Emotion analysis and recognition in 3D space using classifier-dependent feature selection in response to tactile enhanced audio-visual content using EEG.

Traditional media such as text, images, audio, and video primarily target specific senses like vision and hearing. In contrast, multiple sensorial media aims to create immersive experiences by integrating additional sensory modalities such as touch, smell, and taste where applicable. Tactile enhanced audio-visual content leverages the sense of touch in addition to visual and auditory stimuli, aiming to create a more immersive and engaging interaction for users. Previously, tactile enhanced content has been explored in 2D emotional space (valence and arousal). In this paper, EEG data against tactile enhanced audio-visual content is labeled based on a self-assessment manikin scale in 3 dimensions i.e., valence, arousal, and dominance. Statistical significance (with a 95% confidence interval) is also established based on gathered scores, highlighting a significant difference in the arousal and dominance dimension of traditional media and tactile enhanced media. A new methodology is proposed using classifier-dependent feature selection approach to classify valence, arousal, and dominance states using three different classifiers. A highest accuracy of 75%, 73.8%, and 75% is achieved for classifying valence, arousal, and dominance states, respectively. The proposed scheme outperforms previous emotion recognition based studies in response to enhanced multimedia content in terms of accuracy, F-score, and other error parameters.

Prosthetic home intervention induces cortical plasticity in paediatrics with congenital limb reduction.

Paediatrics with congenital upper-limb reduction deficiency often face difficulties with normal development such as motor skills, needing assistance with daily activities such as self-care limitations with certain movements, sports, or activities. The purpose of this non-randomized longitudinal controlled trial was to assess, using intent-to-treat analysis, the effects of an 8-week home intervention of prosthetic use on the sensorimotor cortex in paediatrics with congenital upper-limb reduction deficiency. A paediatric population with congenital upper-limb reduction deficiency (n = 14) who were aged 6-18 years and who had a 20° or greater range of motion in the appropriate joint of the affected arm to move the body-powered prosthesis were enrolled. An age- and sex-matched control group (n = 14) was also enrolled. Participants were non-randomized and fitted with a custom low-cost 3D printed prosthesis and participated in 8 weeks of prosthetic use training at home. Control participants utilized a prosthetic simulator. The home intervention incorporated daily use training and exercises utilizing the prosthesis in direct use and assistive tasks explained by the researchers. After the home intervention, both groups displayed significant improvements in gross manual dexterity. During prosthetic use with the affected limb, significant increases in oxygenated hemodynamic responses were only displayed in the left premotor cortex of the upper-limb reduction deficiency group. The novel findings of this non-randomized longitudinal controlled trial suggest that the intervention may have improved the functional role of the left hemisphere which translated to the improvement of learning direction during adaptation to visuomotor control. The prosthetic home intervention was assumed to provide closed-loop training which could provide a direct benefit to the motor development of paediatrics with upper-limb reduction deficiency.

Correlating maternal and cord-blood inflammatory markers and BDNF with human fetal brain activity recorded by magnetoencephalography: An exploratory study.

Background: During gestation, the brain development of the fetus is affected by many biological markers, where inflammatory processes and neurotrophic factors have been of particular interest in the past decade. Aim: This exploratory study is the first attempt to explore the relationships between biomarker levels in maternal and cord-blood samples and human fetal brain activity measured with non-invasive fetal magnetoencephalography (fMEG). Method: Twenty-three women were enrolled in this study for collection of maternal serum and fMEG tracings immediately prior to their scheduled cesarean delivery. Twelve of these women had a preexisting diabetic condition. At the time of delivery, umbilical cord blood was also collected. Biomarker levels from both maternal and cord blood were measured and subsequently analyzed for correlations with fetal brain activity in four frequency bands extracted from fMEG power spectral densities. Results: Relative power in the delta, alpha, and beta frequency bands exhibited moderate-sized correlations with maternal BDNF and cord-blood CRP levels before and after adjusting for confounding diabetic status. These correlations were negative for the delta band, and positive for the alpha and beta bands. Maternal CRP and cord-blood BDNF and IL-6 exhibited negligible correlations with relative power in all four bands. Diabetes did not appear to be a strong confounding factor affecting the studied biomarkers. Conclusions: Maternal BDNF levels and cord-blood CRP levels appear to have a direct correlation to fetal brain activity. Our findings indicate the potential use of these biomarkers in conjunction with fetal brain electrophysiology to track fetal neurodevelopment.

