Both humans and animals seek primary rewards in the environment, even when such rewards do not correspond to current physiological needs. An example of this is a dissociation between food-seeking behaviour and metabolic needs, a notoriously difficult-to-treat symptom of eating disorders. Feeding relies on distinct cell groups in the hypothalamus1,2,3,4, the activity of which also changes in anticipation of feeding onset5,6,7. The hypothalamus receives strong descending inputs from the lateral septum, which is connected, in turn, with cortical networks8, but cognitive regulation of feeding-related behaviours is not yet understood. Cortical cognitive processing9,10 involves gamma oscillations11,12,13,14,15, which support memory16,17, attention18, cognitive flexibility19 and sensory responses20. These functions contribute crucially to feeding behaviour by unknown neural mechanisms. Here we show that coordinated gamma (30–90 Hz) oscillations in the lateral hypothalamus and upstream brain regions organize food-seeking behaviour in mice. Gamma-rhythmic input to the lateral hypothalamus from somatostatin-positive lateral septum cells evokes food approach without affecting food intake. Inhibitory inputs from the lateral septum enable separate signalling by lateral hypothalamus neurons according to their feeding-related activity, making them fire at distinct phases of the gamma oscillation. Upstream, medial prefrontal cortical projections provide gamma-rhythmic inputs to the lateral septum; these inputs are causally associated with improved performance in a food-rewarded learning task. Overall, our work identifies a top-down pathway that uses gamma synchronization to guide the activity of subcortical networks and to regulate feeding behaviour by dynamic reorganization of functional cell groups in the hypothalamus.
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Saper, C. B., Chou, T. C. & Elmquist, J. K. The need to feed: homeostatic and hedonic control of eating. Neuron 36, 199–211 (2002)
Waterson, M. J. & Horvath, T. L. Neuronal regulation of energy homeostasis: beyond the hypothalamus and feeding. Cell Metab . 22, 962–970 (2015)
Steculorum, S. M. et al. Hypothalamic UDP increases in obesity and promotes feeding via P2Y6-dependent activation of AgRP neurons. Cell 162, 1404–1417 (2015)
Jennings, J. H. et al. Visualizing hypothalamic network dynamics for appetitive and consummatory behaviors. Cell 160, 516–527 (2015)
Rolls, E. T., Burton, M. J. & Mora, F. Hypothalamic neuronal responses associated with the sight of food. Brain Res . 111, 53–66 (1976)
Chen, Y., Lin, Y. C., Kuo, T. W. & Knight, Z. A. Sensory detection of food rapidly modulates arcuate feeding circuits. Cell 160, 829–841 (2015)
Mileykovskiy, B. Y., Kiyashchenko, L. I. & Siegel, J. M. Behavioral correlates of activity in identified hypocretin/orexin neurons. Neuron 46, 787–798 (2005)
Risold, P. Y. & Swanson, L. W. Structural evidence for functional domains in the rat hippocampus. Science 272, 1484–1486 (1996)
Tse, D. et al. Schema-dependent gene activation and memory encoding in neocortex. Science 333, 891–895 (2011)
Euston, D. R., Gruber, A. J. & McNaughton, B. L. The role of medial prefrontal cortex in memory and decision making. Neuron 76, 1057–1070 (2012)
Gray, C. M. & Singer, W. Stimulus-specific neuronal oscillations in orientation columns of cat visual cortex. Proc. Natl Acad. Sci. USA 86, 1698–1702 (1989)
Salinas, E. & Sejnowski, T. J. Correlated neuronal activity and the flow of neural information. Nat. Rev. Neurosci . 2, 539–550 (2001)
Csicsvari, J., Jamieson, B., Wise, K. D. & Buzsáki, G. Mechanisms of gamma oscillations in the hippocampus of the behaving rat. Neuron 37, 311–322 (2003)
