Enhancing Major Depressive Disorder Diagnosis with Dynamic-Static Fusion Graph Neural Networks.

Major Depressive Disorder (MDD) is a debilitating, complex mental condition with unclear mechanisms hindering diagnostic progress. Research links MDD to abnormal brain connectivity using functional magnetic resonance imaging (fMRI). Yet, existing fMRI-based MDD models suffer from limitations, including neglecting dynamic network traits, lacking interpretability, and struggling with small datasets. We present DSFGNN, a novel graph neural network framework addressing these issues for improved MDD diagnosis. DSFGNN employs a graph isomorphism encoder to model static and dynamic brain networks, achieving effective fusion of temporal and spatial information through a spatiotemporal attention mechanism, thereby enhancing interpretability. Furthermore, we incorporate a causal disentangling module and orthogonal regularization module to augment the model's expressiveness. We evaluate DSFGNN on the Rest-meta-MDD dataset, yielding superior results compared to the best baseline. Besides, extensive ablation studies and interpretability analysis confirm DSFGNN's effectiveness and potential for biomarker discovery.

Semantic processing during continuous speech production: an analysis from eye movements and EEG.

Introduction: Speech production involves neurological planning and articulatory execution. How speakers prepare for articulation is a significant aspect of speech production research. Previous studies have focused on isolated words or short phrases to explore speech planning mechanisms linked to articulatory behaviors, including investigating the eye-voice span (EVS) during text reading. However, these experimental paradigms lack real-world speech process replication. Additionally, our understanding of the neurological dimension of speech planning remains limited. Methods: This study examines speech planning mechanisms during continuous speech production by analyzing behavioral (eye movement and speech) and neurophysiological (EEG) data within a continuous speech production task. The study specifically investigates the influence of semantic consistency on speech planning and the occurrence of "look ahead" behavior. Results: The outcomes reveal the pivotal role of semantic coherence in facilitating fluent speech production. Speakers access lexical representations and phonological information before initiating speech, emphasizing the significance of semantic processing in speech planning. Behaviorally, the EVS decreases progressively during continuous reading of regular sentences, with a slight increase for non-regular sentences. Moreover, eye movement pattern analysis identifies two distinct speech production modes, highlighting the importance of semantic comprehension and prediction in higher-level lexical processing. Neurologically, the dual pathway model of speech production is supported, indicating a dorsal information flow and frontal lobe involvement. The brain network linked to semantic understanding exhibits a negative correlation with semantic coherence, with significant activation during semantic incoherence and suppression in regular sentences. Discussion: The study's findings enhance comprehension of speech planning mechanisms and offer insights into the role of semantic coherence in continuous speech production. Furthermore, the research methodology establishes a valuable framework for future investigations in this domain.

Study on neural entrainment to continuous speech using dynamic source connectivity analysis.

Objective.Many recent studies investigating the processing of continuous natural speech have employed electroencephalography (EEG) due to its high temporal resolution. However, most of these studies explored the response mechanism limited to the electrode space. In this study, we intend to explore the underlying neural processing in the source space, particularly the dynamic functional interactions among different regions during neural entrainment to speech.Approach.We collected 128-channel EEG data while 22 participants listened to story speech and time-reversed speech using a naturalistic paradigm. We compared three different strategies to determine the best method to estimate the neural tracking responses from the sensor space to the brain source space. After that, we used dynamic graph theory to investigate the source connectivity dynamics among regions that were involved in speech tracking.Main result.By comparing the correlations between the predicted neural response and the original common neural response under the two experimental conditions, we found that estimating the common neural response of participants in the electrode space followed by source localization of neural responses achieved the best performance. Analysis of the distribution of brain sources entrained to story speech envelopes showed that not only auditory regions but also frontoparietal cognitive regions were recruited, indicating a hierarchical processing mechanism of speech. Further analysis of inter-region interactions based on dynamic graph theory found that neural entrainment to speech operates across multiple brain regions along the hierarchical structure, among which the bilateral insula, temporal lobe, and inferior frontal gyrus are key brain regions that control information transmission. All of these information flows result in dynamic fluctuations in functional connection strength and network topology over time, reflecting both bottom-up and top-down processing while orchestrating computations toward understanding.Significance.Our findings have important implications for understanding the neural mechanisms of the brain during processing natural speech stimuli.

