Time course of gamma-band oscillation associated with face processing in the inferior occipital gyrus and fusiform gyrus: A combined fMRI and MEG study

Altered spontaneous neurological activity in methamphetamine use disorders and its association with cognitive function

BACKGROUND

Methamphetamine (MA) is a widely used and detrimental drug, yet the precise mechanisms by which MA affects cognitive function remain unclear. This study aims to investigate the relationship between cognitive function and brain functional imaging in individuals with MA use disorder (MUD).

METHODS

This study involved 45 patients diagnosed with MUD and 43 healthy controls (HC). Cognitive function assessment utilized the MATRICS Consensus Cognitive Battery, and functional data were acquired using a 3.0 Tesla magnetic resonance imaging scanner.

RESULTS

The MUD group exhibited lower regional homogeneity (ReHo) values in the bilateral postcentral, the left superior temporal, and the left lingual regions compared to the HC group. Additionally, the MUD group displayed higher amplitude of low-frequency fluctuation (ALFF) values in the bilateral fusiform and the left putamen compared to the HC group, along with lower ALFF values in the bilateral postcentral cortices and the left middle cingulate cortex compared to the HC group (all p < 0.05, with false discovery rate corrected). Linear regression analysis revealed a positive correlation between the ReHo value in the right postcentral cortex and the neuropsychology assessment battery-mazes test (p = 0.014). Furthermore, the ALFF value in the left putamen showed negative correlations with the scores of the digit-symbol coding test (p = 0.027), continuous performance test (p = 0.037), and battery-mazes test (p = 0.024).

CONCLUSION

Patients with MUD exhibit altered brain spontaneous neurological activities, and the intensity of spontaneous neurological activity in the left putamen is strongly associated with cognitive function.

Prediction of image interpretation cognitive ability under different mental workloads: a task-state fMRI study

Visual imaging experts play an important role in multiple fields, and studies have shown that the combination of functional magnetic resonance imaging and machine learning techniques can predict cognitive abilities, which provides a possible method for selecting individuals with excellent image interpretation skills. We recorded behavioral data and neural activity of 64 participants during image interpretation tasks under different workloads. Based on the comprehensive image interpretation ability, participants were divided into two groups. general linear model analysis showed that during image interpretation tasks, the high-ability group exhibited higher activation in middle frontal gyrus (MFG), fusiform gyrus, inferior occipital gyrus, superior parietal gyrus, inferior parietal gyrus, and insula compared to the low-ability group. The radial basis function Support Vector Machine (SVM) algorithm shows the most excellent performance in predicting participants' image interpretation abilities (Pearson correlation coefficient = 0.54, R2 = 0.31, MSE = 0.039, RMSE = 0.002). Variable importance analysis indicated that the activation features of the fusiform gyrus and MFG played an important role in predicting this ability. Our study revealed the neural basis related to image interpretation ability when exposed to different mental workloads. Additionally, our results demonstrated the efficacy of machine learning algorithms in extracting neural activation features to predict such ability.

Combining electrodermal activity analysis and dynamic causal modeling to investigate the visual-odor multimodal integration during face perception

Objective. This study presents a novel methodological approach for incorporating information related to the peripheral sympathetic response into the investigation of neural dynamics. Particularly, we explore how hedonic contextual olfactory stimuli influence the processing of neutral faces in terms of sympathetic response, event-related potentials and effective connectivity analysis. The objective is to investigate how the emotional valence of odors influences the cortical connectivity underlying face processing and the role of face-induced sympathetic arousal in this visual-olfactory multimodal integration.Approach. To this aim, we combine electrodermal activity (EDA) analysis and dynamic causal modeling to examine changes in cortico-cortical interactions.Results. The results reveal that stimuli arising sympathetic EDA responses are associated with a more negative N170 amplitude, which may be a marker of heightened arousal in response to faces. Hedonic odors, on the other hand, lead to a more negative N1 component and a reduced the vertex positive potential when they are unpleasant or pleasant. Concerning connectivity, unpleasant odors strengthen the forward connection from the inferior temporal gyrus (ITG) to the middle temporal gyrus, which is involved in processing changeable facial features. Conversely, the occurrence of sympathetic responses after a stimulus is correlated with an inhibition of this same connection and an enhancement of the backward connection from ITG to the fusiform face gyrus.Significance. These findings suggest that unpleasant odors may enhance the interpretation of emotional expressions and mental states, while faces capable of eliciting sympathetic arousal prioritize identity processing.

