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Negative symptoms remain a major therapeutic challenge in schizophrenia, significantly impacting functional outcomes, yet their underlying neural mechanisms remain poorly understood. Traditional static functional connectivity analyses, which examine average correlations over time, may overlook critical temporal features of brain network organization and fail to capture dynamic shifts in connectivity patterns. Resting‐state functional magnetic resonance imaging (rs‐fMRI), particularly when analyzed using co‐activation pattern analysis (CAP), provides a framework to study these dynamic network changes with greater temporal resolution. Using CAP analysis of rs‐fMRI data, we investigated brain network dynamics in 31 schizophrenia patients with predominant negative symptoms, 31 patients without predominant negative symptoms, and 34 healthy controls. Eight distinct brain states were identified, characterized by antagonistic relationships between sensorimotor, default mode, and salience networks. Compared to healthy controls, the overall schizophrenia group showed altered temporal characteristics, including a reduced occurrence of a sensorimotor‐dominant state and excessive transitions from this state to a control‐salience network state. Notably, patients with predominant negative symptoms demonstrated distinct temporal characteristics, including reduced dwell time in sensorimotor‐salience states and excessive transitions from sensorimotor to control‐salience network states. In contrast, patients without predominant negative symptoms did not exhibit such excessive state transitions, while their symptom severity correlated with the occurrence of a cognitive‐sensorimotor network state. Network alterations significantly correlated with symptom severity in both the overall schizophrenia group and the subgroup without predominant negative symptoms, while no significant correlations were observed in patients with predominant negative symptoms. These findings suggest that predominant negative symptoms are associated with stable trait‐like network reorganization characterized by excessive state transitions rather than state‐dependent dysregulation, providing potential neuroimaging markers for clinical assessment. Co‐activation pattern analysis reveals schizophrenia patients with predominant negative symptoms exhibit distinct brain dynamics: impaired sensorimotor–visual stability, reduced sensorimotor–salience maintenance, and excessive sensorimotor‐to‐control transitions. These alterations, independent of symptom severity, represent stable trait‐like neurobiological markers rather than state‐dependent dysregulation, offering potential neuroimaging biomarkers for assessment.

•CAP was conducted for real fMRI data and a stationary null model.•The results of CAP analysis were similar for the real and simulated data.•Similar results for real and simulated data in both ROI- and voxel-based analyses.•Results of CAP analysis may not reflect non-stationarity of resting brain activity. The non-stationarity of resting-state brain activity has received increasing attention in recent years. Functional connectivity (FC) analysis with short sliding windows and coactivation pattern (CAP) analysis are two widely used methods for assessing the dynamic characteristics of brain activity observed with functional magnetic resonance imaging (fMRI). However, the statistical nature of the dynamics captured by these techniques needs to be verified. In this study, we found that the results of CAP analysis were similar for real fMRI data and simulated stationary data with matching covariance structures and spectral contents. We also found that, for both the real and simulated data, CAPs were clustered into spatially heterogeneous modules. Moreover, for each of the modules in the real data, a spatially similar module was found in the simulated data. The present results suggest that care needs to be taken when interpreting observations drawn from CAP analysis as it does not necessarily reflect non-stationarity or a mixture of states in resting brain activity.

Functional magnetic resonance imaging provides rich spatio-temporal data of human brain activity during task and rest. Many recent efforts have focussed on characterising dynamics of brain activity. One notable instance is co-activation pattern (CAP) analysis, a frame-wise analytical approach that disentangles the different functional brain networks interacting with a user-defined seed region. While promising applications in various clinical settings have been demonstrated, there is not yet any centralised, publicly accessible resource to facilitate the deployment of the technique. Here, we release a working version of TbCAPs, a new toolbox for CAP analysis, which includes all steps of the analytical pipeline, introduces new methodological developments that build on already existing concepts, and enables a facilitated inspection of CAPs and resulting metrics of brain dynamics. The toolbox is available on a public academic repository at https://c4science.ch/source/CAP_Toolbox.git. In addition, to illustrate the feasibility and usefulness of our pipeline, we describe an application to the study of human cognition. CAPs are constructed from resting-state fMRI using as seed the right dorsolateral prefrontal cortex, and, in a separate sample, we successfully predict a behavioural measure of continuous attentional performance from the metrics of CAP dynamics (R ​= ​0.59). [Display omitted] •Co-activation pattern (CAP) analysis is a contemporary frame-wise analytical tool.•We provide a toolbox that enables the easy application of all steps of the pipeline.•An intuitive graphical user interface facilitates usage by inexperienced users.•We associate CAP features at rest with performance in a continuous attention task.

