Biology subjects

Langer, N.

Publications and source records attributed to Langer, N..

18 recordsLinked to original sources

bioRxiv2026

The Impact of Non-Neural Sources on Aperiodic EEG Activity

Aperiodic, 1/f-like EEG activity has emerged as a key index of neural population dynamics, offering insights into excitation/inhibition balance, cognitive states, and clinical conditions. However, the foundational assumption that these parameters reflect neural dynamics rather than non-neural sources remains largely untested. Here, we systematically quantify how data quality and physiological artifacts influence aperiodic parameter estimation across two independent datasets (N=99 and N=103). Findings converged across data sets and two complementary analytical approaches: experimental artifact manipulation and selective signal decomposition. We demonstrate that ocular artifacts and poor data quality systematically inflate aperiodic offsets and exponents, while muscular artifacts exert the opposite effects. These spatially widespread influences persist after state-of-the-art preprocessing and reach magnitudes comparable to reported neurophysiologically meaningful group differences. Our results reveal that differential rates of non-neural sources can drive spurious neural interpretations; consequently, we introduce and validate an approach to mitigate these biases, guiding valid inference in basic and clinical aperiodic research.

neuroscience↗

bioRxiv2025

Toward Robust Neuroanatomical Normative Models: Influence of Sample Size and Covariates Distributions

Normative models are increasingly used to characterize individual-level brain deviations in neuroimaging studies, but their performance depends heavily on the reference sample used for training or adaptation. In this study, we systematically investigated how sample size and covariate composition of the reference cohort influence model fit, deviation estimates, and clinical readouts in Alzheimers disease (AD). Using a discovery dataset (OASIS-3, n = 1032), we trained models on healthy control (HC), subsamples ranging from 5 to 600 individuals, while varying age and sex distributions to simulate biases in reference populations. We further assessed the use of adaptive transfer learning by pre-training models on the UK Biobank (n = 42,747) and adapting them to the clinical dataset applying the same sub-sampling strategies. We evaluated model performance on a fixed HC test set and quantified deviation score errors, outlier detection, and classification accuracy in both the HC test set and the AD cohort. The findings were replicated in an external validation sample (AIBL, n = 463). Across all settings, model performance improved with increasing sample size, but demographic alignment of the covariates, particularly in age, was essential for reliable deviation estimates. Models trained directly within the dataset achieved stable fit with approximately 200 HCs, while adapted models reached comparable performance with as few as 50 individuals when pre-trained on large-scale data. These results show that robust individual-level modeling can be achieved using moderately sized but demographically matched cohorts, supporting broader application of normative modeling in ageing and neurodegeneration research.

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bioRxiv2025

EEG-GAN: A Generative EEG Augmentation Toolkit for Enhancing Neural Classification

Electroencephalography (EEG) is a widely applied method for decoding neural activity, offering insights into cognitive function and driving advancements in neurotechnology. However, decoding EEG data remains challenging, as classification algorithms typically require large datasets that are expensive and time-consuming to collect. Recent advances in generative artificial intelligence have enabled the creation of realistic synthetic EEG data, yet no method has consistently demonstrated that such synthetic data can lead to improvements in EEG decodability across diverse datasets. Here, we introduce EEG-GAN, an open-source generative adversarial network (GAN) designed to augment EEG data. In the most comprehensive evaluation study to date, we assessed its capacity to generate realistic EEG samples and enhance classification performance across four datasets, five classifiers, and seven sample sizes, while benchmarking it against six established augmentation techniques. We found that EEG-GAN, when trained to generate raw single-trial EEG signals, produced signals that reproduce grand-averaged waveforms and time-frequency patterns of the original data. Furthermore, training classifiers on additional synthetic data improved their ability to decode held-out empirical data. EEG-GAN achieved up to a 16% improvement in decoding accuracy, with enhancements consistent across datasets but varying among classifiers. Data augmentations were particularly effective for smaller sample sizes (30 and below), significantly improving 70% of these classification analyses and only significantly impairing 4% of analyses. Moreover, EEG-GAN significantly outperformed all benchmark techniques in 69% of the comparisons across datasets, classifiers, and sample sizes and was only significantly outperformed in 3% of comparisons. These findings establish EEG-GAN as a robust toolkit for generating realistic EEG data, which can effectively reduce the costs associated with real-world EEG data collection for neural decoding tasks.