Reorganization of the structural connectome during vision recovery in pituitary adenoma patients post-transsphenoidal surgery

Pituitary adenomas (PAs) can exert pressure on the optic apparatus, leading to visual impairment. A subset of patients may observe a swift improvement in their vision following surgery. Nevertheless, the alterations in the structural connectome during the early postoperative period remain largely unexplored. The research employed probabilistic tractography, graph theoretical analysis, and statistical methods on preoperative and postoperative structural magnetic resonance imaging and diffusion tensor images from 13 PA patients. Postoperative analysis revealed an increase in global and local efficiency, signifying improved network capacity for parallel information transfer and fault tolerance, respectively. Enhanced clustering coefficient and reduced shortest path length were also observed, suggesting a more regular network organization and shortened communication steps within the brain network. Furthermore, alterations in node graphical properties were detected, implying a restructuring of the network's control points, possibly contributing to more efficient visual processing. These findings propose that rapid vision recovery post-surgery may be associated with significant reorganization of the brain's structural connectome, enhancing the efficiency and adaptability of the network, thereby facilitating improved visual processing.

Using multi-modal neuroimaging to characterise social brain specialisation in infants

The specialised regional functionality of the mature human cortex partly emerges through experience-dependent specialisation during early development. Our existing understanding of functional specialisation in the infant brain is based on evidence from unitary imaging modalities and has thus focused on isolated estimates of spatial or temporal selectivity of neural or haemodynamic activation, giving an incomplete picture. We speculate that functional specialisation will be underpinned by better coordinated haemodynamic and metabolic changes in a broadly orchestrated physiological response. To enable researchers to track this process through development, we develop new tools that allow the simultaneous measurement of coordinated neural activity (EEG), metabolic rate, and oxygenated blood supply (broadband near-infrared spectroscopy) in the awake infant. In 4- to 7-month-old infants, we use these new tools to show that social processing is accompanied by spatially and temporally specific increases in coupled activation in the temporal-parietal junction, a core hub region of the adult social brain. During non-social processing, coupled activation decreased in the same region, indicating specificity to social processing. Coupling was strongest with high-frequency brain activity (beta and gamma), consistent with the greater energetic requirements and more localised action of high-frequency brain activity. The development of simultaneous multimodal neural measures will enable future researchers to open new vistas in understanding functional specialisation of the brain.

Estimation of Discriminative Multimodal Brain Network Connectivity Using Message-Passing-Based Nonlinear Network Fusion

Effective estimation of brain network connectivity enables better unraveling of the extraordinary complexity interactions of brain regions and helps in auxiliary diagnosis of psychiatric disorders. Considering different modalities can provide comprehensive characterizations of brain connectivity, we propose the message-passing-based nonlinear network fusion (MP-NNF) algorithm to estimate multimodal brain network connectivity. In the proposed method, the initial functional and structural networks were computed from fMRI and DTI separately. Then, we update every unimodal network iteratively, making it more similar to the others in every iteration, and finally converge to one unified network. The estimated brain connectivities integrate complementary information from multiple modalities while preserving their original structure, by adding the strong connectivities present in unimodal brain networks and eliminating the weak connectivities. The effectiveness of the method was evaluated by applying the learned brain connectivity for the classification of major depressive disorder (MDD). Specifically, 82.18% classification accuracy was achieved even with the simple feature selection and classification pipeline, which significantly outperforms the competing methods. Exploration of brain connectivity contributed to MDD identification suggests that the proposed method not only improves the classification performance but also was sensitive to critical disease-related neuroimaging biomarkers.

