THE HUMAN VISUAL CORTEX

With childhood hemispherectomy, one hemisphere can support—but is suboptimal for—word and face recognition

The right and left cerebral hemispheres are important for face and word recognition, respectively—a specialization that emerges over human development. The question is whether this bilateral distribution is necessary or whether a single hemisphere, be it left or right, can support both face and word recognition. Here, face and word recognition accuracy in patients (median age 16.7 y) with a single hemisphere following childhood hemispherectomy was compared against matched typical controls. In experiment 1, participants viewed stimuli in central vision. Across both face and word tasks, accuracy of both left and right hemispherectomy patients, while significantly lower than controls' accuracy, averaged above 80% and did not differ from each other. To compare patients' single hemisphere more directly to one hemisphere of controls, in experiment 2, participants viewed stimuli in one visual field to constrain initial processing chiefly to a single (contralateral) hemisphere. Whereas controls had higher word accuracy when words were presented to the right than to the left visual field, there was no field/hemispheric difference for faces. In contrast, left and right hemispherectomy patients, again, showed comparable performance to one another on both face and word recognition, albeit significantly lower than controls. Altogether, the findings indicate that a single developing hemisphere, either left or right, may be sufficiently plastic for comparable representation of faces and words. However, perhaps due to increased competition or “neural crowding,” constraining cortical representations to one hemisphere may collectively hamper face and word recognition, relative to that observed in typical development with two hemispheres.

Cognitive decline and hippocampal functional connectivity within older Black adults

While there has been a proliferation of neuroimaging studies on cognitive decline in older non-Hispanic White adults, there is a dearth of knowledge regarding neuroimaging correlates of cognitive decline in Black adults. Resting-state functional neuroimaging approaches may be particularly sensitive to early cognitive decline, but there are no studies that we know of that apply this approach to examining associations of brain function to cognition in older Black adults. We investigated the association of cognitive decline with whole-brain voxel-wise functional connectivity to the hippocampus, a key brain region functionally implicated in early Alzheimer's dementia, in 132 older Black adults without dementia participating in the Minority Aging Research Study and Rush Memory and Aging Project, two longitudinal studies of aging that include harmonized annual cognitive assessments and magnetic resonance imaging brain imaging. In models adjusted for demographic factors (age, education, sex), global cognitive decline was associated with functional connectivity of the hippocampus to three clusters in the right and left frontal regions of the dorsolateral prefrontal cortex. In domain-specific analyses, decline in semantic memory was associated with functional connectivity of the hippocampus to bilateral clusters in the precentral gyrus, and decline in perceptual speed was inversely associated with connectivity of the hippocampus to the bilateral intracalcarine cortex and the right fusiform gyrus. These findings elucidate neurobiological mechanisms underlying cognitive decline in older Black adults and may point to specific targets of intervention for Alzheimer's disease.

Natural scene sampling reveals reliable coarse-scale orientation tuning in human V1

Orientation selectivity in primate visual cortex is organized into cortical columns. Since cortical columns are at a finer spatial scale than the sampling resolution of standard BOLD fMRI measurements, analysis approaches have been proposed to peer past these spatial resolution limitations. It was recently found that these methods are predominantly sensitive to stimulus vignetting - a form of selectivity arising from an interaction of the oriented stimulus with the aperture edge. Beyond vignetting, it is not clear whether orientation-selective neural responses are detectable in BOLD measurements. Here, we leverage a dataset of visual cortical responses measured using high-field 7T fMRI. Fitting these responses using image-computable models, we compensate for vignetting and nonetheless find reliable tuning for orientation. Results further reveal a coarse-scale map of orientation preference that may constitute the neural basis for known perceptual anisotropies. These findings settle a long-standing debate in human neuroscience, and provide insights into functional organization principles of visual cortex.

Modulation of Spectral Representation and Connectivity Patterns in Response to Visual Narrative in the Human Brain

The regional brain networks and the underlying neurophysiological mechanisms subserving the cognition of visual narrative in humans have largely been studied with non-invasive brain recording. In this study, we specifically investigated how regional and cross-regional cortical activities support visual narrative interpretation using intracranial stereotactic electroencephalograms recordings from thirteen human subjects (6 females, and 7 males). Widely distributed recording sites across the brain were sampled while subjects were explicitly instructed to observe images from fables presented in “sequential” order, and a set of images drawn from multiple fables presented in “scrambled” order. Broadband activity mainly within the frontal and temporal lobes were found to encode if a presented image is part of a visual narrative (sequential) or random image set (scrambled). Moreover, the temporal lobe exhibits strong activation in response to visual narratives while the frontal lobe is more engaged when contextually novel stimuli are presented. We also investigated the dynamics of interregional interactions between visual narratives and contextually novel series of images. Interestingly, the interregional connectivity is also altered between sequential and scrambled sequences. Together, these results suggest that both changes in regional neuronal activity and cross-regional interactions subserve visual narrative and contextual novelty processing.

Visual motion: Asymmetrical processing differences between the cerebral hemispheres

Hemispheric differences speak to the functional organisation of the human brain. A new study causally demonstrates such differences are present in bilateral motion-selective areas that are early in the visual cortical hierarchy.

Learning enhances encoding of time and temporal surprise in mouse primary sensory cortex

Primary sensory cortex has long been believed to play a straightforward role in the initial processing of sensory information. Yet, the superficial layers of cortex overall are sparsely active, even during sensory stimulation; additionally, cortical activity is influenced by other modalities, task context, reward, and behavioral state. Our study demonstrates that reinforcement learning dramatically alters representations among longitudinally imaged neurons in superficial layers of mouse primary somatosensory cortex. Learning an object detection task recruits previously unresponsive neurons, enlarging the neuronal population sensitive to touch and behavioral choice. Cortical responses decrease upon repeated stimulus presentation outside of the behavioral task. Moreover, training improves population encoding of the passage of time, and unexpected deviations in trial timing elicit even stronger responses than touches do. In conclusion, the superficial layers of sensory cortex exhibit a high degree of learning-dependent plasticity and are strongly modulated by non-sensory but behaviorally-relevant features, such as timing and surprise.

Activity in the superficial layers of the sensory cortex is believed to be largely driven by incoming sensory stimuli. Here the authors demonstrate how learning changes neural responses to sensations according to both behavioral relevance and timing, suggesting a high degree of non-sensory modulation.