Multimodal evidence for predictive coding in sentence oral reading.

Sentence oral reading requires not only a coordinated effort in the visual, articulatory, and cognitive processes but also supposes a top-down influence from linguistic knowledge onto the visual-motor behavior. Despite a gradual recognition of a predictive coding effect in this process, there is currently a lack of a comprehensive demonstration regarding the time-varying brain dynamics that underlines the oral reading strategy. To address this, our study used a multimodal approach, combining real-time recording of electroencephalography, eye movements, and speech, with a comprehensive examination of regional, inter-regional, sub-network, and whole-brain responses. Our study identified the top-down predictive effect with a phrase-grouping phenomenon in the fixation interval and eye-voice span. This effect was associated with the delta and theta band synchronization in the prefrontal, anterior temporal, and inferior frontal lobes. We also observed early activation of the cognitive control network and its recurrent interactions with the visual-motor networks structurally at the phrase rate. Finally, our study emphasizes the importance of cross-frequency coupling as a promising neural realization of hierarchical sentence structuring and calls for further investigation.

Emotion recognition using spatial-temporal EEG features through convolutional graph attention network.

Objective.Constructing an efficient human emotion recognition model based on electroencephalogram (EEG) signals is significant for realizing emotional brain-computer interaction and improving machine intelligence.Approach.In this paper, we present a spatial-temporal feature fused convolutional graph attention network (STFCGAT) model based on multi-channel EEG signals for human emotion recognition. First, we combined the single-channel differential entropy (DE) feature with the cross-channel functional connectivity (FC) feature to extract both the temporal variation and spatial topological information of EEG. After that, a novel convolutional graph attention network was used to fuse the DE and FC features and further extract higher-level graph structural information with sufficient expressive power for emotion recognition. Furthermore, we introduced a multi-headed attention mechanism in graph neural networks to improve the generalization ability of the model.Main results.We evaluated the emotion recognition performance of our proposed model on the public SEED and DEAP datasets, which achieved a classification accuracy of 99.11% ± 0.83% and 94.83% ± 3.41% in the subject-dependent and subject-independent experiments on the SEED dataset, and achieved an accuracy of 91.19% ± 1.24% and 92.03% ± 4.57% for discrimination of arousal and valence in subject-independent experiments on DEAP dataset. Notably, our model achieved state-of-the-art performance on cross-subject emotion recognition tasks for both datasets. In addition, we gained insight into the proposed frame through both the ablation experiments and the analysis of spatial patterns of FC and DE features.Significance.All these results prove the effectiveness of the STFCGAT architecture for emotion recognition and also indicate that there are significant differences in the spatial-temporal characteristics of the brain under different emotional states.

Detection of Brain Network Communities During Natural Speech Comprehension From Functionally Aligned EEG Sources.

In recent years, electroencephalograph (EEG) studies on speech comprehension have been extended from a controlled paradigm to a natural paradigm. Under the hypothesis that the brain can be approximated as a linear time-invariant system, the neural response to natural speech has been investigated extensively using temporal response functions (TRFs). However, most studies have modeled TRFs in the electrode space, which is a mixture of brain sources and thus cannot fully reveal the functional mechanism underlying speech comprehension. In this paper, we propose methods for investigating the brain networks of natural speech comprehension using TRFs on the basis of EEG source reconstruction. We first propose a functional hyper-alignment method with an additive average method to reduce EEG noise. Then, we reconstruct neural sources within the brain based on the EEG signals to estimate TRFs from speech stimuli to source areas, and then investigate the brain networks in the neural source space on the basis of the community detection method. To evaluate TRF-based brain networks, EEG data were recorded in story listening tasks with normal speech and time-reversed speech. To obtain reliable structures of brain networks, we detected TRF-based communities from multiple scales. As a result, the proposed functional hyper-alignment method could effectively reduce the noise caused by individual settings in an EEG experiment and thus improve the accuracy of source reconstruction. The detected brain networks for normal speech comprehension were clearly distinctive from those for non-semantically driven (time-reversed speech) audio processing. Our result indicates that the proposed source TRFs can reflect the cognitive processing of spoken language and that the multi-scale community detection method is powerful for investigating brain networks.

MXene/Polylactic Acid Fabric-Based Resonant Cavity for Realizing Simultaneous High-Performance Electromagnetic Interference (EMI) Shielding and Efficient Energy Harvesting.

