Cortical Brain Networks Research Articles

Research on the effect of background music on spatial cognitive working memory based on cortical brain network

Background music has been increasingly affecting people's lives. The research on the influence of background music on working memory has become a hot topic in brain science. In this paper, an improved electroencephalography (EEG) experiment based on n-back paradigm was designed. Fifteen university students without musical training were randomly selected to participate in the experiment, and their behavioral data and the EEG data were collected synchronously in order to explore the influence of different types of background music on spatial positioning cognition working memory. The exact low-resolution brain tomography algorithm (eLORETA) was applied to localize the EEG sources and the cross-correlation method was used to construct the cortical brain function networks based on the EEG source signals. Then the characteristics of the networks under different conditions were analyzed and compared to study the effects of background music on people's working memory. The results showed that the difference of peak periods after stimulated by different types of background music were mainly distributed in the signals of occipital lobe and temporal lobe ( P < 0.05). The analysis results showed that the brain connectivity under the condition with background music were stronger than those under the condition without music. The connectivities in the right occipital and temporal lobes under the condition of rock music were significantly higher than those under the condition of classical music. The node degrees, the betweenness centrality and the clustering coefficients under the condition without music were lower than those under the condition with background music. The node degrees and clustering coefficients under the condition of classical music were lower than those under the condition of rock music. It indicates that music stimulation increases the brain activity and has an impact on the working memory, and the effect of rock music is more remarkable than that of classical music. The behavioral data showed that the response accuracy in the state of no music, classical music and rock music were 86.09% ± 0.090%, 80.96% ± 0.960% and 79.36% ± 0.360%, respectively. We conclude that background music has a negative impact on the working memory, for it takes up the cognitive resources and reduces the cognitive ability of spatial location.

Dynamic Reorganization of the Cortical Functional Brain Network in Affective Processing and Cognitive Reappraisal.

Emotion and affect play crucial roles in human life that can be disrupted by diseases. Functional brain networks need to dynamically reorganize within short time periods in order to efficiently process and respond to affective stimuli. Documenting these large-scale spatiotemporal dynamics on the same timescale they arise, however, presents a large technical challenge. In this study, the dynamic reorganization of the cortical functional brain network during an affective processing and emotion regulation task is documented using an advanced multi-model electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) technique. Sliding time window correlation and [Formula: see text]-means clustering are employed to explore the functional brain connectivity (FC) dynamics during the unaltered perception of neutral (moderate valence, low arousal) and negative (low valence, high arousal) stimuli and cognitive reappraisal of negative stimuli. Betweenness centralities are computed to identify central hubs within each complex network. Results from 20 healthy subjects indicate that the cortical mechanism for cognitive reappraisal follows a 'top-down' pattern that occurs across four brain network states that arise at different time instants (0-170[Formula: see text]ms, 170-370[Formula: see text]ms, 380-620[Formula: see text]ms, and 620-1000[Formula: see text]ms). Specifically, the dorsolateral prefrontal cortex (DLPFC) is identified as a central hub to promote the connectivity structures of various affective states and consequent regulatory efforts. This finding advances our current understanding of the cortical response networks of reappraisal-based emotion regulation by documenting the recruitment process of four functional brain sub-networks, each seemingly associated with different cognitive processes, and reveals the dynamic reorganization of functional brain networks during emotion regulation.

Constructing large-scale cortical brain networks from scalp EEG with Bayesian nonnegative matrix factorization

A large-scale network provides a high hierarchical level for understanding the adaptive adjustment of the human brain during cognition processes. Since high spatial resolution is required, most of the related works are based on functional magnetic resonance imaging (fMRI); however, fMRI lacks the temporal information that is important in investigating the high cognition processes. Although combining electroencephalography (EEG) inverse solution and independent component analysis (ICA), researchers detected large-scale functional subnetworks recently, few researchers focus on the unreasonable negative activation, which is biased from the nonnegative electrical source activations in the brain. In this study, considering the favorable nonnegative property of Bayesian nonnegative matrix factorization (Bayesian NMF) and combining EEG source imaging, we developed a robust approach for EEG large-scale network construction and applied it to two independent real EEG datasets (i.e., decision-making and P300). Eight and nine best-fit networks, including such important subnetworks as the somatosensory-motor network (SMN), the default mode network (DMN), etc., were successfully identified for decision-making and P300, respectively. Compared to the networks acquired with ICA, these networks not only lacked confusing negative activations but also showed clear spatial distributions that are compatible with specific brain function. Based on the constructed large-scale network, we further probed that the self-referential network (SRN), the primary visual network (PVN), and the visual network (VN) demonstrated different interaction patterns with other networks between different responses in decision-making. Our results confirm the possibility of probing the neural mechanisms of high cognition processes at a very high temporal and spatial resolution level.

