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Thalamic amplification of cortical connectivity sustains attentional control


Although interactions between the thalamus and cortex are critical for cognitive function1,2,3, the exact contribution of the thalamus to these interactions remains unclear. Recent studies have shown diverse connectivity patterns across the thalamus4,5, but whether this diversity translates to thalamic functions beyond relaying information to or between cortical regions6 is unknown. Here we show, by investigating the representation of two rules used to guide attention in the mouse prefrontal cortex (PFC), that the mediodorsal thalamus sustains these representations without relaying categorical information. Specifically, mediodorsal input amplifies local PFC connectivity, enabling rule-specific neural sequences to emerge and thereby maintain rule representations. Consistent with this notion, broadly enhancing PFC excitability diminishes rule specificity and behavioural performance, whereas enhancing mediodorsal excitability improves both. Overall, our results define a previously unknown principle in neuroscience; thalamic control of functional cortical connectivity. This function, which is dissociable from categorical information relay, indicates that the thalamus has a much broader role in cognition than previously thought.

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Figure 1: Task-specific sequential PFC activity maintains rule representation.
Figure 2: Categorically-free mediodorsal activity is required for PFC rule representation and task performance.
Figure 3: Mediodorsal input amplifies local PFC connectivity.
Figure 4: Enhancing mediodorsal excitability strengthens PFC rule representation and improves performance.


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We thank J. A. Movshon, D. J. Heeger, X.-J. Wang, M. A. Wilson, C. D. Brody and E. K. Miller for helpful discussions. L.I.S. is supported by a NARSAD Young Investigator award and R.D.W. by a fellowship from the Swiss National Science Foundation. M.N. is supported by a JSPS fellowship. M.M.H. is supported by grants from NIMH, NINDS, Brain and Behavior, Sloan and Klingenstein Foundations as well as the Human Frontiers Science Program.

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Authors and Affiliations



L.I.S. designed experiments, performed behavioural studies, analysed the physiological data and contributed to writing the manuscript. R.D.W. designed the 4AFC task, performed the physiological recordings, analysed behavioural data and contributed to writing the manuscript. M.N. validated viral tools, performed tracing studies and contributed to behavioural training. M.H. assisted L.I.S. with analysis. S.M. performed the modelling. M.M.H. conceived experiments and analyses, interpreted the data and wrote the manuscript.

Corresponding author

Correspondence to Michael M. Halassa.

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The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Behavioural and electrophysiological features of the 2AFC task.

a, Mice display equal performance across trial types (n = 4 mice, P = 0.52, Wilcoxon rank-sum test). b, Multi-electrode implant used for PFC neural recordings. Inset, magnification showing electrodes. c, Post-mortem histology in an example brain showing electrode tip locations (arrowheads). d, Example of spike sorting in energy space to identify single units. Two identified clusters reflect two single units. Inset, corresponding spike-waveforms. e, In 17% of rule-tuned cells, tuning is observed for both task rules (example PSTHs shown), albeit with distinct temporal offsets during the delay. f, Schematic showing electrode locations from which rule-tuned neurons (dots) were recorded, illustrating that they are most frequently found in deeper layers. Dot sizes are scaled in proportion to the number of tuned neurons found at that location (n = 594 cells from four mice). g, Fast-spiking (FS) and regular-spiking (RS) neurons are identified on the basis of the peak to trough time of their spike waveform (left, example waveforms; right, peak to through time histogram, dashed line represents cut off for fast-spiking to regular-spiking classification21,43). h, Example rasters and PSTHs for two cells during delay periods of either 400 or 800 ms, randomized within the same recording session. In the first, an early peak is present in both conditions (left), while in the other a late peak is only evident in the 800-ms condition (right). i, Task-variable information for each mouse of our first cohort (manipulation free). Task-variable information is based on the PCA from the divergence of population activity of task-modulated PFC neurons on the axis associated with each variable (see Methods) and is highly informative for task rule (green), but contains no information about movement (side selection, grey). Shaded areas indicate the bootstrapped 95% CI.