The effect of physical exercise during competitions and in simulated conditions on hormonal-neurophysiological relationships in kickboxers.

K1-format kickboxing is a widely followed combat sport that requires intense physical exercise. However, research into the body's response to this type of combat is sparse. This study aims to assess the alterations in hormone levels and brain activity in elite kickboxers following an actual K1 bout and compare these changes with those observed in a control group engaged in a simulated fight exercise with a punchbag. The study included 100 male professional kickboxers, randomly divided into two groups: an experimental group (K1 fight) and a control group (simulated fight with a punchbag). Blood samples were obtained before and after exercise to evaluate testosterone (T) and cortisol concentrations (C). Concurrently, brain activity was recorded using quantitative electroencephalography (QEEG). After the activity in the experimental group mean testosterone level slightly, non-significantly decreased from 13.7 nmol/l to 12.4 nmol/l, while mean cortisol significantly (p < 0.001) increased from 313 to 570 nmol/l. In the control group after the exertion against a punchbag mean cortisol significantly (p < 0.001) increased from 334 to 452 nmol/l and testosterone increased non-significantly, from 15.1 to 16.3 nmol/l. In both groups, the testosterone/cortisol ratio (T/C ratio) showed significantly lower levels after the intervention (p < 0.001 and p < 0.032) in the experimental and control group respectively. The comparison of groups after exercise revealed significantly higher cortisol levels (experimental group x = 570 nmol/l; control group x = 452 nmol/l) and a significantly lower T/C ratio (experimental group x = 2.7; control group x = 3.9), (p = 0.001) in the experimental group. Significantly higher brain activity was found in selected leads after a bout (experimental group). Furthermore, in the experimental group, significant associations of weak to moderate strength were found between hormone fluctuations and selected areas of brain activity (p < 0.05). K1-format kickboxing induces a stress response, evident in the sharp changes in cortisol and testosterone levels. A notable observation was the inverse direction of changes in both hormones. Brain activity analysis indicated the potential influence of raised cortisol concentrations on specific brain areas. This study augments our understanding of the physiological responses during K1 kickboxing bouts and may inform the future evolution of this sport.

EEG/MEG source imaging of deep brain activity within the maximum entropy on the mean framework: Simulations and validation in epilepsy.

Electro/Magneto-EncephaloGraphy (EEG/MEG) source imaging (EMSI) of epileptic activity from deep generators is often challenging due to the higher sensitivity of EEG/MEG to superficial regions and to the spatial configuration of subcortical structures. We previously demonstrated the ability of the coherent Maximum Entropy on the Mean (cMEM) method to accurately localize the superficial cortical generators and their spatial extent. Here, we propose a depth-weighted adaptation of cMEM to localize deep generators more accurately. These methods were evaluated using realistic MEG/high-density EEG (HD-EEG) simulations of epileptic activity and actual MEG/HD-EEG recordings from patients with focal epilepsy. We incorporated depth-weighting within the MEM framework to compensate for its preference for superficial generators. We also included a mesh of both hippocampi, as an additional deep structure in the source model. We generated 5400 realistic simulations of interictal epileptic discharges for MEG and HD-EEG involving a wide range of spatial extents and signal-to-noise ratio (SNR) levels, before investigating EMSI on clinical HD-EEG in 16 patients and MEG in 14 patients. Clinical interictal epileptic discharges were marked by visual inspection. We applied three EMSI methods: cMEM, depth-weighted cMEM and depth-weighted minimum norm estimate (MNE). The ground truth was defined as the true simulated generator or as a drawn region based on clinical information available for patients. For deep sources, depth-weighted cMEM improved the localization when compared to cMEM and depth-weighted MNE, whereas depth-weighted cMEM did not deteriorate localization accuracy for superficial regions. For patients' data, we observed improvement in localization for deep sources, especially for the patients with mesial temporal epilepsy, for which cMEM failed to reconstruct the initial generator in the hippocampus. Depth weighting was more crucial for MEG (gradiometers) than for HD-EEG. Similar findings were found when considering depth weighting for the wavelet extension of MEM. In conclusion, depth-weighted cMEM improved the localization of deep sources without or with minimal deterioration of the localization of the superficial sources. This was demonstrated using extensive simulations with MEG and HD-EEG and clinical MEG and HD-EEG for epilepsy patients.

