Rapid and reversible manipulations of neural activity in behaving animals are transforming our understanding of brain function. An important assumption underlying much of this work is that evoked behavioural changes reflect the function of the manipulated circuits. We show that this assumption is problematic because it disregards indirect effects on the independent functions of downstream circuits. Transient inactivations of motor cortex in rats and nucleus interface (Nif) in songbirds severely degraded task-specific movement patterns and courtship songs, respectively, which are learned skills that recover spontaneously after permanent lesions of the same areas. We resolve this discrepancy in songbirds, showing that Nif silencing acutely affects the function of HVC, a downstream song control nucleus. Paralleling song recovery, the off-target effects resolved within days of Nif lesions, a recovery consistent with homeostatic regulation of neural activity in HVC. These results have implications for interpreting transient circuit manipulations and for understanding recovery after brain lesions.
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Lomber, S. G. The advantages and limitations of permanent or reversible deactivation techniques in the assessment of neural function. J. Neurosci. Methods 86, 109–117 (1999)
Zhang, F., Aravanis, A. M., Adamantidis, A., de Lecea, L. & Deisseroth, K. Circuit-breakers: optical technologies for probing neural signals and systems. Nature Rev. Neurosci. 8, 577–581 (2007)
Carrera, E. & Tononi, G. Diaschisis: past, present, future. Brain 137, 2408–2422 (2014)
Honey, C. J. & Sporns, O. Dynamical consequences of lesions in cortical networks. Hum. Brain Mapp. 29, 802–809 (2008)
Golowasch, J., Casey, M., Abbott, L. F. & Marder, E. Network stability from activity-dependent regulation of neuronal conductances. Neural Comput. 11, 1079–1096 (1999)
Thoby-Brisson, M. & Simmers, J. Long-term neuromodulatory regulation of a motor pattern-generating network: maintenance of synaptic efficacy and oscillatory properties. J. Neurophysiol. 88, 2942–2953 (2002)
Keck, T. et al. Synaptic scaling and homeostatic plasticity in the mouse visual cortex in vivo. Neuron 80, 327–334 (2013)
Marder, E. & Goaillard, J.-M. Variability, compensation and homeostasis in neuron and network function. Nature Rev. Neurosci. 7, 563–574 (2006)
Turrigiano, G. G. Homeostatic plasticity in neuronal networks: the more things change, the more they stay the same. Trends Neurosci. 22, 221–227 (1999)
Bender, D. B. & Baizer, J. S. Saccadic eye movements following kainic acid lesions of the pulvinar in monkeys. Exp. Brain Res. 79, 467–478 (1990)
Wilke, M., Turchi, J., Smith, K., Mishkin, M. & Leopold, D. A. Pulvinar inactivation disrupts selection of movement plans. J. Neurosci. 30, 8650–8659 (2010)
Talwar, S. K., Musial, P. G. & Gerstein, G. L. Role of mammalian auditory cortex in the perception of elementary sound properties. J. Neurophysiol. 85, 2350–2358 (2001)
Van Peppen, R. P. et al. The impact of physical therapy on functional outcomes after stroke: what’s the evidence? Clin. Rehabil. 18, 833–862 (2004)
Newsome, W. T. & Pare, E. B. A selective impairment of motion perception following lesions of the middle temporal visual area (MT). J. Neurosci. 8, 2201–2211 (1988)
Maldonado, M. A., Allred, R. P., Felthauser, E. L. & Jones, T. A. Motor skill training, but not voluntary exercise, improves skilled reaching after unilateral ischemic lesions of the sensorimotor cortex in rats. Neurorehabil. Neural Repair 22, 250–261 (2008)
Kawai, R. et al. Motor cortex is required for learning but not for executing a motor skill. Neuron 86, 800–812 (2015)
Immelmann, K. in Bird Vocalizations (ed. Hinde, R.A. ) 61–74 (Cambridge Univ. Press, 1969)
