close
Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2019 Nov;22(11):1883-1891.
doi: 10.1038/s41593-019-0494-0. Epub 2019 Sep 30.

Widespread temporal coding of cognitive control in the human prefrontal cortex

Elliot H Smith #  1 , Guillermo Horga #  2 , Mark J Yates  3 , Charles B Mikell  4 , Garrett P Banks  3 , Yagna J Pathak  3 , Catherine A Schevon  5 , Guy M McKhann 2nd  3 , Benjamin Y Hayden  6 , Matthew M Botvinick  7 , Sameer A Sheth  8
Affiliations

Widespread temporal coding of cognitive control in the human prefrontal cortex

Elliot H Smith et al. Nat Neurosci. 2019 Nov.

Abstract

When making decisions we often face the need to adjudicate between conflicting strategies or courses of action. Our ability to understand the neuronal processes underlying conflict processing is limited on the one hand by the spatiotemporal resolution of functional MRI and, on the other hand, by imperfect cross-species homologies in animal model systems. Here we examine the responses of single neurons and local field potentials in human neurosurgical patients in two prefrontal regions critical to controlled decision-making, the dorsal anterior cingulate cortex (dACC) and dorsolateral prefrontal cortex (dlPFC). While we observe typical modest conflict-related firing rate effects, we find a widespread effect of conflict on spike-phase coupling in the dACC and on driving spike-field coherence in the dlPFC. These results support the hypothesis that a cross-areal rhythmic neuronal coordination is intrinsic to cognitive control in response to conflict, and provide new evidence to support the hypothesis that conflict processing involves modulation of the dlPFC by the dACC.

PubMed Disclaimer

Conflict of interest statement

Competing Interests

The authors declare no competing interests.