Trial Analysis of Brain Activity Information for the Presymptomatic Disease Detection of Rheumatoid Arthritis.

This study presents a trial analysis that uses brain activity information obtained from mice to detect rheumatoid arthritis (RA) in its presymptomatic stages. Specifically, we confirmed that F759 mice, serving as a mouse model of RA that is dependent on the inflammatory cytokine IL-6, and healthy wild-type mice can be classified on the basis of brain activity information. We clarified which brain regions are useful for the presymptomatic detection of RA. We introduced a matrix completion-based approach to handle missing brain activity information to perform the aforementioned analysis. In addition, we implemented a canonical correlation-based method capable of analyzing the relationship between various types of brain activity information. This method allowed us to accurately classify F759 and wild-type mice, thereby identifying essential features, including crucial brain regions, for the presymptomatic detection of RA. Our experiment obtained brain activity information from 15 F759 and 10 wild-type mice and analyzed the acquired data. By employing four types of classifiers, our experimental results show that the thalamus and periaqueductal gray are effective for the classification task. Furthermore, we confirmed that classification performance was maximized when seven brain regions were used, excluding the electromyogram and nucleus accumbens.

Disrupted default mode network connectivity in bipolar disorder: a resting-state fMRI study.

BACKGROUND: Theoretical and empirical evidence indicates the critical role of the default mode network (DMN) in the pathophysiology of the bipolar disorder (BD). This study aims to identify the specific brain regions of the DMN that is impaired in patients with BD. METHODS: A total of 56 patients with BD and 71 healthy controls (HC) underwent resting-state functional magnetic resonance imaging. Three commonly used functional indices, i.e., fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), and degree centrality (DC), were utilized to identify the brain region showing abnormal spontaneous brain activity in patients with BD. Then, this region served as the seed region for resting-state functional connectivity (rsFC) analysis. RESULTS: Compared to the HC group, the BD group showed reduced fALFF, ReHo, and DC values in the left precuneus. Moreover, patients exhibited decreased rsFCs within the left precuneus and between the left precuneus and the medial prefrontal cortex. Additionally, there was diminished negative connectivity between the left precuneus and the left putamen, extending to the left insula (putamen/insula). The abnormalities in DMN functional connectivity were confirmed through various analysis strategies. CONCLUSIONS: Our findings provide convergent evidence for the abnormalities in the DMN, particularly located in the left precuneus. Decreased functional connectivity within the DMN and the reduced anticorrelation between the DMN and the salience network are found in patients with BD. These findings suggest that the DMN is a key aspect for understanding the neural basis of BD, and the altered functional patterns of DMN may be a potential candidate biomarker for diagnosis of BD.

Large-scale analysis of neural activity and connectivity from high-density electroencephalographic data.

INTRODUCTION: High-density electroencephalography (hdEEG) is a technique used for the characterization of the neural activity and connectivity in the human brain. The analysis of EEG data involves several steps, including signal pre-processing, head modelling, source localization and activity/connectivity quantification. Visual check of the analysis steps is often necessary, making the process time- and resource-consuming and, therefore, not feasible for large datasets. FINDINGS: Here we present the Noninvasive Electrophysiology Toolbox (NET), an open-source software for large-scale analysis of hdEEG data, running on the cross-platform MATLAB environment. NET combines all the tools required for a complete hdEEG analysis workflow, from raw signals to final measured values. By relying on reconstructed neural signals in the brain, NET can perform traditional analyses of time-locked neural responses, as well as more advanced functional connectivity and brain mapping analyses. The extracted quantitative neural data can be exported to provide broad compatibility with other software. CONCLUSIONS: NET is freely available (https://github.com/bind-group-kul/net) under the GNU public license for non-commercial use and open-source development, together with a graphical user interface (GUI) and a user tutorial. While NET can be used interactively with the GUI, it is primarily aimed at unsupervised automation to process large hdEEG datasets efficiently. Its implementation creates indeed a highly customizable program suitable for analysis automation and tight integration into existing workflows.