HFFNet: hierarchical feature fusion network for blind binocular image quality prediction

Compared with monocular images, scene discrepancies between the left- and right-view images impose additional challenges on visual quality predictions in binocular images. Herein, we propose a hierarchical feature fusion network (HFFNet) for blind binocular image quality prediction that handles scene discrepancies and uses multilevel fusion features from the left- and right-view images to reflect distortions in binocular images. Specifically, a feature extraction network based on MobileNetV2 is used to determine the feature layers from distorted binocular images; then, low-level binocular fusion features (or middle-level and high-level binocular fusion features) are obtained by fusing the left and right low-level monocular features (or middle-level and high-level monocular features) using the feature gate module; further, three feature enhancement modules are used to enrich the information of the extracted features at different levels. Finally, the total feature maps obtained from the high-, middle-, and low-level fusion features are applied to a three-input feature fusion module for feature merging. Thus, the proposed HFFNet provides better results, to the best of our knowledge, than existing methods on two benchmark datasets.

Sex-dependent structure of socioemotional salience, executive control, and default mode networks in preschool-aged children with autism

The structure of large-scale intrinsic connectivity networks is atypical in adolescents diagnosed with autism spectrum disorder (ASD or autism). However, the degree to which alterations occur in younger children, and whether these differences vary by sex, is unknown. We utilized structural magnetic resonance imaging (MRI) data from a sex- and age- matched sample of 122 autistic and 122 typically developing (TD) children (2-4 years old) to investigate differences in underlying network structure in preschool-aged autistic children within three large scale intrinsic connectivity networks implicated in ASD: the Socioemotional Salience, Executive Control, and Default Mode Networks. Utilizing structural covariance MRI (scMRI), we report network-level differences in autistic versus TD children, and further report preliminary findings of sex-dependent differences within network topology.

Altered insular functional connectivity correlates to impaired vigilant attention after sleep deprivation: A resting-state functional magnetic resonance imaging study

Objectives

This study used resting-state functional magnetic resonance imaging (rs-fMRI) scans to assess the dominant effects of 36 h total sleep deprivation (TSD) on vigilant attention and changes in the resting-state network.

Materials and methods

Twenty-two healthy college students were enrolled in this study. Participants underwent two rs-fMRI scans, once in rested wakefulness (RW) and once after 36 h of TSD. We used psychomotor vigilance tasks (PVT) to measure vigilant attention. The region-of-interest to region-of-interest correlation was employed to analyze the relationship within the salience network (SN) and between other networks after 36 h of TSD. Furthermore, Pearson’s correlation analysis investigated the relationship between altered insular functional connectivity and PVT performance.

Results

After 36 h of TSD, participants showed significantly decreased vigilant attention. Additionally, TSD induced decreased functional connectivity between the visual and parietal regions, whereas, a significant increase was observed between the anterior cingulate cortex and insula. Moreover, changes in functional connectivity in the anterior cingulate cortex and insula showed a significant positive correlation with the response time to PVT.

Conclusion

Our results suggest that 36 h of TSD impaired vigilant visual attention, resulting in slower reaction times. The decrease in visual-parietal functional connectivity may be related to the decrease in the reception of information in the brain. Enhanced functional connectivity of the anterior cingulate cortex with the insula revealed that the brain network compensation occurs mainly in executive function.

Association Between Vision and Brain Cortical Thickness in a Community-Dwelling Elderly Cohort

Purpose

Visual impairment is a major cause of disability and impairment of cognitive function in older people. Brain structural changes associated with visual function impairment are not well understood. The objective of this study was to assess the association between visual function and cortical thickness in older adults.

Methods

Participants were selected from the French population-based ESPRIT cohort of 2259 community-dwelling adults ≥65 years old enrolled between 1999 and 2001. We considered visual function and brain MRI images at the 12-year follow-up in participants who were right-handed and free of dementia and/or stroke, randomly selected from the whole cohort. High-resolution structural T1-weighted brain scans acquired with a 3-Tesla scanner. Regional reconstruction and segmentation involved using the FreeSurfer image-analysis suite.

Results

A total of 215 participants were included (mean [SD] age 81.8 [3.7] years; 53.0% women): 30 (14.0%) had central vision loss and 185 (86.0%) normal central vision. Vision loss was associated with thinner cortical thickness in the right insula (within the lateral sulcus of the brain) as compared with the control group (mean thickness 2.38 [0.04] vs 2.50 [0.03] mm, 4.8% thinning, p_corrected= 0.04) after adjustment for age, sex, lifetime depression and cardiovascular disease.

Conclusion

The present study describes a significant thinning of the right insular cortex in older adults with vision loss. The insula subserves a wide variety of functions in humans ranging from sensory and affective processing to high-level cognitive processing. Reduced insula thickness associated with vision loss may increase cognitive burden in the ageing brain.

Decoding brain activities of literary metaphor comprehension: An event-related potential and EEG spectral analysis

Novel metaphors in literary texts (hereinafter referred to as literary metaphors) seem to be more creative and open-ended in meaning than metaphors in non-literary texts (non-literary metaphors). However, some disagreement still exists on how literary metaphors differ from non-literary metaphors. Therefore, this study explored the neural mechanisms of literary metaphors extracted from modern Chinese poetry by using the methods of Event-Related Potentials (ERPs) and Event-Related Spectral Perturbations (ERSPs), as compared with non-literary conventional metaphors and literal expressions outside literary texts. Forty-eight subjects were recruited to make the semantic relatedness judgment after reading the prime-target pairs in three linguistic conditions. According to the ERPs results, the earliest differences were presented during the time window of P200 component (170–260 ms) in the frontal and central areas, with the amplitude of P200 for literary metaphors more positive than the other two conditions, reflecting the early allocation of attention and the early conscious experience of the experimental stimuli. Meanwhile, significant differences were presented during the time window of N400 effect (430–530 ms), with the waveform of literary metaphors more negative than others in the frontal and central topography of scalp distributions, suggesting more efforts in retrieving conceptual knowledge for literary metaphors. The ERSPs analysis revealed that the frequency bands of delta and theta were both involved in the cognitive process of literary metaphor comprehension, with delta band distributed in the frontal and central scalp and theta band in parietal and occipital electrodes. Increases in the two power bands during different time windows provided extra evidences that the processing of literary metaphors required more attention and effort than non-literary metaphors and literal expressions in the semantic related tasks, suggesting that the cognitive process of literary metaphors was distinguished by different EEG spectral patterns.

Emotions Modulate Affordances-Related Motor Responses: A Priming Experiment

Traditionally, research on affordances and emotions follows two separate routes. For the first time, this article explicitly links the two phenomena by investigating whether, in a discrimination task (artifact vs. natural object), the motivational states induced by emotional images can modulate affordances-related motor response elicited by dangerous and neutral graspable objects. The results show faster RTs: (i) for both neutral and dangerous objects with neutral images; (ii) for dangerous objects with pleasant images; (iii) for neutral objects with unpleasant images. Overall, these data support a significant effect of emotions on affordances. The article also proposes a brain neural network underlying emotions and affordance interplay.

