Levels Of Cortical Hierarchy Research Articles

Neural Representations in Visual and Parietal Cortex Differentiate between Imagined, Perceived, and Illusory Experiences.

Humans constantly receive massive amounts of information, both perceived from the external environment and imagined from the internal world. To function properly, the brain needs to correctly identify the origin of information being processed. Recent work has suggested common neural substrates for perception and imagery. However, it has remained unclear how the brain differentiates between external and internal experiences with shared neural codes. Here we tested this question in human participants (male and female) by systematically investigating the neural processes underlying the generation and maintenance of visual information from voluntary imagery, veridical perception, and illusion. The inclusion of illusion allowed us to differentiate between objective and subjective internality: while illusion has an objectively internal origin and can be viewed as involuntary imagery, it is also subjectively perceived as having an external origin like perception. Combining fMRI, eye-tracking, multivariate decoding, and encoding approaches, we observed superior orientation representations in parietal cortex during imagery compared with perception, and conversely in early visual cortex. This imagery dominance gradually developed along a posterior-to-anterior cortical hierarchy from early visual to parietal cortex, emerged in the early epoch of imagery and sustained into the delay epoch, and persisted across varied imagined contents. Moreover, representational strength of illusion was more comparable to imagery in early visual cortex, but more comparable to perception in parietal cortex, suggesting content-specific representations in parietal cortex differentiate between subjectively internal and external experiences, as opposed to early visual cortex. These findings together support a domain-general engagement of parietal cortex in internally generated experience.SIGNIFICANCE STATEMENT How does the brain differentiate between imagined and perceived experiences? Combining fMRI, eye-tracking, multivariate decoding, and encoding approaches, the current study revealed enhanced stimulus-specific representations in visual imagery originating from parietal cortex, supporting the subjective experience of imagery. This neural principle was further validated by evidence from visual illusion, wherein illusion resembled perception and imagery at different levels of cortical hierarchy. Our findings provide direct evidence for the critical role of parietal cortex as a domain-general region for content-specific imagery, and offer new insights into the neural mechanisms underlying the differentiation between subjectively internal and external experiences.

Spectrotemporal content of human auditory working memory represented in functional connectivity patterns

Recent research suggests that working memory (WM), the mental sketchpad underlying thinking and communication, is maintained by multiple regions throughout the brain. Whether parts of a stable WM representation could be distributed across these brain regions is, however, an open question. We addressed this question by examining the content-specificity of connectivity-pattern matrices between subparts of cortical regions-of-interest (ROI). These connectivity patterns were calculated from functional MRI obtained during a ripple-sound auditory WM task. Statistical significance was assessed by comparing the decoding results to a null distribution derived from a permutation test considering all comparable two- to four-ROI connectivity patterns. Maintained WM items could be decoded from connectivity patterns across ROIs in frontal, parietal, and superior temporal cortices. All functional connectivity patterns that were specific to maintained sound content extended from early auditory to frontoparietal cortices. Our results demonstrate that WM maintenance is supported by content-specific patterns of functional connectivity across different levels of cortical hierarchy.

Atypical Integration of Sensory-to-Transmodal Functional Systems Mediates Symptom Severity in Autism.

A notable characteristic of autism spectrum disorder (ASD) is co-occurring deficits in low-level sensory processing and high-order social interaction. While there is evidence indicating detrimental cascading effects of sensory anomalies on the high-order cognitive functions in ASD, the exact pathological mechanism underlying their atypical functional interaction across the cortical hierarchy has not been systematically investigated. To address this gap, here we assessed the functional organisation of sensory and motor areas in ASD, and their relationship with subcortical and high-order trandmodal systems. In a resting-state fMRI data of 107 ASD and 113 neurotypical individuals, we applied advanced connectopic mapping to probe functional organization of primary sensory/motor areas, together with targeted seed-based intrinsic functional connectivity (iFC) analyses. In ASD, the connectopic mapping revealed topological anomalies (i.e., excessively more segregated iFC) in the motor and visual areas, the former of which patterns showed association with the symptom severity of restricted and repetitive behaviors. Moreover, the seed-based analysis found diverging patterns of ASD-related connectopathies: decreased iFCs within the sensory/motor areas but increased iFCs between sensory and subcortical structures. While decreased iFCs were also found within the higher-order functional systems, the overall proportion of this anomaly tends to increase along the level of cortical hierarchy, suggesting more dysconnectivity in the higher-order functional networks. Finally, we demonstrated that the association between low-level sensory/motor iFCs and clinical symptoms in ASD was mediated by the high-order transmodal systems, suggesting pathogenic functional interactions along the cortical hierarchy. Findings were largely replicated in the independent dataset. These results highlight that atypical integration of sensory-to-high-order systems contributes to the complex ASD symptomatology.

