A Transient High-dimensional Geometry Affords Stable Conjunctive Subspaces for Efficient Action Selection.

Flexible action selection requires cognitive control mechanisms capable of mapping the same inputs to diverse output actions depending on goals and contexts. How the brain encodes information to enable this capacity remains one of the longstanding and fundamental problems in cognitive neuroscience. From a neural state-space perspective, solving this problem requires a control representation that can disambiguate similar input neural states, making task-critical dimensions separable depending on the context. Moreover, for action selection to be robust and time-invariant, control representations must be stable in time, thereby enabling efficient readout by downstream processing units. Thus, an ideal control representation should leverage geometry and dynamics that maximize the separability and stability of neural trajectories for task computations. Here, using novel EEG decoding methods, we investigated how the geometry and dynamics of control representations constrain flexible action selection in the human brain. Specifically, we tested the hypothesis that encoding a temporally stable conjunctive subspace that integrates stimulus, response, and context (i.e., rule) information in a high-dimensional geometry achieves the separability and stability needed for context-dependent action selection. Human participants performed a task that requires context-dependent action selection based on pre-instructed rules. Participants were cued to respond immediately at varying intervals following stimulus presentation, which forced responses at different states in neural trajectories. We discovered that in the moments before successful responses, there was a transient expansion of representational dimensionality that separated conjunctive subspaces. Further, we found that the dynamics stabilized in the same time window, and that the timing of entry into this stable and high-dimensional state predicted the quality of response selection on individual trials. These results establish the neural geometry and dynamics the human brain needs for flexible control over behavior.

The phase of plasticity-induced neurochemical changes of high-frequency repetitive transcranial magnetic stimulation are different from visual perceptual learning.

Numerous studies have found that repetitive transcranial magnetic stimulation (rTMS) modulates plasticity. rTMS has often been used to change neural networks underlying learning, often under the assumption that the mechanism of rTMS-induced plasticity should be highly similar to that associated with learning. The presence of visual perceptual learning (VPL) reveals the plasticity of early visual systems, which is formed through multiple phases. Hence, we tested how high-frequency (HF) rTMS and VPL modulate the effect of visual plasticity by investigating neurometabolic changes in early visual areas. We employed an excitatory-to-inhibitory (E/I) ratio, which refers to glutamate concentration divided by GABA+ concentration, as an index of the degree of plasticity. We compared neurotransmitter concentration changes after applying HF rTMS to the visual cortex with those after training in a visual task, in otherwise identical procedures. Both the time courses of the E/I ratios and neurotransmitter contributions to the E/I ratio significantly differed between HF rTMS and training conditions. The peak E/I ratio occurred 3.5 h after HF rTMS with decreased GABA+, whereas the peak E/I ratio occurred 0.5 h after visual training with increased glutamate. Furthermore, HF rTMS temporally decreased the thresholds for detecting phosphene and perceiving low-contrast stimuli, indicating increased visual plasticity. These results suggest that plasticity in early visual areas induced by HF rTMS is not as involved in the early phase of development of VPL that occurs during and immediately after training.

Decrease in signal-related activity by visual training and repetitive visual stimulation.

While principles governing encoding mechanisms in visual perceptual learning (VPL) are well-known, findings regarding posttraining processing are still unrelated in terms of their underlying mechanisms. Here, we examined the effect of repetitive high-frequency visual stimulation (H-RVS) on VPL in an orientation detection task. Application of H-RVS after a single task session led to enhanced orientation detection performance (n = 12), but not in a sham condition (n = 12). If prior training-based VPL had been established by seven sessions in the detection task, H-RVS instead led to a performance impairment (n = 12). Both sham (n = 8) and low-frequency stimulation (L-RVS, n = 12) did not lead to a significant impairment. These findings may suggest reversal dynamics in which conditions of elevated network excitation lead to a decrease in a signal-related activity instead of a further increase. These reversal dynamics may represent a means to link various findings regarding posttraining processing.

MRS-measured glutamate versus GABA reflects excitatory versus inhibitory neural activities in awake mice.

