Coactivation Pattern Analysis Research Articles

Age-related unstable transient states and imbalanced activation proportion of brain networks in people with autism spectrum disorder: A resting-state fMRI study using coactivation pattern analyses

Abstract The atypical static brain functions related to the executive control network (ECN), default mode network (DMN), and salience network (SN) in people with autism spectrum disorder (ASD) has been widely reported. However, their transient functions in ASD are not clear. We aim to identify transient network states (TNSs) using coactivation pattern (CAP) analysis to characterize the age-related atypical transient functions in ASD. CAP analysis was performed on a resting-state fMRI dataset (78 ASD and 78 healthy control (CON) juveniles, 54 ASD and 54 CON adults). Six TNSs were divided into the DMN-TNSs, ECN-TNSs, and SN-TNSs. The DMN-TNSs were major states with the highest stability and proportion, and the ECN-TNSs and SN-TNSs were minor states. Age-related abnormalities on spatial stability and TNS proportion were found in ASD. The spatial stability of DMN-TNSs was found increasing with age in CON, but was not found in ASD. A lower proportion of DMN-TNSs was found in ASD compared with CON of the same age, and ASD juveniles had a higher proportion of SN-TNSs while ASD adults had a higher proportion of ECN-TNSs. The abnormalities on spatial stability and TNS proportion were related to social deficits. Our results provided new evidence for atypical transient brain functions in people with ASD.

Similarities and differences in dynamic properties of brain networks between internet gaming disorder and tobacco use disorder

BackgroundInternet gaming disorder (IGD) and tobacco use disorder (TUD) are two major addiction disorders that result in substantial financial loss. Identifying the similarities and differences between these two disorders is important to understand substance addiction and behavioral addiction. The current study was designed to compare these two disorders utilizing dynamic analysis. MethodResting-state data were collected from 35 individuals with IGD, 35 individuals with TUD and 35 healthy controls (HCs). Dynamic coactivation pattern analysis was employed to decipher their dynamic patterns. ResultsIGD participants showed decreased coactivation patterns within the default mode network (DMN) and between the DMN and the salience network (SN). The SN showed reduced coactivation patterns with the executive control network (ECN) and DMN, and the ECN showed decreased coactivation patterns with the DMN. In the TUD group, the DMN exhibited decreased coactivation patterns with the SN, the SN exhibited reduced coactivation patterns with the DMN and ECN, and the ECN showed decreased coactivation patterns with the DMN and within the ECN. Furthermore, the triple network model was fitted to the dynamic properties of the two addiction disorders. Decoding analysis results indicated that addiction-related memory and memory retrieval displayed similar dysfunctions in both addictions. ConclusionThe dynamic characteristics of IGD and TUD suggest that there are similarities in the dynamic features between the SN and DMN and differences in the dynamic features between the DMN and ECN. Our results revealed that the two addiction disorders have dissociable brain mechanisms, indicating that future studies should consider these two addiction disorders as having two separate mechanisms to achieve precise treatment for their individualized targets.

Estimation of static and dynamic functional connectivity in resting-state fMRI using zero-frequency resonator.

Resting-state functional magnetic resonance imaging (rs-fMRI) is increasingly being used to infer the functional organization of the brain. Blood oxygen level-dependent (BOLD) features related to spontaneous neuronal activity, are yet to be clearly understood. Prior studies have hypothesized that rs-fMRI is spontaneous event-related and these events convey crucial information about the neuronal activity in estimating resting state functional connectivity (FC). Attempts have been made to extract these temporal events using a predetermined threshold. However, the thresholding methods in addition to being very sensitive to noise, may consider redundant events or exclude the low-valued inflection points. Here, we extract the event-related temporal onsets from the rs-fMRI time courses using a zero-frequency resonator (ZFR). The ZFR reflects the transient behavior of the BOLD events at its output. The conditional rate (CR) of the BOLD events occurring in a time course with respect to a seed time course is used to derive static FC. The temporal activity around the estimated events called high signal-to-noise ratio (SNR) segments are also obtained in the rs-fMRI time course and are then used to compute static and dynamic FCs during rest. Coactivation pattern (CAP) is the dynamic FC obtained using the high SNR segments driven by the ZFR. The static FC demonstrates that the ZFR-based CR distinguishes the coactivation and non-coactivation scores well in the distribution. CAP analysis demonstrated the stable and longer dwell time dominant resting state functional networks with high SNR segments driven by the ZFR. Static and dynamic FC analysis underpins that the ZFR-driven temporal onsets of BOLD events derive reliable and consistent FCs in the resting brain using a subset of the time points.

