Annalisa Pascarella

The EEGManyPipelines initiative included 396 scientists (168 teams) who independently analyzed the same dataset to test the same hypotheses, mapping the full landscape of analytical decisions. This captured an underexamined source of variability in research results: data processing choices needed to arrive at an analyzable dataset. Considering full analysis pipelines, ERP variability across teams was equivalent to variability across participants, suggesting that who analyses the data is as important as who provides it. In contrast to prior many-analyst projects, typically explaining less than 10% of variance in estimates, our granular codings of researcher choices explained 53%, 75%, and 51% of the variance in difference waves across three hypotheses, with unusual results partly attributable to deviations from the prototypical pipeline. Despite the heterogeneity in approaches and estimates, teams reached a substantial consensus on effect presence. By being transparent across all analysis stages, researchers can identify uncertainty and still draw meaningful inferences.

The EEGManyPipelines initiative included 396 scientists (168 teams) who independently analyzed the same dataset to test the same hypotheses, mapping the full landscape of analytical decisions. This captured an underexamined source of variability in research results: data processing choices needed to arrive at an analyzable dataset. Considering full analysis pipelines, ERP variability across teams was equivalent to variability across participants, suggesting that who analyses the data is as important as who provides it. In contrast to prior many-analyst projects, typically explaining less than 10% of variance in estimates, our granular codings of researcher choices explained 53%, 75%, and 51% of the variance in difference waves across three hypotheses, with unusual results partly attributable to deviations from the prototypical pipeline. Despite the heterogeneity in approaches and estimates, teams reached a substantial consensus on effect presence. By being transparent across all analysis stages, researchers can identify uncertainty and still draw meaningful inferences.

Psychedelics profoundly alter conscious experience, yet how they reshape the relationship between brain anatomy and electrophysiological dynamics remains unclear. Here we use source-localized magnetoencephalography mapped onto connectome harmonics to quantify structure–function coupling in humans under lysergic acid diethylamide (LSD) and placebo. LSD induces a robust decoupling of low-frequency (theta, alpha and beta) activity from anatomical constraints, indicating a global loosening of structure-aligned large-scale dynamics. High-frequency gamma activity shows selective reorganization rather than uniform disruption. Decoupling within core default-mode network regions predicts ego dissolution intensity across individuals, linking frequency-selective DMN reorganization to subjective loss of self. Functional decoding further reveals system-specific rebalancing: visual and attentional systems preferentially decouple while auditory networks exhibit strengthened coupling. Together, these findings provide electrophysiological evidence that psychedelic states emerge from a frequency-dependent relaxation of structural constraints on brain activity and identify default-mode reorganization as a neural correlate of ego dissolution. These results offer a mechanistic framework for understanding how LSD may exert therapeutic effects by transiently relaxing rigid structural constraints and enhancing dynamical flexibility within networks involved in self-related processing.

There is a growing need for large-scale, global studies in response to the replication crisis, which has raised concerns across many scientific fields and prompted efforts to understand the sources of variability in research outcomes. Here we describe the EEGManyPipelines dataset: a many-analyst study aiming to examine how differences in electrophysiology (EEG) data analysis may contribute to differences in the results. We describe data shared and analyzed by 168 analyst teams. The original shared raw data consisted of behavioral data and EEG recordings of 33 participants performing a long-term memory task, together with 8 hypotheses regarding event-related potentials and time-frequency effects. Analyst teams also provided meta-scientific data detailing analysts’ approach to hypotheses: processed EEG data, scripts, and answers to questionnaires. We propose this unique dataset for future use to study analytical flexibility, research culture, and scientific decision-making. It offers a valuable tool for meta-scientific, global and multicenter studies, EEG methodology, and reproducibility studies.

While the beneficial impacts of meditation are increasingly acknowledged, its underlying neural mechanisms remain poorly understood. We examined the electrophysiological brain signals of expert Buddhist monks during two established meditation methods known as Samatha and Vipassana, which employ focused attention and open monitoring technique. By combining source-space magnetoencephalography (MEG) with advanced signal processing and machine learning tools, we provide an unprecedented assessment of the role of brain oscillations, complexity and criticality in meditation. In addition to power spectral density (PSD), we computed long-range temporal correlations (LRTC), deviation from criticality coefficient (DCC), Lempel-Ziv complexity (LZC), 1/f slope, Higuchi fractal dimension (HFD), and spectral entropy. Our findings indicate increased levels of neural signal complexity during both meditation practices compared to the resting state, along-side widespread reductions in gamma-band LRTC and 1/f slope. Importantly, the DCC analysis revealed a separation between Samatha and Vipassana, suggesting that their distinct phenomenological properties are mediated by specific computational characteristics of their dynamic states. Furthermore, in contrast to most previous reports, we observed a decrease in oscillatory gamma power during meditation, a divergence we attribute to the correction of the power spectrum by the 1/f slope. We discuss how these results advance our comprehension of the neural processes associated with focused attention and open monitoring meditation practices.