Information thermodynamics of encoding and encoders

Non-isolated systems have diverse coupling relations with the external environment. These relations generate complex thermodynamics and information transmission between the system and its environment. The framework depicted in the current research attempts to glance at the critical role of the internal orders inside the non-isolated system in shaping the information thermodynamics coupling. We characterize the coupling as a generalized encoding process, where the system acts as an information thermodynamics encoder to encode the external information based on thermodynamics. We formalize the encoding process in the context of the nonequilibrium second law of thermodynamics, revealing an intrinsic difference in information thermodynamics characteristics between information thermodynamics encoders with and without internal correlations. During the information encoding process of an external source Y, specific sub-systems in an encoder X with internal correlations can exceed the information thermodynamics bound on ( X , Y ) and encode more information than system X works as a whole. We computationally verify this theoretical finding in an Ising model with a random external field and a neural data set of the human brain during visual perception and recognition. Our analysis demonstrates that the stronger internal correlation inside these systems implies a higher possibility for specific sub-systems to encode more information than the global one. These findings may suggest a new perspective in studying information thermodynamics in diverse physical and biological systems.

Measuring PM2.5 Concentrations from a Single Smartphone Photograph

PM2.5 participates in light scattering, leading to degraded outdoor views, which forms the basis for estimating PM2.5 from photographs. This paper devises an algorithm to estimate PM2.5 concentrations by extracting visual cues and atmospheric indices from a single photograph. While air quality measurements in the context of complex urban scenes are particularly challenging, when only a single atmospheric index or cue is given, each one can reinforce others to yield a more robust estimator. Therefore, we selected an appropriate atmospheric index in various outdoor scenes to identify reasonable cue combinations for measuring PM2.5. A PM2.5 dataset (PhotoPM-daytime) was built and used to evaluate performance and validate efficacy of cue combinations. Furthermore, a city-wide experiment was conducted using photographs crawled from the Internet to demonstrate the applicability of the algorithm in large-area PM2.5 monitoring. Results show that smartphones equipped with the developed method could potentially be used as PM2.5 sensors.

Brain simulation augments machine-learning-based classification of dementia

Introduction

Computational brain network modeling using The Virtual Brain (TVB) simulation platform acts synergistically with machine learning (ML) and multi-modal neuroimaging to reveal mechanisms and improve diagnostics in Alzheimer's disease (AD).

Methods

We enhance large-scale whole-brain simulation in TVB with a cause-and-effect model linking local amyloid beta (Aβ) positron emission tomography (PET) with altered excitability. We use PET and magnetic resonance imaging (MRI) data from 33 participants of the Alzheimer's Disease Neuroimaging Initiative (ADNI3) combined with frequency compositions of TVB-simulated local field potentials (LFP) for ML classification.

Results

The combination of empirical neuroimaging features and simulated LFPs significantly outperformed the classification accuracy of empirical data alone by about 10% (weighted F1-score empirical 64.34% vs. combined 74.28%). Informative features showed high biological plausibility regarding the AD-typical spatial distribution.

Discussion

The cause-and-effect implementation of local hyperexcitation caused by Aβ can improve the ML-driven classification of AD and demonstrates TVB's ability to decode information in empirical data using connectivity-based brain simulation.

Causal evidence of the roles of the prefrontal and occipital cortices in modulating the impact of color on moral judgement

Moral judgment is known to be affected by factors such as color. Previous research has shown that the colors black and white are particularly important, however, the neural mechanisms underlying this effect remain unclear. This study aimed to investigate the causal relationship between specific brain regions (left dorsolateral prefrontal cortex, left DLPFC and occipital cortex, OC) and their impact of black and white moral judgement by using transcranial direct current stimulation (tDCS). The results of Experiment 1 (N = 54) and Experiment 2 (N = 66) showed that anodal tDCS over the left DLPFC inhibited the impact of black and white on moral judgment while cathodal tDCS over the left DLPFC enhanced the effect. Conversely, anodal tDCS over the OC enhanced the impact of white on moral judgment, while cathodal tDCS over the OC inhibited it. Together these results suggest that moral judgment relies not only on the cognitive control network, but also brain regions important for sensory perception. The current findings provide enhanced insight into how colors can impact moral judgments.

Learning induces coordinated neuronal plasticity of metabolic demands and functional brain networks

The neurobiological basis of learning is reflected in adaptations of brain structure, network organization and energy metabolism. However, it is still unknown how different neuroplastic mechanisms act together and if cognitive advancements relate to general or task-specific changes. Therefore, we tested how hierarchical network interactions contribute to improvements in the performance of a visuo-spatial processing task by employing simultaneous PET/MR neuroimaging before and after a 4-week learning period. We combined functional PET and metabolic connectivity mapping (MCM) to infer directional interactions across brain regions. Learning altered the top-down regulation of the salience network onto the occipital cortex, with increases in MCM at resting-state and decreases during task execution. Accordingly, a higher divergence between resting-state and task-specific effects was associated with better cognitive performance, indicating that these adaptations are complementary and both required for successful visuo-spatial skill learning. Simulations further showed that changes at resting-state were dependent on glucose metabolism, whereas those during task performance were driven by functional connectivity between salience and visual networks. Referring to previous work, we suggest that learning establishes a metabolically expensive skill engram at rest, whose retrieval serves for efficient task execution by minimizing prediction errors between neuronal representations of brain regions on different hierarchical levels.

Brain network analyses reveal coupled changes between functional connectivity and metabolic demands that relate to cognitive performance improvements induced by learning a challenging visuo-spatial task for four weeks.

Depression Classification Using Frequent Subgraph Mining Based on Pattern Growth of Frequent Edge in Functional Magnetic Resonance Imaging Uncertain Network

The brain network structure is highly uncertain due to the noise in imaging signals and evaluation methods. Recent works have shown that uncertain brain networks could capture uncertain information with regards to functional connections. Most of the existing research studies covering uncertain brain networks used graph mining methods for analysis; for example, the mining uncertain subgraph patterns (MUSE) method was used to mine frequent subgraphs and the discriminative feature selection for uncertain graph classification (DUG) method was used to select discriminant subgraphs. However, these methods led to a lack of effective discriminative information; this reduced the classification accuracy for brain diseases. Therefore, considering these problems, we propose an approximate frequent subgraph mining algorithm based on pattern growth of frequent edge (unFEPG) for uncertain brain networks and a novel discriminative feature selection method based on statistical index (dfsSI) to perform graph mining and selection. Results showed that compared with the conventional methods, the unFEPG and dfsSI methods achieved a higher classification accuracy. Furthermore, to demonstrate the efficacy of the proposed method, we used consistent discriminative subgraph patterns based on thresholding and weighting approaches to compare the classification performance of uncertain networks and certain networks in a bidirectional manner. Results showed that classification performance of the uncertain network was superior to that of the certain network within a defined sparsity range. This indicated that if a better classification performance is to be achieved, it is necessary to select a certain brain network with a higher threshold or an uncertain brain network model. Moreover, if the uncertain brain network model was selected, it is necessary to make full use of the uncertain information of its functional connection.

