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

33 Successful human behavior depends on the brain’s ability to extract meaningful structure 34 from information streams and make predictions about future events. Individuals can differ 35 markedly in the decision strategies they use to learn the environment’s statistics, yet we have 36 little idea why. Here, we investigate whether the brain networks involved in learning temporal 37 sequences without explicit reward differ depending on the decision strategy that individuals 38 adopt. We demonstrate that individuals alter their decision strategy in response to changes in 39 temporal statistics and engage dissociable circuits: extracting the exact sequence statistics 40 relates to plasticity in motor cortico-striatal circuits, while selecting the most probable 41 outcomes relates to plasticity in visual, motivational and executive cortico-striatal circuits. 42 Combining graph metrics of functional and structural connectivity, we provide evidence that 43 learning-dependent changes in these circuits predict individual decision strategy. Our findings 44 propose brain plasticity mechanisms that mediate individual ability for interpreting the 45 structure of variable environments. 46 Learning and experience are known to facilitate our ability to extract meaningful structure 47 from streams of information and interpret complex environments. Despite the general 48 consensus that ‘practice makes perfect’, there is striking variability among individuals in the 49 extent to which they take advantage of past experience. In the laboratory, this variability has 50 been demonstrated in tasks such as perceptual decision making 1,2 or statistical learning of 51 regularities (i.e. learning of probabilistic spatial or temporal structures) through mere 52 exposure to the environment 3,4 . Previous work examining individual variability in decision 53 making and probabilistic learning tasks, has highlighted the role of individual decision 54 strategies 5–10 . In particular, humans and animals have been shown to engage in probability 55 matching or maximization when making choices in probabilistic environments (e.g. 9,11,12 ). 56 Probability matching involves making choices stochastically to match the probabilistic 57 distribution of all possible outcomes, while probability maximization involves choosing the 58 most probable or frequently rewarded outcome in a given context. 59 Individual variability in these decision strategies has mainly been investigated in the 60 context of reward learning (e.g. 9,11,12 ). Yet, reward-based learning captures only one aspect of 61 human flexibility in natural environments, as feedback and rewards are often not explicit. 62 Here, we test the role of decision strategies in statistical learning. In particular, we designed a 63 statistical learning task that tests whether individuals learn to extract temporal structure from 64 mere exposure to unfamiliar sequences without explicit reward (i.e. trial-by-trial feedback). 65 We changed the temporal sequence statistics unbeknownst to the participants, to simulate 66 structure in natural environments that may vary from simple regularities to more complex 67 probabilistic combinations. That is, participants were first exposed to sequences determined 68 by frequency statistics (i.e. one item in the sequence occurred more frequently than others) 69 and then sequences that were determined by context-based statistics (i.e. some item 70 combinations were more frequent than others). Participants predicted which item would 71 appear next in the sequence. We modeled the participant responses to interrogate the decision 72 strategy that individuals adopt during learning (i.e. how individuals extract temporal 73 structure). We reasoned that individuals would adapt their decision strategies in response to 74 changes in the temporal sequence statistics and the learning goal (i.e. learning frequency vs. 75 context-based statistics). 76 Previous work has implicated cortico-striatal circuits in sequence and probabilistic 77 learning 13–16 . Here, we sought to determine whether these circuits are involved in statistical 78 learning of temporal structures without explicit reward. We ask whether individual decision 79 strategies (from matching to maximization) involve distinct cortico-striatal circuits and 80 whether learning-dependent plasticity in these circuits can account for individual variability 81 in learning to extract the environment’s statistics. We reasoned that brain plasticity, as 82 expressed by learning-dependent connectivity changes in cortico-striatal circuits, would 83 predict changes in decision strategy when learning frequency vs. context-based statistics. 84 To test these hypotheses, we combined our statistical learning task with multi-session 85 (before vs. after training) measurements of functional (resting-state fMRI: rs-fMRI) and 86 structural (Diffusion Tensor Imaging: DTI) connectivity. rs-fMRI has been shown to reveal 87 functional connectivity within and across brain networks that subserve task performance 17,18 . 88 Moreover, there is accumulating evidence for changes in both functional and structural brain 89 connectivity due to training (e.g. for reviews 19,20 ), suggesting learning-dependent plasticity in 90 human brain networks that mediate adaptive behavior. To map cortico-striatal circuits at fine 91 scale we employed DTI-based segmentation analysis 21 of the striatum into finer sub-regions 92 and computed the functional connectivity between these striatal regions and cortical 93 networks, as revealed by analysis of the rs-fMRI data. Our results show that individuals adapt 94 their decision strategies (from matching towards maximization) in response to changes in the 95 temporal statistics. These adaptive decision strategies relate to distinct cortico-striatal circuits 96 for learning temporal statistics. That is, adopting a strategy closer to matching when learning 97 frequency statistics relates to learning-dependent connectivity changes in the motor circuit. In 98 contrast, deviating from matching towards maximization when learning context-based 99 statistics relates to functional connectivity changes in the visual cortico-striatal circuit. 100 We next combined graph theory analysis with a multivariate statistical analysis 101 (Partial Least Squares-PLS regression) to determine multimodal predictors of decision 102 strategy. This approach allows us to a) combine information from multivariate signals (rs103 fMRI, DTI)– rather than using data from each MRI modality alone, b) test whether plasticity 104 in functional and/or structural connectivity in cortico-striatal circuits predicts– rather than 105 simply relates to– individual decision strategy. In particular, we employed graph theory to 106 extract metrics of brain connectivity that are comparable across brain imaging modalities and 107 have been suggested to relate to learning and brain plasticity 22,23 . We then used PLS modeling 108 to combine these multimodal graph metrics and identify brain connectivity predictors (rs109 fMRI, DTI) of individual decision strategy when learning temporal statistics. Our results 110 demonstrate that learning-dependent changes in resting cortico-striatal connectivity 111 (functional and structural) that predict individual decision strategy for statistical learning. In 112 particular, we discern distinct brain plasticity mechanisms that predict: a) changes in 113 individual decision strategy in response to changes in the environment’s statistics, b) 114 individual variability in decision strategy independent of temporal statistics. Our findings 115 provide evidence for adaptive decision strategies that involve distinct brain routes for 116 statistical learning, proposing a strong link between learning-dependent plasticity in brain 117 connectivity and individual learning ability. 118