Phase-coherence transitions and communication in the gamma range between delay-coupled neuronal populations

Synchronization between neuronal populations plays an important role in information transmission between brain areas. In particular, collective oscillations emerging from the synchronized activity of thousands of neurons can increase the functional connectivity between neural assemblies by coherently coordinating their phases. This synchrony of neuronal activity can take place within a cortical patch or between different cortical regions. While short-range interactions between neurons involve just a few milliseconds, communication through long-range projections between different regions could take up to tens of milliseconds. How these heterogeneous transmission delays affect communication between neuronal populations is not well known. To address this question, we have studied the dynamics of two bidirectionally delayed-coupled neuronal populations using conductance-based spiking models, examining how different synaptic delays give rise to in-phase/anti-phase transitions at particular frequencies within the gamma range, and how this behavior is related to the phase coherence between the two populations at different frequencies. We have used spectral analysis and information theory to quantify the information exchanged between the two networks. For different transmission delays between the two coupled populations, we analyze how the local field potential and multi-unit activity calculated from one population convey information in response to a set of external inputs applied to the other population. The results confirm that zero-lag synchronization maximizes information transmission, although out-of-phase synchronization allows for efficient communication provided the coupling delay, the phase lag between the populations, and the frequency of the oscillations are properly matched.This work was supported by the European Commission through the FP7 Marie Curie Initial Training Network 289146, NETT: Neural Engineering Transformative Technologies, the Ministerio de Economia y Competividad (Spain, project FIS2012-37655), and the Generalitat de Catalunya (projec t 2009SGR1168). JGO acknowledges support from the ICREA Academia programme. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscrip

Collective oscillations of two coupled bidirectionally neural populations.

<p>The inter-areal axonal delay between the two neuronal pools is zero. (A) LFP time trace of the two populations in a interval, for an external mean rate of . The inset shows the averaged time correlation of LFP pairs. (B) Phase coherence between the LFPs of the two networks for varying frequency. The measure is averaged over trials. The black dashed line represents the threshold () above which the phase coherence is considered significant (in red). (C) Time shift between the LFP oscillations of the networks for varying frequency. Red crosses show the time shifts corresponding to the frequencies at which the phase coherence is above threshold. The time shift is calculated as , where is the phase difference at the frequency of maximum phase coherence. The gray bar delimits the gamma peak band (). The measure is averaged over trials.</p

Phase locking between LFP and MUA of a network.

<p>(A) LFP-MUA phase coherence for a single population. (B) Angle histogram of the phase difference between the LFP and MUA. The measures are averaged over 200 trials.</p

Emergent bimodal firing patterns implement different encoding strategies during gamma-band oscillations

Upon sensory stimulation, primary cortical areas readily engage in narrow-band rhythmic activity between 30 and 90 Hz, the so-called gamma oscillations. Here we show that, when embedded in a balanced network, type-I excitable neurons entrained to the collective rhythm show a discontinuity in their firing-rates between a slow and a fast spiking mode. This jump in the spiking frequencies is characteristic to type II neurons, but is not present in the frequency-current curve (f-I curve) of isolated type I neurons. Therefore, this rate bimodality arises as an emerging network property in type I population models. We have studied the mechanisms underlying the generation of these two firing modes, in order to reproduce the spiking activity of in vivo cortical recordings, which is known to be highly irregular and sparse. We have also analyzed the relation between afferent inputs and the single unit activity, and between the latter and the local field potential (LFP) phase, in order to establish how the collective dynamics modulates the spiking activity of the individual neurons. Our results reveal that the inhibitory-excitatory balance allows two encoding mechanisms, for input rate variations and LFP phase, to coexist within the network.This work has been financially supported by the Ministerio de Ciencia e Innovación (project FIS2012-37655) and the Generalitat de Catalunya (project2009SGR1168). J. Garcia-Ojalvo acknowledges financial support from the ICREA Academia program. R. Vicente also acknowledges financial support from the HERTIE Foundation

Phase coherence of two coupled bidirectionally neural populations for three different values of the inter-areal axonal delays .

<p>Phase coherence spectrum and corresponding representative time series for (A,B), (C,D), and (E,F). The inter-areal delays follow a gamma distribution with a mean equal to corresponding inter-areal axonal delay . The gray bars on the x-axes of plots A, C, and E delimit the gamma peak band (). The phase coherence measure is averaged over trials.</p

Phase coherence and time shift behavior in the case of bidirectional symmetric coupling for increasing inter-areal axonal delays .

