Brain Dynamics Research Articles

The Effect of Left Temporal EEG Neurofeedback Training on Cerebral Cortical Activity and Precision Cognitive-Motor Performance

ABSTRACT Objectives: The present study employed individualized magnitudes of electroencephalographic (EEG) alpha (8–12 hz) power in the left temporal (T3) region as a neurofeedback target parameter during the aiming period in pre-elite air pistol shooters to determine its effectiveness on cerebral cortical activation and performance accuracy compared to physical skill training, only. Method: Shooting scores and EEG activity in the left and right temporal regions were collected from 20 healthy air pistol shooters (10 assigned to neurofeedback training) before and after a 16-session intervention completed within 6 weeks. Specifically, EEG low-alpha (8–10 hz), high-alpha (10–13 hz) power, and coherence obtained at the T3-Fz and T4-Fz recording sites over three consecutive 1-s intervals prior to trigger pull, were subjected to three separate 2 × 2 × 2 × 3 (Group x Hemisphere x Time x Epoch) ANOVAs. Results: The neurofeedback group exhibited elevated low- and high-alpha EEG power across both temporal regions, but no differences in EEG broad-band alpha coherence, accompanied by enhanced performance after the intervention compared to the control group. Conclusions: The findings support the influence of neurofeedback training on cerebral cortical arousal and performance of a precision-aiming task, however, the influence of the neurofeedback on brain dynamics (i.e. alpha power) extended beyond the targeted region as a nonspecific manifestation of cerebral cortical inhibition leading to neural efficiency at the homologous sites.

Dynamic mitophagy trajectories hallmark brain aging.

Studies using mitophagy reporter mice have established steady-state landscapes of mitochondrial destruction in mammalian tissues, sparking intense interest in basal mitophagy. Yet how basal mitophagy is modified by healthy aging in diverse brain cell types has remained a mystery. We present a comprehensive spatiotemporal analysis of mitophagy and macroautophagy dynamics in the aging mammalian brain, reporting critical region- and cell-specific turnover trajectories in a longitudinal study. We demonstrate that the physiological regulation of mitophagy in the mammalian brain is cell-specific, dynamic and complex. Mitophagy increases significantly in the cerebellum and hippocampus during midlife, while remaining unchanged in the prefrontal cortex (PFC). Conversely, macroautophagy decreases in the hippocampus and PFC, but remains stable in the cerebellum. We also describe emergent lysosomal heterogeneity, with subsets of differential acidified lysosomes accumulating in the aging brain. We further establish midlife as a critical inflection point for autophagy regulation, which may be important for region-specific vulnerability and resilience to aging. By mapping in vivo autophagy dynamics at the single cell level within projection neurons, interneurons and microglia, to astrocytes and secretory cells, we provide a new framework for understanding brain aging and offer potential targets and timepoints for further study and intervention in neurodegenerative diseases.

Connectome-based prediction of functional impairment in experimental stroke models

Experimental rat models of stroke and hemorrhage are important tools to investigate cerebrovascular disease pathophysiology mechanisms, yet how significant patterns of functional impairment induced in various models of stroke are related to changes in connectivity at the level of neuronal populations and mesoscopic parcellations of rat brains remain unresolved. To address this gap in knowledge, we employed two middle cerebral artery occlusion models and one intracerebral hemorrhage model with variant extent and location of neuronal dysfunction. Motor and spatial memory function was assessed and the level of hippocampal activation via Fos immunohistochemistry. Contribution of connectivity change to functional impairment was analyzed for connection similarities, graph distances and spatial distances as well as the importance of regions in terms of network architecture based on the neuroVIISAS rat connectome. We found that functional impairment correlated with not only the extent but also the locations of the injury among the models. In addition, via coactivation analysis in dynamic rat brain models, we found that lesioned regions led to stronger coactivations with motor function and spatial learning regions than with other unaffected regions of the connectome. Dynamic modeling with the weighted bilateral connectome detected changes in signal propagation in the remote hippocampus in all 3 stroke types, predicting the extent of hippocampal hypoactivation and impairment in spatial learning and memory function. Our study provides a comprehensive analytical framework in predictive identification of remote regions not directly altered by stroke events and their functional implication.

