Label-free, whole-brain in vivo mapping in an adult vertebrate with third harmonic generation microscopy.

Comprehensive understanding of interconnected networks within the brain requires access to high resolution information within large field of views and over time. Currently, methods that enable mapping structural changes of the entire brain in vivo are extremely limited. Third harmonic generation (THG) can resolve myelinated structures, blood vessels, and cell bodies throughout the brain without the need for any exogenous labeling. Together with deep penetration of long wavelengths, this enables in vivo brain-mapping of large fractions of the brain in small animals and over time. Here, we demonstrate that THG microscopy allows non-invasive label-free mapping of the entire brain of an adult vertebrate, Danionella dracula, which is a miniature species of cyprinid fish. We show this capability in multiple brain regions and in particular the identification of major commissural fiber bundles in the midbrain and the hindbrain. These features provide readily discernable landmarks for navigation and identification of regional-specific neuronal groups and even single neurons during in vivo experiments. We further show how this label-free technique can easily be coupled with fluorescence microscopy and used as a comparative tool for studies of other species with similar body features to Danionella, such as zebrafish (Danio rerio) and tetras (Trochilocharax ornatus). This new evidence, building on previous studies, demonstrates how small size and relative transparency, combined with the unique capabilities of THG microscopy, can enable label-free access to the entire adult vertebrate brain.

Modulate the impact of the drowsiness on the resting state functional connectivity.

This research explores different methodologies to modulate the effects of drowsiness on functional connectivity (FC) during resting-state functional magnetic resonance imaging (RS-fMRI). The study utilized a cohort of students (MRi-Share) and classified individuals into drowsy, alert, and mixed/undetermined states based on observed respiratory oscillations. We analyzed the FC group difference between drowsy and alert individuals after five different processing methods: the reference method, two based on physiological and a global signal regression of the BOLD time series signal, and two based on Gaussian standardizations of the FC distribution. According to the reference method, drowsy individuals exhibit higher cortico-cortical FC than alert individuals. First, we demonstrated that each method reduced the differences between drowsy and alert states. The second result is that the global signal regression was quantitively the most effective, minimizing significant FC differences to only 3.3% of the total FCs. However, one should consider the risks of overcorrection often associated with this methodology. Therefore, choosing a less aggressive form of regression, such as the physiological method or Gaussian-based approaches, might be a more cautious approach. Third and last, using the Gaussian-based methods, cortico-subcortical and intra-default mode network (DMN) FCs were significantly greater in alert than drowsy subjects. These findings bear resemblance to the anticipated patterns during the onset of sleep, where the cortex isolates itself to assist in transitioning into deeper slow wave sleep phases, simultaneously disconnecting the DMN.

Investigating the different mechanisms in related neural activities: a focus on auditory perception and imagery.

Neuroimaging studies have shown that the neural representation of imagery is closely related to the perception modality; however, the undeniable different experiences between perception and imagery indicate that there are obvious neural mechanism differences between them, which cannot be explained by the simple theory that imagery is a form of weak perception. Considering the importance of functional integration of brain regions in neural activities, we conducted correlation analysis of neural activity in brain regions jointly activated by auditory imagery and perception, and then brain functional connectivity (FC) networks were obtained with a consistent structure. However, the connection values between the areas in the superior temporal gyrus and the right precentral cortex were significantly higher in auditory perception than in the imagery modality. In addition, the modality decoding based on FC patterns showed that the FC network of auditory imagery and perception can be significantly distinguishable. Subsequently, voxel-level FC analysis further verified the distribution regions of voxels with significant connectivity differences between the 2 modalities. This study complemented the correlation and difference between auditory imagery and perception in terms of brain information interaction, and it provided a new perspective for investigating the neural mechanisms of different modal information representations.

Neurofeedback training of executive function in autism spectrum disorder: distinct effects on brain activity levels and compensatory connectivity changes.