<p>(A) Frequency (black arrow) at which the power spectrum is maximum and extent of the gamma peak (gray bar) (results for only one population are shown, since they are the same for both populations). (B) Frequencies at which the phase coherence exhibits local maxima, . (C) Phase coherence, in color code, as a function of frequency (y-axis) and of the inter-areal axonal delay (x-axis). (D) Time shift at the peak frequency of the power spectrum. (E) Time shift at , the frequencies labeled in (B). The red line corresponds to . The labels in panels B and E correspond to panels of <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003723#pcbi-1003723-g004" target="_blank">Figure 4</a>. (F) Time shift , in color code, as a function of frequency (y-axis) and of the inter-areal axonal delay (x-axis). The solid black lines in panels C and F show (as in panel B) and the dashed black line represents the power spectrum maximum within the gamma range shown in panel A. In plots A, B, and C the total extent of the gamma peak is displayed as a vertical gray bar. In plot D, the arrows point at the gamma period and half of it, being . The measures are averaged over trials for each .</p

Time shift in the case of bidirectional asymmetric coupling for increasing extra inputs.

<p>Effective time shift in milliseconds between LFPs of the two networks, in color code, as a function of frequency (y-axis) and of the inter-areal axonal delay (x-axis) for different stimuli: (A) , (B) , (C) , (D) . The measures are averaged over trials for each and stimulus.</p

Carriers of information and signals.

<p>Diagram of two oscillatory LFPs filtered around the power spectrum peak (), with a short spike train locked at their troughs for different : (A) , representing zero-lag synchronization and (B) , representing anti-phase synchronization.</p

Mutual information carried by LFP and MUA power spectrum of the receiver.

<p>Mutual information between the set of stimuli and the neural response given by the LFP (A) and MUA (B) power spectra for increasing coupling delays . The gray arrow in the color scale refers to significance threshold (, bootstrap test). The measures are averaged over trials for each and stimulus.</p

Overexpression of Dyrk1A, a down syndrome candidate, decreases excitability and impairs gamma oscillations in the prefrontal cortex

The dual-specificity tyrosine phosphorylation-regulated kinase DYRK1A is a serine/threonine kinase involved in neuronal differentiation and synaptic plasticity and a major candidate of Down syndrome brain alterations and cognitive deficits. DYRK1A is strongly expressed in the cerebral cortex, and its overexpression leads to defective cortical pyramidal cell morphology, synaptic plasticity deficits, and altered excitation/inhibition balance. These previous observations, however, do not allow predicting how the behavior of the prefrontal cortex (PFC) network and the resulting properties of its emergent activity are affected. Here, we integrate functional, anatomical, and computational data describing the prefrontal network alterations in transgenic mice overexpressingDyrk1A(TgDyrk1A). Usingin vivoextracellular recordings, we show decreased firing rate and gamma frequency power in the prefrontal network of anesthetized and awakeTgDyrk1Amice. Immunohistochemical analysis identified a selective reduction of vesicular GABA transporter punctae on parvalbumin positive neurons, without changes in the number of cortical GABAergic neurons in the PFC ofTgDyrk1Amice, which suggests that selective disinhibition of parvalbumin interneurons would result in an overinhibited functional network. Using a conductance-based computational model, we quantitatively demonstrate that this alteration could explain the observed functional deficits including decreased gamma power and firing rate. Our results suggest that dysfunction of cortical fast-spiking interneurons might be central to the pathophysiology of Down syndrome. SIGNIFICANCE STATEMENT:DYRK1Ais a major candidate gene in Down syndrome. Its overexpression results into altered cognitive abilities, explained by defective cortical microarchitecture and excitation/inhibition imbalance. An open question is how these deficits impact the functionality of the prefrontal cortex network. Combining functional, anatomical, and computational approaches, we identified decreased neuronal firing rate and deficits in gamma frequency in the prefrontal cortices of transgenic mice overexpressingDyrk1A We also identified a reduction of vesicular GABA transporter punctae specifically on parvalbumin positive interneurons. Using a conductance-based computational model, we demonstrate that this decreased inhibition on interneurons recapitulates the observed functional deficits, including decreased gamma power and firing rate. Our results suggest that dysfunction of cortical fast-spiking interneurons might be central to the pathophysiology of Down syndrome.This work was supported by Foundation Jerome Lejeune Grant 937-SM2011B, Ministerio de Ciencia e Innovación Grants BFU2011-27094 and BFU2014-52467-R, and the EU PF7 FET CORTICONIC contract 600806 (M.V.S.-V.); Ministerio de Economia y Competitividad Grant FIS2012-37655 and the Institució Catalana de Recerca i Estudis Avançats Academia (J.G.O.); and the FRAXA Foundation, Fondation Jerome Lejeune Grant 937-SM2011B, Ministerio de Economia y Competitividad Grants SAF2013-49129-C2-1-R and “Centro de Excelencia Severo Ochoa 2013–2017” SEV-2012-0208, EU ERA-Net Neuron (FOOD for THOUGHT), Secretaria de Universidades e Investigación del Departamento de Economía y Conocimiento de la Generalidad de Cataluña Grant SGR 2014/1125, and CIBERER (Centro de Investigación Biomédica en Red de Enfermedades Raras) (M.D.)