Neurodesk: an accessible, flexible and portable data analysis environment for reproducible neuroimaging.

Neuroimaging research requires purpose-built analysis software, which is challenging to install and may produce different results across computing environments. The community-oriented, open-source Neurodesk platform ( https://www.neurodesk.org/ ) harnesses a comprehensive and growing suite of neuroimaging software containers. Neurodesk includes a browser-accessible virtual desktop, command-line interface and computational notebook compatibility, allowing for accessible, flexible, portable and fully reproducible neuroimaging analysis on personal workstations, high-performance computers and the cloud.

Localized estimation of electromagnetic sources underlying event-related fields using recurrent neural networks.

Objective. To use a recurrent neural network (RNN) to reconstruct neural activity responsible for generating noninvasively measured electromagnetic signals.Approach. Output weights of an RNN were fixed as the lead field matrix from volumetric source space computed using the boundary element method with co-registered structural magnetic resonance images and magnetoencephalography (MEG). Initially, the network was trained to minimise mean-squared-error loss between its outputs and MEG signals, causing activations in the penultimate layer to converge towards putative neural source activations. Subsequently, L1 regularisation was applied to the final hidden layer, and the model was fine-tuned, causing it to favour more focused activations. Estimated source signals were then obtained from the outputs of the last hidden layer. We developed and validated this approach with simulations before applying it to real MEG data, comparing performance with beamformers, minimum-norm estimate, and mixed-norm estimate source reconstruction methods.Main results. The proposed RNN method had higher output signal-to-noise ratios and comparable correlation and error between estimated and simulated sources. Reconstructed MEG signals were also equal or superior to the other methods regarding their similarity to ground-truth. When applied to MEG data recorded during an auditory roving oddball experiment, source signals estimated with the RNN were generally biophysically plausible and consistent with expectations from the literature.Significance. This work builds on recent developments of RNNs for modelling event-related neural responses by incorporating biophysical constraints from the forward model, thus taking a significant step towards greater biological realism and introducing the possibility of exploring how input manipulations may influence localised neural activity.

Deep Factor Learning for Accurate Brain Neuroimaging Data Analysis on Discrimination for Structural MRI and Functional MRI.

Analysis of neuroimaging data (e.g., Magnetic Resonance Imaging, structural and functional MRI) plays an important role in monitoring brain dynamics and probing brain structures. Neuroimaging data are multi-featured and non-linear by nature, and it is a natural way to organise these data as tensors prior to performing automated analyses such as discrimination of neurological disorders like Parkinson's Disease (PD) and Attention Deficit and Hyperactivity Disorder (ADHD). However, the existing approaches are often subject to performance bottlenecks (e.g., conventional feature extraction and deep learning based feature construction), as these can lose the structural information that correlates multiple data dimensions or/and demands excessive empirical and application-specific settings. This study proposes a Deep Factor Learning model on a Hilbert Basis tensor (namely, HB-DFL) to automatically derive latent low-dimensional and concise factors of tensors. This is achieved through the application of multiple Convolutional Neural Networks (CNNs) in a non-linear manner along all possible dimensions with no assumed a priori knowledge. HB-DFL leverages the Hilbert basis tensor to enhance the stability of the solution by regularizing the core tensor to allow any component in a certain domain to interact with any component in the other dimensions. The final multi-domain features are handled through another multi-branch CNN to achieve reliable classification, exemplified here using MRI discrimination as a typical case. A case study of MRI discrimination has been performed on public MRI datasets for discrimination of PD and ADHD. Results indicate that 1) HB-DFL outperforms the counterparts in terms of FIT, mSIR and stability (mSC and umSC) of factor learning; 2) HB-DFL identifies PD and ADHD with an accuracy significantly higher than state-of-the-art methods do. Overall, HB-DFL has significant potentials for neuroimaging data analysis applications with its stability of automatic construction of structural features.

Functional connectivity learning via Siamese-based SPD matrix representation of brain imaging data.

