Research Article

Flexible recruitment of memory-based choice representations by the human medial frontal cortex

See allHide authors and affiliations

Science  26 Jun 2020:
Vol. 368, Issue 6498, eaba3313
DOI: 10.1126/science.aba3313
  • Flexible representations of choices in the human frontal lobe.

    (A) Recording locations. LFP, local field potential. (B) Population response of all recorded neurons (left) and example of a cell signaling memory-based choices (right). (C and D) Representational geometry analysis reveals that different subspaces are used by the two tasks, establishing a memory-specific decision axis. (E) Theta- and gamma-band coherence of MFC choice cells with HA LFPs increased during the memory task.

  • Fig. 1 Task, electrode locations, and behavior.

    (A) Task structure. A session consisted of eight blocks of 40 trials. The task switched with each block (blue = categorization, red = memory), and the response modality switched halfway through each block (saccade or button press, randomly assigned at the beginning of the block). The subject was instructed about the task at the beginning of each block (purple arrows) and how to respond at the beginning and halfway points of each block (green arrows). (B) Example of screens shown to subjects for two example trials. (C and D) Electrode locations. Each dot is the location of a microwire bundle in one subject. Coordinates are in Montreal Neurological Institute (MNI) 152 space. (E and F) Eye tracking data from one session from the button press (E) and eye movement (F) trials. (G) Reaction times as a function of task across all sessions (memory, μ ± SEM, 1.27 ± 0.02 s; categorization, 0.90 ± 0.02 s; P = 7.6 × 10−228, two-sample KS test). (H) Memory performance improves over the course of the experiment (β = 0.56, P = 8.42 × 10−130, logistic mixed effects model). See fig. S1 for an extended summary of the behavior.

  • Fig. 2 Representations of task type and response modality.

    (A and B) Example pre-SMA neuron. (B) Average firing rate during the baseline period (−1 to 0 s relative to stimulus onset) for each block for the cell shown in (A). The average baseline firing rate across all blocks of the same type is shown. (C and D) Population decoding of task type (C) and response modality (D). (E) Cross-condition decoding approach. The background color denotes the type of trials that were used to train a given decoder. (F) Cross-response modality decoding of task type from the baseline firing rate of all recorded cells. (G) Cross-task decoding of response modality. (H) Decoding performance as a function of trial number relative to a task type switch (green arrows in Fig. 1A; transitions from categorization to memory and vice versa were pooled). Error bars indicate SD in all panels, with the exception of (B), where they indicate SEM. (I) Baseline decoding of task type for subsequent trials with short reaction times was more accurate than decoding on long reaction time trials. Performance is shown separately for categorization (Cat) and memory (Mem) trials (P = 2 × 10−11 and 7 × 10−13, respectively, Wilcoxon rank sum test). Error bars denote standard error in decoding accuracy across trials (80 trials in each of the four groups). See fig. S2 for additional analyses that break down context effects by specific anatomical regions.

  • Fig. 3 Representations of image category and familiarity (new versus old).

    (A and B) Example cells that (A) represent image category and (B) differentiate between new and old stimuli. (C) Decoding accuracy of image category from all recorded cells was significantly higher in the HA relative to the MFC (Δtrue = 49%, P < 0.001). (D) Decoding of new versus old (ground truth) was similarly accurate in the HA and MFC (Δtrue = 7%, P = 0.13). For new versus old decoding, trials with images of monkeys were excluded, because the recognition performance for these images was at chance (fig. S1B). (E) Population activity of all recorded HA (left) and MFC (right) cells, plotted in 3D using MDS. Individual points show the mean activity of the population for that specific condition. The highlighted plane contains all locations of state space occupied by a given task for the case of fruits versus faces as the binary category distinction (for illustration only; all analysis uses all categories). The geometry of the representation allows for a decoder that is trained on one task to generalize to the other task (see fig. S4C for example decoder hyperplanes). (F) Approach used for the cross-condition generalization analysis. Color indicates task (blue = categorization, red = memory). (Top) We trained a decoder to discriminate between new and old trials on categorization trials and then tested its performance on new and old stimuli encountered during the memory condition (and vice versa). (Bottom) Similarly, a decoder that is trained to discriminate between image categories (in this example, faces versus fruits; all results include all six possible pairs) on categorization trials was tested on memory trials. (G) Cross-condition generalization performance for image category. (H) Cross-condition generalization performance for new versus old. (I) Difference in cross-task generalization decoding accuracy for familiarity and image category between HA and MFC. Difference is computed between the average cross-task performances in each area (i.e., average of memory→categorization and categorization→memory). The null distribution for the average was estimated from the empirical null estimated by shuffling the labels used to train the decoders. For both variables, decoding from HA had significantly greater cross-task generalization performance than decoding from MFC (the difference in both cases is positive and outside of the 95th percentile of the null distribution). (J) Generalization index (see Materials and methods) for memory (two data points on the left) and image category (two data points on the right). For both image category and familiarity, generalization across task was higher in the HA population than in the MFC population (see figure for statistics; Δ, difference).

  • Fig. 4 Task-specific representation of choice.