To assess if magnetic resonance spectroscopy (MRS)-measured Glutamate (Glu) and GABA reflect excitatory and inhibitory neural activities, respectively, we conducted MRS measurements along with two-photon mesoscopic imaging of calcium signals in excitatory and inhibitory neurons of living, unanesthetized mice. For monitoring stimulus-driven activations of a brain region, MRS signals and mesoscopic neural activities were measured during two consecutive sessions of 15-min prolonged sensory stimulations. In the first session, putative excitatory neuronal activities were increased, while inhibitory neuronal activities remained at the baseline level. In the second half, while excitatory neuronal activities remained elevated, inhibitory neuronal activities were significantly enhanced. We assessed regional neurochemical statuses by measuring MRS signals, which were overall in accordance with the neural activities, and neuronal activities and neurochemical statuses in a mouse model of Dravet syndrome under resting condition. Mesoscopic assessments showed that activities of inhibitory neurons in the cortex were diminished relative to wild-type mice in contrast to spared activities of excitatory neurons. Consistent with these observations, the Dravet model exhibited lower concentrations of GABA than wild-type controls. Collectively, the current investigations demonstrate that MRS-measured Glu and GABA can reflect spontaneous and stimulated activities of neurons producing and releasing these neurotransmitters in an awake condition.

Visual perceptual learning of a primitive feature in human V1/V2 as a result of unconscious processing, revealed by decoded functional MRI neurofeedback (DecNef).

Although numerous studies have shown that visual perceptual learning (VPL) occurs as a result of exposure to a visual feature in a task-irrelevant manner, the underlying neural mechanism is poorly understood. In a previous psychophysical study (Watanabe et al., 2002), subjects were repeatedly exposed to a task-irrelevant Sekuler motion display that induced the perception of not only the local motions, but also a global motionmoving in the direction of the spatiotemporal average of the local motion vectors. As a result of this exposure, subjects enhanced their sensitivity only to the local moving directions, suggesting that early visual areas (V1/V2) that process local motions are involved in task-irrelevant VPL. However, this hypothesis has never been tested directly using neuronal recordings. Here, we employed a decoded neurofeedback technique (DecNef) using functional magnetic resonance imaging in human subjects to examine the involvement of early visual areas (V1/V2) in task-irrelevant VPL of local motion within a Sekuler motion display. During the DecNef training, subjects were trained to induce the activity patterns in V1/V2 that were similar to those evoked by the actual presentation of the Sekuler motion display. The DecNef training was conducted with neither the actual presentation of the display nor the subjects' awareness of the purpose of the experiment. After the experiment, subjects reported that they neither perceived nor imagined the trained motion during the DecNef training. As a result of DecNef training, subjects increased their sensitivity to the local motion directions, but not specifically to the global motion direction. Neuronal changes related to DecNef training were confined to V1/V2. These results suggest that V1/V2 are involved in exposure-based task-irrelevant VPL of local motion.

The DecNef collection, fMRI data from closed-loop decoded neurofeedback experiments.

Decoded neurofeedback (DecNef) is a form of closed-loop functional magnetic resonance imaging (fMRI) combined with machine learning approaches, which holds some promises for clinical applications. Yet, currently only a few research groups have had the opportunity to run such experiments; furthermore, there is no existing public dataset for scientists to analyse and investigate some of the factors enabling the manipulation of brain dynamics. We release here the data from published DecNef studies, consisting of 5 separate fMRI datasets, each with multiple sessions recorded per participant. For each participant the data consists of a session that was used in the main experiment to train the machine learning decoder, and several (from 3 to 10) closed-loop fMRI neural reinforcement sessions. The large dataset, currently comprising more than 60 participants, will be useful to the fMRI community at large and to researchers trying to understand the mechanisms underlying non-invasive modulation of brain dynamics. Finally, the data collection size will increase over time as data from newly run DecNef studies will be added.

Pain Control by Co-adaptive Learning in a Brain-Machine Interface.

