Distinct and distributed functional connectivity patterns across cortex reflect the domain-specific constraints of object, face, scene, body, and tool category-selective modules in the ventral visual pathway

Natural scenes reveal diverse representations of 2D and 3D body pose in the human brain

Human pose, defined as the spatial relationships between body parts, carries instrumental information supporting the understanding of motion and action of a person. A substantial body of previous work has identified cortical areas responsive to images of bodies and different body parts. However, the neural basis underlying the visual perception of body part relationships has received less attention. To broaden our understanding of body perception, we analyzed high-resolution fMRI responses to a wide range of poses from over 4,000 complex natural scenes. Using ground-truth annotations and an application of three-dimensional (3D) pose reconstruction algorithms, we compared similarity patterns of cortical activity with similarity patterns built from human pose models with different levels of depth availability and viewpoint dependency. Targeting the challenge of explaining variance in complex natural image responses with interpretable models, we achieved statistically significant correlations between pose models and cortical activity patterns (though performance levels are substantially lower than the noise ceiling). We found that the 3D view-independent pose model, compared with two-dimensional models, better captures the activation from distinct cortical areas, including the right posterior superior temporal sulcus (pSTS). These areas, together with other pose-selective regions in the LOTC, form a broader, distributed cortical network with greater view-tolerance in more anterior patches. We interpret these findings in light of the computational complexity of natural body images, the wide range of visual tasks supported by pose structures, and possible shared principles for view-invariant processing between articulated objects and ordinary, rigid objects.

How the intrinsic functional connectivity patterns of the semantic network support semantic processing

Semantic processing, a core of language comprehension, involves the activation of brain regions dispersed extensively across the frontal, temporal, and parietal cortices that compose the semantic network. To comprehend the functional structure of this semantic network and how it prepares for semantic processing, we investigated its intrinsic functional connectivity (FC) and the relation between this pattern and semantic processing ability in a large sample from the Human Connectome Project (HCP) dataset. We first defined a well-studied brain network for semantic processing, and then we characterized the within-network connectivity (WNC) and the between-network connectivity (BNC) within this network using a voxel-based global brain connectivity (GBC) method based on resting-state functional magnetic resonance imaging (fMRI). The results showed that 97.73% of the voxels in the semantic network displayed considerably greater WNC than BNC, demonstrating that the semantic network is a fairly encapsulated network. Moreover, multiple connector hubs in the semantic network were identified after applying the criterion of WNC > 1 SD above the mean WNC of the semantic network. More importantly, three of these connector hubs (i.e., the left anterior temporal lobe, angular gyrus, and orbital part of the inferior frontal gyrus) were reliably associated with semantic processing ability. Our findings suggest that the three identified regions use WNC as the central mechanism for supporting semantic processing and that task-independent spontaneous connectivity in the semantic network is essential for semantic processing.

Characterizing the discriminability of visual categorical information in strongly connected voxels

Functional brain responses are strongly influenced by connectivity. Recently, we demonstrated a major example of this: category discriminability within occipitotemporal cortex (OTC) is enhanced for voxel sets that share strong functional connectivity to distal brain areas, relative to those that share lesser connectivity. That is, within OTC regions, sets of 'most-connected' voxels show improved multivoxel pattern discriminability for tool-, face-, and place stimuli relative to voxels with weaker connectivity to the wider brain. However, understanding whether these effects generalize to other domains (e.g. body perception network), and across different levels of the visual processing streams (e.g. dorsal as well as ventral stream areas) is an important extension of this work. Here, we show that this so-called connectivity-guided decoding (CGD) effect broadly generalizes across a wide range of categories (tools, faces, bodies, hands, places). This effect is robust across dorsal stream areas, but less consistent in earlier ventral stream areas. In the latter regions, category discriminability is generally very high, suggesting that extraction of category-relevant visual properties is less reliant on connectivity to downstream areas. Further, CGD effects are primarily expressed in a category-specific manner: For example, within the network of tool regions, discriminability of tool information is greater than non-tool information. The connectivity-guided decoding approach shown here provides a novel demonstration of the crucial relationship between wider brain connectivity and complex local-level functional responses at different levels of the visual processing streams. Further, this approach generates testable new hypotheses about the relationships between connectivity and local selectivity.

Overlapping representations of observed actions and action-related features

The lateral occipitotemporal cortex (LOTC) has been shown to capture the representational structure of a smaller range of actions. In the current study, we carried out an fMRI experiment in which we presented human participants with images depicting 100 different actions and used representational similarity analysis (RSA) to determine which brain regions capture the semantic action space established using judgments of action similarity. Moreover, to determine the contribution of a wide range of action-related features to the neural representation of the semantic action space we constructed an action feature model on the basis of ratings of 44 different features. We found that the semantic action space model and the action feature model are best captured by overlapping activation patterns in bilateral LOTC and ventral occipitotemporal cortex (VOTC). An RSA on eight dimensions resulting from principal component analysis carried out on the action feature model revealed partly overlapping representations within bilateral LOTC, VOTC, and the parietal lobe. Our results suggest spatially overlapping representations of the semantic action space of a wide range of actions and the corresponding action-related features. Together, our results add to our understanding of the kind of representations along the LOTC that support action understanding.

Representation of event and object concepts in ventral anterior temporal lobe and angular gyrus

Semantic knowledge includes understanding of objects and their features and also understanding of the characteristics of events. The hub-and-spoke theory holds that these conceptual representations rely on multiple information sources that are integrated in a central hub in the ventral anterior temporal lobes. The dual-hub theory expands this framework with the claim that the ventral anterior temporal lobe hub is specialized for object representation, while a second hub in angular gyrus is specialized for event representation. To test these ideas, we used representational similarity analysis, univariate and psychophysiological interaction analyses of fMRI data collected while participants processed object and event concepts (e.g. "an apple," "a wedding") presented as images and written words. Representational similarity analysis showed that angular gyrus encoded event concept similarity more than object similarity, although the left angular gyrus also encoded object similarity. Bilateral ventral anterior temporal lobes encoded both object and event concept structure, and left ventral anterior temporal lobe exhibited stronger coding for events. Psychophysiological interaction analysis revealed greater connectivity between left ventral anterior temporal lobe and right pMTG, and between right angular gyrus and bilateral ITG and middle occipital gyrus, for event concepts compared to object concepts. These findings support the specialization of angular gyrus for event semantics, though with some involvement in object coding, but do not support ventral anterior temporal lobe specialization for object concepts.