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bioRxiv2025

Impact of Aging on Theta-Phase Gamma-Amplitude Coupling During Learning: A Multivariate Analysis

Aging is associated with cognitive decline and memory impairment, but the underlying neural mechanisms remain unclear. Phase-amplitude coupling (PAC) between mid-frontal theta (5 Hz) and occipital gamma (>30 Hz) oscillations is a proposed marker for parallel storage of multiple items in working memory. However, research has mainly focused on young individuals and epilepsy patients, with only a few studies on aging populations. Moreover, these studies have relied on univariate PAC methods, which can be flawed by potential spurious or biased PAC estimates due to non-stationarity of EEG signals. Additionally, these methods typically assess PAC at the level of individual electrodes, potentially overlooking the broader functional significance of theta-gamma coupling in coordinating neural activity across distant brain regions. To address these gaps, we employed multivariate PAC (mPAC) through generalized eigendecomposition (GED) analysis, which avoids the pitfalls of non-sinusoidal oscillations. 113 young and 117 older healthy participants engaged in a sequence learning paradigm (6423 sequence repetitions, 55944 stimuli), in which they learned a fixed sequence of visual stimuli over repeated observations, allowing us to track the mPAC during the incremental process of learning. Behavioral results revealed that younger participants learned significantly faster than older participants. Neurophysiological data showed that mPAC increased over the course of learning in both age groups and could identify fast and slow learners. However, older participants exhibited lower mPAC compared to younger counterparts, which suggest compromised parallel storage of items in working memory in older age. Finally, stratification analysis revealed that mPAC effects persist across performance groups with similar mid-frontal theta levels, suggesting that theta alone does not account for these effects. These findings shed light on the age-related differences in memory formation processes and may guide interventions to enhance memory performance in older adults and slow learners.

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bioRxiv2024

Pre-stimulus Activity Mediates Event-Related Theta Synchronization and Alpha Desynchronization During Memory Formation in Healthy Aging

The capacity to learn is a key determinant for the quality of life but is known to decline to varying degrees with age. However, despite mounting evidence of memory deficits in older age, the neural mechanisms contributing to successful or impeded memory remain unclear. Previous research has primarily focused on memory formation through remembered versus forgotten comparisons, lacking the ability to capture the incremental nature of learning. Moreover, previous EEG studies have primarily examined oscillatory brain activity during the encoding phase, such as event-related synchronization (ERS) of mid-frontal theta and desynchronisation (ERD) of parietal alpha, while neglecting the potential influence of pre-stimulus activity. To address these limitations, we employed a sequence learning paradigm, where 113 young and 117 older participants learned a fixed sequence of visual locations through repeated observations (6423 sequence repetitions, 55 944 stimuli). This paradigm enabled us to investigate mid-frontal theta ERS, parietal alpha ERD, and how they are affected by pre-stimulus activity during the incremental learning process. Behavioral results revealed that young subjects learned significantly faster than older subjects, in line with expected age-related cognitive decline. Successful incremental learning was directly linked to decreases of mid-frontal theta ERS and increases of parietal alpha ERD. Notably, these neurophysiological changes were less pronounced in older individuals, reflecting a slower rate of learning. Importantly, the mediation analysis revealed that in both age groups, mid-frontal pre-stimulus theta partially mediated the relationship between learning and mid-frontal theta ERS. Furthermore, the overall impact of learning on parietal alpha ERD was primarily driven by its positive influence on pre-stimulus alpha activity. Our findings offer new insights into the age-related differences in memory formation and highlight the importance of pre-stimulus activity in explaining post-stimulus responses during learning.

