Integrative processing in artificial and biological vision predicts the perceived beauty of natural images.

Previous research shows that the beauty of natural images is already determined during perceptual analysis. However, it is unclear which perceptual computations give rise to the perception of beauty. Here, we tested whether perceived beauty is predicted by spatial integration across an image, a perceptual computation that reduces processing demands by aggregating image parts into more efficient representations of the whole. We quantified integrative processing in an artificial deep neural network model, where the degree of integration was determined by the amount of deviation between activations for the whole image and its constituent parts. This quantification of integration predicted beauty ratings for natural images across four studies with different stimuli and designs. In a complementary functional magnetic resonance imaging study, we show that integrative processing in human visual cortex similarly predicts perceived beauty. Together, our results establish integration as a computational principle that facilitates perceptual analysis and thereby mediates the perception of beauty.

Early language dissociation in bilingual minds: magnetoencephalography evidence through a machine learning approach.

Does neural activity reveal how balanced bilinguals choose languages? Despite using diverse neuroimaging techniques, prior studies haven't provided a definitive solution to this problem. Nonetheless, studies involving direct brain stimulation in bilinguals have identified distinct brain regions associated with language production in different languages. In this magnetoencephalography study with 45 proficient Spanish-Basque bilinguals, we investigated language selection during covert picture naming and word reading tasks. Participants were prompted to name line drawings or read words if the color of the stimulus changed to green, in 10% of trials. The task was performed either in Spanish or Basque. Despite similar sensor-level evoked activity for both languages in both tasks, decoding analyses revealed language-specific classification ~100 ms post-stimulus onset. During picture naming, right occipital-temporal sensors predominantly contributed to language decoding, while left occipital-temporal sensors were crucial for decoding during word reading. Cross-task decoding analysis unveiled robust generalization effects from picture naming to word reading. Our methodology involved a fine-grained examination of neural responses using magnetoencephalography, offering insights into the dynamics of language processing in bilinguals. This study refines our understanding of the neural underpinnings of language selection and bridges the gap between non-invasive and invasive experimental evidence in bilingual language production.

Decoding numeracy and literacy in the human brain: insights from MEG and MVPA.

Numbers and letters are the fundamental building blocks of our everyday social interactions. Previous studies have focused on determining the cortical pathways shaped by numeracy and literacy in the human brain, partially supporting the hypothesis of distinct perceptual neural circuits involved in the visual processing of the two categories. In this study, we aim to investigate the temporal dynamics for number and letter processing. We present magnetoencephalography (MEG) data from two experiments (N = 25 each). In the first experiment, single numbers, letters, and their respective false fonts (false numbers and false letters) were presented, whereas, in the second experiment, numbers, letters, and their respective false fonts were presented as a string of characters. We used multivariate pattern analysis techniques (time-resolved decoding and temporal generalization), testing the strong hypothesis that the neural correlates supporting letter and number processing can be logistically classified as categorically separate. Our results show a very early dissociation (~ 100 ms) between numbers, and letters when compared to false fonts. Number processing can be dissociated with similar accuracy when presented as isolated items or strings of characters, while letter processing shows dissociable classification accuracy for single items compared to strings. These findings reinforce the evidence indicating that early visual processing can be differently shaped by the experience with numbers and letters; this dissociation is stronger for strings compared to single items, thus showing that combinatorial mechanisms for numbers and letters could be categorically distinguished and influence early visual processing.

Temporal uncertainty enhances suppression of neural responses to predictable visual stimuli.

Contextual information triggers predictions about the content ("what") of environmental stimuli to update an internal generative model of the surrounding world. However, visual information dynamically changes across time, and temporal predictability ("when") may influence the impact of internal predictions on visual processing. In this magnetoencephalography (MEG) study, we investigated how processing feature specific information ("what") is affected by temporal predictability ("when"). Participants (N = 16) were presented with four consecutive Gabor patches (entrainers) with constant spatial frequency but with variable orientation and temporal onset. A fifth target Gabor was presented after a longer delay and with higher or lower spatial frequency that participants had to judge. We compared the neural responses to entrainers where the Gabor orientation could, or could not be temporally predicted along the entrainer sequence, and with inter-entrainer timing that was constant (predictable), or variable (unpredictable). We observed suppression of evoked neural responses in the visual cortex for predictable stimuli. Interestingly, we found that temporal uncertainty increased expectation suppression. This suggests that in temporally uncertain scenarios the neurocognitive system invests less resources in integrating bottom-up information. Multivariate pattern analysis showed that predictable visual features could be decoded from neural responses. Temporal uncertainty did not affect decoding accuracy for early visual responses, with the feature specificity of early visual neural activity preserved across conditions. However, decoding accuracy was less sustained over time for temporally jittered than for isochronous predictable visual stimuli. These findings converge to suggest that the cognitive system processes visual features of temporally predictable stimuli in higher detail, while processing temporally uncertain stimuli may rely more heavily on abstract internal expectations.