Disentangling hand and tool processing: Distal effects of neuromodulation

Neural processing within a local brain region that responds to more than one object category (e.g., hands and tools) nonetheless have different functional connectivity patterns with other distal brain areas, which suggests that local processing can affect and/or be affected by processing in distal areas, in a category-specific way. Here we wanted to test whether administering either a hand- or tool-related training task in tandem with transcranial direct current stimulation (tDCS) to a region that responds both to hands and tools (posterior middle temporal gyrus; pMTG), modulated local and distal neural processing more for the trained than the untrained category in a subsequent fMRI task. After each combined tDCS/training session, participants viewed images of tools, hands, and animals, in an fMRI scanner. Using multivoxel pattern analysis, we found that tDCS stimulation to pMTG indeed improved the classification accuracy between tools vs. animals, but only when combined with a tool and not a hand training task. Surprisingly, tDCS stimulation to pMTG also improved classification accuracy between hands vs. animals when combined with a tool but not a hand training task. Our findings suggest that overlapping but functionally-specific networks may be engaged separately by using a category-specific training task together with tDCS - a strategy that can be applied more broadly to other cognitive domains using tDCS. By hypothesis, these effects on local processing are a direct result of within-domain connectivity constraints from domain-specific networks that are at play in the processing and organization of object representations.

More Than the Face: Representations of Bodies in the Inferior Temporal Cortex

Visual representations of bodies, in addition to those of faces, contribute to the recognition of con- and heterospecifics, to action recognition, and to nonverbal communication. Despite its importance, the neural basis of the visual analysis of bodies has been less studied than that of faces. In this article, I review what is known about the neural processing of bodies, focusing on the macaque temporal visual cortex. Early single-unit recording work suggested that the temporal visual cortex contains representations of body parts and bodies, with the dorsal bank of the superior temporal sulcus representing bodily actions. Subsequent functional magnetic resonance imaging studies in both humans and monkeys showed several temporal cortical regions that are strongly activated by bodies. Single-unit recordings in the macaque body patches suggest that these represent mainly body shape features. More anterior patches show a greater viewpoint-tolerant selectivity for body features, which may reflect a processing principle shared with other object categories, including faces. Expected final online publication date for the Annual Review of Vision Science, Volume 8 is September 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Brain Functional Connectivity Asymmetry: Left Hemisphere Is More Modular

Graph-theoretical approaches are increasingly used to study the brain and may enhance our understanding of its asymmetries. In this paper, we hypothesize that the structure of the left hemisphere is, on average, more modular. To this end, we analyzed resting-state functional magnetic resonance imaging data of 90 healthy subjects. We computed functional connectivity by Pearson’s correlation coefficient, turned the matrix into an unweighted graph by keeping a certain percentage of the strongest connections, and quantified modularity separately for the subgraph formed by each hemisphere. Our results show that the left hemisphere is more modular. The result is consistent across a range of binarization thresholds, regardless of whether the two hemispheres are thresholded together or separately. This illustrates that graph-theoretical analysis can provide a robust characterization of lateralization of brain functional connectivity.

Multi-level Functional Connectivity Fusion Classification Framework for Brain Disease Diagnosis

Brain disease diagnosis is a new hotspot in the cross research of artificial intelligence and neuroscience. Quantitative analysis of functional magnetic resonance imaging (fMRI) data can provide valuable biomarkers that contributes to clinical diagnosis, and the analysis of functional connectivity (FC) has become the primary method. However, previous studies mainly focus on brain disease classification based on the low-order FC features, ignoring the potential role of high-order functional relationships among brain regions. To solve this problem, this study proposed a novel multi-level FC fusion classification framework (MFC) for brain disease diagnosis. We firstly designed a deep neural network (DNN) model to extract and learn abstract feature representations for the constructed low-order and high-order FC patterns. Both unsupervised and supervised learning steps were performed during the DNN model training, and the prototype learning was introduced in the supervised fine-tuning to improve the intra-class compactness and inter-class separability of the feature representation. Then, we combined the learned multi-level abstract FC features and trained an ensemble classifier with a hierarchical stacking learning strategy for the brain disease classification. Systematic experiments were conducted on two real large-scale fMRI datasets. Results showed that the proposed MFC model obtained robust classification performance for different preprocessing pipelines, different brain parcellations, and different cross-validation schemes, suggesting the effectiveness and generality of the proposed MFC model. Overall, this study provides a promising solution to combine the informative low-order and high-order FC patterns to further promote the classification of brain diseases.

One object, two networks? Assessing the relationship between the face and body-selective regions in the primate visual system

Faces and bodies are often treated as distinct categories that are processed separately by face- and body-selective brain regions in the primate visual system. These regions occupy distinct regions of visual cortex and are often thought to constitute independent functional networks. Yet faces and bodies are part of the same object and their presence inevitably covary in naturalistic settings. Here, we re-evaluate both the evidence supporting the independent processing of faces and bodies and the organizational principles that have been invoked to explain this distinction. We outline four hypotheses ranging from completely separate networks to a single network supporting the perception of whole people or animals. The current evidence, especially in humans, is compatible with all of these hypotheses, making it presently unclear how the representation of faces and bodies is organized in the cortex.

A computational neuroethology perspective on body and expression perception

Survival prompts organisms to prepare adaptive behavior in response to environmental and social threat. However, what are the specific features of the appearance of a conspecific that trigger such adaptive behaviors? For social species, the prime candidates for triggering defense systems are the visual features of the face and the body. We propose a novel approach for studying the ability of the brain to gather survival-relevant information from seeing conspecific body features. Specifically, we propose that behaviorally relevant information from bodies and body expressions is coded at the levels of midlevel features in the brain. These levels are relatively independent from higher-order cognitive and conscious perception of bodies and emotions. Instead, our approach is embedded in an ethological framework and mobilizes computational models for feature discovery.

Neural encoding and functional interactions underlying pantomimed movements

Pantomimes are a unique movement category which can convey complex information about our intentions in the absence of any interaction with real objects. Indeed, we can pretend to use the same tool to perform different actions or to achieve the same goal adopting different tools. Nevertheless, how our brain implements pantomimed movements is still poorly understood. In our study, we explored the neural encoding and functional interactions underlying pantomimes adopting multivariate pattern analysis (MVPA) and connectivity analysis of fMRI data. Participants performed pantomimed movements, either grasp-to-move or grasp-to-use, as if they were interacting with two different tools (scissors or axe). These tools share the possibility to achieve the same goal. We adopted MVPA to investigate two levels of representation during the planning and execution of pantomimes: (1) distinguishing different actions performed with the same tool, (2) representing the same final goal irrespective of the adopted tool. We described widespread encoding of action information within regions of the so-called “tool” network. Several nodes of the network—comprising regions within the ventral and the dorsal stream—also represented goal information. The spatial distribution of goal information changed from planning—comprising posterior regions (i.e. parietal and temporal)—to execution—including also anterior regions (i.e. premotor cortex). Moreover, connectivity analysis provided evidence for task-specific bidirectional coupling between the ventral stream and parieto-frontal motor networks. Overall, we showed that pantomimes were characterized by specific patterns of action and goal encoding and by task-dependent cortical interactions.