Amodal completion and relationalism

Amodal completion is usually characterized as the representation of those parts of the perceived object that we get no sensory stimulation from. In the case of the visual sense modality, for example, amodal completion is the representation of occluded parts of objects we see. I argue that relationalism about perception, the view that perceptual experience is constituted by the relation to the perceived object, cannot give a coherent account of amodal completion. The relationalist has two options: construe the perceptual relation as the relation to the entire perceived object or as the relation to the unoccluded parts of the perceived object. I argue that neither of these options are viable.

Motor-related signals support localization invariance for stable visual perception

Our ability to perceive a stable visual world in the presence of continuous movements of the body, head, and eyes has puzzled researchers in the neuroscience field for a long time. We reformulated this problem in the context of hierarchical convolutional neural networks (CNNs)-whose architectures have been inspired by the hierarchical signal processing of the mammalian visual system-and examined perceptual stability as an optimization process that identifies image-defining features for accurate image classification in the presence of movements. Movement signals, multiplexed with visual inputs along overlapping convolutional layers, aided classification invariance of shifted images by making the classification faster to learn and more robust relative to input noise. Classification invariance was reflected in activity manifolds associated with image categories emerging in late CNN layers and with network units acquiring movement-associated activity modulations as observed experimentally during saccadic eye movements. Our findings provide a computational framework that unifies a multitude of biological observations on perceptual stability under optimality principles for image classification in artificial neural networks.

Brain grey matter perfusion in primary progressive multiple sclerosis: Mild decrease over years and regional associations with cognition and hand function

BACKGROUND

Extend and dynamic of neurodegeneration in progressive Multiple Sclerosis (MS) might be reflected by global and regional brain perfusion, an outcome at the intercept between structure and function. Here, we provide a first insight in the evolution of brain perfusion and its association with disability in primary progressive MS (PPMS) over several years.

METHODS

77 persons with PPMS were followed over up to 5 years. Visits included a 3T MRI with pulsed Arterial spin labelling (ASL) perfusion, the Timed-25-Foot-Walk, 9-Hole-Peg-Test (NHPT), Symbol-Digit-Modalities-Test (SDMT) and Expanded Disability Status Scale (EDSS). We extracted regional cerebral blood flow surrogates and compared them to 11 controls. Analyses focused in cortical and deep gray matter, the change over time and associations with disability on regional and global level.

RESULTS

Baseline brain perfusion of patients and controls was comparable for the cortex (p=0.716) and deep grey matter (p=0.095). EDSS disability increased mildly (p=0.023) while brain perfusion decreased during follow up (p<0.001) and with disease duration (p=0.009). Lower global perfusion correlated with higher disability as indicated by EDSS, NHPT and Timed-25-Foot-Walk (p<0.001). The motor task NHPT showed associations with twenty gray matter regions. In contrast, better SDMT performance correlated with lower perfusion (p<0.001) in seven predominantly frontal regions indicating a functional maladaptation.

CONCLUSION

Decreasing perfusion indicates a putative association with MS disease mechanisms such as neurodegeneration, reduced metabolism, and loss of resilience. A low alteration rate limits its use in clinical practice, but regional association patterns might provide a snapshot of adaptive and maladaptive functional reorganization.

A Genuine Interindividual Variability in Number and Anatomical Localization of Face-Selective Regions in the Human Brain

Neuroimaging studies have reported regions with more neural activation to face than nonface stimuli in the human occipitotemporal cortex for three decades. Here we used a highly sensitive and reliable frequency-tagging functional magnetic resonance imaging paradigm measuring high-level face-selective neural activity to assess interindividual variability in the localization and number of face-selective clusters. Although the majority of these clusters are located in the same cortical gyri and sulci across 25 adult brains, a volume-based analysis of unsmoothed data reveals a large amount of interindividual variability in their spatial distribution and number, particularly in the ventral occipitotemporal cortex. In contrast to the widely held assumption, these face-selective clusters cannot be objectively related on a one-to-one basis across individual brains, do not correspond to a single cytoarchitectonic region, and are not clearly demarcated by estimated posteroanterior cytoarchitectonic borders. Interindividual variability in localization and number of cortical face-selective clusters does not appear to be due to the measurement noise but seems to be genuine, casting doubt on definite labeling and interindividual correspondence of face-selective "areas" and questioning their a priori definition based on cytoarchitectony or probabilistic atlases of independent datasets. These observations challenge conventional models of human face recognition based on a fixed number of discrete neurofunctional information processing stages.

Larger images are better remembered during naturalistic encoding

We are constantly exposed to multiple visual scenes, and while freely viewing them without an intentional effort to memorize or encode them, only some are remembered. It has been suggested that image memory is influenced by multiple factors, such as depth of processing, familiarity, and visual category. However, this is typically investigated when people are instructed to perform a task (e.g., remember or make some judgment about the images), which may modulate processing at multiple levels and thus, may not generalize to naturalistic visual behavior. Visual memory is assumed to rely on high-level visual perception that shows a level of size invariance and therefore is not assumed to be highly dependent on image size. Here, we reasoned that during naturalistic vision, free of task-related modulations, bigger images stimulate more visual system processing resources (from retina to cortex) and would, therefore, be better remembered. In an extensive set of seven experiments, naïve participants (n = 182) were asked to freely view presented images (sized 3° to 24°) without any instructed encoding task. Afterward, they were given a surprise recognition test (midsized images, 50% already seen). Larger images were remembered better than smaller ones across all experiments (∼20% higher accuracy or ∼1.5 times better). Memory was proportional to image size, faces were better remembered, and outdoors the least. Results were robust even when controlling for image set, presentation order, screen resolution, image scaling at test, or the amount of information. While multiple factors affect image memory, our results suggest that low- to high-level processes may all contribute to image memory.

A Joint Lateral Motion-Stereo Constraint

Purpose

We developed a stereo task that is based on a motion direction discrimination to examine the role that depth can play in disambiguating motion direction.