Spatiotemporal dynamics of postoperative functional plasticity in patients with brain tumors in language areas.

Postoperative functional neuroimaging provides a unique opportunity to investigate the neural mechanisms that facilitate language network reorganization. Previous studies in patients with low grade gliomas (LGGs) in language areas suggest that postoperative recovery is likely due to functional neuroplasticity in peritumoral and contra-tumoral healthy regions, but have attributed varying degrees of importance to specific regions. In this study, we used Magnetoencephalography (MEG) to investigate functional connectivity changes in peritumoral and contra-tumoral regions after brain tumor resection. MEG recordings of cortical activity during resting-state were obtained from 12 patients with LGGs in left-hemisphere language brain areas. MEG data were recorded before (Pre session), and 3 (Post_1 session) and 6 (Post_2 session) months after awake craniotomy. For each MEG session, we measured the functional connectivity of the peritumoral and contra-tumoral regions to the rest of the brain across the 1-100 Hz frequency band. We found that functional connectivity in the Post_1 and Post_2 sessions was higher than in the Pre session only in peritumoral regions and within the alpha frequency band. Functional connectivity in peritumoral regions did not differ between the Post_1 and Post_2 sessions. Alpha connectivity enhancement in peritumoral regions was observed in all patients regardless of the LGG location. Together, these results suggest that postoperative language functional reorganization occurs in peritumoral regions regardless of the location of the tumor and mostly develops within 3 months after surgery.

Trait anxiety and neural efficiency of abstract reasoning: An fMRI investigation.

Worries preoccupy the working memory capacity in anxious individuals, thereby affecting their performance during tasks that require efficient attention regulation. According to the attentional control theory (ACT), trait anxiety affects the processing efficiency, i.e. the effort required for task performance, more than the accuracy of task performance. We investigated the relation between trait anxiety and neural response for a reasoning task in healthy subjects. Functional magnetic resonance imaging (fMRI) was carried out on 22 healthy participants and blood oxygenation level dependent (BOLD) contrast estimates were extracted from a priori regions of interest (ROIs) that were earlier implicated in reasoning (i.e., bilaterally caudate head, globus pallidus, thalamus, prefrontal cortex [[ostral, dorsal and ventral regions]] inferior parietal lobule and middle occipital gyrus). Controlling for the effects of age, gender, state anxiety and depressive symptoms, for equivalent levels of task performance, trait anxiety of the participants was found to be associated with an increase in task related BOLD activation in right globus pallidus, left thalamus and left middle occipital gyrus. Our results suggest a reduced processing efficiency for reasoning in high trait anxiety subjects and provides important brain-behaviour relationships with respect to sub-clinical anxiety.

smRithm: Graphical user interface for heart rate variability analysis.

Over the past 25 years, Heart rate variability (HRV) has become a non-invasive research and clinical tool for indirectly carrying out investigation of both cardiac and autonomic system function in both healthy and diseased. It provides valuable information about a wide range of cardiovascular disorders, pulmonary diseases, neurological diseases, etc. Its primary purpose is to access the functioning of the nervous system. The source of information for HRV analysis is the continuous beat to beat measurement of inter-beat intervals. The electrocardiography (ECG or EKG) is considered as the best way to measure inter-beat intervals. This paper proposes an open source Graphical User Interface (GUI): smRithm developed in MATLAB for HRV analysis that will apply effective techniques on the raw ECG signals to process and decompose it in a simpler manner to obtain more useful information out of signals that can be utilized for more powerful and efficient applications in the near future related to HRV.