Topography of Visual Features in the Human Ventral Visual Pathway

Visual object recognition in humans and nonhuman primates is achieved by the ventral visual pathway (ventral occipital-temporal cortex, VOTC), which shows a well-documented object domain structure. An on-going question is what type of information is processed in the higher-order VOTC that underlies such observations, with recent evidence suggesting effects of certain visual features. Combining computational vision models, fMRI experiment using a parametric-modulation approach, and natural image statistics of common objects, we depicted the neural distribution of a comprehensive set of visual features in the VOTC, identifying voxel sensitivities with specific feature sets across geometry/shape, Fourier power, and color. The visual feature combination pattern in the VOTC is significantly explained by their relationships to different types of response-action computation (fight-or-flight, navigation, and manipulation), as derived from behavioral ratings and natural image statistics. These results offer a comprehensive visual feature map in the VOTC and a plausible theoretical explanation as a mapping onto different types of downstream response-action systems.

Quantifying the variability of neural activation in working memory: A functional probabilistic atlas

Working memory is a fundamental cognitive ability that allows the maintenance and manipulation of information for a brief period of time. Previous studies found a set of brain regions activated during working memory tasks, such as the prefrontal and parietal cortex. However, little is known about the variability of neural activation in working memory. Here, we used functional magnetic resonance imaging to quantify individual, hemispheric, and sex differences of working memory activation in a large cohort of healthy adults (N = 477). We delineated subject-specific activated regions in each individual, including the frontal pole, middle frontal gyrus, frontal eye field, superior parietal lobule, insular, precuneus, and anterior cingulate cortex. A functional probabilistic atlas was created to quantify individual variability in working memory regions. More than 90% of the participants activated all seven regions in both hemispheres, but the intersection of regions across participants was markedly less (50%), indicating significant individual differences in working memory activations. Moreover, we found hemispheric and sex differences in activation location, extent, and magnitude. Most activation regions were larger in the right than in the left hemisphere, but the magnitude of activation did not follow a similar pattern. Men showed more extensive and stronger activations than women. Taken together, our functional probabilistic atlas quantified variabilities of neural activation in working memory, providing a robust spatial reference for standardization of functional localization.

Hand-Selective Visual Regions Represent How to Grasp 3D Tools: Brain Decoding during Real Actions

Most neuroimaging experiments that investigate how tools and their actions are represented in the brain use visual paradigms where tools or hands are displayed as 2D images and no real movements are performed. These studies discovered selective visual responses in occipitotemporal and parietal cortices for viewing pictures of hands or tools, which are assumed to reflect action processing, but this has rarely been directly investigated. Here, we examined the responses of independently visually defined category-selective brain areas when participants grasped 3D tools (N = 20; 9 females). Using real-action fMRI and multivoxel pattern analysis, we found that grasp typicality representations (i.e., whether a tool is grasped appropriately for use) were decodable from hand-selective areas in occipitotemporal and parietal cortices, but not from tool-, object-, or body-selective areas, even if partially overlapping. Importantly, these effects were exclusive for actions with tools, but not for biomechanically matched actions with control nontools. In addition, grasp typicality decoding was significantly higher in hand than tool-selective parietal regions. Notably, grasp typicality representations were automatically evoked even when there was no requirement for tool use and participants were naive to object category (tool vs nontools). Finding a specificity for typical tool grasping in hand-selective, rather than tool-selective, regions challenges the long-standing assumption that activation for viewing tool images reflects sensorimotor processing linked to tool manipulation. Instead, our results show that typicality representations for tool grasping are automatically evoked in visual regions specialized for representing the human hand, the primary tool of the brain for interacting with the world.

High-Level Representations in Human Occipito-Temporal Cortex Are Indexed by Distal Connectivity

Human object recognition is dependent on occipito-temporal cortex (OTC), but a complete understanding of the complex functional architecture of this area must account for how it is connected to the wider brain. Converging functional magnetic resonance imaging evidence shows that univariate responses to different categories of information (e.g., faces, bodies, and nonhuman objects) are strongly related to, and potentially shaped by, functional and structural connectivity to the wider brain. However, to date, there have been no systematic attempts to determine how distal connectivity and complex local high-level responses in occipito-temporal cortex (i.e., multivoxel response patterns) are related. Here, we show that distal functional connectivity is related to, and can reliably index, high-level representations for several visual categories (i.e., tools, faces, and places) within occipito-temporal cortex; that is, voxel sets that are strongly connected to distal brain areas show higher pattern discriminability than less well-connected sets do. We further show that in several cases, pattern discriminability is higher in sets of well-connected voxels than sets defined by local activation (e.g., strong amplitude responses to faces in fusiform face area). Together, these findings demonstrate the important relationship between the complex functional organization of occipito-temporal cortex and wider brain connectivity.SIGNIFICANCE STATEMENT Human object recognition relies strongly on OTC, yet responses in this broad area are often considered in relative isolation to the rest of the brain. We employ a novel connectivity-guided voxel selection approach with functional magnetic resonance imaging data to show higher sensitivity to information (i.e., higher multivoxel pattern discriminability) in voxel sets that share strong connectivity to distal brain areas, relative to (1) voxel sets that are less strongly connected, and in several cases, (2) voxel sets that are defined by strong local response amplitude. These findings underscore the importance of distal contributions to local processing in OTC.

Overlapping but distinct: Distal connectivity dissociates hand and tool processing networks

The processes and organizational principles of information involved in object recognition have been a subject of intense debate. These research efforts led to the understanding that local computations and feedforward/feedback connections are essential to our representations and their organization. Recent data, however, has demonstrated that distal computations also play a role in how information is locally processed. Here we focus on how long-range connectivity and local functional organization of information are related, by exploring regions that show overlapping category-preferences for two categories and testing whether their connections are related with distal representations in a category-specific way. We used an approach that relates functional connectivity with distal areas to local voxel-wise category-preferences. Specifically, we focused on two areas that show an overlap in category-preferences for tools and hands-the inferior parietal lobule/anterior intraparietal sulcus (IPL/aIPS) and the posterior middle temporal gyrus/lateral occipital temporal cortex (pMTG/LOTC) - and how connectivity from these two areas relate to voxel-wise category-preferences in two ventral temporal regions dedicated to the processing of tools and hands separately-the left medial fusiform gyrus and the fusiform body area respectively-as well as across the brain. We show that the functional connections of the two overlap areas correlate with categorical preferences for each category independently. These results show that regions that process both tools and hands maintain object topography in a category-specific way. This potentially allows for a category-specific flow of information that is pertinent to computing object representations.