Abnormal characteristics in disorders of consciousness: A resting-state functional magnetic resonance imaging study.

To explore the functional brain imaging characteristics of patients with disorders of consciousness (DoC). This prospective cohort study consecutively enrolled 27 patients in minimally conscious state (MCS), 23 in vegetative state (VS), and 25 age-matched healthy controls (HC). Resting-state functional magnetic resonance imaging (rs-fMRI) was employed to evaluate the amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), degree centrality (DC), and functional connectivity (FC). Sliding windows approach was conducted to construct dynamic FC (dFC) matrices. Moreover, receiver operating characteristic analysis and Pearson correlation were used to distinguish these altered characteristics in DoC. Both MCS and VS exhibited lower ALFF, ReHo, and DC values, along with reduced FC in multiple brain regions compared with HC. Furthermore, the values in certain regions of VS were lower than those in MCS. The primary differences in brain function between patients with varying levels of consciousness were evident in the cortico-striatopallidal-thalamo-cortical mesocircuit. Significant differences in the temporal properties of dFC (including frequency, mean dwell time, number of transitions, and transition probability) were also noted among the three groups. Moreover, these multimodal alterations demonstrated high classificatory accuracy (AUC>0.8) and were correlated with the Coma Recovery Scale-Revised (CRS-R). Patients with DoC displayed abnormal patterns in local and global dynamic and static brain functions. These alterations in rs-fMRI were closely related to the level of consciousness.

Heterogeneous Brain Dynamics Between Acute Cerebellar and Brainstem Infarction

To evaluate the alterations in brain dynamics in patients suffering from brainstem or cerebellar infarctions and their potential associations with cognitive function. In this study, 37 patients were recruited who had acute cerebellar infarction (CI), 32 patients who had acute brainstem infarction (BsI), and 40 healthy controls (HC). Every participant had their resting-state electroencephalogram (EEG) data captured, and the EEG microstates were analyzed. The cognitive function was measured by the Neuropsychological Cognitive Scale including the Mini-Mental State Examination (MMSE), the Montreal Cognitive Assessment (MoCA), the Boston Naming Test (BNT), the Digit Span Test (Digitspan), and the Symbol Digit Modalities Test (SDMT). Compared with the HC group, the transition probabilities from Microstate A(MsA) and MsD to MsC significantly decreased while the transition probabilities from MsA to MsD and from MsD to MsB significantly increased in the BsI group. By contrast, the CI group showed a significant increase in transition probabilities from MsA and MsD to MsC, whereas the transitions from MsD to MsB significantly decreased. Subgroup analysis within the CI group demonstrated that the CI patients with dizziness showed increased coverage and duration in MsB but decreased MsD occurrence than those of CI patients with vertigo. In addition, the BsI patients with pons infarction performed a decreased transition probability between MsA and MsD than those of BsI patients with medulla oblongata infarctions. Moreover, the changes in Microstate (Ms) were significantly correlated with cognitive scales in patients with CI or BsI. Altered brain dynamics in patients with CI or BsI suggested that disturbances in resting brain networks might play a functional role in the cognitive impairment of the CI or BsI patients. Through the use of microstate analysis, the dizziness or vertigo following CI could be differentiated. These findings may serve as a powerful tool in our future clinical practices.

Iron deficiency negatively affects behavioral measures of learning, indirect neural measures of dopamine, and neural efficiency.

Iron deficiency (ID) is the most prevalent nutrient deficiency in the world, with a growing literature documenting the negative effects of ID on perception, attention, and memory. Animal models of ID suggest that dysregulation of dopamine is responsible for the deficits in memory. However, evidence that ID affects dopamine in humans is extremely limited. We report the results of a study involving college-aged women with and without ID learning two different category structures - a rule-based and an information-integration structure - selected based on the putative differential role of dopamine in learning these two structures. ID non-anemic (IDNA) and iron-sufficient (IS) women completed 1200 learning trials for each structure. EEG was collected to assess the effects of ID on features affected by dopaminergic state: error-related negativity (ERN) and positivity (Pe), feedback-related negativity (FRN), and task-related blink rate. In addition, we examined the EEG data for dynamics distinguishing IDNA from IS women, including a measure of neural efficiency. Both groups of women were able to learn both structures. However, IDNA women were initially slower and less accurate than IS women, specifically for the rule-based structure. There were large and persistent group differences in brain dynamics and neural efficiency measures. The results are discussed with respect to the selective impact of ID on initial rule-based learning and the persistent effect of ID on dopamine signaling and energetic efficiency.