Fries, P. Rhythms for cognition: communication through coherence. Neuron 88, 220–235 (2015)
Colgin, L. L. et al. Frequency of gamma oscillations routes flow of information in the hippocampus. Nature 462, 353–357 (2009)
Yamamoto, J., Suh, J., Takeuchi, D. & Tonegawa, S. Successful execution of working memory linked to synchronized high-frequency gamma oscillations. Cell 157, 845–857 (2014)
Igarashi, K. M., Lu, L., Colgin, L. L., Moser, M. B. & Moser, E. I. Coordination of entorhinal-hippocampal ensemble activity during associative learning. Nature 510, 143–147 (2014)
Kim, H., Ährlund-Richter, S., Wang, X., Deisseroth, K. & Carlén, M. Prefrontal parvalbumin neurons in control of attention. Cell 164, 208–218 (2016)
Cho, K. K. et al. Gamma rhythms link prefrontal interneuron dysfunction with cognitive inflexibility in Dlx5/6+/− mice. Neuron 85, 1332–1343 (2015)
Cardin, J. A. et al. Driving fast-spiking cells induces gamma rhythm and controls sensory responses. Nature 459, 663–667 (2009)
Sartor, G. C. & Aston-Jones, G. S. A septal-hypothalamic pathway drives orexin neurons, which is necessary for conditioned cocaine preference. J. Neurosci . 32, 4623–4631 (2012)
Bender, F. et al. Theta oscillations regulate the speed of locomotion via a hippocampus to lateral septum pathway. Nat. Commun . 6, 8521 (2015)
Sheehan, T. P., Chambers, R. A. & Russell, D. S. Regulation of affect by the lateral septum: implications for neuropsychiatry. Brain Res. Brain Res. Rev . 46, 71–117 (2004)
Takeuchi, T. et al. Locus coeruleus and dopaminergic consolidation of everyday memory. Nature 537, 357–362 (2016)
Nieh, E. H. et al. Decoding neural circuits that control compulsive sucrose seeking. Cell 160, 528–541 (2015)
Herrera, C. G. et al. Hypothalamic feedforward inhibition of thalamocortical network controls arousal and consciousness. Nat. Neurosci . 19, 290–298 (2016)
Ye, L. et al. Wiring and molecular features of prefrontal ensembles representing distinct experiences. Cell 165, 1776–1788 (2016)
Spellman, T. et al. Hippocampal-prefrontal input supports spatial encoding in working memory. Nature 522, 309–314 (2015)
Mendelson, J. & Chorover, S. L. Lateral hypothalamic stimulation in satiated rats: T-maze learning for food. Science 149, 559–561 (1965)
Paxinos, G. & Franklin, K. B. J. The Mouse Brain in Stereotaxic Coordinates (Elsevier Academic Press, 2001)
Vong, L. et al. Leptin action on GABAergic neurons prevents obesity and reduces inhibitory tone to POMC neurons. Neuron 71, 142–154 (2011)
Taniguchi, H. et al. A resource of Cre driver lines for genetic targeting of GABAergic neurons in cerebral cortex. Neuron 71, 995–1013 (2011)
Madisen, L. et al. A robust and high-throughput Cre reporting and characterization system for the whole mouse brain. Nat. Neurosci . 13, 133–140 (2010)
Kim, S. Y. et al. Diverging neural pathways assemble a behavioural state from separable features in anxiety. Nature 496, 219–223 (2013)
Vandecasteele, M. et al. Large-scale recording of neurons by movable silicon probes in behaving rodents. J. Vis. Exp. 61, e3568 (2012)
Jennings, J. H., Rizzi, G., Stamatakis, A. M., Ung, R. L. & Stuber, G. D. The inhibitory circuit architecture of the lateral hypothalamus orchestrates feeding. Science 341, 1517–1521 (2013)
Korotkova, T., Fuchs, E. C., Ponomarenko, A., von Engelhardt, J. & Monyer, H. NMDA receptor ablation on parvalbumin-positive interneurons impairs hippocampal synchrony, spatial representations, and working memory. Neuron 68, 557–569 (2010)
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)
