Visbrain: A Multi-Purpose GPU-Accelerated Open-Source Suite for Multimodal Brain Data Visualization

Music therapy with adult burn patients in the intensive care unit: short-term analysis of electrophysiological signals during music-assisted relaxation

Burn patients often face elevated pain, anxiety, and depression levels. Music therapy adds to integrative care in burn patients, but research including electrophysiological measures is limited. This study reports electrophysiological signals analysis during Music-Assisted Relaxation (MAR) with burn patients in the Intensive Care Unit (ICU). This study is a sub-analysis of an ongoing trial of music therapy with burn patients in the ICU. Electroencephalogram (EEG), electrocardiogram (ECG), and electromyogram (EMG) were recorded during MAR with nine burn patients. Additionally, background pain levels (VAS) and anxiety and depression levels (HADS) were assessed. EEG oscillation power showed statistically significant changes in the delta (p < 0.05), theta (p = 0.01), beta (p < 0.05), and alpha (p = 0.05) bands during music therapy. Heart rate variability tachograms high-frequencies increased (p = 0.014), and low-frequencies decreased (p = 0.046). Facial EMG mean frequency decreased (p = 0.01). VAS and HADS scores decreased - 0.76 (p = 0.4) and - 3.375 points (p = 0.37) respectively. Our results indicate parasympathetic system activity, attention shifts, reduced muscle tone, and a relaxed state of mind during MAR. This hints at potential mechanisms of music therapy but needs to be confirmed in larger studies. Electrophysiological changes during music therapy highlight its clinical relevance as a complementary treatment for ICU burn patients.Trial registration: Clinicaltrials.gov (NCT04571255). Registered September 24th, 2020. https//classic.clinicaltrials.gov/ct2/show/NCT04571255.

Effect of music therapy on short-term psychological and physiological outcomes in mechanically ventilated patients: A randomized clinical pilot study

Background

Elevated anxiety levels are common in patients on mechanical ventilation (MV) and may challenge recovery. Research suggests music-based interventions may reduce anxiety during MV. However, studies investigating specific music therapy techniques, addressing psychological and physiological well-being in patients on MV, are scarce.

Methods

This three-arm randomized clinical pilot study was conducted with MV patients admitted to the intensive care unit (ICU) of Hospital San José in Bogotá, Colombia between March 7, 2022, and July 11, 2022. Patients were divided into three groups: intervention group 1 (IG1), music-assisted relaxation; intervention group 2 (IG2), patient-preferred therapeutic music listening; and control group (CG), standard care. The main outcome measure was the 6-item State-Anxiety Inventory. Secondary outcomes were: pain (measured with a visual analog scale), resilience (measured with the Brief Resilience Scale), agitation/sedation (measured with the Richmond Agitation-Sedation Scale), vital signs (including heart rate, blood pressure, oxygen saturation, and respiratory rate), days of MV, extubation success, and days in the ICU. Additionally, three patients underwent electroencephalography during the interventions.

Results

Data from 23 patients were analyzed in this study. The age range of the patients was 24.0-84.0 years, with a median age of 66.0 years (interquartile range: 57.0-74.0). Of the 23 patients, 19 were female (82.6%). No statistically significant differences between the groups were observed for anxiety (P=0.330), pain (P=0.624), resilience (P=0.916), agitation/sedation (P=0.273), length of ICU stay (P=0.785), or vital signs. A statistically significant difference between the groups was found for days of MV (P=0.019). Electroencephalography measurements showed a trend toward delta and theta band power decrease for two patients and a power increase on both beta frequencies (slow and fast) in the frontal areas of the brain for one patient.

Conclusions

In this pilot study, music therapy did not significantly affect the anxiety levels in patients on MV. However, the interventions were widely accepted by the staff, patients, and caregivers and were safe, considering the critical medical status of the participants. Further large-scale randomized controlled trials are needed to investigate the potential benefits of music therapeutic interventions in this population.Trial Registration ISRCTN trial registry identifier: ISRCTN16964680.

A visual paired associate learning (vPAL) paradigm to study memory consolidation during sleep

Sleep improves the consolidation and long-term stability of newly formed memories and associations. Most research on human declarative memory and its consolidation during sleep uses word-pair associations requiring exhaustive learning. In the present study, we present the visual paired association learning (vPAL) paradigm, in which participants learn new associations between images of celebrities and animals. The vPAL is based on a one-shot exposure that resembles learning in natural conditions. We tested if vPAL can reveal a role for sleep in memory consolidation by assessing the specificity of memory recognition, and the cued recall performance, before and after sleep. We found that a daytime nap improved the stability of recognition memory and discrimination abilities compared to identical intervals of wakefulness. By contrast, cued recall of associations did not exhibit significant sleep-dependent effects. High-density electroencephalography during naps further revealed an association between sleep spindle density and stability of recognition memory. Thus, the vPAL paradigm opens new avenues for future research on sleep and memory consolidation across ages and heterogeneous populations in health and disease.

