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Language control processes allow for the flexible manipulation and access to context‐appropriate verbal representations. Functional magnetic resonance imaging (fMRI) studies have localized the brain regions involved in language control processes usually by comparing high vs. low lexical–semantic control conditions during verbal tasks. Yet, the spectro‐temporal dynamics of associated brain processes remain unexplored, preventing a proper understanding of the neural bases of language control mechanisms. To do so, we recorded functional brain activity using magnetoencephalography (MEG) and fMRI, while 30 healthy participants performed a silent verb generation (VGEN) and a picture naming (PN) task upon confrontation with pictures requiring low or high lexical–semantic control processes. fMRI confirmed the association between stronger language control processes and increased left inferior frontal gyrus (IFG) perfusion, while MEG revealed these controlled mechanisms to be associated with a specific sequence of early (< 500 ms) and late (> 500 ms) beta‐band (de)synchronization processes within fronto‐temporo‐parietal areas. Particularly, beta‐band modulations of event‐related (de)synchronization mechanisms were first observed in the right IFG, followed by bilateral IFG and temporo‐parietal brain regions. Altogether, these results suggest that beyond a specific recruitment of inferior frontal brain regions, language control mechanisms rely on a complex temporal sequence of beta‐band oscillatory mechanisms over antero‐posterior areas. The study aimed at investigating the oscillatory brain dynamics underlying language control processes, a mechanism that critically allows the flexible access to context‐relevant representations during language production tasks. We showed that language control processes rely on a complex sequence of beta‐band brain synchronization processes encompassing fronto‐temporo‐parietal brain regions, with the prefrontal areas being particularly involved at early stages (< 500 ms).

How the brain represents gender identity is largely unknown, but some neural differences have recently been discovered. We used an intrinsic ignition framework to investigate whether there are gender differences in the propagation of neural activity across the whole‐brain and within resting‐state networks. Studying 29 trans men and 17 trans women with gender incongruence, 22 cis women, and 19 cis men, we computed the capability of a given brain area in space to propagate activity to other areas (mean‐ignition), and the variability across time for each brain area (node‐metastability). We found that both measurements differentiated all groups across the whole brain. At the network level, we found that compared to the other groups, cis men showed higher mean‐ignition of the dorsal attention network and node‐metastability of the dorsal and ventral attention, executive control, and temporal parietal networks. We also found higher mean‐ignition values in cis men than in cis women within the executive control network, but higher mean‐ignition in cis women than cis men and trans men for the default mode. Node‐metastability was higher in cis men than cis women in the somatomotor network, while both mean‐ignition and node‐metastability were higher for cis men than trans men in the limbic network. Finally, we computed correlations between these measurements and a body image satisfaction score. Trans men's dissatisfaction as well as cis men's and cis women's satisfaction toward their own body image were distinctively associated with specific networks in each group. Overall, the study of the whole‐brain network dynamical complexity discriminates gender identity groups, functional dynamic approaches could help disentangle the complex nature of the gender dimension in the brain.

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. Significance: 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. Overview of the proposed spatiotemporal dense prediction framework. The model takes 4D fMRI data as input and generates dynamic brain maps that evolve over time. The input is first patchified into a sequence of spatiotemporal tokens. A Vision Transformer (ViT) encoder is used to model complex spatial and temporal dependencies across these tokens. We implement and compare two encoder variants: (1) a space‐time encoder, which jointly attends to spatial and temporal tokens within a unified self‐attention mechanism, and (2) a sequential encoder, which applies separate attention over spatial and temporal dimensions. The encoder output is passed to a lightweight CNN‐based decoder head that reconstructs dense spatiotemporal predictions (4D dynamic maps). Weak supervision is incorporated during training via ICA‐derived components, which guide learning through a soft loss function. This architecture enables learning of subject‐specific, temporally dynamic brain activity patterns from weak supervision.

The human brain, composed of billions of neurons and synaptic connections, is an intricate network coordinating a sophisticated balance of excitatory and inhibitory activities between brain regions. The dynamical balance between excitation and inhibition is vital for adjusting neural input/output relationships in cortical networks and regulating the dynamic range of their responses to stimuli. To infer this balance using connectomics, we recently introduced a computational framework based on the Ising model, which was first developed to explain phase transitions in ferromagnets, and proposed a novel hybrid resting-state structural connectome (rsSC). Here, we show that a generative model based on the Kuramoto phase oscillator can be used to simulate static and dynamic functional connectomes (FC) with rsSC as the coupling weight coefficients, such that the simulated FC aligns well with the observed FC when compared with that simulated traditional structural connectome.