Accumulating evidence suggests that the brain is highly dynamic; thus, investigation of brain dynamics especially in brain connectivity would provide crucial information that stationary functional connectivity could miss. This study investigated temporal expressions of spatial modes within the default mode network (DMN), salience network (SN) and cognitive control network (CCN) using a reliable data-driven co-activation pattern (CAP) analysis in two independent data sets. We found enhanced CAP-to-CAP transitions of the SN in patients with MDD. Results suggested enhanced flexibility of this network in the patients. By contrast, we also found reduced spatial consistency and persistence of the DMN in the patients, indicating reduced variability and stability in individuals with MDD. In addition, the patients were characterized by prominent activation of mPFC. Moreover, further correlation analysis revealed that persistence and transitions of RCCN were associated with the severity of depression. Our findings suggest that functional connectivity in the patients may not be simply attenuated or potentiated, but just alternating faster or slower among more complex patterns. The aberrant temporal–spatial complexity of intrinsic fluctuations reflects functional diaschisis of resting-state networks as characteristic of patients with MDD.

Memory decline is a central cognitive symptom in Parkinson’s Disease (PD). While task-fMRI studies link hippocampal activity (AHA) to poorer memory and olfactory performance, this relationship during rest remains understudied. The objectives of this study are to examine differences in resting-state hippocampal networks, explore the occurrence of reduced AHA within these networks, and investigate its impact on memory and olfaction in PD. Thirty-nine PD patients awaiting evaluation for device-aided Parkinson therapy and 46 healthy controls (HC) underwent resting-state fMRI (rs-fMRI). PD patients also completed a memory and olfactory assessment. Co-activation pattern (CAP) analysis was performed on the rs-fMRI data. Our results demonstrated reduced activity in two hippocampal networks in PD: Network 1, incorporating the visual cortex, cerebellum, superior parietal lobule, and precuneus, and Network 5, incorporating parts of the central executive network. PD subgroups with reduced AHA in Network 1 and 5 performed significantly worse on tests of auditory-verbal short-term, long-term and recognition memory, as well as odor identification. In conclusion, within specific resting-state hippocampal networks, reduced AHA in PD is linked to poorer auditory-verbal memory and odor identification.

The perception that someone is nearby, although nobody can be seen or heard, is called presence hallucination (PH). Being a frequent hallucination in patients with Parkinson's disease, it has been argued to be indicative of a more severe and rapidly advancing form of the disease, associated with psychosis and cognitive decline. PH may also occur in healthy individuals and has recently been experimentally induced, in a controlled manner during fMRI, using MR-compatible robotics and sensorimotor stimulation. Previous neuroimaging correlates of such robot-induced PH, based on conventional time-averaged fMRI analysis, identified altered activity in the posterior superior temporal sulcus and inferior frontal gyrus in healthy individuals. However, no link with the strength of the robot-induced PH was observed, and such activations were also associated with other sensations induced by robotic stimulation. Here we leverage recent advances in dynamic functional connectivity, which have been applied to different psychiatric conditions, to decompose fMRI data during PH-induction into a set of co-activation patterns that are tracked over time, as to characterize their occupancies, durations, and transitions. Our results reveal that, when PH is induced, the identified brain patterns significantly and selectively increase their transition probabilities towards a specific brain pattern, centred on the posterior superior temporal sulcus, angular gyrus, dorso-lateral prefrontal cortex, and middle prefrontal cortex. This change is not observed in any other control conditions, nor is it observed in association with other sensations induced by robotic stimulation. The present findings describe the neural mechanisms of PH in healthy individuals and identify a specific disruption of the dynamics of network interactions, extending previously reported network dysfunctions in psychotic patients with hallucinations to an induced robot-controlled specific hallucination in healthy individuals.