Proliferation in telecommunications and integrated/intelligent devices entails an intense concern for electromagnetic interference (EMI) shielding and versatility. It remains an activated passion to launch infusive EMI shielding materials integrated with self-powered peculiarities. Herein, a double-layered MXene/polylactic acid (PLA) fabric resonance cavity (D-MPF-RC) comprised of two MXene/PLA fabrics (MPFs) with alternating MXene and PLA structures that are separated by a poly(tetrafluoroethylene) (PTFE) frame is developed. The D-MPF-RC achieved 48.5 and 74.8% improvement in SET and SEA, and 24.6% reduction in SER by introducing the double-layered structure and increasing the resonance cavity (RC) distance without varying the material composition and cost. A high shielding efficiency (SE) of 92.3 dB was obtained at an RC distance of 6 mm owing to the synergetic effects of multiple reflections and destructive EM wave interference. The tribopolarity difference between PLA and MXene and the RC structure made the D-MPF-RC a readily available triboelectric nanogenerator (TENG) that could convert mechanical energy into electricity. The D-MPF-RC TENG demonstrated an open-circuit voltage of 88 V and achieved a peak power density of 35.4 mW m-2 on a 6.6 MΩ external resistor, which made it possible to charge capacitors and serve as a self-powered tactile sensor. This report offers new insights into the design of high-performance EMI shielding shields with a resonance cavity and proposes a feasible pathway to integrate them with energy harvesting capabilities.

Inferring Brain State Dynamics Underlying Naturalistic Stimuli Evoked Emotion Changes With dHA-HMM.

The brain functional mechanisms underlying emotional changes have been primarily studied based on the traditional task design with discrete and simple stimuli. However, the brain state transitions when exposed to continuous and naturalistic stimuli with rich affection variations remain poorly understood. This study proposes a dynamic hyperalignment algorithm (dHA) to functionally align the inter-subject neural activity. The hidden Markov model (HMM) was used to study how the brain dynamics responds to emotion during long-time movie-viewing activity. The results showed that dHA significantly improved inter-subject consistency and allowed more consistent temporal HMM states across participants. Afterward, grouping the emotions in a clustering dendrogram revealed a hierarchical grouping of the HMM states. Further emotional sensitivity and specificity analyses of ordered states revealed the most significant differences in happiness and sadness. We then compared the activation map in HMM states during happiness and sadness and found significant differences in the whole brain, but strong activation was observed during both in the superior temporal gyrus, which is related to the early process of emotional prosody processing. A comparison of the inter-network functional connections indicates unique functional connections of the memory retrieval and cognitive network with the cerebellum network during happiness. Moreover, the persistent bilateral connections among salience, cognitive, and sensorimotor networks during sadness may reflect the interaction between high-level cognitive networks and low-level sensory networks. The main results were verified by the second session of the dataset. All these findings enrich our understanding of the brain states related to emotional variation during naturalistic stimuli.

Constructing Accurate and Efficient Deep Spiking Neural Networks With Double-Threshold and Augmented Schemes.

Spiking neural networks (SNNs) are considered as a potential candidate to overcome current challenges, such as the high-power consumption encountered by artificial neural networks (ANNs); however, there is still a gap between them with respect to the recognition accuracy on various tasks. A conversion strategy was, thus, introduced recently to bridge this gap by mapping a trained ANN to an SNN. However, it is still unclear that to what extent this obtained SNN can benefit both the accuracy advantage from ANN and high efficiency from the spike-based paradigm of computation. In this article, we propose two new conversion methods, namely TerMapping and AugMapping. The TerMapping is a straightforward extension of a typical threshold-balancing method with a double-threshold scheme, while the AugMapping additionally incorporates a new scheme of augmented spike that employs a spike coefficient to carry the number of typical all-or-nothing spikes occurring at a time step. We examine the performance of our methods based on the MNIST, Fashion-MNIST, and CIFAR10 data sets. The results show that the proposed double-threshold scheme can effectively improve the accuracies of the converted SNNs. More importantly, the proposed AugMapping is more advantageous for constructing accurate, fast, and efficient deep SNNs compared with other state-of-the-art approaches. Our study, therefore, provides new approaches for further integration of advanced techniques in ANNs to improve the performance of SNNs, which could be of great merit to applied developments with spike-based neuromorphic computing.

