Behaviorally and pathologically relevant cortico-thalamo-cortical oscillations are driven by diverse interacting cell-intrinsic and synaptic processes. However, the mechanism that gives rise to the paroxysmal oscillations of absence seizures (ASs) remains unknown. Here we report that, during ASs in behaving animals, cortico-thalamic excitation drives thalamic firing by preferentially eliciting tonic rather than T-type Ca2+ channel (T-channel)-dependent burst firing in thalamocortical (TC) neurons and by temporally framing thalamic output via feedforward reticular thalamic (NRT)-to-TC neuron inhibition. In TC neurons, overall ictal firing was markedly reduced and bursts rarely occurred. Moreover, blockade of T-channels in cortical and NRT neurons suppressed ASs, but such blockade in TC neurons had no effect on seizures or on ictal thalamic output synchrony. These results demonstrate ictal bidirectional cortico-thalamic communications and provide the first mechanistic understanding of cortico-thalamo-cortical network firing dynamics during ASs in behaving animals.
This is a preview of subscription content
Subscribe to Journal
Get full journal access for 1 year
only $4.92 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
McCormick, D. A. & Bal, T. Sleep and arousal: thalamocortical mechanisms. Annu. Rev. Neurosci. 20, 185–215 (1997).
Crunelli, V. & Hughes, S. W. The slow (1 Hz) rhythm of non-REM sleep: a dialogue between three cardinal oscillators. Nat. Neurosci. 13, 9–17 (2010).
Schmitt, L. I. et al. Thalamic amplification of cortical connectivity sustains attentional control. Nature 545, 219–223 (2017).
Steriade, M., Jones, E. G. & McCormick, D. A. Thalamus – Organisation and Function Vol. 1 (Elsevier Science, New York, 1997).
Crunelli, V. et al. Dual function of thalamic low-vigilance state oscillations: rhythm-regulation and plasticity. Nat. Rev. Neurosci. 19, 107–118 (2018).
Contreras, D. & Steriade, M. Spindle oscillation in cats: the role of corticothalamic feedback in a thalamically generated rhythm. J. Physiol. 490, 159–179 (1996).
McCormick, D. A. & Contreras, D. On the cellular and network bases of epileptic seizures. Annu. Rev. Physiol. 63, 815–846 (2001).
Crunelli, V. & Leresche, N. Childhood absence epilepsy: genes, channels, neurons and networks. Nat. Rev. Neurosci. 3, 371–382 (2002).
Blumenfeld, H. Cellular and network mechanisms of spike-wave seizures. Epilepsia 46 (Suppl. 9), 21–23 (2005).
von Krosigk, M., Bal, T. & McCormick, D. A. Cellular mechanisms of a synchronized oscillation in the thalamus. Science 261, 361–364 (1993).
Huntsman, M. M., Porcello, D. M., Homanics, G. E., DeLorey, T. M. & Huguenard, J. R. Reciprocal inhibitory connections and network synchrony in the mammalian thalamus. Science 283, 541–543 (1999).
Destexhe, A. Spike-and-wave oscillations based on the properties of GABAB receptors. J. Neurosci. 18, 9099–9111 (1998).
Destexhe, A. Neuronal Networks in Brain Function, CNS Disorders, and Therapeutics 11–35 (Elsevier, New York, 2014).
Pinault, D et al. Intracellular recordings in thalamic neurones during spontaneous spike and wave discharges in rats with absence epilepsy. J. Physiol. (Lond.) 509, 449–456 (1998).
Steriade, M. & Contreras, D. Relations between cortical and thalamic cellular events during transition from sleep patterns to paroxysmal activity. J. Neurosci. 15, 623–642 (1995).
Slaght, S. J., Leresche, N., Deniau, J.-M., Crunelli, V. & Charpier, S. Activity of thalamic reticular neurons during spontaneous genetically determined spike and wave discharges. J. Neurosci. 22, 2323–2334 (2002).
Inoue, M., Duysens, J., Vossen, J. M. H. & Coenen, A. M. L. Thalamic multiple-unit activity underlying spike-wave discharges in anesthetized rats. Brain Res. 612, 35–40 (1993).