Effects of non-rapid eye movement sleep on the cortical synaptic expression of GluA1-containing AMPA receptors

Converging electrophysiological, molecular and ultrastructural evidence supports the hypothesis that sleep promotes a net decrease in excitatory synaptic strength, counteracting the net synaptic potentiation caused by ongoing learning during waking. However, several outstanding questions about sleep-dependent synaptic weakening remain. Here, we address some of these questions by using two established molecular markers of synaptic strength, the levels of the AMPA (alpha-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid) receptors containing the GluA1 subunit and the phosphorylation of GluA1 at serine 845 (p-GluA1(845)). We previously found that, in the rat cortex and hippocampus, these markers are lower after 6-8 h of sleep than after the same time spent awake. Here, we measure GluA1 and p-GluA1(845) levels in synaptosomes of mouse cortex after 5 h of either sleep, sleep deprivation, recovery sleep after sleep deprivation or selective REM sleep deprivation (32 C57BL/B6 adult mice, 16 females). We find that relative to after sleep deprivation, these synaptic markers are lower after sleep independent of whether the mice were allowed to enter REM sleep. Moreover, 5 h of recovery sleep following acute sleep deprivation is enough to renormalize their expression. Thus, the renormalization of GluA1 and p-GluA1(845) expression crucially relies on NREM sleep and can occur in a few hours of sleep after acute sleep deprivation.

Human local field potentials in motor and non-motor brain areas encode upcoming movement direction

Limb movement direction can be inferred from local field potentials in motor cortex during movement execution. Yet, it remains unclear to what extent intended hand movements can be predicted from brain activity recorded during movement planning. Here, we set out to probe the directional-tuning of oscillatory features during motor planning and execution, using a machine learning framework on multi-site local field potentials (LFPs) in humans. We recorded intracranial EEG data from implanted epilepsy patients as they performed a four-direction delayed center-out motor task. Fronto-parietal LFP low-frequency power predicted hand-movement direction during planning while execution was largely mediated by higher frequency power and low-frequency phase in motor areas. By contrast, Phase-Amplitude Coupling showed uniform modulations across directions. Finally, multivariate classification led to an increase in overall decoding accuracy (>80%). The novel insights revealed here extend our understanding of the role of neural oscillations in encoding motor plans.

hyve, a compositional visualisation engine for brain imaging data

Visualisations facilitate the interpretation of geometrically structured data and results. However, heterogeneous geometries-such as volumes, surfaces, and networks-have traditionally mandated different software approaches. We introduce hyve, a Python library that uses a compositional functional framework to enable parametric implementation of custom visualisations for different brain geometries. Under this framework, users compose a reusable visualisation protocol from geometric primitives for representing data geometries, input primitives for common data formats and research objectives, and output primitives for producing interactive displays or configurable snapshots. hyve also writes documentation for user-constructed protocols, automates serial production of multiple visualisations, and includes an API for semantically organising an editable multi-panel figure. Through the seamless composition of input, output, and geometric primitives, hyve supports creating visualisations for a range of neuroimaging research objectives.

Performance on complex memory tests is associated with β-amyloid in individuals at risk of developing Alzheimer's disease

The pathophysiological development of Alzheimer's disease (AD) begins in the brain years before the onset of clinical symptoms. The accumulation of beta-amyloid (Aβ) is thought to be the first cortical pathology to occur. Carrying one apolipoprotein E (APOE) ε4 allele increases the risk of developing AD at least 2-3 times and is associated with earlier Aβ accumulation. Although it is difficult to identify Aβ-related cognitive impairment in early AD with standard cognitive tests, more sensitive memory tests may be able to do this. We sought to examine associations between Aβ and performance on three tests within three subdomains of memory, verbal, visual, and associative memory, to elucidate which of these tests were sensitive to Aβ-related cognitive impairment in at-risk subjects. 55 APOE ε4 carriers underwent MRI, 11 C-Pittsburgh Compound B (PiB) PET, and cognitive testing. A composite cortical PiB SUVR cut-off score of 1.5 was used to categorise subjects as either APOE ε4 Aβ+ or APOE ε4 Aβ-. Correlations were carried out using cortical surface analysis. In the whole APOE ε4 group, we found significant correlations between Aβ load and performance on verbal, visual, and associative memory tests in widespread cortical areas, the strongest association being with performance on associative memory tests. In the APOE ε4 Aβ+ group, we found significant correlations between Aβ load and performance of verbal and associative, but not visual, memory in localised cortical areas. Performance on verbal and associative memory tests provides sensitive markers of early Aβ-related cognitive impairment in at-risk subjects.

Citizen neuroscience: Wearable technology and open software to study the human brain in its natural habitat

Citizen science allows the public to participate in various stages of scientific research, including study design, data acquisition, and data analysis. Citizen science has a long history in several fields of the natural sciences, and with recent developments in wearable technology, neuroscience has also become more accessible to citizen scientists. This development was largely driven by the influx of minimal sensing systems in the consumer market, allowing more do-it-yourself (DIY) and quantified-self (QS) investigations of the human brain. While most subfields of neuroscience require sophisticated monitoring devices and laboratories, the study of sleep characteristics can be performed at home with relevant noninvasive consumer devices. The strong influence of sleep quality on waking life and the accessibility of devices to measure sleep are two primary reasons citizen scientists have widely embraced sleep research. Their involvement has evolved from solely contributing to data collection to engaging in more collaborative or autonomous approaches, such as instigating ideas, formulating research inquiries, designing research protocols and methodology, acting upon their findings, and disseminating results. In this article, we introduce the emerging field of citizen neuroscience, illustrating examples of such projects in sleep research. We then provide overviews of the wearable technologies for tracking human neurophysiology and various open-source software used to analyse them. Finally, we discuss the opportunities and challenges in citizen neuroscience projects and suggest how to improve the study of the human brain outside the laboratory.