Linking Pain Sensation to the Autonomic Nervous System: The Role of the Anterior Cingulate and Periaqueductal Gray Resting-State Networks.

There are bi-directional interactions between the autonomic nervous system (ANS) and pain. This is likely underpinned by a substantial overlap between brain areas of the central autonomic network and areas involved in pain processing and modulation. To date, however, relatively little is known about the neuronal substrates of the ANS-pain association. Here, we acquired resting state fMRI scans in 21 healthy subjects at rest and during tonic noxious cold stimulation. As indicators of autonomic function, we examined how heart rate variability (HRV) frequency measures were influenced by tonic noxious stimulation and how these variables related to participants’ pain perception and to brain functional connectivity in regions known to play a role in both ANS regulation and pain perception, namely the right dorsal anterior cingulate cortex (dACC) and periaqueductal gray (PAG). Our findings support a role of the cardiac ANS in brain connectivity during pain, linking functional connections of the dACC and PAG with measurements of low frequency (LF)-HRV. In particular, we identified a three-way relationship between the ANS, cortical brain networks known to underpin pain processing, and participants’ subjectively reported pain experiences. LF-HRV both at rest and during pain correlated with functional connectivity between the seed regions and other cortical areas including the right dorsolateral prefrontal cortex (dlPFC), left anterior insula (AI), and the precuneus. Our findings link cardiovascular autonomic parameters to brain activity changes involved in the elaboration of nociceptive information, thus beginning to elucidate underlying brain mechanisms associated with the reciprocal relationship between autonomic and pain-related systems.

Individual-specific functional connectivity of the amygdala: A substrate for precision psychiatry.

The amygdala is central to the pathophysiology of many psychiatric illnesses. An imprecise understanding of how the amygdala fits into the larger network organization of the human brain, however, limits our ability to create models of dysfunction in individual patients to guide personalized treatment. Therefore, we investigated the position of the amygdala and its functional subdivisions within the network organization of the brain in 10 highly sampled individuals (5 h of fMRI data per person). We characterized three functional subdivisions within the amygdala of each individual. We discovered that one subdivision is preferentially correlated with the default mode network; a second is preferentially correlated with the dorsal attention and fronto-parietal networks; and third subdivision does not have any networks to which it is preferentially correlated relative to the other two subdivisions. All three subdivisions are positively correlated with ventral attention and somatomotor networks and negatively correlated with salience and cingulo-opercular networks. These observations were replicated in an independent group dataset of 120 individuals. We also found substantial across-subject variation in the distribution and magnitude of amygdala functional connectivity with the cerebral cortex that related to individual differences in the stereotactic locations both of amygdala subdivisions and of cortical functional brain networks. Finally, using lag analyses, we found consistent temporal ordering of fMRI signals in the cortex relative to amygdala subdivisions. Altogether, this work provides a detailed framework of amygdala-cortical interactions that can be used as a foundation for models relating aberrations in amygdala connectivity to psychiatric symptoms in individual patients.

Dynamics of functional connectivity in multilayer cortical brain network during sensory information processing

Topology of a functional brain multilayer network is dynamically adjusted to provide optimal performance during accomplishing cognitive tasks, including sensory information processing. Functional connectivity between brain regions is achieved in terms of correlation or synchronization inference in recorded signals of neuronal activity. The promising approach for studying cortical network structure implies considering functional interactions in different frequency bands on the different layers of multilayer network model. Links between these layers can be restored based on cross-frequency couplings. While the topology of functional connectivity within each layer can be effectively restored from registered neurophysiological signals, mechanisms underlying coupling between different layers remain poorly understood. Here we consider evolution of the cortical network topology in alpha and beta frequency bands during visual stimuli processing. For each frequency band the functional connectivity between different brain regions is estimated by comparing Fourier spectra of EEG signals. The obtained functional topologies are considered as the layers of two-layer network. In the framework of a multilayer model we analyze evolution of functional network topology on both layers and reveal features of intralayer interaction underlying visual information processing in the brain.