Myelin development in visual scene-network tracts beyond late childhood: A multimethod neuroimaging study

The visual scene-network-comprising the parahippocampal place area (PPA), retrosplenial cortex (RSC), and occipital place area (OPA)-shows a prolonged functional development. Structural development of white matter that underlies the scene-network has not been investigated despite its potential influence on scene-network function. The key factor for white matter maturation is myelination. However, research on myelination using the gold standard method of post-mortem histology is scarce. In vivo alternatives diffusion-weighted imaging (DWI) and myelin water imaging (MWI) so far report broad-scale findings that prohibit inferences concerning the scene-network. Here, we combine MWI, DWI tractography, and fMRI to investigate myelination in scene-network tracts in middle childhood, late childhood, and adulthood. We report increasing myelin from middle childhood to adulthood in right PPA-OPA, and trends towards increases in the left and right RSC-OPA tracts. Investigating tracts to regions highly connected with the scene-network, such as early visual cortex and the hippocampus, did not yield any significant age group differences. Our findings indicate that structural development coincides with functional development in the scene-network, possibly enabling structure-function interactions.

Sex-Specific Functional Connectivity in the Reward Network Related to Distinct Gender Roles

Gender roles are anti-dichotomous and malleable social constructs that should theoretically be constructed independently from biological sex. However, it is unclear whether and how the factor of sex is related to neural mechanisms involved in social constructions of gender roles. Thus, the present study aimed to investigate sex specificity in gender role constructions and the corresponding underlying neural mechanisms. We measured gender role orientation using the Bem Sex-Role Inventory, used a voxel-based global brain connectivity method based on resting-state functional magnetic resonance imaging to characterize the within-network connectivity in the brain reward network, and analyzed how the integration of the reward network is related to gender role scores between sex groups. An omnibus analysis of voxel-wise global brain connectivity values within a two-level linear mixed model revealed that in female participants, femininity scores were positively associated with integration in the posterior orbitofrontal cortex and subcallosal cortex, whereas masculinity scores were positively associated with integration in the frontal pole. By contrast, in male participants, masculinity was negatively correlated with integration in the nucleus accumbens and subcallosal cortex. For the first time, the present study revealed the sex-specific neural mechanisms underlying distinct gender roles, which elucidates the process of gender construction from the perspective of the interaction between reward sensitivity and social reinforcement.

Resting-State Functional Connectivity of the Punishment Network Associated With Conformity

Fear of punishment prompts individuals to conform. However, why some people are more inclined than others to conform despite being unaware of any obvious punishment remains unclear, which means the dispositional determinants of individual differences in conformity propensity are poorly understood. Here, we explored whether such individual differences might be explained by individuals' stable neural markers to potential punishment. To do this, we first defined the punishment network (PN) by combining all potential brain regions involved in punishment processing. We subsequently used a voxel-based global brain connectivity (GBC) method based on resting-state functional connectivity (FC) to characterize the hubs in the PN, which reflected an ongoing readiness state (i.e., sensitivity) for potential punishment. Then, we used the within-network connectivity (WNC) of each voxel in the PN of 264 participants to explain their tendency to conform by using a conformity scale. We found that a stronger WNC in the right thalamus, left insula, postcentral gyrus, and dACC was associated with a stronger tendency to conform. Furthermore, the FC among the four hubs seemed to form a three-phase ascending pathway, contributing to conformity propensity at every phase. Thus, our results suggest that task-independent spontaneous connectivity in the PN could predispose individuals to conform.

Domain-specific functional coupling between dorsal and ventral systems during action perception

Visual perception of actions and objects has been shown to activate different cortical systems: action perception system spanning more dorsally, across parietal, frontal, and dorsal temporal regions; object perception relying more strongly the ventral occipitotemporal cortex (VOTC). Compared to the well-established object-domain structure (e.g., faces vs. artifacts) in VOTC, it is less known whether the action perception system is constrained by similar domain principle and whether it communicates with the ventral object recognition system in a domain-specific manner. In a fMRI long-block experiment designed to evaluate both regional activity and task-based functional connectivity (FC) patterns, participants viewed animated videos of a human performing two domains of actions to the same set of meaningless shapes without object-domain information: social-communicative-actions (e.g., waving) and manipulation-actions (e.g., folding). We observed action-domain-specific activations, with the superior temporal sulcus and the right precentral region responding more strongly during social-communicative-action perception; the supramarginal gyrus, inferior and superior parietal lobe, and precentral gyrus during manipulation-action perception. The two domains of action perception systems communicated with VOTC in domain-specific manners: FC between the social-communicative-action system and the bilateral fusiform face area was enhanced during social-communicative-action perception; FC between the manipulation-action system and the left tool-preferring lateral occipitoptemporal cortex was enhanced during manipulation-action perception. There was a significant correlation between the FC-with-action-system and the local activity strength across VOTC voxels. Our findings highlight social- and manipulation-domains of human interaction as an overarching principle of both object and action perception systems, with domain-based functional communication across systems.

Spontaneously emerging patterns in human visual cortex and their functional connectivity are linked to the patterns evoked by visual stimuli

The function of spontaneous brain activity is an important issue in neuroscience. Here we test the hypothesis that patterns of spontaneous activity code representational patterns evoked by stimuli. We compared in human visual cortex multivertex patterns of spontaneous activity to patterns evoked by ecological visual stimuli (faces, bodies, scenes) and low-level visual features (e.g., phase-scrambled faces). Specifically, we identified regions that preferred particular stimulus categories during localizer scans (e.g., extrastriate body area for bodies), measured multivertex patterns for each category during event-related task scans, and then correlated over vertices these stimulus-evoked patterns to the pattern measured on each frame of resting-state scans. The mean correlation coefficient was essentially zero for all regions/stimulus categories, indicating that resting multivertex patterns were not biased toward particular stimulus-evoked patterns. However, the spread of correlation coefficients between stimulus-evoked and resting patterns, positive and negative, was significantly greater for the preferred stimulus category of an ROI. The relationship between spontaneous and stimulus-evoked multivertex patterns also governed the temporal correlation or functional connectivity of patterns of spontaneous activity between individual regions (pattern-based functional connectivity). Resting multivertex patterns related to an object category fluctuated preferentially between ROIs preferring the same category, and fluctuations of the pattern for a category (e.g., body) within its preferred ROIs were largely uncorrelated with fluctuations of the pattern for a disparate category (e.g., scene) within its preferred ROIs. These results support the proposal that spontaneous multivertex activity patterns are linked to stimulus-evoked patterns, consistent with a representational function for spontaneous activity.NEW & NOTEWORTHY Spontaneous brain activity was once thought to reflect only noise, but evidence of strong spatiotemporal regularities has motivated a search for functional explanations. Here we show that the spatial pattern of spontaneous activity in human high-level and early visual cortex is related to the spatial patterns evoked by stimuli. Moreover, these patterns partly govern spontaneous spatiotemporal interactions between regions, so-called functional connectivity. These results support the hypothesis that spontaneous activity serves a representational function.

