Information-based functional brain mapping

Interictal suppression in patients with mesial temporal lobe epilepsy: A simultaneous PET/fMRI study

Previous stereotactic-electroencephalography (SEEG) results have suggested that seizure-onset zones (SOZs) could be suppressed by strengthened inward connectivity from the rest of the brain during interictal periods, which might explain why people with epilepsy did not have seizures continuously. However, the limited coverage of SEEG contacts and allocation bias hindered a more comprehensive survey of interictal suppression at the whole-brain level. Previous studies also lacked a direct comparison between patients and healthy controls due to the invasive nature of SEEG. In the present study, we introduced metabolic connectivity mapping (MCM), a simultaneous FDG-PET/fMRI-based measure of effective connectivity, to evaluate the inward and outward connectivity of the SOZs in patients with mesial temporal lobe epilepsy (MTLE). Specifically, simultaneous FDG-PET/fMRI data was acquired from 23 patients with left MTLE, 24 patients with right MTLE, and 25 healthy controls. At the whole-brain level, there was significant increase of inward MCM connectivity to the SOZs, which mostly came from mesial-temporo-limbic, anterior and posterior midline regions of the default mode network (DMN) and subcortical nuclei. There was also significant decrease of outward MCM connectivity from the SOZs, which mainly projected to the regions within DMN. The increased net inward MCM to the SOZs, calculated by subtracting outward MCM from the inward MCM, was positively correlated with seizure frequency. Within DMN, MTLE patients showed decreased MCM from the SOZs to posterior cingulate cortex and right ventromedial prefrontal cortex and increased effective connectivity from posterior cingulate cortex to the SOZs. Based on the MCM patterns within DMN, we were able to classify the epileptic side of MTLE with an accuracy of 91.67 % (79.17 % for MRI-negative patients). Overall, our results provide whole-brain evidences for the interictal suppression hypothesis. We also found that the regions within DMN play a critical role in the suppression of SOZs. The pattern of such suppressive network might also serve as potential features for the localization of SOZs. Our neuroimaging results does not only provide a comprehensive understanding of interictal suppression at the whole-brain level, but also shed lights on a non-invasive and time-efficient way for SOZs localization.

Consistent neural representation of valence in encoding and recall

Recall is an act of elicitation of emotions similar to those emotions previously experienced. Unlike the past experiences where external sensory stimuli triggered emotions, recall does not require external sensory stimuli. This difference is pertinent to the key debate in affective representation, addressing whether the representation of valence is consistent across modalities (modality-general) or dependent on modalities (modality-specific). This study aimed to verify neural representations of valence between encoding and recall. Using neuroimaging data from movie watching and recall (Chen et al., 2017) and behavioral data for valence ratings (Kim et al., 2020), a searchlight analysis was conducted with cross-participant regression-based decoding between movie watching and recall. Multidimensional scaling was employed as a validation analysis of the results from searchlight analysis. The searchlight analysis revealed the right middle temporal and inferior temporal gyrus as well as the left fusiform gyrus. The validation analysis further exhibited significant consistent neural representations of valence in the inferior temporal gyrus and the left fusiform gyrus. This study identified the brain regions where valence is consistently represented between encoding and recall about real events. These findings contribute to debate in affective representations, by comparing conditions utilized little in prior, suggesting the inferior temporal gyrus relates to representations of valence during encoding and recalling natural events.

Abstract sentence meanings are grounded in the sensory-motor regions in a context-dependent fashion

Sentences conveying abstract meanings are crucial tools for high-level thinking and communication. Previous research has sparked a debate on whether abstract concepts rely on the representation of the sensory-motor brain areas. We explored this issue with the assumption that abstract meanings at the sentence level could invoke the sensory-motor regions a context-dependent fashion. With a sentence comprehension task and functional MRI, we measured the neural response patterns of sentences with multimodal abstract meaning, which were presented following context sentences describing either concrete sound-related or action-related events. Multivariate pattern analyses revealed that neural responses to sentences could discriminate abstract sentences in sound- versus action-related contexts, and also context sentences describing these two types of events. The discrimination was manifested in the regions responsible for high-level auditory perception and action execution. Our finding indicates that abstract meanings in modality-specific contexts mayrequire a certain degree of grounded processing in the sensory-motor regions.

Consistency of affective responses to naturalistic stimuli across individuals using intersubject correlation analysis based on neuroimaging data

In this study, we utilized functional magnetic resonance imaging (fMRI) data obtained for naturalistic emotional stimuli to examine the consistency of neural responses among participants in specific regions related to valence. We reanalyzed fMRI data from 17 participants as they watched episodes of "Sherlock" and used emotional ratings from 125 participants. To determine regions where neural response patterns were synchronized across participants based on the pattern of valence changes, intersubject correlation analysis was conducted. As a validation analysis, multidimensional scaling was conducted to investigate emotional representation for significant regions of interest. The results revealed increased neural synchrony in the ventromedial prefrontal cortex, bilateral superior frontal cortex, left posterior cingulate cortex, thalamus, right anterior cingulate cortex, and bilateral inferior frontal cortices during the presentation of positive scenes. Also, the bilateral superior temporal gyrus and bilateral medial temporal gyrus exhibited increased neural synchrony as negative scenes were presented. Moreover, the left inferior frontal cortex and right superior frontal gyrus were found to be engaged in emotion representation and display increased neural synchrony. These findings provide insights into the differential neural responses to emotionally evocative naturalistic stimuli as compared to conventional experimental stimuli. Also, this study highlights the future potential for using intersubject correlation analysis for examining consistency of neural responses to naturalistic stimuli.

Data integration with Fusion Searchlight: Classifying brain states from resting-state fMRI

Resting-state fMRI captures spontaneous neural activity characterized by complex spatiotemporal dynamics. Various metrics, such as local and global brain connectivity and low-frequency amplitude fluctuations, quantify distinct aspects of these dynamics. However, these measures are typically analyzed independently, overlooking their interrelations and potentially limiting analytical sensitivity. Here, we introduce the Fusion Searchlight (FuSL) framework, which integrates complementary information from multiple resting-state fMRI metrics. We demonstrate that combining these metrics enhances the accuracy of pharmacological treatment prediction from rs-fMRI data, enabling the identification of additional brain regions affected by sedation with alprazolam. Furthermore, we leverage explainable AI to delineate the differential contributions of each metric, which additionally improves spatial specificity of the searchlight analysis. Moreover, this framework can be adapted to combine information across imaging modalities or experimental conditions, providing a versatile and interpretable tool for data fusion in neuroimaging.

Spatial and feature-selective attention interact to drive selective coding in frontoparietal cortex

Attention enables the selective processing of relevant information. Two types of selective attention, spatial and feature-selective attention, have separable neural effects but in real life are often used together. Here, we asked how these types of attention interact to affect information coding in a frontoparietal 'multiple-demand' (MD) network, essential for attentional control. Using functional magnetic resonance imaging (fMRI) with multivariate pattern analysis, we examined how covert attention to object features (colour or shape) and spatial locations (left or right) influences coding of task-related stimulus information. We found that spatial and feature-selective attention interacted multiplicatively on information coding in MD and visual regions, such that there was above-chance decoding of the attended feature of the attended object and no detectable coding of visually equivalent but behaviourally irrelevant aspects of the visual display. The attended information had a multidimensional neural representation, with stimulus information (e.g., colour) and discrimination difficulty (distance from the categorical decision boundary) reflected in separate dimensions. Rather than boosting processing of whole objects or relevant features across space, our results suggest neural activity reflects precise tuning to relevant information, indicating a highly selective control process that codes behaviourally relevant information across multiple dimensions.