The neurophysiological brain-fingerprint of Parkinson's disease.

BACKGROUND: Research in healthy young adults shows that characteristic patterns of brain activity define individual "brain-fingerprints" that are unique to each person. However, variability in these brain-fingerprints increases in individuals with neurological conditions, challenging the clinical relevance and potential impact of the approach. Our study shows that brain-fingerprints derived from neurophysiological brain activity are associated with pathophysiological and clinical traits of individual patients with Parkinson's disease (PD). METHODS: We created brain-fingerprints from task-free brain activity recorded through magnetoencephalography in 79 PD patients and compared them with those from two independent samples of age-matched healthy controls (N = 424 total). We decomposed brain activity into arrhythmic and rhythmic components, defining distinct brain-fingerprints for each type from recording durations of up to 4 min and as short as 30 s. FINDINGS: The arrhythmic spectral components of cortical activity in patients with Parkinson's disease are more variable over short periods, challenging the definition of a reliable brain-fingerprint. However, by isolating the rhythmic components of cortical activity, we derived brain-fingerprints that distinguished between patients and healthy controls with about 90% accuracy. The most prominent cortical features of the resulting Parkinson's brain-fingerprint are mapped to polyrhythmic activity in unimodal sensorimotor regions. Leveraging these features, we also demonstrate that Parkinson's symptom laterality can be decoded directly from cortical neurophysiological activity. Furthermore, our study reveals that the cortical topography of the Parkinson's brain-fingerprint aligns with that of neurotransmitter systems affected by the disease's pathophysiology. INTERPRETATION: The increased moment-to-moment variability of arrhythmic brain-fingerprints challenges patient differentiation and explains previously published results. We outline patient-specific rhythmic brain signaling features that provide insights into both the neurophysiological signature and symptom laterality of Parkinson's disease. Thus, the proposed definition of a rhythmic brain-fingerprint of Parkinson's disease may contribute to novel, refined approaches to patient stratification. Symmetrically, we discuss how rhythmic brain-fingerprints may contribute to the improved identification and testing of therapeutic neurostimulation targets. FUNDING: Data collection and sharing for this project was provided by the Quebec Parkinson Network (QPN), the Pre-symptomatic Evaluation of Novel or Experimental Treatments for Alzheimer's Disease (PREVENT-AD; release 6.0) program, the Cambridge Centre for Aging Neuroscience (Cam-CAN), and the Open MEG Archives (OMEGA). The QPN is funded by a grant from Fonds de Recherche du Québec - Santé (FRQS). PREVENT-AD was launched in 2011 as a $13.5 million, 7-year public-private partnership using funds provided by McGill University, the FRQS, an unrestricted research grant from Pfizer Canada, the Levesque Foundation, the Douglas Hospital Research Centre and Foundation, the Government of Canada, and the Canada Fund for Innovation. The Brainstorm project is supported by funding to SB from the NIH (R01-EB026299-05). Further funding to SB for this study included a Discovery grant from the Natural Sciences and Engineering Research Council of Canada of Canada (436355-13), and the CIHR Canada research Chair in Neural Dynamics of Brain Systems (CRC-2017-00311).

Sisterhood predicts similar neural processing of a film.