Coactivation pattern analysis reveals altered whole-brain functional transient dynamics in autism spectrum disorder.

Recent studies on autism spectrum disorder (ASD) have identified recurring states dominated by similar coactivation pattern (CAP) and revealed associations between dysfunction in seed-based large-scale brain networks and clinical symptoms. However, the presence of abnormalities in moment-to-moment whole-brain dynamics in ASD remains uncertain. In this study, we employed seed-free CAP analysis to identify transient brain activity configurations and investigate dynamic abnormalities in ASD. We utilized a substantial multisite resting-state fMRI dataset consisting of 354 individuals with ASD and 446 healthy controls (HCs, from HC groups and 2). CAP were generated from a subgroup of all HC subjects (HC group 1) through temporal K-means clustering, identifying four CAPs. These four CAPs exhibited either the activation or inhibition of the default mode network (DMN) and were grouped into two pairs with opposing spatial CAPs. CAPs for HC group 2 and ASD were identified by their spatial similarity to those for HC group 1. Compared with individuals in HC group 2, those with ASD spent more time in CAPs involving the ventral attention network but less time in CAPs related to executive control and the dorsal attention network. Support vector machine analysis demonstrated that the aberrant dynamic characteristics of CAPs achieved an accuracy of 74.87% in multisite classification. In addition, we used whole-brain dynamics to predict symptom severity in ASD. Our findings revealed whole-brain dynamic functional abnormalities in ASD from a single transient perspective, emphasizing the importance of the DMN in abnormal dynamic functional activity in ASD and suggesting that temporally dynamic techniques offer novel insights into time-varying neural processes.

Differential connectivity of the posterior piriform cortex in Parkinson’s disease and postviral olfactory dysfunction: an fMRI study

Olfactory dysfunction is a common feature of both postviral upper respiratory tract infections (PV) and idiopathic Parkinson’s disease (PD). Our aim was to investigate potential differences in the connectivity of the posterior piriform cortex, a major component of the olfactory cortex, between PV and PD patients. Fifteen healthy controls (median age 66 years, 9 men), 15 PV (median age 63 years, 7 men) and 14 PD patients (median age 70 years, 9 men) were examined with task-based olfactory fMRI, including two odors: peach and fish. fMRI data were analyzed with the co-activation pattern (CAP) toolbox, which allows a dynamic temporal assessment of posterior piriform cortex (PPC) connectivity. CAP analysis revealed 2 distinct brain networks interacting with the PPC. The first network included regions related to emotion recognition and attention, such as the anterior cingulate and the middle frontal gyri. The occurrences of this network were significantly fewer in PD patients compared to healthy controls (p = 0.023), with no significant differences among PV patients and the other groups. The second network revealed a dissociation between the olfactory cortex (piriform and entorhinal cortices), the anterior cingulate gyrus and the middle frontal gyri. This second network was significantly more active during the latter part of the stimulation, across all groups, possibly due to habituation. Our study shows how the PPC interacts with areas that regulate higher order processing and how this network is substantially affected in PD. Our findings also suggest that olfactory habituation is independent of disease.

Spatiotemporal dynamics of hippocampal-cortical networks underlying the unique phenomenological properties of trauma-related intrusive memories

Trauma-related intrusive memories (TR-IMs) possess unique phenomenological properties that contribute to adverse post-traumatic outcomes, positioning them as critical intervention targets. However, transdiagnostic treatments for TR-IMs are scarce, as their underlying mechanisms have been investigated separate from their unique phenomenological properties. Extant models of more general episodic memory highlight dynamic hippocampal-cortical interactions that vary along the anterior-posterior axis of the hippocampus (HPC) to support different cognitive-affective and sensory-perceptual features of memory. Extending this work into the unique properties of TR-IMs, we conducted a study of eighty-four trauma-exposed adults who completed daily ecological momentary assessments of TR-IM properties followed by resting-state functional magnetic resonance imaging (rs-fMRI). Spatiotemporal dynamics of anterior and posterior hippocampal (a/pHPC)-cortical networks were assessed using co-activation pattern analysis to investigate their associations with different properties of TR-IMs. Emotional intensity of TR-IMs was inversely associated with the frequency and persistence of an aHPC-default mode network co-activation pattern. Conversely, sensory features of TR-IMs were associated with more frequent co-activation of the HPC with sensory cortices and the ventral attention network, and the reliving of TR-IMs in the “here-and-now” was associated with more persistent co-activation of the pHPC and the visual cortex. Notably, no associations were found between HPC-cortical network dynamics and conventional symptom measures, including TR-IM frequency or retrospective recall, underscoring the utility of ecological assessments of memory properties in identifying their neural substrates. These findings provide novel insights into the neural correlates of the unique features of TR-IMs that are critical for the development of individualized, transdiagnostic treatments for this pervasive, difficult-to-treat symptom.

