It has been proposed that autistic perception may be marked by a reduced influence of temporal context. Following this theory, prior exposure to a stimulus should lead to a weaker or absent alteration of the behavioral and neural response to the stimulus in autism, compared with a typical population. To examine these hypotheses, we recruited two samples of human volunteers: a student sample (N= 26), which we used to establish our analysis pipeline, and an adolescent sample (N= 36), which consisted of a group of autistic (N= 18) and a group of non-autistic (N= 18) participants. All participants were presented with visual stimulus streams consisting of novel and familiar image pairs, while they attentively monitored each stream. We recorded task performance and used magnetoencephalography (MEG) to measure neural responses, and to compare the responses with familiar and novel images. We found behavioral facilitation as well as a reduction of event-related field (ERF) amplitude for familiar, compared with novel, images in both samples. Crucially, we found statistical evidence against between-group effects of familiarity on both behavioral and neural responses in the adolescent sample, suggesting that the influence of familiarity is comparable between autistic and non-autistic adolescents. These findings challenge the notion that perception in autism is marked by a reduced influence of prior exposure.

Most scientists need software to perform their research (Barker et al., 2020; Carver et al., 2022; Hettrick, 2014; Hettrick et al., 2014; Switters and Osimo, 2019), and neuroscientists are no exception. Whether we work with reaction times, electrophysiological signals, or magnetic resonance imaging data, we rely on software to acquire, analyze, and statistically evaluate the raw data we obtain - or to generate such data if we work with simulations. In recent years there has been a shift toward relying on free, open-source scientific software (FOSSS) for neuroscience data analysis (Poldrack et al., 2019), in line with the broader open science movement in academia (McKiernan et al., 2016) and wider industry trends (Eghbal, 2016). Importantly, FOSSS is typically developed by working scientists (not professional software developers) which sets up a precarious situation given the nature of the typical academic workplace (wherein academics, especially in their early careers, are on short and fixed term contracts). In this paper, we will argue that the existing ecosystem of neuroscientific open source software is brittle, and discuss why and how the neuroscience community needs to come together to ensure a healthy growth of our software landscape to the benefit of all.

Non-invasive evaluation of functional connectivity, based on source-reconstructed estimates of phase-difference-based metrics, is notoriously non-robust. This is due to a combination of factors, ranging from a misspecification of seed regions to suboptimal baseline assumptions, and residual signal leakage. In this work, we propose a new analysis scheme of source level phase-difference-based connectivity, which is aimed at optimizing the detection of interacting brain regions. Our approach is based on the combined use of sensor subsampling and dual-source beamformer estimation of all-to-all connectivity on a prespecified dipolar grid. First, a pairwise two-dipole model, to account for reciprocal leakage in the estimation of the localized signals, allows for a usable approximation of the pairwise bias in connectivity due to residual leakage of ‘third party’ noise. Secondly, using sensor array subsampling, the recreation of multiple connectivity maps using different subsets of sensors allows for the identification of consistent spatially localized peaks in the 6-dimensional connectivity maps, indicative of true brain region interactions. These steps are combined with the subtraction of null coherence estimates to obtain the final coherence maps. With extensive simulations, we compared different analysis schemes for their detection rate of connected dipoles, as a function of signal-to-noise ratio, phase difference and connection strength. We demonstrate superiority of the proposed analysis scheme in comparison to single-dipole models, or an approach that discards the zero phase difference component of the connectivity. We conclude that the proposed pipeline allows for a more robust identification of functional connectivity in experimental data, opening up new possibilities to study brain networks with mechanistically inspired connectivity measures in cognition and in the clinic.

Language is a key part of human cognition, essential for our well-being at all stages of our lives. Whereas many neurocognitive abilities decline with age, for language the picture is much less clear, and how exactly speech comprehension changes with ageing is still unknown. To investigate this, we employed magnetoencephalography (MEG) and recorded neuromagnetic brain responses to auditory linguistic stimuli in healthy participants of younger and older age using a passive task-free paradigm and a range of different linguistic stimulus contrasts, which enabled us to assess neural processing of spoken language at multiple levels (lexical, semantic, morphosyntactic). Using machine learning-based classification algorithms to scrutinise intertrial phase coherence of MEG responses in cortical source space, we found that patterns of oscillatory neural activity diverged between younger and older participants across several frequency bands (alpha, beta, gamma) for all tested linguistic information types. The results suggest multiple age-related changes in the brain's neurolinguistic circuits, which may be due to both healthy ageing in general and compensatory processes in particular.