Relationships between humans are essential for how we see the world. Using fMRI, we explored the neural basis of homophily, a sociological concept that describes the tendency to bond with similar others. Our comparison of brain activity between sisters, friends and acquaintances while they watched a movie, indicate that sisters' brain activity is more similar than that of friends and friends' activity is more similar than that of acquaintances. The increased similarity in brain activity measured as inter-subject correlation (ISC) was found both in higher-order brain areas including the default-mode network (DMN) and sensory areas. Increased ISC could not be explained by genetic relation between sisters neither by similarities in eye-movements, emotional experiences, and physiological activity. Our findings shed light on the neural basis of homophily by revealing that similarity in brain activity in the DMN and sensory areas is the stronger the closer is the relationship between the people.

Prenatal family income, but not parental education, is associated with resting brain activity in 1-month-old infants.

Childhood socioeconomic disadvantage is associated with disparities in development and health, possibly through adaptations in children's brain function. However, it is not clear how early in development such neural adaptations might emerge. This study examined whether prenatal family socioeconomic status, operationalized as family income and average years of parental education, prospectively predicts individual differences in infant resting electroencephalography (EEG; theta, alpha, beta, and gamma power) at approximately 1 month of age (N = 160). Infants of mothers reporting lower family income showed more lower-frequency (theta) and less higher-frequency (beta and gamma) power. These associations held when adjusting for other prenatal and postnatal experiences, as well as infant demographic and health-related factors. In contrast, parental education was not significantly associated with infant EEG power in any frequency band. These data suggest that lower prenatal family income is associated with developmental differences in brain function that are detectable within the first month of life.

Shared genetics linking sociability with the brain's default mode network.

The brain's default mode network (DMN) plays a role in social cognition, with altered DMN function being associated with social impairments across various neuropsychiatric disorders. In the present study, we examined the genetic relationship between sociability and DMN-related resting-state functional magnetic resonance imaging (rs-fMRI) traits. To this end, we used genome-wide association summary statistics for sociability and 31 activity and 64 connectivity DMN-related rs-fMRI traits (N=34,691-342,461). First, we examined global and local genetic correlations between sociability and the rs-fMRI traits. Second, to assess putatively causal relationships between the traits, we conducted bi-directional Mendelian randomisation (MR) analyses. Finally, we prioritised genes influencing both sociability and rs-fMRI traits by combining three methods: gene-expression eQTL MR analyses, the CELLECT framework using single-nucleus RNA-seq data, and network propagation in the context of a protein-protein interaction network. Significant local genetic correlations were found between sociability and two rs-fMRI traits, one representing spontaneous activity within the temporal cortex, the other representing connectivity between the frontal/cingulate and angular/temporal cortices. Sociability affected 12 rs-fMRI traits when allowing for weakly correlated genetic instruments. Combing all three methods for gene prioritisation, we defined 17 highly prioritised genes, with DRD2 and LINGO1 showing the most robust evidence across all analyses. By integrating genetic and transcriptomics data, our gene prioritisation strategy may serve as a blueprint for future studies. The prioritised genes could be explored as potential biomarkers for social dysfunction in the context of neuropsychiatric disorders and as drug target genes.

Prefrontal brain activity and self-injurious behavior in adolescents with major depressive disorder: A functional near-infrared spectroscopy (fNIRS) study.

In clinical practice, accurately identifying self-injurious behavior among adolescents with major depressive disorder (MDD) is crucial for individualized treatment. This study aimed to examine the differences in prefrontal cortex activation using the functional near-infrared spectroscopy (fNIRS) during the verbal fluency task (VFT) assessment of adolescents with MDD and self-harm (SH) compared with those without SH. A total of 60 eligible patients were included for final analysis, with the SH group containing 36 participants, and the Non-SH group containing 24 participants. We found that right middle frontal gyrus (rMFG) was more activated in the SH group than that in the Non-SH group during the VFT assessments (z = -3.591, p = 0.004, FDR correction). The z-scores of beta values of rMFG exhibited a good discriminatory power with the area under the curve (AUC) in distinguishing the two groups (AUC = 0.775, p < 0.001). These findings reveal that the fNIRS-VFT paradigm may be a useful tool for discovering neurobiological differences among adolescents with MDD.

Abnormalities in spontaneous brain activity and functional connectivity are associated with cognitive impairments in children with type 1 diabetes mellitus.