Imbalanced Gamma-band Functional Brain Networks of Autism Spectrum Disorders

Resting gamma-band brain networks are known as an inhibitory component in functional brain networks. Although autism spectrum disorder (ASD) is considered as with imbalanced brain networks, the inhibitory component remains not fully explored. The study reported 10 children with ASD and 10 typically-developing (TD) controls. The power spectral density analysis of the gamma-band signal in the cerebral cortex was performed at the source level. The normalized phase transfer entropy values (nPTEs) were calculated to construct brain connectivity. Gamma-band activity of the ASD group was lower than the TD children. The significantly inhibited brain regions were mainly distributed in the bilateral frontal and temporal lobes. Connectivity analysis showed alterations in the connections from key nodes of the social brain network. The behavior assessments in the ASD group revealed a significantly positive correlation between the total score of Childhood Autism Rating Scale and the regional nPTEs of the right transverse temporal gyrus. Our results provide strong evidence that the gamma-band brain networks of ASD children have a lower level of brain activities and different distribution of information flows. Clinical meanings of such imbalances of both activity and connectivity were also worthy of further explorations.

The Potential Advantages of Tai Chi Chuan in Promoting Inhibitory Control and Spontaneous Neural Activity in Young Adults

Tai Chi Chuan (TCC) is assumed to exert beneficial effects on functional brain activity and cognitive function in elders. Until now, empirical evidence of TCC induced intra-regional spontaneous neural activity and inhibitory control remains inconclusive. Whether the effect of TCC is better than that of other aerobic exercises is still unknown, and the role of TCC in younger adults is not yet fully understood. Here we used resting-state functional MRI (fMRI) to investigate the effects of 8-week TCC (n = 12) and brisk walking (BW, n = 12) on inhibitory control and fractional amplitude of low-frequency fluctuations (fALFF). The results found that TCC had significant effects on inhibitory control performance and spontaneous neural activity that were associated with significantly increased fALFF in the left medial superior frontal gyrus (Cohen's d = 1.533) and the right fusiform gyrus (Cohen's d = 1.436) and decreased fALFF in the right dorsolateral superior frontal gyrus (Cohen's d = 1.405) and the right paracentral lobule (Cohen's d = 1.132).TCC exhibited stronger effects on spontaneous neural activity than the BW condition, as reflected in significantly increased fALFF in the left medial superior frontal gyrus (Cohen's d = 0.862). There was a significant positive correlation between the increase in fALFF in the left medial superior frontal gyrus and the enhancement in inhibitory control performance. The change in fALFF in the left medial superior frontal gyrus was able to explain the change in inhibitory control performance induced by TCC. In conclusion, our results indicated that 8 weeks of TCC intervention could improve processing efficiency related to inhibitory control and alter spontaneous neural activity in young adults, and TCC had potential advantages over BW intervention for optimizing spontaneous neural activity.

Integrative Models of Brain Structure and Dynamics: Concepts, Challenges, and Methods

The anatomical architecture of the brain constrains the dynamics of interactions between various regions. On a microscopic scale, neural plasticity regulates the connections between individual neurons. This microstructural adaptation facilitates coordinated dynamics of populations of neurons (mesoscopic scale) and brain regions (macroscopic scale). However, the mechanisms acting on multiple timescales that govern the reciprocal relationship between neural network structure and its intrinsic dynamics are not well understood. Studies empirically investigating such relationships on the whole-brain level rely on macroscopic measurements of structural and functional connectivity estimated from various neuroimaging modalities such as Diffusion-weighted Magnetic Resonance Imaging (dMRI), Electroencephalography (EEG), Magnetoencephalography (MEG), and functional Magnetic Resonance Imaging (fMRI). dMRI measures the anisotropy of water diffusion along axonal fibers, from which structural connections are estimated. EEG and MEG signals measure electrical activity and magnetic fields induced by the electrical activity, respectively, from various brain regions with a high temporal resolution (but limited spatial coverage), whereas fMRI measures regional activations indirectly via blood oxygen level-dependent (BOLD) signals with a high spatial resolution (but limited temporal resolution). There are several studies in the neuroimaging literature reporting statistical associations between macroscopic structural and functional connectivity. On the other hand, models of large-scale oscillatory dynamics conditioned on network structure (such as the one estimated from dMRI connectivity) provide a platform to probe into the structure-dynamics relationship at the mesoscopic level. Such investigations promise to uncover the theoretical underpinnings of the interplay between network structure and dynamics and could be complementary to the macroscopic level inquiries. In this article, we review theoretical and empirical studies that attempt to elucidate the coupling between brain structure and dynamics. Special attention is given to various clinically relevant dimensions of brain connectivity such as the topological features and neural synchronization, and their applicability for a given modality, spatial or temporal scale of analysis is discussed. Our review provides a summary of the progress made along this line of research and identifies challenges and promising future directions for multi-modal neuroimaging analyses.