    (A) Example MFC choice cells, split by choice (yes or no) and task. (B) Population choice decoding accuracy was significantly greater in MFC than in HA (across all trials, Δtrue = 19% versus empirical null, P < 1 × 10−3). (C) MFC cells represent choice and not the ground truth (i.e., new or old; memory trials only). (D) Population summary (neural state space) of choice-related activity in MFC, plotted in 3D space derived using MDS. (Top) Variability due to response modality. The highlighted planes connect the points of state space occupied by activity when using button presses (purple) or saccades (green). (Bottom) Variability due to task type. The highlighted planes connect the points of state space occupied by activity in the same task. (E) Choice-decoders trained in one task do not generalize to the other task (bin size: 500 ms, step size: 16 ms). (F) Same as (E), but for a fixed 1-s time window starting at 0.2 s after stimulus onset. (G) Choice decoding generalizes across effectors [see (D)]. (H) Generalization index of choice decoding (see Materials and methods) to summarize (F) and (G). The representation of choices generalized across response modality but not task. (I) Generalization between different subtasks of the categorization task but not between task types. The colored bars indicate the 5th to 95th percentile of the null distribution. (J) (Left) State-space trajectories for the four conditions arising from the combination of response (yes or no) and task (categorization or memory). (Right) Trajectory similarity, computed in an 8D latent space (recovered using GPFA, see Materials and methods) across the eight conditions arising from the combinations of choice, effector type, and task. (K) Decoder weight of each cell for decoding choice during the categorization and memory task. The cells in the top 25th percentile are shown in black. The inset shows the angle created by the vector [ωicat,ωimem] with respect to the x axis of the cells marked in black.

  • Fig. 5 Modulation of interareal spike-field coherence by task demands.

    (A) Analysis approach. Inset shows that only data from the baseline was used [except in panel (H)]. (B) Spike-field coherence for a cell in dACC relative to all channels in the ipsilateral hippocampus. (C) Phase-locking of MFC cells to HA LFP. (Top) Average interarea PPC of all cell-electrode pairs for each task. (Bottom) Significance of difference between tasks; peak difference was at f = 5.5 Hz. Dashed line shows the threshold (P = 0.05/56, Bonferroni corrected). (D) Difference in average interarea PPC at f = 5.5 Hz between task conditions for all possible cell-electrode pairs (from left to right, n = 8822 electrode pairs, P = 1.3 × 10−7; n = 3938, P = 8.8 × 10−4; n = 4884, P = 4.3 × 10−5; paired t test). (E) Average spike-triggered power was not significantly different between the two tasks (paired t test, n = 8822 cell electrode pairs, P = 0.08). (F) Single-neuron analysis of choice cells. Importance index assigned by the decoder to each cell for decoding choices in either task. Selected choice cells are indicated in color. (G) MFC-HA spike-field coherence for choice cells. (Top) Average PPC for all choice cells in MFC (209 cells, 2384 cell-electrode pairs) for the two tasks. (Bottom) Significance of difference between tasks, shown separately for memory and categorization choice cells (n = 1176 and 906 cell-electrode pairs, respectively). Only memory choice cells show a significant difference in the gamma band (P = 2 × 10−6, t test). (H) Difference in spike-field coherence between true-positive (correct retrieval) and false negative (incorrect retrieval) trials measured in the [0.2 s, 1.2 s] window after the stimulus onset, shown separately for memory choice cells (left panel) and categorization choice cells (right panel) in the theta-frequency (4 to 10 Hz) and gamma-frequency (30 to 80 Hz) bands. PPC was significantly stronger in correctly retrieved trials in the theta band for memory choice cells (Δmem = 0.003, P = 0.002; Δcat = −0.001, P = 0.3; paired t test) and in the gamma band for both types of choice cells (Δmem = 0.002, P = 0.005; Δcat = 0.004, P = 7.2 × 10−8; paired t test). (I) Spike times of HA cells are coherent with local theta-band (3 to 8 Hz) LFP. (J) Average local PPC in the HA did not differ significantly as a function of the task (f = 5.5 Hz; P = 0.51, paired t test, n = 2321 cell-electrode pairs). Error bars in panels (D) and (H) denote the 95% confidence interval (bootstrap, n = 10,000 iterations). All other error bars are SEM.

Supplementary Materials

  • Flexible recruitment of memory-based choice representations by the human medial frontal cortex

    Juri Minxha, Ralph Adolphs, Stefano Fusi, Adam N. Mamelak, Ueli Rutishauser

    Materials/Methods, Supplementary Text, Tables, Figures, and/or References

    Download Supplement
    • Supplementary Text
    • Figs. S1 to S11
    • Table S1
    • Caption for Movie S1

    Images, Video, and Other Media

    Movie S1
    Dynamics of Neural activity in state space. The video shows trajectories of the average population activity for combinations of choice (yes vs. no) and task type (memory vs. categorization). The 3-dimensional space shown is a projection of an 8-dimensional latent space recovered using Gaussian process factor analysis (GPFA). The gray dots denote the location in state-space of the population activity at the time of the stimulus onset. The trajectories evolve over a period of 750ms from the stimulus onset.

Stay Connected to Science

Navigate This Article