Overview of methods and available tools used in complex brain disorders

Complex brain disorders, including Alzheimer's dementia, sleep disorders, and epilepsy, are chronic conditions that have high prevalence individually and in combination, increasing mortality risk, and contributing to the socioeconomic burden of patients, their families and, their communities at large. Although some literature reviews have been conducted mentioning the available methods and tools used for supporting the diagnosis of complex brain disorders and processing different files, there are still limitations. Specifically, these research works have focused primarily on one single brain disorder, i.e., sleep disorders or dementia or epilepsy. Additionally, existing research initiatives mentioning some tools, focus mainly on one single type of data, i.e., electroencephalography (EEG) signals or actigraphies or Magnetic Resonance Imaging, and so on. To tackle the aforementioned limitations, this is the first study conducting a comprehensive literature review of the available methods used for supporting the diagnosis of multiple complex brain disorders, i.e., Alzheimer's dementia, sleep disorders, epilepsy. Also, to the best of our knowledge, we present the first study conducting a comprehensive literature review of all the available tools, which can be exploited for processing multiple types of data, including EEG, actigraphies, and MRIs, and receiving valuable forms of information which can be used for differentiating people in a healthy control group and patients suffering from complex brain disorders. Additionally, the present study highlights both the benefits and limitations of the existing available tools.

An in-vivo validation of ESI methods with focal sources

Electrophysiological source imaging (ESI) aims at reconstructing the precise origin of brain activity from measurements of the electric field on the scalp. Across laboratories/research centers/hospitals, ESI is performed with different methods, partly due to the ill-posedness of the underlying mathematical problem. However, it is difficult to find systematic comparisons involving a wide variety of methods. Further, existing comparisons rarely take into account the variability of the results with respect to the input parameters. Finally, comparisons are typically performed using either synthetic data, or in-vivo data where the ground-truth is only roughly known. We use an in-vivo high-density EEG dataset recorded during intracranial single pulse electrical stimulation, in which the true sources are substantially dipolar and their locations are precisely known. We compare ten different ESI methods, using their implementation in the MNE-Python package: MNE, dSPM, LORETA, sLORETA, eLORETA, LCMV beamformers, irMxNE, Gamma Map, SESAME and dipole fitting. We perform comparisons under multiple choices of input parameters, to assess the accuracy of the best reconstruction, as well as the impact of such parameters on the localization performance. Best reconstructions often fall within 1 cm from the true source, with most accurate methods hitting an average localization error of 1.2 cm and outperforming least accurate ones erring by 2.5 cm. As expected, dipolar and sparsity-promoting methods tend to outperform distributed methods. For several distributed methods, the best regularization parameter turned out to be the one in principle associated with low SNR, despite the high SNR of the available dataset. Depth weighting played no role for two out of the six methods implementing it. Sensitivity to input parameters varied widely between methods. While one would expect high variability being associated with low localization error at the best solution, this is not always the case, with some methods producing highly variable results and high localization error, and other methods producing stable results with low localization error. In particular, recent dipolar and sparsity-promoting methods provide significantly better results than older distributed methods. As we repeated the tests with "conventional" (32 channels) and dense (64, 128, 256 channels) EEG recordings, we observed little impact of the number of channels on localization accuracy; however, for distributed methods denser montages provide smaller spatial dispersion. Overall findings confirm that EEG is a reliable technique for localization of point sources and therefore reinforce the importance that ESI may have in the clinical context, especially when applied to identify the surgical target in potential candidates for epilepsy surgery.

Sleep/wake changes in perturbational complexity in rats and mice

In humans, the level of consciousness is assessed by quantifying the spatiotemporal complexity of cortical responses using Perturbational Complexity Index (PCI) and related PCIst (st, state transitions). Here we validate PCIst in freely moving rats and mice by showing that it is lower in NREM sleep and slow wave anesthesia than in wake or REM sleep, as in humans. We then show that (1) low PCIst is associated with the occurrence of an OFF period of neuronal silence; (2) stimulation of deep, but not superficial, cortical layers leads to reliable PCIst changes across sleep/wake and anesthesia; (3) consistent PCIst changes are independent of which single area is being stimulated or recorded, except for recordings in mouse prefrontal cortex. These experiments show that PCIst can reliably measure vigilance states in unresponsive animals and support the hypothesis that it is low when an OFF period disrupts causal interactions in cortical networks.

Capillary dysfunction correlates with cortical amyloid load in early Alzheimer's disease

Alterations in cerebral perfusion is increasingly considered to play a crucial role in Alzheimer's disease (AD) and together with accumulated amyloid-β, deficiencies in the brain microvascular circulation may result in local hypoxia. Here, we studied alterations in cerebral circulation and the correlation between amyloid-β load and cerebral perfusion in prodromal AD (pAD). Using dynamic susceptibility contrast MRI and PET, we evaluated cerebral perfusion and amyloid-β levels in 19 individuals with mild cognitive impairment (MCI) and high amyloid-β load (pAD-MCI), 13 MCI individuals without AD pathology and 21 healthy controls. The pAD-MCI group showed significantly lower microvascular blood flow and significantly higher heterogeneity of microvascular blood transit times (p < 0.01) compared with the other 2 groups. Additionally, in the pAD-MCI group raised amyloid-β levels correlated with decreased microvascular blood flow and increased heterogeneity of microvascular blood flow in frontal and temporal areas (p < 0.01). These results indicate a close connection between levels of amyloid-β deposition and brain microvascular perfusion in pAD. A vicious cycle may be established where amyloid-β load and deficiencies in brain perfusion may reinforce each other.