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bioRxiv2022

Neurophysiological markers of successful learning in healthy aging

The capacity to learn and memorize is a key determinant for the quality of life, but is known to decline to varying degrees with age. Previous ERP research methods had the limitation that their design did not allow to track the gradual memory formation process. Thus, the neural mechanisms underlying memory formation and the critical features that determine the extent to which aging affects learning are still unknown. By using a visual sequence learning task, which consists of the repeated presentation of a simple sequence of tokens, we are able to track the progress of gradual memory formation through both neurophysiological and behavioral markers. On a neurophysiological level, we focused on two learning related centroparietal ERP components: the P300 and broad positivity. Our results revealed that although both age groups showed significant learning progress, young individuals learned faster and remembered more stimuli than older participants. Successful learning was directly linked to a decrease of P300 amplitude. However, young participants showed larger P300 amplitude with a sharper decrease during the memory formation process. The P300 amplitude predicted learning success in both age groups, was associated with increased fronto-parietal brain network activation and showed good test-retest reliability. Highly, similar results were found for the broad positivity component, which raises the questions if the BP is a distinct component or just a prolonged P300. In a series of analyses, including topographic analysis of variance (TANOVA), equivalence testing and source reconstruction analysis, we addressed the unresolved questions. These analyses revealed concordant distributed brain activation patterns within parietal circuits. Thus, there is no evidence (rather evidence for equivalence) for distinct underlying neural generators for the two components. Taken together, the results highlight the importance of the P300 as a neurophysiological marker of learning and may enable the development of preventive measures for age-related impeded learning.

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bioRxiv2022

The AI Neuropsychologist: Automatic scoring of memory deficits with deep learning

BackgroundMemory deficits are a hallmark of many different neurological and psychiatric conditions. The Rey-Osterrieth complex figure (ROCF) is the state-of-the-art assessment tool for neuropsychologists across the globe to assess the degree of non-verbal visual memory deterioration. To obtain a score, a trained clinician inspects a patients ROCF drawing and quantifies deviations from the original figure. This manual procedure is time-consuming, slow and scores vary depending on the clinicians experience, motivation and tiredness. MethodsHere, we leverage novel deep learning architectures to automatize the rating of memory deficits. For this, we collected more than 20k hand-drawn ROCF drawings from patients with various neurological and psychiatric disorders as well as healthy participants. Unbiased ground truth ROCF scores were obtained from crowdsourced human intelligence. This dataset was used to train and evaluate a multi-head convolutional neural network. ResultsThe model performs highly unbiased as it yielded predictions very close to the ground truth and the error was similarly distributed around zero. The neural network outperforms both online raters and clinicians. The scoring system can reliably identify and accurately score individual figure elements in previously unseen ROCF drawings, which facilitates explainability of the AI-scoring system. To ensure generalizability and clinical utility, the model performance was successfully replicated in a large independent prospective validation study that was pre-registered prior to data collection. ConclusionsOur AI-powered scoring system provides healthcare institutions worldwide with a digital tool to assess objectively, reliably and time-efficiently the performance in the ROCF test from hand-drawn images.

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bioRxiv2022

The ZuCo Benchmark on Cross-Subject Reading Task Classification with EEG and Eye-Tracking Data

We present a new machine learning benchmark for reading task classification with the goal of advancing EEG and eye-tracking research at the intersection between computational language processing and cognitive neuroscience. The benchmark task consists of a cross-subject classification to distinguish between two reading paradigms: normal reading and task-specific reading. The data for the benchmark is based on the Zurich Cognitive Language Processing Corpus (ZuCo 2.0), which provides simultaneous eye-tracking and EEG signals from natural reading. The training dataset is publicly available, and we present a newly recorded hidden testset. We provide multiple solid baseline methods for this task and discuss future improvements. We release our code and provide an easy-to-use interface to evaluate new approaches with an accompanying public leaderboard: www.zuco-benchmark.com. HighlightsO_LIWe present a new machine learning benchmark for reading task classification with the goal of advancing EEG and eye-tracking research. C_LIO_LIWe provide an interface to evaluate new approaches with an accompanying public leaderboard. C_LIO_LIThe benchmark task consists of a cross-subject classification to distinguish between two reading paradigms: normal reading and task-specific reading. C_LIO_LIThe data is based on the Zurich Cognitive Language Processing Corpus of simultaneous eye-tracking and EEG signals from natural reading. C_LI

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bioRxiv2022

Harmonized-Multinational qEEG Norms (HarMNqEEG)