Sirota, A. et al. Entrainment of neocortical neurons and gamma oscillations by the hippocampal theta rhythm. Neuron 60, 683–697 (2008)
Mitzdorf, U. Current source-density method and application in cat cerebral cortex: investigation of evoked potentials and EEG phenomena. Physiol. Rev . 65, 37–100 (1985)
Kajikawa, Y. & Schroeder, C. E. How local is the local field potential? Neuron 72, 847–858 (2011)
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)
Wulff, P. et al. Hippocampal theta rhythm and its coupling with gamma oscillations require fast inhibition onto parvalbumin-positive interneurons. Proc. Natl Acad. Sci. USA 106, 3561–3566 (2009)
Siapas, A. G., Lubenov, E. V. & Wilson, M. A. Prefrontal phase locking to hippocampal theta oscillations. Neuron 46, 141–151 (2005)
Fidzinski, P. et al. KCNQ5 K+ channels control hippocampal synaptic inhibition and fast network oscillations. Nat. Commun . 6, 6254 (2015)
Csicsvari, J., Hirase, H., Czurkó, A., Mamiya, A. & Buzsáki, G. Oscillatory coupling of hippocampal pyramidal cells and interneurons in the behaving Rat. J. Neurosci . 19, 274–287 (1999)
Zhang, S. J. et al. Optogenetic dissection of entorhinal-hippocampal functional connectivity. Science 340, 1232627 (2013)
Muller, R. U. & Kubie, J. L. The firing of hippocampal place cells predicts the future position of freely moving rats. J. Neurosci . 9, 4101–4110 (1989)
Berndt, A. et al. Structural foundations of optogenetics: Determinants of channelrhodopsin ion selectivity. Proc. Natl Acad. Sci. USA 113, 822–829 (2016)
We thank N. Kopell, A. Adamantidis, and C. Holman for their comments on earlier versions of the manuscript, C. Gutierrez-Herrera for providing ChETA-expressing Vgat-Cre mice for our first experiments, K. Weineck for help with experiments during the manuscript revision, I. Szabo for methodological advice, J. Rösner for help with confocal microscopy, and J. Poulet, M. Larkum, R. Sachdev and N. Takahashi for providing Sst-Cre mice. This work was supported by The Human Frontier Science Program (HFSP; RGY0076/2012, to T.K., D.B.), Deutsche Forschungsgemeinschaft (DFG; Exc 257 NeuroCure, to T.K. and A.P.; SPP1665, to A.P.), NIH (the Collaborative Research in Computational Neuroscience, CRCNS; 1R01 NS067199, to C.B.), The German-Israeli Foundation for Scientific Research and Development (GIF; I-1326-421.13/2015, to T.K.).
The authors declare no competing financial interests.
Extended data figures and tables
a, Schematic of a B32/B64 silicon probe shank. Inset shows the active area of a probe shank with eight recording sites. b, The LS is depicted in blue on sagittal and coronal planes. Bottom, reconstruction of the positions of recording electrodes in the LS; red dots show sites of electrolytic lesions/electrode tracks in each mouse. Lateral (L) 0.24–0.48 mm, distance to midline. CM32 linear probe recordings from caudal (c) and rostral (r) sites are shown in h. LSD, LSI and LSV denote dorsal, intermediate and ventral LS, respectively. c, A representative brain section. Arrow indicates localization of an electrolytic lesion in the LS. d, An example of an isolated single LS unit. Average spike waveforms (left) recorded using a silicon probe (image shown in a), and the corresponding auto-correlogram (right). Bin width, 1 ms. e, Reconstruction of recording electrodes positions in the LH; red dots show sites of electrolytic lesions in each mouse. f, A representative brain section showing a silicon probe track and a lesion in the LH (arrow). g, An example of an isolated single LH unit. Average spike waveforms (left) recorded using a silicon probe (image shown in a), and the corresponding auto-correlogram (right). Bin width, 1 ms. Note prominent gamma rhythmicity of the