The Potential of Trial-by-Trial Variabilities of Ongoing-EEG, Evoked Potentials, Event Related Potentials and fMRI as Diagnostic Markers for Neuropsychiatric Disorders

Leveraging mixed-effects location scale models to assess the ERP mismatch negativity's psychometric properties and trial-by-trial neural variability in toddler-mother dyads

Trial-by-trial neural variability, a measure of neural response stability, has been examined in relation to behavioral indicators using summary measures, but these methods do not characterize meaningful processes underlying variability. Mixed-effects location scale models (MELSMs) overcome these limitations by accounting for predictors and covariates of variability but have been rarely used in developmental studies. Here, we applied MELSMs to the ERP auditory mismatch negativity (MMN), a neural measure often related to language and psychopathology. 84 toddlers and 76 mothers completed a speech-syllable MMN paradigm. We extracted early and late MMN mean amplitudes from trial-level waveforms. We first characterized our sample's psychometric properties using MELSMs and found a wide range of subject-level internal consistency. Next, we examined the relation between toddler MMNs with theoretically relevant child behavioral and maternal variables. MELSMs offered better model fit than analyses that assumed constant variability. We found significant individual differences in trial-by-trial variability but no significant associations between toddler variability and their language, irritability, or mother variability indices. Overall, we illustrate how MELSMs can characterize psychometric properties and answer questions about individual differences in variability. We provide recommendations and resources as well as example code for analyzing trial-by-trial neural variability in future studies.

Electrophysiological variability as marker of dystonia worsening under deep brain stimulation successive withdrawal and renewal effects

DBS has been shown to be an effective intervention for neurological disorders. However, the intervention is complex and many aspects have not been understood. Various clinical situations have no solution and follow trial and error approaches. Dystonia is a movement disorder characterized by involuntary muscle contractions, which gives rise to abnormal movements and postures. Status dystonicus (SD) represents a life-threatening condition that requires urgent assessment and management. Electrophysiological markers for risk of symptom worsening and SD related patterns of evolution in patients treated with long-term deep brain stimulation (DBS), and specially under the effect of withdrawal and renewals of simulation are needed. To this end, we study the variability of neural synchronization as a mechanism for symptom generation under successive perturbations to a system, i.e. withdrawals and renewals of neuromodulation, through computational simulation of clinical profiles under different plasticity conditions. The simulation shows that the neuroplasticity makeup influences the variability of oscillation synchronization patterns in virtual "patients". The difference between the effect of different electrophysiological signatures is remarkable and under a certain condition (equal medium long term potentiation and long term depression) the situation resembles that of a stable equilibrium, putatively making the sudden worsening or change less likely. Stability of variability can only be observed in this condition and is clearly distinct from other scenarios. CONCLUSION: Our results demonstrate that the neuroplasticity makeup affects the variability of the oscillatory synchrony. This i) informs the shaping of the electrophysiological makeup and ii) might serve as a marker for clinical behavior.

Stability, change, and reliable individual differences in electroencephalography measures: a lifespan perspective on progress and opportunities

Electroencephalographic (EEG) methods have great potential to serve both basic and clinical science approaches to understand individual differences in human neural function. Importantly, the psychometric properties of EEG data, such as internal consistency and test-retest reliability, constrain their ability to differentiate individuals successfully. Rapid and recent technological and computational advancements in EEG research make it timely to revisit the topic of psychometric reliability in the context of individual difference analyses. Moreover, pediatric and clinical samples provide some of the most salient and urgent opportunities to apply individual difference approaches, but the changes these populations experience over time also provide unique challenges from a psychometric perspective. Here we take a developmental neuroscience perspective to consider progress and new opportunities for parsing the reliability and stability of individual differences in EEG measurements across the lifespan. We first conceptually map the different profiles of measurement reliability expected for different types of individual difference analyses over the lifespan. Next, we summarize and evaluate the state of the field's empirical knowledge and need for testing measurement reliability, both internal consistency and test-retest reliability, across EEG measures of power, event-related potentials, nonlinearity, and functional connectivity across ages. Finally, we highlight how standardized pre-processing software for EEG denoising and empirical metrics of individual data quality may be used to further improve EEG-based individual differences research moving forward. We also include recommendations and resources throughout that individual researchers can implement to improve the utility and reproducibility of individual differences analyses with EEG across the lifespan.

Assessment of a single trial impact on the amplitude of the averaged event related potentials

Widely used in neuroscience the averaging of event related potentials is based on the assumption that small responses to the investigated events are present in every trial but can be hidden under the random noise. This situation often takes place, especially in experiments performed at hierarchically lower levels of sensory systems. However, in the studies of higher order complex neuronal networks evoked responses might appear only under particular conditions and be absent otherwise. We encountered this problem studying a propagation of interoceptive information to the cortical areas in the sleep-wake cycle. Cortical responses to various visceral events were present during some periods of sleep, then disappeared for a while and restored again after a period of absence. Further investigation of the viscero-cortical communication required a method that would allow labeling the trials contributing to the averaged event related responses-"efficient trials," and separating them from the trials without any response. Here we describe a heuristic approach to solving this problem in the context of viscero-cortical interactions occurring during sleep. However, we think that the proposed technique can be applicable to any situation where neuronal processing of the same events is expected to be variable due to internal or external factors modulating neuronal activity. The method was first implemented as a script for Spike 2 program version 6.16 (CED). However, at present a functionally equivalent version of this algorithm is also available as Matlab code at https://github.com/george-fedorov/erp-correlations.