Project, toolkit, and database of neuroinformatics ecosystem: A summary of previous studies on "Frontiers in Neuroinformatics"

In the field of neuroscience, the core of the cohort study project consists of collection, analysis, and sharing of multi-modal data. Recent years have witnessed a host of efficient and high-quality toolkits published and employed to improve the quality of multi-modal data in the cohort study. In turn, gleaning answers to relevant questions from such a conglomeration of studies is a time-consuming task for cohort researchers. As part of our efforts to tackle this problem, we propose a hierarchical neuroscience knowledge base that consists of projects/organizations, multi-modal databases, and toolkits, so as to facilitate researchers' answer searching process. We first classified studies conducted for the topic "Frontiers in Neuroinformatics" according to the multi-modal data life cycle, and from these studies, information objects as projects/organizations, multi-modal databases, and toolkits have been extracted. Then, we map these information objects into our proposed knowledge base framework. A Python-based query tool has also been developed in tandem for quicker access to the knowledge base, (accessible at https://github.com/Romantic-Pumpkin/PDT_fninf). Finally, based on the constructed knowledge base, we discussed some key research issues and underlying trends in different stages of the multi-modal data life cycle.

Transfreq: A Python package for computing the theta-to-alpha transition frequency from resting state electroencephalographic data

A classic approach to estimate individual theta-to-alpha transition frequency (TF) requires two electroencephalographic (EEG) recordings, one acquired in a resting state condition and one showing alpha desynchronisation due, for example, to task execution. This translates into long recording sessions that may be cumbersome in studies involving patients. Moreover, an incomplete desynchronisation of the alpha rhythm may compromise TF estimates. Here we present transfreq, a publicly available Python library that allows TF computation from resting state data by clustering the spectral profiles associated to the EEG channels based on their content in alpha and theta bands. A detailed overview of transfreq core algorithm and software architecture is provided. Its effectiveness and robustness across different experimental setups are demonstrated on a publicly available EEG data set and on in-house recordings, including scenarios where the classic approach fails to estimate TF. We conclude with a proof of concept of the predictive power of transfreq TF as a clinical marker. Specifically, we present a scenario where transfreq TF shows a stronger correlation with the mini mental state examination score than other widely used EEG features, including individual alpha peak and median/mean frequency. The documentation of transfreq and the codes for reproducing the analysis of the article with the open-source data set are available online at https://elisabettavallarino.github.io/transfreq/. Motivated by the results showed in this article, we believe our method will provide a robust tool for discovering markers of neurodegenerative diseases.

Group-level inference of information-based measures for the analyses of cognitive brain networks from neurophysiological data

The reproducibility crisis in neuroimaging and in particular in the case of underpowered studies has introduced doubts on our ability to reproduce, replicate and generalize findings. As a response, we have seen the emergence of suggested guidelines and principles for neuroscientists known as Good Scientific Practice for conducting more reliable research. Still, every study remains almost unique in its combination of analytical and statistical approaches. While it is understandable considering the diversity of designs and brain data recording, it also represents a striking point against reproducibility. Here, we propose a non-parametric permutation-based statistical framework, primarily designed for neurophysiological data, in order to perform group-level inferences on non-negative measures of information encompassing metrics from information-theory, machine-learning or measures of distances. The framework supports both fixed- and random-effect models to adapt to inter-individuals and inter-sessions variability. Using numerical simulations, we compared the accuracy in ground-truth retrieving of both group models, such as test- and cluster-wise corrections for multiple comparisons. We then reproduced and extended existing results using both spatially uniform MEG and non-uniform intracranial neurophysiological data. We showed how the framework can be used to extract stereotypical task- and behavior-related effects across the population covering scales from the local level of brain regions, inter-areal functional connectivity to measures summarizing network properties. We also present an open-source Python toolbox called Frites¹ that includes the proposed statistical pipeline using information-theoretic metrics such as single-trial functional connectivity estimations for the extraction of cognitive brain networks. Taken together, we believe that this framework deserves careful attention as its robustness and flexibility could be the starting point toward the uniformization of statistical approaches.

N-Tools-Browser: Web-Based Visualization of Electrocorticography Data for Epilepsy Surgery

Epilepsy affects more than three million people in the United States. In approximately one-third of this population, anti-seizure medications do not control seizures. Many patients pursue surgical treatment that can include a procedure involving the implantation of electrodes for intracranial monitoring of seizure activity. For these cases, accurate mapping of the implanted electrodes on a patient’s brain is crucial in planning the ultimate surgical treatment. Traditionally, electrode mapping results are presented in static figures that do not allow for dynamic interactions and visualizations. In collaboration with a clinical research team at a Level 4 Epilepsy Center, we developed N-Tools-Browser, a web-based software using WebGL and the X-Toolkit (XTK), to help clinicians interactively visualize the location and functional properties of implanted intracranial electrodes in 3D. Our software allows the user to visualize the seizure focus location accurately and simultaneously display functional characteristics (e.g., results from electrical stimulation mapping). Different visualization modes enable the analysis of multiple electrode groups or individual anatomical locations. We deployed a prototype of N-Tools-Browser for our collaborators at the New York University Grossman School of Medicine Comprehensive Epilepsy Center. Then, we evaluated its usefulness with domain experts on clinical cases.