Methods

In this study, we quantified normal adults' static and dynamic (i.e., laterally moving) stereoscopic performance using a psychophysical task, where we dichoptically presented randomly arranged, limited lifetime Gabor elements at two depth planes (one plane was at the fixation plane and the other at an uncrossed disparity relative to the fixation plane). Each plane contained half of the elements. For the dynamic condition, all elements were vertically oriented and moved to the left in one plane and to the right in another plane; for the static condition, the elements were horizontally oriented in one plane and vertically oriented in another plane.

Results

For the range of motion speed that we measured (from 0.17°/s to 5.33°/s), we observed clear speed tuning of the stereo sensitivity (P = 3.0 × 10-5). The shape of this tuning did not significantly change with different spatial frequencies. We also found a significant difference in stereo sensitivity between stereopsis with static and laterally moving stimuli (speed = 0.67°/s; P = 0.004). Such difference was not evident when we matched the task between the static and moving stimuli.

Conclusions

We report that lateral motion modulates human global depth perception. This motion/stereo constraint is related to motion velocity not stimulus temporal frequency. We speculate that the processing of motion-based stereopsis of the kind reported here occurs in dorsal extrastriate cortex.

Domain-specific connectivity drives the organization of object knowledge in the brain

The goal of this chapter is to review neuropsychological and functional MRI findings that inform a theory of the causes of functional specialization for semantic categories within occipito-temporal cortex-the ventral visual processing pathway. The occipito-temporal pathway supports visual object processing and recognition. The theoretical framework that drives this review considers visual object recognition through the lens of how "downstream" systems interact with the outputs of visual recognition processes. Those downstream processes include conceptual interpretation, grasping and object use, navigating and orienting in an environment, physical reasoning about the world, and inferring future actions and the inner mental states of agents. The core argument of this chapter is that innately constrained connectivity between occipito-temporal areas and other regions of the brain is the basis for the emergence of neural specificity for a limited number of semantic domains in the brain.

A massive 7T fMRI dataset to bridge cognitive neuroscience and artificial intelligence

Extensive sampling of neural activity during rich cognitive phenomena is critical for robust understanding of brain function. Here we present the Natural Scenes Dataset (NSD), in which high-resolution functional magnetic resonance imaging responses to tens of thousands of richly annotated natural scenes were measured while participants performed a continuous recognition task. To optimize data quality, we developed and applied novel estimation and denoising techniques. Simple visual inspections of the NSD data reveal clear representational transformations along the ventral visual pathway. Further exemplifying the inferential power of the dataset, we used NSD to build and train deep neural network models that predict brain activity more accurately than state-of-the-art models from computer vision. NSD also includes substantial resting-state and diffusion data, enabling network neuroscience perspectives to constrain and enhance models of perception and memory. Given its unprecedented scale, quality and breadth, NSD opens new avenues of inquiry in cognitive neuroscience and artificial intelligence.

Automatic parcellation of resting-state cortical dynamics by iterative community detection and similarity measurements

To investigate the properties of a large-scale brain network, it is a common practice to reduce the dimension of resting state functional magnetic resonance imaging (rs-fMRI) data to tens to hundreds of nodes. This study presents an analytic streamline that incorporates modular analysis and similarity measurements (MOSI) to fulfill functional parcellation (FP) of the cortex. MOSI is carried out by iteratively dividing a module into sub-modules (via the Louvain community detection method) and unifying similar neighboring sub-modules into a new module (adjacent sub-modules with a similarity index <0.05) until the brain modular structures of successive runs become constant. By adjusting the gamma value, a parameter in the Louvain algorithm, MOSI may segment the cortex with different resolutions. rs-fMRI scans of 33 healthy subjects were selected from the dataset of the Rockland sample. MOSI was applied to the rs-fMRI data after standardized pre-processing steps. The results indicate that the parcellated modules by MOSI are more homogeneous in content. After reducing the grouped voxels to representative neural nodes, the network structures were explored. The resultant network components were comparable with previous reports. The validity of MOSI in achieving data reduction has been confirmed. MOSI may provide a novel starting point for further investigation of the network properties of rs-fMRI data. Potential applications of MOSI are discussed.

The neurobiology of treatment-resistant depression: A systematic review of neuroimaging studies

Treatment-resistant depression (TRD) is a debilitating condition associated with higher medical costs, increased illness burden, and reduced quality of life compared to non-treatment-resistant major depressive disorder (MDD). The question arises whether TRD can be considered a distinct MDD sub-type based on neurobiological features. To answer this question we conducted a systematic review of neuroimaging studies investigating the neurobiological differences between TRD and non-TRD. Our main findings are that patients with TRD show 1) reduced functional connectivity (FC) within the default mode network (DMN), 2) reduced FC between components of the DMN and other brain areas, and 3) hyperactivity of DMN regions. In addition, aberrant activity and FC in the occipital lobe may play a role in TRD. The main limitations of most studies were related to inherent confounding factors for comparing TRD with non-TRD, such as differences in disease chronicity/severity and medication history. Future studies may use prospective longitudinal neuroimaging designs to delineate which effects are present in treatment-naive patients and which effects are the result of disease progression.

Multi-timepoint pattern analysis: Influence of personality and behavior on decoding context-dependent brain connectivity dynamics

Behavioral traits are rarely considered in task‐evoked functional magnetic resonance imaging (MRI) studies, yet these traits can affect how an individual engages with the task, and thus lead to heterogeneity in task‐evoked brain responses. We aimed to investigate whether interindividual variation in behavior associates with the accuracy of predicting task‐evoked changes in the dynamics of functional brain connectivity measured with functional MRI. We developed a novel method called multi‐timepoint pattern analysis (MTPA), in which binary logistic regression classifiers were trained to distinguish rest from each of 7 tasks (i.e., social cognition, working memory, language, relational, motor, gambling, emotion) based on functional connectivity dynamics measured in 1,000 healthy adults. We found that connectivity dynamics for multiple pairs of large‐scale networks enabled individual classification between task and rest with accuracies exceeding 70%, with the most discriminatory connections relatively unique to each task. Crucially, interindividual variation in classification accuracy significantly associated with several behavioral, cognition and task performance measures. Classification between task and rest was generally more accurate for individuals with higher intelligence and task performance. Additionally, for some of the tasks, classification accuracy improved with lower perceived stress, lower aggression, higher alertness, and greater endurance. We conclude that heterogeneous dynamic adaptations of functional brain networks to changing cognitive demands can be reliably captured as linearly separable patterns by MTPA. Future studies should account for interindividual variation in behavior when investigating context‐dependent dynamic functional connectivity.