Special Patterns of Dynamic Brain Networks Discriminate Between Face and Non-face Processing: A Single-Trial EEG Study

Face processing is a spatiotemporal dynamic process involving widely distributed and closely connected brain regions. Although previous studies have examined the topological differences in brain networks between face and non-face processing, the time-varying patterns at different processing stages have not been fully characterized. In this study, dynamic brain networks were used to explore the mechanism of face processing in human brain. We constructed a set of brain networks based on consecutive short EEG segments recorded during face and non-face (ketch) processing respectively, and analyzed the topological characteristic of these brain networks by graph theory. We found that the topological differences of the backbone of original brain networks (the minimum spanning tree, MST) between face and ketch processing changed dynamically. Specifically, during face processing, the MST was more line-like over alpha band in 0–100 ms time window after stimuli onset, and more star-like over theta and alpha bands in 100–200 and 200–300 ms time windows. The results indicated that the brain network was more efficient for information transfer and exchange during face processing compared with non-face processing. In the MST, the nodes with significant differences of betweenness centrality and degree were mainly located in the left frontal area and ventral visual pathway, which were involved in the face-related regions. In addition, the special MST patterns can discriminate between face and ketch processing by an accuracy of 93.39%. Our results suggested that special MST structures of dynamic brain networks reflected the potential mechanism of face processing in human brain.

The Essentials of Brain Anatomy for Physiatrists: Magnetic Resonance Imaging Findings

ABSTRACT

Detailed knowledge of the brain anatomy is important for the treatment of patients with brain disorders. In this study, we conducted a review of essential parts of human brain anatomy based on magnetic resonance imaging of the brain. Using T2-weighted brain magnetic resonance imaging, we explained how to recognize several structures in each brain lobe (the frontal, parietal, temporal, and occipital lobes). We depicted the boundary of each structure on brain magnetic resonance imaging and described their functions. The limbic system controls various functions such as emotion, motivation, behavior, memory, and olfaction. Broca's and Wernicke's areas and arcuate fasciculus are important structures for human language functions. Emotion, memory, and language function are one of the main functions of human. Therefore, the anatomical knowledge of the limbic system and language-related structures is important for physiatrists. We described the anatomical location and function of each substructure of the limbic system and language centers. In addition, we indicated the exact points of motor- and sensory-related neural tracts (corticospinal tract, corticoreticular pathway, medial lemniscus, and spinothalamic tract) on brain magnetic resonance imaging. We believe that our review on brain anatomy would be helpful for physiatrists to accurately identify the damage of each function from brain disorders and elucidate proper plan for rehabilitative treatment.