Low-frequency neural oscillations contribute to the parsing of continuous speech into linguistic units. Little is known however on the coupling of brain rhythms to higher-frequencies in speech such as fundamental frequency (F0) or pitch. Using magnetoencephalography, we investigated whole-brain cortical tracking of F0 while participants listened to sentences produced at normal rate or fast rate, where pitch naturally increases, and to artificially accelerated sentences, where F0 remains unchanged. Our results revealed significant brain-to-F0 coupling across all speech rates not only in right auditory but also in right parietal, insular, and pre- and postcentral regions, likely including the ventral larynx area. Importantly, the cortico-acoustic coupling peak frequency was higher for natural fast speech to reflect the corresponding F0 increase compared to normal rate and time-compressed speech. These findings demonstrate the engagement of an auditory-somato-motor network in F0 tracking, supporting its role in facilitating phonemic processing during the perception of naturally-produced speech.

While the beneficial impacts of meditation are increasingly acknowledged, its underlying neural mechanisms remain poorly understood. We examined the electrophysiological brain signals of expert Buddhist monks during two established meditation methods known as Samatha and Vipassana, which employ focused attention and open-monitoring technique. By combining source-space magnetoencephalography with advanced signal processing and machine learning tools, we provide an unprecedented assessment of the role of brain oscillations, complexity, and criticality in meditation. In addition to power spectral density, we computed long-range temporal correlations (LRTC), deviation from criticality coefficient (DCC), Lempel–Ziv complexity, 1/f slope, Higuchi fractal dimension, and spectral entropy. Our findings indicate increased levels of neural signal complexity during both meditation practices compared to the resting state, alongside widespread reductions in gamma-band LRTC and 1/f slope. Importantly, the DCC analysis revealed a separation between Samatha and Vipassana, suggesting that their distinct phenomenological properties are mediated by specific computational characteristics of their dynamic states. Furthermore, in contrast to most previous reports, we observed a decrease in oscillatory gamma power during meditation, a divergence likely due to the correction of the power spectrum by the 1/f slope, which could reduce potential confounds from broadband 1/f activity. We discuss how these results advance our comprehension of the neural processes associated with focused attention and open-monitoring meditation practices.

The ongoing reproducibility crisis in psychology and cognitive neuroscience has sparked increasing calls to re-evaluate and reshape scientific culture and practices. Heeding those calls, we have recently launched the EEGManyPipelines project as a means to assess the robustness of EEG research in naturalistic conditions and experiment with an alternative model of conducting scientific research. One hundred sixty-eight analyst teams, encompassing 396 individual researchers from 37 countries, independently analyzed the same unpublished, representative EEG data set to test the same set of predefined hypotheses and then provided their analysis pipelines and reported outcomes. Here, we lay out how large-scale scientific projects can be set up in a grassroots, community-driven manner without a central organizing laboratory. We explain our recruitment strategy, our guidance for analysts, the eventual outputs of this project, and how it might have a lasting impact on the field.

Electroencephalography (EEG) source imaging aims to reconstruct brain activity maps from the neuroelectric potential difference measured on the skull. To obtain the brain activity map, we need to solve an ill-posed and ill-conditioned inverse problem that requires regularization techniques to make the solution viable. When dealing with real-time applications, dimensionality reduction techniques can be used to reduce the computational load required to evaluate the numerical solution of the EEG inverse problem. To this end, in this paper we use the random dipole sampling method, in which a Monte Carlo technique is used to reduce the number of neural sources. This is equivalent to reducing the number of the unknowns in the inverse problem and can be seen as a first regularization step. Then, we solve the reduced EEG inverse problem with two popular inversion methods, the weighted Minimum Norm Estimate (wMNE) and the standardized LOw Resolution brain Electromagnetic TomogrAphy (sLORETA). The main result of this paper is the error estimates of the reconstructed activity map obtained with the randomized version of wMNE and sLORETA. Numerical experiments on synthetic EEG data demonstrate the effectiveness of the random dipole sampling method.