Effects of polygenic risk for suicide attempt and risky behavior on brain structure in young people with familial risk of bipolar disorder

Bipolar disorder (BD) is associated with a 20-30-fold increased suicide risk compared to the general population. First-degree relatives of BD patients show inflated rates of psychopathology including suicidal behaviors. As reliable biomarkers of suicide attempts (SA) are lacking, we examined associations between suicide-related polygenic risk scores (PRSs)-a quantitative index of genomic risk-and variability in brain structures implicated in SA. Participants (n = 206; aged 12-30 years) were unrelated individuals of European ancestry and comprised three groups: 41 BD cases, 96 BD relatives ("high risk"), and 69 controls. Genotyping employed PsychArray, followed by imputation. Three PRSs were computed using genome-wide association data for SA in BD (SA-in-BD), SA in major depressive disorder (SA-in-MDD) (Mullins et al., 2019, The American Journal of Psychiatry, 176(8), 651-660), and risky behavior (Karlsson Linnér et al., 2019, Nature Genetics, 51(2), 245-257). Structural magnetic resonance imaging processing employed FreeSurfer v5.3.0. General linear models were constructed using 32 regions-of-interest identified from suicide neuroimaging literature, with false-discovery-rate correction. SA-in-MDD and SA-in-BD PRSs negatively predicted parahippocampal thickness, with the latter association modified by group membership. SA-in-BD and Risky Behavior PRSs inversely predicted rostral and caudal anterior cingulate structure, respectively, with the latter effect driven by the "high risk" group. SA-in-MDD and SA-in-BD PRSs positively predicted cuneus structure, irrespective of group. This study demonstrated associations between PRSs for suicide-related phenotypes and structural variability in brain regions implicated in SA. Future exploration of extended PRSs, in conjunction with a range of biological, phenotypic, environmental, and experiential data in high risk populations, may inform predictive models for suicidal behaviors.

Predicting the retinotopic organization of human visual cortex from anatomy using geometric deep learning

Whether it be in a single neuron or a more complex biological system like the human brain, form and function are often directly related. The functional organization of human visual cortex, for instance, is tightly coupled with the underlying anatomy with cortical shape having been shown to be a useful predictor of the retinotopic organization in early visual cortex. Although the current state-of-the-art in predicting retinotopic maps is able to account for gross individual differences, such models are unable to account for any idiosyncratic differences in the structure-function relationship from anatomical information alone due to their initial assumption of a template. Here we developed a geometric deep learning model capable of exploiting the actual structure of the cortex to learn the complex relationship between brain function and anatomy in human visual cortex such that more realistic and idiosyncratic maps could be predicted. We show that our neural network was not only able to predict the functional organization throughout the visual cortical hierarchy, but that it was also able to predict nuanced variations across individuals. Although we demonstrate its utility for modeling the relationship between structure and function in human visual cortex, our approach is flexible and well-suited for a range of other applications involving data structured in non-Euclidean spaces.

Artificial Intelligence for Brain Molecular Imaging

AI has been applied to brain molecular imaging for over 30 years. The past two decades, have seen explosive progress. AI applications span from operations processes such as attenuation correction and image generation, to disease diagnosis and prediction. As sophistication in AI software platforms increases, and the availability of large imaging data repositories become common, future studies will incorporate more multidimensional datasets and information that may truly reach "superhuman" levels in the field of brain imaging. However, even with a growing level of complexity, these advanced networks will still require human supervision for appropriate application and interpretation in medical practice.

Brain Strategy Algorithm for Multiple Object Tracking Based on Merging Semantic Attributes and Appearance Features

The human brain can effortlessly perform vision processes using the visual system, which helps solve multi-object tracking (MOT) problems. However, few algorithms simulate human strategies for solving MOT. Therefore, devising a method that simulates human activity in vision has become a good choice for improving MOT results, especially occlusion. Eight brain strategies have been studied from a cognitive perspective and imitated to build a novel algorithm. Two of these strategies gave our algorithm novel and outstanding results, rescuing saccades and stimulus attributes. First, rescue saccades were imitated by detecting the occlusion state in each frame, representing the critical situation that the human brain saccades toward. Then, stimulus attributes were mimicked by using semantic attributes to reidentify the person in these occlusion states. Our algorithm favourably performs on the MOT17 dataset compared to state-of-the-art trackers. In addition, we created a new dataset of 40,000 images, 190,000 annotations and 4 classes to train the detection model to detect occlusion and semantic attributes. The experimental results demonstrate that our new dataset achieves an outstanding performance on the scaled YOLOv4 detection model by achieving a 0.89 mAP 0.5.

Direct comparison of contralateral bias and face/scene selectivity in human occipitotemporal cortex

Human visual cortex is organised broadly according to two major principles: retinotopy (the spatial mapping of the retina in cortex) and category-selectivity (preferential responses to specific categories of stimuli). Historically, these principles were considered anatomically separate, with retinotopy restricted to the occipital cortex and category-selectivity emerging in the lateral-occipital and ventral-temporal cortex. However, recent studies show that category-selective regions exhibit systematic retinotopic biases, for example exhibiting stronger activation for stimuli presented in the contra- compared to the ipsilateral visual field. It is unclear, however, whether responses within category-selective regions are more strongly driven by retinotopic location or by category preference, and if there are systematic differences between category-selective regions in the relative strengths of these preferences. Here, we directly compare contralateral and category preferences by measuring fMRI responses to scene and face stimuli presented in the left or right visual field and computing two bias indices: a contralateral bias (response to the contralateral minus ipsilateral visual field) and a face/scene bias (preferred response to scenes compared to faces, or vice versa). We compare these biases within and between scene- and face-selective regions and across the lateral and ventral surfaces of the visual cortex more broadly. We find an interaction between surface and bias: lateral surface regions show a stronger contralateral than face/scene bias, whilst ventral surface regions show the opposite. These effects are robust across and within subjects, and appear to reflect large-scale, smoothly varying gradients. Together, these findings support distinct functional roles for the lateral and ventral visual cortex in terms of the relative importance of the spatial location of stimuli during visual information processing.

Oscillatory visual mechanisms revealed by random temporal sampling

It is increasingly apparent that functionally significant neural activity is oscillatory in nature. Demonstrating the implications of this mode of operation for perceptual/cognitive function remains somewhat elusive. This report describes the technique of random temporal sampling for the investigation of visual oscillatory mechanisms. The technique is applied in visual recognition experiments using different stimulus classes (words, familiar objects, novel objects, and faces). Classification images reveal variations of perceptual effectiveness according to the temporal features of stimulus visibility. These classification images are also decomposed into their power and phase spectra. Stimulus classes lead to distinct outcomes and the power spectra of classification images are highly generalizable across individuals. Moreover, stimulus class can be reliably decoded from the power spectrum of individual classification images. These findings and other aspects of the results validate random temporal sampling as a promising new method to study oscillatory visual mechanisms.