•CAP analysis reveals dynamic brain states during reasoning tasks.•CAP2 (visual network) and CAP3 (DMN-sensorimotor) dominate during reasoning.•Longer engagement in specific CAPs correlates with better reasoning performance.•Aging reduces task-relevant CAP engagement, increasing transitions to baseline states.•CAP analysis provides novel insights into transient brain network reconfigurations. Brain activity exhibits substantial temporal variability during cognitive processes, yet traditional fMRI analyses often fail to capture these dynamic patterns. Co-activation pattern (CAP) analysis has emerged as a promising method to study brain dynamics. CAP analysis provides a powerful framework for capturing transient brain states, however, its application to cognitive tasks remains very limited, with no prior studies specifically investigating its role in reasoning performance. This study investigated CAPs during reasoning tasks, their relationship with cognitive performance, age and other individual differences. We applied CAP analysis to fMRI data from 303 participants performing three reasoning tasks—Matrix Reasoning, Letter Sets, and Paper Folding—along with resting-state data. Using K-means clustering, we identified four distinct CAPs, each exhibiting unique spatial and temporal characteristics. These CAPs were analyzed in relation to predefined resting-state networks, revealing their functional relevance to cognitive task engagement. Key temporal metrics, including fraction occupancy, dwelling time, and transition probabilities, were assessed across reasoning tasks and resting state. The results demonstrate that CAP2 and CAP3 are predominantly engaged during reasoning tasks, with CAP2 strongly overlapping with the visual network and CAP3 exhibiting concurrent default mode and sensorimotor network activations. CAP1, primarily dominant during rest, showed prolonged engagement in older individuals, while CAP4 appeared to function as a transitional state facilitating network reorganization. Regression analyses link longer dwelling times and higher fraction occupancy of CAP2 and CAP3 to superior reasoning performance, whereas excessive transitions to CAP4 negatively impacted cognitive task outcomes. Additionally, aging was associated with reduced engagement in task-relevant CAPs and an increased tendency to transition into baseline-like states. These findings underscore the critical role of dynamic brain state reconfigurations in supporting cognition specifically reasoning and highlight CAP analysis as a powerful tool for studying transient brain function and individual cognitive differences.

Brain activity is time varying and dynamic, even in the resting state. However, little attention has been paid to the dynamic alterations in regional brain activity in Parkinson’s disease (PD). We aimed to test for differences in dynamic regional homogeneity (dReHo) between PD patients and healthy controls (HCs) and to further investigate the pathophysiological meaning of this altered dReHo in PD. We included 57 PD patients and 31 HCs with rs-fMRI scans and neuropsychological examinations. Then, ReHo and dReHo were calculated for all subjects. We compared ReHo and dReHo between PD patients and HCs and then analyzed the associations between altered dReHo variability and clinical/neuropsychological measurements. Support vector machines (SVMs) were also used to assist in differentiating PD patients from HCs using the classification values of dReHo. The results showed that PD patients had increased ReHo in the bilateral medial temporal lobe and decreased ReHo in the right posterior cerebellar lobe, right precentral gyrus, and supplementary motor area, compared with controls. The coefficient of variation (CV) of dReHo was considerably higher in the precuneus in PD patients compared with HCs, and the CV of dReHo in the precuneus was found to be highly associated with HAMD, HAMA, and NMSQ scores. Multiple linear regression analysis controlling for demographic, clinical, and neuropsychiatric variables confirmed the association between altered dReHo and HAMD. Using the leave-one-out cross validation procedure, 98% (p < 0.001) of individuals were properly identified using the SVM classifier. These results provide new evidence for the aberrant resting-state brain activity in the precuneus of PD patients and its role in neuropsychiatric symptoms in PD.

The brain is regarded as the most complex anatomical structure in the human body that executes various functions. The electromagnetic brainwaves or energy with its discrete network is commonly thought of as the sole contributor to various brain functions. However, the discrete pattern of the brain network seems insufficient to explain consciousness, binding problems in neural communication, brain heat, psychiatric manifestations and higher-order of thinking. Therefore, it seems that the brain must possess additional energy for it to have a much higher degree of functional freedom. Plasma brain dynamics (PBD) and quantum brain dynamics (QBD) are two hypothetical brain energies that have a strong scientific basis to exist together with the conventional brain electromagnetic energy. The presence of these energies may explain the puzzling brain functions, thus creating an opportunity to correct any abnormalities arising from them.