Representations of personally familiar voices are better resolved in the brain

The human voice is highly flexible, allowing for diverse expression during communication,1 but presents perceptual challenges through large acoustic variability.2,3,4,5,6,7,8,9,10,11 The ability to recognize an individual person's voice depends on the listener's ability to overcome this within-speaker variability to extract a single identity percept.2,18 Previous work has found that this process is greatly assisted by familiarity,6,9,13 with evidence suggesting that more extensive and varied exposure to a voice is associated with the formation of a more robust mental representation of it.4,8 Here, we used functional magnetic resonance imaging (fMRI) with representational similarity analysis14 to characterize how personal familiarity with a voice is reflected in neural representations. We measured and compared brain responses with voices of differing familiarity-a personally familiar voice, a voice familiarized through lab training, and a new (untrained) voice-while listeners identified these voices from naturally varying, spontaneous speech clips. Personally familiar voices elicited brain response patterns in voice-, face-, and person-selective corticesthat showed higher within- and between-speaker dissimilarity, compared with lower-familiarity lab-trained and untrained voices. These findings indicated that representations for the sounds of personally familiar voices are better resolved from each other in the brain, and they align with other research reporting intelligibility advantages for speech produced by familiar talkers.15,16,17,18 Overall, our findings suggest that extensive and varied exposure to personally familiar voices results in the development of finer-grained representations of those voices, which cannot be achieved via short-term lab training.

Neural signatures underlying the effect of social structure on empathy and altruistic behaviors

Humans inhabit complex social networks, monitoring social structures that encompass both direct and indirect relationships. However, previous research primarily focused on direct relationships, leaving the neural basis of how social structure influences socioemotional processes understudied. This study addressed this gap by investigating the neural pathways underlying the influence of social structure on empathy and altruistic behaviors. During fMRI scanning, participants viewed painful or non-painful stimulation to innocent strangers who shared preferences with targets who had either treated participants fairly or unfairly. Afterwards, participants rated the pain experienced by these innocents and shared money with other innocents. Participants showed reduced empathic and altruistic responses toward innocents resembling unfair (vs. fair) targets, accompanied by heightened activation in regions crucial for emotion regulation and mentalizing, such as the lateral and medial prefrontal cortex. Furthermore, whole-brain and local neural patterns in the anterior insula and premotor cortex robustly discriminated painful (but not non-painful) stimulation of different innocents, suggesting that social structure altered emotional and sensorimotor aspects of empathy. These alterations might be driven by top-down regulation, as indicated by heightened functional connectivity between the lateral prefrontal cortex and sensorimotor areas, as well as between the anterior insula and subgenual anterior cingulate cortex when witnessing the pain of innocents resembling fair (vs. unfair) targets. Together, our work is the first to uncover the neural underpinnings through which human empathy and altruistic behaviors are shaped by social structure beyond direct self-other relationships.

Cortical representational geometry of diverse tasks reveals subject-specific and subject-invariant cognitive structures

The variability in brain function forms the basis for our uniqueness. Prior studies indicate smaller individual differences and larger inter-subject correlation (ISC) in sensorimotor areas than in the association cortex. These studies, deriving information from brain activity, leave individual differences in cognitive structures based on task similarity relations unexplored. This study quantitatively evaluates these differences by integrating ISC, representational similarity analysis, and vertex-wise encoding models using functional magnetic resonance imaging across 25 cognitive tasks. ISC based on cognitive structures enables subject identification with 100% accuracy using at least 14 tasks. ISC is larger in the fronto-parietal association and higher-order visual cortices, suggesting subject-invariant cognitive structures in these regions. Principal component analysis reveals different cognitive structure configurations within these regions. This study provides evidence of individual variability and similarity in abstract cognitive structures.

Object knowledge representation in the human visual cortex requires a connection with the language system

How world knowledge is stored in the human brain is a central question in cognitive neuroscience. Object knowledge effects have been commonly observed in higher-order sensory association cortices, with the role of language being highly debated. Using object color as a test case, we investigated whether communication with the language system plays a necessary role in knowledge neural representation in the visual cortex and corresponding behaviors, combining diffusion imaging (measuring white-matter structural integrity), functional MRI (fMRI; measuring functional neural representation of knowledge), and neuropsychological assessments (measuring behavioral integrity) in a group of patients who suffered from stroke (N = 33; 18 with left-hemisphere lesions, 11 with right-hemisphere lesions, and 4 with bilateral lesions). The structural integrity loss of the white-matter connection between the anterior temporal language region and the ventral visual cortex had a significant effect on the neural representation strength of object color knowledge in the ventral visual cortex and on object color knowledge behavior across modalities. These contributions could not be explained by the potential effects of the early visual perception pathway or potential confounding brain or cognitive variables. Our experiments reveal the contribution of the vision-language connection in the ventral occipitotemporal cortex (VOTC) object knowledge neural representation and object knowledge behaviors, highlighting the significance of the language-sensory system interface.

A neural geometry approach comprehensively explains apparently conflicting models of visual perceptual learning

Visual perceptual learning (VPL), defined as long-term improvement in a visual task, is considered a crucial tool for elucidating underlying visual and brain plasticity. Previous studies have proposed several neural models of VPL, including changes in neural tuning or in noise correlations. Here, to adjudicate different models, we propose that all neural changes at single units can be conceptualized as geometric transformations of population response manifolds in a high-dimensional neural space. Following this neural geometry approach, we identified neural manifold shrinkage due to reduced trial-by-trial population response variability, rather than tuning or correlation changes, as the primary mechanism of VPL. Furthermore, manifold shrinkage successfully explains VPL effects across artificial neural responses in deep neural networks, multivariate blood-oxygenation-level-dependent signals in humans and multiunit activities in monkeys. These converging results suggest that our neural geometry approach comprehensively explains a wide range of empirical results and reconciles previously conflicting models of VPL.

Brain Representation in Conscious and Unconscious Vision

The development of robust frameworks to understand how the human brain represents conscious and unconscious perceptual contents is paramount to make progress in the neuroscience of consciousness. Recent functional MRI studies using multi-voxel pattern classification analyses showed that unconscious contents could be decoded from brain activity patterns. However, decoding does not imply a full understanding of neural representations. Here we re-analysed data from a high-precision fMRI study coupled with representational similarity analysis based on convolutional neural network models to provide a detailed information-based approach to neural representations of both unconscious and conscious perceptual content. The results showed that computer vision model representations strongly predicted brain responses in ventral visual cortex and in fronto-parietal regions to both conscious and unconscious contents. Moreover, this pattern of results generalised when the models were trained and tested with different participants. Remarkably, these observations results held even when the analysis was restricted to observers that showed null perceptual sensitivity. In light of the highly distributed brain representation of unconscious information, we suggest that the functional role of fronto-parietal cortex in conscious perception is unlikely to be related to the broadcasting of information, as proposed by the global neuronal workspace theory, and may instead relate to the generation of meta-representations as proposed by higher-order theories.