Measurement of brain functional connectivity has become a dominant approach to explore the interaction dynamics between brain regions of subjects under examination. Conventional functional connectivity measures largely originate from deterministic models on empirical analysis, usually demanding application-specific settings (e.g., Pearson's Correlation and Mutual Information). To bridge the technical gap, this study proposes a Siamese-based Symmetric Positive Definite (SPD) Matrix Representation framework (SiameseSPD-MR) to derive the functional connectivity of brain imaging data (BID) such as Electroencephalography (EEG), thus the alternative application-independent measure (in the form of SPD matrix) can be automatically learnt: (1) SiameseSPD-MR first exploits graph convolution to extract the representative features of BID with the adjacency matrix computed considering the anatomical structure; (2) Adaptive Gaussian kernel function then applies to obtain the functional connectivity representations from the deep features followed by SPD matrix transformation to address the intrinsic functional characteristics; and (3) Two-branch (Siamese) networks are combined via an element-wise product followed by a dense layer to derive the similarity between the pairwise inputs. Experimental results on two EEG datasets (autism spectrum disorder, emotion) indicate that (1) SiameseSPD-MR can capture more significant differences in functional connectivity between neural states than the state-of-the-art counterparts do, and these findings properly highlight the typical EEG characteristics of ASD subjects, and (2) the obtained functional connectivity representations conforming to the proposed measure can act as meaningful markers for brain network analysis and ASD discrimination.

Filling your time: Auditory flutter alters perceived duration via stimulus-locked responses.

In the current article, we examined the flutter-duration illusion; the extension of perceived duration when an interval is filled with auditory flutter. Participants reproduced flutter-filled and empty durations while electrophysiological activity was recorded. As expected, participants over-produced durations when they were filled with auditory flutter rather than unfilled. Using multivariate pattern analysis, we found several differentiating patterns of neural activity while participants listened to either flutter-filled or empty intervals. However, in subsequent single trial analysis, only two of these clusters predicted perceived duration in the flutter condition; one occurring in line with the second click of the flutter, and one in line with the fourth click. We relate this finding to the N1P2 component and P3a component to timing initiation and arousal, respectively.

Investigating predictive coding in younger and older children using MEG and a multi-feature auditory oddball paradigm.

There is mounting evidence for predictive coding theory from computational, neuroimaging, and psychological research. However, there remains a lack of research exploring how predictive brain function develops across childhood. To address this gap, we used pediatric magnetoencephalography to record the evoked magnetic fields of 18 younger children (M = 4.1 years) and 19 older children (M = 6.2 years) as they listened to a 12-min auditory oddball paradigm. For each child, we computed a mismatch field "MMF": an electrophysiological component that is widely interpreted as a neural signature of predictive coding. At the sensor level, the older children showed significantly larger MMF amplitudes relative to the younger children. At the source level, the older children showed a significantly larger MMF amplitude in the right inferior frontal gyrus relative to the younger children, P < 0.05. No differences were found in 2 other key regions (right primary auditory cortex and right superior temporal gyrus) thought to be involved in mismatch generation. These findings support the idea that predictive brain function develops during childhood, with increasing involvement of the frontal cortex in response to prediction errors. These findings contribute to a deeper understanding of the brain function underpinning child cognitive development.

Neurodesk: An accessible, flexible, and portable data analysis environment for reproducible neuroimaging.

Neuroimaging data analysis often requires purpose-built software, which can be challenging to install and may produce different results across computing environments. Beyond being a roadblock to neuroscientists, these issues of accessibility and portability can hamper the reproducibility of neuroimaging data analysis pipelines. Here, we introduce the Neurodesk platform, which harnesses software containers to support a comprehensive and growing suite of neuroimaging software (https://www.neurodesk.org/). Neurodesk includes a browser-accessible virtual desktop environment and a command line interface, mediating access to containerized neuroimaging software libraries on various computing platforms, including personal and high-performance computers, cloud computing and Jupyter Notebooks. This community-oriented, open-source platform enables a paradigm shift for neuroimaging data analysis, allowing for accessible, flexible, fully reproducible, and portable data analysis pipelines.

Neural correlates of face perception modeled with a convolutional recurrent neural network.

