Category Trumps Shape as an Organizational Principle of Object Space in the Human Occipitotemporal Cortex.

The organizational principles of the object space represented in the human ventral visual cortex are debated. Here we contrast two prominent proposals that, in addition to an organization in terms of animacy, propose either a representation related to aspect ratio (stubby-spiky) or to the distinction between faces and bodies. We designed a critical test that dissociates the latter two categories from aspect ratio and investigated responses from human fMRI (of either sex) and deep neural networks (BigBiGAN). Representational similarity and decoding analyses showed that the object space in the occipitotemporal cortex and BigBiGAN was partially explained by animacy but not by aspect ratio. Data-driven approaches showed clusters for face and body stimuli and animate-inanimate separation in the representational space of occipitotemporal cortex and BigBiGAN, but no arrangement related to aspect ratio. In sum, the findings go in favor of a model in terms of an animacy representation combined with strong selectivity for faces and bodies.SIGNIFICANCE STATEMENT We contrasted animacy, aspect ratio, and face-body as principal dimensions characterizing object space in the occipitotemporal cortex. This is difficult to test, as typically faces and bodies differ in aspect ratio (faces are mostly stubby and bodies are mostly spiky). To dissociate the face-body distinction from the difference in aspect ratio, we created a new stimulus set in which faces and bodies have a similar and very wide distribution of values along the shape dimension of the aspect ratio. Brain imaging (fMRI) with this new stimulus set showed that, in addition to animacy, the object space is mainly organized by the face-body distinction and selectivity for aspect ratio is minor (despite its wide distribution).

Two distinct networks containing position-tolerant representations of actions in the human brain.

Humans can recognize others' actions in the social environment. This action recognition ability is rarely hindered by the movement of people in the environment. The neural basis of this position tolerance for observed actions is not fully understood. Here, we aimed to identify brain regions capable of generalizing representations of actions across different positions and investigate the representational content of these regions. In a functional magnetic resonance imaging experiment, participants viewed point-light displays of different human actions. Stimuli were presented in either the upper or the lower visual field. Multivariate pattern analysis and a surface-based searchlight approach were employed to identify brain regions that contain position-tolerant action representation: Classifiers were trained with patterns in response to stimuli presented in one position and were tested with stimuli presented in another position. Results showed above-chance classification in the left and right lateral occipitotemporal cortices, right intraparietal sulcus, and right postcentral gyrus. Further analyses exploring the representational content of these regions showed that responses in the lateral occipitotemporal regions were more related to subjective judgments, while those in the parietal regions were more related to objective measures. These results provide evidence for two networks that contain abstract representations of human actions with distinct representational content.

Predicting Blood Oxygenation Level-Dependent Activity in Fusiform Face Area from the Activity in Other Visual Areas.

Neuroimaging studies have shown that discrete regions in ventral visual pathway respond selectively to specific object categories. For example, the fusiform face area (FFA) in humans is consistently more responsive to face than nonface images. However, it is not clear how other cortical regions contribute to this preferential response in FFA. To address this question, we performed a functional magnetic resonance imaging study on human subjects watching naturalistic movie clips from human actions. We then used correlation and multivariate regression (partial least-squares regression) analyses to estimate/predict the mean BOLD (blood oxygenation level-dependent) activity in FFA, from the mean and pattern of responses in 24 visual cortical areas. Higher tier retinotopic areas V3, hV4, and LO2, motion-selective area middle temporal, body-selective areas, and non-FFA face-selective areas had the best prediction accuracy particularly when they were located ipsilateral to FFA. All non-FFA collectively could explain up to 75% of variance in the FFA response. The regression models were also designed to predict the mean activity in one face area from the pattern of activity in another face area. The prediction power was significantly higher between the occipital face area and FFA. The multivariate regression analysis provides a new framework for investigating functional connectivity between cortical areas, and it could inform hierarchical models of visual cortex.

Brain Decoding-Classification of Hand Written Digits from fMRI Data Employing Bayesian Networks.

We are frequently exposed to hand written digits 0-9 in today's modern life. Success in decoding-classification of hand written digits helps us understand the corresponding brain mechanisms and processes and assists seriously in designing more efficient brain-computer interfaces. However, all digits belong to the same semantic category and similarity in appearance of hand written digits makes this decoding-classification a challenging problem. In present study, for the first time, augmented naïve Bayes classifier is used for classification of functional Magnetic Resonance Imaging (fMRI) measurements to decode the hand written digits which took advantage of brain connectivity information in decoding-classification. fMRI was recorded from three healthy participants, with an age range of 25-30. Results in different brain lobes (frontal, occipital, parietal, and temporal) show that utilizing connectivity information significantly improves decoding-classification and capability of different brain lobes in decoding-classification of hand written digits were compared to each other. In addition, in each lobe the most contributing areas and brain connectivities were determined and connectivities with short distances between their endpoints were recognized to be more efficient. Moreover, data driven method was applied to investigate the similarity of brain areas in responding to stimuli and this revealed both similarly active areas and active mechanisms during this experiment. Interesting finding was that during the experiment of watching hand written digits, there were some active networks (visual, working memory, motor, and language processing), but the most relevant one to the task was language processing network according to the voxel selection.