discharge. h, Examples of multisite recordings of LFP from caudal (top) and rostral (bottom) LS using linear silicon probes. The top trace in the caudal recording shows cortical LFP; recording epochs highlighted in grey are expanded. Colour panels: current source density (CSD) maps showing local gamma oscillatory activity in the LS. CSD was computed from the average depth profiles of gamma oscillations in respective recording sessions. Black waveform on each colour panel shows average gamma-band signal in the channel used to detect oscillations. i, Left, power spectral density of gamma oscillation episodes, with leading frequencies in one of the three sub-bands: black: 30–60 Hz; red: 60–90 Hz; blue: 90–120 Hz. Right, occurrence of gamma episodes. Top, average 1–200 Hz LFP signal centred at peaks of gamma envelopes with respective leading frequencies. j, Rhythmic modulation of neuronal discharge in the LS was higher during gamma oscillations of larger amplitude (30–90 Hz gamma amplitude: P < 0.00001, ANOVA, n = 75 cells). k, Power of LFP gamma oscillations (30–60 Hz) in the LS and LH matched the time required to reach food zone from a given location (approach rate, LS: n = 79 experiments, n = 13 mice, P = 0.000023; LH: n = 83 experiments, n = 16 mice; P < 0.00001, ANOVA). l, Power of LFP gamma oscillations (30–60 Hz) in the LS and LH did not match the time required to reach the drinking zone (approach rate, LS: n = 43 experiments, n = 7 mice, P = 0.13; LH: n = 48 experiments, n = 11 mice, P = 0.98, ANOVA). Data are mean ± s.e.m. The mouse brain was reproduced with permission from ref. 30.
a, Fluorescence image of a coronal LH section showing fibres of ChR2-tdTomato-expressing LSVgat cells (red), and ChETA–YFP expressing LHVgat neurons (green). The two constructs were used in the same mouse to visualize LSVgat projections to the LH. For behavioural experiments, only one brain region was targeted in each mouse. b, Expression of Cre-dependent ChETA in the LS in a Sst-Cre mouse. c, Reconstruction of termination sites of optic fibres (blue circles) in the LH; ten representative sites for a given bregma position, representing the whole range of termination positions, are shown. d, Latency to reach the drinking zone after the onset of LSSST–LH pathway stimulation at gamma frequency (YFP: n = 6 mice, opsin: n = 9 mice; P = 0.6, t-test). e, Latency to reach the food zone after the onset of brief (30 s) stimulation of the LSSST–LH pathway at gamma frequency (YFP: n = 9 mice, opsin: n = 11 mice; *P = 0.01 t-test). f, LSSST–LH optostimulation with ChETA (Ch) at gamma frequency did not change the average running speed (YFP: n = 7 mice, Ch opsin: n = 12 mice; P = 0.6, t-test). g, Latency to reach the food zone after the onset of LSSST–LH pathway stimulation at non-gamma (theta) frequency (opsin: n = 7 mice) compared to intensity-matched stimulation in the YFP group (n = 8 mice; P = 0.7, t-test). h, LSSST–LH optostimulation at non-gamma (theta) frequency did not change the fraction of trials in which a mouse visited the food zone before the other three corners of the enclosure (opsin: n = 8 mice, YFP, intensity-matched stimulation: n = 8 mice; P = 0.5, binomial test). i, Amount of food consumed during control stimulation (black) or optogenetic stimulation (blue) of LSSST–LH projections at gamma frequency performed in the same mice (n = 6 mice; P = 0.13 ANOVA). j, Amount of high-fat food (60% energy from fat) consumed per 10 min after LSSST–LH pathway stimulation at gamma frequency (YFP: n = 7 mice, opsin: n = 6 mice; P = 0.97. t-test). Data are mean ± s.e.m. The mouse brain was reproduced with permission from ref. 30.
