Schizophrenia is a devastating disease with a prevalence of 1% in populations around the world. Current diagnostic techniques of schizophrenia and high-risk population are based on subjective psychiatric interviews. Early diagnosis and intervention can mitigate progression and improve treatment outcomes. However, the lack of biomarkers that support objective examinations has been a long-term bottleneck in clinical diagnosis and assessment of schizophrenia and its high-risk state. In the present study, resting-state 128-channel electroencephalogram (EEG) data were acquired from 65 participants, including clinically-stable individuals with first-episode schizophrenia (FESZ), individuals at ultra-high-risk (UHR) and high-risk (HR), and healthy controls (HC). Microstate analysis was used to assess the dynamics of functional networks in these participants. Three features were extracted for each class of microstate (A, B, C, D, E, F): duration, occurrence and time coverage. Furthermore, clinical examinations and cognitive tests were performed. Behavioral results showed poorer performances in the participants as the disease progressed. Moreover, microstate features computed from resting-state EEG microstates (especially microstate class D) were capable of distinguishing the four groups of individuals. Combined biomarkers including clinical examinations, cognitive tests and EEG microstate parameters were identified as a potential effective diagnostic tool, achieving the highest classification performance using the random forest model compared with the support vector machine (SVM) and long short term memory (LSTM) networks, with an average classification of 92%, mean sensitivity of 91.8%, and specificity of 90.8% among the four groups, which were much higher than that only using behavioral features. The results demonstrate that microstate-based indicators together with behavioral results may act as biomarkers for early diagnosis and prediction of at-risk individuals of schizophrenia. Furthermore, our findings illustrate the potential use of resting-state EEG in clinical screening, classification and quantitative evaluation of patients with neurodevelopmental disorders.

Real-world scenes usually contain a set of cluttered and yet contextually related objects. Here we used fMRI to investigate where and how contextually related multiple objects were represented in the human ventral visual pathway. Specifically, we measured the responses in face-selective and body-selective regions along the ventral pathway when faces and bodies were presented either simultaneously or in isolation. We found that, in the posterior regions, the response for the face and body pair was the weighted average response for faces and bodies presented in isolation. In contrast, the anterior regions encoded the face and body pair in a mutually facilitative fashion, with the response for the pair significantly higher than that for its constituent objects. Furthermore, in the right fusiform face area, the face and body pair was represented as one inseparable object, possibly to reduce perceptual load and increase representation efficiency. Therefore, our study suggests that the visual system uses a hierarchical representation scheme to process multiple objects in natural scenes: the average mechanism in posterior regions helps retaining information of individual objects in clutter, whereas the nonaverage mechanism in the anterior regions uses the contextual information to optimize the representation for multiple objects.

Sustained attention is crucial for higher-order cognition and real-world activities. The idea that tactile training improves sustained attention is appealing and has clinical significance. The aim of this study was to explore whether tactile training could improve visual sustained attention. Using 128-channel electroencephalography (EEG), we found that participants with tactile training outperformed non-trainees in the accuracy and calculation efficiency measured by the Math task. Furthermore, trainees demonstrated significantly decreased omission error measured by the sustained attention to response task (SART). We also found that the improvements in behavioral performance were associated with parietal P300 amplitude enhancements. EEG source imaging analyses revealed stronger brain activation among the trainees in the prefrontal and sensorimotor regions at P300. These results suggest that the tactile training can improve sustained attention in young adults, and the improved sustained attention following training may be due to more effective attentional resources allocation. Our findings also indicate the use of a noninvasive tactile training paradigm to improve cognitive functions (e.g., sustained attention) in young adults, potentially leading to new training and rehabilitative protocols.

The chapters in this Handbook reveal the breadth of brilliant imaging and analysis techniques designed to fulfill the mandate of cognitive neuroscience: to understand how anatomical structures and physiological processes in the brain cause typical and atypical behavior. Yet merely producing data from the latest imaging method is insufficient to truly achieve this goal. We also need a mental toolbox that contains methods of inference that allow us to derive true scientific explanation from these data. Causal inference is not easy as regards human brain function, where we are limited primarily to observational data, and our methods of experimental perturbation in the service of causal explanation are limited. As a case study, we reverse engineer one of the most influential accounts of a neuropsychiatric disorder that is derived from observational imaging data: the connectivity theories of autism spectrum disorder (ASD). We take readers through an approach of first considering all possible causal paths that are allowed by preliminary imaging-behavioral correlations. By progressively sharpening the specificity of the measures and brain/behavioral constructs, we iteratively chip away at this space of allowable causal paths, like the sculptor chipping away the excess marble to reveal the statue. To assist in this process, we consider how current imaging methods that are lumped together under the term “connectivity” may actually offer a differentiated set of connectivity constructs that can more specifically relate notions of information transmission in the mind to the physiology of the brain.

Although attention plays an important role in perception and cognition, it is challenging to develop an effective way to train sustained attention and find biomarkers to measure attention level. The primary purpose of this investigation is to identify effective biomarkers of sustained attention and demonstrate adaptive force control tasks can improve attention skill. 128-channel electroencephalogram (EEG) data was collected from 14 participants during the haptic training: a five-consecutive-day force control task with one, two, three and four fingers. The EEG recordings were then divided into four-second epochs for analysis. We used tolerance, variance and duration of the forces of involved fingers to design a score (called 'integrated parameter') to measure the performance level of each four-second epoch. Those epochs with a high score (called 'good epochs') and a low score (called 'bad epochs') were selected for comparison. Two different brain connectivity patterns were calculated to classify the good and bad epochs. Discriminative connections were extracted and kernel support vector machine was applied to find relevant frequency bands and brain areas that can effectively separate the two types of epochs. The results showed that enhanced connection strength located at frontal and partial lobes in gamma band and reduced connection strength at frontal-parietal regions in alpha band during good epochs. Furthermore, executive control network showed significant improvement after training. It indicates that force control tasks may not only improve the behavioral performance, but also the sustained attention from different sensory channel.

Brain-computer interfaces (BCIs) offer a direct channel for communication between external devices and the user's central nervous system, holding significant promise for restoring limb mobility in patients, particularly those afflicted by conditions such as peripheral nerve injury. However, the widespread utilization of BCIs is impeded by the absence of an objective and user-centric evaluation system. This paper proposes a novel BCI evaluation index termed "brain resource occupancy," serving as a quantitative measure of an individual's cognitive resource utilization at any given moment. Derived from cognitive psychology, this metric is perceived as a crucial determinant of performance across various single-task and multi-task scenarios. The paper delves into the concept of brain resource occupancy, its significance, measurement techniques, and underscores its potential to objectively assess BCI system performance from the user's viewpoint. Additionally, it explores the correlation between brain resource occupancy and the privacy protection and security facets within the realm of BCIs.