Childhood Trauma, Emotional Awareness, and Neural Correlates of Long-Term Nicotine Smoking

Temporal dynamic measures provide insight into the neurobiological properties of nicotine use. It is critical to determine whether brain-based measures are associated with substance use risk factors, such as childhood trauma-related emotion dysregulation. To assess temporal dynamic differences based on smoking status and examine the associations between childhood trauma, alexithymia, nicotine smoking, and default mode network (DMN) states. This cross-sectional study was conducted in the Baltimore, Maryland, area at the National Institute on Drug Abuse. Participants included individuals aged 18 to 65 years who smoked nicotine long term and matched controls with no co-occurring substance use or psychiatric disorders. Participants were enrolled from August 8, 2013, to August 9, 2022. Analysis was conducted from August 2022 to July 2023. Long-term nicotine smoking. The main outcome was temporal dynamic differences based on smoking status. Coactivation pattern analysis was conducted based on 16-minute resting-state functional magnetic resonance imaging; total time in, persistence of, and frequency of transitions into states were evaluated. The associations between childhood trauma (Childhood Trauma Questionnaire), alexithymia (20-item Toronto Alexithymia Scale), and DMN temporal dynamics were assessed. The sample included 204 participants (102 individuals who smoked nicotine and 102 control individuals) with a mean (SD) age of 37.53 (10.64) years (109 [53.4%] male). Compared with controls, individuals who smoked nicotine spent more time in the frontoinsular DMN (FI-DMN) state (mean difference, 25.63 seconds; 95% CI, 8.05-43.20 seconds; η2p = 0.04; P = .004 after Bonferroni correction). In those who smoked nicotine, greater alexithymia was associated with less time spent in the FI-DMN state (r, -0.26; 95% CI, -0.44 to -0.07; P = .007). In a moderated mediation analysis, alexithymia mediated the association between childhood trauma and time spent in the FI-DMN state only in individuals who smoked nicotine (c' = -0.24; 95% CI, -0.58 to -0.03; P = .02). Compared with controls, individuals who smoked nicotine spent more time in the FI-DMN state. Among those who smoked nicotine, childhood trauma-related alexithymia was associated with less time spent in the FI-DMN state, indicating that considering trauma-related factors may reveal alternative neurobiological underpinnings of substance use. These data may aid in reconciling contradictory findings in prior literature regarding the role of FI-DMN regions in substance use.

Aberrant resting-state co-activation network dynamics in major depressive disorder

Major depressive disorder (MDD) is a globally prevalent and highly disabling disease characterized by dysfunction of large-scale brain networks. Previous studies have found that static functional connectivity is not sufficient to reflect the complicated and time-varying properties of the brain. The underlying dynamic interactions between brain functional networks of MDD remain largely unknown, and it is also unclear whether neuroimaging-based dynamic properties are sufficiently robust to discriminate individuals with MDD from healthy controls since the diagnosis of MDD mainly depends on symptom-based criteria evaluated by clinical observation. Resting-state functional magnetic resonance imaging (fMRI) data of 221 MDD patients and 215 healthy controls were shared by REST-meta-MDD consortium. We investigated the spatial-temporal dynamics of MDD using co-activation pattern analysis and made individual diagnoses using support vector machine (SVM). We found that MDD patients exhibited aberrant dynamic properties (such as dwell time, occurrence rate, transition probability, and entropy of Markov trajectories) in some transient networks including subcortical network (SCN), activated default mode network (DMN), de-activated SCN-cerebellum network, a joint network, activated attention network (ATN), and de-activated DMN-ATN, where some dynamic properties were indicative of depressive symptoms. The trajectories of other networks to deactivated DMN-ATN were more accessible in MDD patients. Subgroup analyses also showed subtle dynamic changes in first-episode drug-naïve (FEDN) MDD patients. Finally, SVM achieved preferable accuracies of 84.69%, 76.77%, and 88.10% in discriminating patients with MDD, FEDN MDD, and recurrent MDD from healthy controls with their dynamic metrics. Our findings reveal that MDD is characterized by aberrant dynamic fluctuations of brain network and the feasibility of discriminating MDD patients using dynamic properties, which provide novel insights into the neural mechanism of MDD.