Objective.Event-related potential (ERP) sensitivity to faces is predominantly characterized by an N170 peak that has greater amplitude and shorter latency when elicited by human faces than images of other objects. We aimed to develop a computational model of visual ERP generation to study this phenomenon which consisted of a three-dimensional convolutional neural network (CNN) connected to a recurrent neural network (RNN).Approach.The CNN provided image representation learning, complimenting sequence learning of the RNN for modeling visually-evoked potentials. We used open-access data from ERP Compendium of Open Resources and Experiments (40 subjects) to develop the model, generated synthetic images for simulating experiments with a generative adversarial network, then collected additional data (16 subjects) to validate predictions of these simulations. For modeling, visual stimuli presented during ERP experiments were represented as sequences of images (time x pixels). These were provided as inputs to the model. By filtering and pooling over spatial dimensions, the CNN transformed these inputs into sequences of vectors that were passed to the RNN. The ERP waveforms evoked by visual stimuli were provided to the RNN as labels for supervised learning. The whole model was trained end-to-end using data from the open-access dataset to reproduce ERP waveforms evoked by visual events.Main results.Cross-validation model outputs strongly correlated with open-access (r= 0.98) and validation study data (r= 0.78). Open-access and validation study data correlated similarly (r= 0.81). Some aspects of model behavior were consistent with neural recordings while others were not, suggesting promising albeit limited capacity for modeling the neurophysiology of face-sensitive ERP generation.Significance.The approach developed in this work is potentially of significant value for visual neuroscience research, where it may be adapted for multiple contexts to study computational relationships between visual stimuli and evoked neural activity.

Theta tACS impairs episodic memory more than tDCS.

Episodic memory deficits are a common consequence of aging and are associated with a number of neurodegenerative disorders (e.g., Alzheimer's disease). Given the importance of episodic memory, a great deal of research has investigated how we can improve memory performance. Transcranial electrical stimulation (TES) represents a promising tool for memory enhancement but the optimal stimulation parameters that reliably boost memory are yet to be determined. In our double-blind, randomised, sham-controlled study, 42 healthy adults (36 females; 23.3 ± 7.7 years of age) received anodal transcranial direct current stimulation (tDCS), theta transcranial alternating current stimulation (tACS) and sham stimulation during a list-learning task, over three separate sessions. Stimulation was applied over the left temporal lobe, as encoding and recall of information is typically associated with mesial temporal lobe structures (e.g., the hippocampus and entorhinal cortex). We measured word recall within each stimulation session, as well as the average number of intrusion and repetition errors. In terms of word recall, participants recalled fewer words during tDCS and tACS, compared to sham stimulation, and significantly fewer words recalled during tACS compared with tDCS. Significantly more memory errors were also made during tACS compared with sham stimulation. Overall, our findings suggest that TES has a deleterious effect on memory processes when applied to the left temporal lobe.

A Guided Tutorial on Modelling Human Event-Related Potentials with Recurrent Neural Networks.

In cognitive neuroscience research, computational models of event-related potentials (ERP) can provide a means of developing explanatory hypotheses for the observed waveforms. However, researchers trained in cognitive neurosciences may face technical challenges in implementing these models. This paper provides a tutorial on developing recurrent neural network (RNN) models of ERP waveforms in order to facilitate broader use of computational models in ERP research. To exemplify the RNN model usage, the P3 component evoked by target and non-target visual events, measured at channel Pz, is examined. Input representations of experimental events and corresponding ERP labels are used to optimize the RNN in a supervised learning paradigm. Linking one input representation with multiple ERP waveform labels, then optimizing the RNN to minimize mean-squared-error loss, causes the RNN output to approximate the grand-average ERP waveform. Behavior of the RNN can then be evaluated as a model of the computational principles underlying ERP generation. Aside from fitting such a model, the current tutorial will also demonstrate how to classify hidden units of the RNN by their temporal responses and characterize them using principal component analysis. Statistical hypothesis testing can also be applied to these data. This paper focuses on presenting the modelling approach and subsequent analysis of model outputs in a how-to format, using publicly available data and shared code. While relatively less emphasis is placed on specific interpretations of P3 response generation, the results initiate some interesting discussion points.

Automatic morpheme identification across development: Magnetoencephalography (MEG) evidence from fast periodic visual stimulation.

