Mental rotation meets the motion aftereffect: the role of hV5/MT+ in visual mental imagery

A growing number of studies show that visual mental imagery recruits the same brain areas as visual perception. Although the necessity of hV5/MT+ for motion perception has been revealed by means of TMS, its relevance for motion imagery remains unclear. We induced a direction-selective adaptation in hV5/MT+ by means of an MAE while subjects performed a mental rotation task that elicits imagined motion. We concurrently measured behavioral performance and neural activity with fMRI, enabling us to directly assess the effect of a perturbation of hV5/MT+ on other cortical areas involved in the mental rotation task. The activity in hV5/MT+ increased as more mental rotation was required, and the perturbation of hV5/MT+ affected behavioral performance as well as the neural activity in this area. Moreover, several regions in the posterior parietal cortex were also affected by this perturbation. Our results show that hV5/MT+ is required for imagined visual motion and engages in an interaction with parietal cortex during this cognitive process

Laminar fMRI: applications for cognitive neuroscience

The cortex is a massively recurrent network, characterized by feedforward and feedback connections between brain areas as well as lateral connections within an area. Feedforward, horizontal and feedback responses largely activate separate layers of a cortical unit, meaning they can be dissociated by lamina-resolved neurophysiological techniques. Such techniques are invasive and are therefore rarely used in humans. However, recent developments in high spatial resolution fMRI allow for non-invasive, in vivo measurements of brain responses specific to separate cortical layers. This provides an important opportunity to dissociate between feedforward and feedback brain responses, and investigate communication between brain areas at a more fine- grained level than previously possible in the human species. In this review, we highlight recent studies that successfully used laminar fMRI to isolate layer-specific feedback responses in human sensory cortex. In addition, we review several areas of cognitive neuroscience that stand to benefit from this new technological development, highlighting contemporary hypotheses that yield testable predictions for laminar fMRI. We hope to encourage researchers with the opportunity to embrace this development in fMRI research, as we expect that many future advancements in our current understanding of human brain function will be gained from measuring lamina-specific brain responses

Brief stimuli cast a persistent long-term trace in visual cortex

Visual processing is strongly influenced by the recent stimulus history - a phenomenon termed adaptation. Prominent theories cast adaptation as a consequence of optimized encoding of visual information, by exploiting the temporal statistics of the world. However, this would require the visual system to track the history of individual briefly experienced events, within a stream of visual input, to build up statistical representations over longer timescales. Here, using an openly available dataset from the Allen Brain Observatory, we show that neurons in the early visual cortex of the mouse indeed maintain long-term traces of individual past stimuli that persist despite the presentation of several intervening stimuli, leading to long-term and stimulus-specific adaptation over dozens of seconds. Long-term adaptation was selectively expressed in cortical, but not in thalamic neurons, which only showed short-term adaptation. Early visual cortex thus maintains concurrent stimulus-specific memory traces of past input, enabling the visual system to build up a statistical representation of the world to optimize the encoding of new information in a changing environment.SIGNIFICANCE STATEMENTIn the natural world, previous sensory input is predictive of current input over multi-second timescales. The visual system could exploit these predictabilities by adapting current visual processing to the long-term history of visual input. However, it is unclear whether the visual system can track the history of individual briefly experienced images, within a stream of input, to build up statistical representations over such long timescales. Here, we show that neurons in early visual cortex of the mouse brain exhibit remarkably long-term adaptation to brief stimuli, persisting over dozens of seconds, and despite the presentation of several intervening stimuli. The visual cortex thus maintains long-term traces of individual briefly experienced past images, enabling the formation of statistical representations over extended timescales

The role of consciousness in cognitive control and decision making

Here we review studies on the complexity and strength of unconscious information processing. We focus on empirical evidence that relates awareness of information to cognitive control processes (e.g., response inhibition, conflict resolution, and task-switching), the life-time of information maintenance (e.g., working memory) and the possibility to integrate multiple pieces of information across space and time. Overall, the results that we review paint a picture of local and specific effects of unconscious information on various (high-level) brain regions, including areas in the prefrontal cortex. Although this neural activation does not elicit any conscious experience, it is functional and capable of influencing many perceptual, cognitive (control) and decision-related processes, sometimes even for relatively long periods of time. However, recent evidence also points out interesting dissociations between conscious and unconscious information processing when it comes to the duration, flexibility and the strategic use of that information for complex operations and decision-making. Based on the available evidence, we conclude that the role of task-relevance of subliminal information and meta-cognitive factors in unconscious cognition need more attention in future work

Interactions Between Posterior Gamma and Frontal Alpha/Beta Oscillations During Imagined Actions

Several studies have revealed that posterior parietal and frontal regions support planning of hand movements but far less is known about how these cortical regions interact during the mental simulation of a movement. Here, we have used magnetoencephalography (MEG) to investigate oscillatory interactions between posterior and frontal areas during the performance of a well-established motor imagery task that evokes motor simulation: mental rotation of hands. Motor imagery induced sustained power suppression in the alpha and beta band over the precentral gyrus and a power increase in the gamma band over bilateral occipito-parietal cortex. During motor imagery of left hand movements, there was stronger alpha and beta band suppression over the right precentral gyrus. The duration of these power changes increased, on a trial-by-trial basis, as a function of the motoric complexity of the imagined actions. Crucially, during a specific period of the movement simulation, the power fluctuations of the frontal beta-band oscillations became coupled with the occipito-parietal gamma-band oscillations. Our results provide novel information about the oscillatory brain activity of posterior and frontal regions. The persistent functional coupling between these regions during task performance emphasizes the importance of sustained interactions between frontal and occipito-parietal areas during mental simulation of action