Psychological and physiological evidence for an initial 'Rough Sketch' calculation of personal space

Personal space has been defined as “the area individuals maintain around themselves into which others cannot intrude without arousing discomfort”. However, the precise relationship between discomfort (or arousal) responses as a function of distance from an observer remains incompletely understood. Also the mechanisms involved in recognizing conspecifics and distinguishing them from other objects within personal space have not been identified. Accordingly, here we measured personal space preferences in response to real humans and human-like avatars (in virtual reality), using well-validated “stop distance” procedures. Based on threshold measurements of personal space, we examined within-subject variations in discomfort-related responses across multiple distances (spanning inside and outside each individual’s personal space boundary), as reflected by psychological (ratings) and physiological (skin conductance) responses to both humans and avatars. We found that the discomfort-by-distance functions for both humans and avatars were closely fit by a power law. These results suggest that the brain computation of visually-defined personal space begins with a ‘rough sketch’ stage, which generates responses to a broad range of human-like stimuli, in addition to humans. Analogous processing mechanisms may underlie other brain functions which respond similarly to both real and simulated human body parts.

Local neuronal relational structures underlying the contents of human conscious experience

While most theories of consciousness posit some kind of dependence on global network activities, I consider here an alternative, localist perspective-in which localized cortical regions each underlie the emergence of a unique category of conscious experience. Under this perspective, the large-scale activation often found in the cortex is a consequence of the complexity of typical conscious experiences rather than an obligatory condition for the emergence of conscious awareness-which can flexibly shift, depending on the richness of its contents, from local to more global activation patterns. This perspective fits a massive body of human imaging, recordings, lesions and stimulation data but opens a fundamental problem: how can the information, defining each content, be derived locally in each cortical region. Here, I will discuss a solution echoing pioneering structuralist ideas in which the content of a conscious experience is defined by its relationship to all other contents within an experiential category. In neuronal terms, this relationship structure between contents is embodied by the local geometry of similarity distances between cortical activation patterns generated during each conscious experience, likely mediated via networks of local neuronal connections. Thus, in order for any conscious experience to appear in an individual's mind, two central conditions must be met. First, a specific configural pattern ("bar-code") of neuronal activity must appear within a local relational geometry, i.e. a cortical area. Second, the individual neurons underlying the activated pattern must be bound into a unified functional ensemble through a burst of recurrent neuronal firing: local "ignitions".

On the relationship between maps and domains in inferotemporal cortex

How does the brain encode information about the environment? Decades of research have led to the pervasive notion that the object-processing pathway in primate cortex consists of multiple areas that are each specialized to process different object categories (such as faces, bodies, hands, non-face objects and scenes). The anatomical consistency and modularity of these regions have been interpreted as evidence that these regions are innately specialized. Here, we propose that ventral-stream modules do not represent clusters of circuits that each evolved to process some specific object category particularly important for survival, but instead reflect the effects of experience on a domain-general architecture that evolved to be able to adapt, within a lifetime, to its particular environment. Furthermore, we propose that the mechanisms underlying the development of domains are both evolutionarily old and universal across cortex. Topographic maps are fundamental, governing the development of specializations across systems, providing a framework for brain organization.

Cortical patterning of morphometric similarity gradient reveals diverged hierarchical organization in sensory-motor cortices

The topological organization of the cerebral cortex provides hierarchical axes, namely gradients, which reveal systematic variations of brain structure and function. However, the hierarchical organization of macroscopic brain morphology and how it constrains cortical function along the organizing axes remains unclear. We map the gradient of cortical morphometric similarity (MS) connectome, combining multiple features conceptualized as a "fingerprint" of an individual's brain. The principal MS gradient is anchored by motor and sensory cortices at two extreme ends, which are reliable and reproducible. Notably, divergences between motor and sensory hierarchies are consistent with the laminar histological thickness gradient but contrary to the canonical functional connectivity (FC) gradient. Moreover, the MS dissociates with FC gradients in the higher-order association cortices. The MS gradient recapitulates fundamental properties of cortical organization, from gene expression and cyto- and myeloarchitecture to evolutionary expansion. Collectively, our findings provide a heuristic hierarchical organization of cortical morphological neuromarkers.

Cortical networks of dynamic scene category representation in the human brain

Humans have an impressive ability to rapidly process global information in natural scenes to infer their category. Yet, it remains unclear whether and how scene categories observed dynamically in the natural world are represented in cerebral cortex beyond few canonical scene-selective areas. To address this question, here we examined the representation of dynamic visual scenes by recording whole-brain blood oxygenation level-dependent (BOLD) responses while subjects viewed natural movies. We fit voxelwise encoding models to estimate tuning for scene categories that reflect statistical ensembles of objects and actions in the natural world. We find that this scene-category model explains a significant portion of the response variance broadly across cerebral cortex. Cluster analysis of scene-category tuning profiles across cortex reveals nine spatially-segregated networks of brain regions consistently across subjects. These networks show heterogeneous tuning for a diverse set of dynamic scene categories related to navigation, human activity, social interaction, civilization, natural environment, non-human animals, motion-energy, and texture, suggesting that the organization of scene category representation is quite complex.

Dissociable Neural Representations of Adversarially Perturbed Images in Convolutional Neural Networks and the Human Brain

Despite the remarkable similarities between convolutional neural networks (CNN) and the human brain, CNNs still fall behind humans in many visual tasks, indicating that there still exist considerable differences between the two systems. Here, we leverage adversarial noise (AN) and adversarial interference (AI) images to quantify the consistency between neural representations and perceptual outcomes in the two systems. Humans can successfully recognize AI images as the same categories as their corresponding regular images but perceive AN images as meaningless noise. In contrast, CNNs can recognize AN images similar as corresponding regular images but classify AI images into wrong categories with surprisingly high confidence. We use functional magnetic resonance imaging to measure brain activity evoked by regular and adversarial images in the human brain, and compare it to the activity of artificial neurons in a prototypical CNN-AlexNet. In the human brain, we find that the representational similarity between regular and adversarial images largely echoes their perceptual similarity in all early visual areas. In AlexNet, however, the neural representations of adversarial images are inconsistent with network outputs in all intermediate processing layers, providing no neural foundations for the similarities at the perceptual level. Furthermore, we show that voxel-encoding models trained on regular images can successfully generalize to the neural responses to AI images but not AN images. These remarkable differences between the human brain and AlexNet in representation-perception association suggest that future CNNs should emulate both behavior and the internal neural presentations of the human brain.