The present study combined magnetoencephalography (MEG) recordings with fast periodic visual stimulation (FPVS) to investigate automatic neural responses to morphemes in developing and skilled readers. Native English-speaking children (N = 17, grade 5-6) and adults (N = 28) were presented with rapid streams of base stimuli (6 Hz) interleaved periodically with oddballs (i.e., every fifth item, oddball stimulation frequency: 1.2 Hz). In a manipulation-check condition, tapping into word recognition, oddballs featured familiar words (e.g., roll) embedded in a stream of consonant strings (e.g., ktlq). In the experimental conditions, the contrast between oddball and base stimuli was manipulated in order to probe selective stem and suffix identification in morphologically structured pseudowords (e.g., stem + suffix pseudowords such as softity embedded in nonstem + suffix pseudowords such as trumess). Neural responses at the oddball frequency and harmonics were analyzed at the sensor level using non-parametric cluster-based permutation tests. As expected, results in the manipulation-check condition revealed a word-selective response reflected by a predominantly left-lateralized cluster that emerged over temporal, parietal, and occipital sensors in both children and adults. However, across the experimental conditions, results yielded a differential pattern of oddball responses in developing and skilled readers. Children displayed a significant response that emerged in a mostly central occipital cluster for the condition tracking stem identification in the presence of suffixes (e.g., softity vs. trumess). In contrast, adult participants showed a significant response that emerged in a cluster located in central and left occipital sensors for the condition tracking suffix identification in the presence of stems (e.g., softity vs. stopust). The present results suggest that while the morpheme identification system in Grade 5-6 children is not yet adult-like, it is sufficiently mature to automatically analyze the morphemic structure of novel letter strings. These findings are discussed in the context of theoretical accounts of morphological processing across reading development.

Comprehensive behavioural intervention for tics: a neurophysiological intervention.

BACKGROUND: Gilles de la Tourette Syndrome (GTS) is a childhood-onset neuropsychiatric disorder characterised by motor and vocal tics. While Comprehensive Behavioural Intervention for Tics (CBIT) is an effective, non-pharmacological treatment for patients with GTS, the underlying neurophysiological basis of this intervention has not been investigated. METHODS: To investigate the clinical effectiveness of CBIT in reducing tic severity in young people with GTS and explore neurophysiological mechanisms associated with clinical change. RESULTS: There was a significant overall improvement in tic severity of large effect size. The Cortical Silent Period (CSP) to motor evoked potential (MEP) ratio (CSP/MEP ratio) increased after the intervention with a small effect size. Other neurophysiological measures of inhibition were not significantly related to the change in tic severity. CONCLUSIONS: Alongside significant clinical improvements, these results suggest a role for motor cortical Gamma-aminobutyric acid (GABA)-ergic inhibitory circuitry in the neurophysiological changes underlying CBIT treatment. These findings need to be replicated in larger studies using control samples.

Head and Neck Characteristics as Risk Factors For and Protective Factors Against Mild Traumatic Brain Injury in Military and Sporting Populations: A Systematic Review.

BACKGROUND: Investigators have proposed that various physical head and neck characteristics, such as neck strength and head and neck size, are associated with protection from mild traumatic brain injury (mTBI/concussion). OBJECTIVES: To systematically review the literature and investigate potential relationships between physical head and neck characteristics and mTBI risk in athletic and military populations. METHODS: A comprehensive search of seven databases was conducted: MEDLINE, EMBASE, CINAHL, Scopus, SPORTDiscus, Cochrane Library, and Web of Science. Potential studies were systematically screened and reviewed. Studies on military and athletic cohorts were included if they assessed the relationship between physical head-neck characteristics and mTBI risk or proxy risk measures such as head impact kinematics. RESULTS: The systematic search yielded a total of 11,723 original records. From these, 22 studies met our inclusion criteria (10 longitudinal, 12 cross-sectional). Relevant to our PECO (Population, Exposure, Comparator, and Outcomes) question, exposures included mTBI incidence and head impact kinematics (acceleration, velocity, displacement) for impacts during sport play and training and in controlled laboratory conditions. Outcome characteristics included head and neck size (circumference, mass, length, ratios between these measures), neck strength and endurance, and rate of force development of neck muscles. DISCUSSION: We found mixed evidence for head and neck characteristics acting as risk factors for and protective factors against mTBI and increased susceptibility to head impacts. Head-neck strength and size variables were at times associated with protection against mTBI incidence and reduced impact kinematics (14/22 studies found one or more head-neck variable to be associated with protection); however, some studies did not find these relationships (8/22 studies found no significant associations or relationships). Interestingly, two studies found stronger and larger athletes were more at risk of sustaining high impacts during sport. Strength and size metrics may have some predictive power, but impact mitigation seems to be influenced by many other variables, such as behaviour, sex, and impact anticipation. A meta-analysis could not be performed due to heterogeneity in study design and reporting. CONCLUSION: There is mixed evidence in the literature for the protective capacity of head and neck characteristics. We suggest field-based mTBI research in the future should include more dynamic anthropometric metrics, such as neck stiffness and response to perturbation. In addition, laboratory-based mTBI studies should aim to standardise design and reporting to help further uncover these complicated relationships.