Cardin, J. A. Sensorimotor nucleus NIf is necessary for auditory processing but not vocal motor output in the avian song system. J. Neurophysiol. 93, 2157–2166 (2005)
Huber, D. et al. Multiple dynamic representations in the motor cortex during sensorimotor learning. Nature 484, 473–478 (2012)
Peters, A. J., Chen, S. X. & Komiyama, T. Emergence of reproducible spatiotemporal activity during motor learning. Nature 510, 263–267 (2014)
Martin, J. H. Autoradiographic estimation of the extent of reversible inactivation produced by microinjection of lidocaine and muscimol in the rat. Neurosci. Lett. 127, 160–164 (1991)
Roberts, T. F., Gobes, S. M. H., Murugan, M., Ölveczky, B. P. & Mooney, R. Motor circuits are required to encode a sensory model for imitative learning. Nature Neurosci. 15, 1454–1459 (2012)
Zhang, F., Wang, L.-P., Boyden, E. S. & Deisseroth, K. Channelrhodopsin-2 and optical control of excitable cells. Nature Methods 3, 785–792 (2006)
Klapoetke, N. C. et al. Independent optical excitation of distinct neural populations. Nature Methods 11, 338–346 (2014)
Long, M. A. & Fee, M. S. Using temperature to analyse temporal dynamics in the songbird motor pathway. Nature 456, 189–194 (2008)
Aronov, D., Andalman, A. S. & Fee, M. S. A specialized forebrain circuit for vocal babbling in the juvenile songbird. Science 320, 630–634 (2008)
Fee, M. S. & Scharff, C. The songbird as a model for the generation and learning of complex sequential behaviors. ILAR J. 51, 362–377 (2010)
Simpson, H. B. & Vicario, D. S. Brain pathways for learned and unlearned vocalizations differ in zebra finches. J. Neurosci. 10, 1541–1556 (1990)
Ali, F. et al. The basal ganglia is necessary for learning spectral, but not temporal, features of birdsong. Neuron 80, 494–506 (2013)
Hahnloser, R. H. R. & Fee, M. S. Sleep-related spike bursts in HVC are driven by the nucleus interface of the nidopallium. J. Neurophysiol. 97, 423–435 (2007)
Schmidt, M. F., Ashmore, R. C. & Vu, E. T. Bilateral control and interhemispheric coordination in the avian song motor system. Ann. NY Acad. Sci. 1016, 171–186 (2004)
Long, M. A., Jin, D. Z. & Fee, M. S. Support for a synaptic chain model of neuronal sequence generation. Nature 468, 394–399 (2010)
McCasland, J. S. Neuronal control of bird song production. J. Neurosci. 7, 23–39 (1987)
Hahnloser, R. H., Kozhevnikov, A. A. & Fee, M. S. An ultra-sparse code underlies the generation of neural sequences in a songbird. Nature 419, 65–70 (2002)
Watt, A. J. & Desai, N. S. Homeostatic plasticity and STDP: keeping a neuron’s cool in a fluctuating world. Front. Synaptic Neurosci. 2, 5 (2010)
van Welie, I., van Hooft, J. A. & Wadman, W. J. Homeostatic scaling of neuronal excitability by synaptic modulation of somatic hyperpolarization-activated Ih channels. Proc. Natl Acad. Sci. USA 101, 5123–5128 (2004)
Turrigiano, G. G., Leslie, K. R., Desai, N. S., Rutherford, L. C. & Nelson, S. B. Activity-dependent scaling of quantal amplitude in neocortical neurons. Nature 391, 892–896 (1998)
van Vreeswijk, C. & Sompolinsky, H. Chaotic balanced state in a model of cortical circuits. Neural Comput. 10, 1321–1371 (1998)
London, M., Roth, A., Beeren, L., Häusser, M. & Latham, P. E. Sensitivity to perturbations in vivo implies high noise and suggests rate coding in cortex. Nature 466, 123–127 (2010)
Shu, Y., Hasenstaub, A. & McCormick, D. A. Turning on and off recurrent balanced cortical activity. Nature 423, 288–293 (2003)
Feeney, D. M. & Baron, J. C. Diaschisis. Stroke 17, 817–830 (1986)
Phillips, P. C. Epistasis—the essential role of gene interactions in the structure and evolution of genetic systems. Nature Rev. Genet. 9, 855–867 (2008)
Miyashita, T., Kubik, S., Lewandowski, G. & Guzowski, J. F. Networks of neurons, networks of genes: an integrated view of memory consolidation. Neurobiol. Learn. Mem. 89, 269–284 (2008)
