Rhythmic TMS as a Feasible Tool to Uncover the Oscillatory Signatures of Audiovisual Integration

Silicon Spike: An Arduino-based low-cost and open-access triggerbox to precisely control TMS devices

Transcranial magnetic stimulation (TMS) is a widely used tool in the field of clinical and cognitive neuroscience. To exploit its excellent temporal properties, TMS usually relies on triggerbox devices, which temporize the delivery of magnetic pulses according to the paradigm requirements. However, a main limitation of most of the widely used triggerbox devices is that they rely solely on the experimental computer processor, which might add temporal uncertainty in delivering the TMS pulse when the computer's resources are drained by other experimental devices or by task execution itself, especially during repetitive TMS or dual-coil protocols. We aimed at developing a low-cost and easily reproducible triggerbox device which could overcome these limitations by relying on an external processor to handle the timing precision. We used an Arduino Uno R4 Minima to build Silicon Spike, a low-cost ($60) triggerbox device. We tested the device's precision in delivering the TMS pulses under different working load conditions, and the impact over time. All of the tests were ecological, delivering real TMS pulses during dual-coil, repetitive, and patterned TMS protocols. We obtained extremely high precision (< 0.022 ms) in all of the tests. This means that, for smaller or longer latencies, the error remains negligible for TMS studies. Thus, the Silicon Spike device demonstrated microsecond precision in handling the TMS pulse delivery, establishing itself as a simple and yet precise device. We freely provide the source code and the hardware schematics, allowing anyone to reproduce our work.

Individual Alpha Frequency Contributes to the Precision of Human Visual Processing

Brain oscillatory activity within the alpha band has been associated with a wide range of processes encompassing perception, memory, decision-making, and overall cognitive functioning. Individual alpha frequency (IAF) is a specific parameter accounting for the mean velocity of the alpha cycling activity, conventionally ranging between ∼7 and ∼13 Hz. One influential hypothesis has proposed a fundamental role of this cycling activity in the segmentation of sensory input and in the regulation of the speed of sensory processing, with faster alpha oscillations resulting in greater temporal resolution and more refined perceptual experience. However, although several recent theoretical and empirical studies would support this account, contradictory evidence suggests caution and more systematic approaches in the assessment and interpretation of this hypothesis. For example, it remains to be explored to what degree IAF shapes perceptual outcomes. In the present study, we investigated whether inter-individual differences in bias-free visual contrast detection threshold in a large sample of individuals in the general population (n = 122) could be explained by inter-individual differences in alpha pace. Our results show that the contrast needed to correctly identify target stimuli (individual perceptual threshold) is associated with alpha peak frequency (not amplitude). Specifically, individuals who require reduced contrast show higher IAF than individuals requiring higher contrasts. This suggests that inter-individual differences in alpha frequency contribute to performance variability in low-level perceptual tasks, supporting the hypothesis that IAF underlies a fundamental temporal sampling mechanism that shapes visual objective performance, with higher frequencies promoting enhanced sensory evidence per time unit.

Alpha Oscillations and Temporal Binding Windows in Perception-A Critical Review and Best Practice Guidelines

An intriguing question in cognitive neuroscience is whether alpha oscillations shape how the brain transforms the continuous sensory inputs into distinct percepts. According to the alpha temporal resolution hypothesis, sensory signals arriving within a single alpha cycle are integrated, whereas those in separate cycles are segregated. Consequently, shorter alpha cycles should be associated with smaller temporal binding windows and higher temporal resolution. However, the evidence supporting this hypothesis is contentious, and the neural mechanisms remain unclear. In this review, we first elucidate the alpha temporal resolution hypothesis and the neural circuitries that generate alpha oscillations. We then critically evaluate study designs, experimental paradigms, psychophysics, and neurophysiological analyses that have been employed to investigate the role of alpha frequency in temporal binding. Through the lens of this methodological framework, we then review evidence from between-subject, within-subject, and causal perturbation studies. Our review highlights the inherent interpretational ambiguities posed by previous study designs and experimental paradigms and the extensive variability in analysis choices across studies. We also suggest best practice recommendations that may help to guide future research. To establish a mechanistic role of alpha frequency in temporal parsing, future research is needed that demonstrates its causal effects on the temporal binding window with consistent, experimenter-independent methods.

Neural Signatures of Predictive Strategies Track Individuals Along the Autism-Schizophrenia Continuum

BACKGROUND AND HYPOTHESIS

Humans develop a constellation of different representations of the external environment, even in the face of the same sensory exposure. According to the Bayesian framework, these differentiations could be grounded in a different weight assigned to prior knowledge vs. new external inputs in predictive inference. Since recent advances in computational psychiatry suggest that autism (ASD) and schizophrenia (SSD) lie on the two diametric poles of the same predictive continuum, the adoption of a specific inferential style could be routed by dispositional factors related to autistic and schizotypal traits. However, no studies have directly investigated the role of ASD-SSD dimension in shaping the neuro-behavioral markers underlying perceptual inference.

STUDY DESIGN

We used a probabilistic detection task while simultaneously recording EEG to investigate whether neurobehavioral signatures related to prior processing were diametrically shaped by ASD and SSD traits in the general population (n = 80).

RESULTS

We found that the position along the ASD-SSD continuum directed the predictive strategies adopted by the individuals in decision-making. While proximity to the positive schizotypy pole was associated with the adoption of the predictive approach associated to the hyper-weighting of prior knowledge, proximity to ASD pole was related to strategies that favored sensory evidence in decision-making.

CONCLUSIONS

These findings revealed that the weight assigned to prior knowledge is a marker of the ASD-SSD continuum, potentially useful for identifying individuals at-risk of developing mental disorders and for understanding the mechanisms contributing to the onset of symptoms observed in ASD and SSD clinical forms.