The mediodorsal thalamus (MD) shares reciprocal connectivity with the prefrontal cortex (PFC), and decreased MD–PFC connectivity is observed in schizophrenia patients. Patients also display cognitive deficits including impairments in working memory, but a mechanistic link between thalamo–prefrontal circuit function and working memory is missing. Using pathway-specific inhibition, we found directional interactions between mouse MD and medial PFC (mPFC), with MD-to-mPFC supporting working memory maintenance and mPFC-to-MD supporting subsequent choice. We further identify mPFC neurons that display elevated spiking during the delay, a feature that was absent on error trials and required MD inputs for sustained maintenance. Strikingly, delay-tuned neurons had minimal overlap with spatially tuned neurons, and each mPFC population exhibited mutually exclusive dependence on MD and hippocampal inputs. These findings indicate a role for MD in sustaining prefrontal activity during working memory maintenance. Consistent with this idea, we found that enhancing MD excitability was sufficient to enhance task performance.
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Fuster, J.M. The Prefrontal Cortex. Anatomy, Physiology, and Neuropsychology of the Frontal Lobe. 2nd ed. (Raven, 1989).
Minzenberg, M.J., Laird, A.R., Thelen, S., Carter, C.S. & Glahn, D.C. Meta-analysis of 41 functional neuroimaging studies of executive function in schizophrenia. Arch. Gen. Psychiatry 66, 811–822 (2009).
Weinberger, D.R. & Berman, K.F. Prefrontal function in schizophrenia: confounds and controversies. Phil. Trans. R. Soc. Lond. B 351, 1495–1503 (1996).
Perlstein, W.M., Carter, C.S., Noll, D.C. & Cohen, J.D. Relation of prefrontal cortex dysfunction to working memory and symptoms in schizophrenia. Am. J. Psychiatry 158, 1105–1113 (2001).
Mitchell, A.S. The mediodorsal thalamus as a higher order thalamic relay nucleus important for learning and decision-making. Neurosci. Biobehav. Rev. 54, 76–88 (2015).
Saalmann, Y.B. Intralaminar and medial thalamic influence on cortical synchrony, information transmission and cognition. Front. Syst. Neurosci. 8, 83 (2014).
Baxter, M.G. Mediodorsal thalamus and cognition in non-human primates. Front. Syst. Neurosci. 7, 38 (2013).
Jones, E.G. The Thalamus (2nd Edition). (Cambridge University Press, 2007).
Parnaudeau, S. et al. Mediodorsal thalamus hypofunction impairs flexible goal-directed behavior. Biol. Psychiatry 77, 445–453 (2015).
Parnaudeau, S. et al. Inhibition of mediodorsal thalamus disrupts thalamofrontal connectivity and cognition. Neuron 77, 1151–1162 (2013).
Browning, P.G., Chakraborty, S. & Mitchell, A.S. Evidence for mediodorsal thalamus and prefrontal cortex interactions during cognition in macaques. Cereb. Cortex 25, 4519–4534 (2015).
Bailey, K.R. & Mair, R.G. Lesions of specific and nonspecific thalamic nuclei affect prefrontal cortex-dependent aspects of spatial working memory. Behav. Neurosci. 119, 410–419 (2005).
Byne, W., Hazlett, E.A., Buchsbaum, M.S. & Kemether, E. The thalamus and schizophrenia: current status of research. Acta Neuropathol. 117, 347–368 (2009).
Andrews, J., Wang, L., Csernansky, J.G., Gado, M.H. & Barch, D.M. Abnormalities of thalamic activation and cognition in schizophrenia. Am. J. Psychiatry 163, 463–469 (2006).
Woodward, N.D., Karbasforoushan, H. & Heckers, S. Thalamocortical dysconnectivity in schizophrenia. Am. J. Psychiatry 169, 1092–1099 (2012).
Anticevic, A. et al. Characterizing thalamo-cortical disturbances in schizophrenia and bipolar illness. Cereb. Cortex 24, 3116–3130 (2014).
Anticevic, A. et al. Association of thalamic dysconnectivity and conversion to psychosis in youth and young adults at elevated clinical risk. JAMA Psychiatry 72, 882–891 (2015).
Spellman, T. et al. Hippocampal-prefrontal input supports spatial encoding in working memory. Nature 522, 309–314 (2015).