Resting-State Activity in High-Order Visual Areas as a Window into Natural Human Brain Activations

A major limitation of conventional human brain research has been its basis in highly artificial laboratory experiments. Due to technical constraints, little is known about the nature of cortical activations during ecological real life. We have previously proposed the "spontaneous trait reactivation (STR)" hypothesis arguing that resting-state patterns, which emerge spontaneously in the absence of external stimulus, reflect the statistics of habitual cortical activations during real life. Therefore, these patterns can serve as a window into daily life cortical activity. A straightforward prediction of this hypothesis is that spontaneous patterns should preferentially correlate to patterns generated by naturalistic stimuli compared with artificial ones. Here we targeted high-level category-selective visual areas and tested this prediction by comparing BOLD functional connectivity patterns formed during rest to patterns formed in response to naturalistic stimuli, as well as to more artificial category-selective, dynamic stimuli. Our results revealed a significant correlation between the resting-state patterns and functional connectivity patterns generated by naturalistic stimuli. Furthermore, the correlations to naturalistic stimuli were significantly higher than those found between resting-state patterns and those generated by artificial control stimuli. These findings provide evidence of a stringent link between spontaneous patterns and the activation patterns during natural vision.

Connectivity at the origins of domain specificity in the cortical face and place networks

It is well established that the adult brain contains a mosaic of domain-specific networks. But how do these domain-specific networks develop? Here we tested the hypothesis that the brain comes prewired with connections that precede the development of domain-specific function. Using resting-state fMRI in the youngest sample of newborn humans tested to date, we indeed found that cortical networks that will later develop strong face selectivity (including the "proto" occipital face area and fusiform face area) and scene selectivity (including the "proto" parahippocampal place area and retrosplenial complex) by adulthood, already show domain-specific patterns of functional connectivity as early as 27 d of age (beginning as early as 6 d of age). Furthermore, we asked how these networks are functionally connected to early visual cortex and found that the proto face network shows biased functional connectivity with foveal V1, while the proto scene network shows biased functional connectivity with peripheral V1. Given that faces are almost always experienced at the fovea, while scenes always extend across the entire periphery, these differential inputs may serve to facilitate domain-specific processing in each network after that function develops, or even guide the development of domain-specific function in each network in the first place. Taken together, these findings reveal domain-specific and eccentricity-biased connectivity in the earliest days of life, placing new constraints on our understanding of the origins of domain-specific cortical networks.

Network Representations of Facial and Bodily Expressions: Evidence From Multivariate Connectivity Pattern Classification

Emotions can be perceived from both facial and bodily expressions. Our previous study has found the successful decoding of facial expressions based on the functional connectivity (FC) patterns. However, the role of the FC patterns in the recognition of bodily expressions remained unclear, and no neuroimaging studies have adequately addressed the question of whether emotions perceiving from facial and bodily expressions are processed rely upon common or different neural networks. To address this, the present study collected functional magnetic resonance imaging (fMRI) data from a block design experiment with facial and bodily expression videos as stimuli (three emotions: anger, fear, and joy), and conducted multivariate pattern classification analysis based on the estimated FC patterns. We found that in addition to the facial expressions, bodily expressions could also be successfully decoded based on the large-scale FC patterns. The emotion classification accuracies for the facial expressions were higher than that for the bodily expressions. Further contributive FC analysis showed that emotion-discriminative networks were widely distributed in both hemispheres, containing regions that ranged from primary visual areas to higher-level cognitive areas. Moreover, for a particular emotion, discriminative FCs for facial and bodily expressions were distinct. Together, our findings highlight the key role of the FC patterns in the emotion processing, indicating how large-scale FC patterns reconfigure in processing of facial and bodily expressions, and suggest the distributed neural representation for the emotion recognition. Furthermore, our results also suggest that the human brain employs separate network representations for facial and bodily expressions of the same emotions. This study provides new evidence for the network representations for emotion perception and may further our understanding of the potential mechanisms underlying body language emotion recognition.

Decoding disparity categories in 3-dimensional images from fMRI data using functional connectivity patterns

Humans use binocular disparity to extract depth information from two-dimensional retinal images in a process called stereopsis. Previous studies usually introduce the standard univariate analysis to describe the correlation between disparity level and brain activity within a given brain region based on functional magnetic resonance imaging (fMRI) data. Recently, multivariate pattern analysis has been developed to extract activity patterns across multiple voxels for deciphering categories of binocular disparity. However, the functional connectivity (FC) of patterns based on regions of interest or voxels and their mapping onto disparity category perception remain unknown. The present study extracted functional connectivity patterns for three disparity conditions (crossed disparity, uncrossed disparity, and zero disparity) at distinct spatial scales to decode the binocular disparity. Results of 27 subjects' fMRI data demonstrate that FC features are more discriminatory than traditional voxel activity features in binocular disparity classification. The average binary classification of the whole brain and visual areas are respectively 87% and 79% at single subject level, and thus above the chance level (50%). Our research highlights the importance of exploring functional connectivity patterns to achieve a novel understanding of 3D image processing.

Functional Localization of the Frontal Eye Fields in the Common Marmoset Using Microstimulation

The frontal eye field (FEF) is a critical region for the deployment of overt and covert spatial attention. Although investigations in the macaque continue to provide insight into the neural underpinnings of the FEF, due to its location within a sulcus, the macaque FEF is virtually inaccessible to electrophysiological techniques such as high-density and laminar recordings. With a largely lissencephalic cortex, the common marmoset (Callithrix jacchus) is a promising alternative primate model for studying FEF microcircuitry. Putative homologies have been established with the macaque FEF on the basis of cytoarchitecture and connectivity; however, physiological investigation in awake, behaving marmosets is necessary to physiologically locate this area. Here, we addressed this gap using intracortical microstimulation in a broad range of frontal cortical areas in three adult marmosets (two males, one female). We implanted marmosets with 96-channel Utah arrays and applied microstimulation trains while they freely viewed video clips. We evoked short-latency fixed vector saccades at low currents (<50 μA) in areas 45, 8aV, 8C, and 6DR. We observed a topography of saccade direction and amplitude consistent with findings in macaques and humans: small saccades in ventrolateral FEF and large saccades combined with contralateral neck and shoulder movements encoded in dorsomedial FEF. Our data provide compelling evidence supporting homology between marmoset and macaque FEF and suggest that the marmoset is a useful primate model for investigating FEF microcircuitry and its contributions to oculomotor and cognitive functions.SIGNIFICANCE STATEMENT The frontal eye field (FEF) is a critical cortical region for overt and covert spatial attention. The microcircuitry of this area remains poorly understood because in the macaque, the most commonly used model, it is embedded within a sulcus and is inaccessible to modern electrophysiological and imaging techniques. The common marmoset is a promising alternative primate model due to its lissencephalic cortex and potential for genetic manipulation. However, evidence for homologous cortical areas in this model remains limited and unclear. Here, we applied microstimulation in frontal cortical areas in marmosets to physiologically identify FEF. Our results provide compelling evidence for an FEF in the marmoset and suggest that the marmoset is a useful model for investigating FEF microcircuitry.