Inoue, M., Ates, N., Vossen, J. M. H. & Coenen, A. M. L. Effects of the neuroleptanalgesic fentanyl-fluanisone (Hypnorm) on spike-wave discharges in epileptic rats. Pharmacol. Biochem. Behav. 48, 547–551 (1994).
Depaulis, A., David, O. & Charpier, S. The genetic absence epilepsy rat from Strasbourg as a model to decipher the neuronal and network mechanisms of generalized idiopathic epilepsies. J. Neurosci. Methods 260, 159–174 (2016).
Taylor, H. et al. Investigating local and long-range neuronal network dynamics by simultaneous optogenetics, reverse microdialysis and silicon probe recordings in vivo. J. Neurosci. Methods 235, 83–91 (2014).
Venzi, M., Di Giovanni, G. & Crunelli, V. A critical evaluation of the gamma-hydroxybutyrate (GHB) model of absence seizures. CNS Neurosci. Ther. 21, 123–140 (2015).
Meeren, H. K. M., Pijn, J. P. M., Van Luijtelaar, E. L. J. M., Coenen, A. M. L. & Lopes da Silva, F.H. Cortical focus drives widespread corticothalamic networks during spontaneous absence seizures in rats. J. Neurosci. 22, 1480–1495 (2002).
Polack, P.-O. et al. Deep layer somatosensory cortical neurons initiate spike-and-wave discharges in a genetic model of absence seizures. J. Neurosci. 27, 6590–6599 (2007).
Hazan, L., Zugaro, M. & Buzsáki, G. Klusters, NeuroScope, NDManager: a free software suite for neurophysiological data processing and visualization. J. Neurosci. Methods 155, 207–216 (2006).
Barthó, P. et al. Ongoing network state controls the length of sleep spindles via inhibitory activity. Neuron 82, 1367–1379 (2014).
Halassa, M. M. et al. State-dependent architecture of thalamic reticular subnetworks. Cell 158, 808–821 (2014).
Dreyfus, F. M. et al. Selective T-type calcium channel block in thalamic neurons reveals channel redundancy and physiological impact of I Twindow. J. Neurosci. 30, 99–109 (2010).
David, F. et al. Essential thalamic contribution to slow waves of natural sleep. J. Neurosci. 33, 19599–19610 (2013).
Talley, E. M. et al. Differential distribution of three members of a gene family encoding low voltage-activated (T-type) calcium channels. J. Neurosci. 19, 1895–1911 (1999).
Kim, D. et al. Lack of the burst firing of thalamocortical relay neurons and resistance to absence seizures in mice lacking α1G T-type Ca2+ channels. Neuron 31, 35–45 (2001).
Ernst, W. L., Zhang, Y., Yoo, J. W., Ernst, S. J. & Noebels, J. L. Genetic enhancement of thalamocortical network activity by elevating α1G-mediated low-voltage-activated calcium current induces pure absence epilepsy. J. Neurosci. 29, 1615–1625 (2009).
Buzsáki, G. The thalamic clock: emergent network properties. Neuroscience 41, 351–364 (1991).
Sorokin, J. M. et al. Bidirectional control of generalized epilepsy networks via rapid real-time switching of firing mode. Neuron 93, 194–210 (2017).
Cope, D. W. et al. Enhanced tonic GABAA inhibition in typical absence epilepsy. Nat. Med. 15, 1392–1398 (2009).
Connelly, W. M. et al. GABAB receptors regulate extrasynaptic GABAA receptors. J. Neurosci. 33, 3780–3785 (2013).
Herd, M. B., Brown, A. R., Lambert, J. J. & Belelli, D. Extrasynaptic GABAA receptors couple presynaptic activity to postsynaptic inhibition in the somatosensory thalamus. J. Neurosci. 33, 14850–14868 (2013).
Cope, D. W., Hughes, S. W. & Crunelli, V. GABAA receptor-mediated tonic inhibition in thalamic neurons. J. Neurosci. 25, 11553–11563 (2005).
