Discriminative Analysis of Schizophrenia Patients Using Topological Properties of Structural and Functional Brain Networks: A Multimodal Magnetic Resonance Imaging Study

Interest in the application of machine learning (ML) techniques to multimodal magnetic resonance imaging (MRI) data for the diagnosis of schizophrenia (SZ) at the individual level is growing. However, a few studies have applied the features of structural and functional brain networks derived from multimodal MRI data to the discriminative analysis of SZ patients at different clinical stages. In this study, 205 normal controls (NCs), 61 first-episode drug-naive SZ (FESZ) patients, and 79 chronic SZ (CSZ) patients were recruited. We acquired their structural MRI, diffusion tensor imaging, and resting-state functional MRI data and constructed brain networks for each participant, including the gray matter network (GMN), white matter network (WMN), and functional brain network (FBN). We then calculated 3 nodal properties for each brain network, including degree centrality, nodal efficiency, and betweenness centrality. Two classifications (SZ vs. NC and FESZ vs. CSZ) were performed using five ML algorithms. We found that the SVM classifier with the input features of the combination of nodal properties of both the GMN and FBN achieved the best performance to discriminate SZ patients from NCs [accuracy, 81.2%; area under the receiver operating characteristic curve (AUC), 85.2%; p < 0.05]. Moreover, the SVM classifier with the input features of the combination of the nodal properties of both the GMN and WMN achieved the best performance to discriminate FESZ from CSZ patients (accuracy, 86.2%; AUC, 92.3%; p < 0.05). Furthermore, the brain areas in the subcortical/cerebellum network and the frontoparietal network showed significant importance in both classifications. Together, our findings provide new insights to understand the neuropathology of SZ and further highlight the potential advantages of multimodal network properties for identifying SZ patients at different clinical stages.

Comparative Study of Brain fMRI of Olfactory Stimulation in Neuromyelitis Optica Spectrum Disease and Multiple Sclerosis

Objective: To observe the characteristics of brain fMRI during olfactory stimulation in patients with neuromyelitis optica spectrum disease (NMOSD) and multiple sclerosis (MS), compare the differences of brain functional activation areas between patients with NMOSD and MS, and explore the characteristics of olfactory-related brain networks of NMOSD and MS.Methods: Nineteen patients with NMOSD and 16 patients with MS who met the diagnostic criteria were recruited, and 19 healthy controls matched by sex and age were recruited. The olfactory function of all participants was assessed using the visual analog scale (VAS). Olfactory stimulation was alternately performed using a volatile body (lavender and rose solution) and the difference in brain activation was evaluated by task-taste fMRI scanning simultaneously.Results: Activation intensity was weaker in the NMOSD group than in the healthy controls, including the left rectus, right superior temporal gyrus, and left cuneus. The activation intensity was stronger for the NMOSD than the controls in the left insula and left middle frontal gyrus (P < 0.05). Activation intensity was weaker in the MS group than the healthy controls in the bilateral hippocampus, right parahippocampal gyrus, right insula, left rectus gyrus, and right precentral gyrus, and stronger in the left paracentral lobule among the MS than the controls (P < 0.05). Compared with the MS group, activation intensity in the NMOSD group was weaker in the right superior temporal gyrus and left paracentral lobule, while it was stronger among the NMOSD group in the bilateral insula, bilateral hippocampus, bilateral parahippocampal gyrus, left inferior orbital gyrus, left superior temporal gyrus, left putamen, and left middle frontal gyrus (P < 0.05).Conclusion: Olfactory-related brain networks are altered in both patients, and there are differences between their olfactory-related brain networks. It may provide a new reference index for the clinical differentiation and disease evaluation of NMOSD and MS. Moreover, further studies are needed.

Genetic background of grey matter brain networks related to attention-deficit/hyperactivity disorder

Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder and is associated with structural grey matter differences in the brain. We investigated the genetic background of some of these brain differences in a sample of 899 adults and adolescents consisting of individuals with ADHD and healthy controls. Previous work in an overlapping sample identified three ADHD-related grey matter brain networks located in areas of the superior, middle, and inferior frontal gyrus as well as the cerebellar tonsil and culmen. We associated these brain networks with protein coding genes using a statistical stability selection approach. We identified ten genes, the most promising of which were NR3C2, TRHDE, SCFD1, GNAO1, and UNC5D. These genes are expressed in brain and linked to neuropsychiatric disorders including ADHD. With our results we aid in the growing understanding of the aetiology of ADHD from genes to brain to behaviour.

