Multimodal imaging of brain connectivity reveals predictors of individual decision strategy in statistical learning

Abstract

Successful human behaviour depends on the brain's ability to extract meaningful structure from information streams and make predictions about future events. Individuals can differ markedly in the decision strategies they use to learn the environment's statistics, yet we have little idea why. Here, we investigate whether the brain networks involved in learning temporal sequences without explicit reward differ depending on the decision strategy that individuals adopt. We demonstrate that individuals alter their decision strategy in response to changes in temporal statistics and engage dissociable circuits: extracting the exact sequence statistics relates to plasticity in motor corticostriatal circuits, while selecting the most probable outcomes relates to plasticity in visual, motivational and executive corticostriatal circuits. Combining graph metrics of functional and structural connectivity, we provide evidence that learning-dependent changes in these circuits predict individual decision strategy. Our findings propose brain plasticity mechanisms that mediate individual ability for interpreting the structure of variable environments.

Conflict of interest statement

Competing interests The authors declare no competing interests.

Figures

Fig. 1. Trial and sequence design.

a, Trial design: stimuli comprised four symbols chosen from Ndjuká syllabary. A temporal sequence of 8–14 symbols was presented, followed by a cue and the test display. b, Sequence design: the three Markov models used in the study. In the zero-order model (level 0), each of the four symbols (A, B, C and D) constitutes a different state that occurred with a different probability. In the first-order (level 1) and second-order (level 2) models, each state (indicated by a circle) is associated with two transitional probabilities—one high probability (solid arrow) and one low probability (dashed arrow). Rows in the conditional probability matrix represent the temporal context, whereas columns represent the corresponding target.

Fig. 2. Behavioural performance.

a, Normalized performance index for the training group (n = 21) per level and test session (pre-training, grey bars; post-training, black bars). Error bars indicate s.e.m. across participants. b, Box plots of strategy index show individual variability for each level (levels 0, 1 and 2). The upper and lower error bars display the minimum and maximum data values, and the central boxes represent the interquartile range (25th–75th percentiles). The thick line in the central boxes represents the median. The open circle denotes an outlier. The strategy index for frequency statistics was not significantly different from matching (that is, zero strategy index; t(20) = −0.23; P = 0.82; CI = −0.08 to 0.07; Cohen’s d = −0.050). Note that the variability across participants around zero could be due to the fact that the task is probabilistic and the participants were not given trial-by-trial feedback. In contrast, the strategy index for context-based statistics (mean strategy index for levels 1 and 2) was significantly higher than zero (t(20) = 4.01; P<0.001; CI = 0.08 to 0.26; Cohen’s d = 0.874). c, Scatter plot of strategy index for frequency and context-based statistics. Individual participant data are shown with open circles (n = 21). Points below the diagonal indicate participants who showed a higher strategy index for context-based compared with frequency statistics.

Fig. 3. Striatal segments and iCA components.

a, Four striatal segments as estimated by a DTI connectivity-based and hypothesis-free classification method. Segments are displayed in neurological convention (left is left) and overlaid on the Montreal Neurological Institute (MNI) template (green, ventral striatum; blue, caudate head and anterior putamen; yellow, caudate body/tail; red, posterior putamen). b, The seven selected ICA components are depicted, organized into known cortical networks. Group spatial maps are thresholded at z = 1.96 for visualization purposes and displayed in neurological convention on the MNI template. The x, y and z coordinates denote the location of the sagittal, coronal and axial slices, respectively.

Fig. 4. Intrinsic and extrinsic connectivity analysis.

a,b, Significant skipped Pearson correlations (two-sided; n = 21) of the intrinsic connectivity change (post- minus pre-training) (a) and the extrinsic connectivity change (b) with strategy index for frequency (top) and context-based statistics (bottom). Open circles denote outliers as detected by the Robust Correlation Toolbox.

Fig. 5. rs-fMRI and DTI graphs.

a,b, Whole-brain graphs for rs-fMRI (a) and DTI data (b). Graphs were generated based on the AAL parcellation (90 areas excluding the cerebellum and vermis) and displayed at 5% density for visualization. The thickness of the edges is proportional to the average functional and structural connectivity, respectively. The selected nodes are coloured to represent regions within known corticostriatal circuits: caudate and putamen (magenta); right MFG and left IFG (red); postcentral gyrus (cyan); calcarine sulcus (blue); and ACC (yellow). Graphs are displayed in neurological convention (left is left) in axial (left) and sagittal (right) views. Three-dimensional videos illustrating the rs-fMRI and DTI graphs are included in the Supplementary Information.

Fig. 6. PLS weights for degree and clustering coefficient.

a,b, Scatter plot of PLS-1 and PLS-2 weights for change (that is, post- minus pre-training) in degree (a) and clustering coefficient (b). PLS predictor weights for each selected node are indicated by symbols separately for DTI (circles) and rs-fMRI data (squares). The colour of the symbols corresponds to the nodes (see Fig. 5) in corticostriatal circuits: caudate and putamen (magenta); right MFG and left IFG (red); postcentral gyrus (cyan); calcarine sulcus (blue); and ACC (yellow). PLS predictor weights with |z| > 2.576 (P = 0.01) are marked by an asterisk to denote significant predictors for the respective PLS component. Supplementary Table 4a shows the numerical values of the PLS weights for each predictor.

Fig. 7. PLS components predicting decision strategy.

a, Scatter plot of PLS-1 and PLS-2 weights (values akin to the z-score) for the response variables (that is, the strategy index for frequency versus context-based statistics). Supplementary Table 4b shows the numerical values of the PLS weights for each response variable. PLS-1 separates decision strategies for frequency versus context-based statistics (that is, negative versus positive weight), capturing changes in decision strategy across sequence levels. PLS-2 weights equally the strategy for frequency and context-based statistics, capturing variability in decision strategy across participants independent of the sequence levels. b, Pearson correlations (two-sided; n = 21) of PLS-1 score with difference in strategy index for frequency and context-based statistics (r(19) = 0.89; P < 0.001; CI = 0.68 to 0.96) (left) and PLS-2 score with mean strategy index (r(19) = 0.79; P < 0.001; CI = 0.49 to 0.92) (right). c, Significant predictors (|z| > 2.576; P = 0.01) for the first two PLS components are shown on axial (left) and sagittal (right) views of the DTI graph for illustration purposes only (neurological convention: left is left). Red nodes indicate the significant predictors for PLS-1 and blue nodes the significant predictors for PLS-2, irrespective of imaging modality (rs-fMRI or DTI) or graph metric (degree change or clustering coefficient change).