This paper extends our frequency domain quantitative electroencephalography (qEEG) methods pursuing higher sensitivity to detect Brain Developmental Disorders. Prior qEEG work lacked integration of cross-spectral information omitting important functional connectivity descriptors. Lack of geographical diversity precluded accounting for site-specific variance, increasing qEEG nuisance variance. We ameliorate these weaknesses. i) Create lifespan Hermitian Riemannian multinational qEEG norms for cross-spectral tensors. These norms result from the HarMNqEEG project fostered by the Global Brain Consortium. We calculate the norms with data from 9 countries, 12 devices, and 14 studies, including 1564 subjects. Instead of raw data, only anonymized metadata and EEG cross-spectral tensors were shared. After visual and automatic quality control developmental equations for the mean and standard deviation of qEEG traditional and Hermitian Riemannian descriptive parameters were calculated using additive mixed-effects models. We demonstrate qEEG "batch effects" and provide methods to calculate harmonized z-scores. ii) We also show that the multinational harmonized Hermitian Riemannian norms produce z-scores with increased diagnostic accuracy to predict brain dysfunction at school-age produced by malnutrition only in the first year of life. We provide data and software for constructing norms. iii) We offer open code and data to calculate different individual z-scores from the HarMNqEEG dataset. These results contribute to developing bias-free, low-cost neuroimaging technologies applicable in various health settings. HighlightsO_LIWe create lifespan Hermitian Riemannian qEEG norms for cross-spectral tensors. C_LIO_LIThe norms are based on 9 countries, 12 devices, and 14 studies, with 1564 subjects. C_LIO_LIWe demonstrate qEEG "batch effects", providing harmonization methods to remove them. C_LIO_LIMultinational harmonized z-scores increase diagnostic accuracy of brain dysfunction. C_LIO_LIData and software are available for norm and individual z-scores calculation. C_LI

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bioRxiv2021

Alpha oscillations link action to cognition: An oculomotor account of the brain's dominant rhythm

Power modulations in alpha oscillations (8-14Hz) have been associated with most human cognitive functions and psychopathological conditions studied. These reports are often inconsistent with the prevailing view of a specific relationship of alpha oscillations to attention and working memory (WM). We propose that conceptualizing the role of alpha oscillations in oculomotor control resolves this inconsistency. This proposition is based on a review of results across species (human Npooled=295, one non-human primate, honey bee N=5), experimental conditions (rest, attention, and working memory), and recording techniques (EEG, ECOG, eye-tracking, and MEG) that encourage the following relationships between alpha oscillations and eye-movement control: (i) saccade initiation prompts power decrease in brain circuits associated with saccadic control; (ii) the direction of a saccade is consistent with alpha lateralization, both during task and resting conditions; (iii) the phase of alpha activity informs saccade occurrence and biases miniature eye movements during fixation (e.g. fixational tremor); and (iv) oculomotor action differentiates WM load. A new theory on how alpha oscillations link oculomotor action to cognition is proposed. Generalizing across tasks and species: low oculomotor activity is associated with high alpha power and vice versa. Alpha oscillations regulate how long to look at a given target and how fast to saccade to a next. By ensuring steady gaze position, any potential input outside foveal vision is "suppressed".

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bioRxiv2021

Decomposing age effects in EEG alpha power

Increasing life expectancy is prompting the need to understand how the brain changes during healthy aging. Research utilizing Electroencephalography (EEG) has found that the power of alpha oscillations decrease from adulthood on. However, non-oscillatory (aperiodic) components in the data may confound results and thus require re-investigation of these findings. The present report aims at analyzing a pilot and two additional independent samples (total N = 533) of resting-state EEG from healthy young and elderly individuals. A newly developed algorithm will be utilized that allows the decomposition of the measured signal into aperiodic and aperiodic-adjusted signal components. By using multivariate sequential Bayesian updating of the age effect in each signal component, evidence across the datasets will be accumulated. It is hypothesized that previously reported age-related alpha power differences will disappear when absolute power is adjusted for the aperiodic signal component. Consequently, age-related differences in the intercept and slope of the aperiodic signal component are expected. Importantly, using a battery of neuropsychological tests, we will assess how the previously reported relationship between cognitive functions and alpha oscillations changes when taking the aperiodic signal into account; this will be done on data of the young and aged individuals separately. The aperiodic signal components and adjusted alpha parameters could potentially offer a promising biomarker for cognitive decline, thus finally the test-retest reliability of the aperiodic and aperiodic-adjusted signal components will be assessed.

