Patients with schizophrenia demonstrate reduced cortical sensitivity to auditory oddball regularities

Cortical excitation-inhibition ratio mediates the effect of pre-attentive auditory processing deficits on interpersonal difficulties

Several lines of evidence identify aberrant excitatory-inhibitory neural processes across autism and schizophrenia spectrum disorders, particularly within the psychosocial domain. Such neural processes include increased excitatory glutamate and reduced inhibitory GABA concentrations, which may affect auditory pre-attentive processing as indexed by the mismatch negativity (MMN); thus, an excitation-inhibition imbalance might lead to aberrant MMN, which might in turn drive the relationship between the MMN and psychosocial difficulties. This research has the potential to enhance the neurochemical understanding of the relationship between electrophysiology (MMN) and behavioural/clinical measures (psychosocial difficulties). Thirty-eight adults (18 male, 18-40 years) completed the Schizotypal Personality Questionnaire (SPQ) and Autism-Spectrum Quotient (AQ). Glutamate and GABA concentrations in bilateral superior temporal cortex (STC) were quantified using proton magnetic resonance spectroscopy (1H-MRS) while auditory MMN to a duration deviant was measured with magnetoencephalography. Spearman correlations probed the relationships between STC glutamate/GABA ratios, MMN amplitude and latency, and AQ and SPQ dimensions. Mediation effects of glutamate/GABA ratios on the relationship between MMN and AQ-SPQ dimensions were probed using causal mediation analysis. Only SPQ-interpersonal and AQ-communication were significantly correlated with right hemisphere glutamate/GABA ratios and MMN latency (ps < 0.05), which were themselves correlated (p = .035). Two mediation models were investigated, with right MMN latency as predictor and SPQ-interpersonal and AQ-communication as outcome variables. Right STC glutamate/GABA ratios significantly mediated the relationship between MMN latency and SPQ-interpersonal scores, but only partially mediated the relationship between MMN latency and AQ-communication scores. These findings support the growing body of literature pointing toward an excitation-inhibition imbalance that is central to psychosocial functioning across multi-dimensional spectrum disorders, such as autism and schizophrenia, and provides neurochemical indicators of the processes that underlie psychosocial dysfunction.

Moving Beyond ERP Components: A Selective Review of Approaches to Integrate EEG and Behavior

Relationships between neuroimaging measures and behavior provide important clues about brain function and cognition in healthy and clinical populations. While electroencephalography (EEG) provides a portable, low cost measure of brain dynamics, it has been somewhat underrepresented in the emerging field of model-based inference. We seek to address this gap in this article by highlighting the utility of linking EEG and behavior, with an emphasis on approaches for EEG analysis that move beyond focusing on peaks or “components” derived from averaging EEG responses across trials and subjects (generating the event-related potential, ERP). First, we review methods for deriving features from EEG in order to enhance the signal within single-trials. These methods include filtering based on user-defined features (i.e., frequency decomposition, time-frequency decomposition), filtering based on data-driven properties (i.e., blind source separation, BSS), and generating more abstract representations of data (e.g., using deep learning). We then review cognitive models which extract latent variables from experimental tasks, including the drift diffusion model (DDM) and reinforcement learning (RL) approaches. Next, we discuss ways to access associations among these measures, including statistical models, data-driven joint models and cognitive joint modeling using hierarchical Bayesian models (HBMs). We think that these methodological tools are likely to contribute to theoretical advancements, and will help inform our understandings of brain dynamics that contribute to moment-to-moment cognitive function.

Magnetoencephalography reveals an increased non-target P3a, but not target P3b, that is associated with high non-clinical psychosocial deficits

Auditory processing deficits are frequently identified in autism and schizophrenia, and the two disorders have been shown to share psychosocial difficulties. This study used magnetoencephalography to investigate auditory processing differences for those with a high degree of a non-clinical autistic and schizotypal trait phenotype, Social Disorganisation (SD). Participants were 18 low (9 female) and 19 high (9 female) SD scorers (18-40 years) who completed a three-stimulus auditory oddball paradigm of speech sounds (standard: 100ms 'o', deviant: 150ms 'o', novel: 150ms 'e'). Spatio-temporal cluster analysis revealed increased amplitude for the high SD group in a left (p = 0.006) and a right (p = 0.020) hemisphere cluster in response to the novel non-target. No cluster differences were found in response to the target deviant. These findings suggest that those with a high degree of the SD phenotype recruit more cortical resources when processing unattended, novel speech stimuli, which may lead to psychosocial deficits.