SynCoPa: Visualizing Connectivity Paths and Synapses Over Detailed Morphologies

Brain complexity has traditionally fomented the division of neuroscience into somehow separated compartments; the coexistence of the anatomical, physiological, and connectomics points of view is just a paradigmatic example of this situation. However, there are times when it is important to combine some of these standpoints for getting a global picture, like for fully analyzing the morphological and topological features of a specific neuronal circuit. Within this framework, this article presents SynCoPa, a tool designed for bridging gaps among representations by providing techniques that allow combining detailed morphological neuron representations with the visualization of neuron interconnections at the synapse level. SynCoPa has been conceived for the interactive exploration and analysis of the connectivity elements and paths of simple to medium complexity neuronal circuits at the connectome level. This has been done by providing visual metaphors for synapses and interconnection paths, in combination with the representation of detailed neuron morphologies. SynCoPa could be helpful, for example, for establishing or confirming a hypothesis about the spatial distributions of synapses, or for answering questions about the way neurons establish connections or the relationships between connectivity and morphological features. Last, SynCoPa is easily extendable to include functional data provided, for example, by any of the morphologically-detailed simulators available nowadays, such as Neuron and Arbor, for providing a deep insight into the circuits features prior to simulating it, in particular any analysis where it is important to combine morphology, network topology, and physiology.

Capillary function progressively deteriorates in prodromal Alzheimer's disease: A longitudinal MRI perfusion study

Cardiovascular risk factors are associated with the development of Alzheimer's disease (AD), and increasing evidence suggests that cerebral microvascular dysfunction plays a vital role in the disease progression. Using magnetic resonance imaging, we investigated the two-year changes of the cerebral microvascular blood flow in 11 mild cognitively impaired (MCI) patients with prodromal AD compared to 12 MCI patients without evidence of AD and 10 cognitively intact age-matched controls. The pAD-MCI patients displayed widespread deterioration in microvascular cerebral perfusion associated with capillary dysfunction. No such changes were observed in the other two groups, suggesting that the dysfunction in capillary perfusion is linked to the AD pathophysiology. The observed capillary dysfunction may limit local oxygenation in AD leading to downstream β-amyloid aggregation, tau hyperphosphorylation, neuroinflammation and neuronal dysfunction. The findings are in agreement with the capillary dysfunction hypothesis of AD, suggesting that increasing heterogeneity of capillary blood flow is a primary pathological event in AD.

Altered Brain Criticality in Schizophrenia: New Insights From Magnetoencephalography

Schizophrenia has a complex etiology and symptomatology that is difficult to untangle. After decades of research, important advancements toward a central biomarker are still lacking. One of the missing pieces is a better understanding of how non-linear neural dynamics are altered in this patient population. In this study, the resting-state neuromagnetic signals of schizophrenia patients and healthy controls were analyzed in the framework of criticality. When biological systems like the brain are in a state of criticality, they are thought to be functioning at maximum efficiency (e.g., optimal communication and storage of information) and with maximum adaptability to incoming information. Here, we assessed the self-similarity and multifractality of resting-state brain signals recorded with magnetoencephalography in patients with schizophrenia patients and in matched controls. Schizophrenia patients had similar, although attenuated, patterns of self-similarity and multifractality values. Statistical tests showed that patients had higher values of self-similarity than controls in fronto-temporal regions, indicative of more regularity and memory in the signal. In contrast, patients had less multifractality than controls in the parietal and occipital regions, indicative of less diverse singularities and reduced variability in the signal. In addition, supervised machine-learning, based on logistic regression, successfully discriminated the two groups using measures of self-similarity and multifractality as features. Our results provide new insights into the baseline cognitive functioning of schizophrenia patients by identifying key alterations of criticality properties in their resting-state brain data.

NEST Desktop, an Educational Application for Neuroscience

Simulation software for spiking neuronal network models matured in the past decades regarding performance and flexibility. But the entry barrier remains high for students and early career scientists in computational neuroscience since these simulators typically require programming skills and a complex installation. Here, we describe an installation-free Graphical User Interface (GUI) running in the web browser, which is distinct from the simulation engine running anywhere, on the student's laptop or on a supercomputer. This architecture provides robustness against technological changes in the software stack and simplifies deployment for self-education and for teachers. Our new open-source tool, NEST Desktop, comprises graphical elements for creating and configuring network models, running simulations, and visualizing and analyzing the results. NEST Desktop allows students to explore important concepts in computational neuroscience without the need to learn a simulator control language before. Our experiences so far highlight that NEST Desktop helps advancing both quality and intensity of teaching in computational neuroscience in regular university courses. We view the availability of the tool on public resources like the European ICT infrastructure for neuroscience EBRAINS as a contribution to equal opportunities.

Mining the Mind: Linear Discriminant Analysis of MEG Source Reconstruction Time Series Supports Dynamic Changes in Deep Brain Regions During Meditation Sessions

Meditation practices have been claimed to have a positive effect on the regulation of mood and emotions for quite some time by practitioners, and in recent times there has been a sustained effort to provide a more precise description of the influence of meditation on the human brain. Longitudinal studies have reported morphological changes in cortical thickness and volume in selected brain regions due to meditation practice, which is interpreted as an evidence its effectiveness beyond the subjective self reporting. Using magnetoencephalography (MEG) or electroencephalography to quantify the changes in brain activity during meditation practice represents a challenge, as no clear hypothesis about the spatial or temporal pattern of such changes is available to date. In this article we consider MEG data collected during meditation sessions of experienced Buddhist monks practicing focused attention (Samatha) and open monitoring (Vipassana) meditation, contrasted by resting state with eyes closed. The MEG data are first mapped to time series of brain activity averaged over brain regions corresponding to a standard Destrieux brain atlas. Next, by bootstrapping and spectral analysis, the data are mapped to matrices representing random samples of power spectral densities in [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] frequency bands. We use linear discriminant analysis to demonstrate that the samples corresponding to different meditative or resting states contain enough fingerprints of the brain state to allow a separation between different states, and we identify the brain regions that appear to contribute to the separation. Our findings suggest that the cingulate cortex, insular cortex and some of the internal structures, most notably the accumbens, the caudate and the putamen nuclei, the thalamus and the amygdalae stand out as separating regions, which seems to correlate well with earlier findings based on longitudinal studies.