A neurocomputational account of the link between social perception and social action

People selectively help others based on perceptions of their merit or need. Here, we develop a neurocomputational account of how these social perceptions translate into social choice. Using a novel fMRI social perception task, we show that both merit and need perceptions recruited the brain's social inference network. A behavioral computational model identified two non-exclusive mechanisms underlying variance in social perceptions: a consistent tendency to perceive others as meritorious/needy (bias) and a propensity to sample and integrate normative evidence distinguishing high from low merit/need in other people (sensitivity). Variance in people's merit (but not need) bias and sensitivity independently predicted distinct aspects of altruism in a social choice task completed months later. An individual's merit bias predicted context-independent variance in people's overall other-regard during altruistic choice, biasing people toward prosocial actions. An individual's merit sensitivity predicted context-sensitive discrimination in generosity toward high and low merit recipients by influencing other- and self-regard during altruistic decision-making. This context-sensitive perception-action link was associated with activation in the right temporoparietal junction. Together, these findings point toward stable, biologically based individual differences in perceptual processes related to abstract social concepts like merit, and suggest that these differences may have important behavioral implications for an individual's tendency toward favoritism or discrimination in social settings.

Touching-untouching patterns organize action representation in the inferior parietal cortex

At an abstract temporospatial level, object-directed actions can be described as sequences of touchings and untouchings of objects, hands, and the ground. These sparse action codes can effectively guide automated systems like robots in recognizing and responding to human actions without the need for object identification. The aim of the current study was to investigate whether the neural processing of actions and their behavioral classification relies on the action categorization derived from the touching-untouching structure. Here we show, using a representational similarity analysis of functional MRI data from two experiments, that action representations in left anterior intraparietal sulcus (aIPS) are particularly associated with this categorization of touching-untouching structures. Within the examined action observation network, only the touching-untouching category model selectively correlated with the representational profile of the left aIPS. The behavioral results showed a significant relation between the touching-untouching structure and the observers' judgments on the similarity of actions with weakly-informative objects. Extending prior research on touchings and untouchings as meaningful anchor points for explicit action segmentation, our findings suggest that touching-untouching sequences serve as an organizing principle in inferior parietal action representation.

The neural representation of body orientation and emotion from biological motion

The perception of human body orientation and emotion in others provides crucial insights into their intentions. While significant research has explored the brain's representation of body orientation and emotion processing, their possible combined representation remains less well understood. In this study, functional magnetic resonance imaging was employed to investigate this issue. Participants were shown point-light displays and tasked with recognizing both body emotion and orientation. The analysis of functional activation revealed that the extrastriate body area encodesd emotion, while the precentral gyrus and postcentral gyrus encoded body orientation. Additionally, results from multivariate pattern analysis and representational similarity analysis demonstrated that the lingual gyrus, precentral gyrus, and postcentral gyrus played a critical role in processing body orientation, whereas the lingual gyrus and extrastriate body area were crucial for processing emotion. Furthermore, the commonality analysis found that the neural representations of emotion and body orientation in the lingual and precentral gyrus were not interacting, but rather competing. Lastly, a remarkable interaction between hemisphere and body orientation revealed in the connection analysis showed that the coupling between the inferior parietal lobule and the left precentral gyrus was more sensitive to a 90° body orientation, while the coupling between the inferior parietal lobule and the right precentral gyrus was sensitive to 0° and 45° body orientation. Overall, these findings suggest that the conflicted relationship between the neural representation of body orientation and emotion in LING and PreCG when point-light displays were shown, and the different hemispheres play different role in encoding different body orientations.

Fingerprint patterns of human brain activity reveal a dynamic mix of emotional responses during virtual intergroup encounters

The Stereotype Content Model (SCM) states that different social groups elicit different emotions according to their perceived level of competence and warmth. Because of this relationship between stereotypes and emotional states and because emotions are highly predictive of intergroup behaviors, emotional evaluation is crucial for research on intergroup relations. However, emotional assessment heavily relies on self-reports, which are often compromised by social desirability and challenges in reporting immediate emotional appraisals. In this study, we used machine learning to identify emotional brain patterns using functional magnetic resonance imaging. Subsequently, those patterns were used to monitor emotional reactions during virtual intergroup encounters. Specifically, we showed Finnish majority group members 360-videos depicting members of their ethnic ingroup and immigrant outgroups approaching and entering participants' personal space. All the groups showed different levels of perceived competence and warmth. In alignment with the SCM, our results showed that the groups perceived as low in competence and warmth evoked contempt and discomfort. Moreover, the ambivalent low-competent/high-warm group elicited both happiness and discomfort. Additionally, upon the protagonists' approach into personal space, emotional reactions were modulated differently for each group. Taken together, our findings suggest that our method could be used to explore the temporal dynamics of emotional responses during intergroup encounters.

A reduction in energy costs induces integrated states of brain dynamics

In the human brain, interactions between multiple regions organize stable dynamics that enable enhanced cognitive processes. However, it is unclear how collective activities in the brain network can generate stable states while preserving unity across the whole brain scale under successive environmental changes. Herein, a network model was introduced in which network connections were adjusted to reduce the energy consumption level by avoiding excess changes in the activated states of each region during successive interactions. For time series data obtained from fMRI images, a connection matrix was generated by a simulation, and the predictions made by this matrix yielded accurate results relative to the real data. In this simulation, the adjustment process was activity-dependent, in which the interregional connections between intense active regions were reinforced to prohibit free behaviours. This resulted in a reduced excess energy loss and the integration of multiple regional activities into integrated dynamic states under constraints imposed by other regions. It was suggested that the simple rule of saving excess energy costs plays an important role in the mechanism that regulates large-scale brain networks and dynamics.

Dissociable neural networks for processing fearful bodily expressions at different spatial frequencies

The human brain processes visual input across various spatial frequency (SF) ranges to extract emotional cues. Prior studies have extensively explored SF processing in facial expressions, yielding partly conflicting results. However, bodily expressions, which provide complementary emotional and survival-relevant cues, remain unexplored. We investigated the neural mechanisms underlying the processing of low (LSF), high (HSF), and broad spatial frequency (BSF) components in fearful versus neutral bodily postures. Using functional Magnetic Resonance Imaging, we examined brain activity in 20 participants viewing SF-filtered images of bodily expressions in a semi-passive task. A multivariate "searchlight" analysis based on Multi-Voxel Pattern Analysis was employed to decode the non-linear activation patterns associated with each SF band. Our findings reveal that SF processing engages distinct neural networks in response to fearful bodily expressions. BSF stimuli activated a widespread network, including the amygdala, pulvinar, frontal, and temporal cortices. These findings suggest a general threat-detection system integrating information across all SFs. HSF stimuli engaged cortical regions associated with detailed emotional evaluation and motor planning, such as the orbitofrontal cortex, anterior cingulate cortex, and premotor areas, suggesting that processing fine-grained fear cues involves computationally demanding networks related to emotional resonance and action preparation. In contrast, LSF stimuli primarily activated motor-preparatory regions linked to rapid, action-oriented responses, highlighting the brain prioritization of quick readiness to low-detail threats. Notably, the amygdala showed no SF selectivity, supporting its role as a generalized "relevance detector" in emotional processing. The present study demonstrates that the brain flexibly adapts its SF processing strategy based on the visual details available in fearful bodily expressions, underscoring the complexity and adaptability of emotional processing from bodily signals.