The relative contributions of visual and semantic information in the neural representation of object categories

INTRODUCTION

How do multiple sources of information interact to form mental representations of object categories? It is commonly held that object categories reflect the integration of perceptual features and semantic/knowledge-based features. To explore the relative contributions of these two sources of information, we used functional magnetic resonance imaging (fMRI) to identify regions involved in the representation object categories with shared visual and/or semantic features.

METHODS

Participants (N = 20) viewed a series of objects that varied in their degree of visual and semantic overlap in the MRI scanner. We used a blocked adaptation design to identify sensitivity to visual and semantic features in a priori visual processing regions and in a distributed network of object processing regions with an exploratory whole-brain analysis.

RESULTS

Somewhat surprisingly, within higher-order visual processing regions-specifically lateral occipital cortex (LOC)-we did not obtain any difference in neural adaptation for shared visual versus semantic category membership. More broadly, both visual and semantic information affected a distributed network of independently identified category-selective regions. Adaptation was seen a whole-brain network of processing regions in response to visual similarity and semantic similarity; specifically, the angular gyrus (AnG) adapted to visual similarity and the dorsomedial prefrontal cortex (DMPFC) adapted to both visual and semantic similarity.

CONCLUSIONS

Our findings suggest that perceptual features help organize mental categories throughout the object processing hierarchy. Most notably, visual similarity also influenced adaptation in nonvisual brain regions (i.e., AnG and DMPFC). We conclude that category-relevant visual features are maintained in higher-order conceptual representations and visual information plays an important role in both the acquisition and neural representation of conceptual object categories.

Action at a distance on object-related ventral temporal representations

The representation of objects in ventral temporal cortex is relatively resilient to transformations in the stimuli. There is emerging recognition that ventral temporal object representations are forged via interactions among a broader network of regions that receive independent inputs about a stimulus. Here we test whether ventral temporal representations are causally modulated by disrupting processing in distal associative areas. We used transcranial direct current stimulation (tDCS) to stimulate left parietal areas and functional Magnetic Resonance Imaging (fMRI) to measure object-related neural responses in the ventral stream. We find that representational geometries and category discriminability within ventral temporal cortex, as well as functional connectivity between ventral temporal and parietal areas, are enhanced by anodal compared to cathodal stimulation of left parietal associative cortex. These results demonstrate that ventral temporal representations can be causally modulated by processing distal to the ventral stream.

Domain-Specific Diaschisis: Lesions to Parietal Action Areas Modulate Neural Responses to Tools in the Ventral Stream

Neural responses to small manipulable objects ("tools") in high-level visual areas in ventral temporal cortex (VTC) provide an opportunity to test how anatomically remote regions modulate ventral stream processing in a domain-specific manner. Prior patient studies indicate that grasp-relevant information can be computed about objects by dorsal stream structures independently of processing in VTC. Prior functional neuroimaging studies indicate privileged functional connectivity between regions of VTC exhibiting tool preferences and regions of parietal cortex supporting object-directed action. Here we test whether lesions to parietal cortex modulate tool preferences within ventral and lateral temporal cortex. We found that lesions to the left anterior intraparietal sulcus, a region that supports hand-shaping during object grasping and manipulation, modulate tool preferences in left VTC and in the left posterior middle temporal gyrus. Control analyses demonstrated that neural responses to "place" stimuli in left VTC were unaffected by lesions to parietal cortex, indicating domain-specific consequences for ventral stream neural responses in the setting of parietal lesions. These findings provide causal evidence that neural specificity for "tools" in ventral and lateral temporal lobe areas may arise, in part, from online inputs to VTC from parietal areas that receive inputs via the dorsal visual pathway.

Neurochemical correlates of scene processing in the precuneus/posterior cingulate cortex: A multimodal fMRI and 1 H-MRS study

Precuneus/posterior cingulate cortex (PCu/PCC) are key components of a midline network, activated during rest but also in tasks that involve construction of scene or situation models. Despite growing interest in PCu/PCC functional alterations in disease and disease risk, the underlying neurochemical modulators of PCu/PCC's task‐evoked activity are largely unstudied. Here, a multimodal imaging approach was applied to investigate whether interindividual differences in PCu/PCC fMRI activity, elicited during perceptual discrimination of scene stimuli, were correlated with local brain metabolite levels, measured during resting‐state ¹H‐MRS. Forty healthy young adult participants completed an fMRI perceptual odd‐one‐out task for scenes, objects and faces. ¹H‐MRS metabolites N‐acetyl‐aspartate (tNAA), glutamate (Glx) and γ‐amino‐butyric acid (GABA+) were quantified via PRESS and MEGA‐PRESS scans in a PCu/PCC voxel and an occipital (OCC) control voxel. Whole brain fMRI revealed a cluster in right dorsal PCu/PCC that showed a greater BOLD response to scenes versus faces and objects. When extracted from an independently defined PCu/PCC region of interest, scene activity (vs. faces and objects and also vs. baseline) was positively correlated with PCu/PCC, but not OCC, tNAA. A voxel‐wise regression analysis restricted to the PCu/PCC ¹H‐MRS voxel area identified a significant PCu/PCC cluster, confirming the positive correlation between scene‐related BOLD activity and PCu/PCC tNAA. There were no correlations between PCu/PCC activity and Glx or GABA+ levels. These results demonstrate, for the first time, that scene activity in PCu/PCC is linked to local tNAA levels, identifying a neurochemical influence on interindividual differences in the task‐driven activity of a key brain hub.

Connectivity between the visual word form area and the parietal lobe improves after the first year of reading instruction: a longitudinal MRI study in children

Shortly after reading instruction, a region in the ventral occipital temporal cortex (vOTC) of the left hemisphere, the Visual Word Form Area (VWFA), becomes specialized for written words. Its reproducible location across scripts suggests important anatomical constraints, such as specific patterns of connectivity, notably to spoken language areas. Here, we explored the structural connectivity of the emerging VWFA in terms of its specificity relative to other ventral visual regions and its stability throughout the process of reading instruction in ten children studied longitudinally over 2 years. Category-specific regions for words, houses, faces, and tools were identified in the left vOTC of each subject with functional MRI. With diffusion MRI and tractography, we reconstructed the connections of these regions at two time points (mean age ± standard deviation: 6.2 ± 0.3, 7.2 ± 0.4 years). We first showed that the regions for each visual category harbor their own specific connectivity, all of which precede reading instruction and remain stable throughout development. The most specific connections of the VWFA were to the dorsal posterior parietal cortex. We then showed that microstructural changes in these connections correlated with improvements in reading scores over the first year of instruction but not 1 year later in a subsample of eight children (age: 8.4 ± 0.3 years). These results suggest that the VWFA location depends on its connectivity to distant regions, in particular, the left inferior parietal region which may play a crucial role in visual field maps and eye movement dynamics in addition to attentional control in letter-by-letter reading and disambiguation of mirror-letters during the first stages of learning to read.