Cortical connectivity in the face of congenital structural changes-A case of homozygous LAMC3 mutation

The homozygous LAMC3 gene mutation is associated with severe bilateral smoothening and thickening of the lateral occipital cortex . Despite this and further significant changes in gray matter structure, a patient harboring this mutation exhibited a range of remarkably intact perceptual abilities . One possible explanation of this perceptual sparing could be that the white matter structural integrity and functional connectivity in relevant pathways remained intact. To test this idea, we used diffusion tensor and functional magnetic resonance imaging to investigate functional connectivity in resting-state networks in major structural pathways involved in object perception and visual attention and corresponding microstructural integrity in a patient with homozygous LAMC3 mutation and sex, age, education, and socioeconomically matched healthy control group. White matter microstructural integrity results indicated widespread disruptions in both intra- and interhemispheric structural connections except inferior longitudinal fasciculus. With a few exceptions, the functional connectivity between the patient's adjacent gray matter regions of major white matter tracts of interest was conserved. In addition, functional localizers for face, object, and place areas showed similar results with a representative control, providing an explanation for the patient's intact face, place, and object recognition abilities. To generalize this finding, we also compared functional connectivity between early visual areas and face, place, and object category-selective areas, and we found that the functional connectivity of the patient was not different from the control group. Overall, our results provided complementary information about the effects of LAMC3 gene mutation on the human brain including intact temporo-occipital structural and functional connectivity that are compatible with preserved perceptual abilities.

Unveiling functions of the visual cortex using task-specific deep neural networks

The human visual cortex enables visual perception through a cascade of hierarchical computations in cortical regions with distinct functionalities. Here, we introduce an AI-driven approach to discover the functional mapping of the visual cortex. We related human brain responses to scene images measured with functional MRI (fMRI) systematically to a diverse set of deep neural networks (DNNs) optimized to perform different scene perception tasks. We found a structured mapping between DNN tasks and brain regions along the ventral and dorsal visual streams. Low-level visual tasks mapped onto early brain regions, 3-dimensional scene perception tasks mapped onto the dorsal stream, and semantic tasks mapped onto the ventral stream. This mapping was of high fidelity, with more than 60% of the explainable variance in nine key regions being explained. Together, our results provide a novel functional mapping of the human visual cortex and demonstrate the power of the computational approach.

The default mode network in cognition: a topographical perspective

The default mode network (DMN) is a set of widely distributed brain regions in the parietal, temporal and frontal cortex. These regions often show reductions in activity during attention-demanding tasks but increase their activity across multiple forms of complex cognition, many of which are linked to memory or abstract thought. Within the cortex, the DMN has been shown to be located in regions furthest away from those contributing to sensory and motor systems. Here, we consider how our knowledge of the topographic characteristics of the DMN can be leveraged to better understand how this network contributes to cognition and behaviour.

Self-reported experiences of discrimination in older black adults are associated with insula functional connectivity

Self-reported experiences of discrimination are associated with a number of negative health outcomes. However, the neurobiological correlates of discrimination remain elusive. Recent neuroimaging work suggests that the amygdala is sensitive to forms of social adversity and the insula is involved in assessments of trust. We hypothesized that functional connectivity (FC) of these brain regions may be associated with discrimination in older Black adults. One-hundred and twenty-four nondemented older Black adults participating in the Minority Aging Research Study or the Clinical Core study of the Rush Alzheimer's Disease Center completed a measure of self-reported experiences of discrimination and a 3T MRI brain scan including structural T1 and resting-state fMRI EPIBOLD sequences. The right and left amygdala and insula regions were anatomically delineated as ROIs according to the Harvard-Oxford Brain Atlas and whole-brain voxelwise FC analyses were conducted using default parameters in the CONN toolbox. In regression analyses controlling for demographics and global cognition, self-reported experiences of discrimination were associated with greater FC between the left insula and the bilateral intracalcarine cortex, weaker FC between the left insula and the left dorsolateral prefrontal cortex, and weaker FC between the right insula and the left supplementary motor area. Amygdala analyses yielded no significant findings. Greater self-reported experiences of discrimination are associated with differential insula functional connectivity in older adults. More specifically, results suggest that discrimination is associated with differential connectivity of a key region (the insula) involved in trust perception.

Reading-Related Brain Changes in Audiovisual Processing: Cross-Sectional and Longitudinal MEG Evidence

The ability to establish associations between visual objects and speech sounds is essential for human reading. Understanding the neural adjustments required for acquisition of these arbitrary audiovisual associations can shed light on fundamental reading mechanisms and help reveal how literacy builds on pre-existing brain circuits. To address these questions, the present longitudinal and cross-sectional MEG studies characterize the temporal and spatial neural correlates of audiovisual syllable congruency in children (age range, 4-9 years; 22 males and 20 females) learning to read. Both studies showed that during the first years of reading instruction children gradually set up audiovisual correspondences between letters and speech sounds, which can be detected within the first 400 ms of a bimodal presentation and recruit the superior portions of the left temporal cortex. These findings suggest that children progressively change the way they treat audiovisual syllables as a function of their reading experience. This reading-specific brain plasticity implies (partial) recruitment of pre-existing brain circuits for audiovisual analysis.SIGNIFICANCE STATEMENT Linking visual and auditory linguistic representations is the basis for the development of efficient reading, while dysfunctional audiovisual letter processing predicts future reading disorders. Our developmental MEG project included a longitudinal and a cross-sectional study; both studies showed that children's audiovisual brain circuits progressively change as a function of reading experience. They also revealed an exceptional degree of neuroplasticity in audiovisual neural networks, showing that as children develop literacy, the brain progressively adapts so as to better detect new correspondences between letters and speech sounds.

How Action Shapes Body Ownership Momentarily and Throughout the Lifespan

Objects which a human agent controls by efferent activities (such as real or virtual tools) can be perceived by the agent as belonging to his or her body. This suggests that what an agent counts as “body” is plastic, depending on what she or he controls. Yet there are possible limitations for such momentary plasticity. One of these limitations is that sensations stemming from the body (e.g., proprioception) and sensations stemming from objects outside the body (e.g., vision) are not integrated if they do not sufficiently “match”. What “matches” and what does not is conceivably determined by long–term experience with the perceptual changes that body movements typically produce. Children have accumulated less sensorimotor experience than adults have. Consequently, they express higher flexibility to integrate body-internal and body-external signals, independent of their “match” as suggested by rubber hand illusion studies. However, children’s motor performance in tool use is more affected by mismatching body-internal and body-external action effects than that of adults, possibly because of less developed means to overcome such mismatches. We review research on perception-action interactions, multisensory integration, and developmental psychology to build bridges between these research fields. By doing so, we account for the flexibility of the sense of body ownership for actively controlled events and its development through ontogeny. This gives us the opportunity to validate the suggested mechanisms for generating ownership by investigating their effects in still developing and incomplete stages in children. We suggest testable predictions for future studies investigating both body ownership and motor skills throughout the lifespan.