The Bodily Appearance of a Virtual Partner Affects the Activity of the Action Observation and Action Monitoring Systems in a Minimally Interactive Task

One pending question in social neuroscience is whether interpersonal interactions are processed differently by the brain depending on the bodily characteristics of the interactor, i.e., their physical appearance. To address this issue, we engaged participants in a minimally interactive task with an avatar either showing bodily features or not while recording their brain activity using electroencephalography (EEG) in order to investigate indices of action observation and action monitoring processing. Multivariate results showed that bodily compared with nonbodily appearance modulated parieto-occipital neural patterns throughout the entire duration of the observed movement and that, importantly, such patterns differ from the ones related to initial shape processing. Furthermore, among the electrocortical indices of action monitoring, only the early observational positivity (oPe) was responsive to the bodily appearance of the observed agent under the specific task requirement to predict the partner movement. Taken together, these findings broaden the understanding of how bodily appearance shapes the spatiotemporal processing of an interactor's movements. This holds particular relevance in our modern society, where human-artificial (virtual or robotic) agent interactions are rapidly becoming ubiquitous.

Time-dependent consolidation mechanisms of durable memory in spaced learning

Emerging studies suggest that time-dependent consolidation enables memory stabilization by promoting memory integration and hippocampal-cortical transfer. Compared to massed learning, how time-dependent consolidation contributes to forming durable memory and what neural signatures predict durable memory in spaced learning remain unclear. We recruited 48 participants who underwent either 3-day spaced learning or 1-day massed learning, and both resting-state and task-based fMRI data were collected in multiple delayed tests (i.e., immediate, 1-week, and 1-month). We use representational similarity analysis to assess neural integration and replay in the hippocampus and default mode network (DMN) subsystems. In contrast with massed learning, spaced learning induces higher neural pattern similarity during immediate retrieval only in DMN subsystems. Particularly, the neural pattern similarity in the dorsal-medial DMN (DMNdm) and medial-temporal DMN subsystems predicts the durable memory defined by 1-month delay. Moreover, we find increased neural replay of durable memory in the DMNdm for spaced learning and in the hippocampus for both spaced and massed learning. Our findings suggest that time-dependent consolidation promotes neural integration and replay in the cortex rather than in the hippocampus, which may underlie the formation of durable memory after spaced learning.

Neural dynamics of semantic control underlying generative storytelling

Storytelling has been pivotal for the transmission of knowledge across human history, yet the role of semantic control and its associated neural dynamics has been poorly investigated. Here, human participants generated stories that were either appropriate (ordinary), novel (random), or balanced (creative), while recording functional magnetic resonance imaging (fMRI). Deep language models confirmed participants adherence to task instructions. At the neural level, linguistic and visual areas exhibited neural synchrony across participants regardless of the semantic control level, with parietal and frontal regions being more synchronized during random ideation. Importantly, creative stories were differentiated by a multivariate pattern of neural activity in frontal and fronto-temporo-parietal cortices compared to ordinary and random stories. Crucially, similar brain regions were also encoding the features that distinguished the stories. Moreover, we found specific spatial frequency patterns underlying the modulation of semantic control during story generation, while functional coupling in default, salience, and control networks differentiated creative stories with their controls. Remarkably, the temporal irreversibility between visual and high-level areas was higher during creative ideation, suggesting the enhanced hierarchical structure of causal interactions as a neural signature of creative storytelling. Together, our findings highlight the neural mechanisms underlying the regulation of semantic exploration during narrative ideation.

Distinct and content-specific neural representations of self- and other-produced actions in joint piano performance

During ensemble performance, musicians predict their own and their partners' action outcomes to smoothly coordinate in real time. The neural auditory-motor system is thought to contribute to these predictions by running internal forward models that simulate self- and other-produced actions slightly ahead of time. What remains elusive, however, is whether and how own and partner actions can be represented simultaneously and distinctively in the sensorimotor system, and whether these representations are content-specific. Here, we applied multivariate pattern analysis (MVPA) to functional magnetic resonance imaging (fMRI) data of duetting pianists to dissociate the neural representation of self- and other-produced actions during synchronous joint music performance. Expert pianists played familiar right-hand melodies in a 3 T MR-scanner, in duet with a partner who played the corresponding left-hand basslines in an adjacent room. In half of the pieces, pianists were motorically familiar (or unfamiliar) with their partner's left-hand part. MVPA was applied in primary motor and premotor cortices (M1, PMC), cerebellum, and planum temporale of both hemispheres to classify which piece was performed. Classification accuracies were higher in left than right M1, reflecting the content-specific neural representation of self-produced right-hand melodies. Notably, PMC showed the opposite lateralization, with higher accuracies in the right than left hemisphere, likely reflecting the content-specific neural representation of other-produced left-hand basslines. Direct physiological support for the representational alignment of partners' M1 and PMC should be gained in future studies using novel tools like interbrain representational similarity analyses. Surprisingly, motor representations in PMC were similarly precise irrespective of familiarity with the partner's part. This suggests that expert pianists may generalize contents of familiar actions to unfamiliar pieces with similar musical structure, based on the auditory perception of the partner's part. Overall, these findings support the notion of parallel, distinct, and content-specific self and other internal forward models that are integrated within cortico-cerebellar auditory-motor networks to support smooth coordination in musical ensemble performance and possibly other forms of social interaction.

Reduced temporal and spatial stability of neural activity patterns predict cognitive control deficits in children with ADHD

This study investigates the neural underpinnings of cognitive control deficits in attention-deficit/hyperactivity disorder (ADHD), focusing on trial-level variability of neural coding. Using fMRI, we apply a computational approach to single-trial neural decoding on a cued stop-signal task, probing proactive and reactive control within the dual control model. Reactive control involves suppressing an automatic response when interference is detected, and proactive control involves implementing preparatory strategies based on prior information. In contrast to typically developing children (TD), children with ADHD show disrupted neural coding during both proactive and reactive control, characterized by increased temporal variability and diminished spatial stability in neural responses in salience and frontal-parietal network regions. This variability correlates with fluctuating task performance and ADHD symptoms. Additionally, children with ADHD exhibit more heterogeneous neural response patterns across individuals compared to TD children. Our findings underscore the significance of modeling trial-wise neural variability in understanding cognitive control deficits in ADHD.

Models of actor-specific range of motion are encoded in the extrastriate body area

Models of actor-specific range of motion (or biomechanical limits) shapes perception and (inter)actions. This functional magnetic resonance imaging study tested the hypothesis that these models are encoded in the extrastriate body area. Participants were first introduced with the maximal amplitude of arm and leg movements of a "rigid" and a "flexible" actor. Then, we measured the blood oxygenation level dependent response in 25 participants while they watched video clips depicting these actors performing either "small" movements that were "possible" to perform for both actors, "large" ones that were "impossible" for both actors and "intermediate" ones that were possible only for the "flexible" actor. Results aligned with the 2 predictions of our hypothesis: (i) extrastriate body area responded more strongly to impossible than possible movements; (ii) extrastriate body area categorized intermediate movements as "possible" or "impossible" depending on each actor's specific range of motion. The results of additional analyses suggested that extrastriate body area encodes actor-specific range of motion at the level of specific body parts, and as a probability function. Finally, the results of whole brain and functional connectivity analyses suggested that the right posterior superior temporal sulcus may also play an important role in encoding information about actor-specific biomechanical limits.