Ray, J.P. & Price, J.L. The organization of projections from the mediodorsal nucleus of the thalamus to orbital and medial prefrontal cortex in macaque monkeys. J. Comp. Neurol. 337, 1–31 (1993).
Groenewegen, H.J. Organization of the afferent connections of the mediodorsal thalamic nucleus in the rat, related to the mediodorsal-prefrontal topography. Neuroscience 24, 379–431 (1988).
Alcaraz, F., Marchand, A.R., Courtand, G., Coutureau, E. & Wolff, M. Parallel inputs from the mediodorsal thalamus to the prefrontal cortex in the rat. Eur. J. Neurosci. 44, 1972–1986 (2016).
Mátyás, F., Lee, J., Shin, H.S. & Acsády, L. The fear circuit of the mouse forebrain: connections between the mediodorsal thalamus, frontal cortices and basolateral amygdala. Eur. J. Neurosci. 39, 1810–1823 (2014).
Kellendonk, C. et al. Transient and selective overexpression of dopamine D2 receptors in the striatum causes persistent abnormalities in prefrontal cortex functioning. Neuron 49, 603–615 (2006).
Yoon, T., Okada, J., Jung, M.W. & Kim, J.J. Prefrontal cortex and hippocampus subserve different components of working memory in rats. Learn. Mem. 15, 97–105 (2008).
Padilla-Coreano, N. et al. Direct ventral hippocampal-prefrontal input is required for anxiety-related neural activity and behavior. Neuron 89, 857–866 (2016).
Stujenske, J.M., Spellman, T. & Gordon, J.A. Modeling the spatiotemporal dynamics of light and heat propagation for in vivo optogenetics. Cell Rep. 12, 525–534 (2015).
Adhikari, A., Sigurdsson, T., Topiwala, M.A. & Gordon, J.A. Cross-correlation of instantaneous amplitudes of field potential oscillations: a straightforward method to estimate the directionality and lag between brain areas. J. Neurosci. Methods 191, 191–200 (2010).
Jung, M.W., Qin, Y., McNaughton, B.L. & Barnes, C.A. Firing characteristics of deep layer neurons in prefrontal cortex in rats performing spatial working memory tasks. Cereb. Cortex 8, 437–450 (1998).
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).
Harvey, C.D., Coen, P. & Tank, D.W. Choice-specific sequences in parietal cortex during a virtual-navigation decision task. Nature 484, 62–68 (2012).
Baeg, E.H. et al. Dynamics of population code for working memory in the prefrontal cortex. Neuron 40, 177–188 (2003).
Mello, G.B., Soares, S. & Paton, J.J. A scalable population code for time in the striatum. Curr. Biol. 25, 1113–1122 (2015).
Akhlaghpour, H. et al. Dissociated sequential activity and stimulus encoding in the dorsomedial striatum during spatial working memory. Elife 5, e19507 (2016).
Yizhar, O. et al. Neocortical excitation/inhibition balance in information processing and social dysfunction. Nature 477, 171–178 (2011).
Goldman-Rakic, P.S. Cellular basis of working memory. Neuron 14, 477–485 (1995).
Funahashi, S. Space representation in the prefrontal cortex. Prog. Neurobiol. 103, 131–155 (2013).
Rao, S.C., Rainer, G. & Miller, E.K. Integration of what and where in the primate prefrontal cortex. Science 276, 821–824 (1997).
Fuster, J.M. Unit activity in prefrontal cortex during delayed-response performance: neuronal correlates of transient memory. J. Neurophysiol. 36, 61–78 (1973).
Niki, H. Differential activity of prefrontal units during right and left delayed response trials. Brain Res. 70, 346–349 (1974).
Niki, H. Prefrontal unit activity during delayed alternation in the monkey. I. Relation to direction of response. Brain Res. 68, 185–196 (1974).
Rich, E.L. & Shapiro, M. Rat prefrontal cortical neurons selectively code strategy switches. J. Neurosci. 29, 7208–7219 (2009).
Durstewitz, D., Vittoz, N.M., Floresco, S.B. & Seamans, J.K. Abrupt transitions between prefrontal neural ensemble states accompany behavioral transitions during rule learning. Neuron 66, 438–448 (2010).
Wallis, J.D., Anderson, K.C. & Miller, E.K. Single neurons in prefrontal cortex encode abstract rules. Nature 411, 953–956 (2001).
Cromer, J.A., Roy, J.E. & Miller, E.K. Representation of multiple, independent categories in the primate prefrontal cortex. Neuron 66, 796–807 (2010).
