Analysis of ongoing dynamics in neural networks

Learning populations with hubs govern the initiation and propagation of spontaneous bursts in neuronal networks after learning

Spontaneous bursts in neuronal networks with propagation involving a large number of synchronously firing neurons are considered to be a crucial feature of these networks both in vivo and in vitro. Recently, learning has been shown to improve the association and synchronization of spontaneous events in neuronal networks by promoting the firing of spontaneous bursts. However, little is known about the relationship between the learning phase and spontaneous bursts. By combining high-resolution measurement with a 4,096-channel complementary metal-oxide-semiconductor (CMOS) microelectrode array (MEA) and graph theory, we studied how the learning phase influenced the initiation of spontaneous bursts in cultured networks of rat cortical neurons in vitro. We found that a small number of selected populations carried most of the stimulus information and contributed to learning. Moreover, several new burst propagation patterns appeared in spontaneous firing after learning. Importantly, these "learning populations" had more hubs in the functional network that governed the initiation of spontaneous burst activity. These results suggest that changes in the functional structure of learning populations may be the key mechanism underlying increased bursts after learning. Our findings could increase understanding of the important role that synaptic plasticity plays in the regulation of spontaneous activity.

Highly active neurons emerging in vitro

Mean firing rates vary across neurons in a neuronal network. Although most neurons infrequently emit spikes, a small fraction of neurons exhibit extremely high frequencies of spikes; this fraction of neurons plays a pivotal role in information processing, however, little is known about how these outliers emerge and whether they are maintained over time. In primary cultures of mouse hippocampal neurons, we traced highly active neurons every 24 h for 7 wk by optically observing the fluorescent protein dVenus; the expression of dVenus was controlled by the promoter of Arc, an immediate early gene that is induced by neuronal activity. Under default-mode conditions, 0.3%-0.4% of neurons were spontaneously Arc-dVenus positive, exhibiting high firing rates. These neurons were spatially clustered, exhibited intermittently repeated dVenus expression, and often continued to express Arc-dVenus for approximately 2 wk. Thus, highly active neurons constitute a few select functional subpopulations in the neuronal network.NEW & NOTEWORTHY The overdispersion of neuronal activity levels can often be attributed to very few neurons exhibiting extremely high firing rates, but due to technical difficulty, no studies have examined how these outliers are selected during development and whether they are maintained over time. We optically monitored highly active neurons for as long as 7 wk in vitro and found that they constituted a unique population that was different from other "mediocre" neurons with normal firing rates.

The inner fluctuations of the brain in presymptomatic Frontotemporal Dementia: The chronnectome fingerprint

Frontotemporal Dementia (FTD) is preceded by a long period of subtle brain changes, occurring in the absence of overt cognitive symptoms, that need to be still fully characterized. Dynamic network analysis based on resting-state magnetic resonance imaging (rs-fMRI) is a potentially powerful tool for the study of preclinical FTD. In the present study, we employed a "chronnectome" approach (recurring, time-varying patterns of connectivity) to evaluate measures of dynamic connectivity in 472 at-risk FTD subjects from the Genetic Frontotemporal dementia research Initiative (GENFI) cohort. We considered 249 subjects with FTD-related pathogenetic mutations and 223 mutation non-carriers (HC). Dynamic connectivity was evaluated using independent component analysis and sliding-time window correlation to rs-fMRI data, and meta-state measures of global brain flexibility were extracted. Results show that presymptomatic FTD exhibits diminished dynamic fluidity, visiting less meta-states, shifting less often across them, and travelling through a narrowed meta-state distance, as compared to HC. Dynamic connectivity changes characterize preclinical FTD, arguing for the desynchronization of the inner fluctuations of the brain. These changes antedate clinical symptoms, and might represent an early signature of FTD to be used as a biomarker in clinical trials.