Neural mechanisms for the attention-mediated propagation of conceptual information in the human brain

The visual environment is complicated, and humans and other animals accordingly prioritize some sources of information over others through the deployment of spatial attention. Cognitive theories propose that one core purpose of this is to gather information that can be used in downstream cognitive processes, including the development of concepts and categories. However, neuroscientific investigation has focused closely on the identification of the systems and algorithms that support attentional control or that instantiate the effect of attention on sensation and perception. Much less is known about how attention impacts the acquisition and activation of concepts. Here, we use machine learning of EEG and concurrently recorded EEG/MRI to temporally and anatomically characterize the neural network that abstracts from attended perceptual information to activate and construct semantic and conceptual representations. We find that variance in the amplitude of N2pc-an event-related potential (ERP) component closely linked to selective attention-predicts the emergence of conceptual information in a network including prefrontal, posterior parietal, and anterior insular cortex. This network appears to play a key role in the attention-mediated translation of perceptual information to concepts, semantics, and action plans.

Emergence of Categorical Representations in Parietal and Ventromedial Prefrontal Cortex across Extended Training

How do the neural representations underlying category learning change as skill develops? We examined perceptual category learning using a prototype learning task known to recruit a corticostriatal system including the posterior striatum, motor cortex, visual cortex, and intraparietal sulcus (IPS). Male and female human participants practiced categorizing stimuli as category members or nonmembers (A vs not-A) across 3 d, with fMRI data collected at the beginning and end. Univariate analyses found that corticostriatal activity in regions associated with habitual instrumental learning was recruited across both sessions, but activity in regions associated with goal-directed instrumental learning decreased from Day 1 to Day 3. Multivoxel pattern analysis (MVPA) indicated that after training, the trained category could be more easily decoded from the IPS when compared with a novel category. Representational similarity analysis (RSA) showed development of category representations in the IPS and motor cortex. In addition, RSA revealed evidence for category-related representations including prototype representation in the ventromedial prefrontal cortex which may reflect parallel development of schematic memory for the category structure. Overall, the results converge to show how performance of category decisions and representations of the category structure emerge after extensive training across the corticostriatal system underlying perceptual category learning.

Neurostimulation improves reading and alters communication within reading networks in dyslexia

The left temporo-parietal cortex (TPC) is critical for phonological decoding during reading and appears hypoactive in dyslexia. Therefore, a promising approach to alleviating phonological deficits in dyslexia is to modulate left TPC functioning. However, it is unclear how neurostimulation alters activity and network interactions in dyslexia. To address this gap, we combined facilitatory transcranial magnetic stimulation (TMS) to the left TPC in adults with dyslexia with an overt word and pseudoword reading task during functional neuroimaging. We found TMS-induced improvements in pseudoword reading, reduced contributions of right-hemispheric regions during reading, and substantial changes between the core reading nodes and an extended network involving the right cerebellum. Stronger coupling between temporo-occipital and frontal cortices was further directly linked to improvements in pseudoword reading. Collectively, we provide evidence for a crucial role of the left TPC for phonological decoding and show that TMS can successfully modulate reading networks to improve reading in dyslexia.

Decoding dynamic faces and scenes without awareness under dis-continuous flash suppression

In the perceptual sciences, there is an ongoing debate about the depth of unconscious processing. Here, we address this issue by implementing three improvements with regards to paradigm, stimuli and analyses to explore the neural correlates of unconscious face processing. Our results demonstrated that conscious faces elicited broader univariate activations than conscious scenes. Such results were absent when faces/scenes were suppressed and invisible (n = 43). However, further whole-brain multivariate classification revealed that both static and dynamic invisible faces could be distinguished from scenes in the occipital-temporal region. ROI analysis showed that bilateral FFA and OFA could differentiate dynamic invisible faces from dynamic invisible scenes. These findings suggest that interocularly suppressed faces are still processed in-depth in the ventral visual stream. Therefore, our study highlights the importance of optimizing stimulus signal, experimental paradigm, and analysis to extract unconscious signals in the brain.

Neural processing of naturalistic audiovisual events in space and time

Our brain seamlessly integrates distinct sensory information to form a coherent percept. However, when real-world audiovisual events are perceived, the specific brain regions and timings for processing different levels of information remain less investigated. To address that, we curated naturalistic videos and recorded functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) data when participants viewed videos with accompanying sounds. Our findings reveal early asymmetrical cross-modal interaction, with acoustic information represented in both early visual and auditory regions, while visual information only identified in visual cortices. The visual and auditory features were processed with similar onset but different temporal dynamics. High-level categorical and semantic information emerged in multisensory association areas later in time, indicating late cross-modal integration and its distinct role in converging conceptual information. Comparing neural representations to a two-branch deep neural network model highlighted the necessity of early cross-modal connections to build a biologically plausible model of audiovisual perception. With EEG-fMRI fusion, we provided a spatiotemporally resolved account of neural activity during the processing of naturalistic audiovisual stimuli.

The Effect of Cognitive-Motor Dual Tasks on the Risk of Falls in Female Saudi Students: A Cross-Sectional Study

Introduction

Dual tasking (DT) requires individuals to carry out two actions simultaneously, comparable to how the brain can perform a cognitive function while the body is in motion, which eventually enhances human balance. This paper aims to examine and compare the impact of DT on the risk of falling (ROF) among Saudi female students.

Methods

A cross-sectional design was used. 120 female students were recruited and divided into two groups: literary group (LG) (n = 34) and scientific group (SG) (n = 86). Participants, aged 18-25, had a normal body mass index (BMI) and cognitive and balancing skills. ROF was measured using the Biodex balancing device for balance alone (no DT) and with DT (motor and two cognitive tasks). After three trials, the mean and average were calculated. The ICC calculation showed a reliable result of <0.8. BMI was represented as the mean (M) and standard deviation (SD) for both groups. ROF was compared within and between groups using paired and unpaired T-tests. Mann-Whitney compared the two groups throughout DT. The level of significance was P = 0.05.

Results

There was no significant difference in ROF in SG (P = 0.06) between the performance with and without a DT; on the contrary, LG demonstrated a significant difference (P = 0.001) for the same tests. In addition, the only time there was a significant difference between the two groups was when they performed DT (P = 0.006).

Conclusion

Female students who used critical and analytical thinking and motor performance in their study and daily routine were more balanced and resistant to falling than their peers who did not. This study may improve efficient treatments for fall prevention and balance. Future research could investigate the complex nature of additional DT that may be complicated by gender and BMI outside of the normal range.

The integration of self-efficacy and response-efficacy in decision making

The belief that we can exert an influence in our environment is dependent on distinct components of perceived control. Here, we investigate the neural representations that differentially code for self-efficacy (belief in successfully executing a behavior) and response-efficacy (belief that the behavior leads to an expected outcome) and how such signals may be integrated to inform decision-making. Participants provided confidence ratings related to executing a behavior (self-efficacy), and the potential for a rewarding outcome (response-efficacy). Computational modeling was used to measure the subjective weight of self-efficacy and response-efficacy while making decisions and to examine the neural mechanisms of perceived control computation. While participants factored in both self-efficacy and response-efficacy during decision-making, we observed that integration of these two components was dependent on neural responses within the vmPFC, OFC and striatum. Further, the dlPFC was observed to assign importance to self-efficacy and response-efficacy in specific trials, while dACC computed the trade-off between both components, taking into account individual differences. These findings highlight the contributions of perceived control components in decision-making, and identify key neural pathways involved in computing perceived control.