Renormalization of Thalamic Sub-Regional Functional Connectivity Contributes to Improvement of Cognitive Function after Liver Transplantation in Cirrhotic Patients with Overt Hepatic Encephalopathy.

OBJECTIVE: The role of preoperative overt hepatic encephalopathy (OHE) in the neurophysiological mechanism of cognitive improvement after liver transplantation (LT) remains elusive. This study aimed to explore changes in sub-regional thalamic functional connectivity (FC) after LT and their relationship with neuropsychological improvement using resting-state functional MRI (rs-fMRI) data in cirrhotic patients with and without a history of OHE. MATERIALS AND METHODS: A total of 51 cirrhotic patients, divided into the OHE group (n = 21) and no-OHE group (n = 30), and 30 healthy controls were enrolled in this prospective study. Each patient underwent rs-fMRI before and 1 month after LT. Using 16 bilateral thalamic subregions as seeds, we conducted a seed-to-voxel FC analysis to compare the thalamic FC alterations before and after LT between the OHE and no-OHE groups, as well as differences in FC between the two groups of cirrhotic patients and the control group. Correction for multiple comparisons was conducted using the false discovery rate (p < 0.05). RESULTS: We found abnormally increased FC between the thalamic sub-region and prefrontal cortex, as well as an abnormally decreased FC between the bilateral thalamus in both OHE and no-OHE cirrhotic patients before LT, which returned to normal levels after LT. Compared with the no-OHE group, the OHE group exhibited more extensive abnormalities prior to LT, and the increased FC between the right thalamic subregions and right inferior parietal lobe was markedly reduced to normal levels after LT. CONCLUSION: The renormalization of FC in the cortico-thalamic loop might be a neuro-substrate for the recovery of cognitive function after LT in cirrhotic patients. In addition, hyperconnectivity between thalamic subregions and the inferior parietal lobe might be an important feature of OHE. Changes in FC in the thalamus might be used as potential biomarkers for recovery of cognitive function after LT in cirrhotic patients.

Investigation of functional brain network reconfiguration during exposure to naturalistic stimuli using graph-theoretical analysis.

Objective.One of the most significant features of the human brain is that it can dynamically reconfigure itself to adapt to a changing environment. However, dynamic interaction characteristics of the brain networks in naturalistic scenes remain unclear.Approach.We used open-source functional magnetic resonance imaging (fMRI) data from 15 participants who underwent fMRI scans while watching an audio-visual movie 'Forrest Gump'. The community detection algorithm based on inter-subject functional correlation was used to study the time-varying functional networks only induced by the movie stimuli. The whole brain reconfiguration patterns were quantified by the temporal co-occurrence matrix that describes the probability of two brain regions engage in the same community (or putative functional module) across time and the time-varying brain modularity. Four graph metrics of integration, recruitment, spatio-temporal diversity and within-community normalised centrality were further calculated to summarise the brain network dynamic roles and hub features in their spatio-temporal topology.Main results.Our results suggest that the networks that were involved in attention and audio-visual information processing, such as the visual network, auditory network, and dorsal attention network, were considered to play a role of 'stable loners'. By contrast, 'unstable loner' networks such as the default mode network (DMN) and fronto-parietal network tended to interact more flexibly with the other networks. In addition, global brain network showed significant fluctuations in modularity. The 'stable loner' networks always maintained high functional connectivity (FC) strength while 'unstable loner' networks, especially the DMN, exhibited high intra- and inter-network FC only during a low modularity period. Finally, changes in brain modularity were significantly associated with variations in emotions induced by the movie.Significance.Our findings provide new insight for understanding the dynamic interaction characteristics of functional brain networks during naturalistic stimuli.

Identification of minimal hepatic encephalopathy based on dynamic functional connectivity.