BACKGROUND: It remains unclear whether alterations in brain function occur in the early stage of pediatric type 1 diabetes mellitus(T1DM). We aimed to examine changes in spontaneous brain activity and functional connectivity (FC) in children with T1DM using resting-state functional magnetic resonance imaging (rs-fMRI), and to pinpoint potential links between neural changes and cognitive performance. METHODS: In this study, 22 T1DM children and 21 age-, sex-matched healthy controls underwent rs-fMRI. The amplitude of low frequency fluctuations (ALFF) and seed-based FC analysis were performed to examine changes in intrinsic brain activity and functional networks in T1DM children. Partial correlation analyses were utilized to explore the correlations between ALFF values and clinical parameters. RESULTS: The ALFF values were significantly lower in the lingual gyrus (LG) and higher in the left medial superior frontal gyrus (MSFG) in T1DM children compared to controls. Subsequent FC analysis indicated that the LG had decreased FC with bilateral inferior occipital gyrus, and the left MSFG had decreased FC with right precentral gyrus, right inferior parietal gyrus and right postcentral gyrus in children with T1DM. The ALFF values of LG were positively correlated with full-scale intelligence quotient and age at disease onset in T1DM children, while the ALFF values of left MSFG were positively correlated with working memory scores. CONCLUSION: Our findings revealed abnormal spontaneous activity and FC in brain regions related to visual, memory, default mode network, and sensorimotor network in the early stage of T1DM children, which may aid in further understanding the mechanisms underlying T1DM-associated cognitive dysfunction.

Neuronal activity induces symmetry breaking in neurodegenerative disease spreading.

Dynamical systems on networks typically involve several dynamical processes evolving at different timescales. For instance, in Alzheimer's disease, the spread of toxic protein throughout the brain not only disrupts neuronal activity but is also influenced by neuronal activity itself, establishing a feedback loop between the fast neuronal activity and the slow protein spreading. Motivated by the case of Alzheimer's disease, we study the multiple-timescale dynamics of a heterodimer spreading process on an adaptive network of Kuramoto oscillators. Using a minimal two-node model, we establish that heterogeneous oscillatory activity facilitates toxic outbreaks and induces symmetry breaking in the spreading patterns. We then extend the model formulation to larger networks and perform numerical simulations of the slow-fast dynamics on common network motifs and on the brain connectome. The simulations corroborate the findings from the minimal model, underscoring the significance of multiple-timescale dynamics in the modeling of neurodegenerative diseases.

Substance use disorders are characterised by increased voxel-wise intrinsic measures in sensorimotor cortices: An ALE meta-analysis.

Substance use disorders (SUDs) are severe psychiatric illnesses. Seed region and independent component analyses are currently the dominant connectivity measures but carry the risk of false negatives due to selection. They can be complemented by a data-driven and whole-brain usage of voxel-wise intrinsic measures (VIMs). We meta-analytically integrated VIMs, namely regional homogeneity (ReHo), amplitude of low-frequency fluctuations (ALFF), voxel-mirrored homotopy connectivity (VMHC) and degree centrality (DC) across different SUDs using the Activation Likelihood Estimation (ALE) algorithm, functionally decoded emerging clusters, and analysed their connectivity profiles. Our systematic search identified 51 studies including 1439 SUD participants. Although no overall convergent pattern of alterations across VIMs in SUDs was found, sensitivity analyses demonstrated two ALE-derived clusters of increased ReHo and ALFF in SUDs, which peaked in the left pre- and postcentral cortices. Subsequent analyses showed their involvement in action execution, somesthesis, finger tapping and vibrotactile monitoring/discrimination. Their numerous clinical correlates across included studies highlight the under-discussed role of sensorimotor cortices in SUD, urging a more attentive exploration of their clinical significance.

Shades of gravity - effects of planetary gravity levels on electrocortical activity and neurocognitive performance.