Whole brain functional connectivity: Insights from next generation neural mass modelling incorporating electrical synapses.

The ready availability of brain connectome data has both inspired and facilitated the modelling of whole brain activity using networks of phenomenological neural mass models that can incorporate both interaction strength and tract length between brain regions. Recently, a new class of neural mass model has been developed from an exact mean field reduction of a network of spiking cortical cell models with a biophysically realistic model of the chemical synapse. Moreover, this new population dynamics model can naturally incorporate electrical synapses. Here we demonstrate the ability of this new modelling framework, when combined with data from the Human Connectome Project, to generate patterns of functional connectivity (FC) of the type observed in both magnetoencephalography and functional magnetic resonance neuroimaging. Some limited explanatory power is obtained via an eigenmode description of frequency-specific FC patterns, obtained via a linear stability analysis of the network steady state in the neigbourhood of a Hopf bifurcation. However, direct numerical simulations show that empirical data is more faithfully recapitulated in the nonlinear regime, and exposes a key role of gap junction coupling strength in generating empirically-observed neural activity, and associated FC patterns and their evolution. Thereby, we emphasise the importance of maintaining known links with biological reality when developing multi-scale models of brain dynamics. As a tool for the study of dynamic whole brain models of the type presented here we further provide a suite of C++ codes for the efficient, and user friendly, simulation of neural mass networks with multiple delayed interactions.

A Novel Self-Supervised Learning-Based Method for Dynamic CT Brain Perfusion Imaging.

Dynamic computed tomography (CT)-based brain perfusion imaging is a non-invasive technique that can provide quantitative measurements of cerebral blood flow (CBF), cerebral blood volume (CBV), and mean transit time (MTT). However, due to high radiation dose, dynamic CT scan with a low tube voltage and current protocol is commonly used. Because of this reason, the increased noise degrades the quality and reliability of perfusion maps. In this study, we aim to propose and investigate the feasibility of utilizing a convolutional neural network and a bi-directional long short-term memory model with an attention mechanism to self-supervisedly yield the impulse residue function (IRF) from dynamic CT images. Then, the predicted IRF can be used to compute the perfusion parameters. We evaluated the performance of the proposed method using both simulated and real brain perfusion data and compared the results with those obtained from two existing methods: singular value decomposition and tensor total-variation. The simulation results showed that the overall performance of parameter estimation obtained from the proposed method was superior to that obtained from the other two methods. The experimental results showed that the perfusion maps calculated from the three studied methods were visually similar, but small and significant differences in perfusion parameters between the proposed method and the other two methods were found. We also observed that there were several low-CBF and low-CBV lesions (i.e., suspected infarct core) found by all comparing methods, but only the proposed method revealed longer MTT. The proposed method has the potential to self-supervisedly yield reliable perfusion maps from dynamic CT images.

Counting on AR: EEG responses to incongruent information with real-world context

Augmented Reality (AR) technologies enhance the real world by integrating contextual digital information about physical entities. However, inconsistencies between physical reality and digital augmentations, which may arise from errors in the visualized information or the user's mental context, can considerably impact user experience. This work characterizes the brain dynamics associated with processing incongruent information within an AR environment. To study these effects, we designed an interactive paradigm featuring the manipulation of a Rubik's cube serving as a physical referent. Congruent and incongruent information regarding the cube's current status was presented via symbolic (digits) and non-symbolic (graphs) stimuli, thus examining the impact of different means of data representation. The analysis of electroencephalographic signals from 19 participants revealed the presence of centro-parietal N400 and P600 components following the processing of incongruent information, with significantly increased latencies for non-symbolic stimuli. Additionally, we explored the feasibility of exploiting incongruency effects for brain-computer interfaces. Hence, we implemented decoders using linear discriminant analysis, support vector machines, and EEGNet, achieving comparable performances with all methods. Therefore, this work contributes to the design of adaptive AR systems by demonstrating that above-chance detection of incongruent information based on physiological signals is feasible. The successful decoding of incongruency-induced modulations can inform systems about the current mental state of users without making it explicit, aiming for more coherent and contextually appropriate AR interactions.