To investigate whether dynamic functional connectivity (DFC) metrics can better identify minimal hepatic encephalopathy (MHE) patients from cirrhotic patients without any hepatic encephalopathy (noHE) and healthy controls (HCs). Resting-state functional MRI data were acquired from 62 patients with cirrhosis (MHE, n = 30; noHE, n = 32) and 41 HCs. We used the sliding time window approach and functional connectivity analysis to extract the time-varying properties of brain connectivity. Three DFC characteristics (i.e., strength, stability, and variability) were calculated. For comparison, we also calculated the static functional connectivity (SFC). A linear support vector machine was used to differentiate MHE patients from noHE and HCs using DFC and SFC metrics as classification features. The leave-one-out cross-validation method was used to estimate the classification performance. The strength of DFC (DFC-Dstrength) achieved the best accuracy (MHE vs. noHE, 72.5%; MHE vs. HCs, 84%; and noHE vs. HCs, 88%) compared to the other dynamic features. Compared to static features, the classification accuracies of the DFC-Dstrength feature were improved by 10.5%, 8%, and 14% for MHE vs. noHE, MHE vs. HC, and noHE vs. HCs, respectively. Based on the DFC-Dstrength, seven nodes were identified as the most discriminant features to classify MHE from noHE, including left inferior parietal lobule, left supramarginal gyrus, left calcarine, left superior frontal gyrus, left cerebellum, right postcentral gyrus, and right insula. In summary, DFC characteristics have a higher classification accuracy in identifying MHE from cirrhosis patients. Our findings suggest the usefulness of DFC in capturing neural processes and identifying disease-related biomarkers important for MHE identification.

EEG Assessment in a 2-Year-Old Child with Prolonged Disorders of Consciousness: 3 Years' Follow-up.

A 2-year-old girl, diagnosed with traumatic brain injury and epilepsy following car trauma, was followed up for 3 years (a total of 15 recordings taken at 0, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 14, 19, 26, and 35 months). There is still no clear guidance on the diagnosis, treatment, and prognosis of children with disorders of consciousness. At each appointment, recordings included the child's height, weight, pediatric Glasgow Coma Scale (pGCS), Coma Recovery Scale-Revised (CRS-R), Gesell Developmental Schedule, computed tomography or magnetic resonance imaging, electroencephalogram, frequency of seizures, oral antiepileptic drugs, stimulation with subject's own name (SON), and median nerve electrical stimulation (MNS). Growth and development were deemed appropriate for the age of the child. The pGCS and Gesell Developmental Schedule provided a comprehensive assessment of consciousness and mental development; the weighted Phase Lag Index (wPLI ) in the ß-band (13-25 Hz) can distinguish unresponsive wakefulness syndrome from minimally conscious state and confirm that the SON and MNS were effective. The continuous increase of delta-band power indicates a poor prognosis. Interictal epileptiform discharges (IEDs) have a cumulative effect and seizures seriously affect the prognosis.

Identifying Mild Hepatic Encephalopathy Based on Multi-Layer Modular Algorithm and Machine Learning.

Hepatic encephalopathy (HE) is a neurocognitive dysfunction based on metabolic disorders caused by severe liver disease, which has a high one-year mortality. Mild hepatic encephalopathy (MHE) has a high risk of converting to overt HE, and thus the accurate identification of MHE from cirrhosis with no HE (noHE) is of great significance in reducing mortality. Previously, most studies focused on studying abnormality in the static brain networks of MHE to find biomarkers. In this study, we aimed to use multi-layer modular algorithm to study abnormality in dynamic graph properties of brain network in MHE patients and construct a machine learning model to identify individual MHE from noHE. Here, a time length of 500-second resting-state functional MRI data were collected from 41 healthy subjects, 32 noHE patients and 30 MHE patients. Multi-layer modular algorithm was performed on dynamic brain functional connectivity graph. The connection-stability score was used to characterize the loyalty in each brain network module. Nodal flexibility, cohesion and disjointness were calculated to describe how the node changes the network affiliation across time. Results show that significant differences between MHE and noHE were found merely in nodal disjointness in higher cognitive network modules (ventral attention, fronto-parietal, default mode networks) and these abnormalities were associated with the decline in patients' attention and visual memory function evaluated by Digit Symbol Test. Finally, feature extraction from node disjointness with the support vector machine classifier showed an accuracy of 88.71% in discrimination of MHE from noHE, which was verified by different window sizes, modular partition parameters and machine learning parameters. All these results show that abnormal nodal disjointness in higher cognitive networks during brain network evolution can be seemed as a biomarker for identification of MHE, which help us understand the disease mechanism of MHE at a fine scale.

Decoding sound categories based on whole-brain functional connectivity patterns.