Transient resting-state salience-limbic co-activation patterns in functional neurological disorders

BackgroundFunctional neurological disorders were historically regarded as the manifestation of a dynamic brain lesion which might be linked to trauma or stress, although this association has not yet been directly tested yet. Analysing large-scale brain network dynamics at rest in relation to stress biomarkers assessed by salivary cortisol and amylase could provide new insights into the pathophysiology of functional neurological symptoms. MethodsCase-control resting-state functional magnetic resonance imaging study of 79 patients with mixed functional neurological disorders (i.e., functional movement disorders, functional seizures, persistent perceptual-postural dizziness) and 74 age- and sex-matched healthy controls. Using a two-step hierarchical data-driven neuroimaging approach, static functional connectivity was first computed between 17 resting-state networks. Second, dynamic alterations in these networks were examined using co-activation pattern analysis. Using a partial least squares correlation analysis, the multivariate pattern of correlation between altered temporal characteristics and stress biomarkers as well as clinical scores were evaluated. ResultsCompared to healthy controls, patients presented with functional aberrancies of the salience-limbic network connectivity. Thus, the insula and amygdala were selected as seed-regions for the subsequent analyses. Insular co-(de)activation patterns related to the salience network, the somatomotor network and the default mode network were detected, which patients entered more frequently than controls. Moreover, an insular co-(de)activation pattern with subcortical regions together with a wide-spread co-(de)activation with diverse cortical networks was detected, which patients entered less frequently than controls. In patients, dynamic alterations conjointly correlated with amylase measures and duration of symptoms. ConclusionThe relationship between alterations in insular co-activation patterns, stress biomarkers and clinical data proposes inter-related mechanisms involved in stress regulation and functional (network) integration. In summary, altered functional brain network dynamics were identified in patients with functional neurological disorder supporting previously raised concepts of impaired attentional and interoceptive processing.

Spatial and Spectral Components of the BOLD Global Signal in Rat Resting-State Functional MRI.

In resting-state fMRI (rs-fMRI), the global signal average captures widespread fluctuations related to unwanted sources of variance such as motion and respiration, as well as widespread neural activity; however, relative contributions of neural and non-neural sources to the global signal remain poorly understood. This study sought to tackle this problem through the comparison of the BOLD global signal to an adjacent non-brain tissue signal, where neural activity was absent, from the same rs-fMRI scan obtained from anesthetized rats. In this dataset, motion was minimal and ventilation was phase-locked to image acquisition to minimize respiratory fluctuations. Data were acquired using three different anesthetics: isoflurane, dexmedetomidine, and a combination of dexmedetomidine and light isoflurane. A power spectral density estimate, a voxel-wise spatial correlation via Pearson's correlation, and a co-activation pattern analysis were performed using the global signal and the non-brain tissue signal. Functional connectivity was calculated using Pearson's linear correlation on default mode network (DMN) regions. We report differences in the spectral composition of the two signals and show spatial selectivity within DMN structures that show an increased correlation to the global signal and decreased intra-network connectivity after global signal regression. All of the observed differences between the global signal and the non-brain tissue signal were maintained across anesthetics. These results show that the global signal is distinct from the noise contained in the tissue signal, as support for a neural contribution. This study provides a unique perspective to the contents of the global signal and their origins.

Cortico–subcortical spatiotemporal dynamics in Parkinson’s disease can be modulated by transcranial alternating current stimulation