Apperceptive Prosopagnosia Secondary to an Ischemic Infarct of the Lingual Gyrus: A Case Report and an Update on the Neuroanatomy, Neurophysiology, and Phenomenology of Prosopagnosia

Phenomenology is the philosophical study of experience and its core feature of sentience, the very ability to be conscious of a sensation and how we perceive it. Nowhere is this idea more vivid, as in the phenomenon of vision and the ability to form and sense a visual percept. The clinical entity of prosopagnosia, the ability to sense but not recognize a face, strikes at the heart of this phenomenon. We describe a classic case of selective apperceptive prosopagnosia due to an ischemic infarct of the left occipital lobe with extension to the lingual gyrus. It is well-established that acquired prosopagnosia usually involves the right more than the left occipital cortex, with localization of lesions bilaterally more than unilaterally. The ischemic infarcts strategically involve the fusiform gyrus, inferior occipital gyrus, the fundus of the posterior temporal sulcus, parahippocampal gyrus, and, rarely, lingual gyrus, which is almost always not a solitary finding. We seize upon this opportunity to explore the concept of visual prosopagnosia and outline the latest ideas on the neuroanatomical localization, neurophysiology, and classification of this intriguing phenomenon.

Short-range and long-range neuronal oscillatory coupling in multiple frequency bands during face perception

Neuronal oscillatory activity has been considered to play a key role in face processing through its functional effect on information flow and exchange in human brain. Specifically, most neuronal oscillatory activity is measured in different rhythm based on the electrophysiological signal at single channel level. Although, the neuronal oscillatory coupling between neuronal assembles is associated with the information flow and exchange between brain regions, few studies focus on this type of neuronal oscillatory activity in face processing. In this study, the neuronal oscillatory coupling was investigated based on electroencephalographic (EEG) data of 20 participants, which were recorded when the participants were in a face/non-face perceptual task. The phase lag index (PLI) was used to assess the neuronal oscillatory coupling between brain regions in typical frequency bands. Enhanced short-range coupling was observed in theta (4-8 Hz) and alpha (8-12 Hz) band over the frontal region, in gamma1 (30-49 Hz) band over the left posterior and occipito-temporal regions, and in gamma2 (51-75 Hz) over the right temporal region during face perception compared with non-face perception. Long-range coupling was increased in theta and gamma band over the right hemisphere during face perception. Moreover, increased long-range coupling was observed in alpha band over the left and right hemisphere respectively during face perception. The results suggested that frequency-specific neuronal oscillatory coupling within and between regions of frontal cortex and the ventral visual pathway played an important role in face perception, which might reflect underlying neural mechanism of face perception.

Theta- and Gamma-Band Activity Discriminates Face, Body and Object Perception

Face and body perception is mediated by configural mechanisms, which allow the perception of these stimuli as a whole, rather than the sum of individual parts. Indirect measures of configural processing in visual cognition are the face and body inversion effects (FIE and BIE), which refer to the drop in performance when these stimuli are perceived upside-down. Albeit FIE and BIE have been well characterized at the behavioral level, much still needs to be understood in terms of the neurophysiological correlates of these effects. Thus, in the current study, the brain’s electrical activity has been recorded by a 128 channel electroencephalogram (EEG) in 24 healthy participants while perceiving (upright and inverted) faces, bodies and houses. EEG data were analyzed in both the time domain (i.e., event-related potentials—ERPs) and the frequency domain [i.e., induced theta (5–7 Hz) and gamma (28–45 Hz) oscillations]. ERPs amplitude results showed increased N170 amplitude for inverted faces and bodies (compared to the same stimuli presented in canonical position) but not for houses. ERPs latency results showed delayed N170 components for inverted (vs. upright) faces, houses, but not bodies. Spectral analysis of induced oscillations indicated physiological FIE and BIE; that is decreased gamma-band synchronization over right occipito-temporal electrodes for inverted (vs. upright) faces, and increased bilateral frontoparietal theta-band synchronization for inverted (vs. upright) faces. Furthermore, increased left occipito-temporal and right frontal theta-band synchronization for upright (vs. inverted) bodies was found. Our findings, thus, demonstrate clear differences in the neurophysiological correlates of face and body perception. The neurophysiological FIE suggests disruption of feature binding processes (decrease in occipital gamma oscillations for inverted faces), together with enhanced feature-based attention (increase in frontoparietal theta oscillations for inverted faces). In contrast, the BIE may suggest that structural encoding for bodies is mediated by the first stages of configural processing (decrease in occipital theta oscillations for inverted bodies).

