Identifying Clinically and Functionally Distinct Groups Among Healthy Controls and First Episode Psychosis Patients by Clustering on EEG Patterns.

OBJECTIVE: The mismatch negativity (MMN) is considered as a promising biomarker that can inform future therapeutic studies. However, there is a large variability among patients with first episode psychosis (FEP). Also, most studies report a single electrode site and on comparing case-control group differences. Few have taken advantage of the full wealth of multi-channel EEG signals to examine observable patterns. None, to our knowledge, have used machine learning (ML) approaches to investigate neurophysiological derived subgroups with distinct cognitive and functional outcome characteristics. In this study, we applied ML to empirically stratify individuals into homogeneous subgroups based on multi-channel MMN data. We then characterized the functional, cognitive, and clinical profiles of these neurobiologically derived subgroups. We also explored the underlying low frequency range responses (delta, theta, alpha) during MMN. METHODS: Clinical, neurocognitive, functioning data of 33 healthy controls and 20 FEP patients were collected. 90% of the patients had 6-month follow-up data. Neurocognition, social cognition, and functioning measures were assessed using the NCCB Cognitive Battery, the Awareness of Social Inference Test, UCSD Performance-Based Skills Assessment, and Multnomah Community Ability Scale. Symptom severity was collected using the PANSS. MMN amplitude and single-trial derived low frequency activity across 24 frontocentral channels were used as main variables in the ML k-means clustering analyses. RESULTS: We found a consistent pattern of two distinctive subgroups. We labeled them as "better functioning" and "poorer functioning" clusters, respectively. Each subgroup can be mapped onto either better or poorer clinical, cognitive, and functioning profiles. Also, we identified two subgroups of patients: one showed improved MMN and one showed worsening of MMN over time. Patients with improved MMN had better follow-up clinical, cognitive, and functioning profile than those with worsening MMN. Among the low frequency bands, delta frequency appeared to be the most relevant to the observed MMN responses in all individuals. However, higher delta responses were not necessarily associated with a better functioning profile, suggesting that delta frequency alone may not be useful in clinical characterization. CONCLUSIONS: The ML approach could be a robust tool to explore heterogeneity and facilitate the identification of neurobiological homogeneous subgroups in FEP.

Catching Audiovisual Interactions With a First-Person Fisherman Video Game.

The human brain is excellent at integrating information from different sources across multiple sensory modalities. To examine one particularly important form of multisensory interaction, we manipulated the temporal correlation between visual and auditory stimuli in a first-person fisherman video game. Subjects saw rapidly swimming fish whose size oscillated, either at 6 or 8 Hz. Subjects categorized each fish according to its rate of size oscillation, while trying to ignore a concurrent broadband sound seemingly emitted by the fish. In three experiments, categorization was faster and more accurate when the rate at which a fish oscillated in size matched the rate at which the accompanying, task-irrelevant sound was amplitude modulated. Control conditions showed that the difference between responses to matched and mismatched audiovisual signals reflected a performance gain in the matched condition, rather than a cost from the mismatched condition. The performance advantage with matched audiovisual signals was remarkably robust over changes in task demands between experiments. Performance with matched or unmatched audiovisual signals improved over successive trials at about the same rate, emblematic of perceptual learning in which visual oscillation rate becomes more discriminable with experience. Finally, analysis at the level of individual subjects' performance pointed to differences in the rates at which subjects can extract information from audiovisual stimuli.

Policing Fish at Boston's Museum of Science: Studying Audiovisual Interaction in the Wild.

Boston's Museum of Science supports researchers whose projects advance science and provide educational opportunities to the Museum's visitors. For our project, 60 visitors to the Museum played "Fish Police!!," a video game that examines audiovisual integration, including the ability to ignore irrelevant sensory information. Players, who ranged in age from 6 to 82 years, made speeded responses to computer-generated fish that swam rapidly across a tablet display. Responses were to be based solely on the rate (6 or 8 Hz) at which a fish's size modulated, sinusoidally growing and shrinking. Accompanying each fish was a task-irrelevant broadband sound, amplitude modulated at either 6 or 8 Hz. The rates of visual and auditory modulation were either Congruent (both 6 Hz or 8 Hz) or Incongruent (6 and 8 or 8 and 6 Hz). Despite being instructed to ignore the sound, players of all ages responded more accurately and faster when a fish's auditory and visual signatures were Congruent. In a controlled laboratory setting, a related task produced comparable results, demonstrating the robustness of the audiovisual interaction reported here. Some suggestions are made for conducting research in public settings.

Structure and visualization of high-dimensional conductance spaces.

Neurons, and realistic models of neurons, typically express several different types of voltage-gated conductances. These conductances are subject to continual regulation. Therefore it is essential to understand how changes in the conductances of a neuron affect its intrinsic properties, such as burst period or delay to firing after inhibition of a particular duration and magnitude. Even in model neurons, it can be difficult to visualize how the intrinsic properties vary as a function of their underlying maximal conductances. We used a technique, called clutter-based dimension reordering (CBDR), which enabled us to visualize intrinsic properties in high-dimensional conductance spaces. We applied CBDR to a family of models with eight different types of voltage- and calcium-dependent channels. CBDR yields images that reveal structure in the underlying conductance space. CBDR can also be used to visualize the results of other types of analysis. As examples, we use CBDR to visualize the results of a connected-components analysis, and to visually evaluate the results of a separating-hyperplane (i.e., linear classifier) analysis. We believe that CBDR will be a useful tool for visualizing the conductance spaces of neuronal models in many systems.