Brain Networks Underlying Eye's Pupil Dynamics.

Phasic changes in eye’s pupil diameter have been repeatedly observed during cognitive, emotional and behavioral activity in mammals. Although pupil diameter is known to be associated with noradrenergic firing in the pontine Locus Coeruleus (LC), thus far the causal chain coupling spontaneous pupil dynamics to specific cortical brain networks remains unknown. In the present study, we acquired steady-state blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) data combined with eye-tracking pupillometry from fifteen healthy subjects that were trained to maintain a constant attentional load. Regression analysis revealed widespread visual and sensorimotor BOLD-fMRI deactivations correlated with pupil diameter. Furthermore, we found BOLD-fMRI activations correlated with pupil diameter change rate within a set of brain regions known to be implicated in selective attention, salience, error-detection and decision-making. These regions included LC, thalamus, posterior cingulate cortex (PCC), dorsal anterior cingulate and paracingulate cortex (dACC/PaCC), orbitofrontal cortex (OFC), and right anterior insular cortex (rAIC). Granger-causality analysis performed on these regions yielded a complex pattern of interdependence, wherein LC and pupil dynamics were far apart in the network and separated by several cortical stages. Functional connectivity (FC) analysis revealed the ubiquitous presence of the superior frontal gyrus (SFG) in the networks identified by the brain regions correlated to the pupil diameter change rate. No significant correlations were observed between pupil dynamics, regional activation and behavioral performance. Based on the involved brain regions, we speculate that pupil dynamics reflects brain processing implicated in changes between self- and environment-directed awareness.

Complex Oscillatory Waves Emerging from Cortical Organoids Model Early Human Brain Network Development.

Structural and transcriptional changes during early brain maturation follow fixed developmental programs defined by genetics. However, whether this is true for functional network activity remains unknown, primarily due to experimental inaccessibility of the initial stages of the living human brain. Here, we developed human cortical organoids that dynamically change cellular populations during maturation and exhibited consistent increases in electrical activity over the span of several months. The spontaneous network formation displayed periodic and regular oscillatory events that were dependent on glutamatergic and GABAergic signaling. The oscillatory activity transitioned to more spatiotemporally irregular patterns, and synchronous network events resembled features similar to those observed in preterm human electroencephalography. These results show that the development of structured network activity in a human neocortex model may follow stable genetic programming. Our approach provides opportunities for investigating and manipulating the role of network activity in the developing human cortex.

Task-induced functional brain connectivity mediates the relationship between striatal D2/3 receptors and working memory.

Working memory performance is thought to depend on both striatal dopamine 2/3 receptors (D2/3Rs) and task-induced functional organisation in key cortical brain networks. Here, we combine functional magnetic resonance imaging and D2/3R positron emission tomography in 51 healthy volunteers, to investigate the relationship between working memory performance, task-induced default mode network (DMN) functional connectivity changes, and striatal D2/3R availability. Increasing working memory load was associated with reduced DMN functional connectivity, which was itself associated with poorer task performance. Crucially, the magnitude of the DMN connectivity reduction correlated with striatal D2/3R availability, particularly in the caudate, and this relationship mediated the relationship between striatal D2/3R availability and task performance. These results inform our understanding of natural variation in working memory performance, and have implications for understanding age-related cognitive decline and cognitive impairments in neuropsychiatric disorders where dopamine signalling is altered.

Large-scale brain modes reorganize between infant sleep states and carry prognostic information for preterms

Sleep architecture carries vital information about brain health across the lifespan. In particular, the ability to express distinct vigilance states is a key physiological marker of neurological wellbeing in the newborn infant although systems-level mechanisms remain elusive. Here, we demonstrate that the transition from quiet to active sleep in newborn infants is marked by a substantial reorganization of large-scale cortical activity and functional brain networks. This reorganization is attenuated in preterm infants and predicts visual performance at two years. We find a striking match between these empirical effects and a computational model of large-scale brain states which uncovers fundamental biophysical mechanisms not evident from inspection of the data. Active sleep is defined by reduced energy in a uniform mode of neural activity and increased energy in two more complex anteroposterior modes. Preterm-born infants show a deficit in this sleep-related reorganization of modal energy that carries novel prognostic information.