Transition to Piscivory Seen Through Brain Transcriptomics in a Juvenile Percid Fish: Complex Interplay of Differential Gene Transcription, Alternative Splicing, and ncRNA Activity.

Pikeperch (Sander Lucioperca) belongs to main predatory fish species in freshwater bodies throughout Europe playing the key role by reducing planktivorous fish abundance. Two size classes of the young-of-the-year (YOY) pikeperch are known in Europe and North America. Our long-term fish survey elucidates late-summer size distribution of YOY pikeperch in the Lipno Reservoir (Czechia) and recognizes two distinct subcohorts: smaller pelagic planktivores heavily outnumber larger demersal piscivores. To explore molecular mechanisms accompanying the switch from planktivory to piscivory, we compared brain transcriptomes of both subcohorts and identified 148 differentially transcribed genes. The pathway enrichment analyses identified the piscivorous phase to be associated with genes involved in collagen and extracellular matrix generation with numerous Gene Ontology (GO), while the planktivorous phase was associated with genes for non-muscle-myosins (NMM) with less GO terms. Transcripts further upregulated in planktivores from the periphery of the NMM network were Pmchl, Pomcl, and Pyyb, all involved also in appetite control and producing (an)orexigenic neuropeptides. Noncoding RNAs were upregulated in transcriptomes of planktivores including three transcripts of snoRNA U85. Thirty genes mostly functionally unrelated to those differentially transcribed were alternatively spliced between the subcohorts. Our results indicate planktivores as potentially driven by voracity to initiate the switch to piscivory, while piscivores undergo a dynamic brain development. We propose a spatiotemporal spreading of juvenile development over a longer period and larger spatial scales through developmental plasticity as an adaptation to exploiting all types of resources and decreasing the intraspecific competition.

Analyzing the Brain’s Dynamic Response to Targeted Stimulation using Generative Modeling

Abstract Generative models of brain activity have been instrumental in testing hypothesized mechanisms underlying brain dynamics against experimental datasets. Beyond capturing the key mechanisms underlying spontaneous brain dynamics, these models hold an exciting potential for understanding the mechanisms underlying the dynamics evoked by targeted brain-stimulation techniques. This paper delves into this emerging application, using concepts from dynamical systems theory to argue that the stimulus-evoked dynamics in such experiments may be shaped by new types of mechanisms distinct from those that dominate spontaneous dynamics. We review and discuss: (i) the targeted experimental techniques across spatial scales that can both perturb the brain to novel states and resolve its relaxation trajectory back to spontaneous dynamics; and (ii) how we can understand these dynamics in terms of mechanisms using physiological, phenomenological, and data-driven models. A tight integration of targeted stimulation experiments with generative quantitative modeling provides an important opportunity to uncover novel mechanisms of brain dynamics that are difficult to detect in spontaneous settings.

Individualized mouse brain network models produce asymmetric patterns of functional connectivity after simulated traumatic injury

Abstract The functional and cognitive effects of traumatic brain injury (TBI) are poorly understood, as even mild injuries (concussion) can lead to long-lasting, untreatable symptoms. Simplified brain dynamics models may help researchers better understand the relationship between brain injury patterns and functional outcomes. Properly developed, these computational models provide an approach to investigate the effects of both computational and in vivo injury on simulated dynamics and cognitive function, respectively, for model organisms. In this study, we apply the Kuramoto model and an existing mesoscale mouse brain structural network to develop a simplified computational model of mouse brain dynamics. We explore how to optimize our initial model to predict existing mouse brain functional connectivity collected from mice under various anesthetic protocols. Finally, to determine how strongly the changes in our optimized models’ dynamics can predict the extent of a brain injury, we investigate how our simulations respond to varying levels of structural network damage. Results predict a mixture of hypo- and hyperconnectivity after experimental TBI, similar to results in TBI survivors, and also suggest a compensatory remodeling of connections which may have an impact on functional outcome after TBI.