Innovation in the field of brain-machine interfacing offers a new approach to managing human pain. In principle, it should be possible to use brain activity to directly control a therapeutic intervention in an interactive, closed-loop manner. But this raises the question as to whether the brain activity changes as a function of this interaction. Here, we used real-time decoded functional MRI responses from the insula cortex as input into a closed-loop control system aimed at reducing pain and looked for co-adaptive neural and behavioral changes. As subjects engaged in active cognitive strategies orientated toward the control system, such as trying to enhance their brain activity, pain encoding in the insula was paradoxically degraded. From a mechanistic perspective, we found that cognitive engagement was accompanied by activation of the endogenous pain modulation system, manifested by the attentional modulation of pain ratings and enhanced pain responses in pregenual anterior cingulate cortex and periaqueductal gray. Further behavioral evidence of endogenous modulation was confirmed in a second experiment using an EEG-based closed-loop system. Overall, the results show that implementing brain-machine control systems for pain induces a parallel set of co-adaptive changes in the brain, and this can interfere with the brain signals and behavior under control. More generally, this illustrates a fundamental challenge of brain decoding applications-that the brain inherently adapts to being decoded, especially as a result of cognitive processes related to learning and cooperation. Understanding the nature of these co-adaptive processes informs strategies to mitigate or exploit them.

Spatial variability induces generalization in contextual cueing.

We can incidentally learn regularities in a visual scene, and this kind of learning facilitates subsequent processing of similar scenes. One example of incidental learning is referred to as contextual cueing, a phenomenon in which repetitive exposure to a particular spatial configuration facilitates visual search performance in the configuration. Previous studies have demonstrated that effects of contextual cueing generalize to similar, but not entirely identical configurations. Although humans may be capable of extracting regularity from variable instances and applying it to a new instance, the mechanisms underlying generalization in contextual cueing are not fully understood. We hypothesized that contextual cueing results from extraction of variance in item locations, and the variance is used to calculate the similarity between the learned and new configuration. Based on this hypothesis, we predicted that contextual cueing would generalize more widely when the variability of item locations during learning is large compared with when it is small. The results supported our hypothesis, indicating that spatial variability induced generalization in contextual cueing. This finding suggests that, in incidental learning, the similarity between a learned and new representation is computed based on the variance of inputs. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Neural signals in amygdala predict implicit prejudice toward an ethnic outgroup.

Racial and ethnic prejudice is one of the most pressing problems in modern societies. Although previous social neuroscience research has suggested the amygdala as a key structure in racial prejudice, it still remains elusive whether the amygdala activity reflects negative attitudes toward an outgroup or other unrelated processes. The present study aims to rigorously test the role of the amygdala in negative prejudice toward an outgroup. Seventy Japanese individuals passively viewed images related to an ethnic outgroup (South Korea) inside a functional magnetic resonance imaging scanner. Using Multi-Voxel Pattern Analysis (MVPA), we found that Japanese individuals' level of implicit (but not explicit) evaluations of South Korea could be predicted from neural signals in the left amygdala. Our result further suggested that the medial and lateral parts of amygdala play different roles in implicit evaluations. In contrast to the MVPA findings, conventional univariate analyses failed to find any reliable relationship between brain activation and both implicit and explicit evaluations. Our findings provide evidence for the amygdala's role in representing an implicit form of prejudice and highlight the utility of the multivariate approach to reveal neural signatures of this complex social phenomenon.

Toward a comprehensive understanding of the neural mechanisms of decoded neurofeedback.

Real-time functional magnetic resonance imaging (fMRI) neurofeedback is an experimental framework in which fMRI signals are presented to participants in a real-time manner to change their behaviors. Changes in behaviors after real-time fMRI neurofeedback are postulated to be caused by neural plasticity driven by the induction of specific targeted activities at the neuronal level (targeted neural plasticity model). However, some research groups argued that behavioral changes in conventional real-time fMRI neurofeedback studies are explained by alternative accounts, including the placebo effect and physiological artifacts. Recently, decoded neurofeedback (DecNef) has been developed as a result of adapting new technological advancements, including implicit neurofeedback and fMRI multivariate analyses. DecNef provides strong evidence for the targeted neural plasticity model while refuting the abovementioned alternative accounts. In this review, we first discuss how DecNef refutes the alternative accounts. Second, we propose a model that shows how targeted neural plasticity occurs at the neuronal level during DecNef training. Finally, we discuss computational and empirical evidence that supports the model. Clarification of the neural mechanisms of DecNef would lead to the development of more advanced fMRI neurofeedback methods that may serve as powerful tools for both basic and clinical research.