Abstract TP175: Changes in Brain Network After High Frequency Repetitive Transcranial Magnetic Stimulation Based on Brain Activation During Language Performance in Chronic Stroke Patients With Non-Fluent Aphasia

Introduction: In post-stroke aphasia rehabilitation, repetitive transcranial stimulation (rTMS) aimed to reinforce the activity of the brain regions in the left hemisphere. In the present study, we performed functional near-infrared spectroscopy (fNIRS) with excitatory rTMS treatment for selection of the most appropriate application of stimulation and detection of the changes in cortical brain network. The purpose of this study is to assess the changes in brain network after high frequency rTMS in post-stroke non-fluent aphasia patients. Methods: Five right-handed patients with post-stroke non-fluent aphasia were included in this study. The most activated area of left hemisphere in fNIRS analysis was selected for stimulation. 34 channels were set by arranging 12 sensors and detectors at intervals of 3cm covering Broca and Wernicke areas. Excitatory rTMS (10Hz, 800 stimuli, 100% of resting motor threshold, tangential to scalp) at the most activated area with navigation system was done in combination with 30 minutes’ speech therapy for 10 consecutive days. Western aphasia battery (WAB) and fNIRS evaluation before (T0), right after 10 sessions of treatment (T1) and 2 months after the last treatment session (T2) were done. The changes in brain network were analyzed by the graph theoretical approach. Results: The scores from subtests in WAB have increased after excitatory rTMS combined with speech therapy in all subjects (Figure 1). The thresholded correlation in the area of stimulation became higher in all patients. The changes of network parameters such as global efficiency and small-worldness in Broca and Wernicke area was significant after rTMS in all patients. Conclusion: Aphasia quotient and scores from all subtests in WAB have increased significantly after targeted excitatory rTMS combined with speech therapy in all subjects. The efficiency in brain language network has been changed after excitatory rTMS.

Cortical gamma-synchrony measured with magnetoencephalography is a marker of clinical status and predicts clinical outcome in stroke survivors.

The outcome of stroke survivors is difficult to anticipate. While the extent of the anatomical brain lesion is only poorly correlated with the prognosis, functional measures of cortical synchrony, brain networks and cortical plasticity seem to be good predictors of clinical recovery. In this field, gamma (>30Hz) cortical synchrony is an ideal marker of brain function, as it plays a crucial role for the integration of information, it is an indirect marker of Glutamate/GABA balance and it directly estimates the reserve of parvalbulin-positive neurons, key players in synaptic plasticity. In this study we measured gamma synchronization driven by external auditory stimulation with magnetoencephalography and tested whether it was predictive of the clinical outcome in stroke survivors undergoing intensive rehabilitation in a tertiary rehabilitation center. Eleven stroke survivors undergoing intensive rehabilitation were prospectively recruited. Gamma synchrony was measured non-invasively within one month from stroke onset with magnetoencephalography, both at rest and during entrainment with external 40Hz amplitude modulated binaural sounds. Lesion location and volume were quantitatively assessed through a high-resolution anatomical MRI. Barthel index (BI) and Functional Independence Measure (FIM) scales were measured at the beginning and at the end of the admission to the rehabilitation unit. The spatial distribution of cortical gamma synchrony was altered, and the physiological right hemispheric dominance observed in healthy controls was attenuated or lost. Entrained gamma synchronization (but not resting state gamma synchrony) showed a very high correlation with the clinical status at both admission and discharge (both BI and FIM). Neither clinical status nor gamma synchrony showed a correlation with lesion volume. Cortical gamma synchrony related to auditory entrainment can be reliably measured in stroke patients. Gamma synchrony is strongly associated with the clinical outcome of stroke survivors undergoing rehabilitation.

Modeling brain dynamic state changes with adaptive mixture independent component analysis