Cortical oscillatory dysrhythmias in visual snow syndrome: a magnetoencephalography study.

Visual snow refers to the persistent visual experience of static in the whole visual field of both eyes. It is often reported by patients with migraine and co-occurs with conditions such as tinnitus and tremor. The underlying pathophysiology of the condition is poorly understood. Previously, we hypothesized that visual snow syndrome may be characterized by disruptions to rhythmical activity within the visual system. To test this, data from 18 patients diagnosed with visual snow syndrome, and 16 matched controls, were acquired using magnetoencephalography. Participants were presented with visual grating stimuli, known to elicit decreases in alpha-band (8-13 Hz) power and increases in gamma-band power (40-70 Hz). Data were mapped to source-space using a beamformer. Across both groups, decreased alpha power and increased gamma power localized to early visual cortex. Data from the primary visual cortex were compared between groups. No differences were found in either alpha or gamma peak frequency or the magnitude of alpha power, p > 0.05. However, compared with controls, our visual snow syndrome cohort displayed significantly increased primary visual cortex gamma power, p = 0.035. This new electromagnetic finding concurs with previous functional MRI and PET findings, suggesting that in visual snow syndrome, the visual cortex is hyperexcitable. The coupling of alpha-phase to gamma amplitude within the primary visual cortex was also quantified. Compared with controls, the visual snow syndrome group had significantly reduced alpha-gamma phase-amplitude coupling, p < 0.05, indicating a potential excitation-inhibition imbalance in visual snow syndrome, as well as a potential disruption to top-down 'noise-cancellation' mechanisms. Overall, these results suggest that rhythmical brain activity in the primary visual cortex is both hyperexcitable and disorganized in visual snow syndrome, consistent with this being a condition of thalamocortical dysrhythmia.

Natural vs forced language switching: Free selection and consistent language use eliminate significant performance costs and cognitive demands in the brain.

Bilinguals are known to switch language spontaneously in everyday conversations, even if there are no external requirements to do so. However, in the laboratory setting, language control is often investigated using forced switching tasks, which result in significant performance costs. The present study assessed whether switching would be less costly when performed in a more natural fashion, and what factors might account for this. Mandarin-English bilinguals engaged in language switching under three different contexts with varied task demands. We examined two factors which may be characteristic of natural switching: (i) freedom of language selection; (ii) consistency of language used to name each item. Participants' brain activities were recorded using magnetoencephalography (MEG), along with behavioural measures of reaction speed and accuracy. The natural context (with both free selection and consistent language use for each item) produced better performance overall, showing reduced mixing cost and no significant switch cost. The neural effect of language mixing was also reversed in this context, suggesting that freely mixing two languages was easier than staying in a single language. Further, while switching in the forced context elicited increased brain activity in the right inferior frontal gyrus, this switch effect disappeared when the language used to name each item was consistent. Together, these findings demonstrate that the two factors above conjointly contribute to eliminating significant performance costs and cognitive demands associated with language switching and mixing. Such evidence aligns with lexical selection models which do not assume bilingual production to be inherently effortful.

Performance of Bimanual Finger Coordination Tasks in Speakers Who Stutter.

