Combining Repetition Suppression and Pattern Analysis Provides New Insights into the Role of M1 and Parietal Areas in Skilled Sequential Actions.

How does the brain change during learning? In functional magnetic resonance imaging (fMRI) studies, both multivariate pattern analysis (MVPA) and repetition suppression (RS) have been used to detect changes in neuronal representations. In the context of motor sequence learning, the two techniques have provided discrepant findings: pattern analysis showed that only premotor and parietal regions, but not primary motor cortex (M1), develop a representation of trained sequences. In contrast, RS suggested trained sequence representations in all these regions. Here, we applied both analysis techniques to a five-week finger sequence training study, in which participants executed each sequence twice before switching to a different sequence. Both RS and pattern analysis indicated learning-related changes for parietal areas, but only RS showed a difference between trained and untrained sequences in M1. A more fine-grained analysis, however, revealed that the RS effect in M1 reflects a fundamentally different process than in parietal areas. On the first execution, M1 represents especially the first finger of each sequence, likely reflecting preparatory processes. This effect dramatically reduces during the second execution. In contrast, parietal areas represent the identity of a sequence, and this representation stays relatively stable on the second execution. These results suggest that the RS effect does not reflect a trained sequence representation in M1, but rather a preparatory signal for movement initiation. More generally, our study demonstrates that across regions RS can reflect different representational changes in the neuronal population code, emphasizing the importance of combining pattern analysis and RS techniques.SIGNIFICANCE STATEMENT Previous studies using pattern analysis have suggested that primary motor cortex (M1) does not represent learnt sequential actions. However, a study using repetition suppression (RS) has reported M1 changes during motor sequence learning. Combining both techniques, we first replicate the discrepancy between them, with learning-related changes in M1 in RS, but not pattern dissimilarities. We further analyzed the representational changes with repetition, and found that the RS effects differ across regions. M1's activity represents the starting finger of the sequence, an effect that vanishes with repetition. In contrast, activity patterns in parietal areas exhibit sequence dependency, which persists with repetition. These results demonstrate the importance of combining RS and pattern analysis to understand the function of brain regions.

Mapping Frequency-Specific Tone Predictions in the Human Auditory Cortex at High Spatial Resolution.

Auditory inputs reaching our ears are often incomplete, but our brains nevertheless transform them into rich and complete perceptual phenomena such as meaningful conversations or pleasurable music. It has been hypothesized that our brains extract regularities in inputs, which enables us to predict the upcoming stimuli, leading to efficient sensory processing. However, it is unclear whether tone predictions are encoded with similar specificity as perceived signals. Here, we used high-field fMRI to investigate whether human auditory regions encode one of the most defining characteristics of auditory perception: the frequency of predicted tones. Two pairs of tone sequences were presented in ascending or descending directions, with the last tone omitted in half of the trials. Every pair of incomplete sequences contained identical sounds, but was associated with different expectations about the last tone (a high- or low-frequency target). This allowed us to disambiguate predictive signaling from sensory-driven processing. We recorded fMRI responses from eight female participants during passive listening to complete and incomplete sequences. Inspection of specificity and spatial patterns of responses revealed that target frequencies were encoded similarly during their presentations, as well as during omissions, suggesting frequency-specific encoding of predicted tones in the auditory cortex (AC). Importantly, frequency specificity of predictive signaling was observed already at the earliest levels of auditory cortical hierarchy: in the primary AC. Our findings provide evidence for content-specific predictive processing starting at the earliest cortical levels.SIGNIFICANCE STATEMENT Given the abundance of sensory information around us in any given moment, it has been proposed that our brain uses contextual information to prioritize and form predictions about incoming signals. However, there remains a surprising lack of understanding of the specificity and content of such prediction signaling; for example, whether a predicted tone is encoded with similar specificity as a perceived tone. Here, we show that early auditory regions encode the frequency of a tone that is predicted yet omitted. Our findings contribute to the understanding of how expectations shape sound processing in the human auditory cortex and provide further insights into how contextual information influences computations in neuronal circuits.

Ipsilateral finger representations in the sensorimotor cortex are driven by active movement processes, not passive sensory input.

