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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.

•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.

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 advanced absorptive concentration meditation (ACAM-J), concurrently with 7-T fMRI and phenomenological reporting. We identified three brain states that marked differences between ACAM-J and nonmeditative 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 thalamocortical 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. Advanced meditation research investigates states and stages of practice that unfold with increasing mastery and time, which may include altered states of consciousness such as a diminished sense of self. In the current study, we examined a 7-T fMRI case study of , an advanced concentrative absorptive meditation (ACAM-J). Specifically, we examined the temporal properties of dynamic connectivity brain states that could reflect mental states and phenomena during ACAM-J. We identified two brain states that were more prevalent during ACAM-J than control conditions. One state, involving default-mode network anticorrelations with the rest of the brain, increased across ACAM-J. Another state, involving hyperconnectivity across many cortical networks, was correlated with reports of narrow attention and greater sensory awareness, as well as diminished across ACAM-J.

Modern approaches to investigate complex brain dynamics suggest to represent the brain as a functional network of brain regions defined by a brain atlas, while edges represent the structural or functional connectivity among them. This approach is also utilized for mathematical modeling of the resting-state brain dynamics, where the applied brain parcellation plays an essential role in deriving the model network and governing the modeling results. There is however no consensus and empirical evidence on how a given brain atlas affects the model outcome, and the choice of parcellation is still rather arbitrary. Accordingly, we explore the impact of brain parcellation on inter-subject and inter-parcellation variability of model fitting to empirical data. Our objective is to provide a comprehensive empirical evidence of potential influences of parcellation choice on resting-state whole-brain dynamical modeling. We show that brain atlases strongly influence the quality of model validation and propose several variables calculated from empirical data to account for the observed variability. A few classes of such data variables can be distinguished depending on their inter-subject and inter-parcellation explanatory power.

•Reliability of whole-brain dynamical models ranges from ”poor” to ”good”.•Reliability and specificity of modeling results may exceed those of empirical data.•Model personalization has a positive influence on the reliability and specificity.•Parcellations have a much larger effect on modeling results than on empirical data. Dynamical whole-brain models were developed to link structural (SC) and functional connectivity (FC) together into one framework. Nowadays, they are used to investigate the dynamical regimes of the brain and how these relate to behavioral, clinical and demographic traits. However, there is no comprehensive investigation on how reliable and subject specific the modeling results are given the variability of the empirical FC. In this study, we show that the parameters of these models can be fitted with a ”poor” to ”good” reliability depending on the exact implementation of the modeling paradigm. We find, as a general rule of thumb, that enhanced model personalization leads to increasingly reliable model parameters. In addition, we observe no clear effect of the model complexity evaluated by separately sampling results for linear, phase oscillator and neural mass network models. In fact, the most complex neural mass model often yields modeling results with ”poor” reliability comparable to the simple linear model, but demonstrates an enhanced subject specificity of the model similarity maps. Subsequently, we show that the FC simulated by these models can outperform the empirical FC in terms of both reliability and subject specificity. For the structure-function relationship, simulated FC of individual subjects may be identified from the correlations with the empirical SC with an accuracy up to 70%, but not vice versa for non-linear models. We sample all our findings for 8 distinct brain parcellations and 6 modeling conditions and show that the parcellation-induced effect is much more pronounced for the modeling results than for the empirical data. In sum, this study provides an exploratory account on the reliability and subject specificity of dynamical whole-brain models and may be relevant for their further development and application. In particular, our findings suggest that the application of the dynamical whole-brain modeling should be tightly connected with an estimate of the reliability of the results.