Perception, Cognition, and Action in Hyperspaces: Implications on Brain Plasticity, Learning, and Cognition.

We live in a three-dimensional (3D) spatial world; however, our retinas receive a pair of 2D projections of the 3D environment. By using multiple cues, such as disparity, motion parallax, perspective, our brains can construct 3D representations of the world from the 2D projections on our retinas. These 3D representations underlie our 3D perceptions of the world and are mapped into our motor systems to generate accurate sensorimotor behaviors. Three-dimensional perceptual and sensorimotor capabilities emerge during development: the physiology of the growing baby changes hence necessitating an ongoing re-adaptation of the mapping between 3D sensory representations and the motor coordinates. This adaptation continues in adulthood and is quite general to successfully deal with joint-space changes (longer arms due to growth), skull and eye size changes (and still being able of accurate eye movements), etc. A fundamental question is whether our brains are inherently limited to 3D representations of the environment because we are living in a 3D world, or alternatively, our brains may have the inherent capability and plasticity of representing arbitrary dimensions; however, 3D representations emerge from the fact that our development and learning take place in a 3D world. Here, we review research related to inherent capabilities and limitations of brain plasticity in terms of its spatial representations and discuss whether with appropriate training, humans can build perceptual and sensorimotor representations of spatial 4D environments, and how the presence or lack of ability of a solid and direct 4D representation can reveal underlying neural representations of space.

Neural activity in the reward-related brain regions predicts implicit self-esteem: A novel validity test of psychological measures using neuroimaging.

Self-esteem, arguably the most important attitudes an individual possesses, has been a premier research topic in psychology for more than a century. Following a surge of interest in implicit attitude measures in the 90s, researchers have tried to assess self-esteem implicitly to circumvent the influence of biases inherent in explicit measures. However, the validity of implicit self-esteem measures remains elusive. Critical tests are often inconclusive, as the validity of such measures is examined in the backdrop of imperfect behavioral measures. To overcome this serious limitation, we tested the neural validity of the most widely used implicit self-esteem measure, the implicit association test (IAT). Given the conceptualization of self-esteem as attitude toward the self, and neuroscience findings that the reward-related brain regions represent an individual's attitude or preference for an object when viewing its image, individual differences in implicit self-esteem should be associated with neural signals in the reward-related regions during passive-viewing of self-face (the most obvious representation of the self). Using multi-voxel pattern analysis (MVPA) on functional MRI (fMRI) data, we demonstrate that the neural signals in the reward-related regions were robustly associated with implicit (but not explicit) self-esteem, thus providing unique evidence for the neural validity of the self-esteem IAT. In addition, both implicit and explicit self-esteem were related, although differently, to neural signals in regions involved in self-processing. Our finding highlights the utility of neuroscience methods in addressing fundamental psychological questions and providing unique insights into important psychological constructs. (PsycINFO Database Record

Advances in fMRI Real-Time Neurofeedback.

Functional magnetic resonance imaging (fMRI) neurofeedback is a type of biofeedback in which real-time online fMRI signals are used to self-regulate brain function. Since its advent in 2003 significant progress has been made in fMRI neurofeedback techniques. Specifically, the use of implicit protocols, external rewards, multivariate analysis, and connectivity analysis has allowed neuroscientists to explore a possible causal involvement of modified brain activity in modified behavior. These techniques have also been integrated into groundbreaking new neurofeedback technologies, specifically decoded neurofeedback (DecNef) and functional connectivity-based neurofeedback (FCNef). By modulating neural activity and behavior, DecNef and FCNef have substantially advanced both basic and clinical research.

Thermosensory Perceptual Learning Is Associated with Structural Brain Changes in Parietal-Opercular (SII) Cortex.