BACKGROUND: Deficits in executive function (EF) are consistently reported in autism spectrum disorders (ASD). Tailored cognitive training tools, such as neurofeedback, focused on executive function enhancement might have a significant impact on the daily life functioning of individuals with ASD. We report the first real-time fMRI neurofeedback (rt-fMRI NF) study targeting the left dorsolateral prefrontal cortex (DLPFC) in ASD. METHODS: Thirteen individuals with autism without intellectual disability and seventeen neurotypical individuals completed a rt-fMRI working memory NF paradigm, consisting of subvocal backward recitation of self-generated numeric sequences. We performed a region-of-interest analysis of the DLPFC, whole-brain comparisons between groups and, DLPFC-based functional connectivity. RESULTS: The ASD and control groups were able to modulate DLPFC activity in 84% and 98% of the runs. Activity in the target region was persistently lower in the ASD group, particularly in runs without neurofeedback. Moreover, the ASD group showed lower activity in premotor/motor areas during pre-neurofeedback run than controls, but not in transfer runs, where it was seemingly balanced by higher connectivity between the DLPFC and the motor cortex. Group comparison in the transfer run also showed significant differences in DLPFC-based connectivity between groups, including higher connectivity with areas integrated into the multidemand network (MDN) and the visual cortex. CONCLUSIONS: Neurofeedback seems to induce a higher between-group similarity of the whole-brain activity levels (including the target ROI) which might be promoted by changes in connectivity between the DLPFC and both high and low-level areas, including motor, visual and MDN regions.

Correlations between developmental trajectories of brain functional connectivity, neurocognitive functions, and clinical symptoms in patients with attention-deficit hyperactivity disorder.

Several studies on attention-deficit hyperactivity disorder (ADHD) have suggested a developmental sequence of brain changes: subcortico-subcortical connectivity in children, evolving to subcortico-cortical in adolescence, and culminating in cortico-cortical connectivity in young adulthood. This study hypothesized that children with ADHD would exhibit decreased functional connectivity (FC) between the cortex and striatum compared to adults with ADHD, who may show increased FC in these regions. Seventy-six patients with ADHD (26 children, 26 adolescents, and 24 adults) and 74 healthy controls (25 children, 24 adolescents, and 25 adults) participated in the study. Resting state magnetic resonance images were acquired using a 3.0 T Philips Achieva scanner. The results indicated a gradual decrease in the number of subcategories representing intelligence quotient deficits in the ADHD group with age. In adulthood, the ADHD group exhibited lower working memory compared to the healthy control group. The number of regions showing decreased FC from the cortex to striatum between the ADHD and control groups reduced with age, while regions with increased FC from the default mode network and attention network in the ADHD group increased with age. In adolescents and adults, working memory was positively associated with brain activity in the postcentral gyrus and negatively correlated with ADHD clinical symptoms. In conclusion, the findings suggest that intelligence deficits in certain IQ subcategories may diminish as individuals with ADHD age. Additionally, the study indicates an increasing anticorrelation between cortical and subcortical regions with age in individuals with ADHD.

Neural patterns associated with mixed valence feelings differ in consistency and predictability throughout the brain.

Mixed feelings, the simultaneous presence of feelings with positive and negative valence, remain an understudied topic. They pose a specific set of challenges due to individual variation, and their investigation requires analtyic approaches focusing on individually self-reported states. We used functional magnetic resonance imaging (fMRI) to scan 27 subjects watching an animated short film chosen to induce bittersweet mixed feelings. The same subjects labeled when they had experienced positive, negative, and mixed feelings. Using hidden-Markov models, we found that various brain regions could predict the onsets of new feeling states as determined by self-report. The ability of the models to identify these transitions suggests that these states may exhibit unique and consistent neural signatures. We next used the subjects' self-reports to evaluate the spatiotemporal consistency of neural patterns for positive, negative, and mixed states. The insula had unique and consistent neural signatures for univalent states, but not for mixed valence states. The anterior cingulate and ventral medial prefrontal cortex had consistent neural signatures for both univalent and mixed states. This study is the first to demonstrate that subjectively reported changes in feelings induced by naturalistic stimuli can be predicted from fMRI and the first to show direct evidence for a neurally consistent representation of mixed feelings.

Favoritism or bias? Cooperation and competition under different intergroup relationships: evidence from EEG hyperscanning.

Cooperation and competition are the most common forms of social interaction in various social relationships. Intergroup relationships have been posited to influence individuals' interpersonal interactions significantly. Using electroencephalography hyperscanning, this study aimed to establish whether intergroup relationships influence interpersonal cooperation and competition and the underlying neural mechanisms. According to the results, the in-group Coop-index is better than the out-group, whereas the out-group Comp-index is stronger than the in-group. The in-group functional connectivity between the frontal-central region and the right temporoparietal junction in the ß band was stronger in competition than cooperation. The out-group functional connectivity between the frontal-central region and the left temporoparietal junction in the α band was stronger in cooperation than competition. In both cooperation and competition, the in-group exhibited higher interbrain synchronization between the prefrontal cortex and parietal region in the θ band, as well as between the frontal-central region and frontal-central region in the α band, compared to the out-group. The intrabrain phase-locking value in both the α and ß bands can effectively predict performance in competition tasks. Interbrain phase-locking value in both the α and θ bands can be effectively predicted in a performance cooperation task. This study offers neuroscientific evidence for in-group favoritism and out-group bias at an interpersonal level.