The dynamics of the brain's intrinsic networks have been recently studied using co-activation pattern (CAP) analysis. The CAP method relies on few model assumptions and CAP-based measurements provide quantitative information of network temporal dynamics. One limitation of existing CAP-related methods is that the computed CAPs share considerable spatial overlap that may or may not be functionally distinct relative to specific network dynamics. To more accurately describe network dynamics with spatially distinct CAPs, and to compare network dynamics between different populations, a novel data-driven CAP group analysis method is proposed in this study. In the proposed method, a dominant-CAP (d-CAP) set is synthesized across CAPs from multiple clustering runs for each group with the constraint of low spatial similarities among d-CAPs. Alternating d-CAPs with less overlapping spatial patterns can better capture overall network dynamics. The number of d-CAPs, the temporal fraction and spatial consistency of each d-CAP, and the subject-specific switching probability among all d-CAPs are then calculated for each group and used to compare network dynamics between groups. The spatial dissimilarities among d-CAPs computed with the proposed method were first demonstrated using simulated data. High consistency between simulated ground-truth and computed d-CAPs was achieved, and detailed comparisons between the proposed method and existing CAP-based methods were conducted using simulated data. In an effort to physiologically validate the proposed technique and investigate network dynamics in a relevant brain network disorder, the proposed method was then applied to data from the Parkinson's Progression Markers Initiative (PPMI) database to compare the network dynamics in Parkinson's disease (PD) and normal control (NC) groups. Fewer d-CAPs, skewed distribution of temporal fractions of d-CAPs, and reduced switching probabilities among final d-CAPs were found in most networks in the PD group, as compared to the NC group. Furthermore, an overall negative association between switching probability among d-CAPs and disease severity was observed in most networks in the PD group as well. These results expand upon previous findings from in vivo electrophysiological recording studies in PD. Importantly, this novel analysis also demonstrates that changes in network dynamics can be measured using resting-state fMRI data from subjects with early stage PD. •A group analysis method is proposed to compute less overlapping dominant co-activation patterns (d-CAPs) that represent network dynamics.•Detailed comparisons are conducted between the proposed method and existing CAP-related methods using simulations.•Four d-CAP based measurements are derived to compare network dynamics between different subject groups.•Reduced temporal dynamics in most networks are found in the Parkinson’s disease group when compared to the normal control group.

•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.

While resting‐state brain dysfunctions have been extensively investigated in Alzheimer's disease (AD), the dynamic alterations of functional systems remain poorly understood. We employed co‐activation pattern (CAP) analysis to characterize the functional‐state alterations in 243 participants using resting‐state fMRI data and applied graph theory analysis to estimate corresponding topological properties. The CAP analysis identified five distinct brain states across groups: State 1 (limbic network dominated), State 2 (dorsal attention network (DAN) and central executive network dominated), State 3 (default mode network and central executive network dominated), State 4 (somatomotor network and ventral attention network dominated), and State 5 (DAN, sensorimotor, and visual networks dominated). Compared to cognitively unimpaired individuals, State 3 demonstrated significantly reduced persistence and resilience in both mild cognitive impairment (MCI) and AD groups. Additionally, both clinical groups (MCI and AD) exhibited decreased transitions from State 2 to State 5 and reduced self‐transitions within State 3. Graph theory analysis revealed that compared to cognitively unimpaired individuals, MCI and AD individuals had increased node degree centrality and node efficiency, alongside decreased node local efficiency in regions within the default mode network (DAN) and visual network, which corresponded well with CAP analysis results. Our findings provide a multiscale framework linking dynamic state instability to static network reorganization, advancing understanding of the dynamic functional alterations underlying cognitive decline in AD spectrum disorders. State 3 exhibited significantly reduced persistence and resilience in both MCI and AD compared to CU. Individuals with MCI and AD showed reduced transition probability from State 2 to State 5 and decreased self‐transitions in State 3. Graph theoretical results corresponded well to the findings of the above CAP analysis.

Recently, fMRI researchers have begun to realize that the brain's intrinsic network patterns may undergo substantial changes during a single resting state (RS) scan. However, despite the growing interest in brain dynamics, metrics that can quantify the variability of network patterns are still quite limited. Here, we first introduce various quantification metrics based on the extension of co-activation pattern (CAP) analysis, a recently proposed point-process analysis that tracks state alternations at each individual time frame and relies on very few assumptions; then apply these proposed metrics to quantify changes of brain dynamics during a sustained 2-back working memory (WM) task compared to rest. We focus on the functional connectivity of two prominent RS networks, the default-mode network (DMN) and executive control network (ECN). We first demonstrate less variability of global Pearson correlations with respect to the two chosen networks using a sliding-window approach during WM task compared to rest; then we show that the macroscopic decrease in variations in correlations during a WM task is also well characterized by the combined effect of a reduced number of dominant CAPs, increased spatial consistency across CAPs, and increased fractional contributions of a few dominant CAPs. These CAP metrics may provide alternative and more straightforward quantitative means of characterizing brain network dynamics than time-windowed correlation analyses. •Utilize Co-Activation Patterns to develop detailed metrics of brain dynamics.•Compare brain dynamics during rest and sustained working memory with these metrics.•Demonstrate reduced brain dynamics in the DMN and ECN during WM compared to rest.