The location of a sensory cortex for temperature perception remains a topic of substantial debate. Both the parietal-opercular (SII) and posterior insula have been consistently implicated in thermosensory processing, but neither region has yet been identified as the locus of fine temperature discrimination. Using a perceptual learning paradigm in male and female humans, we show improvement in discrimination accuracy for subdegree changes in both warmth and cool detection over 5 d of repetitive training. We found that increases in discriminative accuracy were specific to the temperature (cold or warm) being trained. Using structural imaging to look for plastic changes associated with perceptual learning, we identified symmetrical increases in gray matter volume in the SII cortex. Furthermore, we observed distinct, adjacent regions for cold and warm discrimination, with cold discrimination having a more anterior locus than warm. The results suggest that thermosensory discrimination is supported by functionally and anatomically distinct temperature-specific modules in the SII cortex.SIGNIFICANCE STATEMENT We provide behavioral and neuroanatomical evidence that perceptual learning is possible within the temperature system. We show that structural plasticity localizes to parietal-opercular (SII), and not posterior insula, providing the best evidence to date resolving a longstanding debate about the location of putative "temperature cortex." Furthermore, we show that cold and warm pathways are behaviorally and anatomically dissociable, suggesting that the temperature system has distinct temperature-dependent processing modules.

[Associative Learning between Orientation and Color in Early Visual Areas].

Associative learning is an essential neural phenomenon where the contingency of different items increases after training. Although associative learning has been found to occur in many brain regions, there is no clear evidence that associative learning of visual features occurs in early visual areas. Here, we developed an associative decoded functional magnetic resonance imaging (fMRI) neurofeedback (A-DecNef) to determine whether associative learning of color and orientation can be induced in early visual areas. During the three days' training, A-DecNef induced fMRI signal patterns that corresponded to a specific target color (red) mostly in early visual areas while a vertical achromatic grating was simultaneously, physically presented to participants. Consequently, participants' perception of "red" was significantly more frequently than that of "green" in an achromatic vertical grating. This effect was also observed 3 to 5 months after training. These results suggest that long-term associative learning of two different visual features such as color and orientation, was induced most likely in early visual areas. This newly extended technique that induces associative learning may be used as an important tool for understanding and modifying brain function, since associations are fundamental and ubiquitous with respect to brain function.

Overlearning hyperstabilizes a skill by rapidly making neurochemical processing inhibitory-dominant.

Overlearning refers to the continued training of a skill after performance improvement has plateaued. Whether overlearning is beneficial is a question in our daily lives that has never been clearly answered. Here we report a new important role: overlearning in humans abruptly changes neurochemical processing, to hyperstabilize and protect trained perceptual learning from subsequent new learning. Usually, learning immediately after training is so unstable that it can be disrupted by subsequent new learning until after passive stabilization occurs hours later. However, overlearning so rapidly and strongly stabilizes the learning state that it not only becomes resilient against, but also disrupts, subsequent new learning. Such hyperstabilization is associated with an abrupt shift from glutamate-dominant excitatory to GABA-dominant inhibitory processing in early visual areas. Hyperstabilization contrasts with passive and slower stabilization, which is associated with a mere reduction of excitatory dominance to baseline levels. Using hyperstabilization may lead to efficient learning paradigms.

Neural predictors of evaluative attitudes toward celebrities.

Our attitudes toward others influence a wide range of everyday behaviors and have been the most extensively studied concept in the history of social psychology. Yet they remain difficult to measure reliably and objectively, since both explicit and implicit measures are typically confounded by other psychological processes. We here address the feasibility of decoding incidental attitudes based on brain activations. Participants were presented with pictures of members of a Japanese idol group inside an functional magnetic resonance imaging scanner while performing an unrelated detection task, and subsequently (outside the scanner) performed an incentive-compatible choice task that revealed their attitude toward each celebrity. We used a real-world election scheme that exists for this idol group, which confirmed both strongly negative and strongly positive attitudes toward specific individuals. Whole-brain multivariate analyses (searchlight-based support vector regression) showed that activation patterns in the anterior striatum predicted each participant's revealed attitudes (choice behavior) using leave-one-out (as well as 4-fold) cross-validation across participants. In contrast, attitude extremity (unsigned magnitude) could be decoded from a distinct region in the posterior striatum. The findings demonstrate dissociable striatal representations of valenced attitude and attitude extremity and constitute a first step toward an objective and process-pure neural measure of attitudes.