Dynamic brain states underlying advanced concentrative absorption meditation: A 7T fMRI intensive case study

Abstract Advanced meditation consists of states and stages of practice that unfold with mastery and time. Dynamic functional connectivity (dFC) analysis of fMRI could identify brain states underlying advanced meditation. We conducted an intensive dFC case study of a meditator who completed 27 runs of jhāna advanced absorptive concentration meditation (ACAM-J), concurrently with 7-Tesla fMRI and phenomenological reporting. We identified three brain states that marked differences between ACAM-J and non-meditative control conditions. These states were characterized as a DMN-anticorrelated brain state, a hyperconnected brain state, and a sparsely connected brain state. Our analyses indicate higher prevalence of the DMN-anticorrelated brain state during ACAM-J than control states, and the prevalence increased significantly with deeper ACAM-J states. The hyperconnected brain state was also more common during ACAM-J, and was characterized by elevated thalamo-cortical connectivity and somatomotor network connectivity. The hyperconnected brain state significantly decreased over the course of ACAM-J, associating with self-reports of wider attention and diminished physical sensations. This brain state may be related to sensory awareness. Advanced meditators have developed well-honed abilities to move in and out of different altered states of consciousness, and this study provides initial evidence that functional neuroimaging can objectively track their dynamics.

Mapping Longitudinally Consistent Intrinsic Connectivity Networks in Macaque Brain via Longitudinal Sparse Dictionary Learning

Mapping consistent longitudinal intrinsic connectivity networks (ICNs) is crucial for understanding brain functional development over various life stages. However, achieving consistent longitudinal ICNs has been challenging due to the lack of methodologies that maintain temporal consistency. To address this gap, we introduce an innovative approach named Longitudinal Sparse Dictionary Learning (LSDL). This method utilizes an additional Frobenius norm to bridge gaps between consecutive ICNs, facilitating the continuous transfer of the learned feature matrix to subsequent stages. Moreover, Matrix Backpropagation (MBP) is employed to effectively mitigate potential accumulative errors. Our validation results demonstrate that LSDL can successfully extract 21 consistent longitudinal ICNs in macaque brains. In comparative empirical evaluations with established methodologies, Fast Independent Component Analysis (FICA) and Sparse Dictionary Learning (SDL), LSDL showcases superior efficacy in modeling longitudinal functional Magnetic Resonance Imaging (fMRI) data. This approach opens new avenues for research into developmental brain dynamics and neurodegenerative disorders, providing a robust framework for tracking the evolution of brain connectivity over time.

Quality-aware fuzzy min-max neural networks for dynamic brain network analysis and its application to schizophrenia identification

Dynamic functional connections (dFCs) refer to the observed phenomenon that functional connectivity changes over a short time, which has become a pioneering method in the diagnosis of brain diseases. However, current dynamic brain network analysis methods are insufficient to address the fuzzy information of brain networks caused by the quality issues of rs-fMRI data, and the uncertainty arising from inconsistent data quality across different windows due to missing values and noise in individual windows. These results in unreliable integration of multiple windows. To this end, this paper proposes a dynamic brain network analysis method based on quality-aware fuzzy min–max neural networks (QFMMNet). The individual window of dFCs is treated as a view, and we define three convolution filters to extract features from the brain network under the multi-view learning framework, thereby obtaining multi-view evidence for dFCs. We design the multi-view fuzzy min–max neural networks (MVFMM) based on fuzzy sets to deal with the fuzzy information of the brain network, which takes evidence as input patterns to generate hyperboxes and serves as the classification layer of each view. A quality-aware ensemble module is introduced to deal with uncertainty, which employs Dempster-Shafer Theory (D-S theory) to directly model the uncertainty and evaluate the dynamic quality-aware weighting of each view. At the decision level of model, a weighted fusion strategy is employed for multi-view fusion based on the dynamic quality-aware weighting, thereby the quality of each view is fully considered to improve the trustworthiness of the decision. We verified the effectiveness of QFMMNet through comparison with advanced algorithms on three real schizophrenia datasets, and the results show that QFMMNet significantly improved the classification performance of brain disease diagnosis tasks. The accuracy on the Huaxi, COBRE, and Xiangya datasets reached 87.14%, 86.67%, and 85.27%, respectively.