A network view on brain regions involved in experts' object and pattern recognition: Implications for the neural mechanisms of skilled visual perception

Skilled visual object and pattern recognition form the basis of many everyday behaviours. The game of chess has often been used as a model case for studying how long-term experience aides in perceiving objects and their spatio-functional interrelations. Earlier research revealed two brain regions, posterior middle temporal gyrus (pMTG) and collateral sulcus (CoS), to be linked to chess experts' superior object and pattern recognition, respectively. Here we elucidated the brain networks these two expertise-related regions are embedded in, employing resting-state functional connectivity analysis and meta-analytic connectivity modelling with the BrainMap database. pMTG was preferentially connected with dorsal visual stream areas and a parieto-prefrontal network for action planning, while CoS was preferentially connected with posterior medial cortex and hippocampus, linked to scene perception, perspective-taking and navigation. Functional profiling using BrainMap meta-data revealed that pMTG was linked to semantic processing as well as inhibition and attention, while CoS was linked to face and shape perception as well as passive viewing. Our findings suggest that pMTG subserves skilled object recognition by mediating the link between object identity and object affordances, while CoS subserves skilled pattern recognition by linking the position of individual objects with typical spatio-functional layouts of their environment stored in memory.

Action and semantic tool knowledge - Effective connectivity in the underlying neural networks

Evidence from neuropsychological and imaging studies indicate that action and semantic knowledge about tools draw upon distinct neural substrates, but little is known about the underlying interregional effective connectivity. With fMRI and dynamic causal modeling (DCM) we investigated effective connectivity in the left-hemisphere (LH) while subjects performed (i) a function knowledge and (ii) a value knowledge task, both addressing semantic tool knowledge, and (iii) a manipulation (action) knowledge task. Overall, the results indicate crosstalk between action nodes and semantic nodes. Interestingly, effective connectivity was weakened between semantic nodes and action nodes during the manipulation task. Furthermore, pronounced modulations of effective connectivity within the fronto-parietal action system of the LH (comprising lateral occipito-temporal cortex, intraparietal sulcus, supramarginal gyrus, inferior frontal gyrus) were observed in a bidirectional manner during the processing of action knowledge. In contrast, the function and value knowledge tasks resulted in a significant strengthening of the effective connectivity between visual cortex and fusiform gyrus. Importantly, this modulation was present in both semantic tasks, indicating that processing different aspects of semantic knowledge about tools evokes similar effective connectivity patterns. Data revealed that interregional effective connectivity during the processing of tool knowledge occurred in a bidirectional manner with a weakening of connectivity between areas engaged in action and semantic knowledge about tools during the processing of action knowledge. Moreover, different semantic tool knowledge tasks elicited similar effective connectivity patterns.

Large-Scale Organization of the Hand Action Observation Network in Individuals Born Without Hands

The human high-level visual cortex comprises regions specialized for the processing of distinct types of stimuli, such as objects, animals, and human actions. How does this specialization emerge? Here, we investigated the role of effector-specific visuomotor coupling experience in shaping the organization of the action observation network (AON) as a window on this question. Observed body movements are frequently coupled with corresponding motor codes, e.g., during monitoring one’s own movements and imitation, resulting in bidirectionally connected circuits between areas involved in body movements observation (e.g., of the hand) and the motor codes involved in their execution. If the organization of the AON is shaped by this effector-specific visuomotor coupling, then, it should not form for body movements that do not belong to individuals’ motor repertoire. To test this prediction, we used fMRI to investigate the spatial arrangement and functional properties of the hand and foot action observation circuits in individuals born without upper limbs. Multivoxel pattern decoding, pattern similarity, and univariate analyses revealed an intact hand AON in the individuals born without upper limbs. This suggests that the organization of the AON does not require effector-specific visuomotor coupling.

Image categorization from functional magnetic resonance imaging using functional connectivity

BACKGROUND

Previous studies have attempted to infer the category of objects in a stimulus image from functional magnetic resonance imaging (fMRI) data recoded during image-viewing. Most studies focus on extracting activity patterns within a given region or across multiple voxels, and utilize the relationships among voxels to decipher the category of a stimulus image. Yet, the functional connectivity (FC) patterns across regions of interest in response to image categories, and their potential contributions to category classification are largely unknown.

NEW METHOD

We investigated whole-brain FC patterns in response to 4 image category stimuli (cats, faces, houses, and vehicles) using fMRI in healthy adult volunteers, and classified FC patterns using machine learning framework (Support Vector Machine [SVM] and Random Forest). We further examined the FC robustness and the influence of the window length on FC patterns for neural decoding.

RESULTS

The average one-vs.-one classification accuracy of the two classification models were 74% within subjects and 80% between subjects, which are higher than the chance level (50%). The Random Forest results were better than SVM results, and the 48-s FC results were better than the 24-s FC results.

COMPARISON WITH EXISTING METHOD(S)

We compared the classification performance of our FC patterns with two other existing methods, inter-block and intra-block, without overlapping temporal information.

CONCLUSIONS

Whole-brain FC patterns for different window lengths (24 and 48 s) can predict images categories with high accuracy. These results reveal novel mechanisms underlying the representation of categorical information in large-scale FC patterns in the human brain.

Fast periodic visual stimulation to study tool-selective processing in the human brain

Because tools are manipulated for the purpose of action, they are often considered to be a specific object category that associates perceptual and motor properties. Their neural processing has been studied extensively by comparing the cortical activity elicited by the separate presentation of tool and non-tool objects, assuming that observed differences are solely due to activity selective for processing tools. Here, using a fast periodic visual stimulation (FPVS) paradigm, we isolated EEG activity selectively related to the processing of tool objects embedded in a stream of non-tool objects. Participants saw a continuous sequence of tool and non-tool images at a 3.7 Hz presentation rate, arranged as a repeating pattern of four non-tool images followed by one tool image. We expected the stimulation to generate an EEG response at the frequency of image presentation (3.7 Hz) and its harmonics, reflecting activity common to the processing of tool and non-tool images. Most importantly, if tool and non-tool images evoked different neural responses, we expected this differential activity to generate an additional response at the frequency of tool images (3.7 Hz/5 = 0.74 Hz). To ensure that this response was not due to unaccounted for systematic differences in low-level visual features, we also tested a phase-scrambled version of the sequence. The periodic insertion of tool stimuli within a stream of non-tool stimuli elicited a significant EEG response at the tool-selective frequency and its harmonics. This response was reduced when the images were phase-scrambled. We conclude that FPVS is a promising technique to selectively measure tool-related activity.