Objective: We investigated changes in cortico–subcortical spatiotemporal dynamics to explore the treatment mechanisms of transcranial alternating current stimulation (tACS) in patients with Parkinson’s disease (PD). Methods: Resting-state functional magnetic resonance imaging (rs-fMRI) data were collected from 20 patients with PD and 20 normal controls (NC). Each patient with PD received successive multidisciplinary intensive rehabilitation treatment and tACS treatment over a one-year interval. Individual functional brain network mapping and co-activation pattern (CAP) analysis were performed to characterize cortico–subcortical dynamics. Results: The same tACS electrode placement stimulated different proportions of functional brain networks across the participants. CAP analysis revealed that the visual network, attentional network, and default mode network co-activated with the thalamus, accumbens, and amygdala, respectively. The pattern characterized by the de-activation of the visual network and the activation of the thalamus showed a significantly low amplitude in the patients with PD than in NCs, and this amplitude increased after tACS treatment. Furthermore, the co-occurrence of cortico–subcortical CAPs was significantly higher in patients with PD than in NCs and decreased after tACS treatment. Conclusions: This study investigated cortico–subcortical spatiotemporal dynamics in patients with PD and further revealed the tACS treatment mechanism. These findings contribute to understanding cortico– subcortical dynamics and exploring noninvasive neuromodulation targets of cortico–subcortical circuits in brain diseases, such as PD, Alzheimer’s disease, and depression.

Deficient dynamics of prefrontal-striatal and striatal-default mode network (DMN) neural circuits in internet gaming disorder

BackgroundStudies have proven that individuals with internet gaming disorder (IGD) show impaired cognitive control over game craving; however, the neural mechanism underlying this process remains unclear. Accordingly, the present study aimed to investigate the dynamic features of brain functional networks of individuals with IGD during rest, which have barely been understood until now. MethodsResting-state fMRI data were collected from 333 subjects (123 subjects with IGD (males/females: 73/50) and 210 healthy controls (males/females: 135/75)). First, the data-driven methodology, named co-activation pattern analysis, was applied to investigate the dynamic features of nucleus accumbens (the core region involved in craving/reward processing and addiction)-centered brain networks in IGD. Further, machine learning analysis was conducted to investigate the prediction effect of the dynamic features on participants' addiction severity. ResultsCompared to controls, subjects in the IGD group showed decreased resilience, betweenness centrality and occurrence in the prefrontal-striatal neural circuit, and decreased in-degree in the striatal-default mode network (DMN) circuit. Moreover, these decreased dynamic features could significantly predict participants' addiction severity. LimitationsThe causal relationship between IGD and the abnormal dynamic features cannot be identified in this study. All the subjects were university students. ConclusionsThe present results revealed the underlying brain networks of uncontrollable craving and game-seeking behaviors in individuals with IGD during rest. The decreased dynamics of the prefrontal-striatal and striatal-DMN neural circuits might be potential biomarkers for predicting the addiction severity of IGD and potential targets for effective interventions to reduce game craving of this disorder.

On co-activation pattern analysis and 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.

Criticality Creates a Functional Platform for Network Transitions Between Internal and External Processing Modes in the Human Brain.

Continuous switching between internal and external modes in the brain appears important for generating models of the self and the world. However, how the brain transitions between these two modes remains unknown. We propose that a large synchronization fluctuation of brain networks, emerging only near criticality (i.e., a balanced state between order and disorder), spontaneously creates temporal windows with distinct preferences for integrating the network’s internal information or for processing external stimuli. Using a computational model, electroencephalography (EEG) analysis, and functional magnetic resonance imaging (fMRI) analysis during alterations of consciousness in humans, we report that synchronized and incoherent networks, respectively, bias toward internal and external information with specific network configurations. In the brain network model and EEG-based network, the network preferences are the most prominent at criticality and in conscious states associated with the bandwidth 4−12 Hz, with alternating functional network configurations. However, these network configurations are selectively disrupted in different states of consciousness such as general anesthesia, psychedelic states, minimally conscious states, and unresponsive wakefulness syndrome. The network preference for internal information integration is only significant in conscious states and psychedelic states, but not in other unconscious states, suggesting the importance of internal information integration in maintaining consciousness. The fMRI co-activation pattern analysis shows that functional networks that are sensitive to external stimuli–such as default mode, dorsal attentional, and frontoparietal networks–are activated in incoherent states, while insensitive networks, such as global activation and deactivation networks, are dominated in highly synchronized states. We suggest that criticality produces a functional platform for the brain’s capability for continuous switching between two modes, which is crucial for the emergence of consciousness.