Hand and finger movements are mostly controlled through crossed corticospinal projections from the contralateral hemisphere. During unimanual movements, activity in the contralateral hemisphere is increased while the ipsilateral hemisphere is suppressed below resting baseline. Despite this suppression, unimanual movements can be decoded from ipsilateral activity alone. This indicates that ipsilateral activity patterns represent parameters of ongoing movement, but the origin and functional relevance of these representations is unclear. In this study, we asked whether ipsilateral representations are caused by active movement or whether they are driven by sensory input. Participants alternated between performing single finger presses and having fingers passively stimulated while we recorded brain activity using high-field (7T) functional imaging. We contrasted active and passive finger representations in sensorimotor areas of ipsilateral and contralateral hemispheres. Finger representations in the contralateral hemisphere were equally strong under passive and active conditions, highlighting the importance of sensory information in feedback control. In contrast, ipsilateral finger representations in the sensorimotor cortex were stronger during active presses. Furthermore, the spatial distribution of finger representations differed between hemispheres: the contralateral hemisphere showed the strongest finger representations in Brodmann areas 3a and 3b, whereas the ipsilateral hemisphere exhibited stronger representations in premotor and parietal areas. Altogether, our results suggest that finger representations in the two hemispheres have different origins: contralateral representations are driven by both active movement and sensory stimulation, whereas ipsilateral representations are mainly engaged during active movement. NEW & NOTEWORTHY Movements of the human body are mostly controlled by contralateral cortical regions. The function of ipsilateral activity during movements remains elusive. Using high-field neuroimaging, we investigated how human contralateral and ipsilateral hemispheres represent active and passive finger presses. We found that representations in contralateral sensorimotor cortex are equally strong during both conditions. Ipsilateral representations were mostly present during active movement, suggesting that sensorimotor areas do not receive direct sensory input from the ipsilateral hand.

Predictive processing of scenes and objects.

Real-world visual input consists of rich scenes that are meaningfully composed of multiple objects which interact in complex, but predictable, ways. Despite this complexity, we recognize scenes, and objects within these scenes, from a brief glance at an image. In this review, we synthesize recent behavioral and neural findings that elucidate the mechanisms underlying this impressive ability. First, we review evidence that visual object and scene processing is partly implemented in parallel, allowing for a rapid initial gist of both objects and scenes concurrently. Next, we discuss recent evidence for bidirectional interactions between object and scene processing, with scene information modulating the visual processing of objects, and object information modulating the visual processing of scenes. Finally, we review evidence that objects also combine with each other to form object constellations, modulating the processing of individual objects within the object pathway. Altogether, these findings can be understood by conceptualizing object and scene perception as the outcome of a joint probabilistic inference, in which "best guesses" about objects act as priors for scene perception and vice versa, in order to concurrently optimize visual inference of objects and scenes.

Flexible recoding of visual input for memory storage.

Working memory enables us to maintain relevant past information for future behavior. In this issue of Neuron, Kwak and Curtis (2022) demonstrate that early visual areas do not simply maintain but flexibly recode sensory percepts into mnemonic codes containing goal-relevant information.

A critical re-evaluation of fMRI signatures of motor sequence learning.

Despite numerous studies, there is little agreement about what brain changes accompany motor sequence learning, partly because of a general publication bias that favors novel results. We therefore decided to systematically reinvestigate proposed functional magnetic resonance imaging correlates of motor learning in a preregistered longitudinal study with four scanning sessions over 5 weeks of training. Activation decreased more for trained than untrained sequences in premotor and parietal areas, without any evidence of learning-related activation increases. Premotor and parietal regions also exhibited changes in the fine-grained, sequence-specific activation patterns early in learning, which stabilized later. No changes were observed in the primary motor cortex (M1). Overall, our study provides evidence that human motor sequence learning occurs outside of M1. Furthermore, it shows that we cannot expect to find activity increases as an indicator for learning, making subtle changes in activity patterns across weeks the most promising fMRI correlate of training-induced plasticity.

It has famously been claimed that it takes 10,000 hours to become an expert at something. But while most of us will never become concert pianists, we can all learn new motor skills and improve existing ones ­ from touch-typing to tennis ­ by practicing. What happens in the brain to produce these improvements in performance? Researchers have tried to answer this question by scanning the brains of people as they practice motor skills, but the results have proved inconsistent. Some studies find that specific brain areas become more active as people practice. This could indicate that these areas are 'storing' new skills. But others report that brain activity decreases with practice. This might indicate that practice instead makes certain brain areas work more efficiently. It is also unclear where in the brain these learning-related changes occur. Some studies suggest that most occur in the primary motor cortex, or M1 ­ the area that sends commands to muscles. Others suggest that most changes take place outside of M1, in areas that plan movements. Berlot et al. set out to resolve these inconsistencies by scanning the brains of healthy volunteers as they learned to play six 9-digit sequences on a keyboard. Each volunteer completed about 4,000 training trials over 5 weeks, and had their brain scanned four times. As the weeks passed, the volunteers became faster and more accurate at playing the sequences. However, the activity of their primary motor cortex did not change. By contrast, the activity of areas involved in planning movements decreased throughout training. The patterns of activity for each individual sequence reorganized throughout learning in the areas outside of the M1. This happened most quickly during the early stages of training when the volunteers showed the fastest improvements in performance. Overall, these findings suggest that when we learn a new skill, activity in the brain areas supporting that skill decrease as the brain becomes more efficient. Increases in brain activity are thus unlikely to reflect the acquired skill. Instead, more subtle changes, in which the brain uses more specific patterns of activity to encode the skill, may underlie improved performance. This may also be true for other types of learning, such as acquiring a new language.