Electroencephalography Searchlight Decoding Reveals Person- and Place-specific Responses for Semantic Category and Familiarity

Proper names are linguistic expressions referring to unique entities, such as individual people or places. This sets them apart from other words like common nouns, which refer to generic concepts. And yet, despite both being individual entities, one's closest friend and one's favorite city are intuitively associated with very different pieces of knowledge-face, voice, social relationship, autobiographical experiences for the former, and mostly visual and spatial information for the latter. Neuroimaging research has revealed the existence of both domain-general and domain-specific brain correlates of semantic processing of individual entities; however, it remains unclear how such commonalities and similarities operate over a fine-grained temporal scale. In this work, we tackle this question using EEG and multivariate (time-resolved and searchlight) decoding analyses. We look at when and where we can accurately decode the semantic category of a proper name and whether we can find person- or place-specific effects of familiarity, which is a modality-independent dimension and therefore avoids sensorimotor differences inherent among the two categories. Semantic category can be decoded in a time window and with spatial localization typically associated with lexical semantic processing. Regarding familiarity, our results reveal that it is easier to distinguish patterns of familiarity-related evoked activity for people, as opposed to places, in both early and late time windows. Second, we discover that within the early responses, both domain-general (left posterior-lateral) and domain-specific (right fronto-temporal, only for people) neural patterns can be individuated, suggesting the existence of person-specific processes.

Functional Connectivity-Based Searchlight Multivariate Pattern Analysis for Discriminating Schizophrenia Patients and Predicting Clinical Variables

BACKGROUND

Schizophrenia, a multifaceted psychiatric disorder characterized by functional dysconnectivity, poses significant challenges in clinical practice. This study explores the potential of functional connectivity (FC)-based searchlight multivariate pattern analysis (CBS-MVPA) to discriminate between schizophrenia patients and healthy controls while also predicting clinical variables.

STUDY DESIGN

We enrolled 112 schizophrenia patients and 119 demographically matched healthy controls. Resting-state functional magnetic resonance imaging data were collected, and whole-brain FC subnetworks were constructed. Additionally, clinical assessments and cognitive evaluations yielded a dataset comprising 36 clinical variables. Finally, CBS-MVPA was utilized to identify subnetworks capable of effectively distinguishing between the patient and control groups and predicting clinical scores.

STUDY RESULTS

The CBS-MVPA approach identified 63 brain subnetworks exhibiting significantly high classification accuracies, ranging from 62.2% to 75.6%, in distinguishing individuals with schizophrenia from healthy controls. Among them, 5 specific subnetworks centered on the dorsolateral superior frontal gyrus, orbital part of inferior frontal gyrus, superior occipital gyrus, hippocampus, and parahippocampal gyrus showed predictive capabilities for clinical variables within the schizophrenia cohort.

CONCLUSION

This study highlights the potential of CBS-MVPA as a valuable tool for localizing the information related to schizophrenia in terms of brain network abnormalities and capturing the relationship between these abnormalities and clinical variables, and thus, deepens our understanding of the neurological mechanisms of schizophrenia.

Distinguishing deception from its confounds by improving the validity of fMRI-based neural prediction

Deception is a universal human behavior. Yet longstanding skepticism about the validity of measures used to characterize the biological mechanisms underlying deceptive behavior has relegated such studies to the scientific periphery. Here, we address these fundamental questions by applying machine learning methods and functional magnetic resonance imaging (fMRI) to signaling games capturing motivated deception in human participants. First, we develop an approach to test for the presence of confounding processes and validate past skepticism by showing that much of the predictive power of neural predictors trained on deception data comes from processes other than deception. Specifically, we demonstrate that discriminant validity is compromised by the predictor's ability to predict behavior in a control task that does not involve deception. Second, we show that the presence of confounding signals need not be fatal and that the validity of the neural predictor can be improved by removing confounding signals while retaining those associated with the task of interest. To this end, we develop a "dual-goal tuning" approach in which, beyond the typical goal of predicting the behavior of interest, the predictor also incorporates a second compulsory goal that enforces chance performance in the control task. Together, these findings provide a firmer scientific foundation for understanding the neural basis of a neglected class of behavior, and they suggest an approach for improving validity of neural predictors.

The transformation of sensory to perceptual braille letter representations in the visually deprived brain

Experience-based plasticity of the human cortex mediates the influence of individual experience on cognition and behavior. The complete loss of a sensory modality is among the most extreme such experiences. Investigating such a selective, yet extreme change in experience allows for the characterization of experience-based plasticity at its boundaries. Here, we investigated information processing in individuals who lost vision at birth or early in life by probing the processing of braille letter information. We characterized the transformation of braille letter information from sensory representations depending on the reading hand to perceptual representations that are independent of the reading hand. Using a multivariate analysis framework in combination with functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and behavioral assessment, we tracked cortical braille representations in space and time, and probed their behavioral relevance. We located sensory representations in tactile processing areas and perceptual representations in sighted reading areas, with the lateral occipital complex as a connecting 'hinge' region. This elucidates the plasticity of the visually deprived brain in terms of information processing. Regarding information processing in time, we found that sensory representations emerge before perceptual representations. This indicates that even extreme cases of brain plasticity adhere to a common temporal scheme in the progression from sensory to perceptual transformations. Ascertaining behavioral relevance through perceived similarity ratings, we found that perceptual representations in sighted reading areas, but not sensory representations in tactile processing areas are suitably formatted to guide behavior. Together, our results reveal a nuanced picture of both the potentials and limits of experience-dependent plasticity in the visually deprived brain.

Semantic fMRI neurofeedback of emotions: from basic principles to clinical applications

During fMRI neurofeedback participants learn to self-regulate activity in relevant brain areas and networks based on ongoing feedback extracted from measured responses in those regions. This closed-loop approach has been successfully applied to reduce symptoms in mood disorders such as depression by showing participants a thermometer-like display indicating the strength of activity in emotion-related brain areas. The hitherto employed conventional neurofeedback is, however, 'blind' with respect to emotional content, i.e. patients instructed to engage in a specific positive emotion could drive the neurofeedback signal by engaging in a different (positive or negative) emotion. In this future perspective, we present a new form of neurofeedback that displays semantic information of emotions to the participant. Semantic information is extracted online using real-time representational similarity analysis of emotion-specific activity patterns. The extracted semantic information can be provided to participants in a two-dimensional semantic map depicting the current mental state as a point reflecting its distance to pre-measured emotional mental states (e.g. 'happy', 'content', 'sad', 'angry'). This new approach provides transparent feedback during self-regulation training, and it has the potential to enable more specific training effects for future therapeutic applications such as clinical interventions in mood disorders.This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'.

Encoding models for developmental cognitive computational neuroscience: Promise, challenges, and potential

Cognitive computational neuroscience has received broad attention in recent years as an emerging area integrating cognitive science, neuroscience, and artificial intelligence. At the heart of this field, approaches using encoding models allow for explaining brain activity from latent and high-dimensional features, including artificial neural networks. With the notable exception of temporal response function models that are applied to electroencephalography, most prior studies have focused on adult subjects, making it difficult to capture how brain representations change with learning and development. Here, we argue that future developmental cognitive neuroscience studies would benefit from approaches relying on encoding models. We provide an overview of encoding models used in adult functional magnetic resonance imaging research. This research has notably used data with a small number of subjects, but with a large number of samples per subject. Studies using encoding models also generally require task-based neuroimaging data. Though these represent challenges for developmental studies, we argue that these challenges may be overcome by using functional alignment techniques and naturalistic paradigms. These methods would facilitate encoding model analysis in developmental neuroimaging research, which may lead to important theoretical advances.