Visual perception of highly memorable images is mediated by a distributed network of ventral visual regions that enable a late memorability response.

Behavioral and neuroscience studies in humans and primates have shown that memorability is an intrinsic property of an image that predicts its strength of encoding into and retrieval from memory. While previous work has independently probed when or where this memorability effect may occur in the human brain, a description of its spatiotemporal dynamics is missing. Here, we used representational similarity analysis (RSA) to combine functional magnetic resonance imaging (fMRI) with source-estimated magnetoencephalography (MEG) to simultaneously measure when and where the human cortex is sensitive to differences in image memorability. Results reveal that visual perception of High Memorable images, compared to Low Memorable images, recruits a set of regions of interest (ROIs) distributed throughout the ventral visual cortex: a late memorability response (from around 300 ms) in early visual cortex (EVC), inferior temporal cortex, lateral occipital cortex, fusiform gyrus, and banks of the superior temporal sulcus. Image memorability magnitude results are represented after high-level feature processing in visual regions and reflected in classical memory regions in the medial temporal lobe (MTL). Our results present, to our knowledge, the first unified spatiotemporal account of visual memorability effect across the human cortex, further supporting the levels-of-processing theory of perception and memory.

Inferior parietal cortex represents relational structures for explicit transitive inference.

The human brain is distinguished by its ability to perform explicit logical reasoning like transitive inference. This study investigated the functional role of the inferior parietal cortex in transitive inference with functional MRI. Participants viewed premises describing abstract relations among items. They accurately recalled the relationship between old pairs of items, effectively inferred the relationship between new pairs of items, and discriminated between true and false relationships for new pairs. First, the inferior parietal cortex, but not the hippocampus or lateral prefrontal cortex, was associated with transitive inference. The inferior parietal activity and functional connectivity were modulated by inference (new versus old pairs) and discrimination (true versus false pairs). Moreover, the new/old and true/false pairs were decodable from the inferior parietal representation. Second, the inferior parietal cortex represented an integrated relational structure (ordered and directed series). The inferior parietal activity was modulated by serial position (larger end versus center pairs). The inferior parietal representation was modulated by symbolic distance (adjacent versus distant pairs) and direction (preceding versus following pairs). It suggests that the inferior parietal cortex may flexibly integrate observed relations into a relational structure and use the relational structure to infer unobserved relations and discriminate between true and false relations.

Specialization of amygdala subregions in emotion processing.

The amygdala is important for human fear processing. However, recent research has failed to reveal specificity, with evidence that the amygdala also responds to other emotions. A more nuanced understanding of the amygdala's role in emotion processing, particularly relating to fear, is needed given the importance of effective emotional functioning for everyday function and mental health. We studied 86 healthy participants (44 females), aged 18-49 (mean 26.12 ± 6.6) years, who underwent multiband functional magnetic resonance imaging. We specifically examined the reactivity of four amygdala subregions (using regions of interest analysis) and related brain connectivity networks (using generalized psycho-physiological interaction) to fear, angry, and happy facial stimuli using an emotional face-matching task. All amygdala subregions responded to all stimuli (p-FDR < .05), with this reactivity strongly driven by the superficial and centromedial amygdala (p-FDR < .001). Yet amygdala subregions selectively showed strong functional connectivity with other occipitotemporal and inferior frontal brain regions with particular sensitivity to fear recognition and strongly driven by the basolateral amygdala (p-FDR < .05). These findings suggest that amygdala specialization to fear may not be reflected in its local activity but in its connectivity with other brain regions within a specific face-processing network.

The association between glymphatic system dysfunction and alterations in cerebral function and structure in patients with white matter hyperintensities.