There is a growing interest in neuroscience in assessing the continuous, endogenous, and nonstationary dynamics of brain network activity supporting the fluidity of human cognition and behavior. This non-stationarity may involve ever-changing formation and dissolution of active cortical sources and brain networks. However, unsupervised approaches to identify and model these changes in brain dynamics as continuous transitions between quasi-stable brain states using unlabeled, noninvasive recordings of brain activity have been limited. This study explores the use of adaptive mixture independent component analysis (AMICA) to model multichannel electroencephalographic (EEG) data with a set of ICA models, each of which decomposes an adaptively learned portion of the data into statistically independent sources. We first show that AMICA can segment simulated quasi-stationary EEG data and accurately identify ground-truth sources and source model transitions. Next, we demonstrate that AMICA decomposition, applied to 6–13 channel scalp recordings from the CAP Sleep Database, can characterize sleep stage dynamics, allowing 75% accuracy in identifying transitions between six sleep stages without use of EEG power spectra. Finally, applied to 30-channel data from subjects in a driving simulator, AMICA identifies models that account for EEG during faster and slower response to driving challenges, respectively. We show changes in relative probabilities of these models allow effective prediction of subject response speed and moment-by-moment characterization of state changes within single trials. AMICA thus provides a generic unsupervised approach to identifying and modeling changes in EEG dynamics. Applied to continuous, unlabeled multichannel data, AMICA may likely be used to detect and study any changes in cognitive states.

Alterations in Normal Aging Revealed by Cortical Brain Network Constructed Using IBASPM.

Normal aging has been linked with the decline of cognitive functions, such as memory and executive skills. One of the prominent approaches to investigate the age-related alterations in the brain is by examining the cortical brain connectome. IBASPM is a toolkit to realize individual atlas-based volume measurement. Hence, this study seeks to determine what further alterations can be revealed by cortical brain networks formed by IBASPM-extracted regional gray matter volumes. We found the reduced strength of connections between the superior temporal pole and middle temporal pole in the right hemisphere, global hubs as the left fusiform gyrus and right Rolandic operculum in the young and aging groups, respectively, and significantly reduced inter-module connection of one module in the aging group. These new findings are consistent with the phenomenon of normal aging mentioned in previous studies and suggest that brain network built with the IBASPM could provide supplementary information to some extent. The individualization of morphometric features extraction deserved to be given more attention in future cortical brain network research.

Brain structural covariance network centrality in maltreated youth with PTSD and in maltreated youth resilient to PTSD.

Child maltreatment is a major cause of pediatric posttraumatic stress disorder (PTSD). Previous studies have not investigated potential differences in network architecture in maltreated youth with PTSD and those resilient to PTSD. High-resolution magnetic resonance imaging brain scans at 3 T were completed in maltreated youth with PTSD (n = 31), without PTSD (n = 32), and nonmaltreated controls (n = 57). Structural covariance network architecture was derived from between-subject intraregional correlations in measures of cortical thickness in 148 cortical regions (nodes). Interregional positive partial correlations controlling for demographic variables were assessed, and those correlations that exceeded specified thresholds constituted connections in cortical brain networks. Four measures of network centrality characterized topology, and the importance of cortical regions (nodes) within the network architecture were calculated for each group. Permutation testing and principle component analysis method were employed to calculate between-group differences. Principle component analysis is a methodological improvement to methods used in previous brain structural covariance network studies. Differences in centrality were observed between groups. Larger centrality was found in maltreated youth with PTSD in the right posterior cingulate cortex; smaller centrality was detected in the right inferior frontal cortex compared to youth resilient to PTSD and controls, demonstrating network characteristics unique to pediatric maltreatment-related PTSD. Larger centrality was detected in right frontal pole in maltreated youth resilient to PTSD compared to youth with PTSD and controls, demonstrating structural covariance network differences in youth resilience to PTSD following maltreatment. Smaller centrality was found in the left posterior cingulate cortex and in the right inferior frontal cortex in maltreated youth compared to controls, demonstrating attributes of structural covariance network topology that is unique to experiencing maltreatment. This work is the first to identify cortical thickness-based structural covariance network differences between maltreated youth with and without PTSD. We demonstrated network differences in both networks unique to maltreated youth with PTSD and those resilient to PTSD. The networks identified are important for the successful attainment of age-appropriate social cognition, attention, emotional processing, and inhibitory control. Our findings in maltreated youth with PTSD versus those without PTSD suggest vulnerability mechanisms for developing PTSD.

A biophysical model of dynamic balancing of excitation and inhibition in fast oscillatory large-scale networks.