High-level cognition during story listening is reflected in high-order dynamic correlations in neural activity patterns

Our thoughts arise from coordinated patterns of interactions between brain structures that change with our ongoing experiences. High-order dynamic correlations in neural activity patterns reflect different subgraphs of the brain's functional connectome that display homologous lower-level dynamic correlations. Here we test the hypothesis that high-level cognition is reflected in high-order dynamic correlations in brain activity patterns. We develop an approach to estimating high-order dynamic correlations in timeseries data, and we apply the approach to neuroimaging data collected as human participants either listen to a ten-minute story or listen to a temporally scrambled version of the story. We train across-participant pattern classifiers to decode (in held-out data) when in the session each neural activity snapshot was collected. We find that classifiers trained to decode from high-order dynamic correlations yield the best performance on data collected as participants listened to the (unscrambled) story. By contrast, classifiers trained to decode data from scrambled versions of the story yielded the best performance when they were trained using first-order dynamic correlations or non-correlational activity patterns. We suggest that as our thoughts become more complex, they are reflected in higher-order patterns of dynamic network interactions throughout the brain.

Fiber Clustering Acceleration With a Modified Kmeans++ Algorithm Using Data Parallelism

Fiber clustering methods are typically used in brain research to study the organization of white matter bundles from large diffusion MRI tractography datasets. These methods enable exploratory bundle inspection using visualization and other methods that require identifying brain white matter structures in individuals or a population. Some applications, such as real-time visualization and inter-subject clustering, need fast and high-quality intra-subject clustering algorithms. This work proposes a parallel algorithm using a General Purpose Graphics Processing Unit (GPGPU) for fiber clustering based on the FFClust algorithm. The proposed GPGPU implementation exploits data parallelism using both multicore and GPU fine-grained parallelism present in commodity architectures, including current laptops and desktop computers. Our approach implements all FFClust steps in parallel, improving execution times in all of them. In addition, our parallel approach includes a parallel Kmeans++ algorithm implementation and defines a new variant of Kmeans++ to reduce the impact of choosing outliers as initial centroids. The results show that our approach provides clustering quality results very similar to FFClust, and it requires an execution time of 3.5 s for processing about a million fibers, achieving a speedup of 11.5 times compared to FFClust.

Sex differences in brain modular organization in chronic pain

ABSTRACT

Men and women can exhibit different pain sensitivities, and many chronic pain conditions are more prevalent in one sex. Although there is evidence of sex differences in the brain, it is not known whether there are sex differences in the organization of large-scale functional brain networks in chronic pain. Here, we used graph theory with modular analysis and machine-learning of resting-state-functional magnetic resonance imaging data from 220 participants: 155 healthy controls and 65 individuals with chronic low back pain due to ankylosing spondylitis, a form of arthritis. We found an extensive overlap in the graph partitions with the major brain intrinsic systems (ie, default mode, central, visual, and sensorimotor modules), but also sex-specific network topological characteristics in healthy people and those with chronic pain. People with chronic pain exhibited higher cross-network connectivity, and sex-specific nodal graph properties changes (ie, hub disruption), some of which were associated with the severity of the chronic pain condition. Females exhibited atypically higher functional segregation in the mid cingulate cortex and subgenual anterior cingulate cortex and lower connectivity in the network with the default mode and frontoparietal modules, whereas males exhibited stronger connectivity with the sensorimotor module. Classification models on nodal graph metrics could classify an individual's sex and whether they have chronic pain with high accuracies (77%-92%). These findings highlight the organizational abnormalities of resting-state-brain networks in people with chronic pain and provide a framework to consider sex-specific pain therapeutics.

Decoding Saccade Intention From Primate Prefrontal Cortical Local Field Potentials Using Spectral, Spatial, and Temporal Dimensionality Reduction

Most invasive Brain Computer Interfaces (iBCIs) use spike and Local Field Potentials (LFPs) from the motor or parietal cortices to decode movement intentions. It has been debated whether harvesting signals from other brain areas that encode global cognitive variables, such as the allocation of attention and eye movement goals in a variety of spatial reference frames, may improve the outcome of iBCIs. Here, we explore the ability of LFP signals, sampled from the lateral prefrontal cortex (LPFC) of macaque monkeys, to encode eye-movement intention during the pre-movement fixation period of a delayed saccade task. We use spectral dimensionality reduction to examine the spatiotemporal properties of the extracted non-rhythmic broadband activity and explore its usefulness in decoding saccade goals. The dynamics of the broadband signal in low spatial dimensions across the pre-movement fixation period uncovered saccade target separation; its discriminative potential was confirmed using support vector machine classifications. These findings reveal that broadband LFP from the LPFC can be used to decode intended saccade target location during pre-movement periods. We further provide a general workflow that can be implemented in iBCIs and it is relatively robust to the loss of spikes in individual electrodes.