Extended Data Figure 2 Behavioural errors are primarily driven by inappropriate rule encoding.

a, Mice show comparable performance on trials with one target modality presented compared to performance in conflict trials (n = 4 mice, P = 0.81). b, Example PSTHs of a neuron whose appropriate tuning to the attend to vision rule is observed in error trials of the attend to audition rule. c, Rule information derived from PCA of sessions in which sufficient numbers of errors allowed for their analysis (93 neurons, 18 sessions from four mice) show that they contain information about the other rule; directionality of rule-related axis in error trials are along the same axis used in correct trials (see Methods). Shaded areas indicate bootstrapped 95% CI. d, Schematic of the 4AFC task developed to distinguish between errors related to rule encoding (executive) and those related to target cue perception (sensory, see Methods). Visual and auditory targets are reported at different response port pairs (inner versus outer), making it possible to distinguish between outcomes in which the animal makes a selection on the basis of prior cueing, the spatial location of sensory targets, both, only one or neither. e, 4AFC task outcomes illustrated in a confusion matrix showing outcomes conditional upon sensory target modality and location. Note that sensory conflict is not specified for these trials, as it can be either spatially congruent or in conflict with the appropriate target. f, Executive errors represent the majority of those observed, accounting for about 50% of all errors across mice (n = 4 mice). Dashed line represents chance performance (25%). All behavioural data was compared using a Wilcoxon rank-sum test.

Extended Data Figure 3 Combining PFC recordings with local optogenetic control of inhibitory interneurons.

a, Tuning peak examples of multiple PFC neurons simultaneously recorded in a single recording session. Tuning peaks associated with either rule occur at multiple times across the delay period in different neurons suggesting precisely timed, sequential activation. b, Top, two examples of a short-latency cross-correlation (shuffle corrected; see Methods) observed between pairs of tuned neurons. Bottom, histogram of cross-correlation peak times (n = 914 pairs). c, Increased connection probability between tuned neurons (all, 914 pairs; tuned, 97 pairs; same rule, 50 pairs; opposite rule, 47 pairs; comparison with a binomial test). d, Cross-correlation strength is significantly higher for neurons representing the same rule. e, f, Co-modulation probability (e) and strength (f) show dependence on temporal distance between tuning peaks among same rule-representing pairs (n = 138). g, Photograph of a multi-electrode implant used to record from PFC with simultaneous optogenetic manipulation. Inset, enlargement of the drive component targeting bilateral PFC with optic fibres and electrodes (top) and enlargement showing electrodes and optic fibre for one hemisphere (bottom) (e, electrodes and f, optic fibre). h, Examples of a fast-spiking neuron that is driven (top) and a regular-spiking neuron that is inhibited (bottom) by exposure to blue light (blue bar, 473 nm). i, Quantification of laser effects on fast-spiking and regular-spiking cell firing rate shows that this holds true at the population level (albeit with the population mean of regular-spiking neurons being generally smaller than the example). Grey shading represents 95% CI of the no laser condition. j, Top, example task-modulated spike rasters showing laser effect on tuning peak. Bottom, visualization of peak strength measurement for these examples (see Methods).

Extended Data Figure 4 Causal evidence for task-specific sequential PFC activity maintaining rule representation.

a, Effect of bilateral optogenetic enhancement of local inhibition on PFC rule tuning and behaviour. Left, raster and PSTH examples of neurons tuned either early or late in the delay (blue shading indicates laser presentation), shows loss of tuning with minimal impact on overall spiking (see Extended Data Fig. 3i for quantification of laser on spike rates). Middle, laser effect on population rule information (n = 94 neurons, three mice). Right, laser effect on behaviour (n = 12 sessions; four from each mouse). bd, Temporally limited optogenetic manipulations show that later PFC tuning is dependent on early tuning. b, Manipulation limited to the first 250 ms is sufficient to mimic the effect of suppression during the full delay period on neuronal tuning with a smaller impact on behaviour probably owing to a smaller laser dose and being close to the optical fibres (n = 52 neurons, 12 sessions). c, The effect from b persists even when rule presentation period is spared (n = 46 neurons, 12 sessions). d, Late laser activation only impacts late activity (n = 53 neurons, 12 sessions). e, Cartoon of experimental comparison of the effect of sensory selection rule presentation inside and outside of the task. f, Left, example of two neurons that display tuning following rule-related cue presentation inside the task but not outside of it. Right, group quantification for population tuning shown on the left (n = 283 neurons from five mice). For peak size, shaded error regions show the 95% CI of the measurement, while the grey bar denotes the subsampled bootstrapped 95% CI for baseline error estimate. For rule information shaded areas indicate bootstrapped 95% CI. Wilcoxon rank-sum test was used for all behavioural comparisons.