A Platform for Spatiotemporal “Matrix” Stimulation in Brain Networks Reveals Novel Forms of Circuit Plasticity

Here we demonstrate a facile method by which to deliver complex spatiotemporal stimulation to neural networks in fast patterns, to trigger interesting forms of circuit-level plasticity in cortical areas. We present a complete platform by which patterns of electricity can be arbitrarily defined and distributed across a brain circuit, either simultaneously, asynchronously, or in complex patterns that can be easily designed and orchestrated with precise timing. Interfacing with acute slices of mouse cortex, we show that our system can be used to activate neurons at many locations and drive synaptic transmission in distributed patterns, and that this elicits new forms of plasticity that may not be observable via traditional methods, including interesting measurements of associational and sequence plasticity. Finally, we introduce an automated “network assay” for imaging activation and plasticity across a circuit. Spatiotemporal stimulation opens the door for high-throughput explorations of plasticity at the circuit level, and may provide a basis for new types of adaptive neural prosthetics.

An EEG Neurofeedback Interactive Model for Emotional Classification of Electronic Music Compositions Considering Multi-Brain Synergistic Brain-Computer Interfaces

This paper presents an in-depth study and analysis of the emotional classification of EEG neurofeedback interactive electronic music compositions using a multi-brain collaborative brain-computer interface (BCI). Based on previous research, this paper explores the design and performance of sound visualization in an interactive format from the perspective of visual performance design and the psychology of participating users with the help of knowledge from various disciplines such as psychology, acoustics, aesthetics, neurophysiology, and computer science. This paper proposes a specific mapping model for the conversion of sound to visual expression based on people’s perception and aesthetics of sound based on the phenomenon of audiovisual association, which provides a theoretical basis for the subsequent research. Based on the mapping transformation pattern between audio and visual, this paper investigates the realization path of interactive sound visualization, the visual expression form and its formal composition, and the aesthetic style, and forms a design expression method for the visualization of interactive sound, to benefit the practice of interactive sound visualization. In response to the problem of neglecting the real-time and dynamic nature of the brain in traditional brain network research, dynamic brain networks proposed for analyzing the EEG signals induced by long-time music appreciation. During prolonged music appreciation, the connectivity of the brain changes continuously. We used mutual information on different frequency bands of EEG signals to construct dynamic brain networks, observe changes in brain networks over time and use them for emotion recognition. We used the brain network for emotion classification and achieved an emotion recognition rate of 67.3% under four classifications, exceeding the highest recognition rate available.

Oxytocin modulates social brain network correlations in resting and task state

Oxytocin (OT) is a neuropeptide that modulates social behaviors and the social brain. The effects of OT on the social brain can be tracked by assessing the neural activity in the resting and task states, providing a system-level framework for characterizing state-based functional relationships of its distinct effect. Here, we contribute to this framework by examining how OT modulates social brain network correlations during the resting and task states using fMRI. Firstly, we investigated network activation, followed by analyzing the relationship between networks and individual differences measured by the Positive and Negative Affect Schedule and the Big-Five scales. Subsequently, we evaluated functional connectivity in both states. Finally, the relationship between networks across the states was represented by the predictive power of networks in the resting state for task-evoked activity. The difference in predicted accuracy between subjects displayed individual variations in this relationship. Our results showed decreased dorsal default mode network (DDMN) for OT group in the resting state. Additionally, only in the OT group, the activity of the DDMN in the resting state had the largest predictive power for task-evoked activation of the precuneus network (PN). The results also demonstrated OT reduced individual variation of PN, specifically, the difference of accuracy between predicting a subject's own and others' PN task activation. These findings suggest a distributed but modulatory effect of OT on the association between resting brain networks and task-dependent brain networks, showing increased DDMN to PN connectivity after OT administration, which may support OT-induced distributed processing during task performance.