a, Representative spectrograms (30–90 Hz band) computed from the LH LFP during the food approach and feeding in the same recording session, power is scaled from minimum to maximum across the two plots. b, Power of LFP gamma oscillations (30–90 Hz) in the LH during food approach and feeding (n = 3 mice; **P = 0.0088, ANOVA). c, d, When LSSST–LH gamma-rhythmic optogenetic stimulation was limited to the area outside the food zone (c), no changes in food intake were observed in ChETA-expressing mice compared to YFP-expressing mice (d, YFP: n = 6 mice, Ch opsin: n = 6 mice; P = 0.33, t-test). Data are mean ± s.e.m.
a, b, Optogenetic activation of excitatory opsin-expressing LSSST–LH projections did not change the time spent in the photostimulation-paired side in a place-preference test compared with YFP-expressing controls (YFP: n = 7 mice, opsin: n = 7 mice; P = 0.2, t-test). c, Representative spectrograms (30–90 Hz) computed from the LH LFP during approach to a familiar or a novel object. Dotted line marks the beginning of a contact with the object. d, Left, power of 30–60 Hz gamma oscillations during approach to a familiar object (factor ‘approach rate’, P = 0.7, ANOVA) and to a novel object (P = 0.63, ANOVA). Right, power of 60–90 Hz gamma oscillations during approach to a familiar object (P = 0.7, ANOVA) and a novel object (n = 3 mice; P = 0.07, ANOVA). e, Gamma-rhythmic optogenetic stimulation of the LSSST–LH pathway did not change latency to reach a novel object (YFP: n = 5 mice, opsin: n = 8 mice; P = 0.45, t-test). Data are mean ± s.e.m.
Extended Data Figure 5 Opposing control of neuronal excitability in the same cell with the eNPAC2.0 construct.
a, Sample image of an eNPAC2.0-expressing neuron under eYFP fluorescence. b, Example voltage-clamp recordings from an eNPAC2.0-expressing neuron after yellow (586/20 nm, top trace) and blue (475/28 nm, bottom trace) light delivery. Escape spike within peak inward current is truncated for clarity. c, Bar graph summaries of steady-state photocurrent amplitudes after yellow and blue light application. d, Example current-clamp recording showing yellow-light-mediated inhibition of electrically induced spiking in an eNPAC2.0-expressing neuron (200 pA electrical current injection). e, Example current-clamp recordings showing blue-light-induced spiking in the same neuron. Blue light pulse widths were 5 ms, delivered at 5 Hz (top trace) or 20 Hz (bottom trace). f, Bar graph summary of spike inhibition probability during delivery of yellow light. g, Spike generation probability under 5 Hz and 20 Hz blue-light pulse trains. Light power density: 5 mW mm−2 for yellow and blue light. n = 9 cells. Data are mean ± s.e.m.
a, Optogenetic inhibition of LSSST–LH projections in eNPAC2.0-expressing Sst-Cre mice. b, Average spike waveforms and firing probability during gamma cycle of a representative LH cell, responding to LSSST–LH inhibition, before (black) and during (orange) 593 nm light delivery onto LSSST–LH projections. c, Changes of gamma phase response in LH cells after LSSST–LH inhibition (n = 138 cells). Grey bars denote significantly (P < 0.05) responding cells (see Supplementary Information, Statistical Analysis). d, LH gamma amplitude changes after LSSST–LH optogenetic inhibition; orange rectangles show 1-min epochs when the yellow light was delivered. e, Yellow light was delivered onto eNPAC2.0-expressing LSSST–LH projections as mice entered the food approach zone (orange dotted line). Data are mean ± s.e.m. The mouse brain was reproduced with permission from ref. 30.
