase variability can be attributed to residual noise or modulation of cognitive state, the present results illustrate that resting-state functional connectivity is not static, and it may therefore prove valuable to consider measures of variability, in addition to average quantities, when characterizing resting-state networks.

The intracerebral local field potential (LFP) is a measure of brain activity that reflects the highly dynamic flow of information across neural networks. This is a composite signal that receives contributions from multiple neural sources, yet interpreting its nature and significance may be hindered by several confounding factors and technical limitations. By and large, the main factor defining the amplitude of LFPs is the geometry of the current sources, over and above the degree of synchronization or the properties of the media. As such, similar levels of activity may result in potentials that differ in several orders of magnitude in different populations. The geometry of these sources has been experimentally inaccessible until intracerebral high density recordings enabled the co-activating sources to be revealed. Without this information, it has proven difficult to interpret a century's worth of recordings that used temporal cues alone, such as event or spike related potentials and frequency bands. Meanwhile, a collection of biophysically ill-founded concepts have been considered legitimate, which can now be corrected in the light of recent advances. The relationship of LFPs to their sources is often counterintuitive. For instance, most LFP activity is not local but remote, it may be larger further from rather than close to the source, the polarity does not define its excitatory or inhibitory nature, and the amplitude may increase when source's activity is reduced. As technological developments foster the use of LFPs, the time is now ripe to raise awareness of the need to take into account spatial aspects of these signals and of the errors derived from neglecting to do so.

Neuronal activity has been shown to be essential for the proper formation of neuronal circuits, affecting developmental processes like neurogenesis, migration, programmed cell death, cellular differentiation, formation of local and long-range axonal connections, synaptic plasticity or myelination. Accordingly, neocortical areas reveal distinct spontaneous and sensory-driven neuronal activity patterns already at early phases of development. At embryonic stages, when immature neurons start to develop voltage-dependent channels, spontaneous activity is highly synchronized within small neuronal networks and governed by electrical synaptic transmission. Subsequently, spontaneous activity patterns become more complex, involve larger networks and propagate over several neocortical areas. The developmental shift from local to large-scale network activity is accompanied by a gradual shift from electrical to chemical synaptic transmission with an initial excitatory action of chloride-gated channels activated by GABA, glycine and taurine. Transient neuronal populations in the subplate (SP) support temporary circuits that play an important role in tuning early neocortical activity and the formation of mature neuronal networks. Thus, early spontaneous activity patterns control the formation of developing networks in sensory cortices, and disturbances of these activity patterns may lead to long-lasting neuronal deficits.

Neuronal activity in the cerebral cortex comes in surprisingly early and influences or even controls a number of important developmental process like neurogenesis, neuronal migration, myelination, formation of cortical maps and local circuits, and programmed cell death. During the late prenatal and early postnatal period, the neocortical network shows a developmental transition from sparse, synchronized, low activity patterns to continuous, desynchronized, high activity patterns. This developmental sequence has been demonstrated in various neocortical areas of different mammalian species. This review article aims to provide a comprehensive overview of the early development of neuronal network activity in the cerebral cortex. We mainly focus on the rodent barrel cortex and a developmental period when the cortex gains mature functional properties at the cellular and network level. After briefly summarizing the developmental processes underlying the construction, reconstruction, and deconstruction of neocortical circuits, we describe the age-dependent changes in spontaneous and sensory driven network activity. Next we discuss the functional role of transient cortical structures and cell types in the generation of early activity patterns and in the activity-dependent maturation of local and large-scale cortical networks. Finally, we present an outlook on the models and techniques to study the cellular and network mechanisms underlying neuronal activity in the developing cerebral cortex.