Multi-Phase Cross-modal Learning for Noninvasive Gene Mutation Prediction in Hepatocellular Carcinoma

Electroencephalographic Resting-State Microstates are Unstable in Visual Snow Syndrome

OBJECTIVE

To study large-scale network processing in visual snow syndrome (VSS) with and without migraine, we applied resting-state electroencephalographic microstate analysis.

BACKGROUND

VSS is characterized by a spectrum of visual symptoms, with the main symptom being perceived as flickering dots throughout the visual field. The syndrome is associated with migraine and tinnitus and is considered a network disorder, but the cause and pathophysiology are still largely unknown.

METHODS

In this case-control study, resting-state electroencephalography (EEG) recordings were selected from a cohort of 21 subjects with VSS (8 females, 33 ± 9.56 years, 14 migraines) and 21 matched controls (8 females, 33 ± 11.1 years, 14 migraines). An analysis of parameters between the four canonical microstate Classes A-D was performed.

RESULTS

VSS patients showed an overall shorter duration (p = 0.001, Cohen's d = -0.48) and lower mean amplitude (p < 0.001, Cohen's d = -0.76) of microstates compared to the controls. In addition, we found an aberrant syntax of microstate Class A (auditive and visual processing) with more (p = 0.001, Cohen's d = 0.57) transitions to Class B (visual) and less (p = 0.011, d = -0.71) to Class C (interoceptive, salience) compared to controls.

CONCLUSION

Visual snow syndrome (VSS) is a complex disorder, affecting widespread neural network activity. In VSS, electroencephalography (EEG) microstate analysis revealed unstable microstates as well as aberrant transition probabilities, indicating disturbed large-scale network activity. The analysis of microstate dynamics in VSS complements the results of imaging with high temporal resolution and could contribute to the development of future treatment approaches.

The Functional Aspects of Resting EEG Microstates: A Systematic Review

A growing body of clinical and cognitive neuroscience studies have adapted a broadband EEG microstate approach to evaluate the electrical activity of large-scale cortical networks. However, the functional aspects of these microstates have not yet been systematically reviewed. Here, we present an overview of the existing literature and systematize the results to provide hints on the functional role of electrical brain microstates. Studies that evaluated and manipulated the temporal properties of resting-state microstates and utilized questionnaires, task-initiated thoughts, specific tasks before or between EEG session(s), pharmacological interventions, neuromodulation approaches, or localized sources of the extracted microstates were selected. Fifty studies that met the inclusion criteria were included. A new microstate labeling system has been proposed for a comprehensible comparison between the studies, where four classical microstates are referred to as A-D, and the others are labeled by the frequency of their appearance. Microstate A was associated with both auditory and visual processing and links to subjects' arousal/arousability. Microstate B showed associations with visual processing related to self, self-visualization, and autobiographical memory. Microstate C was related to processing personally significant information, self-reflection, and self-referential internal mentation rather than autonomic information processing. In contrast, microstate E was related to processing interoceptive and emotional information and to the salience network. Microstate D was associated with executive functioning. Microstate F is suggested to be a part of the Default Mode Network and plays a role in personally significant information processing, mental simulations, and theory of mind. Microstate G is potentially linked to the somatosensory network.

From mechanisms to markers: novel noninvasive EEG proxy markers of the neural excitation and inhibition system in humans

Brain function is a product of the balance between excitatory and inhibitory (E/I) brain activity. Variation in the regulation of this activity is thought to give rise to normal variation in human traits, and disruptions are thought to potentially underlie a spectrum of neuropsychiatric conditions (e.g., Autism, Schizophrenia, Downs' Syndrome, intellectual disability). Hypotheses related to E/I dysfunction have the potential to provide cross-diagnostic explanations and to combine genetic and neurological evidence that exists within and between psychiatric conditions. However, the hypothesis has been difficult to test because: (1) it lacks specificity-an E/I dysfunction could pertain to any level in the neural system- neurotransmitters, single neurons/receptors, local networks of neurons, or global brain balance - most researchers do not define the level at which they are examining E/I function; (2) We lack validated methods for assessing E/I function at any of these neural levels in humans. As a result, it has not been possible to reliably or robustly test the E/I hypothesis of psychiatric disorders in a large cohort or longitudinal patient studies. Currently available, in vivo markers of E/I in humans either carry significant risks (e.g., deep brain electrode recordings or using Positron Emission Tomography (PET) with radioactive tracers) and/or are highly restrictive (e.g., limited spatial extent for Transcranial Magnetic Stimulation (TMS) and Magnetic Resonance Spectroscopy (MRS). More recently, a range of novel Electroencephalography (EEG) features has been described, which could serve as proxy markers for E/I at a given level of inference. Thus, in this perspective review, we survey the theories and experimental evidence underlying 6 novel EEG markers and their biological underpinnings at a specific neural level. These cheap-to-record and scalable proxy markers may offer clinical utility for identifying subgroups within and between diagnostic categories, thus directing more tailored sub-grouping and, therefore, treatment strategies. However, we argue that studies in clinical populations are premature. To maximize the potential of prospective EEG markers, we first need to understand the link between underlying E/I mechanisms and measurement techniques.