Schmitt, L. I. et al. Thalamic amplification of cortical connectivity sustains attentional control. Nature http://dx.doi.org/10.1038/nature22073 (2017).
Narayanan, N.S. & Laubach, M. Top-down control of motor cortex ensembles by dorsomedial prefrontal cortex. Neuron 52, 921–931 (2006).
Sherman, S.M. & Guillery, R.W. The role of the thalamus in the flow of information to the cortex. Phil. Trans. R. Soc. Lond. B 357, 1695–1708 (2002).
Oh, S.W. et al. A mesoscale connectome of the mouse brain. Nature 508, 207–214 (2014).
Hunnicutt, B.J. et al. A comprehensive thalamocortical projection map at the mesoscopic level. Nat. Neurosci. 17, 1276–1285 (2014).
Bolkan, S.S., Carvalho Poyraz, F. & Kellendonk, C. Using human brain imaging studies as a guide toward animal models of schizophrenia. Neuroscience 321, 77–98 (2016).
Johansson, J.D. Spectroscopic method for determination of the absorption coefficient in brain tissue. J. Biomed. Opt. 15, 057005 (2010).
Mattis, J. et al. Principles for applying optogenetic tools derived from direct comparative analysis of microbial opsins. Nat. Methods 9, 159–172 (2011).
Paxinos, G. & Franklin, K.B.J. The Mouse Brain in Stereotaxic Coordinates. (Academic Press, 2001).
Delevich, K., Tucciarone, J., Huang, Z.J. & Li, B. The mediodorsal thalamus drives feedforward inhibition in the anterior cingulate cortex via parvalbumin interneurons. J. Neurosci. 35, 5743–5753 (2015).
Vinck, M., van Wingerden, M., Womelsdorf, T., Fries, P. & Pennartz, C.M. The pairwise phase consistency: a bias-free measure of rhythmic neuronal synchronization. Neuroimage 51, 112–122 (2010).
We thank members of the Gordon and Kellendonk labs for technical assistance and discussions. We also thank M. Halassa for discussions and commentary on an initial draft of the manuscript. This work was supported by grants from the NIMH (R01 MH096274 to J.A.G., F31 MH102041 to S.S.B. and F30 MH107204 to J.M.S.); by the Hope for Depression Research Foundation (to J.A.G.); and by the Irma Hirschl Trust (to C.K.). This article was prepared while J.A.G. was employed at the Department of Psychiatry at Columbia University and NYSPI. The opinions expressed in this article are the author's own and do not reflect the view of the National Institutes of Health, the Department of Health and Human Services or the United States government.
The authors declare no competing financial interests.
Integrated supplementary information
Supplementary Figure 1 Mediodorsal thalamic connectivity between the medial and orbital walls of the prefrontal cortex.
(a) Schema of dual antereograde/retrograde tracing of mPFC and OFC inputs to the MD and projections from the MD back to PFC. (b) Epifluorescent microscope image at PFC injection sites in an example animal. Fluoro-emerald (green) and fluoro-ruby (red) in the mPFC and OFC, respectively. Blue reports DAPI nuclear staining. (c) Direct fluorescence of MD-projecting mPFC terminals (green) and OFC terminals (red), as well as prefrontal-projecting MD cell bodies from the same example animal in (b). Red arrows and green arrows depict OFC-projecting and mPFC-projecting MD cell bodies, respectively. Blue reports DAPI nuclear staining. (d) Schema of observed reciprocal MD-PFC connectivity pattern from 4 mice. Abbreviations: prelimbic (PL), anterior cingulate (ACC), secondary motor (M2), primary motor (M1), anterior insula (AI), dorsolateral (dlO), lateral (LO), ventral (VO), and medial (MO) orbital cortex. Central (c), medial (m), and lateral (l) MD.
Supplementary Figure 2 Modeling the propagation of light in MD and mPFC during pathway-specific optogenetic experiments.
(a,b) Volume of mPFC (a) and MD (b) tissue predicted to receive an effective power density sufficient to achieve half maximal activation (EPD50) of eArch3.0 protein according to our optogenetic parameters and targeted fiber placements (10mW, 532nm light delivered via flat-tipped, 200μm diameter, 0.22 NA fiber optics). See Online Methods for modeling details.