St-DenseViT: A Weakly Supervised Spatiotemporal Vision Transformer for Dense Prediction of Dynamic Brain Networks.

Modeling dynamic neuronal activity within brain networks enables the precise tracking of rapid temporal fluctuations across different brain regions. However, current approaches in computational neuroscience fall short of capturing and representing the spatiotemporal dynamics within each brain network. We developed a novel weakly supervised spatiotemporal dense prediction model capable of generating personalized 4D dynamic brain networks from fMRI data, providing a more granular representation of brain activity over time. We developed a model that leverages the vision transformer (ViT) as its backbone, jointly encoding spatial and temporal information from fMRI inputs using two different configurations: space-time and sequential encoders. The model generates 4D brain network maps that evolve over time, capturing dynamic changes in both spatial and temporal dimensions. In the absence of ground-truth data, we used spatially constrained windowed independent component analysis (ICA) components derived from fMRI data as weak supervision to guide the training process. The model was evaluated using large-scale resting-state fMRI datasets, and statistical analyses were conducted to assess the effectiveness of the generated dynamic maps using various metrics. Our model effectively produced 4D brain maps that captured both inter-subject and temporal variations, offering a dynamic representation of evolving brain networks. Notably, the model demonstrated the ability to produce smooth maps from noisy priors, effectively denoising the resulting brain dynamics. Additionally, statistically significant differences were observed in the temporally averaged brain maps, as well as in the summation of absolute temporal gradient maps, between patients with schizophrenia and healthy controls. For example, within the Default Mode Network (DMN), significant differences emerged in the temporally averaged space-time configurations, particularly in the thalamus, where healthy controls exhibited higher activity levels compared to subjects with schizophrenia. These findings highlight the model's potential for differentiating between clinical populations. The proposed spatiotemporal dense prediction model offers an effective approach for generating dynamic brain maps by capturing significant spatiotemporal variations in brain activity. Leveraging weak supervision through ICA components enables the model to learn dynamic patterns without direct ground-truth data, making it a robust and efficient tool for brain mapping. This work presents an important new approach for dynamic brain mapping, potentially opening up new opportunities for studying brain dynamics within specific networks. By framing the problem as a spatiotemporal dense prediction task in computer vision, we leverage the spatiotemporal ViT architecture combined with weakly supervised learning techniques to efficiently and effectively estimate these maps.

Abnormal stability of dynamic functional architecture in drug‐naïve children with attention‐deficit/hyperactivity disorder

Background and aimsAttention-deficit/hyperactivity disorder (ADHD) is most commonly diagnosed neurodevelopmental disorder in childhood, characterized by developmentally inappropriate inattention and/or hyperactivity/impulsivity symptoms. Static and dynamic functional connectivity (FC) studies have revealed brain dysfunction in ADHD. However, few studies have estimated the stability of dynamic functional architecture of children with ADHD. The present study attempted to identify the functional stability (FS) abnormalities associated with ADHD in drug‐naïve children.Materials and methodsThe resting-state fMRI of 42 children with ADHD and 30 healthy controls (HCs) were collected. Using the sliding window approach, FS of each voxel was obtained by measuring the concordance of dynamic FC over time. Further, the seed based dynamic FC (dFC) was conducted to explore the specific brain regions with dFC alteration related to these brain regions with altered FS. Then, the inter-group comparison and correlation analysis were performed.ResultsWe found that children with ADHD exhibited (1) decreased FS in the bilateral superior frontal gyrus (SFG) and increased FS in the right middle temporal gyrus (MTG), which both belong to the default mode network (DMN); (2) increased dFC between the bilateral SFG of DMN and the left insula of salience networks (SN) (GRF, voxel-wise p < 0.001, cluster-wise p < 0.05); (3) decreased dFC between the right MTG and the left cerebellum posterior lobe, and (3) worse performance in the Stroop test that significantly correlate with decreased FS in the bilateral SFG (p = 0.043, FDR corrected).ConclusionsOur findings showed that the abnormal functional architecture involved the DMN (the bilateral SFG and right MTG) and SN (left insula) regions in children with ADHD. This preliminary study provides novel insight into the dynamic brain functional networks in ADHD.