Crandall, S. R., Cruikshank, S. J. & Connors, B. W. A corticothalamic switch: controlling the thalamus with dynamic synapses. Neuron 86, 768–782 (2015).
Deleuze, C. et al. T-type calcium channels consolidate tonic action potential output of thalamic neurons to neocortex. J. Neurosci. 32, 12228–12236 (2012).
Powell, K. L. et al. A Cav3.2 T-type calcium channel point mutation has splice-variant-specific effects on function and segregates with seizure expression in a polygenic rat model of absence epilepsy. J. Neurosci. 29, 371–380 (2009).
Chen, Y. et al. Association between genetic variation of CACNA1H and childhood absence epilepsy. Ann. Neurol. 54, 239–243 (2003).
Lee, S. E. et al. Rebound burst firing in the reticular thalamus is not essential for pharmacological absence seizures in mice. Proc. Natl Acad. Sci. USA 111, 11828–11833 (2014).
Gentet, L. J. & Ulrich, D. Strong, reliable and precise synaptic connections between thalamic relay cells and neurones of the nucleus reticularis in juvenile rats. J. Physiol. (Lond.) 546, 801–811 (2003).
Kim, U. & McCormick, D. A. The functional influence of burst and tonic firing mode on synaptic interactions in the thalamus. J. Neurosci. 18, 9500–9516 (1998).
Cruikshank, S. J., Urabe, H., Nurmikko, A. V. & Connors, B. W. Pathway-specific feedforward circuits between thalamus and neocortex revealed by selective optical stimulation of axons. Neuron 65, 230–245 (2010).
Mease, R. A., Krieger, P. & Groh, A. Cortical control of adaptation and sensory relay mode in the thalamus. Proc. Natl Acad. Sci. USA 111, 6798–6803 (2014).
Maheshwari, A. & Noebels, J. L. Monogenic models of absence epilepsy: windows into the complex balance between inhibition and excitation in thalamocortical microcircuits. Prog. Brain Res. 213, 223–252 (2014).
Paz, J. T. et al. A new mode of corticothalamic transmission revealed in the Gria4(-/-) model of absence epilepsy. Nat. Neurosci. 14, 1167–1173 (2011).
Mangan, K. P. et al. Tonic inhibition is abolished in GABAA receptor γ2R43Q knock-in mice with absence epilepsy and febrile seizures. Preprint at bioRxiv https://www.biorxiv.org/content/early/2017/06/26/155556 (2017).
Guo, J. N. et al. Impaired consciousness in patients with absence seizures investigated by functional MRI, EEG, and behavioural measures: a cross-sectional study. Lancet Neurol. 15, 1336–1345 (2016).
Lidster, K. et al. Opportunities for improving animal welfare in rodent models of epilepsy and seizures. J. Neurosci. Methods 260, 2–25 (2016).
Paxinos, G. & Watson, C. The Rat Brain in Stereotaxic Coordinates 6th edn. (Academic, New York, 2007).
Vandecasteele, M. et al. Large-scale recording of neurons by movable silicon probes in behaving rodents. J. Vis. Exp. 2012, e3568 (2012).
Harris, K. D., Henze, D. A., Csicsvari, J., Hirase, H. & Buzsáki, G. Accuracy of tetrode spike separation as determined by simultaneous intracellular and extracellular measurements. J. Neurophysiol. 84, 401–414 (2000).
Celeux, G. & Govaert, G. A classification EM algorithm for clustering and two stochastic versions. Comput. Stat. Data Anal. 14, 315–332 (1992).
Henze, D. A. et al. Intracellular features predicted by extracellular recordings in the hippocampus in vivo. J. Neurophysiol. 84, 390–400 (2000).
Pedreira, C., Martinez, J., Ison, M. J. & Quian Quiroga, R. How many neurons can we see with current spike sorting algorithms? J. Neurosci. Methods 211, 58–65 (2012).
Pouzat, C., Delescluse, M., Viot, P. & Diebolt, J. Improved spike-sorting by modeling firing statistics and burst-dependent spike amplitude attenuation: a Markov chain Monte Carlo approach. J. Neurophysiol. 91, 2910–2928 (2004).