Sleep parameters improvement in PTSD soldiers after symptoms remission

Eye movement desensitization and reprocessing (EMDR) is a psychotherapy for the treatment of posttraumatic stress disorder (PTSD). It is still unclear whether symptoms remission through EMDR therapy is associated with a beneficial effect on one of the PTSD symptoms, sleep disturbance. Our objective was therefore to study sleep parameters before and after symptom remission in soldiers with PTSD. The control group consisted of 20 healthy active duty military men who slept in a sleep lab with standard polysomnography (PSG) on two sessions separated by one month. The patient group consisted of 17 active duty military with PTSD who underwent EMDR therapy. PSG-recorded sleep was assessed 1 week before the EMDR therapy began and 1 week after PTSD remission. We found that the increased REMs density after remission was positively correlated with a greater decrease of symptoms. Also, the number of EMDR sessions required to reach remission was correlated with intra-sleep awakenings before treatment. These results confirm the improvement of some sleep parameters in PTSD after symptoms remission in a soldier's population and provide a possible predictor of treatment success. Further experiments will be required to establish whether this effect is specific to the EMDR therapy.

Decoding the neural dynamics of free choice in humans

How do we choose a particular action among equally valid alternatives? Nonhuman primate findings have shown that decision-making implicates modulations in unit firing rates and local field potentials (LFPs) across frontal and parietal cortices. Yet the electrophysiological brain mechanisms that underlie free choice in humans remain ill defined. Here, we address this question using rare intracerebral electroencephalography (EEG) recordings in surgical epilepsy patients performing a delayed oculomotor decision task. We find that the temporal dynamics of high-gamma (HG, 60-140 Hz) neural activity in distinct frontal and parietal brain areas robustly discriminate free choice from instructed saccade planning at the level of single trials. Classification analysis was applied to the LFP signals to isolate decision-related activity from sensory and motor planning processes. Compared with instructed saccades, free-choice trials exhibited delayed and longer-lasting HG activity during the delay period. The temporal dynamics of the decision-specific sustained HG activity indexed the unfolding of a deliberation process, rather than memory maintenance. Taken together, these findings provide the first direct electrophysiological evidence in humans for the role of sustained high-frequency neural activation in frontoparietal cortex in mediating the intrinsically driven process of freely choosing among competing behavioral alternatives.

The Modular Organization of Pain Brain Networks: An fMRI Graph Analysis Informed by Intracranial EEG

Intracranial EEG (iEEG) studies have suggested that the conscious perception of pain builds up from successive contributions of brain networks in less than 1 s. However, the functional organization of cortico-subcortical connections at the multisecond time scale, and its accordance with iEEG models, remains unknown. Here, we used graph theory with modular analysis of fMRI data from 60 healthy participants experiencing noxious heat stimuli, of whom 36 also received audio stimulation. Brain connectivity during pain was organized in four modules matching those identified through iEEG, namely: 1) sensorimotor (SM), 2) medial fronto-cingulo-parietal (default mode-like), 3) posterior parietal-latero-frontal (central executive-like), and 4) amygdalo-hippocampal (limbic). Intrinsic overlaps existed between the pain and audio conditions in high-order areas, but also pain-specific higher small-worldness and connectivity within the sensorimotor module. Neocortical modules were interrelated via “connector hubs” in dorsolateral frontal, posterior parietal, and anterior insular cortices, the antero-insular connector being most predominant during pain. These findings provide a mechanistic picture of the brain networks architecture and support fractal-like similarities between the micro-and macrotemporal dynamics associated with pain. The anterior insula appears to play an essential role in information integration, possibly by determining priorities for the processing of information and subsequent entrance into other points of the brain connectome.

Patient, interrupted: MEG oscillation dynamics reveal temporal dysconnectivity in schizophrenia

Current theories of schizophrenia emphasize the role of altered information integration as the core dysfunction of this illness. While ample neuroimaging evidence for such accounts comes from investigations of spatial connectivity, understanding temporal disruptions is important to fully capture the essence of dysconnectivity in schizophrenia. Recent electrophysiology studies suggest that long-range temporal correlation (LRTC) in the amplitude dynamics of neural oscillations captures the integrity of transferred information in the healthy brain. Thus, in this study, 25 schizophrenia patients and 25 controls (8 females/group) were recorded during two five-minutes of resting-state magnetoencephalography (once with eyes-open and once with eyes-closed). We used source-level analyses to investigate temporal dysconnectivity in patients by characterizing LRTCs across cortical and sub-cortical brain regions. In addition to standard statistical assessments, we applied a machine learning framework using support vector machine to evaluate the discriminative power of LRTCs in identifying patients from healthy controls. We found that neural oscillations in schizophrenia patients were characterized by reduced signal memory and higher variability across time, as evidenced by cortical and subcortical attenuations of LRTCs in the alpha and beta frequency bands. Support vector machine significantly classified participants using LRTCs in key limbic and paralimbic brain areas, with decoding accuracy reaching 82%. Importantly, these brain regions belong to networks that are highly relevant to the symptomology of schizophrenia. These findings thus posit temporal dysconnectivity as a hallmark of altered information processing in schizophrenia, and help advance our understanding of this pathology.