2Sound decoding is important for patients with sensory loss, such as the blind. Previous studies on sound categorization were conducted by estimating brain activity using univariate analysis or voxel-wise multivariate decoding methods and suggested some regions were sensitive to auditory categories. It is proposed that feedback connections between brain areas may facilitate auditory object selection. Therefore, it is important to explore whether functional connectivity among regions can be used to decode sound category. In this study, we constructed whole-brain functional connectivity patterns when subjects perceived four different sound categories and combined them with multivariate pattern classification analysis for sound decoding. The categorical discriminative networks and regions were determined based on the weight maps. Results showed that a high accuracy in multi-category classification was obtained based on the whole-brain functional connectivity patterns and the results were verified by different preprocessing parameters. Insight into the category discriminative functional networks showed that contributive connections crossed the left and right brain, and ranged from primary regions to high-level cognitive regions, which provide new evidence for the distributed representation of auditory object. Further analysis of brain regions in the discriminative networks showed that superior temporal gyrus and Heschl's gyrus significantly contributed to discriminating sound categories. Together, the findings reveal that functional connectivity based multivariate classification method provides rich information for auditory category decoding. The successful decoding results implicate the interactive properties of the distributed brain areas in auditory sound representation.

Tracking the Dynamic Functional Network Interactions During Goal-Directed Auditory Tasks by Brain State Clustering.

Both perceiving and processing external sound stimuli as well as actively maintaining and updating relevant information (i.e., working memory) are critical for communication and problem solving in everyday acoustic environments. The translation of sensory information into perceptual decisions for goal-directed tasks hinges on dynamic changes in neural activity. However, the underlying brain network dynamics involved in this process are not well specified. In this study, we collected functional MRI data of participants engaging in auditory discrimination and auditory working memory tasks. Independent component analysis (ICA) was performed to extract the brain networks involved and the sliding-window functional connectivity (FC) among networks was calculated. Next, a temporal clustering technique was used to identify the brain states underlying auditory processing. Our results identified seven networks configured into four brain states. The number of brain state transitions was negatively correlated with auditory discrimination performance, and the fractional dwell time of State 2-which included connectivity among the triple high-order cognitive networks and the auditory network (AN)-was positively correlated with working memory performance. A comparison of the two tasks showed significant differences in the connectivity of the frontoparietal, default mode, and sensorimotor networks (SMNs), which is consistent with previous studies of the modulation of task load on brain network interaction. In summary, the dynamic network analysis employed in this study allowed us to isolate moment-to-moment fluctuations in inter-network synchrony, find network configuration in each state, and identify the specific state that enables fast, effective performance during auditory processing. This information is important for understanding the key neural mechanisms underlying goal-directed auditory tasks.

Revealing the Dynamic Brain Connectivity from Perception of Speech Sound to Semantic Processing by EEG.

Understanding brain processing mechanisms from the perception of speech sounds to high-level semantic processing is vital for effective human-robot communication. In this study, 128-channel electroencephalograph (EEG) signals were recorded when subjects were listening to real and pseudowords in Mandarin. By using an EEG source reconstruction method and a sliding-window Granger causality analysis, we analyzed the dynamic brain connectivity patterns. Results showed that the bilateral temporal cortex (lTC and rTC), the bilateral motor cortex (lMC and rMC), the frontal cortex (FC), and the occipital cortex (OC) were recruited in the process, with complex patterns in the real word condition than in the pseudoword condition. The spatial pattern is basically consistent with previous functional MRI studies on the understanding of spoken Chinese. For the real word condition, speech perception and processing involved different connection patterns in the initial phoneme perception and processing phase, the phonological processing and lexical selection phase, and the semantic integration phase. Specifically, compared with pseudowords, a hub region in the FC and unique patterns of lMC → rMC and lTC → FC connectivity were found during processing real words after 180 ms, while a distributed network of temporal, motor, and frontal brain areas was involved after 300 ms. This may be related to semantic processing and integration. The involvement of both bottom-up input and top-down modulation in real word processing may support the previously proposed TRACE model. In sum, the findings of this study suggest that representations of speech involve dynamic interactions among distributed brain regions that communicate through time-specific functional networks.

Investigating the Brain Neural Mechanism when Signature Objects were Masked during a Scene Categorization Task using Functional MRI.