Figures

Figure 1.
Figure 1.. Recording configuration, task description, and behavioral performance |
a, Diagram of the intracranial implant including a stereotactically placed intra-cerebral depth electrode with macroelectrodes (blue squares) along the shaft from dlPFC to dACC and microwire electrodes (orange star) in dACC. A, anterior; L, lateral; CS, central sulcus; SFS, superior frontal sulcus; IFS, inferior frontal sulcus. b, Multi-source interference task (MSIT). The subject sees a cue consisting of 3 numbers and has to identify the unique number (“target”) and respond with a button push: left button if the target is “1”, middle if “2”, right if “3”. Incongruence between the location of the target number in the 3-digit sequence and location of the correct button in the 3-button pad produces spatial (Simon) conflict (orange). The distracting presence of numbers that are valid button choices (“1”, “2”, “3”, vs. “0”, which is not a valid choice) produces flanker (Eriksen) conflict (green). Trials can also have neither type of conflict (magenta) or both types (violet). In all 4 example trials shown, “2” is the target; thus the middle button is the correct response. Following the response, valenced (green/red for correct/incorrect; 2 example trials shown above dashed line) or unvalenced (blue regardless of correctness; 2 example trials below dashed line) feedback is provided in alternating blocks of 10 trials. c, Line plots of RT distributions across all patient. Each colored line represents two standard deviations of RTs centered on the mean RT and color-coded each conflict condition. Black and gray lines connect the means across conflict conditions within each patient. There was a statistically significant difference among conflict conditions (generalized linear mixed effects model, t3881 = 2.36, p = 0.01). RT distributions for each subject and session are shown in Supplementary Figure 7.
Figure 2.
Figure 2.. Rate coding of task-relevant variables in human dACC neurons |
a, Microwire recording locations, with different colors per subject. b, Example dACC raster plot and firing rate over conflict conditions for a representative neuron that shows rate coding for decision conflict. Conflict conditions are color-coded as in Figure 1. Shaded regions represent standard error (n = 72, 77, 85, and 66 trials for none, spatial, flanker, and both conditions, respectively). c, Venn diagram showing only those dACC neurons that were selective for specific task features, as determined by the sliding GLM. Each colored square represents one neuron; percentage of total n = 136 neurons indicated in parentheses.
Figure 3.
Figure 3.. Robust phase coding in dACC neurons |
a, Example dACC neuron whose firing rate does not vary by conflict level (no firing rate code). b, Mean phase of SFC for a single beta-coherent neuron for each conflict condition. Shaded regions in a, and b represent standard error (n = 72, 77, 85, and 66 trials for none, spatial, flanker, and both conditions, respectively). c, Mean phase of SFC across all beta-coherent neurons color coded by conflict condition (LMM t-test, t510 = 3.6, p = 4*10−4). Shading represents standard error across 50 beta coherent neurons. d, Schematic showing mean spike phase for each conflict condition in beta-coherent neurons. e,f, Same as a,b for a different dACC neuron that again shows no firing rate code, but shows increased theta coherence (N = 69 trials for each conflict condition). g,h, Same as c,d, for theta-coherent neurons (LMM t-test, t510 = 3.1, p = 2*10−3). Shading represents standard error across 43 theta coherent neurons. i, Venn diagram showing only the proportions of neurons that were either beta phase coding (green), theta phase coding (blue), or rate coding (pink) neurons for decision conflict. The proportion of beta- or theta-phase coding neurons was significantly greater than that of rate coding neurons (73 vs 14 neurons; McNemar’s Test, χ2 = 13.5, p < 10−3). j,k, For each neuron, the maximum F statistic from the beta (j) or theta (k) phase code F-statistics plotted against the maximum F statistic from the firing rate GLM (Spearman’s rho for theta: ρ = 0.03; p = 0.76; for beta: ρ = - 0.08; p = 0.32; two-sided t-tests). Scatter plots show statistics for all 136 neurons. Significant rate coding cells are shown in pink, and significant phase coding cells are indicated with colored circles for each frequency range, as in i.
Figure 4.
Figure 4.. dACC neuronal interactions within a broader control network |
a, Spike-triggered LFP (stLFP) waveforms evoked by dACC neurons on dACC LFP (“dACC-dACC”) for conflict selective rate coding neurons (red, n = 2,370 spikes), non-coding neurons (gray, n = 10,292 spikes), and null distribution (black). b, dACC-dACC stLFP waveforms for temporal coding neurons: beta-coherent (green, n = 8,398 spikes), theta-coherent (blue, n = 7,581 spikes), and null distribution (black). c, Distributions of dACC-dACC stLFP amplitudes for each neuron group in a,b. stLFP amplitudes were decorrelated by their covariance matrices and Z-scored. d-f, same spikes and details as in a-c, but for stLFP evoked by dACC neurons on dlPFC LFP (“dACC-dlPFC”). Gradient bars in c,f show significant pairwise post hoc comparisons of fixed effects (following omnibus LMM t-test, t20,576 < 2.2, p < 0.02; post-hoc two-sided t-tests, all p < 0.05).
Figure 5.
Figure 5.. Temporal coding in human dlPFC neurons |
a, Recording locations in the 9 subjects from which dlPFC units were recorded. Circles represent DBS patients and the square represents the epilepsy patient. b, Venn diagram showing numbers (and percent, total n = 367) of only those neurons that were selective for specific task features using a rate code. c, A representative dlPFC neuron with firing rate coding and temporal (theta) coding (two-sided SFC permutation test, p < 0.05) for decision conflict. Conflict conditions are color-coded as in Figure 1. Gray arrows highlight clusters of single unit spikes in a theta-coherent pattern. Shaded regions indicate standard error (n = 58, 86, 88, and 68 trials for none, spatial, flanker, and both conditions, respectively). d,e, Representative coherogram for another dlPFC neuron averaged over “none” trials (d) and “both” trials (e). f, Difference coherogram between d and e illustrating increased coherence between spike timing and theta oscillations (boxed region) in higher conflict trials. g, Mean difference coherogram averaged across all 367 dlPFC neurons recorded in all 9 subjects (boxed region shows significant cluster −0.1 to 1.2 s and 4.8 to 10.7 Hz, two-sided SFC permutation tests, all p < 0.05). 191 (52.0%) neurons demonstrated spike-theta coupling that scaled with conflict level (LMM t-test, t886 = 4.2, p = 3*10−5).
Figure 6.
Figure 6.. Trial-to-trial encoding of conflict via population theta coherence |
a, Mean difference coherogram averaged across trials (sample sizes listed below) with >100 simultaneously recorded neurons on the dlPFC UMA, illustrating significant difference in coherence across conflict conditions (The cluster of significant coherence was from 0.8 to 1.9 s and 5.4 to 10.7 Hz, two-sided SFC permutation test, all p < 0.05). b, Box plots (box height: interquartile range, ticks: most extreme points, lines: medians) of theta coherence fixed effect quintiles from the LMM versus log(RT) with overlaid regression lines for each conflict condition, color coded as in Figure 1 (n = 215, 222, 240, and 211 trials for none, spatial, flanker, and both conditions, respectively). c, Same as b, using down-sampled and filtered data from the UMA for coherence calculations (n = 187, 198, 208, and 176 trials for none, spatial, flanker, and both conditions, respectively).
See this image and copyright information in PMC

References

    1. Botvinick M & Braver T Motivation and Cognitive Control: From Behavior to Neural Mechanism. Annu. Rev. Psychol 66, 83–113 (2015). - PubMed
    1. Shenhav A et al. Toward a Rational and Mechanistic Account of Mental Effort. Annu. Rev. Neurosci 40, 99–124 (2017). - PubMed
    1. Miller EK & Cohen JD An Integrative Theory of Prefrontal Cortex Function. Annu. Rev. Neurosci 24, 167–202 (2001). - PubMed
    1. Abi-Dargham A & Horga G The search for imaging biomarkers in psychiatric disorders. Nat. Med 22, 1248–1255 (2016). - PubMed
    1. Cole MW, Repovš G & Anticevic A The frontoparietal control system: a central role in mental health. The Neuroscientist 20, 652–664 (2014). - PMC - PubMed

Publication types

LinkOut - more resources