Tensorpac: An open-source Python toolbox for tensor-based phase-amplitude coupling measurement in electrophysiological brain signals

Despite being the focus of a thriving field of research, the biological mechanisms that underlie information integration in the brain are not yet fully understood. A theory that has gained a lot of traction in recent years suggests that multi-scale integration is regulated by a hierarchy of mutually interacting neural oscillations. In particular, there is accumulating evidence that phase-amplitude coupling (PAC), a specific form of cross-frequency interaction, plays a key role in numerous cognitive processes. Current research in the field is not only hampered by the absence of a gold standard for PAC analysis, but also by the computational costs of running exhaustive computations on large and high-dimensional electrophysiological brain signals. In addition, various signal properties and analyses parameters can lead to spurious PAC. Here, we present Tensorpac, an open-source Python toolbox dedicated to PAC analysis of neurophysiological data. The advantages of Tensorpac include (1) higher computational efficiency thanks to software design that combines tensor computations and parallel computing, (2) the implementation of all most widely used PAC methods in one package, (3) the statistical analysis of PAC measures, and (4) extended PAC visualization capabilities. Tensorpac is distributed under a BSD-3-Clause license and can be launched on any operating system (Linux, OSX and Windows). It can be installed directly via pip or downloaded from Github (https://github.com/EtienneCmb/tensorpac). By making Tensorpac available, we aim to enhance the reproducibility and quality of PAC research, and provide open tools that will accelerate future method development in neuroscience.

A Gaussian Process Model of Human Electrocorticographic Data

We present a model-based method for inferring full-brain neural activity at millimeter-scale spatial resolutions and millisecond-scale temporal resolutions using standard human intracranial recordings. Our approach makes the simplifying assumptions that different people's brains exhibit similar correlational structure, and that activity and correlation patterns vary smoothly over space. One can then ask, for an arbitrary individual's brain: given recordings from a limited set of locations in that individual's brain, along with the observed spatial correlations learned from other people's recordings, how much can be inferred about ongoing activity at other locations throughout that individual's brain? We show that our approach generalizes across people and tasks, thereby providing a person- and task-general means of inferring high spatiotemporal resolution full-brain neural dynamics from standard low-density intracranial recordings.

Predicting reading ability from brain anatomy and function: From areas to connections

Reading is a complex task involving different brain areas. As a crystallized ability, reading is also known to have effects on brain structure and function development. However, there are still open questions about what are the elements of the reading networks and how structural and functional brain measures shape the reading ability. The present study used a data-driven approach to investigate whether reading-related brain structural measures of cortical thickness, myelination, sulcus depth and structural connectivity and functional connectivity from the whole brain can predict individual differences in reading skills. It used different brain measures and performance scores from the Oral Reading Recognition Test (ORRT) measuring reading ability from 998 participants. We revealed reading-related brain areas and connections, and evaluated how well area and connection measures predict reading performance. Interestingly, the combination of all brain measures obtained the best predictions. We further grouped reading-related areas into positive and negative networks, each with four different levels (Core Regions, Extended-Regions 1, 2, 3), representing different correlation levels with the reading scores, and the non-correlated Region irrelevant to reading ability. The Core Regions are composed of areas that are most strongly correlated with reading performance. Insular and frontal opercular cortex, lateral temporal cortex, and early auditory cortex occupy the positive Core Region, while inferior temporal and motor cortex occupy the negative Core Region. Aside from those areas, the present study also found more reading-related areas including visual and language-related areas. In addition, connections predicting reading scores are denser inside the reading-related networks than outside. Together, the present study reveals extended reading networks of the brain and provides an extended data-driven analytical framework to study interpretable brain-behavior relationships, which are transferable also to studying other abilities.

Low plasma neurofilament light levels associated with raised cortical microglial activation suggest inflammation acts to protect prodromal Alzheimer's disease

BACKGROUND

Plasma and cerebrospinal fluid levels of neurofilament light (NfL), a marker of axonal degeneration, have previously been reported to be raised in patients with clinically diagnosed Alzheimer's disease (AD). Activated microglia, an intrinsic inflammatory response to brain lesions, are also known to be present in a majority of Alzheimer or mild cognitive impaired (MCI) subjects with raised β-amyloid load on their positron emission tomography (PET) imaging. It is now considered that the earliest phase of inflammation may be protective to the brain, removing amyloid plaques and remodelling synapses. Our aim was to determine whether the cortical inflammation/microglial activation load, measured with the translocator protein marker ¹¹C-PK11195 PET, was correlated with plasma NfL levels in prodromal and early Alzheimer subjects.

METHODS

Twenty-seven MCI or early AD cases with raised cortical β-amyloid load had ¹¹C-(R)-PK11195 PET, structural and diffusion magnetic resonance imaging, and levels of their plasma NfL measured. Correlation analyses were performed using surface-based cortical statistics.

RESULTS

Statistical maps localised areas in MCI cases where levels of brain inflammation correlated inversely with plasma NfL levels. These areas were localised in the frontal, parietal, precuneus, occipital, and sensorimotor cortices. Brain inflammation correlated negatively with mean diffusivity (MD) of water with regions overlapping.

CONCLUSION

We conclude that an inverse correlation between levels of inflammation in cortical areas and plasma NfL levels indicates that microglial activation may initially be protective to axons in AD. This is supported by the finding of an inverse association between cortical water diffusivity and microglial activation in the same regions. Our findings suggest a rationale for stimulating microglial activity in early and prodromal Alzheimer cases-possibly using immunotherapy. Plasma NfL levels could be used as a measure of the protective efficacy of immune stimulation and for monitoring efficacy of putative neuroprotective agents.