Perceptually Visible but Emotionally Subliminal Stimuli to Improve Exposure Therapies

Subliminal stimuli are gaining growing interest due to their capability to induce desensitization to pathologically feared (e.g., phobic) pictures without inducing exaggerated emotional reactions. However, unresolved methodological issues cast significant doubt on the reliability of these findings and their interpretation. The studies most robustly assessing stimulus detection found that ~30% of the supposed-to-be-subliminal stimuli were, in fact, detected, suggesting that the beneficial effects attributed to subliminal stimuli may result from those actually seen. Nevertheless, a deeper analysis of the data underlying this misinterpretation unveils theoretical and clinical implications. Since the purpose of subliminal stimulation is to reduce the aversiveness of exposure therapies while maintaining their efficacy, researchers should measure the emotional relevance of supposed-to-be-subliminal stimuli that are, in fact, detected. A distinction is needed between perceptually- and emotionally-subliminal stimuli: the former is not consciously detected; the latter just fails to elicit emotional reactions. Emotionally-subliminal stimuli could represent an intermediate step of exposure in addition to those involving perceptually subliminal or supraliminal stimuli. Importantly, emotionally subliminal stimuli could make patients able to sustain a conscious exposure to feared stimuli without exaggeratedly reacting to them: if confirmed by empirical data, this unexpected disconfirmation of patients' beliefs could pave the way for successful therapy while increasing their self-efficacy and compliance to treatment.

Hyperbolic trade-off: The importance of balancing trial and subject sample sizes in neuroimaging

Here we investigate the crucial role of trials in task-based neuroimaging from the perspectives of statistical efficiency and condition-level generalizability. Big data initiatives have gained popularity for leveraging a large sample of subjects to study a wide range of effect magnitudes in the brain. On the other hand, most task-based FMRI designs feature a relatively small number of subjects, so that resulting parameter estimates may be associated with compromised precision. Nevertheless, little attention has been given to another important dimension of experimental design, which can equally boost a study's statistical efficiency: the trial sample size. The common practice of condition-level modeling implicitly assumes no cross-trial variability. Here, we systematically explore the different factors that impact effect uncertainty, drawing on evidence from hierarchical modeling, simulations and an FMRI dataset of 42 subjects who completed a large number of trials of cognitive control task. We find that, due to an approximately symmetic hyperbola-relationship between trial and subject sample sizes in the presence of relatively large cross-trial variability, 1) trial sample size has nearly the same impact as subject sample size on statistical efficiency; 2) increasing both the number of trials and subjects improves statistical efficiency more effectively than focusing on subjects alone; 3) trial sample size can be leveraged alongside subject sample size to improve the cost-effectiveness of an experimental design; 4) for small trial sample sizes, trial-level modeling, rather than condition-level modeling through summary statistics, may be necessary to accurately assess the standard error of an effect estimate. We close by making practical suggestions for improving experimental designs across neuroimaging and behavioral studies.

Understanding the Error in Psychopathology: Notable Intraindividual Differences in Neural Variability of Performance Monitoring

BACKGROUND

Abnormal performance monitoring is a possible transdiagnostic marker of psychopathology. Research on neural indices of performance monitoring, including the error-related negativity (ERN), typically examines group and interindividual (between-person) differences in mean/average scores. Intraindividual (within-person) variability in activity captures the capacity to dynamically adjust from moment to moment, and excessive variability appears maladaptive. Intraindividual variability in ERN represents a unique and largely unexamined dimension that might impact functioning. We tested whether psychopathology group differences (major depressive disorder [MDD], generalized anxiety disorder [GAD], obsessive-compulsive disorder [OCD]) or corresponding psychiatric symptoms account for intraindividual variability in single-trial ERN scores.

METHODS

High-density electroencephalogram (Electrical Geodesics, Inc.) was recorded during a semantic flanker task in 51 participants with MDD, 44 participants with GAD, 31 participants with OCD, and 56 psychiatrically-healthy participants. Mean ERN amplitude was scored 0-125ms following participant response across four fronto-central sites. Multilevel location-scale models were used to simultaneously examine interindividual and intraindividual differences in ERN.

RESULTS

Analyses indicated considerable intraindividual variability in ERN that was common across groups. However, we did not find strong evidence to support relationships between ERN and psychopathology groups or transdiagnostic symptoms.

CONCLUSIONS

These findings point to important methodological implications for studies of performance monitoring in healthy and clinical populations-the common assumption of fixed intraindividual variability (i.e., residual variance) may be inappropriate for ERN studies. Implementation of multilevel location-scale models in future research can leverage between-person differences in intraindividual variability in performance monitoring to gain a rich understanding of trial-to-trial performance monitoring dynamics.