Schmitzer-Torbert, N., Jackson, J., Henze, D., Harris, K. & Redish, A. D. Quantitative measures of cluster quality for use in extracellular recordings. Neuroscience 131, 1–11 (2005).
Domich, L., Oakson, G. & Steriade, M. Thalamic burst patterns in the naturally sleeping cat: a comparison between cortically projecting and reticularis neurones. J. Physiol. (Lond.) 379, 429–449 (1986).
Steriade, M., Domich, L. & Oakson, G. Reticularis thalami neurons revisited: activity changes during shifts in states of vigilance. J. Neurosci. 6, 68–81 (1986).
Barthó, P. et al. Characterization of neocortical principal cells and interneurons by network interactions and extracellular features. J. Neurophysiol. 92, 600–608 (2004).
Vinck, M., Battaglia, F. P., Womelsdorf, T. & Pennartz, C. Improved measures of phase-coupling between spikes and the local field potential. J. Comput. Neurosci. 33, 53–75 (2012).
Hines, M. L. & Carnevale, N. T. NEURON: a tool for neuroscientists. Neuroscientist 7, 123–135 (2001).
Destexhe, A., Bal, T., McCormick, D. A. & Sejnowski, T. J. Ionic mechanisms underlying synchronized oscillations and propagating waves in a model of ferret thalamic slices. J. Neurophysiol. 76, 2049–2070 (1996).
Destexhe, A., Contreras, D. & Steriade, M. LTS cells in cerebral cortex and their role in generating spike-and-wave oscillations. Neurocomputing 38, 555–563 (2001).
Wolfart, J., Debay, D., Le Masson, G., Destexhe, A. & Bal, T. Synaptic background activity controls spike transfer from thalamus to cortex. Nat. Neurosci. 8, 1760–1767 (2005).
Destexhe, A., Mainen, Z. F. & Sejnowski, T. J. Synthesis of models for excitable membranes, synaptic transmission and neuromodulation using a common kinetic formalism. J. Comput. Neurosci. 1, 195–230 (1994).
Fujisawa, S., Amarasingham, A., Harrison, M. T. & Buzsáki, G. Behavior-dependent short-term assembly dynamics in the medial prefrontal cortex. Nat. Neurosci. 11, 823–833 (2008).
We wish to thank T. Gould for technical assistance and V. N. Uebele (Merck Inc., USA) for the generous gift of TTA-P2. This work was supported by the MRC (grant G0900671 to V.C.), the Wellcome Trust (grant 91882 to V.C.), the Hungarian Scientific Research Fund (grant NN125601 to M.L.L.), the Hungarian Brain Research Program (grant KTIA_NAP_13-2-2014-0014 to M.L.L.), the CNRS (grant LIA 528 to N.L., R.C.L. and V.C.), the European Union (grant COST Action CM1103 to G.D.G.) and the Malta Council of Science and Technology (MCST, grant R&I-2013-14 “EPILEFREE” to G.D.G. and V.C.). M.V. and Z.A. were supported by Wellcome Trust 4-year PhD studentships (grants 93763 and 204014, respectively) and F.D. by a Marie Curie ITN PhD studentship (grant H2020-MSCA-ITN-2016 -722053).
The authors declare no competing financial interests.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Integrated supplementary information
(a) Example of high-pass filtered extracellular recording traces with TC (blue) and NRT (green) neuron action potentials recorded during periods of wakefulness (top), ASs (middle) and non-REM sleep (bottom) highlight the consistency of action potential sorting across different behavioral states and levels of synchrony. (b) Firing rate of all NRT neurons during wakefulness showing the demarcation between wake-active (WA, n=13) and wake-quiescent (WQ, n=9) NRT neurons at 10 Hz (dashed black line), as originally indicated by Halassa et al. (Cell, 2014). Black lines are mean±SEM. (c) Log of the ratio of wake firing rate to sleep firing rate of NRT neurons, with all but one WA neuron having a negative value, confirms the presence of two NRT neuron groups with different firing rate during wakefulness. (d) Merged brightfield and TRITC-filtered images showing the final position of a silicone probe marked with Vybrant-Dil dye within the VB. Orange outlines indicate a portion of the NRT. (e) Burst signature of GAERS TC (blue) and NRT (green, both WA and WQ) neurons during non-REM sleep show their characteristic decelerando and accelerando-decelerando patterns, respectively (n=139 TC neurons; n=25 NRT neurons). Points are mean and error bars are ±SEM. (f) Temporal dynamics of the rate of occurrence of bursts containing ≥ 3 action potentials (purple line: mean; shaded areas: ±SEM) and action potential doublets (black) of GAERS TC neurons (n=124) before, during and after ASs. Note the strong qualitative similarities between the two firing types. Dashed red lines indicate times of start and end of SWDs in the EEG.