The objective of this study is to explore the relationship between the glymphatic system and alterations in the structure and function of the brain in white matter hyperintensity (WMH) patients. MRI data were collected from 27 WMH patients and 23 healthy controls. We calculated the along perivascular space (ALPS) indices, the anterior corner distance of the lateral ventricle, and the width of the third ventricle for each subject. The DPABISurf tool was used to calculate the cortical thickness and cortical area. In addition, data processing assistant for resting-state fMRI was used to calculate regional homogeneity, degree centrality, amplitude low-frequency fluctuation (ALFF), fractional amplitude of low-frequency fluctuation (fALFF), and voxel-mirrored homotopic connectivity (VMHC). In addition, each WMH patient was evaluated on the Fazekas scale. Finally, the correlation analysis of structural indicators and functional indicators with bilateral ALPS indices was investigated using Spearman correlation analysis. The ALPS indices of WMH patients were lower than those of healthy controls (left: t = -4.949, P < 0.001; right: t = -3.840, P < 0.001). This study found that ALFF, fALFF, regional homogeneity, degree centrality, and VMHC values in some brain regions of WMH patients were alternated (AlphaSim corrected, P < 0.005, cluster size > 26 voxel, rmm value = 5), and the cortical thickness and cortical area of WMH patients showed trend changes (P < 0.01, cluster size > 20 mm2, uncorrected). Interestingly, we found significantly positive correlations between the left ALPS indices and degree centrality values in the superior temporal gyrus (r = 0.494, P = 0.009, P × 5 < 0.05, Bonferroni correction). Our results suggest that glymphatic system impairment is related to the functional centrality of local connections in patients with WMH. This provides a new perspective for understanding the pathological mechanisms of cognitive impairment in the WMH population.

Functional brain networks in Developmental Topographical Disorientation.

Despite a decade-long study on Developmental Topographical Disorientation, the underlying mechanism behind this neurological condition remains unknown. This lifelong selective inability in orientation, which causes these individuals to get lost even in familiar surroundings, is present in the absence of any other neurological disorder or acquired brain damage. Herein, we report an analysis of the functional brain network of individuals with Developmental Topographical Disorientation ($n = 19$) compared against that of healthy controls ($n = 21$), all of whom underwent resting-state functional magnetic resonance imaging, to identify if and how their underlying functional brain network is altered. While the established resting-state networks (RSNs) are confirmed in both groups, there is, on average, a greater connectivity and connectivity strength, in addition to increased global and local efficiency in the overall functional network of the Developmental Topographical Disorientation group. In particular, there is an enhanced connectivity between some RSNs facilitated through indirect functional paths. We identify a handful of nodes that encode part of these differences. Overall, our findings provide strong evidence that the brain networks of individuals suffering from Developmental Topographical Disorientation are modified by compensatory mechanisms, which might open the door for new diagnostic tools.

Limited but specific engagement of the mature language network during linguistic statistical learning.

Statistical learning (SL) is the ability to detect and learn regularities from input and is foundational to language acquisition. Despite the dominant role of SL as a theoretical construct for language development, there is a lack of direct evidence supporting the shared neural substrates underlying language processing and SL. It is also not clear whether the similarities, if any, are related to linguistic processing, or statistical regularities in general. The current study tests whether the brain regions involved in natural language processing are similarly recruited during auditory, linguistic SL. Twenty-two adults performed an auditory linguistic SL task, an auditory nonlinguistic SL task, and a passive story listening task as their neural activation was monitored. Within the language network, the left posterior temporal gyrus showed sensitivity to embedded speech regularities during auditory, linguistic SL, but not auditory, nonlinguistic SL. Using a multivoxel pattern similarity analysis, we uncovered similarities between the neural representation of auditory, linguistic SL, and language processing within the left posterior temporal gyrus. No other brain regions showed similarities between linguistic SL and language comprehension, suggesting that a shared neurocomputational process for auditory SL and natural language processing within the left posterior temporal gyrus is specific to linguistic stimuli.

Behaviour-correlated profiles of cerebellar-cerebral functional connectivity observed in independent neurodevelopmental disorder cohorts.