A 7 Tesla fMRI investigation of human tinnitus percept in cortical and subcortical auditory areas.

Tinnitus is a clinical condition defined by hearing a sound in the absence of an objective source. Early experiments in animal models have suggested that tinnitus stems from an alteration of processing in the auditory system. However, translating these results to humans has proven challenging. One limiting factor has been the insufficient spatial resolution of non-invasive measurement techniques to investigate responses in subcortical auditory nuclei, like the inferior colliculus and the medial geniculate body (MGB). Here we employed ultra-high field functional magnetic resonance imaging (UHF-fMRI) at 7 Tesla to investigate the frequency-specific processing in sub-cortical and cortical regions in a cohort of six tinnitus patients and six hearing loss matched controls. We used task-based fMRI to perform tonotopic mapping and compared the magnitude and tuning of frequency-specific responses between the two groups. Additionally, we used resting-state fMRI to investigate the functional connectivity. Our results indicate frequency-unspecific reductions in the selectivity of frequency tuning that start at the level of the MGB and continue in the auditory cortex, as well as reduced thalamocortical and cortico-cortical connectivity with tinnitus. These findings suggest that tinnitus may be associated with reduced inhibition in the auditory pathway, potentially leading to increased neural noise and reduced functional connectivity. Moreover, these results indicate the relevance of high spatial resolution UHF-fMRI for the investigation of the role of sub-cortical auditory regions in tinnitus.

Vestibular contributions to a right-hemisphere network for bodily awareness: combining galvanic vestibular stimulation and the "Rubber Hand Illusion".

An altered sense of one's own body is a common consequence of vestibular damage, and also of damage to vestibular networks in the right hemisphere. However, few experimental studies have investigated whether vestibular signals contribute to bodily awareness. We addressed this issue by combining an established experimental model of bodily awareness (Rubber Hand Illusion -RHI) with galvanic vestibular stimulation (GVS) in healthy participants. Brief left anodal and right cathodal GVS (which predominantly activates vestibular networks in the right hemisphere), or right anodal and left cathodal GVS, or sham stimulation were delivered at random, while participants experienced either synchronous or asynchronous visuo-tactile stimulation of a rubber hand and their own hand. The drift in the perceived position of the participant's hand towards the rubber hand was used as a proxy measure of the resulting multisensory illusion of body ownership. GVS induced strong polarity-dependent effects on this measure of RHI: left anodal and right cathodal GVS produced significantly lower proprioceptive drift than right anodal and left cathodal GVS. We suggest that vestibular inputs influence the multisensory weighting functions that underlie bodily awareness: the right hemisphere vestibular projections activated by the left anodal and right cathodal GVS increased the weight of intrinsic proprioceptive signals about hand position, and decreased the weight of visual information responsible for visual capture during the RHI.

Moving time: the influence of action on duration perception.

Perceiving the sensory consequences of action accurately is essential for appropriate interaction with our physical and social environments. Prediction mechanisms are considered necessary for fine-tuned sensory control of action, yet paradoxically may distort perception. Here, we examine this paradox by addressing how movement influences the perceived duration of sensory outcomes congruent with action. Experiment 1 required participants to make judgments about the duration of vibrations applied to a moving or stationary finger. In Experiments 2 and 3, participants judged observed finger movements that were congruent or incongruent with their own actions. In all experiments, target events were perceived to be longer when congruent with movement. Interestingly, this temporal dilation did not differ as a function of stimulus perspective (1st or 3rd person) or spatial location. We propose that this bias may reflect the operation of an adaptive mechanism for sensorimotor selection and control that preactivates anticipated outcomes of action. The bias itself may have surprising implications for both action control and perception of others: we may be in contact with grasped objects for less time than we realize, and others' reactions to us may be briefer than we believe.