Parvalbumin neurons and gamma rhythms enhance cortical circuit performance

Abstract

Synchronized oscillations and inhibitory interneurons have important and interconnected roles within cortical microcircuits. In particular, interneurons defined by the fast-spiking phenotype and expression of the calcium-binding protein parvalbumin have been suggested to be involved in gamma (30-80 Hz) oscillations, which are hypothesized to enhance information processing. However, because parvalbumin interneurons cannot be selectively controlled, definitive tests of their functional significance in gamma oscillations, and quantitative assessment of the impact of parvalbumin interneurons and gamma oscillations on cortical circuits, have been lacking despite potentially enormous significance (for example, abnormalities in parvalbumin interneurons may underlie altered gamma-frequency synchronization and cognition in schizophrenia and autism). Here we use a panel of optogenetic technologies in mice to selectively modulate multiple distinct circuit elements in neocortex, alone or in combination. We find that inhibiting parvalbumin interneurons suppresses gamma oscillations in vivo, whereas driving these interneurons (even by means of non-rhythmic principal cell activity) is sufficient to generate emergent gamma-frequency rhythmicity. Moreover, gamma-frequency modulation of excitatory input in turn was found to enhance signal transmission in neocortex by reducing circuit noise and amplifying circuit signals, including inputs to parvalbumin interneurons. As demonstrated here, optogenetics opens the door to a new kind of informational analysis of brain function, permitting quantitative delineation of the functional significance of individual elements in the emergent operation and function of intact neural circuitry.

Figure 1. Inhibiting PV cells suppresses gamma oscillations in vivo

a, Double-floxed Cre-dependent AAV vector design. hGH, human growth hormone polyadenylation signal; ITR, inverted terminal repeat; WPRE, woodchuck heptatitis virus post-transcriptional regulatory element. b, Recombination pathways to low-leak Cre-dependent expression. c, Left: antibody-stained PV cells (red) expressing ChR2–eYFP (green) in prefrontal cortex (PFC) of PV::Cre mice after injection of Cre-dependent AAV. Right: absent ChR2–eYFP expression in similarly treated wild-type mice. d, Yellow-light-evoked outward current in eNpHR-expressing PV cells in acute slice. e, Effect of yellow light on current-ramp-evoked spiking in the same cell. f, Experimental design: in vivo local field potentials (LFPs) recorded in mouse PFC; blue and yellow light modulate ChR2(+) PY and eNpHR(+) fast-spiking (FS)/PV cells, respectively. g, Sample blue-light-evoked LFPs; g–k show data from a single location in vivo; red traces denote recordings in yellow light. h, Filtered (35–45 Hz)-light-evoked LFPs. i, Spontaneous LFP power spectra. j, Blue-light-evoked LFP power spectra. k, Yellow light modulation of phase-locking (Supplementary Information). Dotted line denotes statistical significance. l, Effect of fast-spiking cell inhibition on peak power at gamma and lower frequencies (n = 4 recording locations; ***P < 0.001). Error bars, mean and s.e.m.

Figure 2. Feedback inhibition from PV cells generates emergent gamma frequency synchrony

a, Light-evoked responses in a fast-spiking PV interneuron (FS). b, Responses of a ChR2(–) PY cell during photoactivation of ChR2(+) PV interneurons. c, Experimental design: sEPSCs drive PY cells. Light flashes triggered by PY cell spikes activate FS/PV interneurons (optical feedback inhibition). V_m, measured membrane potential; I_m, injected current. d, PY cell responses to non-rhythmic sEPSCs with and without this optical feedback inhibition (inh.). g_EPSC, unitary sEPSC conductance; f_EPSC, sEPSC frequency. e, Power spectra obtained by convolving the spike trains of a PY cell with wavelets of varying frequencies; red trace represents optical feedback inhibition via PV interneurons. f, Summary of spectral data at gamma (30–80 Hz) and lower frequencies (n = 4 cells; *P < 0.05). Error bars, mean and s.e.m.

Figure 3. Gamma oscillations amplify signals and reduce noise in PY cells

a, PY cell responses (top traces) to non-rhythmic (NR), repeated non-rhythmic (Rep), or rhythmic defined sEPSC trains (lower traces; Supplementary Information; depicted rates = 40–372 Hz). b, Spike rates of representative PY cell under each rhythmicity condition. c, Maximum input–output (I–O) gain. d, Response variability for each condition. e, Left: mutual information between output spike number and input sEPSCs; gamma oscillations consistently enhanced mutual information. Right: contrasting effect on sEPSC spike rate information in simulated integrate and-fire cells using the same sEPSC trains (n = 14 cells in c–e; ***P < 0.001). Key in e is the same as for b. Error bars, mean and s.e.m.

Figure 4. Gamma oscillations enhance information flow from PY to PV cells

a, eYFP (green) and PV (red) cells in layer V PFC of Thy1–ChR2–eYFP transgenic mice. b, Experimental design: light flashes excite PY neurons, which synaptically excite PV cells. c, Light directly excites PY cells. Current evoked by 1-ms flashes with/without synaptic blockers (CNQX and D-AP5). d, Responses of PY neurons from different slices to the same light train (blue). e, Light indirectly excites fast-spiking (FS) interneurons. Current evoked by 1-ms flashes with/without synaptic blockers. f, Power spectra of PY neuron spiking elicited by distinct-rhythmicity light trains. g, h, Mutual information (Inf) between responses of fast-spiking or regular-spiking (RS) neurons and PY neurons to the same rhythmic or non-rhythmic light trains (Supplementary Information). i, j, Response variability of fast-spiking and regular-spiking cells for rhythmic and non-rhythmic stimuli (n = 12 PY, 7 fast-spiking, and 9 regular-spiking cells). k, Interplay of gamma (γ) oscillations and fast-spiking neurons. Error bars, mean and s.e.m.