The cerebellum, through its connectivity with the cerebral cortex, plays an integral role in regulating cognitive and affective processes, and its dysregulation can result in neurodevelopmental disorder (NDD)-related behavioural deficits. Identifying cerebellar-cerebral functional connectivity (FC) profiles in children with NDDs can provide insight into common connectivity profiles and their correlation to NDD-related behaviours. 479 participants from the Province of Ontario Neurodevelopmental Disorders (POND) network (typically developing = 93, Autism Spectrum Disorder = 172, Attention Deficit/Hyperactivity Disorder = 161, Obsessive-Compulsive Disorder = 53, mean age = 12.2) underwent resting-state functional magnetic resonance imaging and behaviour testing (Social Communication Questionnaire, Toronto Obsessive-Compulsive Scale, and Child Behaviour Checklist - Attentional Problems Subscale). FC components maximally correlated to behaviour were identified using canonical correlation analysis. Results were then validated by repeating the investigation in 556 participants from an independent NDD cohort provided from a separate consortium (Healthy Brain Network (HBN)). Replication of canonical components was quantified by correlating the feature vectors between the two cohorts. The two cerebellar-cerebral FC components that replicated to the greatest extent were correlated to, respectively, obsessive-compulsive behaviour (behaviour feature vectors, rPOND-HBN = -0.97; FC feature vectors, rPOND-HBN = -0.68) and social communication deficit contrasted against attention deficit behaviour (behaviour feature vectors, rPOND-HBN = -0.99; FC feature vectors, rPOND-HBN = -0.78). The statistically stable (|z| > 1.96) features of the FC feature vectors, measured via bootstrap re-sampling, predominantly comprised of correlations between cerebellar attentional and control network regions and cerebral attentional, default mode, and control network regions. In both cohorts, spectral clustering on FC loading values resulted in subject clusters mixed across diagnostic categories, but no cluster was significantly enriched for any given diagnosis as measured via chi-squared test (p > 0.05). Overall, two behaviour-correlated components of cerebellar-cerebral functional connectivity were observed in two independent cohorts. This suggests the existence of generalizable cerebellar network differences that span across NDD diagnostic boundaries.

Interscanner reproducibility of volumetric quantitative susceptibility mapping about cerebral subcortical gray nuclei at different MR vendors with the same magnetic strength.

BACKGROUND AND PURPOSE: Quantitative susceptibility mapping (QSM) technique was a new quantitative magnetic resonance imaging technique to evaluate the cerebral iron deposition in clinical practice. The current study was aimed to investigate the reproducibility of the volumetric susceptibility value of the subcortical gray nuclei at two different MR vendor with the same magnetic strength. METHODS: Cerebral magnitude and phase images of 21 normal subjects were acquired from a 3D multiecho enhanced gradient recalled echo sequence at two different 3.0T MR scanner, and then the magnetic susceptibility images were generated by STI software. The brain structural images were coregistered with magnitude images and generated the normalized parameters, and then generated the normalized susceptibility images. The subcortical gray nuclei template was applied to extract the volumetric susceptibility value of the target nuclei. RESULTS: ICC value (95% CI) of the caudate, putamen and GP were 0.847 (0.660-0.935), 0.848 (0.663-0.935) and 0.838 (0.643-0.931), respectively. The ICC value of the thalamus was 0.474 (0.064-0.747). Ninety-five point two percent (20/21) of the difference points of the susceptibility located between the 95% LA for the caudate at the two different 3.0T MR scanner, while the less than 95% of the difference points of the susceptibility value located between the 95% LA for the putamen, globus pallidus and thalamus. CONCLUSION: The current study identified that the caudate had the stable reproducibility of the magnetic susceptibility value, and the other basal ganglion nuclei should be cautious for the quantitative evaluation of the magnetic susceptibility value at different 3.0T MR scanner.

Commonality and variance of resting-state networks in common marmoset brains.

Animal models of brain function are critical for the study of human diseases and development of effective interventions. Resting-state network (RSN) analysis is a powerful tool for evaluating brain function and performing comparisons across animal species. Several studies have reported RSNs in the common marmoset (Callithrix jacchus; marmoset), a non-human primate. However, it is necessary to identify RSNs and evaluate commonality and inter-individual variance through analyses using a larger amount of data. In this study, we present marmoset RSNs detected using > 100,000 time-course image volumes of resting-state functional magnetic resonance imaging data with careful preprocessing. In addition, we extracted brain regions involved in the composition of these RSNs to understand the differences between humans and marmosets. We detected 16 RSNs in major marmosets, three of which were novel networks that have not been previously reported in marmosets. Since these RSNs possess the potential for use in the functional evaluation of neurodegenerative diseases, the data in this study will significantly contribute to the understanding of the functional effects of neurodegenerative diseases.

Permutation entropy analysis of EEG signals for distinguishing eyes-open and eyes-closed brain states: Comparison of different approaches.