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bioRxiv2020

Decomposing the role of alpha oscillations during brain maturation

Childhood and adolescence are critical stages of the human lifespan, in which fundamental neural reorganizational processes take place. A substantial body of literature investigated neurophysiological changes during brain maturation by focusing on the most dominant feature of the human EEG signal: the alpha oscillation. Ambiguous results were reported for the developmental trajectory of the power of the alpha oscillation. Simulations in this study show that conventional measures of alpha power are confounded by various factors and need to be decomposed into periodic and aperiodic components, which represent distinct underlying brain mechanisms. It is therefore unclear how each part of the signal relates to changes during brain maturation. Using multivariate Bayesian generalized linear mixed models, we examined aperiodic and periodic parameters of alpha activity in the largest openly available pediatric dataset (N=2529, age range 5-21 years) and replicated these findings in a preregistered analysis of an independent validation sample (N=369, age range 6-21yrs). First, the well documented age-related decrease in total alpha power was replicated. However, when controlling for the aperiodic signal component, our findings provide strong evidence for a reversed developmental trajectory of the periodic alpha power, whereas the aperiodic signal components, slope and offset, decreased. Consequently, earlier interpretations on age related changes of alpha power need to be fundamentally reconsidered, incorporating changes in the aperiodic signal. The interpretation of decreased total alpha power as elimination of active synapses rather links to decreases in the aperiodic intercept. Instead, additional analyses of diffusion tensor imaging data indicate that the maturational increase in periodic alpha power is related to increased thalamocortical connectivity. Functionally, our results suggest that increased thalamic control of cortical alpha power is linked to improved attentional performance during brain maturation.

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bioRxiv2018

AUTOMAGIC: Standardized Preprocessing of Big EEG Data

Electroencephalography (EEG) recordings have been rarely included in large-scale studies. This is arguably not due to a lack of information that lies in EEG recordings but mainly on account of methodological issues. In many cases, particularly in clinical, pediatric and aging populations, the EEG has a high degree of artifact contamination and the quality of EEG recordings often substantially differs between subjects. Although there exists a variety of standardized preprocessing methods to clean EEG from artifacts, currently there is no method to objectively quantify the quality of preprocessed EEG. This makes the commonly accepted procedure of excluding subjects from analyses due to exceeding contamination of artifacts highly subjective. As a consequence, P-hacking is fostered, the replicability of results is decreased, and it is difficult to pool data from different study sites. In addition, in large-scale studies, data are collected over years or even decades, requiring software that controls and manages the preprocessing of ongoing and dynamically growing studies. To address these challenges, we developed Automagic, an open-source MATLAB toolbox that acts as a wrapper to run currently available preprocessing methods and offers objective standardized quality assessment for growing studies. The software is compatible with the Brain Imaging Data Structure (BIDS) standard and hence facilitates data sharing. In the present paper we outline the functionality of Automagic and examine the effect of applying combinations of methods on a sample of resting EEG data. This examination suggests that applying a pipeline of algorithms to detect artifactual channels in combination with Multiple Artifact Rejection Algorithm (MARA), an independent component analysis (ICA)-based artifact correction method, is sufficient to reduce a large extent of artifacts.

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bioRxiv2018

Microstate EEGlab toolbox: An introductory guide

EEG microstate analysis offers a sparse characterisation of the spatio-temporal features of large-scale brain network activity. However, despite the concept of microstates is straight-forward and offers various quantifications of the EEG signal with a relatively clear neurophysiological interpretation, a few important aspects about the currently applied methods are not readily comprehensible. Here we aim to increase the transparency about the methods to facilitate widespread application and reproducibility of EEG microstate analysis by introducing a new EEGlab toolbox for Matlab. EEGlab and the Microstate toolbox are open source, allowing the user to keep track of all details in every analysis step. The toolbox is specifically designed to facilitate the development of new methods. While the toolbox can be controlled with a graphical user interface (GUI), making it easier for newcomers to take their first steps in exploring the possibilities of microstate analysis, the Matlab framework allows advanced users to create scripts to automatise analysis for multiple subjects to avoid tediously repeating steps for every subject. This manuscript provides an overview of the most commonly applied microstate methods as well as a tutorial consisting of a comprehensive walk-through of the analysis of a small, publicly available dataset.