Task- and domain-specific modulation of functional connectivity in the ventral and dorsal object-processing pathways

A whole-brain network of regions collectively supports the ability to recognize and use objects-the Tool Processing Network. Little is known about how functional interactions within the Tool Processing Network are modulated in a task-dependent manner. We designed an fMRI experiment in which participants were required to either generate object pantomimes or to carry out a picture matching task over the same images of tools, while holding all aspects of stimulus presentation constant across the tasks. The Tool Processing Network was defined with an independent functional localizer, and functional connectivity within the network was measured during the pantomime and picture matching tasks. Relative to tool picture matching, tool pantomiming led to an increase in functional connectivity between ventral stream regions and left parietal and frontal-motor areas; in contrast, the matching task was associated with an increase in functional connectivity among regions in ventral temporo-occipital cortex, and between ventral temporal regions and the left inferior parietal lobule. Graph-theory analyses over the functional connectivity data indicated that the left premotor cortex and left lateral occipital complex were hub-like (exhibited high betweenness centrality) during tool pantomiming, while ventral stream regions (left medial fusiform gyrus and left posterior middle temporal gyrus) were hub-like during the picture matching task. These results demonstrate task-specific modulation of functional interactions among a common set of regions, and indicate dynamic coupling of anatomically remote regions in task-dependent manner.

Stimulus Dependent Dynamic Reorganization of the Human Face Processing Network

Using the "face inversion effect", a hallmark of face perception, we examined network mechanisms supporting face representation by tracking functional magnetic resonance imaging (fMRI) stimulus-dependent dynamic functional connectivity within and between brain networks associated with the processing of upright and inverted faces. We developed a novel approach adapting the general linear model (GLM) framework classically used for univariate fMRI analysis to capture stimulus-dependent fMRI dynamic connectivity of the face network. We show that under the face inversion manipulation, the face and non-face networks have complementary roles that are evident in their stimulus-dependent dynamic connectivity patterns as assessed by network decomposition into components or communities. Moreover, we show that connectivity patterns are associated with the behavioral face inversion effect. Thus, we establish "a network-level signature" of the face inversion effect and demonstrate how a simple physical transformation of the face stimulus induces a dramatic functional reorganization across related brain networks. Finally, we suggest that the dynamic GLM network analysis approach, developed here for the face network, provides a general framework for modeling the dynamics of blocked stimulus-dependent connectivity experimental designs and hence can be applied to a host of neuroimaging studies.

The Large-Scale Organization of Object-Responsive Cortex Is Reflected in Resting-State Network Architecture

Neural responses to visually presented objects have a large-scale spatial organization across the cortex, related to the dimensions of animacy and object size. Most proposals about the origins of this organization point to the influence of differential connectivity with other cortical regions as the key organizing force that drives distinctions in object-responsive cortex. To explore this possibility, we used resting-state functional connectivity to examine the relationship between stimulus-evoked organization of objects, and distinctions in functional network architecture. Using a data-driven analysis, we found evidence for three distinct whole-brain resting-state networks that route through object-responsive cortex, and these naturally manifest the tripartite structure of the stimulus-evoked organization. However, object-responsive regions were also highly correlated with each other at rest. Together, these results point to a nested network architecture, with a local interconnected network across object-responsive cortex and distinctive subnetworks that specifically route these key object distinctions to distinct long-range regions. Broadly, these results point to the viability that long-range connections are a driving force of the large-scale organization of object-responsive cortex.

Connectivity of the ventral visual cortex is necessary for object recognition in patients

The functional profiles of regions in the ventral occipital-temporal cortex (VTC), a critical region for object visual recognition, are associated with the VTC connectivity patterns to nonvisual regions relevant to the corresponding object domain. However, whether and how whole-brain connections affect recognition behavior remains untested. We directly examined the necessity of VTC connectivity in object recognition behavior by testing 82 patients whose lesion spared relevant VTC regions but affected various white matter (WM) tracts and other regions. In these patients, we extracted the whole-brain anatomical connections of two VTC domain-selective (large manmade objects and animals) clusters with probabilistic tractography, and examined whether such connectivity pattern can predict recognition performance of the corresponding domains with support vector regression (SVR) analysis. We found that the whole-brain anatomical connectivity of large manmade object-specific cluster successfully predicted patients' large object recognition performance but not animal recognition or control tasks, even after we excluded connections with early visual regions. The contributing connections to large object recognition included tracts between VTC-large object cluster and distributed regions both within and beyond the visual cortex (e.g., putamen, superior, and middle temporal gyrus). These results provide causal evidence that the VTC whole-brain anatomical connectivity is necessary for at least certain domains of object recognition behavior.

Multivariate Pattern Classification of Facial Expressions Based on Large-Scale Functional Connectivity

It is an important question how human beings achieve efficient recognition of others’ facial expressions in cognitive neuroscience, and it has been identified that specific cortical regions show preferential activation to facial expressions in previous studies. However, the potential contributions of the connectivity patterns in the processing of facial expressions remained unclear. The present functional magnetic resonance imaging (fMRI) study explored whether facial expressions could be decoded from the functional connectivity (FC) patterns using multivariate pattern analysis combined with machine learning algorithms (fcMVPA). We employed a block design experiment and collected neural activities while participants viewed facial expressions of six basic emotions (anger, disgust, fear, joy, sadness, and surprise). Both static and dynamic expression stimuli were included in our study. A behavioral experiment after scanning confirmed the validity of the facial stimuli presented during the fMRI experiment with classification accuracies and emotional intensities. We obtained whole-brain FC patterns for each facial expression and found that both static and dynamic facial expressions could be successfully decoded from the FC patterns. Moreover, we identified the expression-discriminative networks for the static and dynamic facial expressions, which span beyond the conventional face-selective areas. Overall, these results reveal that large-scale FC patterns may also contain rich expression information to accurately decode facial expressions, suggesting a novel mechanism, which includes general interactions between distributed brain regions, and that contributes to the human facial expression recognition.

Exploring biased attention towards body-related stimuli and its relationship with body awareness

Stimuli of great social relevance exogenously capture attention. Here we explored the impact of body-related stimuli on endogenous attention. Additionally, we investigate the influence of internal states on biased attention towards this class of stimuli. Participants were presented with a body, face, or chair cue to hold in memory (Memory task) or to merely attend (Priming task) and, subsequently, they were asked to find a circle in an unrelated visual search task. In the valid condition, the circle was flanked by the cue. In the invalid condition, the pre-cued picture re-appeared flanking the distracter. In the neutral condition, the cue item did not re-appear in the search display. We found that although bodies and faces benefited from a general faster visual processing compared to chairs, holding them in memory did not produce any additional advantage on attention compared to when they are merely attended. Furthermore, face cues generated larger orienting effect compared to body and chairs cues in both Memory and Priming task. Importantly, results showed that individual sensitivity to internal bodily responses predicted the magnitude of the memory-based orienting of attention to bodies, shedding new light on the relationship between body awareness and visuo-spatial attention.