•L1 and L2 processing in late bilinguals could be captured by brain dynamic states.•Distinct brain state dynamics were found in L1 and L2 processing in late bilinguals.•L1 processing was associated with more integrated states and greater transition flexibility.•L2 processing was associated with more segregated states and insufficient transition flexibility. The process of complex cognition, which includes language processing, is dynamic in nature and involves various network modes or cognitive modes. This dynamic process can be manifested by a set of brain states and transitions between them. Previous neuroimaging studies have shed light on how bilingual brains support native language (L1) and second language (L2) through a shared network. However, the mechanism through which this shared brain network enables L1 and L2 processing remains unknown. This study examined this issue by testing the hypothesis that L1 and L2 processing is associated with distinct brain state dynamics in terms of brain state integration and transition flexibility. A group of late Chinese-English bilinguals was scanned using functional magnetic resonance imaging (fMRI) while listening to eight short narratives in Chinese (L1) and English (L2). Brain state dynamics were modeled using the leading eigenvector dynamic analysis framework. The results show that L1 processing involves more integrated states and frequent transitions between integrated and segregated states, while L2 processing involves more segregated states and fewer transitions. Our work provides insight into the dynamic process of narrative listening comprehension in late bilinguals and sheds new light on the neural representation of language processing and related disorders.

Abstract Identifying the neural characteristics of impulsivity and anxiety is important, as both traits confer risk for mental health conditions. In this study, we applied a Hidden Semi-Markov Model (HSMM) to capture the temporal patterns of brain activity to identify brain states associated with impulsivity and anxiety. Using the Leipzig Study for Mind-Body Emotion Interactions (LEMON) resting-state fMRI dataset of healthy individuals (N = 56), we analyzed three groups: High Impulsivity (HI), High Anxiety (HA), and High Impulsivity & High Anxiety (HIHA), assessed with the STAI-T and UPPS scales. HSMM identified three distinct functional brain states characterized by mean activation, functional connectivity, and topological properties of the frontoparietal, default mode, salience/ventral attention, and limbic networks. Notably, the HI group spent more time in a state with an inverse pattern between the default mode network and the salience/ventral attention network, with anticorrelated connectivity and opposing activation, compared with the HA and HIHA groups. Furthermore, the HI group showed a stronger tendency to persist in this state, which may reflect the neural characteristic distinguishing impulsivity from anxiety. However, no distinctive features were observed in the HIHA group. Nevertheless, these findings provide initial insights into the time-varying characteristics of impulsivity and anxiety.

Anesthetisia is an important surgical and explorative tool in the study of consciousness. Much work has been done to connect the deeply anesthetized condition with decreased complexity. However, anesthesia-induced unconsciousness is also a dynamic condition in which functional activity and complexity may fluctuate, being perturbed by internal or external (e.g., noxious) stimuli. We use fMRI data from a cohort undergoing deep propofol anesthesia to investigate resting state dynamics using dynamic brain state models and spatiotemporal network analysis. We focus our analysis on group-level dynamics of brain state temporal complexity, functional activity, connectivity, and spatiotemporal modularization in deep anesthesia and wakefulness. We find that in contrast to dynamics in the wakeful condition, anesthesia dynamics are dominated by a handful of sink states that act as low-complexity attractors to which subjects repeatedly return. On a subject level, our analysis provides tentative evidence that these low-complexity attractor states appear to depend on subject-specific age and anesthesia susceptibility factors. Finally, our spatiotemporal analysis, including a novel spatiotemporal clustering of graphs representing hidden Markov models, suggests that dynamic functional organization in anesthesia can be characterized by mostly unchanging, isolated regional subnetworks that share some similarities with the brain’s underlying structural connectivity, as determined from normative tractography data. The imperturbability of subjects undergoing general anesthesia is surgically important and is well studied in the neuroscience of consciousness. However, research is ongoing on how this perturbation resistance is realized dynamically, on the level of changes in functional connectivity. Here, we demonstrate a possible explanation in terms of brain states of reduced functional complexity to which anesthetized subjects repeatedly return. These states act as sinks that reduce the overall temporal complexity of anesthesia dynamics in a manner that may depend on surgically relevant susceptibility and age-related factors. Using a novel graph-based spatiotemporal clustering method, we show that anesthesia induces not just temporal but also spatiotemporal reductions in complexity, resulting in states that are functionally fragmented into unchanging, anatomically restricted subregions with minimal information sharing between these fragments.

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.

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.