Functional connectivity (FC) sheds light on the interactions between different brain regions. Besides basic research, it is clinically relevant for applications in Alzheimer's disease, schizophrenia, presurgical planning, epilepsy, and traumatic brain injury. Simulations of whole-brain mean-field computational models with realistic connectivity determined by tractography studies enable us to reproduce with accuracy aspects of average FC in the resting state. Most computational studies, however, did not address the prominent non-stationarity in resting state FC, which may result in large intra- and inter-subject variability and thus preclude an accurate individual predictability. Here we show that this non-stationarity reveals a rich structure, characterized by rapid transitions switching between a few discrete FC states. We also show that computational models optimized to fit time-averaged FC do not reproduce these spontaneous state transitions and, thus, are not qualitatively superior to simplified linear stochastic models, which account for the effects of structure alone. We then demonstrate that a slight enhancement of the non-linearity of the network nodes is sufficient to broaden the repertoire of possible network behaviors, leading to modes of fluctuations, reminiscent of some of the most frequently observed Resting State Networks. Because of the noise-driven exploration of this repertoire, the dynamics of FC qualitatively change now and display non-stationary switching similar to empirical resting state recordings (Functional Connectivity Dynamics (FCD)). Thus FCD bear promise to serve as a better biomarker of resting state neural activity and of its pathologic alterations. •Resting state Functional Connectivity (FC) displays switching non-stationarity.•Previous whole-brain models reproduce average FC, but not its dynamic switching.•Enhancing the dynamic repertoire of the whole-brain model leads to FC switching.•The simulated FC states are reminiscent of known resting state networks.

This magnetoencephalography (MEG) study addresses (i) how Friedreich ataxia (FRDA) affects the sub‐second dynamics of resting‐state brain networks, (ii) the main determinants of their dynamic alterations, and (iii) how these alterations are linked with FRDA‐related changes in resting‐state functional brain connectivity (rsFC) over long timescales. For that purpose, 5 min of resting‐state MEG activity were recorded in 16 FRDA patients (mean age: 27 years, range: 12–51 years; 10 females) and matched healthy subjects. Transient brain network dynamics was assessed using hidden Markov modeling (HMM). Post hoc median‐split, nonparametric permutations and Spearman rank correlations were used for statistics. In FRDA patients, a positive correlation was found between the age of symptoms onset (ASO) and the temporal dynamics of two HMM states involving the posterior default mode network (DMN) and the temporo‐parietal junctions (TPJ). FRDA patients with an ASO <11 years presented altered temporal dynamics of those two HMM states compared with FRDA patients with an ASO > 11 years or healthy subjects. The temporal dynamics of the DMN state also correlated with minute‐long DMN rsFC. This study demonstrates that ASO is the main determinant of alterations in the sub‐second dynamics of posterior associative neocortices in FRDA patients and substantiates a direct link between sub‐second network activity and functional brain integration over long timescales. This magnetoencephalography (MEG) study addresses (i) how Friedreich ataxia (FRDA) affects the sub‐second dynamics of resting‐state brain networks, (ii) the main determinants of their dynamic alterations, and (iii) how these alterations are linked with FRDA‐related changes in resting‐state functional brain connectivity (rsFC) over long timescales. Transient brain network dynamics was assessed using hidden Markov modeling (HMM). In FRDA patients, a positive correlation was found between the age of symptoms onset (ASO) and the temporal dynamics of two HMM states involving the posterior default mode network (DMN) and the temporo‐parietal junctions (TPJ). The temporal dynamics of the DMN state also correlated with minute‐long DMN rsFC. FRDA patients with an ASO <11 years presented altered temporal dynamics of those two HMM states.

The brain is a complex, multiscale dynamical system composed of many interacting regions. Knowledge of the spatiotemporal organization of these interactions is critical for establishing a solid understanding of the brain’s functional architecture and the relationship between neural dynamics and cognition in health and disease. The possibility of studying these dynamics through careful analysis of neuroimaging data has catalyzed substantial interest in methods that estimate time-resolved fluctuations in functional connectivity (often referred to as “dynamic” or time-varying functional connectivity; TVFC). At the same time, debates have emerged regarding the application of TVFC analyses to resting fMRI data, and about the statistical validity, physiological origins, and cognitive and behavioral relevance of resting TVFC. These and other unresolved issues complicate interpretation of resting TVFC findings and limit the insights that can be gained from this promising new research area. This article brings together scientists with a variety of perspectives on resting TVFC to review the current literature in light of these issues. We introduce core concepts, define key terms, summarize controversies and open questions, and present a forward-looking perspective on how resting TVFC analyses can be rigorously and productively applied to investigate a wide range of questions in cognitive and systems neuroscience.