Static and temporal dynamic changes of intrinsic brain activity in early-onset and adult-onset schizophrenia: a fMRI study of interaction effects.

Schizophrenia is characterized by altered static and dynamic spontaneous brain activity. However, the conclusions regarding this are inconsistent. Evidence has revealed that this inconsistency could be due to mixed effects of age of onset. We enrolled 66/84 drug-naïve first-episode patients with early-onset/adult-onset schizophrenia (EOS/AOS) and matched normal controls (NCs) (46 adolescents, 73 adults), undergoing resting-state functional magnetic resonance imaging. Two-way ANOVA was used to determine the amplitude of low-frequency fluctuation (ALFF) and dynamic ALFF (dALFF) among the four groups. Compared to NCs, EOS had a higher ALFF in inferior frontal gyrus bilateral triangular part (IFG-tri), left opercular part (IFG-oper), left orbital part (IFG-orb), and left middle frontal gyrus (MFG). The AOS had a lower ALFF in left IFG-tri, IFG-oper, and lower dALFF in left IFG-tri. Compared to AOS, EOS had a higher ALFF in the left IFG-orb, and MFG, and higher dALFF in IFG-tri. Adult NCs had higher ALFF and dALFF in the prefrontal cortex (PFC) than adolescent NCs. The main effects of diagnosis were found in the PFC, medial temporal structures, cerebrum, visual and sensorimotor networks, the main effects of age were found in the visual and motor networks of ALFF and PFC of dALFF. Our findings unveil the static and dynamic neural activity mechanisms involved in the interaction between disorder and age in schizophrenia. Our results underscore age-related abnormalities in the neural activity of the PFC, shedding new light on the neurobiological mechanisms underlying the development of schizophrenia. This insight may offer valuable perspectives for the specific treatment of EOS in clinical settings.

Performance of optically pumped magnetometer magnetoencephalography: validation in large samples and multiple tasks.

Objective&#xD;Current commercial magnetoencephalography (MEG) systems detect neuro-magnetic signals using superconducting quantum interferometers devices (SQUIDs), which require liquid helium as cryogen and have many limitations during operation. In contrast, optical pumped magnetometers (OPMs) technology provides a promising alternative to conventional SQUID-MEG. OPMs can operate at room temperature, offering benefits such as flexible deployment and lower costs. However, the validation of OPM-MEG has primarily been conducted on small sample sizes and specific regions of interest in the brain, lacking comprehensive validation for larger sample sizes and assessment of whole-brain. &#xD;Approach&#xD;We recruited 100 participants, including healthy and neurological disorders individuals. Whole-brain OPM-MEG and SQUID-MEG data were recorded sequentially during auditory (n = 50) and visual (n = 50) stimulation experiments. By comparing the task-evoked responses of the two systems, we aimed to validate the performance of the next-generation OPM-MEG. &#xD;Main results&#xD;The results showed that OPM-MEG enhanced the amplitude of task-related responses and exhibited similar magnetic field patterns and neural oscillatory activity as SQUID-MEG. There was no difference in the task-related latencies measured by the two systems. The signal-to-noise ratio was lower for the OPM-MEG in the auditory experiment, but did not differ in the visual experiment, suggesting that the results may be task-dependent. &#xD;Significance&#xD;These results demonstrate that OPM-MEG, as an alternative to traditional SQUID-MEG, shows superior response amplitude and comparable performance in capturing brain dynamics. This study provides evidence for the effectiveness of OPM-MEG as a next-generation neuroimaging technique.

Covariant Formulation of the Brain’s Emerging Ohm’s Law

It is essential to establish the validity of Ohm’s law in any reference frame if we aim to implement a relativistic approach to brain dynamics based on a Lorentz covariant microscopic response relation. Here, we obtain a covariant formulation of Ohm’s law for an electromagnetic field tensor of any order derived from the emergent conductivity tensor in highly non-isotropic systems, employing the bidomain theory framework within brain tissue cells. With this, we offer a different perspective that we hope will lead to understanding the close relationship between brain dynamics and a seemingly ordinary yet profoundly crucial element: space.