Developing reliable methodologies to decode brain state information from electroencephalogram (EEG) signals is an open challenge, crucial to implementing EEG-based brain-computer interfaces (BCIs). For example, signal processing methods that identify brain states could allow motor-impaired patients to communicate via non-invasive, EEG-based BCIs. In this work, we focus on the problem of distinguishing between the states of eyes closed (EC) and eyes open (EO), employing quantities based on permutation entropy (PE). An advantage of PE analysis is that it uses symbols (ordinal patterns) defined by the ordering of the data points (disregarding the actual values), hence providing robustness to noise and outliers due to motion artifacts. However, we show that for the analysis of multichannel EEG recordings, the performance of PE in discriminating the EO and EC states depends on the symbols' definition and how their probabilities are estimated. Here, we study the performance of PE-based features for EC/EO state classification in a dataset of N=107 subjects with one-minute 64-channel EEG recordings in each state. We analyze features obtained from patterns encoding temporal or spatial information, and we compare different approaches to estimate their probabilities (by averaging over time, over channels, or by "pooling"). We find that some PE-based features provide about 75% classification accuracy, comparable to the performance of features extracted with other statistical analysis techniques. Our work highlights the limitations of PE methods in distinguishing the eyes' state, but, at the same time, it points to the possibility that subject-specific training could overcome these limitations.

Repertoire of timescales in uni - and transmodal regions mediate working memory capacity.

Working memory (WM) describes the dynamic process of maintenance and manipulation of information over a certain time delay. Neuronally, WM recruits a distributed network of cortical regions like the visual and dorsolateral prefrontal cortex as well as the subcortical hippocampus. How the input dynamics and subsequent neural dynamics impact WM remains unclear though. To answer this question, we combined the analysis of behavioral WM capacity with measuring neural dynamics through task-related power spectrum changes, e.g., median frequency (MF) in functional magnetic resonance imaging (fMRI). We show that the processing of the input dynamics, e.g., the task structure's specific timescale, leads to changes in the unimodal visual cortex's corresponding timescale which also relates to working memory capacity. While the more transmodal hippocampus relates to working memory capacity through its balance across multiple timescales or frequencies. In conclusion, we here show the relevance of both input dynamics and different neural timescales for WM capacity in uni - and transmodal regions like visual cortex and hippocampus for the subject's WM performance.

Noninvasive- and invasive mapping reveals similar language network centralities - A function-based connectome analysis.

BACKGROUND: Former comparisons between direct cortical stimulation (DCS) and navigated transcranial magnetic stimulation (nTMS) only focused on cortical mapping. While both can be combined with diffusion tensor imaging, their differences in the visualization of subcortical and even network levels remain unclear. Network centrality is an essential parameter in network analysis to measure the importance of nodes identified by mapping. Those include Degree centrality, Eigenvector centrality, Closeness centrality, Betweenness centrality, and PageRank centrality. While DCS and nTMS have repeatedly been compared on the cortical level, the underlying network identified by both has not been investigated yet. METHOD: 27 patients with brain lesions necessitating preoperative nTMS and intraoperative DCS language mapping during awake craniotomy were enrolled. Function-based connectome analysis was performed based on the cortical nodes obtained through the two mapping methods, and language-related network centralities were compared. RESULTS: Compared with DCS language mapping, the positive predictive value of cortical nTMS language mapping is 74.1%, with good consistency of tractography for the arcuate fascicle and superior longitudinal fascicle. Moreover, network centralities did not differ between the two mapping methods. However, ventral stream tracts can be better traced based on nTMS mappings, demonstrating its strengths in acquiring language-related networks. In addition, it showed lower centralities than other brain areas, with decentralization as an indicator of language function loss. CONCLUSION: This study deepens the understanding of language-related functional anatomy and proves that non-invasive mapping-based network analysis is comparable to the language network identified via invasive cortical mapping.

Dynamic brain communication underwriting face pareidolia.

Face pareidolia is a tendency to seeing faces in nonface images that reflects high tuning to a face scheme. Yet, studies of the brain networks underwriting face pareidolia are scarce. Here, we examined the time course and dynamic topography of gamma oscillatory neuromagnetic activity while administering a task with nonface images resembling a face. Images were presented either with canonical orientation or with display inversion that heavily impedes face pareidolia. At early processing stages, the peaks in gamma activity (40 to 45 Hz) to images either triggering or not face pareidolia originate mainly from the right medioventral and lateral occipital cortices, rostral and caudal cuneus gyri, and medial superior occipital gyrus. Yet, the difference occurred at later processing stages in the high-frequency range of 80 to 85 Hz over a set of the areas constituting the social brain. The findings speak rather for a relatively late neural network playing a key role in face pareidolia. Strikingly, a cutting-edge analysis of brain connectivity unfolding over time reveals mutual feedforward and feedback intra- and interhemispheric communication not only within the social brain but also within the extended large-scale network of down- and upstream regions. In particular, the superior temporal sulcus and insula strongly engage in communication with other brain regions either as signal transmitters or recipients throughout the whole processing of face-pareidolia images.