The application of decoding models to electrophysiological data has become standard practice in neuroscience. The use of such methods on sensor space data can, however, limit the interpretability of the results, since brain sources cannot be readily estimated from the decoding of sensor space responses. Here, we propose a new method that combines the common spatial patterns (CSP) algorithm with beamformer source reconstruction for the decoding of oscillatory activity. We compare this method to sensor and source space decoding and show that it performs equally well as source space decoding with respect to both decoding accuracy and source localization without the extensive computational cost. We confirm our simulation results on a real MEG data set. In conclusion, our proposed method performs as good as source space decoding, is highly interpretable in the spatial domain, and has low computational cost.

Parkinson’s disease is caused by degeneration of dopaminergic neurons, originating in thesubstantia nigra pars compactaand characterised by bradykinesia, rest tremor and rigidity. In addition, visual disorders and retinal abnormalities are often present and can be identified by decreased visual acuity, abnormal spatial contrast sensitivity or even difficulty in complex visual task completion. Because of their early onset in patients with de novo Parkinson’s disease, the anatomical retinal changes and electrophysiological modification could be valuable markers even at early stages of the disease. However, due to the concomitant occurrence of normal ageing, the relevance and specificity of these predictive values can be difficult to interpret. This review examines retinal dysfunction arising in Parkinson’s disease. We highlight the electrophysiological delays and decreased amplitude in the electroretinography recorded in patients and animal models. We relate this to coexisting anatomical changes such as retinal nerve fibre layer and macular thinning, measured using optical coherence tomography, and show that functional measures are more consistent overall than optical coherence-measured structural changes. We review the underlying chemical changes seen with loss of retinal dopaminergic neurons and the effect of levodopa treatment on the retina in Parkinson’s disease. Finally, we consider whether retinal abnormalities in Parkinson’s disease could have a role as potential markers of poorer outcomes and help stratify patients at early stages of the disease. We emphasise that retinal measures can be valuable, accessible and cost-effective methods in the early evaluation of Parkinson’s disease pathogenesis with potential for patient stratification.

Many Parkinson’s disease (PD) patients are able to ride a bicycle despite being severely compromised by gait disturbances up to freezing of gait. This review [PROSPERO CRD 42019137386] aimed to find out, which PD-related symptoms improve from bicycling, and which type of bicycling exercise would be most beneficial. Following a systematic database literature search, peer-reviewed studies with randomized control trials (RCT) and with non-randomized trials (NRCT) investigating the interventional effects of bicycling on PD patients were included. A quality analysis addressing reporting, design and possible bias of the studies, as well as a publication bias test was done. Out of 202 references, 22 eligible studies with 505 patients were analysed. An inverse variance-based analysis revealed that primary measures, defined as motor outcomes, benefitted from bicycling significantly more than cognitive measures. Additionally, secondary measures of balance, walking speed and capacity, and the PDQ-39 ratings improved with bicycling. The interventions varied in durations, intensities and target cadences. Conclusively, bicycling is particularly beneficial for the motor performance of PD patients, improving crucial features of gait. Furthermore, our findings suggest that bicycling improves the overall quality-of-life of PD patients.

Beamforming is a popular method for functional source reconstruction using magnetoencephalography (MEG) and electroencephalography (EEG) data. Beamformers, which were first proposed for MEG more than two decades ago, have since been applied in hundreds of studies, demonstrating that they are a versatile and robust tool for neuroscience. However, certain characteristics of beamformers remain somewhat elusive and there currently does not exist a unified documentation of the mathematical underpinnings and computational subtleties of beamformers as implemented in the most widely used academic open source software packages for MEG analysis (Brainstorm, FieldTrip, MNE, and SPM). Here, we provide such documentation that aims at providing the mathematical background of beamforming and unifying the terminology. Beamformer implementations are compared across toolboxes and pitfalls of beamforming analyses are discussed. Specifically, we provide details on handling rank deficient covariance matrices, prewhitening, the rank reduction of forward fields, and on the combination of heterogeneous sensor types, such as magnetometers and gradiometers. The overall aim of this paper is to contribute to contemporary efforts towards higher levels of computational transparency in functional neuroimaging.