Plasma luteinizing hormone level affects the brain activity of patients with polycystic ovary syndrome

OBJECTIVE

Cognitive function has been reported to be impaired in women with polycystic ovary syndrome (PCOS). This study aimed to investigate the effect of PCOS on brain activity and explore the relationship between brain activity and sex hormone levels in women with PCOS (WPCOS).

METHODS

Twenty-one women aged 18-45 years old with new-diagnosed PCOS were enrolled. Plasma levels of six sex hormones including luteinizing hormone (LH) and follicle-stimulating hormone (FSH) were tested during the 2-5 days of their menstrual periods. Twenty-seven healthy controls (HC) were recruited. Every subject underwent a resting-state functional magnetic resonance imaging (fMRI). The amplitude of low-frequency fluctuation (ALFF) of the whole brain was evaluated followed by the functional connectivity (FC) analysis. Finally, the correlation between the ALFF, FC of the significant areas and the plasma hormone levels were analyzed.

RESULTS

The patients showed increased ALFF value in the left inferior temporal gyrus (ITG.L) and decreased ALFF value in the left inferior occipital gyrus (IOG.L) as well as the superior frontal gyrus (SFG.R, P < 0.005). For the FC analysis, patients showed decreased FC in SFG.R with the right middle frontal gyrus (MFG.R, P < 0.05). The FC between SFG.R and MFG.R was negatively correlated with LH level (R=-0.594, P = 0.005) and with the LH/FSH ratio (R=-0.521, P = 0.015).

CONCLUSION

PCOS can induce changes in activities of brain regions responsible for visuospatial working memory, face processing and episodic memory. The reduced functional connectivity within the right frontal lobe is related with the high LH level in WPCOS.

Becoming sexy: Contrapposto pose increases attractiveness ratings and modulates observers' brain activity

Previous neurophysiological studies have revealed the neural correlates of human body form perception, as well as those related to the perception of attractive body sizes. In the current study we aimed to extend the neurophysiological studies regarding body perception by investigating the perception of human body posture to provide insights into the cognitive mechanisms responsive to bodily form, and the processing of its attractiveness. To achieve these aims, we used the contrapposto posture which creates an exaggeration of low waist to hip ratio (WHR), an indicator of women's attractiveness. Electroencephalogram (EEG) signals were recorded while participants completed both (i) an oddball task presenting female body forms differing in pose (contrapposto vs. standing) and viewing angle (anterior vs. posterior), and (ii) a subsequent active attractiveness judgement task. Behavioral results showed that a contrapposto pose is considered more attractive than a neutral standing pose. Results at the neural level showed that body posture modulates the visual information processing in early ERP components, indicating attentional variations depending on human body posture; as well as in late components, indicating further differences in attention and attractiveness judgement of stimuli varying in body pose. Furthermore, the LORETA results identified the middle temporal gyrus as well as angular gyrus as the key brain regions activated in association with the perception and attractiveness judgment of females' bodies with different body poses. Overall, the current paper suggests the evolutionary adaptive preference for lower WHRs as in the contrapposto pose activating brain regions associated with visual perception and attractiveness judgement.

Visual event-related potentials in external emotional conditions in bipolar disorders I and II

OBJECTIVES

Mutual influences of cognitive and emotional functions occur in bipolar disorder, but specific alterations in relation to external emotional stimuli in bipolar I (BD I) and II (BD II) subtypes remain unknown. This study aimed to explore the effects of external emotional stimuli on cerebral attentional function in BD I and BD II.

METHODS

We tested visual oddball event-related potentials (ERPs) during various external emotional stimuli (Disgust, Fear, Erotica, Happiness, Neutral and Sadness) in 31 patients with BD I, 19 BD II and 47 healthy volunteers. Participants' concurrent affective states were also evaluated.