Socioeconomic status (SES) and cognitive outcomes are predicted by resting-state EEG in school-aged children

Children's socioeconomic status (SES) is related to patterns of intrinsic resting-state brain function that subserve relevant cognitive processes over the course of development. Although infant research has demonstrated the association between children's environments, cognitive outcomes, and resting-state electroencephalography (rsEEG), it remains unknown how these aspects of their environment, tied to SES, impact neural and cognitive development throughout the school years. To address this gap, we applied a multivariate pattern analysis (MVPA) to rsEEG data to identify which neural frequencies at rest are differentially associated with unique aspects of socioeconomic status (SES; income and maternal education) and cognitive (vocabulary, working memory) outcomes among school-aged children (8-15 years). We find that the alpha frequency is associated with both income and maternal education, while lower gamma and theta fluctuations are tied to dissociable aspects of SES and cognitive outcomes. Specifically, changes in the gamma frequency are predictive of both maternal education and vocabulary outcome, while changes in the theta frequency are related to both income and working memory ability. The current findings extend our understanding of unique pathways by which SES influences cognitive and neural development in school-aged children.

The representation of abstract goals in working memory is supported by task-congruent neural geometry

Successful goal-directed behavior requires the maintenance and implementation of abstract task goals on concrete stimulus information in working memory. Previous working memory research has revealed distributed neural representations of task information across cortex. However, how the distributed task representations emerge and communicate with stimulus-specific information to implement flexible goal-directed computations is still unclear. Here, leveraging electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) in human participants along with state space analyses, we provided converging evidence in support of a low-dimensional neural geometry of goal information congruent with a designed task space, which first emerged in frontal cortex during goal maintenance and then transferred to posterior cortex through frontomedial-to-posterior theta coherence for implementation on stimulus-specific representations. Importantly, the fidelity of the goal geometry was associated with memory performance. Collectively, our findings suggest that abstract goals in working memory are represented in an organized, task-congruent neural geometry for communications from frontal to posterior cortex to enable computations necessary for goal-directed behaviors.

Differential neural representations of syntactic and semantic information across languages in Chinese-English bilinguals

Bilingual individuals manage multiple languages that align in conceptual meaning but differ in forms and structures. While prior research has established foundational insights into the neural mechanisms in bilingual processing, the extent to which the first (L1) and second language (L2) systems overlap or diverge across different linguistic components remains unclear. This study probed the neural underpinnings of syntactic and semantic processing for L1 and L2 in Chinese-English bilinguals (N = 44) who performed sentence comprehension tasks and an N-back working memory task during functional MRI scanning. We observed that the increased activation for L2 processing was within the verbal working memory network, suggesting a greater cognitive demand for processing L2. Crucially, we looked for brain regions showing adaptation to the repetition of semantic information and syntactic structure, and found more robust adaptation effects in L1 in the middle and superior temporal cortical areas. The differential adaptation effects between L1 and L2 were more pronounced for the semantic condition. Multivariate pattern analysis further revealed distinct neural sensitivities to syntactic and semantic representations between L1 and L2 across frontotemporal language regions. Our findings suggest that while L1 and L2 engage similar neural systems, finer representation analyses uncover distinct neural patterns for both semantic and syntactic aspects in the two languages. This study advances our understanding of neural representations involved in different language components in bilingual individuals.

Behaviorally-relevant features of observed actions dominate cortical representational geometry in natural vision

We effortlessly extract behaviorally relevant information from dynamic visual input in order to understand the actions of others. In the current study, we develop and test a number of models to better understand the neural representational geometries supporting action understanding. Using fMRI, we measured brain activity as participants viewed a diverse set of 90 different video clips depicting social and nonsocial actions in real-world contexts. We developed five behavioral models using arrangement tasks: two models reflecting behavioral judgments of the purpose (transitivity) and the social content (sociality) of the actions depicted in the video stimuli; and three models reflecting behavioral judgments of the visual content (people, objects, and scene) depicted in still frames of the stimuli. We evaluated how well these models predict neural representational geometry and tested them against semantic models based on verb and nonverb embeddings and visual models based on gaze and motion energy. Our results revealed that behavioral judgments of similarity better reflect neural representational geometry than semantic or visual models throughout much of cortex. The sociality and transitivity models in particular captured a large portion of unique variance throughout the action observation network, extending into regions not typically associated with action perception, like ventral temporal cortex. Overall, our findings expand the action observation network and indicate that the social content and purpose of observed actions are predominant in cortical representation.

Family lexicon: Using language models to encode memories of personally familiar and famous people and places in the brain

Knowledge about personally familiar people and places is extremely rich and varied, involving pieces of semantic information connected in unpredictable ways through past autobiographical memories. In this work, we investigate whether we can capture brain processing of personally familiar people and places using subject-specific memories, after transforming them into vectorial semantic representations using language models. First, we asked participants to provide us with the names of the closest people and places in their lives. Then we collected open-ended answers to a questionnaire, aimed at capturing various facets of declarative knowledge. We collected EEG data from the same participants while they were reading the names and subsequently mentally visualizing their referents. As a control set of stimuli, we also recorded evoked responses to a matched set of famous people and places. We then created original semantic representations for the individual entities using language models. For personally familiar entities, we used the text of the answers to the questionnaire. For famous entities, we employed their Wikipedia page, which reflects shared declarative knowledge about them. Through whole-scalp time-resolved and searchlight encoding analyses, we found that we could capture how the brain processes one's closest people and places using person-specific answers to questionnaires, as well as famous entities. Overall encoding performance was significant in a large time window (200-800ms). Using spatio-temporal EEG searchlight, we found that we could predict brain responses significantly better than chance earlier (200-500ms) in bilateral temporo-parietal electrodes and later (500-700ms) in frontal and posterior central electrodes. We also found that XLM, a contextualized (or large) language model, provided superior encoding scores when compared with a simpler static language model as word2vec. Overall, these results indicate that language models can capture subject-specific semantic representations as they are processed in the human brain, by exploiting small-scale distributional lexical data.

A data-driven analysis of the perceptual and neural responses to natural objects reveals organising principles of human visual cognition

A key challenge in understanding the functional organisation of visual cortex stems from the fact that only a small proportion of the objects experienced during natural viewing can be presented in a typical experiment. This constraint often leads to experimental designs that compare responses to objects from experimenter-defined stimulus conditions, potentially limiting the interpretation of the data. To overcome this issue, we used images from the THINGS initiative, which provides a systematic sampling of natural objects. A data-driven analysis was then applied to reveal the functional organisation of the visual brain, incorporating both perceptual and neural responses to these objects. Perceptual properties of the objects were taken from an analysis of similarity judgements, and neural properties were taken from whole brain fMRI responses to the same objects. Partial least squares regression (PLSR) was then used to predict neural responses across the brain from the perceptual properties while simultaneously applying dimensionality reduction. The PLSR model accurately predicted neural responses across visual cortex using only a small number of components. These components revealed smooth, graded neural topographies, which were similar in both hemispheres, and captured a variety of object properties including animacy, real-world size, and object category. However, they did not accord in any simple way with previous theoretical perspectives on object perception. Instead, our findings suggest that visual cortex encodes information in a statistically efficient manner, reflecting natural variability among objects.Significance statement The ability to recognise objects is fundamental to how we interact with our environment, yet the organising principles underlying neural representations of visual objects remain contentious. In this study, we sought to address this question by analysing perceptual and neural responses to a large, unbiased sample of objects. Using a data-driven approach, we leveraged perceptual properties of objects to predict neural responses using a small number of components. This model predicted neural responses with a high degree of accuracy across visual cortex. The components did not directly align with previous explanations of object perception. Instead, our findings suggest the organisation of the visual brain is based on the statistical properties of objects in the natural world.