Optically pumped magnetometers (OPMs) have been adopted for the measurement of brain activity. Without the need to be cooled to cryogenic temperatures, an array of these sensors can be placed more flexibly, which allows for the recording of neuronal structures other than neocortex. Here we use eight OPM sensors to record human retinal activity following flash stimulation. We compare this magnetoretinographic (MRG) activity to the simultaneously recorded electroretinogram of the eight participants. The MRG shows the familiar flash-evoked potentials (a-wave and b-wave) and shares a highly significant amount of information with the electroretinogram (both in a simultaneous and separate measurement). We conclude that OPM sensors have the potential to become a contactless alternative to fiber electrodes for the measurement of retinal activity. Such a contactless solution can benefit both clinical and neuroscientific settings.

Beamformers are applied for estimating spatiotemporal characteristics of neuronal sources underlying measured MEG/EEG signals. Several MEG analysis toolboxes include an implementation of a linearly constrained minimum-variance (LCMV) beamformer. However, differences in implementations and in their results complicate the selection and application of beamformers and may hinder their wider adoption in research and clinical use. Additionally, combinations of different MEG sensor types (such as magnetometers and planar gradiometers) and application of preprocessing methods for interference suppression, such as signal space separation (SSS), can affect the results in different ways for different implementations. So far, a systematic evaluation of the different implementations has not been performed. Here, we compared the localization performance of the LCMV beamformer pipelines in four widely used open-source toolboxes (FieldTrip, SPM12, Brainstorm, and MNE-Python) using datasets both with and without SSS interference suppression. We analyzed MEG data that were i) simulated, ii) recorded from a static and moving phantom, and iii) recorded from a healthy volunteer receiving auditory, visual, and somatosensory stimulation. We also investigated the effects of SSS and the combination of the magnetometer and gradiometer signals. We quantified how localization error and point-spread volume vary with SNR in all four toolboxes. When applied carefully to MEG data with a typical SNR (3–15 dB), all four toolboxes localized the sources reliably; however, they differed in their sensitivity to preprocessing parameters. As expected, localizations were highly unreliable at very low SNR, but we found high localization error also at very high SNRs. We also found that the SNR improvement offered by SSS led to more accurate localization.

This data set is a part of the study 'Comparison of beamformers implementations for MEG source localization'. The dataset includes 64 phantom datasets, 2 human datasets, and 50 simulated datasets. The data also include segmented MRI files from FreeSurfer for MEG phantom (Megin Oy, Helsinki, Finland) and a human subject MRI. It also includes the used versions of the four beamforming packages (MNE-Python, FieldTrip, SPM12(DAiSS), and Brainstorm) and codes used for the analysis.

Hemodynamic research has recently clarified key nodes and links in brain networks implementing inhibitory control. Although fMRI methods are optimized for identifying the structure of brain networks, the relatively slow temporal course of fMRI limits the ability to characterize network operation. The latter is crucial for developing a mechanistic understanding of how brain networks shift dynamically to support inhibitory control. To address this critical gap, we applied spectrally resolved Granger causality (GC) and random forest machine learning tools to human EEG data in two large samples of adults (test sample n = 96, replication sample n = 237, total N = 333, both sexes) who performed a color–word Stroop task. Time–frequency analysis confirmed that recruitment of inhibitory control accompanied by slower behavioral responses was related to changes in theta and alpha/beta power. GC analyses revealed directionally asymmetric exchanges within frontal and between frontal and parietal brain areas: top-down influence of superior frontal gyrus (SFG) over both dorsal ACC (dACC) and inferior frontal gyrus (IFG), dACC control over middle frontal gyrus (MFG), and frontal–parietal exchanges (IFG, precuneus, MFG). Predictive analytics confirmed a combination of behavioral and brain-derived variables as the best set of predictors of inhibitory control demands, with SFG theta bearing higher classification importance than dACC theta and posterior beta tracking the onset of behavioral response. The present results provide mechanistic insight into the biological implementation of a psychological phenomenon: inhibitory control is implemented by dynamic routing processes during which the target response is upregulated via theta-mediated effective connectivity within key PFC nodes and via beta-mediated motor preparation. SIGNIFICANCE STATEMENT Hemodynamic neuroimaging research has recently clarified regional structures in brain networks supporting inhibitory control. However, due to inherent methodological constraints, much of this research has been unable to characterize the temporal dynamics of such networks (e.g., direction of information flow between nodes). Guided by fMRI research identifying the structure of brain networks supporting inhibitory control, results of EEG source analysis in a test sample (n = 96) and replication sample (n = 237) using effective connectivity and predictive analytics strategies advance a model of inhibitory control by characterizing the precise temporal dynamics by which this network operates and exemplify an approach by which mechanistic models can be developed for other key psychological processes.