Extended Data Figure 5 Optogenetic dissection of error types in the 4AFC task.

a, Inactivation of PFC in four VGAT–ChR2 mice during the delay period specifically increases executive errors, whereas the sensory errors remain comparable. b, Mediodorsal inhibition leads to a similar increase in executive errors. c, By contrast, LGN inactivation specifically increases sensory errors in attend to vision trials, whereas executive errors remain comparable. Coloured bars show median values and dots represent average performance of each mouse (4–5 sessions per mouse). For visual clarity, error bars were not included. Wilcoxon rank-sum test was used for all comparisons.

Extended Data Figure 6 Mediodorsal recruitment by the PFC is related to delay period length in the 2AFC task.

a, Bilateral optogenetic LGN suppression through activation of NpHR3.0 (orange bar) had no effect on PFC tuning during the delay period, but did increase errors in the 2AFC task. Left, raster and PSTH examples of neurons tuned either early or late in the delay (shading indicates laser presentation), shows that rule tuning persists during LGN inactivation. Middle, laser effect on population rule information over the delay (n = 33 cells, two mice). Right, laser effect on behaviour (n = 2 mice, three sessions each). be, Mediodorsal suppression using the same approach as in the LGN leads to loss of tuning and disrupts behavioural performance. Data are presented as example units (be, left), followed by the PCA for the laser on verus off conditions (second left) and behavioural impact (bd, right; e, third graph). e, The right most graph shows the direct comparison between late PFC (Extended Data Fig. 4d (middle)) suppression and late mediodorsal suppression (second left) on PFC rule information in the last 100 ms (mean ± 95% CI).

Extended Data Figure 7 Connectivity pattern and response profile of mediodorsal and PFC neurons.

a, Cumulative distributions of neuronal peak widths (measured as full-width at half-maximum) for mediodorsal thalamus and PFC. b, c, Two nonlinear decoding methods, PNB (b) and MCC (c), fail to reveal rule information among tuned mediodorsal neurons (PFC, n = 604 neurons, six mice; mediodorsal thalamus, n = 156 neurons, three mice). d, Rule information obtained from nonlinear decoding does not depend on the number of simultaneously recorded neurons. Decoding of rule information among tuned mediodorsal and PFC neurons is similar in sessions containing 1–5 neurons (PFC, n = 318 neurons, six mice; mediodorsal thalamus, n = 73 neurons, three mice) and sessions containing more than 5 neurons (PFC, n = 286 neurons, six mice; mediodorsal thalamus, n = 83 neurons, three mice). Similar results were obtained in single sessions with the highest population of simultaneously recorded mediodorsal neurons containing temporal peaks (n = 16), and an equivalent session containing the same number of simultaneously recorded tuned PFC neurons. Error bars are 95% CI. e, Rule information is not encoded by mediodorsal neurons that do not show peaks. f, Schematic diagram showing that tetrodes yielding mediodorsal neurons with peaks were located exclusively in the lateral mediodorsal thalamus (dots). g, Anterograde labelling of the PFC shows that their terminals are located in the lateral mediodorsal thalamus. h, Retrograde labelling from PFC identifies cells in the lateral mediodorsal thalamus. Insets show enlarged view. i, Firing rates are comparable in correct and incorrect trials for mediodorsal (left) or PFC (right) neurons. j, Left, example PSTH of a single mediodorsal neuron in correct or error trials showing similar peaks across all conditions. Right, quantification of peak size for the same rule in incorrect trials shows that mediodorsal peaks are retained, whereas PFC peaks are diminished. Shade indicates bootstrapped 95% CI.