Shobe, J. The role of PKA, CaMKII, and PKC in avoidance conditioning: permissive or instructive? Neurobiol. Learn. Mem. 77, 291–312 (2002)
Taha, S. A. & Fields, H. L. Inhibitions of nucleus accumbens neurons encode a gating signal for reward-directed behavior. J. Neurosci. 26, 217–222 (2006)
Stoltz, S., Humm, J. L. & Schallert, T. Cortical injury impairs contralateral forelimb immobility during swimming: a simple test for loss of inhibitory motor control. Behav. Brain Res. 106, 127–132 (1999)
Tononi, G. & Cirelli, C. Sleep function and synaptic homeostasis. Sleep Med. Rev. 10, 49–62 (2006)
Walker, M. P. & Stickgold, R. Sleep-dependent learning and memory consolidation. Neuron 44, 121–133 (2004)
Siccoli, M. M., Rölli-Baumeler, N., Achermann, P. & Bassetti, C. L. Correlation between sleep and cognitive functions after hemispheric ischaemic stroke. Eur. J. Neurol. 15, 565–572 (2008)
Levin, H. S. & Grafman, J. Cerebral Reorganization of Function after Brain Damage (Oxford Univ. Press, 2000)
Poddar, R., Kawai, R. & Ölveczky, B. P. A fully automated high-throughput training system for rodents. PLoS ONE 8, e83171 (2013)
Neafsey, E. J. et al. The organization of the rat motor cortex: a microstimulation mapping study. Brain Res. 396, 77–96 (1986)
Allen, T. A. et al. Imaging the spread of reversible brain inactivations using fluorescent muscimol. J. Neurosci. Methods 171, 30–38 (2008)
Ölveczky, B. P., Andalman, A. S. & Fee, M. S. Vocal experimentation in the juvenile songbird requires a basal ganglia circuit. PLoS Biol. 3, e153 (2005)
Naie, K. & Hahnloser, R. H. R. Regulation of learned vocal behavior by an auditory motor cortical nucleus in juvenile zebra finches. J. Neurophysiol. 106, 291–300 (2011)
Ravbar, P., Lipkind, D., Parra, L. C. & Tchernichovski, O. Vocal exploration is locally regulated during song learning. J. Neurosci. 32, 3422–3432 (2012)
Tchernichovski, O., Nottebohm, F., Ho, C. E., Pesaran, B. & Mitra, P. P. A procedure for an automated measurement of song similarity. Anim. Behav. 59, 1167–1176 (2000)
Fiete, I. R., Senn, W., Wang, C. Z. H. & Hahnloser, R. H. R. Spike-time-dependent plasticity and heterosynaptic competition organize networks to produce long scale-free sequences of neural activity. Neuron 65, 563–576 (2010)
We thank E. Soucy and J. Greenwood for technical assistance. We are grateful to M. Meister, J. Sanes, N. Uchida, K. Blum, A. Dhawale, M. Josch, A. Kampff, and E. Feinberg for their feedback on our manuscript. This work was supported by a McKnight Scholars Award to B.P.Ö., HSFP and EMBO fellowships to S.B.E.W., an NRSA fellowship to R.K., and a Rubicon fellowship from the Netherlands Organization for Scientific Research to S.M.H.G.
The authors declare no competing financial interests.
Extended data figures and tables
Extended Data Figure 1 Light stimulation of motor cortical neurons expressing the optogenetic activator Chrimson.
a, Representative example of AAV-injections into motor cortex, showing Chrimson-tdTomato expression at different magnifications in a coronal brain section (~1.5 mm anterior to bregma). The scheme of the brain (right) is adapted from Paxinos’ rat atlas. The estimated spread of the injections was 8.3 ± 1.3 mm3 (mean ± s.d., n = 2 rats), with an average of 31 ± 2% infected cells (Methods). b, Heatmap showing the instantaneous firing rates of 28 single units recorded in an anaesthetized rat in response to a 1 s light pulse, averaged over 30 stimulations (Methods). c, A custom-built battery-operated wireless optogenetic stimulation device, consisting of a printed circuit board with integrated IR sensor and LED (λ = 615 nm). The IR sensor gates the circuit and allows the LED to be triggered by an IR light-source placed on top of the rat’s cage. During surgery, the LED is affixed atop a small craniotomy above motor cortex.