Differences in Cortical Sources of the Event-Related P3 Potential Between Young and Old Participants Indicate Frontal Compensation

The event-related P3 potential, as elicited in auditory signal detection tasks, originates from neural activity of multiple cortical structures and presumably reflects an overlap of several cognitive processes. The fact that the P3 is affected by aging makes it a potential metric for age-related cognitive change. The P3 in older participants is thought to encompass frontal compensatory activity in addition to task-related processes. The current study investigates this by decomposing the P3 using group independent component analysis (ICA). Independent components (IC) of young and old participants were compared in order to investigate the effects of aging. Exact low-resolution tomography analysis (eLORETA) was used to compare current source densities between young and old participants for the P3-ICs to localize differences in cortical source activity for every IC. One of the P3-related ICs reflected a different constellation of cortical generators in older participants compared to younger participants, suggesting that this P3-IC reflects shifts in neural activations and compensatory processes with aging. This P3-IC was localized to the orbitofrontal/temporal, and the medio-parietal regions. For this IC, older participants showed more frontal activation and less parietal activation as measured on the scalp. The differences in cortical sources were localized in the precentral gyrus and the parahippocampal gyrus. This finding might reflect compensatory activity recruited from these cortical sources during a signal detection task.

Stable Scalp EEG Spatiospectral Patterns Across Paradigms Estimated by Group ICA

Electroencephalography (EEG) oscillations reflect the superposition of different cortical sources with potentially different frequencies. Various blind source separation (BSS) approaches have been developed and implemented in order to decompose these oscillations, and a subset of approaches have been developed for decomposition of multi-subject data. Group independent component analysis (Group ICA) is one such approach, revealing spatiospectral maps at the group level with distinct frequency and spatial characteristics. The reproducibility of these distinct maps across subjects and paradigms is relatively unexplored domain, and the topic of the present study. To address this, we conducted separate group ICA decompositions of EEG spatiospectral patterns on data collected during three different paradigms or tasks (resting-state, semantic decision task and visual oddball task). K-means clustering analysis of back-reconstructed individual subject maps demonstrates that fourteen different independent spatiospectral maps are present across the different paradigms/tasks, i.e. they are generally stable.

Blind Source Separation for Unimodal and Multimodal Brain Networks: A Unifying Framework for Subspace Modeling

In the past decade, numerous advances in the study of the human brain were fostered by successful applications of blind source separation (BSS) methods to a wide range of imaging modalities. The main focus has been on extracting "networks" represented as the underlying latent sources. While the broad success in learning latent representations from multiple datasets has promoted the wide presence of BSS in modern neuroscience, it also introduced a wide variety of objective functions, underlying graphical structures, and parameter constraints for each method. Such diversity, combined with a host of datatype-specific know-how, can cause a sense of disorder and confusion, hampering a practitioner's judgment and impeding further development. We organize the diverse landscape of BSS models by exposing its key features and combining them to establish a novel unifying view of the area. In the process, we unveil important connections among models according to their properties and subspace structures. Consequently, a high-level descriptive structure is exposed, ultimately helping practitioners select the right model for their applications. Equipped with that knowledge, we review the current state of BSS applications to neuroimaging. The gained insight into model connections elicits a broader sense of generalization, highlighting several directions for model development. In light of that, we discuss emerging multi-dataset multidimensional (MDM) models and summarize their benefits for the study of the healthy brain and disease-related changes.