Supplementary Figure 2 SWDs occur in cortical S1 earlier than in VB and other cortical regions of GAERS.
(a) EEG traces showing the start of a GAERS bilateral SWD in the perioral region of the primary somatosensory cortex (S1) before the ventrobasal thalamic nucleus (VB), the primary motor cortex (M1), the primary auditory cortex (A1) and the medial prefrontal cortex (mPFC). (b) Left: difference in the start time of SWDs between the recorded areas (the start of the SWDs in S1 is taken as time zero). Note the statistically significant delay in the start time between S1 (p=8.1x10−17, n=41, two-sided Mann-Whitney U test) and VB, and between VB and the other cortical areas (p=4.8x10−5, n=41, two-sided Mann-Whitney U test). Points are mean and error bars are ±SEM. Right: lack of any significant difference in the start of SWDs between the right and left hemisphere in the brain regions examined (p=0.9, 0.95, 0.89, 0.95 and 0.99, n=41, two-sided Mann-Whitney U test). Values are mean±SEM. (c) Superimposed cross-correlograms of SWC-spikes show no time-lag among left and right hemispheres for different brain regions (n= 41 SWDs in 3 GAERS for b and c).
Supplementary Figure 3 Firing properties of GAERS TC and NRT neurons in different behavioral states.
(a,b,c) Rate of different firing types (total, bursts, tonic) of GAERS TC (blue, n=139) and NRT (wake-active, WA, n=13; wake-quiescent, WQ, n=12) neurons during ASs, wakefulness and non-REM sleep. Left and right axes refer to TC and NRT neurons, respectively. Horizontal red lines indicate mean (further data in Supplementary Table 1). (d) Autocorrelograms of GAERS TC (blue, n=139) and NRT (green, n=25) neuron firing during ASs (left) and wakefulness (right). Lines are mean and shaded areas are ±SEM.
(a) Cross-correlograms of total firing of 6 simultaneously recorded GAERS TC neurons during interictal periods (black plots) and ASs (red plots). (b) Cross-correlograms of total firing of 7 simultaneously recorded WA NRT neurons during interictal periods (black plots) and GHB-elicited ASs (red plots) in a Wistar rat injected with 100 mg/kg i.p. of γ-butyrolactone, a GHB pro-drug (see Venzi et al., CNS Neurosci. Ther., 2015) (cf. Supplementary Fig. 7 and Online Methods).
Total (a) and burst (b) firing rate plotted individually for each AS and each TC (blue, n=139) and NRT (green, n=25) neuron, ranked on the x-axis from lowest to highest mean firing rate. The high variability for many neurons, and the absence of a bimodal distribution within individual neurons, indicate a large graduated range of neuronal behaviors during ASs in GAERS.
(a) Examples of ictal total firing XCors of a TC-WA NRT neuron pair with a symmetric peak centered on the SWC spike (left), a TC-WA NRT neuron pair with no peak or trough (middle), and a TC-WQ NRT neuron pair (right) from GAERS. Orange lines indicate confidence intervals of expected correlations (CI: 5%–95%) estimated from surrogate firing time for each neuron in its respective firing distribution relative to the SWC (Online Methods). Top left insets in (a,f,g) show superimposed autocorrelograms for the respective TC (blue) and NRT (green) neurons calculated between −400 and +400 ms (each curve maximum amplitude is normalized to 1). Top right insets in (a) show superimposed spike distribution of the respective TC (blue) and NRT (green) neuron calculated for the time period −40 to +40 ms with respect to the SWC spike. (b-g) XCors of the same pairs as in (a) for different types of firing (as indicated) during ASs (b-e), awake interictal periods (f) and non-REM sleep (g).