Extended Data Figure 8 Mediodorsal thalamus to PFC and PFC to mediodorsal thalamus pathways are functionally asymmetric.

a, b, Scatter plots comparing firing rates of PFC neurons during the delay period with and without mediodorsal suppression (each data point represents a neuron). Regular-spiking (a) and fast-spiking (b) cells show significantly reduced firing when the mediodorsal thalamus is optogenetically suppressed during the task delay period (P < 0.001). c, By contrast, mediodorsal suppression outside of the task only reduces fast-spiking firing rates (regular-spiking, n = 245 neurons fast-spiking, n = 114 neurons; data are presented as mean ± 95% CI; grey shading indicates 95% CI of null distribution). df, Increasing excitability in the mediodorsal thalamus through activation of SSFOs has no effect on the firing rate of regular-spiking neurons (d, n = 303 neurons, P > 0.05), but significantly increases spiking of fast-spiking (e, n = 131 neurons, P < 0.001) and mediodorsal spiking neurons (f, n = 254 neurons, P = 0.001). gi, The same manipulation in the PFC increased firing rates in cortical regular-spiking (g, n = 140 neurons, P < 0.001), fast-spiking (h, n = 91 neurons, P < 0.001) and mediodorsal (i, n = 111 neurons, P = 0.004) neurons. j, All scatter plot data were compared using Wilcoxon signed-rank tests. Other than c, the scatter plots are the raw data used for the normalized values in Fig. 3.

Extended Data Figure 9 Experimental and modelling results clarifying the attributes of the mediodorsal thalamus–PFC network.

a, While LGN activation drives spiking in the V1, mediodorsal activation does not drive PFC spiking. b, Data-based schematic of the conceptual model showing mediodorsal, cortical fast-spiking and regular-spiking neurons in three different conditions. Triangles represent PFC regular-spiking cells tuned to a single rule which send convergent input to mediodorsal neurons (purple). The mediodorsal thalamus sends a modulatory like signal that enhances spiking in fast-spiking cells (orange) and amplifies connections among regular-spiking cells. Task engagement enhances mediodorsal activity and in turn fast-spiking neural activity. Rule information is simulated as synchronized input to starter regular-spiking neurons. c, Example data from the model in b, rasters of two regular-spiking cells (red, cell 1; black, cell 2) at different positions within a chain showing changes in activity across conditions. Overall spike rates do not change, but coordinated spiking (grey shading) increases. d, Systematically exploring the degree of convergence in the mediodorsal thalamus–PFC model suggests that 3–4 links in the PFC chain converge onto individual mediodorsal neurons (n = 250 neurons, three simulations per condition). e, f, The model captures firing rate (e) and connectivity changes (f) observed experimentally. g, Enhancing excitability in mediodorsal neurons by 10% significantly increases the number of rule-tuned cells in the PFC. h, Enhancing excitability in the PFC population by 8% markedly increased the proportion of neurons that show inappropriate tuning. g, h, Data are mean ± s.e.m.; n = 250 neurons, 10 simulations; P = 0.002, Wilcoxon rank-sum test. i, Example raster from a neuron tuned to one rule, showing that mediodorsal activation is sufficient to generate appropriate tuning outside the task. j, Population data shows that mediodorsal activation is sufficient to partially generate tuning outside the task (n = 2 mice, 31 tuned neurons). Grey shading indicates 95% CI of null distribution. All data are presented as mean ± 95% CI. k, Example of the effect of SSFO-based activation on a mediodorsal neuron containing only one peak, showing the addition of a second peak at the same time point in the opposite trial type. l, Relative to the population average (8.4%, dotted line), mediodorsal neurons showed significantly fewer single peaks in the SSFO condition despite the presence of an average number of single peaks in the same neurons without SSFO (cumulative binomial test versus population average).

Extended Data Figure 10 Behavioural effects of excitability changes in MGB.

a, Diagram showing task design and SSFO activation/termination timing in a Go/No-go auditory discrimination task (see Methods for task description). b, Comparison showing the probability of a Go response after either Go or No-go stimuli were presented across sessions (points) and mice (columns). NS, non-significant (P = 0.52), Wilcoxon signed-rank test.

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Schmitt, L., Wimmer, R., Nakajima, M. et al. Thalamic amplification of cortical connectivity sustains attentional control. Nature 545, 219–223 (2017).

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