Reconstruction of digit images from human brain fMRI activity through connectivity informed Bayesian networks.

BACKGROUND: Newly emerged developments in decoding of stimulus images from fMRI measurements have shown promising results. Decoding-classification has been the main concern of decoding studies, whereas the matter of reconstruction (decoding) of stimulus images from fMRI data, especially natural images, lacks adequate examination and it requires plenty of efforts to improve. NEW METHOD: The present study employs Bayesian networks for decoding-reconstruction which is a novel application of this tool. Moreover, as a novel approach, we exploit the brain connectivity information in decoding-reconstruction procedure through Bayesian networks. RESULTS: The proposed method was applied to reconstruct 100 images of digits 6 and 9 from the fMRI measurements obtained when showing some handwritten images of 6 and 9 to the subject. The information of only 10 brain voxels were exploited and an average (standard deviation) city-block distance error of 0.1071(0.0134) was obtained for all stimuli's reconstruction. In comparison with current common methods: The results reveal that Bayesian networks are successful in decoding-reconstruction of handwritten digits and inclusion of brain connectivity information makes them perform even more efficiently and improves decoding-reconstruction as well (reducing average error by almost 5%). CONCLUSION: In the task of decoding-reconstruction, the models including brain connectivity appear significantly superior to other existing models.

Chaos-chaos transition of left hemisphere EEGs during standard tasks of Waterloo-Stanford Group Scale of hypnotic susceptibility.

A recent study, recurrence quantification analysis of EEG signals during standard tasks of Waterloo-Stanford Group Scale of hypnotic susceptibility investigated recurrence quantifiers (RQs) of hypnotic electroencephalograph (EEG) signals recorded after hypnotic induction while subjects were doing standard tasks of Waterloo-Stanford Group Scale (WSGS) of hypnotic susceptibility to distinguish subjects of different hypnotizability levels. Following the same analysis, the current study determines the capability of different RQs to distinguish subjects of low, medium and high hypnotizability level and studies the influence of hypnotizability level on underlying dynamic of tasks. Besides, EEG channels were sorted according to the number of their RQs, which differed significantly among subjects of different hypnotizability levels. Another valuable result was determination of major brain regions in observing significant differences in various task types (ideomotors, hallucination, challenge and memory).

Recurrence quantification analysis of electroencephalograph signals during standard tasks of Waterloo-Stanford group scale of hypnotic susceptibility.

The purpose of this study was to apply RQA (recurrence quantification analysis) on hypnotic electroencephalograph (EEG) signals recorded after hypnotic induction while subjects were doing standard tasks of the Waterloo-Stanford Group Scale (WSGS) of hypnotic susceptibility. Then recurrence quantifiers were used to analyse the influence of hypnotic depth on EEGs. By the application of this method, the capability of tasks to distinguish subjects of different hypnotizability levels was determined. Besides, medium hypnotizable subjects showed the highest disposition to be inducted by hypnotizer. Similarities between brain governing dynamics during tasks of the same type were also observed. The present study demonstrated two remarkable innovations; investigating the EEGs of the hypnotized as doing mental tasks of Waterloo-Stanford Group Scale (WSGS) and applying RQA on hypnotic EEGs.

The impacts of hypnotic susceptibility on chaotic dynamics of EEG signals during standard tasks of Waterloo-Stanford Group Scale.

Chaotic features of hypnotic EEG (electroencephalograph), recorded during standard tasks of Waterloo-Stanford Group Scale of hypnotic susceptibility (WSGS), were used to investigate the underlying dynamic of tasks and analyse the effect of hypnotic depth and concentration on EEG signals. Results demonstrate: (1) More efficiency of Higuchi dimension in comparison with Correlation dimension to distinguish subjects from different hypnotizable groups, (2) Channels with significantly different chaotic features among people from various hypnotizability levels in tasks, (3) High level of consistency among discriminating channels of tasks with function of brain's lobes, (4) Most affectability of medium hypnotizable subjects and (5) Rise in fractal dimensions due to increase in hypnosis depth.