Mesoscale Architecture Shapes Initiation and Richness of Spontaneous Network Activity

Spontaneous activity in the absence of external input, including propagating waves of activity, is a robust feature of neuronal networks in vivo and in vitro The neurophysiological and anatomical requirements for initiation and persistence of such activity, however, are poorly understood, as is their role in the function of neuronal networks. Computational network studies indicate that clustered connectivity may foster the generation, maintenance, and richness of spontaneous activity. Since this mesoscale architecture cannot be systematically modified in intact tissue, testing these predictions is impracticable in vivo Here, we investigate how the mesoscale structure shapes spontaneous activity in generic networks of rat cortical neurons in vitro In these networks, neurons spontaneously arrange into local clusters with high neurite density and form fasciculating long-range axons. We modified this structure by modulation of protein kinase C, an enzyme regulating neurite growth and cell migration. Inhibition of protein kinase C reduced neuronal aggregation and fasciculation of axons, i.e., promoted uniform architecture. Conversely, activation of protein kinase C promoted aggregation of neurons into clusters, local connectivity, and bundling of long-range axons. Supporting predictions from theory, clustered networks were more spontaneously active and generated diverse activity patterns. Neurons within clusters received stronger synaptic inputs and displayed increased membrane potential fluctuations. Intensified clustering promoted the initiation of synchronous bursting events but entailed incomplete network recruitment. Moderately clustered networks appear optimal for initiation and propagation of diverse patterns of activity. Our findings support a crucial role of the mesoscale architectures in the regulation of spontaneous activity dynamics.SIGNIFICANCE STATEMENT Computational studies predict richer and persisting spatiotemporal patterns of spontaneous activity in neuronal networks with neuron clustering. To test this, we created networks of varying architecture in vitro Supporting these predictions, the generation and spatiotemporal patterns of propagation were most variable in networks with intermediate clustering and lowest in uniform networks. Grid-like clustering, on the other hand, facilitated spontaneous activity but led to degenerating patterns of propagation. Neurons outside clusters had weaker synaptic input than neurons within clusters, in which increased membrane potential fluctuations facilitated the initiation of synchronized spike activity. Our results thus show that the intermediate level organization of neuronal networks strongly influences the dynamics of their activity.

Pharmacological modulation of functional connectivity: α2-adrenergic receptor agonist alters synchrony but not neural activation

Correlative low frequency fluctuations in functional MRI (fMRI) signals across brain regions at rest have been taken as a measure of functional connectivity to map large-scale neural networks; however, the neural origin is still not clear. Receptor-targeted pharmacological manipulation could elucidate the role of neuroreceptor systems in resting-state functional connectivity to provide another perspective on the mechanism. In this study, the dose-dependent effects of an α(2)-adrenergic receptor agonist, medetomidine, on brain activation and functional connectivity were investigated. Forepaw stimulation-induced activation and resting-state fluctuation in the rat somatosensory cortices and caudate putamen were measured using the blood oxygenation level dependent (BOLD) fMRI. The results showed significant dose-dependent suppression of inter-hemispheric correlation but not the amplitude in the somatosensory areas, while the stimulation-induced activation in the same areas remained unchanged. To clarify the potential change in the hemodynamic response caused by the vasoconstrictive effect of medetomidine, the resting perfusion fluctuation was studied by arterial spin labeling and showed similar results as the BOLD. This suggests that the oxygen metabolic rate and hence the neural activity may not be affected by medetomidine but only the synchrony between brain regions was suppressed. Furthermore, no change in functional connectivity with medetomidine dosages was seen in the caudate putamen, a region with much lower α(2)-receptor density. These results indicate that resting-state signal correlation may reflect underlying neuroreceptor activity and a potential role of the adrenergic system in the functional connectivity.

Spatial attention decorrelates intrinsic activity fluctuations in macaque area V4

Attention typically amplifies neuronal responses evoked by task-relevant stimuli while attenuating responses to task-irrelevant distracters. In this context, visual distracters constitute an external source of noise that is diminished to improve attended signal quality. Activity that is internal to the cortex itself, stimulus-independent ongoing correlated fluctuations in firing, might also act as task-irrelevant noise. To examine this, we recorded from area V4 of macaques performing an attention-demanding task. The firing of neurons to identically repeated stimuli was highly variable. Much of this variability originates from ongoing low-frequency (<5 Hz) fluctuations in rate correlated across the neuronal population. When attention is directed to a stimulus inside a neuron's receptive field, these correlated fluctuations in rate are reduced. This attention-dependent reduction of ongoing cortical activity improves the signal-to-noise ratio of pooled neural signals substantially more than attention-dependent increases in firing rate.