Supplementary Figure 3 Time-limited MD-to-mPFC delay-phase inhibition is sufficient to impair T-maze performance.
(a) Average duration of the sample, delay and choice phases of the DNMS T-maze based on mouse behavior used in Fig. 2. (b) Schema of comparable and temporally-limited light on conditions during the T-maze: sample phase terminal illumination (left) and 17-seconds of terminal illumination during a 60 second delay (right). (c) Task performance in eYFP and eArch animals with MD-to-mPFC terminal illumination (left schema) during light off trials and the light on conditions displayed above in b. Middle: Behavioral results from terminal illumination during the sample phase. Right: Behavioral results from 17-seconds of terminal illumination during a 60s delay (2-tailed rmANOVA light x group, p=0.031, F(1,26)=5.22; 2-tailed paired t-test eArch light OFF vs. ON, p=0.0002, t(15)=4.91). Error bars depict SEM throughout.
Supplementary Figure 4 Functional directionality of MD-mPFC LFP cross-correlations dynamically shifts across task phases.
(a) Example of simultaneously recorded MD (red trace) and mPFC (black trace) filtered LFP beta oscillations (13-30Hz) during the sample (ai), delay (aii), and choice (aiii) phases of a single session of the DNMS T-maze. Black arrows indicate the temporal relationship in peak-to-peak power across successive oscillatory cycles. (b) Histogram of the lag time in which the peak MD-mPFC power cross-correlation was observed when shifting mPFC LFP +/-100ms in 1ms steps during the sample phase (n=90 recording sessions) (mean lag=-2.9ms; 2-tailed Signrank: z(89)=0.59, p=0.55). (c) As in b but during the delay phase (mean lag=-4.2ms; 2-tailed Signrank: z(89)=-6.59, ***p=0.0001). (d) As in b but during the choice phase (mean lag=9.8ms; 2-tailed Signrank: z(89)=2.02, p=0.044).
Supplementary Figure 5 Significantly spatially tuned mPFC units do not represent arm-locations during delays and are independent of MD inputs.
(a) Schema of behavior timestamps for spike alignment on a single DNMS T-maze trial. (b) Peri-event time histograms of normalized firing rate across mPFC units that exhibited significant spatially-tuning determined by Wilcoxon rank-sum test of firing rates on left versus right trials during light off conditions (250/891 units from 9 eArch mice). (c) As in b but for light on sample (left) or light on delay (middle, right) trials. Preferred arm during delay (middle) was assigned based on firing rate difference +/-500ms of sample goal arrival or choice goal arrival (insets). Preferred arm during choice (right) was based on firing rate difference +/-500ms of choice goal arrival or sample goal arrival (insets). Throughout, red asterisk indicate bins with Wilcoxon sign-rank significance at Bonferroni-corrected p values (p<0.0005 for sample/choice; p<0.00083 for delay). Error bars depict SEM throughout.
(a-d) Peri-event time histograms and raster plots from mPFC single-units during light off and light on trials of the DNMS T-maze. Examples are of high firing rate units that significantly decrease (a) or increase (b) in response to light and low firing rate units that significantly decrease (c) or increase (d) in response to light. (e) Summary data of average firing rate of all well-isolated eArch single units on light off and light on trials (top: 0-80Hz, bottom: 0-10Hz blow-up). Inset is the proportion of significantly light-increased (red pie) and light-decreased (blue pie) units at p<0.05 (solid fill) and p<0.01 (open) levels (538 units; 17%, 92 decrease; 15%, 83 increase at p<0.05). (f) Same as e for mPFC units recorded from eYFP mice (447 units; 4%, 18 decrease; 9%, 41 increase at p<0.05).
Supplementary Figure 7 Delay-suppressed mPFC neurons are distinct from and largely exclusive of delay-elevated neurons.
(a) Normalized firing rates of mPFC neurons exhibiting significantly suppressed activity during the delay period DNMS T-maze on light off trials (260/891 from 9 eArch mice). Neurons are sorted by peak time of suppression during the delay period. (b) Proportion of all mPFC single-units identified as delay-elevated (30%, 266), delay-suppressed (29%, 260), or spatially-tuned during the sample phase (28%, 250) and the respective overlap between groups.
Supplementary Figure 8 Delay-elevated mPFC neurons do not scale activity according to delay-interval duration.