In 3 Wistar rats injected with 100 mg/kg i.p. of γ-butyrolactone, a GHB pro-drug (Online Methods), we recorded 39 TC neurons, 18 WA and 3 WQ NRT neurons during 332 ASs that had a duration of 3.03±1.08 s and a SWD frequency of 5.09±0.85 Hz. (a) Spike-time raster plots (bottom traces) from 2 TC (blue) and 2 WA NRT (green) neurons with time-matched EEG (top trace) during GHB-elicited ASs. (b) Same as in (a) for 2 TC (blue) and 7 WA NRT (green) neurons from another GBL-injected Wistar rat. Note the sparse firing of TC and WA NRT neurons during ASs. (c,d,e) Temporal evolution of different firing types before, during and after GHB-elicited ASs for 39 TC (blue) and NRT (green) neurons (n=18 WA and 3 WQ) (lines: mean, shaded areas: ±SEM). Red dashed lines indicate the start and end of ASs. Note that the ictal firing dynamics of TC neurons during these seizures were remarkably similar to those observed during spontaneous ASs in GAERS, in particular i) the marked ictal decrease in total and tonic firing (52% and 55%, respectively), ii) the low burst rate (0.1 burst/SWC) and iii) the average ictal total firing (0.94 spike/SWC) (Supplementary Table 1). However, the sharp peak in burst firing observed within the first second of the ictal activity in GAERS was absent in the GHB model. WA NRT neurons had overall ictal activity profiles similar to GAERS WA NRT neurons, with two notable exceptions: i) total firing exhibited a 26% decrease ictally compared to interictally, and ii) burst firing increased monotonically without a peak around the interictal-to-ictal transition. Notwithstanding the decrease in ictal total firing, there was a consistent output of the NRT population during ASs as indicated by the peaks in the XCors between simultaneously recorded NRT neurons (Supplementary Fig. 4b). Notably, the mean interictal rate of different firing types of TC and particularly of WA-NRT neurons was on average lower in the GHB model than the respective rates in GAERS (cf. Fig. 2), probably as a result of GHB-induced sedation (see Venzi et al., CNS Neurosci. Ther., 2015). (f,g) Percentage of SWCs accompanied by electrical silence, bursts and tonic single action potentials for TC neurons (f), WA and WQ NRT neurons (g). (h,i) Events of each firing type per SWC in TC (h) and NRT (i) neurons. Red lines in (f-i) indicate mean values. As in GAERS, WQ-NRT neurons in the GHB model did not show any significant AS-associated variation in firing or any significant AS-linked feature in subsequent analyses.
(a) SWC-spike-triggered averages (lines: mean; shaded areas: ±SEM) of total, burst and tonic firing for GHB TC (n=44), and WA (n=18) and WQ (n=3) NRT neurons. Note the different peak time (color coded arrows) of TC and WA NRT neurons relative to SWC spike (red vertical line) and the strong temporal overlap between the ascending phase of the curves of the two thalamic populations. Ictal burst and tonic firing peaks had similar shape and latency (to the SWC-spike) to those of total firing (Supplementary Table 2). No clear peaks are evident for WQ NRT neurons. (b) Typical examples of ictal firing XCors of a TC-WA NRT neuron pair recorded during GBL-elicited ASs. Orange lines indicate confidence intervals (5–95%) of expected correlations estimated as in Fig. 5 (Online Methods). Thirty-two pairs of simultaneously recorded TC and NRT neuron pairs had broad peaks before time-zero indicating an increased probability of NRT neurons to fire after TC neurons. Note the presence of a significant trough when TC neuron total and tonic firing (b,c), but not burst firing (d), are used for the XCors, a result similar to that in GAERS. Top left inset in (b) shows superimposed autocorrelograms for the respective TC (blue) and NRT (green) neurons calculated between −400 and +400 ms. Top right inset in (b) shows superimposed spike distribution of the respective TC (blue) and NRT (green) neuron calculated for the time period −40 to +40 ms with respect to the SWC-spike.