Spatiospectral Decomposition of Multi-subject EEG: Evaluating Blind Source Separation Algorithms on Real and Realistic Simulated Data

Electroencephalographic (EEG) oscillations predominantly appear with periods between 1 s (1 Hz) and 20 ms (50 Hz), and are subdivided into distinct frequency bands which appear to correspond to distinct cognitive processes. A variety of blind source separation (BSS) approaches have been developed and implemented within the past few decades, providing an improved isolation of these distinct processes. Within the present study, we demonstrate the feasibility of multi-subject BSS for deriving distinct EEG spatiospectral maps. Multi-subject spatiospectral EEG decompositions were implemented using the EEGIFT toolbox ( http://mialab.mrn.org/software/eegift/ ) with real and realistic simulated datasets (the simulation code is available at http://mialab.mrn.org/software/simeeg ). Twelve different decomposition algorithms were evaluated. Within the simulated data, WASOBI and COMBI appeared to be the best performing algorithms, as they decomposed the four sources across a range of component numbers and noise levels. RADICAL ICA, ERBM, INFOMAX ICA, ICA EBM, FAST ICA, and JADE OPAC decomposed a subset of sources within a smaller range of component numbers and noise levels. INFOMAX ICA, FAST ICA, WASOBI, and COMBI generated the largest number of stable sources within the real dataset and provided partially distinct views of underlying spatiospectral maps. We recommend the multi-subject BSS approach and the selected algorithms for further studies examining distinct spatiospectral networks within healthy and clinical populations.

Cortical Response Similarities Predict which Audiovisual Clips Individuals Viewed, but Are Unrelated to Clip Preference

Cortical responses to complex natural stimuli can be isolated by examining the relationship between neural measures obtained while multiple individuals view the same stimuli. These inter-subject correlation’s (ISC’s) emerge from similarities in individual’s cortical response to the shared audiovisual inputs, which may be related to their emergent cognitive and perceptual experience. Within the present study, our goal is to examine the utility of using ISC’s for predicting which audiovisual clips individuals viewed, and to examine the relationship between neural responses to natural stimuli and subjective reports. The ability to predict which clips individuals viewed depends on the relationship of the EEG response across subjects and the nature in which this information is aggregated. We conceived of three approaches for aggregating responses, i.e. three assignment algorithms, which we evaluated in Experiment 1A. The aggregate correlations algorithm generated the highest assignment accuracy (70.83% chance = 33.33%) and was selected as the assignment algorithm for the larger sample of individuals and clips within Experiment 1B. The overall assignment accuracy was 33.46% within Experiment 1B (chance = 06.25%), with accuracies ranging from 52.9% (Silver Linings Playbook) to 11.75% (Seinfeld) within individual clips. ISC’s were significantly greater than zero for 15 out of 16 clips, and fluctuations within the delta frequency band (i.e. 0-4 Hz) primarily contributed to response similarities across subjects. Interestingly, there was insufficient evidence to indicate that individuals with greater similarities in clip preference demonstrate greater similarities in cortical responses, suggesting a lack of association between ISC and clip preference. Overall these results demonstrate the utility of using ISC’s for prediction, and further characterize the relationship between ISC magnitudes and subjective reports.

The relationship between somatic and cognitive-affective depression symptoms and error-related ERPs

BACKGROUND

The symptoms that contribute to the clinical diagnosis of depression likely emerge from, or are related to, underlying cognitive deficits. To understand this relationship further, we examined the relationship between self-reported somatic and cognitive-affective Beck'sDepression Inventory-II (BDI-II) symptoms and aspects of cognitive control reflected in error event-related potential (ERP) responses.

METHODS

Task and assessment data were analyzed within 51 individuals. The group contained a broad distribution of depressive symptoms, as assessed by BDI-II scores. ERPs were collected following error responses within a go/no-go task. Individual error ERP amplitudes were estimated by conducting group independent component analysis (ICA) on the electroencephalographic (EEG) time series and analyzing the individual reconstructed source epochs. Source error amplitudes were correlated with the subset of BDI-II scores representing somatic and cognitive-affective symptoms.

RESULTS

We demonstrate a negative relationship between somatic depression symptoms (i.e. fatigue or loss of energy) (after regressing out cognitive-affective scores, age and IQ) and the central-parietal ERP response that peaks at 359 ms. The peak amplitudes within this ERP response were not significantly related to cognitive-affective symptom severity (after regressing out the somatic symptom scores, age, and IQ).

LIMITATIONS

These findings were obtained within a population of female adults from a maximum-security correctional facility. Thus, additional research is required to verify that they generalize to the broad population.

CONCLUSIONS

These results suggest that individuals with greater somatic depression symptoms demonstrate a reduced awareness of behavioral errors, and help clarify the relationship between clinical measures of self-reported depression symptoms and cognitive control.