Extended Data Figure 7 Firing of functionally and genetically identified LH neurons in vivo and in vitro.
a, Firing of individual LH cells during locally recorded slow gamma oscillations (30–60 Hz, n = 291 cells). Colour scale indicates normalized for each neuron firing probability. b, Example firing maps of LH cells that fire preferentially in the food zone (top, FZ-match cells) and outside the food zone (bottom, FZ-mismatch cells). Maximal firing rate of each cell is shown above the colour maps. c, Changes in firing probability of LH FZ-mismatch cells during spontaneous LS (top) and LH (bottom) gamma oscillations (30–60 Hz, n = 55 cells). Firing probability of LS cells according to phase of LS gamma oscillation (n = 69 cells). d, To identify MCH cells, MCH-Cre mice were injected into the LH with a Cre-dependent ChR2-mCherry virus. e, Voltage responses of an identified MCH cell to current pulses (−60, −40, −20, 0, +20, +40 pA). f, To identify Vgat-expressing cells, a cross between Vgat-ires-Cre and CAG-tdTomato mice was used. g, Voltage responses of an identified Vgat-expressing LH cell to current pulses (−60, −40, −20, 0, +20, +40 pA). h, Average spike waveforms of representative presumed LH Vgat cells before (black) and during (blue) LHVgat optostimulation. Data are mean ± s.e.m. The mouse brain was reproduced with permission from ref. 30.
Extended Data Figure 8 Opposing optogenetic control of LHVgat cells bidirectionally manipulates food intake.
a, Photomicrographs showing anterior-posterior coronal brain sections from Vgat-Cre mice transduced with AAV-ChETA-eYFP in the LH area. b, For optostimulation of LHVgat cells, Cre-dependent opsins ChETA or eNPAC2.0 were expressed in Vgat-ires-Cre mice, and blue light (473 nm) was delivered at 20 Hz to the LH. c, d, Optostimulation of LHVgat cells increased food intake (c, optogenetic stimulation: n = 5 mice, control stimulation: n = 7 mice, *P = 0.0078 (LH 20 Hz versus baseline), P = 0.23 (control stimulation versus control baseline), ANOVA; also in a subset of experiments (d) in which the mice were satiated (consumed <3 pellets during the baseline recording; optogenetic stimulation: n = 5 mice; control: n = 6 mice; *P = 0.0016, ANOVA). e, For optogenetic inhibition of LHVgat cells, yellow light (593 nm) was bilaterally delivered into the LH in Vgat-ires-Cre mice, expressing Cre-dependent opsin (eNPAC2.0). f, Optogenetic inhibition of the LHVgat cells decreased food intake in food-deprived mice (LH: n = 12 experiments, n = 3 mice; baseline: n = 12 experiments, n = 3 mice; yellow-light stimulation (orange open bar) decreased amount of pellets eaten compared to control stimulation (orange filled bar), **P = 0.0003, ANOVA, Tukey tests). g, Examples of fast (within 3 s) discharge changes in different LH cells during optostimulation of Vgat cells at 20 Hz. h, Examples of fast (within 3 s) discharge changes in different LH cells during optostimulation of LSVgat cells at gamma frequency. Data are mean ± s.e.m. The mouse brain was reproduced with permission from ref. 30.
a–d, Сomputational modelling of gamma-rhythmic entrainment in the LS. Model network diagrams (left): synaptic connections between inhibitory cells in the LS and inputs to the network, spike rastergrams of 100 cells (top, right) and input current (arbitrary units) to the network (b–d, bottom, right). a, A deterministic interneuronal network gamma (ING) rhythm with the average cell firing frequency of approximately 12 Hz and a population frequency of 30 Hz. b, An external weak gamma-rhythmic input to the network raises average firing rates to around 17 Hz and increases temporal precision of the discharge. c, An increase in drive heterogeneity from 2% (a) to 20% (c) abolishes gamma oscillation. d, A weak gamma-rhythmicity of the input in c recovers gamma oscillation. e, Representative Nissl-stained slices. Arrows highlight electrolytic lesions indicating the locations of probe shanks in the LS and mPFC, at anterior-posterior levels shown on the scheme (right). f–h, mPFC–LS and hippocampus–LS LFP coherence spectra (current flow density in the LS, LFP in mPFC or hippocampus, f), raw LFP coherence (g) and LFP power spectral density (h), computed for the recording session, excerpt traces of which are shown in Fig. 4a. A notch at 50 Hz corresponds to the omitted part of the spectrum at mains electricity frequency. i, An example image (horizontal view) showing eYFP expression in the projections of mPFC neurons. Images are shown as maximum projection over a 100-μm volume, digitally sliced from the 3D volume shown in Fig. 4c. Note that posterior to the LS, in contrast to caudate putamen, no fibres of passage are seen. j, Representative images of eYFP-expressing mPFC fibres in the LS, counterstained with anti-synapsin antibody (1 out of 3 mice). Scale bars, 5 μm (first three images) and 1 μm (right image, which is a magnification of the dotted-line area). An example from a different mouse is shown in Fig. 4e. k, Representative images of synaptophysin-mCherry-expressing mPFC fibres in the LS (1 out of 3 mice). Scale bars, 5 μm (middle) and 1 μm (right). The mouse brain was reproduced with permission from ref. 30.