Objects play vital roles in scene categorization. Although a number of studies have researched on the neural responses during object and object-based scene recognition, few studies have investigated the neural mechanism underlying object-masked scene categorization. Here, we used functional magnetic resonance imaging (fMRI) to measure the changes in brain activations and functional connectivity (FC) while subjects performed a visual scene-categorization task with different numbers of 'signature objects' masked. The object-selective region in the lateral occipital complex (LOC) showed a decrease in activations and changes in FC with the default mode network (DMN), indicating changes in object attention after the masking of signature objects. Changes in top-down modulation effect were revealed in the FC from the dorsolateral prefrontal cortex (DLPFC) to LOC and the extrastriate visual cortex, possibly participating in conscious object recognition. The whole-brain analyses showed the participation of fronto-parietal network (FPN) in scene categorization judgment, and right DLPFC served as the core hub in this network. Another core hub was found in left middle temporal gyrus (MTG) and its connection with middle cingulate cortex (MCC), supramarginal gyrus (SMG) and insula might serve in the processing of motor response and the semantic relations between objects and scenes. Brain-behavior correlation analysis substantiated the contributions of the FC to the different processes in the object-masked scene-categorization tasks. Altogether, the results suggest that masking of objects significantly affected the object attention, cognitive demand, top-down modulation effect, and semantic judgment.

Brain Regional Homogeneity Changes in Cirrhotic Patients with or without Hepatic Encephalopathy Revealed by Multi-Frequency Bands Analysis Based on Resting-State Functional MRI.

Objective: To investigate brain regional homogeneity (ReHo) changes of multiple sub-frequency bands in cirrhotic patients with or without hepatic encephalopathy using resting-state functional MRI. Materials and Methods: This study recruited 46 cirrhotic patients without clinical hepatic encephalopathy (noHE), 38 cirrhotic patients with clinical hepatic encephalopathy (HE), and 37 healthy volunteers. ReHo differences were analyzed in slow-5 (0.010-0.027 Hz), slow-4 (0.027-0.073 Hz), and slow-3 (0.073-0.198 Hz) bands. Routine analysis of (0.010-0.080 Hz) band was used as a benchmark. Associations of abnormal ReHo values in each frequency band with neuropsychological scores and blood ammonia level were analyzed. Pattern classification analyses were conducted to determine whether ReHo differences in each band could differentiate the three groups of subjects (patients with or without hepatic encephalopathy and healthy controls). Results: Compared to routine analysis, more differences between HE and noHE were observed in slow-5 and slow-4 bands (p < 0.005, cluster > 12, overall corrected p < 0.05). Sub-frequency band analysis also showed that ReHo abnormalities were frequency-dependent (overall corrected p < 0.05). In addition, ReHo abnormalities in each sub-band were correlated with blood ammonia level and neuropsychological scores, especially in the left inferior parietal lobe (overall corrected p < 0.05 for all frequency bands). Pattern classification analysis demonstrated that ReHo differences in lower slow-5 and slow-4 bands (both p < 0.05) and higher slow-3 band could differentiate the three groups (p < 0.05). Compared to routine analysis, ReHo features in slow-4 band obtained better classification accuracy (89%). Conclusion: Cirrhotic patients showed frequency-dependent changes in ReHo. Sub-frequency band analysis is important for understanding HE and clinical monitoring.

Semantically Congruent Sounds Facilitate the Decoding of Degraded Images.

Semantically congruent sounds can facilitate perception of visual objects in the human brain. However, the manner in which semantically congruent sounds affect cognitive processing for degraded visual stimuli remains unclear. We presented participants with naturalistic degraded images and semantically congruent sounds from different conceptual categories in three modalities: degraded visual only, auditory only, and auditory and degraded visual. Functional magnetic resonance imaging was performed to assess variations in brain-activation spatial patterns. In order to account for the facilitation of auditory modulation at different levels, four conceptual categories of stimuli were divided into coarse and fine groups. Conjunction analysis and multivariate pattern analysis were used to investigate integrative properties. Superadditive interactions were found in the visual association cortex and subadditive interactions were observed in the superior temporal sulcus/superior temporal gyrus (STS/STG). Our results demonstrate that the visual association cortex and STS/STG are involved in the integration of auditory and degraded visual information. In addition, the pattern classification results imply that semantically congruent sounds may facilitate identification of degraded images in both coarse and fine groups. Importantly, when naturalistic visual stimuli were further subdivided, facilitation through auditory modulation exhibited category selectivity.