Extended Data Figure 2 Both brief and sustained optogenetic stimulation of motor cortex cause significant performance deficits in our task.
a, Optogenetic stimulation was triggered on the first lever press in a trial, and lasted for either 50 ms or 1 s. b, Both sustained (1 s, left, compare Fig. 1h, n = 5 rats, P = 3 × 10−5, paired t-test) and brief (50 ms, right, n = 3 rats, P = 0.01, paired t-test) optogenetic activation of motor cortex interfered with normal task performance. c, Comparing the effects of the two stimulation protocols on task performance (ratio light on/light off) shows that sustained stimulation has a significantly larger effect (1 s: n = 5; 50 ms: n = 3, P = 0.004, unpaired t-test). Error bars represent s.e.m.
a, Injection of fluorescently labelled cholera toxin subunit B (green) into HVC retrogradely labels Nif and anterogradely labels downstream control nucleus RA. b, Bilateral injections of the excitotoxin NMA produced focal lesions of Nif. Shown are Nissl stained sections from both hemispheres in the same example bird. Red arrows indicate the estimated boundaries of Nif; dashed green line shows the extent of the lesion.
a, Top, Nissl-stained parasagittal section of a zebra finch brain. Middle, magnified view of the region demarcated with a green square atop. Red arrows (left) indicate the estimated boundaries of Nif; violet overlay (right) shows the spread of fluorescent dye co-injected with muscimol. Orange star indicates estimated centre of injection based on brightness of the fluorescence. Bottom, estimated injection sites relative to the boundaries of Nif for all muscimol-injected birds. Colours denote different animals. b, Syllable spectrograms (left) and entropy-duration distributions (right) for a bird injected with different volumes of muscimol in Nif. Example spectrograms for 9 nl and 27 nl injections are from recordings made 3 min and 7 min after the injections, respectively. That song disruption was similarly rapid and severe for both volumes (in conjunction with the lack of effect from injections above Nif) limits the possibility that the effects on song were due to diffusion of the drug into HVC.
Data for the example bird in Fig. 3. All examples were recorded within the first hour of singing after lesion. Top, example spectrogram of a motif recorded just before lesion. Left, example spectrograms of vocalizations in which motif syllables could not be reliably identified and thus were excluded from subsequent analysis. Middle, example spectrograms of identifiable motifs that were included in the alignment-dependent analysis (Fig. 3f–j). Right, example spectrograms of songs with identifiable syllables, but truncated motifs.
Extended Data Figure 6 Different mechanisms for homeostatic regulation of neural activity produce similar effects.
a, Top, effect of Nif removal on membrane excitability during simulated songs in a model neuron (from the 40th node), smoothed with a 100-point moving average and averaged over 40 model ‘experiments’ (shaded regions denote standard deviation across ‘experiments’). A rule for homeostatic regulation of activity drives a reduction in spiking threshold after Nif removal. Middle, fraction of simulations in a 100-point window for which activity in the model HVC network propagated to the end, averaged over 40 model ‘experiments’ (Methods). Bottom, a 100-point moving average over the time to complete a full chain propagation, averaged over 40 model ‘experiments’. Orange triangle denotes time of Nif removal. Same as in Fig. 4b, d. b, c, Same as in a, but with homeostatic regulation of membrane leak conductance (b) and synaptic input strength (c) (Methods).
Video shows a trained rat engaging in our lever pressing task before and after injections of muscimol into left motor cortex (100 nl of 1 mM and 200 nl of 4 mM respectively). Eights trials are shown for each conditions. The videos are slowed down by a factor of two. (WMV 28810 kb)
Video shows a trained rat engaging in our lever pressing task with and without optogentic stimulation of right motor cortex. Four trials are shown. The first two are without stimulation; the last two are with a 1 second light stimulation triggered on the first lever press in the trial. (WMV 12686 kb)
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Otchy, T., Wolff, S., Rhee, J. et al. Acute off-target effects of neural circuit manipulations. Nature 528, 358–363 (2015). https://doi.org/10.1038/nature16442
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