The gamma model analysis (GMA): Introducing a novel scoring method for the shape of components of the event-related potential

Optimal filters for ERP research I: A general approach for selecting filter settings

Filtering plays an essential role in event-related potential (ERP) research, but filter settings are usually chosen on the basis of historical precedent, lab lore, or informal analyses. This reflects, in part, the lack of a well-reasoned, easily implemented method for identifying the optimal filter settings for a given type of ERP data. To fill this gap, we developed an approach that involves finding the filter settings that maximize the signal-to-noise ratio for a specific amplitude score (or minimizes the noise for a latency score) while minimizing waveform distortion. The signal is estimated by obtaining the amplitude score from the grand average ERP waveform (usually a difference waveform). The noise is estimated using the standardized measurement error of the single-subject scores. Waveform distortion is estimated by passing noise-free simulated data through the filters. This approach allows researchers to determine the most appropriate filter settings for their specific scoring methods, experimental designs, subject populations, recording setups, and scientific questions. We have provided a set of tools in ERPLAB Toolbox to make it easy for researchers to implement this approach with their own data.

Optimal Filters for ERP Research I: A General Approach for Selecting Filter Settings

Filtering plays an essential role in event-related potential (ERP) research, but filter settings are usually chosen on the basis of historical precedent, lab lore, or informal analyses. This reflects, in part, the lack of a well-reasoned, easily implemented method for identifying the optimal filter settings for a given type of ERP data. To fill this gap, we developed an approach that involves finding the filter settings that maximize the signal-to-noise ratio for a specific amplitude score (or minimizes the noise for a latency score) while minimizing waveform distortion. The signal is estimated by obtaining the amplitude score from the grand average ERP waveform (usually a difference waveform). The noise is estimated using the standardized measurement error of the single-subject scores. Waveform distortion is estimated by passing noise-free simulated data through the filters. This approach allows researchers to determine the most appropriate filter settings for their specific scoring methods, experimental designs, subject populations, recording setups, and scientific questions. We have provided a set of tools in ERPLAB Toolbox to make it easy for researchers to implement this approach with their own data.

Acceleration of inferred neural responses to oddball targets in an individual with bilateral amygdala lesion compared to healthy controls

Detecting unusual auditory stimuli is crucial for discovering potential threat. Locus coeruleus (LC), which coordinates attention, and amygdala, which is implicated in resource prioritization, both respond to deviant sounds. Evidence concerning their interaction, however, is sparse. Seeking to elucidate if human amygdala affects estimated LC activity during this process, we recorded pupillary responses during an auditory oddball and an illuminance change task, in a female with bilateral amygdala lesions (BG) and in n = 23 matched controls. Neural input in response to oddballs was estimated via pupil dilation, a reported proxy of LC activity, harnessing a linear-time invariant system and individual pupillary dilation response function (IRF) inferred from illuminance responses. While oddball recognition remained intact, estimated LC input for BG was compacted to an impulse rather than the prolonged waveform seen in healthy controls. This impulse had the earliest response mean and highest kurtosis in the sample. As a secondary finding, BG showed enhanced early pupillary constriction to darkness. These findings suggest that LC-amygdala communication is required to sustain LC activity in response to anomalous sounds. Our results provide further evidence for amygdala involvement in processing deviant sound targets, although it is not required for their behavioral recognition.

Larger error negativity peak amplitudes for accuracy versus speed instructions may reflect more neuro-cognitive alignment, not more intense error processing

Understanding human error processing is a highly relevant interdisciplinary goal. More than 30 years of research in this field have established the error negativity (Ne) as a fundamental electrophysiological marker of various types of erroneous decisions (e.g. perceptual, economic) and related clinically relevant variations. A common finding is that the Ne is more pronounced when participants are instructed to focus on response accuracy rather than response speed, an observation that has been interpreted as reflecting more thorough error processing. We challenge this wide-spread interpretation by demonstrating that when controlling for the level of non-event-related noise in the participant-average waveform and for single-trial peak latency variability, the significant speed-accuracy difference in the participant-average waveform vanishes. This suggests that the previously reported Ne differences may be mostly attributable to a more precise alignment of neuro-cognitive processes and not (only) to more intense error processing under accuracy instructions, opening up novel perspectives on previous findings.