Translating noisy sensory signals to perceptual decisions is critical for successful interactions in complex environments. Learning is known to improve perceptual judgments by filtering external noise and task-irrelevant information. Yet, little is known about the brain mechanisms that mediate learning-dependent suppression. Here, we employ ultra-high field magnetic resonance spectroscopy of GABA to test whether suppressive processing in decision-related and visual areas facilitates perceptual judgments during training. We demonstrate that parietal GABA relates to suppression of task-irrelevant information, while learning-dependent changes in visual GABA relate to enhanced performance in target detection and feature discrimination tasks. Combining GABA measurements with functional brain connectivity demonstrates that training on a target detection task involves local connectivity and disinhibition of visual cortex, while training on a feature discrimination task involves inter-cortical interactions that relate to suppressive visual processing. Our findings provide evidence that learning optimizes perceptual decisions through suppressive interactions in decision-related networks.

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One important barrier in the development of complex models of human brain organization is the lack of a large and comprehensive task-based neuro-imaging dataset. Therefore, current atlases of functional brain organization are mainly based on single and homogeneous resting-state datasets. Here, we propose a hierarchical Bayesian framework that can learn a probabilistically defined brain parcellation across numerous task-based and resting-state datasets, exploiting their combined strengths. The framework is partitioned into a spatial arrangement model that defines the probability of a specific individual brain parcellation, and a set of dataset-specific emission models that defines the probability of the observed data given the individual brain organization. We show that the framework optimally combines information from different datasets to achieve a new population-based atlas of the human cerebellum. Furthermore, we demonstrate that, using only 10 min of individual data, the framework is able to generate individual brain parcellations that outperform group atlases.

SummaryInteracting with our ever-changing physical environment requires continual recalibration of the motor system. One mechanism by which this occurs is motor adaptation. Understanding how motor adaptation is implemented by the human brain, how different regions work in concert to retain adaptive movement accuracy, and how this function is linked to metabolic use of neurochemicals poses an important challenge in neuroscience. In humans, motor sequence learning is related to concentration of γ-aminobutyric acid (GABA) in the primary motor cortex (M1). However, the role of M1 GABA in adaptation – where behaviour is acquired outside M1 but retained within M1 – is unclear. Here, we used an ultra-high field MR multimodal acquisition to address the hypothesis that M1 GABA and M1-Cerebellar functional connectivity would relate to retention of adaptation, but not acquisition of adaptation. As such, we demonstrate higher baseline M1 [GABA] relates to greater retention but does not relate to adaptation-acquisition. This relationship is mediated by change in M1-Cerebellar functional connectivity: higher M1 [GABA] relates to a decreased M1-Cerebellar connectivity, resulting in greater retention. These findings showed anatomical, neurochemical and behavioural specificity: As expected, no relationship was found between retention and a control metabolite, M1 [Glutamate], as well as retention and connectivity change between control regions and no relationship was found between M1 [GABA] and behaviour in a control condition. The implication of a mechanistic link from neurochemistry to retention significantly advances our understanding of population variability in retention behaviour and provides a crucial step towards developing therapeutic interventions to restore motor abilities.