Single-trial analyses have the potential to uncover meaningful brain dynamics that are obscured when averaging across trials. However, low signal-to-noise ratio (SNR) can impede the use of single-trial analyses and decoding methods. In this study, we investigate the applicability of a single-trial approach to decode stimulus modality from magnetoencephalographic (MEG) high frequency activity. In order to classify the auditory versus visual presentation of words, we combine beamformer source reconstruction with the random forest classification method. To enable group level inference, the classification is embedded in an across-subjects framework. We show that single-trial gamma SNR allows for good classification performance (accuracy across subjects: 66.44%). This implies that the characteristics of high frequency activity have a high consistency across trials and subjects. The random forest classifier assigned informational value to activity in both auditory and visual cortex with high spatial specificity. Across time, gamma power was most informative during stimulus presentation. Among all frequency bands, the 75 Hz to 95 Hz band was the most informative frequency band in visual as well as in auditory areas. Especially in visual areas, a broad range of gamma frequencies (55 Hz to 125 Hz) contributed to the successful classification. Thus, we demonstrate the feasibility of single-trial approaches for decoding the stimulus modality across subjects from high frequency activity and describe the discriminative gamma activity in time, frequency, and space.

Visual processing of dark visual stimuli has been hypothesized to occur faster relative to bright stimuli. We investigated the timing, processing, and propagation of neural activity in response to darks and lights, operationalized as light offset and onset, in the human visual system by recording electroretinography (ERG) simultaneously with magnetoencephalography (MEG) in humans. We discovered that dark onset resulted in 75–95 Hz retinal activity that we call the dark retinal oscillatory potential , occurring with the same latency as the analogous but more broadband (55–195 Hz) oscillatory potential at light onset. Both retinal oscillations coupled with subsequent cortical activity of corresponding bandwidths, but cortical responses for darks indeed occurred earlier than for lights. Darks therefore propagate from retina to cortex more quickly than lights, potentially resulting from a thalamic advantage. Furthermore, we found that this propagation is effectuated by high frequency retinocortical coupling of narrow bandwidth for darks but wide bandwidth for lights.

Research has identified appetitive aggression, i.e., the perception of committed, violent acts as appealing, exciting and fascinating, as a common phenomenon within populations living in precarious and violent circumstances. Investigating demobilized soldiers in the Democratic Republic of Congo (DRC) demonstrated that violent offending is associated with appetitive aggression and not necessarily with symptoms of posttraumatic stress. In the present study, we sought to replicate these results in an independent and larger sample of demobilized soldiers from Burundi. As with the Congolese ex-combatants, random forest regression revealed that the number of lifetime perpetrated violent acts is the most important predictor of appetitive aggression and the number of lifetime experienced traumatic events is the main predictor for posttraumatic stress. Perpetrated violent acts with salient cues of hunting (pursuing the victim, the sight of blood, etc.) were most predictive for perceiving violent cues appealingly after demobilization. Moreover, the association of violent acts and appetitive aggression as well as traumatic events and posttraumatic stress remains strong even years after demobilization. Patterns of traumatic events and perpetrated acts as predictors for posttraumatic stress and appetitive aggression seem to be robust among different samples of ex-combatants who fought in civil wars. Psychotherapeutic interventions that address these complementary facets of combat-related disorders namely, posttraumatic stress and appetitive aggression are indispensable for a successful reintegration of those who fought in armed conflicts and to achieve a successful transition to peace.