Stuttering is a neurodevelopmental speech disorder characterized by the symptoms of speech repetition, prolongation, and blocking. Stuttering-related dysfluency can be transiently alleviated by providing an external timing signal such as a metronome or the voice of another person. Therefore, the existence of a core motor timing deficit in stuttering has been speculated. If this is the case, then motoric behaviors other than speech should be disrupted in stuttering. This study examined motoric performance on four complex bimanual tasks in 37 adults who stutter and 31 fluent controls. Two tasks utilized bimanual rotation to examine motor dexterity, and two tasks used the bimanual mirror and parallel tapping movements to examine timing control ability. Video-based analyses were conducted to determine performance accuracy and speed. The results showed that individuals who stutter performed worse than fluent speakers on tapping tasks but not on bimanual rotation tasks. These results suggest stuttering is associated with timing control for general motor behavior.

Analysis methods for measuring passive auditory fNIRS responses generated by a block-design paradigm.

Significance: Functional near-infrared spectroscopy (fNIRS) is an increasingly popular tool in auditory research, but the range of analysis procedures employed across studies may complicate the interpretation of data. Aim: We aim to assess the impact of different analysis procedures on the morphology, detection, and lateralization of auditory responses in fNIRS. Specifically, we determine whether averaging or generalized linear model (GLM)-based analysis generates different experimental conclusions when applied to a block-protocol design. The impact of parameter selection of GLMs on detecting auditory-evoked responses was also quantified. Approach: 17 listeners were exposed to three commonly employed auditory stimuli: noise, speech, and silence. A block design, comprising sounds of 5 s duration and 10 to 20 s silent intervals, was employed. Results: Both analysis procedures generated similar response morphologies and amplitude estimates, and both indicated that responses to speech were significantly greater than to noise or silence. Neither approach indicated a significant effect of brain hemisphere on responses to speech. Methods to correct for systemic hemodynamic responses using short channels improved detection at the individual level. Conclusions: Consistent with theoretical considerations, simulations, and other experimental domains, GLM and averaging analyses generate the same group-level experimental conclusions. We release this dataset publicly for use in future development and optimization of algorithms.

Rates of false positive outcomes on the A-WPTAS picture items in a sample of non-concussed athletes.

Accurate diagnosis of sports related concussion ensures that athletes are removed from play if concussed and prevents incorrect removal when a concussion has not occurred. Although various screening tools are currently in use, there is no gold standard measure with which to diagnose sports related concussion.Objective: The current study aimed to investigate the diagnostic accuracy of the Abbreviated Westmead Post Traumatic Amnesia Scale (A-WPTAS) picture task, a neurocognitive measure used to assess mild traumatic brain injury. The incidence of false positive classifications and the potential confounding effect of exercise on scores on the A-WPTAS picture items were examined.Methods: The study included an athlete group comprising 33 players and a control group comprising 37 subjects. The A-WPTAS picture task was completed on three testing occasions, separated by three week intervals.Results: Results revealed that the A-WPTAS picture task was highly accurate (>95%) in correctly classifying participants with no concussion across all three testing occasions. There was no significant difference between the two groups in relation to false positive outcomes on any testing occasion, suggesting that exercise was not a confounding factor.Conclusions: Findings provide preliminary evidence to support the use of the A-WPTAS picture task in a sporting context.

Oddball onset timing: Little evidence of early gating of oddball stimuli from tapping, reacting, and producing.

Oddballs, rare or novel stimuli, appear to last longer than non-oddballs. This illusion is often attributed to the perceived time that an oddball occupies being longer than that of a non-oddball. However, it is also possible that oddball stimuli are perceived to onset earlier than non-oddballs; they are "gated" earlier in time and thus the perceived duration of those stimuli are longer. In the current article, we directly investigate this proposal by asking participants to react to, produce durations initiated with, and tap along to either oddball or standard stimuli. Tapping provided some support for earlier perceived onset of an oddball in the visual modality. However, both reaction time and duration production experiments provided evidence against an oddball being gated earlier than a standard stimulus. Contrarily, these experiments showed an oddball resulted in longer reaction times and productions, respectively. Taken together, these three experiments indicate it is unlikely that the expansion of time attributed to oddball presentation is purely due to the earlier gating of oddball stimuli. In fact, the first two experiments provide some evidence that the effect of an oddball must compensate for the later gating of these stimuli.