Abstract Finger representations in the sensorimotor cortex can be activated even in the absence of somatosensory input or motor output through mere top-down processes, such as motor imagery. While executed finger movements activate finger representations in the primary sensorimotor cortex that are spatially consistent over time within participants, the stability of top-down activated finger representations remains largely unexplored. Given the increasing use of top-down activated sensorimotor representations to both plan implantation of and control brain-computer interfaces, it is crucial to understand the stability of these representations. Here, we investigated the spatial consistency, and thereby reliability, of finger representations activated through motor imagery in the primary somatosensory cortex over time. To assess this, participants performed imagined and executed individual finger movements in two 3T fMRI sessions that were ~2 weeks apart. We observed highly consistent univariate finger-selective activity clusters and multivariate vertex-wise activity patterns within participants over time in both the motor imagery and motor execution task. Using a multivariate across-tasks decoding approach, we further found that motor execution and motor imagery activate similar finger representations in the primary somatosensory cortex. This demonstrates that motor imagery can be used to identify finger representations related to movement execution. Our findings not only validate the use of top-down processes for brain-computer interface planning and control, but also open up new opportunities for the development of sensorimotor training interventions that do not rely on overt movements.
Abstract Contemporary neuroimaging has been optimised for experimental control rather than for the conditions under which neural systems evolved to operate. Accumulating theoretical and empirical work suggests that cognition, perception, and regulation emerge from ongoing sensorimotor coupling between brain, body, and environment. As a result, movement-restricted paradigms may systematically limit the questions we can ask and the phenomena we can observe. Recent advances in movement-tolerant neuroimaging now make it possible to study these processes directly, motivating a shift toward more naturalistic experimental designs. We focus on optically pumped magnetometers (OPMs) as they are uniquely placed to record neural and bodily signals within a single measurement framework. We synthesise evidence from computational neuroscience, interoception research, and embodied cognition to demonstrate why naturalistic neuroimaging is not merely complementary to static methods but necessary for understanding brain function as it evolved to operate. We detail how this relaxation of compliance constraints might accelerate clinical advances. This review makes the case that conceptualising the brain as a body-regulator engaged in active inference offers a broader perspective on the importance of movement-tolerant neuroimaging.
Abstract Children with type C hepatic encephalopathy (HE) suffer from irreversible neurological damage even after liver transplantation. The metabolic and structural mechanisms underlying the vulnerability of the developing brain are still poorly understood. We used here the unique combination of diffusion magnetic resonance spectroscopy (dMRS) and diffusion magnetic resonance imaging (dMRI) to probe cell-type specific microstructural changes in vivo, non-invasively and longitudinally, in the cerebellum of a rat model of early onset type C HE. Glial and neuronal metabolites, present intracellularly only, exhibited increased diffusivities and decreased kurtosis in HE rats compared to controls, indicating reduced intracellular microstructure complexity. Water, present both intra and extracellularly, exhibited higher diffusivity both in white and grey matter and, through biophysical modelling, probed increased intra-fiber water diffusivity, decreased fiber fraction, increased soma radius and tentatively, decreased water intra-to-extracellular exchange rate in HE rats versus controls. These results suggest that early HE causes irreversible reduction of astrocytic and neuronal network complexity and arborization in the cerebellum, as confirmed by immunohistochemistry of the granular layer, Bergmann glia and Purkinje neurons in the same animals. These observations reorient the current and restrictive hypothesis of astrocytes swelling towards a broader one of complex multi-cellular microstructure alterations in type C HE.
Abstract Individuals typically acquire knowledge through multiple exposures. While some information is accurately remembered after a single exposure, much of it requires correction through repeated practice and feedback. How distinct brain regions support memory retrieval with different practice histories of correctness is unclear. To this end, 31 participants learned 120 Swahili–Chinese word pairs, followed by two retrieval practice–answer feedback cycles and final cued-recall tests at 10 min and 24 h. Based on participants’ performance during two practice cycles (correct or incorrect; C or I), correct responses during the final tests were categorized into three types (CCC, ICC, and IIC). The results showed that activation in the salience network and executive control network was significantly stronger for the pairs with more practice failures (i.e., IIC > ICC/CCC trials). In contrast, regions in the default mode network (DMN), especially the angular gyrus (AG), exhibited linearly increased activation and distinct representations as a function of practice success. The hippocampus had comparable activation for different types, but the posterior hippocampus showed more distinct representations for CCC than IIC trials. Moreover, the distinct representations in the AG supported memory of CCC trials by the mediation of increased activity in the medial prefrontal cortex (mPFC) and the (posterior) hippocampus. These findings highlight the important role of practice history (i.e., repeated failures and success) in organizing and consolidating cortical regions and networks during memory retrieval.
Abstract Any study of learning must capture neural or behavioural changes over time. Stimuli are therefore presented more than once, before and after learning takes place—a sequence that cannot be randomised across time. This design creates vulnerability to temporal confounds that cause illusory neural effects. We investigate this issue in a classic ‘one-shot’ learning paradigm, in which ambiguous two-tone images become recognisable after viewing a photo cue. Across common fMRI designs, we found that spurious correlations driven by the temporal proximity of stimuli created strong artefacts in multivariate data. Among certain experimental designs, we found strong neural markers of perceptual learning depending on how stimuli were distributed across these artefacts. This paradigm-dependent effect was present not only in the primary visual cortex (V1), but also in a non-visual control region (S1), indicative of an artefactual origin rather than a genuine perceptual learning effect. We present a principled approach to correct for this temporal order confound. Once applied, the artefactual learning signature was eliminated from both V1 and S1, and while the corrected signal retained information about image identity in V1, this was absent in S1. Our work demonstrates how current multivariate neuroimaging approaches to understanding learning could be influenced by methodological artefacts that misdirect interpretations towards distributed neural processing, and offers a practical framework to distinguish true learning from measurement error.