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bioRxiv2017

A multi-modal approach to decomposing standard neuropsychological test performance: Symbol Search

Neuropsychological test batteries provide normed assessments of cognitive performance across multiple functional domains. Although each test emphasizes a certain component of cognition, a poor score can reflect many possible processing deficits. Here we explore the use of simultaneous eye tracking and EEG to decompose test performance into interpretable, components of cognitive processing. We examine the specific case of Symbol Search, a \"processing speed\" subtest of the WISC, which involves searching for the presence of either of two target symbols among five search symbols. To characterize the signatures of effective performance of the test, we asked 26 healthy adults to perform a computerized version of it while recording continuous EEG and eye tracking. We first established basic gaze-shifting patterns in the task, such as more frequent and prolonged fixation of each target than each search symbol, and longer search symbol fixations and overall trial duration for target-absent trials. We then entered multiple such metrics into a least absolute shrinkage and selection operator (LASSO) analysis, which revealed that short trial completion times were mainly predicted by longer initial fixations on the targets and fewer subsequent confirmatory saccades directed back to the targets. Further, the tendency to make confirmatory saccades was associated with stronger gamma-amplitude modulation by mid-frontal theta-phase in the EEG during initial target symbol encoding. Taken together, these findings indicate that efficient Symbol Search performance depends more on effective memory encoding than on general \"processing speed\".

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bioRxiv2017

The Healthy Brain Network Biobank: An open resource for transdiagnostic research in pediatric mental health and learning disorders

Technological and methodological innovations are equipping researchers with unprecedented capabilities for detecting and characterizing pathologic processes in the developing human brain. As a result, ambitions to achieve clinically useful tools to assist in the diagnosis and management of mental health and learning disorders are gaining momentum. To this end, it is critical to accrue large-scale multimodal datasets that capture a broad range of commonly encountered clinical psychopathology. The Child Mind Institute has launched the Healthy Brain Network (HBN), an ongoing initiative focused on creating and sharing a biobank of data from 10,000 New York area participants (ages 5-21). The HBN Biobank houses data about psychiatric, behavioral, cognitive, and lifestyle phenotypes, as well as multimodal brain imaging (resting and naturalistic viewing fMRI, diffusion MRI, morphometric MRI), electroencephalography, eye-tracking, voice and video recordings, genetics, and actigraphy. Here, we present the rationale, design and implementation of HBN protocols. We describe the first data release (n = 664) and the potential of the biobank to advance related areas (e.g., biophysical modeling, voice analysis).

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bioRxiv2016

A Resource for Assessing Information Processing in the Developing Brain Using EEG and Eye Tracking

We present a dataset combining electrophysiology and eye tracking intended as a resource for the investigation of information processing in the developing brain. The dataset includes high-density task-based and task-free EEG, eye tracking, and cognitive and behavioral data collected from 126 individuals (ages: 6-44). The task battery spans both the simple/complex and passive/active dimensions to cover a range of approaches prevalent in modern cognitive neuroscience. The active task paradigms facilitate principled deconstruction of core components of task performance in the developing brain, whereas the passive paradigms permit the examination of intrinsic functional network activity during varying amounts of external stimulation. Alongside these neurophysiological data, we include an abbreviated cognitive test battery and questionnaire-based measures of psychiatric functioning. We hope that this dataset will lead to the development of novel assays of neural processes fundamental to information processing, which can be used to index healthy brain development as well as detect pathologic processes.

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bioRxiv2016

Age and sex affect intersubject correlation of EEGthroughout development

Neural development is generally marked by an increase in the efficiency and diversity of neural processes. In a large sample (N = 114) of children and adults with ages ranging from 5 -44 years, we investigated the neural responses to naturalistic video stimuli. Videos from both real-life classroom settings and Hollywood feature films were used to probe different aspects of attention and engagement. For all stimuli, older ages were marked by more variable neural responses. Variability was assessed by the inter-subject correlation of evoked electroencephalographic (EEG) responses. Young males also had more variable responses than young females. These results were replicated in an independent cohort (N = 303). When interpreted in the context of neural maturation, we conclude that neural function becomes more variable with maturity, at least in during the passive viewing of real-world stimuli.\n\nSignificance StatementNaturalistic videos were used to measure how a large sample of children and adults process environmentally meaningful stimuli. As age increased, neural responses were more variable, and females responded more variably than males - a difference that disappeared with age. These results are consistent with developmental theories positing that neural variability increases with maturation, and that neural maturation typically occurs earlier in females. This is the first study to investigate neural variability under naturalistic conditions in a developmental sample.

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