Biased Neural Representation of Feature-Based Attention in the Human Frontoparietal Network.

Selective attention is a core cognitive function for efficient processing of information. Although it is well known that attention can modulate neural responses in many brain areas, the computational principles underlying attentional modulation remain unclear. Contrary to the prevailing view of a high-dimensional, distributed neural representation, here we show a surprisingly simple, biased neural representation for feature-based attention in a large dataset including five human fMRI studies. We found that when human participants (both sexes) selected one feature from a compound stimulus, voxels in many cortical areas responded consistently higher to one attended feature over the other. This univariate bias was consistent across brain areas within individual subjects. Importantly, this univariate bias showed a progressively stronger magnitude along the cortical hierarchy. In frontoparietal areas, the bias was strongest and contributed largely to pattern-based decoding, whereas early visual areas lacked such a bias. These findings suggest a gradual transition from a more analog to a more abstract representation of attentional priority along the cortical hierarchy. Biased neural responses in high-level areas likely reflect a low-dimensional neural code that can facilitate a robust representation and simple readout of cognitive variables.SIGNIFICANCE STATEMENT It is typically assumed that cognitive variables are represented by distributed population activities. Although this view is rooted in decades of work in the sensory system, it has not been rigorously tested at different levels of cortical hierarchy. Here we show a novel, low-dimensional coding scheme that dominated the representation of feature-based attention in frontoparietal areas. The simplicity of such a biased code may confer a robust representation of cognitive variables, such as attentional selection, working memory, and decision-making.

Компоненты вызванного потенциала в исследовании перцептивного научения

Perceptual learning is defined by increased effectiveness of completing perceptual tasks as a result of experience or training. This review presents the analysis of changes in the components of event-related potentials (ERPs) after visual and auditory perceptual learning in humans. The use of the EEG method, which has a high temporal resolution, makes it possible to trace the spatio-temporal dynamics of changes in the functioning of the brain during learning, which remains hidden in behavioral experimental studies. A review of neurophysiological studies indicates that perceptual learning induces changes across all levels of cortical hierarchy, starting with the early sensory components of ERPs (C1) and ending with the later integrative components (N170, MMN, P2). We also analyzed the short-term and long-term effects of learning. The reviewed neurophysiological data can serve as the basis for the development of new approaches of effective learning, as well as for the objective evaluation of existing methodics by assessing neuronal dynamics at different stages of stimuli processing.

Input and output gain modulation by the lateral interhemispheric network in early visual cortex.

Neurons in the cerebral cortex are constantly integrating different types of inputs. Dependent on their origin, these inputs can be modulatory in many ways and, for example, change the neuron's responsiveness, sensitivity, or selectivity. To investigate the modulatory role of lateral input from the same level of cortical hierarchy, we recorded in the primary visual cortex of cats while controlling synaptic input from the corresponding contralateral hemisphere by reversible deactivation. Most neurons showed a pronounced decrease in their response to a visual stimulus of different contrasts and orientations. This indicates that the lateral network acts via an unspecific gain-setting mechanism, scaling the output of a neuron. However, the interhemispheric input also changed the contrast sensitivity of many neurons, thereby acting on the input. Such a contrast gain mechanism has important implications because it extends the role of the lateral network from pure response amplification to the modulation of a specific feature. Interestingly, for many neurons, we found a mixture of input and output gain modulation. Based on these findings and the known physiology of callosal connections in the visual system, we developed a simple model of lateral interhemispheric interactions. We conclude that the lateral network can act directly on its target, leading to a sensitivity change of a specific feature, while at the same time it also can act indirectly, leading to an unspecific gain setting. The relative contribution of these direct and indirect network effects determines the outcome for a particular neuron.