Convergent neural signatures of speech prediction error are a biological marker for spoken word recognition

We use MEG and fMRI to determine how predictions are combined with speech input in superior temporal cortex. We compare neural responses to words in which first syllables strongly or weakly predict second syllables (e.g., "bingo", "snigger" versus "tango", "meagre"). We further compare neural responses to the same second syllables when predictions mismatch with input during pseudoword perception (e.g., "snigo" and "meago"). Neural representations of second syllables are suppressed by strong predictions when predictions match sensory input but show the opposite effect when predictions mismatch. Computational simulations show that this interaction is consistent with prediction error but not alternative (sharpened signal) computations. Neural signatures of prediction error are observed 200 ms after second syllable onset and in early auditory regions (bilateral Heschl's gyrus and STG). These findings demonstrate prediction error computations during the identification of familiar spoken words and perception of unfamiliar pseudowords.

Neural Predictors of Fear Depend on the Situation

The extent to which neural representations of fear experience depend on or generalize across the situational context has remained unclear. We systematically manipulated variation within and across three distinct fear-evocative situations including fear of heights, spiders, and social threats. Participants (n = 21; 10 females and 11 males) viewed ∼20 s clips depicting spiders, heights, or social encounters and rated fear after each video. Searchlight multivoxel pattern analysis was used to identify whether and which brain regions carry information that predicts fear experience and the degree to which the fear-predictive neural codes in these areas depend on or generalize across the situations. The overwhelming majority of brain regions carrying information about fear did so in a situation-dependent manner. These findings suggest that local neural representations of fear experience are unlikely to involve a singular pattern but rather a collection of multiple heterogeneous brain states.

Predictive learning shapes the representational geometry of the human brain

Predictive coding theories propose that the brain constantly updates internal models to minimize prediction errors and optimize sensory processing. However, the neural mechanisms that link prediction error encoding and optimization of sensory representations remain unclear. Here, we provide evidence how predictive learning shapes the representational geometry of the human brain. We recorded magnetoencephalography (MEG) in humans listening to acoustic sequences with different levels of regularity. We found that the brain aligns its representational geometry to match the statistical structure of the sensory inputs, by clustering temporally contiguous and predictable stimuli. Crucially, the magnitude of this representational shift correlates with the synergistic encoding of prediction errors in a network of high-level and sensory areas. Our findings suggest that, in response to the statistical regularities of the environment, large-scale neural interactions engaged in predictive processing modulate the representational content of sensory areas to enhance sensory processing.

Using imagination and the contents of memory to create new scene and object representations: A functional MRI study

Humans can use the contents of memory to construct scenarios and events that they have not encountered before, a process colloquially known as imagination. Much of our current understanding of the neural mechanisms mediating imagination is limited by paradigms that rely on participants' subjective reports of imagined content. Here, we used a novel behavioral paradigm that was designed to systematically evaluate the contents of an individual's imagination. Participants first learned the layout of four distinct rooms containing five wall segments with differing geometrical characteristics, each associated with a unique object. During functional MRI, participants were then shown two different wall segments or objects on each trial and asked to first, retrieve the associated objects or walls, respectively (retrieval phase) and then second, imagine the two objects side-by-side or combine the two wall segments (imagination phase). Importantly, the contents of each participant's imagination were interrogated by having them make a same/different judgment about the properties of the imagined objects or scenes. Using univariate and multivariate analyses, we observed widespread activity across occipito-temporal cortex for the retrieval of objects and for the imaginative creation of scenes. Interestingly, a classifier, whether trained on the imagination or retrieval data, was able to successfully differentiate the neural patterns associated with the imagination of scenes from that of objects. Our results reveal neural differences in the cued retrieval of object and scene memoranda, demonstrate that different representations underlie the creation and/or imagination of scene and object content, and highlight a novel behavioral paradigm that can be used to systematically evaluate the contents of an individual's imagination.

Language proficiency is associated with neural representational dimensionality of semantic concepts

Previous studies suggest that semantic concepts are characterized by high-dimensional neural representations and that language proficiency affects semantic processing. However, it is not clear whether language proficiency modulates the dimensional representations of semantic concepts at the neural level. To address this question, the present study adopted principal component analysis (PCA) and representational similarity analysis (RSA) to examine the differences in representational dimensionalities (RDs) and in semantic representations between words in highly proficient (Chinese) and less proficient (English) language. PCA results revealed that language proficiency increased the dimensions of lexical representations in the left inferior frontal gyrus, temporal pole, inferior temporal gyrus, supramarginal gyrus, angular gyrus, and fusiform gyrus. RSA results further showed that these regions represented semantic information and that higher semantic representations were observed in highly proficient language relative to less proficient language. These results suggest that language proficiency is associated with the neural representational dimensionality of semantic concepts.

Functional PET/MRI reveals active inhibition of neuronal activity during optogenetic activation of the nigrostriatal pathway

The dopaminergic system is a central component of the brain's neurobiological framework, governing motor control and reward responses and playing an essential role in various brain disorders. Within this complex network, the nigrostriatal pathway represents a critical circuit for dopamine neurotransmission from the substantia nigra to the striatum. However, stand-alone functional magnetic resonance imaging is unable to study the intricate interplay between brain activation and its molecular underpinnings. In our study, the use of a functional [fluorine-18]2-fluor-2-deoxy-d-glucose positron emission tomography approach, simultaneously with blood oxygen level-dependent functional magnetic resonance imaging, provided an important insight that demonstrates an active suppression of the nigrostriatal activity during optogenetic stimulation. This result increases our understanding of the molecular mechanisms of brain function and provides an important perspective on how dopamine influences hemodynamic responses in the brain.

Convergent representation of values from tactile and visual inputs for efficient goal-directed behavior in the primate putamen

Animals can discriminate diverse sensory values with a limited number of neurons, raising questions about how the brain utilizes neural resources to efficiently process multi-dimensional inputs for decision-making. Here, we demonstrate that this efficiency is achieved by reducing sensory dimensions and converging towards the value dimension essential for goal-directed behavior in the putamen. Humans and monkeys performed tactile and visual value discrimination tasks while their neural responses were examined. Value information, whether originating from tactile or visual stimuli, was found to be processed within the human putamen using fMRI. Notably, at the single-neuron level in the macaque putamen, half of the individual neurons encode values independently of sensory inputs, while the other half selectively encode tactile or visual value. The responses of bimodal value neurons correlate with value-guided finger insertion behavior in both tasks, whereas modality-selective value neurons show task-specific correlations. Simulation using these neurons reveals that the presence of bimodal value neurons enables value discrimination with a significantly reduced number of neurons compared to simulations without them. Our data indicate that individual neurons in the primate putamen process different values in a convergent manner, thereby facilitating the efficient use of constrained neural resources for value-guided behavior.