Supplementary Figure 9 Action potential classification and firing dynamics of GAERS cortical putative excitatory neurons.
(a) EEG trace (top) and raster plots (bottom) showing SWDs and firing times, respectively, of simultaneously recorded single cortical and TC neurons. (b) High-pass filtered traces showing typical isolated cortical action potentials. (c) Auto-correlogram of a putative excitatory cortical neuron. (d) Action potential half-widths vs trough-to-peak times for putative inhibitory (n=5) (black, FS for fast-spiking) and excitatory (n=41) (red, RS for regular spiking) cortical neurons. Average action potential waveforms are shown in the inset. (e) Total firing rate of all cortical pyramidal neurons (n=41). Horizontal dashed line indicates the start and end of SWDs in the EEG. Lines are mean and shaded areas are ±SEM. (f) XCors of total firing of 3 simultaneously recorded cortical excitatory neurons during interictal periods (black) and ASs (red). (g) Cumulative distributions of the peak of total firing for all simultaneously recorded cortical excitatory (n=41) and TC (n=43) neurons show no difference between the two neuronal populations (p=0.4892, Kolmogorov-Smirnov test).
Supplementary Figure 10 Firing-mode-specific, cortico-thalamic interactions in GAERS for SWC-spikes of different amplitude.
(a) XCors of different firing types (TC total and CX tonic) and (TC tonic and CX burst) for the same TC and cortical (CX) neuron pairs shown in Fig. 6a. (b) XCors of different types of TC and CX neuron firing (same pairs as in Fig. 6a) for SWC-spikes of largest (4th quartile, orange line) or smallest (1st quartile, yellow line) amplitude. (c) Same as in (b) for the same pairs of NRT and CX neurons as in Fig. 6b.
Supplementary Figure 11 Effect of bilateral microdialysis application of TTA-P2 on the number and duration of ASs in GAERS.
(a) Each plot show the effect of TTA-P2 applied in the indicated brain regions (c and m indicate central to the VB and medial to the VB, respectively) on the mean number of ASs and mean length of individual seizures (from left to right and top to bottom: n=7, 6, 10, 9, and 16 animals, p=0.742, 0.547, 1, 0.031, 0.014, 0.024, 1, 0.313, 0.433, 0.013, paired two-sided Wilcoxon signed-rank test). (b) Effect of TTA-P2 applied in the indicated brain regions on the mean peak frequency of SWDs for the same groups of animals, compared to vehicle microdialysis application (from left to right: n=7, 6, 7, 8, and 16 animals, p=0.563, 0.438, 0.625, 0.945, 0.025, paired two-sided Wilcoxon signed-rank test). In all subfigures, black lines are mean and error bars are ±SEM.
(a-h) Each panel shows the firing distribution of simulated activity in TC (blue) and NRT (light green depolarized, brown hyperpolarized, dark green mean of both) neurons of the cortico-thalamic model during repetitive sequences of trains of 5 EPSPs delivered to the cortical neurons at 7 Hz to mimic the GAERS SWD frequency. Time-zero is that of the 3rd EPSP in each stimulation train (cf. Fig. 8).
About this article
Cite this article
McCafferty, C., David, F., Venzi, M. et al. Cortical drive and thalamic feed-forward inhibition control thalamic output synchrony during absence seizures. Nat Neurosci 21, 744–756 (2018). https://doi.org/10.1038/s41593-018-0130-4
Nonlinear Dynamics (2021)
Nature Communications (2019)
Robust closed-loop control of spike-and-wave discharges in a thalamocortical computational model of absence epilepsy
Scientific Reports (2019)
Nature Neuroscience (2019)
Regulatory Architecture of the Neuronal Cacng2/Tarpγ2 Gene Promoter: Multiple Repressive Domains, a Polymorphic Regulatory Short Tandem Repeat, and Bidirectional Organization with Co-regulated lncRNAs
Journal of Molecular Neuroscience (2019)