RESULTS

The ERP N2 latencies during Fear and Happiness were prolonged, P3 amplitudes during Disgust and Erotica were decreased in BD I; P3 amplitudes during Disgust, Erotica, Happiness and Neutral conditions were decreased in BD II. Increased frontal and parietal and decreased temporal and occipital activations were found in BD I, while increased occipital and parietal and decreased frontal and limbic activations in BD II in relation to different external emotions. ERP components were not correlated with concurrent affective states in patients.

CONCLUSIONS

Automatic attention during Happiness and Fear, and voluntary attention during Disgust and Erotica conditions were impaired in BD I; and voluntary attention during Disgust, Happiness, Erotica and Neutral conditions was impaired in BD II. Our study illustrates different patterns of visual attentional deficits associated with different external emotional stimuli in BD I and BD II.

Prestimulus feedback connectivity biases the content of visual experiences

Ongoing fluctuations in neural excitability and in networkwide activity patterns before stimulus onset have been proposed to underlie variability in near-threshold stimulus detection paradigms-that is, whether or not an object is perceived. Here, we investigated the impact of prestimulus neural fluctuations on the content of perception-that is, whether one or another object is perceived. We recorded neural activity with magnetoencephalography (MEG) before and while participants briefly viewed an ambiguous image, the Rubin face/vase illusion, and required them to report their perceived interpretation in each trial. Using multivariate pattern analysis, we showed robust decoding of the perceptual report during the poststimulus period. Applying source localization to the classifier weights suggested early recruitment of primary visual cortex (V1) and ∼160-ms recruitment of the category-sensitive fusiform face area (FFA). These poststimulus effects were accompanied by stronger oscillatory power in the gamma frequency band for face vs. vase reports. In prestimulus intervals, we found no differences in oscillatory power between face vs. vase reports in V1 or in FFA, indicating similar levels of neural excitability. Despite this, we found stronger connectivity between V1 and FFA before face reports for low-frequency oscillations. Specifically, the strength of prestimulus feedback connectivity (i.e., Granger causality) from FFA to V1 predicted not only the category of the upcoming percept but also the strength of poststimulus neural activity associated with the percept. Our work shows that prestimulus network states can help shape future processing in category-sensitive brain regions and in this way bias the content of visual experiences.

Frequency-Dependent Changes of the Resting BOLD Signals Predicts Cognitive Deficits in Asymptomatic Carotid Artery Stenosis

“Asymptomatic” carotid artery stenosis (aCAS) patients usually have cognitive impairment in the domains of executive, psychomotor speed, and memory function. However, the pathophysiology of this impairment in aCAS patients is still unclear. In this study, amplitude of low-frequency fluctuation (ALFF) method was used based on resting-state blood oxygenation level dependent (BOLD) signals, to investigate local brain activity in 19 aCAS patients and 24 healthy controls, aimed to explore this pathophysiology mechanism. We analyzed this intrinsic activity in four individual frequency bands: Slow-2 (0.198–0.25 Hz), Slow-3 (0.073–0.198 Hz), Slow-4 (0.027–0.073 Hz), and Slow-5 (0.01–0.027 Hz). The aCAS-related ALFF changes were mainly distributed in (1) cortical midline structure, including bilateral dorsomedial prefrontal (dmPFC), cingulate cortex (CC) and precuneus (PCu); (2) hippocampus and its adjacent structures, including bilateral hippocampus, thalamus and medial temporal regions. We found these spatial patterns were frequency-dependent. Significant interaction between frequency and group was found distributed in left putamen, triangle part of inferior temporal and bilateral precentral/postcentral gyrus when Slow-4 and Slow-5 were considered. The delay recall ability of aCAS patient was significantly positive correlated to the mean ALFF in dmPFC within Slow-4 band and the mean ALFF in the bilateral hippocampus within Slow-3 band, respectively. We also found the Montreal Cognitive Assessme score of aCAS patient was significantly positive correlated to the mean ALFF in right fusiform and parahippocampus within Slow-3 band. Furthermore, we built the automatic diagnosis and prediction models based on support vector machine (SVM) and back propagation neural network (BPNN), respectively. Both two types of models could achieve relatively competent performance, which meant the frequency-dependent changes in ALFF could not only reveal the pathophysiology mechanism of cognitive impairment of aCAS, but also could be used as neuroimaging marker in the analysis of cognition impairment for aCAS patients.