Low attention impairs optimal incorporation of prior knowledge in perceptual decisions

When visual attention is directed away from a stimulus, neural processing is weak and strength and precision of sensory data decreases. From a computational perspective, in such situations observers should give more weight to prior expectations in order to behave optimally during a discrimination task. Here we test a signal detection theoretic model that counter-intuitively predicts subjects will do just the opposite in a discrimination task with two stimuli, one attended and one unattended: when subjects are probed to discriminate the unattended stimulus, they rely less on prior information about the probed stimulus’ identity. The model is in part inspired by recent findings that attention reduces trial-by-trial variability of the neuronal population response and that they use a common criterion for attended and unattended trials. In five different visual discrimination experiments, when attention was directed away from the target stimulus, subjects did not adjust their response bias in reaction to a change in stimulus presentation frequency despite being fully informed and despite the presence of performance feedback and monetary and social incentives. This indicates that subjects did not rely more on the priors under conditions of inattention as would be predicted by a Bayes-optimal observer model. These results inform and constrain future models of Bayesian inference in the human brain

Stubborn Predictions in Primary Visual Cortex

Perceivers can use past experiences to make sense of ambiguous sensory signals. However, this may be inappropriate when the world changes and past experiences no longer predict what the future holds. Optimal learning models propose that observers decide whether to stick with or update their predictions by tracking the uncertainty or "precision" of their expectations. However, contrasting theories of prediction have argued that we are prone to misestimate uncertainty-leading to stubborn predictions that are difficult to dislodge. To compare these possibilities, we had participants learn novel perceptual predictions before using fMRI to record visual brain activity when predictive contingencies were disrupted-meaning that previously "expected" events become objectively improbable. Multivariate pattern analyses revealed that expected events continued to be decoded with greater fidelity from primary visual cortex, despite marked changes in the statistical structure of the environment, which rendered these expectations no longer valid. These results suggest that our perceptual systems do indeed form stubborn predictions even from short periods of learning-and more generally suggest that top-down expectations have the potential to help or hinder perceptual inference in bounded minds like ours

Independent causal contributions of alpha- and beta-band oscillations during movement selection

To select a movement, specific neuronal populations controlling particular features of that movement need to be activated, whereas other populations are downregulated. The selective (dis)inhibition of cortical sensorimotor populations is governed by rhythmic neural activity in the alpha (8–12 Hz) and beta (15–25 Hz) frequency range. However, it is unclear whether and how these rhythms contribute independently to motor behavior. Building on a recent dissociation of the sensorimotor alpha- and beta-band rhythms, we test the hypothesis that the beta-band rhythm governs the disinhibition of task-relevant neuronal populations, whereas the alpha-band rhythm suppresses neurons that may interfere with task performance. Cortical alpha- and beta-band rhythms were manipulated with transcranial alternating current stimulation (tACS) while human participants selected how to grasp an object. Stimulation was applied at either 10 or 20 Hz and was imposed on the sensorimotor cortex contralaterally or ipsilaterally to the grasping hand. In line with task-induced changes in endogenous spectral power, the effect of the tACS intervention depended on the frequency and site of stimulation. Whereas tACS stimulation generally increased movement selection times, 10 Hz stimulation led to relatively faster selection times when applied to the hemisphere ipsilateral to the grasping hand, compared with other stimulation conditions. These effects occurred selectively when multiple movements were considered. These observations functionally differentiate the causal contribution of alpha- and beta-band oscillations to movement selection. The findings suggest that sensorimotor beta-band rhythms disinhibit task-relevant populations, whereas alpha-band rhythms inhibit neuronal populations that could interfere with movement selection

osl-dynamics, a toolbox for modeling fast dynamic brain activity

Neural activity contains rich spatiotemporal structure that corresponds to cognition. This includes oscillatory bursting and dynamic activity that span across networks of brain regions, all of which can occur on timescales of tens of milliseconds. While these processes can be accessed through brain recordings and imaging, modeling them presents methodological challenges due to their fast and transient nature. Furthermore, the exact timing and duration of interesting cognitive events are often a priori unknown. Here, we present the OHBA Software Library Dynamics Toolbox (osl-dynamics), a Python-based package that can identify and describe recurrent dynamics in functional neuroimaging data on timescales as fast as tens of milliseconds. At its core are machine learning generative models that are able to adapt to the data and learn the timing, as well as the spatial and spectral characteristics, of brain activity with few assumptions. osl-dynamics incorporates state-of-the-art approaches that can be, and have been, used to elucidate brain dynamics in a wide range of data types, including magneto/electroencephalography, functional magnetic resonance imaging, invasive local field potential recordings, and electrocorticography. It also provides novel summary measures of brain dynamics that can be used to inform our understanding of cognition, behavior, and disease. We hope osl-dynamics will further our understanding of brain function, through its ability to enhance the modeling of fast dynamic processes