Theta-coupled periodic replay in working memory - PubMed (original) (raw)

Theta-coupled periodic replay in working memory

Lluís Fuentemilla et al. Curr Biol. 2010.

Abstract

Working memory allows information from transient events to persist as active neural representations that can be used for goal-directed behaviors such as decision making and learning. Computational modeling based on neuronal firing patterns in animals suggests that one putative mechanism enabling working memory is periodic reactivation (henceforth termed "replay") of the maintained information coordinated by neural oscillations at theta (4-8 Hz) and gamma (30-80 Hz) frequency. To investigate this possibility, we trained multivariate pattern classifier decoding algorithms on oscillatory brain responses to images depicting natural scenes, recorded with high temporal resolution via magnetoencephalography. These classifiers were applied to brain activity recorded during the subsequent five second maintenance of the scenes. This decoding revealed replay during the entire maintenance interval. Replay was specific to whether an indoor or an outdoor scene was maintained and whether maintenance centered on configural associations of scene elements or just single scene elements. Replay was coordinated by the phase of theta and the amount of theta coordination was correlated with working memory performance. By confirming the predictions of a mechanistic model and linking these to behavioral performance in humans, these findings identify theta-coupled replay as a mechanism of working memory maintenance.

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Figure 1

Figure 1

The Trial Structure, Subjects' Behavioral Performance, and MVPC Accuracy during Sample Presentation (A) Trial structure of the two variants of a blocked DMS working memory task, one with (configural) and the other without (nonconfigural) associative configural maintenance demands and a control task without maintenance requirements. (B) Behavioral performance at probe for each experimental condition. Working memory performance was better in the nonconfigural than the configural condition [paired t test: t(7) = 4.02, p = 0.005] and accuracy in control and configural was similar [paired t test: t(7) = 0.8, p = 0.45], showing that the two conditions were equated for difficulty. ∗p < 0.05; ns: p > 0.4. (C) Single-subject indoor and outdoor MVPCs were computed separately every 80 ms from −36 ms prior to 764 ms after sample onset during encoding. X axis labels time points where the MVPC was trained and tested. Plots represent subjects' mean MVPC accuracy at sample encoding for control (Cont; black line), nonconfigural (N-Conf; blue line), and configural (Conf; red line) conditions. MVPC results showed correct classification of sample pictures into indoor and outdoor categories from 200–300 ms onward. The statistical threshold for correct MVPC classification was set at p < 0.04 and at p < 0.002 after correcting for multiple comparisons. Error bars denote standard error of the mean (SEM) in (B) and (C).

Figure 2

Figure 2

Category-, Condition-, and Task-Specific Reactivations during Maintenance (A) Category-specific replay during the maintenance period (4.5 s; x axis) for each experimental condition and for the 11 different classifiers trained at different time points of sample picture encoding (y axis). Plots represent the percentage of subjects that showed significant (p < 1.8 × 10−5) reactivations for different classifiers (y) and time points (x). (B) Sum of all significant reactivations for all ten (44 to 764 ms after onset of sample image) classifiers collapsed across categories and time points (paired t test one-tailed, ∗p < 0.05 and ∗∗p < 0.01). (C) Similar replay count as in (B) but displayed for each classifier. The x axis refers to each of the classifiers trained at different time points during sample picture encoding. (D)–(F) Condition specificity (nonconfigural versus configural DMS condition) and task specificity (DMS tasks versus control task) of reactivations. (D) Number of significant indoor/outdoor neural pattern reactivations when classifiers trained during control and nonconfigural encoding were tested along indoor/outdoor scene maintenance of the configural condition. This was contrasted (paired t test) with the number of significant reactivations obtained when trained and tested classifiers belonged to configural task. (E) As in (D), but contrasting the number of reactivations obtained during the delay of the control task when classifiers were trained during configural, nonconfigural, and control encoding. (F) As in (D), but contrasting the number of reactivations obtained during the delay of the nonconfigural condition when classifiers were trained during configural, nonconfigural, and control encoding. ∗∗p < 0.01; ∗p < 0.05; ns denotes nonsignificant. In (B)–(F), error bars denote SEM.

Figure 3

Figure 3

Theta Phase Coupling of Category-Specific Reactivations during Maintenance (A) Sensor-specific significant (p < 0.05) 6 Hz theta phase locking of reactivations during nonconfigural and configural maintenance. In the topographic plots, C1–C4 denote clusters (minimum of eight significant adjacent sensors) of sensors where phase locking in nonconfigural and configural conditions exceeded phase locking in the control task. Of these clusters, C2 and C3 in the nonconfigural condition and C2, C3, and C4 in the configural condition survived correction for multiple comparisons (see Supplemental Information and Figure S3 for details). (B) Mean 6 Hz PLVs obtained for each cluster identified in (A). Error bars denote SEM. (C) Topographic distribution of significant (red; p < 0.05, minimum of eight significant adjacent sensors) correlations between PLVs (6 Hz) at bilateral frontotemporal sensors and behavioral working memory accuracy in the configural condition.

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