(a) Normalized firing rates during the delay phase in delay-elevated mPFC neurons recorded at two delay durations – 60s and 20s (238/657). Delay-elevated neurons were identified from 60s delay trials, sorted according to time of peak elevation in firing (left), and normalized firing rates on 20s delay trials were plotted to match (right). (b) Time-triggered histograms and raster plots of firing rates for three example neurons that exhibited delay-elevated peaks at early (<20s) or late (>40s) periods of the 60s delay. Red histograms and rasters denote firing on 60s trials, and blue denotes firing on 20s trials. (c) Scatterplots of delay-elevated mPFC neurons exhibiting peak activity at early (0-20s), middle (21-40s) and late (41-60s) periods of the 60s delay, and the corresponding peak activity time observed at 20s delays (early: 111/238; middle: 40/238; late: 77/238). Data points with identical peak/peak values are shown as intersecting points for visualization. Colors denote clustered subgroups based on temporal correlation in firing rate as performed and shown in Fig. 5bi and elsewhere. Dotted lines depict linear regression fits to each subset of delay-elevated neurons (above each plot the linear model, r2, and p value for model fit vs. a constant model is displayed). Early: f(109)=64.2; Middle: f(41)=-0.02; Late: f(75)=2.72.
Supplementary Figure 9 Elevated mPFC delay activity is unaffected by terminal illumination in eYFP mice.
(a) Normalized firing rate in delay-elevated mPFC units during the delay phase on all light off trials. Units are arranged by time of peak firing rate. (b) Mean normalized firing rate across populations of delay-elevated units grouped based on correlations in single-unit firing rate across time. (c) Heat plots of normalized firing rates sorted as in a but displayed separately for correct (left) or incorrect (right) trials in the light off condition. (d) As in b but displayed separately for correct (left) and incorrect (right) trials in the light off condition. (e,f) As in c and d but for light on delay trials only. (g) Ratio of normalized firing rate at peak elevation on incorrect versus correct trials averaged across units grouped by early (73 units), middle (69 units) or late (79 units) peak times (the first two, middle two, or last two clusters from b, respectively). Open grey circles display all individual single-units in each group, while symbols above each group indicate 2-tailed t-test significance from a distribution with a mean of 1 (***p<0.0001, t(72)=-4.6; t(68)=-5.82; **p=0.004, t(78)=-3.01). (h) As in g but for light on delay trials only (***p<0.0001, t(72)=-3.42, t(68)=-4.1; *p=0.014, t(78)=-2.52). Error bars depict SEM throughout.
(a) Schema of maximum and minimum viral spread in the MD for all MD-to-mPFC optogenetic experiments (n=34 AAV5-hSyn-eYFP; n=46 AAV5-hSyn-eArch-eYFP). (b) Schema of maximum and minimum viral spread in the mPFC for all mPFC-to-MD optogenetic experiments (n=13 eYFP; n=14 eArch). (c) Example of electrolytic lesion from an MD targeted LFP wire from combined MD-to-mPFC optogenetic/physiology experiments (top). Summary of all MD LFP recording sites (bottom, red 'x', n=7 recording sites from 9 eArch mice). (d) Example of mPFC lesion at final site of recording from combined MD-to-mPFC optogenetic/physiology experiments (top). Summary of all final mPFC recording sites (bottom, red 'x', n=9 recording sites from 9 eArch mice).
Supplementary Figure 11 Histological summary of projection-specific vHPC-to-mPFC and MD SSFO experiments.
(a) Example of AAV5-hSyn-eArch-eYFP expression in ventral hippocampus from vHPC-to-mPFC optogenetic inhibition experiments. (b) Schema of maximum and minimum spread of vHPC targeted eArch-expressing virus for vHPC-to-mPFC optogenetic experiments (n=6 mice). (c) Example mPFC recording site lesion and vHPC terminal expression from vHPC-to-mPFC experiments. (d) Summary of mPFC lesions at final site of recording in vHPC-to-mPFC experiments (red 'x', n=6 eArch mice). (e) Example of AAV2-CaMKIIa-hChr2(C128S/D156A)-mCherry (SSFO: stabilized step function opsin) expression in MD. (f) Schema of maximum and minimum spread of MD targeted SSFO-expressing virus (n=9 SSFO mice).
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Bolkan, S., Stujenske, J., Parnaudeau, S. et al. Thalamic projections sustain prefrontal activity during working memory maintenance. Nat Neurosci 20, 987–996 (2017). https://doi.org/10.1038/nn.4568
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