a, Gamma-rhythmic coordination between firing in the LS and mPFC. LS gamma phase preference of individual slow-firing mPFC cells, that is, a population also including pyramidal cells (<6 Hz, n = 59; P < 0.0001, Rayleigh test) and LS cells (n = 73, P < 0.0001, Rayleigh test). Sine waves indicate reference oscillation cycles. Maximal discharge of LS cells followed mPFC neurons by approximately 87° (difference of mean discharge phases: P < 0.05, Mardia–Watson test). b, Expression of CaMKIIa-dependent ChR2 (AAV2-CaMKIIa-hChR2(H134R)-eYFP) in the somata of mPFC cells (left) and their fibres in the LS (right). IL, infralimbic cortex; PL, prelimbic cortex. c, Reconstruction of the termination sites of optic fibres (blue circles) in the LS. Ten representative sites for a given bregma position, representing the whole range of termination positions, are shown. d, mPFC–LS stimulation at gamma frequency did not affect the latency to enter the control zone (YFP: n = 8 mice, opsin: n = 7 mice; P = 0.7, t-test). e, mPFC–LS stimulation at gamma frequency did not change the average running speed (YFP: n = 8 mice, opsin: n = 7 mice; P = 0.7, t-test). f, The occurrence of spontaneous slow (30–60 Hz) gamma oscillation episodes in the choice segment of the T-maze was increased in correct trials (c) compared to incorrect trials (i) (mPFC: P = 0.004, LS: **P = 0.004. ANOVA), but was not increased in the start arm of T-maze (P = 0.4, ANOVA). n = 54 trials, n = 4 mice. g, The number of correct choices in the T-maze task was increased during mPFC–LS optogenetic stimulation at gamma frequency in water-restricted, water-rewarded mice (YFP: n = 6 mice, opsin: n = 6 mice, trials 1–20: **P = 0.0097, t-test; trials 21–40: P = 0.4). h, mPFC–LS stimulation at gamma frequency significantly increased the fraction of repeated correct trials (cc) (*P = 0.02 t-test), but did not significantly decrease the fraction of repeated incorrect trials (YFP: n = 6 mice, opsin: n = 6 mice, P = 0.052, t-test) in water-restricted, water-rewarded mice. i, j, mPFC–LS optostimulation at theta frequency did not change the fraction of correct trials (i, trials 1–20, opsin: n = 7 mice, YFP, intensity-matched stimulation (same control group as in Fig. 4r, s): n = 7 mice, P > 0.99, t-test), repeated correct trials (j, cc, P = 0.74, t-test), or repeated incorrect trials (j, ii, P = 0.7, t-test). Data are mean ± s.e.m. The mouse brain was reproduced with permission from ref. 30.
This file contains Supplementary Text and Data, Supplementary Results, a Supplementary Discussion and additional references. (PDF 361 kb)
This 3D CLARITY video shows PFC fibers terminating in the lateral septum (highlighted in red). Note that posterior to lateral septum, in contrast to caudate putamen, no fibers of passage are present. (MP4 21954 kb)
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Carus-Cadavieco, M., Gorbati, M., Ye, L. et al. Gamma oscillations organize top-down signalling to hypothalamus and enable food seeking. Nature 542, 232–236 (2017) doi:10.1038/nature21066
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