PAGANI Toolkit: Parallel graph-theoretical analysis package for brain network big data

The recent collection of unprecedented quantities of neuroimaging data with high spatial resolution has led to brain network big data. However, a toolkit for fast and scalable computational solutions is still lacking. Here, we developed the PArallel Graph-theoretical ANalysIs (PAGANI) Toolkit based on a hybrid central processing unit-graphics processing unit (CPU-GPU) framework with a graphical user interface to facilitate the mapping and characterization of high-resolution brain networks. Specifically, the toolkit provides flexible parameters for users to customize computations of graph metrics in brain network analyses. As an empirical example, the PAGANI Toolkit was applied to individual voxel-based brain networks with ∼200,000 nodes that were derived from a resting-state fMRI dataset of 624 healthy young adults from the Human Connectome Project. Using a personal computer, this toolbox completed all computations in ∼27 h for one subject, which is markedly less than the 118 h required with a single-thread implementation. The voxel-based functional brain networks exhibited prominent small-world characteristics and densely connected hubs, which were mainly located in the medial and lateral fronto-parietal cortices. Moreover, the female group had significantly higher modularity and nodal betweenness centrality mainly in the medial/lateral fronto-parietal and occipital cortices than the male group. Significant correlations between the intelligence quotient and nodal metrics were also observed in several frontal regions. Collectively, the PAGANI Toolkit shows high computational performance and good scalability for analyzing connectome big data and provides a friendly interface without the complicated configuration of computing environments, thereby facilitating high-resolution connectomics research in health and disease.

Low-frequency oscillations employ a general coding of the spatio-temporal similarity of dynamic faces

Brain networks use neural oscillations as information transfer mechanisms. Although the face perception network in occipitotemporal cortex is well-studied, contributions of oscillations to face representation remain an open question. We tested for links between oscillatory responses that encode facial dimensions and the theoretical proposal that faces are encoded in similarity-based "face spaces". We quantified similarity-based encoding of dynamic faces in magnetoencephalographic sensor-level oscillatory power for identity, expression, physical and perceptual similarity of facial form and motion. Our data show that evoked responses manifest physical and perceptual form similarity that distinguishes facial identities. Low-frequency induced oscillations (< 20Hz) manifested more general similarity structure, which was not limited to identity, and spanned physical and perceived form and motion. A supplementary fMRI-constrained source reconstruction implicated fusiform gyrus and V5 in this similarity-based representation. These findings introduce a potential link between "face space" encoding and oscillatory network communication, which generates new hypotheses about the potential oscillation-mediated mechanisms that might encode facial dimensions.

Bidirectional electric communication between the inferior occipital gyrus and the amygdala during face processing

Faces contain multifaceted information that is important for human communication. Neuroimaging studies have revealed face-specific activation in multiple brain regions, including the inferior occipital gyrus (IOG) and amygdala; it is often assumed that these regions constitute the neural network responsible for the processing of faces. However, it remains unknown whether and how these brain regions transmit information during face processing. This study investigated these questions by applying dynamic causal modeling of induced responses to human intracranial electroencephalography data recorded from the IOG and amygdala during the observation of faces, mosaics, and houses in upright and inverted orientations. Model comparisons assessing the experimental effects of upright faces versus upright houses and upright faces versus upright mosaics consistently indicated that the model having face-specific bidirectional modulatory effects between the IOG and amygdala was the most probable. The experimental effect between upright versus inverted faces also favored the model with bidirectional modulatory effects between the IOG and amygdala. The spectral profiles of modulatory effects revealed both same-frequency (e.g., gamma-gamma) and cross-frequency (e.g., theta-gamma) couplings. These results suggest that the IOG and amygdala communicate rapidly with each other using various types of oscillations for the efficient processing of faces. Hum Brain Mapp 38:4511-4524, 2017. © 2017 Wiley Periodicals, Inc.