Over long timescales, neuronal dynamics can be robust to quite large perturbations, such as changes in white matter connectivity and grey matter structure through processes including learning, aging, development and certain disease processes. One possible explanation is that robust dynamics are facilitated by homeostatic mechanisms that can dynamically rebalance brain networks. In this study, we simulate a cortical brain network using the Wilson-Cowan neural mass model with conduction delays and noise, and use inhibitory synaptic plasticity (ISP) to dynamically achieve a spatially local balance between excitation and inhibition. Using MEG data from 55 subjects we find that ISP enables us to simultaneously achieve high correlation with multiple measures of functional connectivity, including amplitude envelope correlation and phase locking. Further, we find that ISP successfully achieves local E/I balance, and can consistently predict the functional connectivity computed from real MEG data, for a much wider range of model parameters than is possible with a model without ISP.

Optical imaging during toddlerhood: brain responses during naturalistic social interactions.

Despite the importance of our ability to interact and communicate with others, the early development of the social brain network remains poorly understood. We examined brain activity in 12- to 14-month-old infants while they were interacting live with an adult in two different naturalistic social scenarios (i.e., reading a picture book versus singing nursery rhymes with gestures), as compared to baseline (i.e., showing infants a toy without eye contact or speech). We used functional near-infrared spectroscopy (fNIRS) recorded over the right temporal lobe of infants to assess the role of the superior temporal sulcus-temporoparietal junction (STS-TPJ) region during naturalistic social interactions. We observed increased cortical activation in the STS-TPJ region to live social stimuli in both socially engaging conditions compared to baseline during real life interaction, with greater activation evident for the joint attention (reading book) condition relative to the social nursery rhymes. These results supported the view that the STS-TPJ region, engaged in the cortical social brain network, is already specialized in infants for processing social signals and is sensitive to communicative situations. This study also highlighted the potential of fNIRS for studying brain function in infants entering toddlerhood during live social interaction.

Cortical and Sensory Causes of Individual Differences in Selective Attention Ability Among Listeners With Normal Hearing Thresholds.

This review provides clinicians with an overview of recent findings relevant to understanding why listeners with normal hearing thresholds (NHTs) sometimes suffer from communication difficulties in noisy settings. The results from neuroscience and psychoacoustics are reviewed. In noisy settings, listeners focus their attention by engaging cortical brain networks to suppress unimportant sounds; they then can analyze and understand an important sound, such as speech, amidst competing sounds. Differences in the efficacy of top-down control of attention can affect communication abilities. In addition, subclinical deficits in sensory fidelity can disrupt the ability to perceptually segregate sound sources, interfering with selective attention, even in listeners with NHTs. Studies of variability in control of attention and in sensory coding fidelity may help to isolate and identify some of the causes of communication disorders in individuals presenting at the clinic with "normal hearing." How well an individual with NHTs can understand speech amidst competing sounds depends not only on the sound being audible but also on the integrity of cortical control networks and the fidelity of the representation of suprathreshold sound. Understanding the root cause of difficulties experienced by listeners with NHTs ultimately can lead to new, targeted interventions that address specific deficits affecting communication in noise. http://cred.pubs.asha.org/article.aspx?articleid=2601617.

Disrupted cortical brain network in post-traumatic stress disorder patients: a resting-state electroencephalographic study

This study aimed to examine the source-level cortical brain networks of post-traumatic stress disorder (PTSD) based on the graph theory using electroencephalography (EEG). Sixty-six cortical source signals were estimated from 78 PTSD and 58 healthy controls (HCs) of resting-state EEG. Four global indices (strength, clustering coefficient (CC), path length (PL) and efficiency) and one nodal index (CC) were evaluated in six frequency bands (delta, theta, alpha, low beta, high beta and gamma). PTSD showed decreased global strength, CC and efficiency, in delta, theta, and low beta band and enhanced PL in theta and low beta band. In low beta band, the strength and CC correlated positively with the anxiety scores, while PL had a negative correlation. In addition, nodal CCs were reduced in PTSD in delta, theta and low beta band. Nodal CCs of theta band correlated negatively with rumination and re-experience symptom scores; while, nodal CCs in low beta band correlated positively with anxiety and pain severity. Inefficiently altered and symptom-dependent changes in cortical networks were seen in PTSD. Our source-level cortical network indices might be promising biomarkers for evaluating PTSD.

55. Abnormal Claustral–Cortical Functional Brain Networks in People With Schizophrenia During Perception and Resting: Brain Network Dynamics Underlying Psychotic Symptoms

55. Abnormal Claustral–Cortical Functional Brain Networks in People With Schizophrenia During Perception and Resting: Brain Network Dynamics Underlying Psychotic Symptoms