The plans of international space agencies to return to the Moon and explore deep space, including Mars, highlight the challenges of human adaptation and stress the need for a thorough analysis of the factors that facilitate, limit and modify human performance under extreme environments. This study investigates the influence of partial gravity on behavioural (error rate and reaction time) and neuronal parameters (event-related potentials) through parabolic flights. Brain cortical activity was assessed using EEG from 18 participants who solved a neurocognitive task, consisting of a mental arithmetic task and an auditory oddball paradigm, during Earth (1G), Lunar (0.16G + 0.25G) and Martian gravity (0.38G + 0.5G) for 15 consecutive parabolas. Data shows higher electrocortical activity in Earth gravity compared to Lunar and Martian gravity in the parietal lobe. No differences in participants' performance were found among the gravity levels. Event-related potentials displayed gravity-dependent variations, though limited stimuli recording suggests caution in interpretation. Data suggests a threshold between Earth and Martian gravity within the different gravities responsible for physiological changes, but it seems to vary greatly between individuals. The altered neuronal communication could be explained with a model developed by Kohn and Ritzmann in 2018. The increasing intracranial pressure in weightlessness changes the properties of the cell membrane of neurons and leads to a depolarisation of the resting membrane potential. The findings underscore the individuality of physiological changes in response to gravity alterations, signalling the need for further investigations in future studies.

Vertebrates on a Chip: Noninvasive Electrical and Optical Mapping of Whole Brain Activity Associated with Pharmacological Treatments.

Mapping brain activities is necessary for understanding brain physiology and discovering new treatments for neurological disorders. Such efforts have greatly benefited from the advancement in technologies for analyzing neural activity with improving temporal or spatial resolution. Here, we constructed a multielectrode array based brain activity mapping (BAM) system capable of stabilizing and orienting zebrafish larvae for recording electroencephalogram (EEG) like local field potential (LFP) signals and brain-wide calcium dynamics in awake zebrafish. Particularly, we designed a zebrafish trap chip that integrates with an eight-by-eight surface electrode array, so that brain electrophysiology can be noninvasively recorded in an agarose-free and anesthetic-free format with a high temporal resolution of 40 µs, matching the capability typically achieved by invasive LFP recording. Benefiting from the specially designed hybrid system, we can also conduct calcium imaging directly on immobilized awake larval zebrafish, which further supplies us with high spatial resolution brain-wide activity data. All of these innovations reconcile the limitations of sole LFP recording or calcium imaging, emphasizing a synergy of combining electrical and optical modalities within one unified device for activity mapping across a whole vertebrate brain with both improved spatial and temporal resolutions. The compatibility with in vivo drug treatment further makes it suitable for pharmacology studies based on multimodal measurement of brain-wide physiology.

Aberrant concordance among dynamics of spontaneous brain activity in patients with migraine without aura: A multivariate pattern analysis study.

Background: Alterations in the static and dynamic characteristics of spontaneous brain activity have been extensively studied to investigate functional brain changes in migraine without aura (MwoA). However, alterations in concordance among the dynamics of spontaneous brain activity in MwoA remain largely unknown. This study aimed to determine the possibilities of diagnosis based on the concordance indices. Methods: Resting-state functional MRI scans were performed on 32 patients with MwoA and 33 matched healthy controls (HCs) in the first cohort, as well as 36 patients with MwoA and 32 HCs in the validation cohort. The dynamic indices including fractional amplitude of low-frequency fluctuation, regional homogeneity, voxel-mirrored homotopic connectivity, degree centrality and global signal connectivity were analyzed. We calculated the concordance of grey matter volume-wise (across voxels) and voxel-wise (across time windows) to quantify the degree of integration among different functional levels represented by these dynamic indices. Subsequently, the voxel-wise concordance alterations were analyzed as features for multi-voxel pattern analysis (MVPA) utilizing the support vector machine. Results: Compared with that of HCs, patients with MwoA had lower whole-grey matter volume-wise concordance, and the mean value of volume-wise concordance was negatively correlated with the frequency of migraine attacks. The MVPA results revealed that the most discriminative brain regions were the right thalamus, right cerebellar Crus II, left insula, left precentral gyrus, right cuneus, and left inferior occipital gyrus. Conclusions: Concordance alterations in the dynamics of spontaneous brain activity in brain regions could be an important feature in the identification of patients with MwoA.