Using multiband multi-echo imaging to improve the robustness and repeatability of co-activation pattern analysis for dynamic functional connectivity

Emerging evidence has shown that functional connectivity is dynamic and changes over the course of a scan. Furthermore, connectivity patterns can arise from short periods of co-activation on the order of seconds. Recently, a dynamic co-activation patterns (CAPs) analysis was introduced to examine the co-activation of voxels resulting from individual timepoints. The goal of this study was to apply CAPs analysis on resting state fMRI data collected using an advanced multiband multi-echo (MBME) sequence, in comparison with a multiband (MB) sequence with a single echo. Data from 28 healthy control subjects were examined. Subjects underwent two resting state scans, one MBME and one MB, and 19 subjects returned within two weeks for a repeat scan session. Data preprocessing included advanced denoising namely multi-echo independent component analysis (ME-ICA) for the MBME data and an ICA-based strategy for Automatic Removal of Motion Artifacts (ICA-AROMA) for the MB data. The CAPs analysis was conducted using the newly published TbCAPs toolbox. CAPs were extracted using both seed-based and seed-free approaches. Timepoints were clustered using k-means clustering. The following metrics were compared between MBME and MB datasets: mean activation in each CAP, the spatial correlation and mean squared error (MSE) between each timepoint and the centroid CAP it was assigned to, within-dataset variance across timepoints assigned to the same CAP, and the between-session spatial correlation of each CAP. Co-activation was heightened for MBME data for the majority of CAPs. Spatial correlation and MSE between each timepoint and its assigned centroid CAP were higher and lower respectively for MBME data. The within-dataset variance was also lower for MBME data. Finally, the between-session spatial correlation was higher for MBME data. Overall, our findings suggest that the advanced MBME sequence is a promising avenue for the measurement of dynamic co-activation patterns by increasing the robustness and reproducibility of the CAPs.

Dynamic functional brain networks underlying the temporal inertia of negative emotions

Affective inertia represents the lasting impact of transient emotions at one time point on affective state at a subsequent time point. Here we describe the neural underpinnings of inertia following negative emotions elicited by sad events in movies. Using a co-activation pattern analysis of dynamic functional connectivity, we examined the temporal expression and reciprocal interactions among brain-wide networks during movies and subsequent resting periods in twenty healthy subjects. Our findings revealed distinctive spatiotemporal expression of visual (VIS), default mode (DMN), central executive (CEN), and frontoparietal control (FPCN) networks both in negative movies and in rest periods following these movies. We also identified different reciprocal relationships among these networks, in transitions from movie to rest. While FPCN and DMN expression increased during and after negative movies, respectively, FPCN occurrences during the movie predicted lower DMN and higher CEN expression during subsequent rest after neutral movies, but this relationship was reversed after the elicitation of negative emotions. Changes in FPCN and DMN activity correlated with more negative subjective affect. These findings provide new insights into the transient interactions of intrinsic brain networks underpinning the inertia of negative emotions. More specifically, they describe a major role of FPCN in emotion elicitation processes, with prolonged impact on DMN activity in subsequent rest, presumably involved in emotion regulation and restoration of homeostatic balance after negative events.

Whole-Brain Functional Dynamics Track Depressive Symptom Severity

Depressive symptoms are reported by 20% of the population and are related to altered functional integrity of large-scale brain networks. The link between moment-to-moment brain function and depressive symptomatology, and the implications of these relationships for clinical and community populations alike, remain understudied. The present study examined relationships between functional brain dynamics and subclinical-to-mild depressive symptomatology in a large community sample of adults with and without psychiatric diagnoses. This study used data made available through the Enhanced Nathan Kline Institute-Rockland Sample; 445 participants between 18 and 65years of age completed a 10-min resting-state functional MRI scan. Coactivation pattern analysis was used to examine the dimensional relationship between depressive symptoms and whole-brain states. Elevated levels of depressive symptoms were associated with increased frequency and dwell time of the default mode network, a brain network associated with self-referential thought, evaluative judgment, and social cognition. Furthermore, increased depressive symptom severity was associated with less frequent occurrences of a hybrid brain network implicated in cognitive control and goal-directed behavior, which may impair the inhibition of negative thinking patterns in depressed individuals. These findings demonstrate how temporally dynamic techniques offer novel insights into time-varying neural processes underlying subclinical and clinically meaningful depressive symptomatology.

Sex differences in functional network dynamics observed using coactivation pattern analysis

ABSTRACT Sex differences in the organization of large-scale resting-state brain networks have been identified using traditional static measures, which average functional connectivity over extended time periods. In contrast, emerging dynamic measures have the potential to define sex differences in network changes over time, providing additional understanding of neurobiological sex differences. To meet this goal, we used a Coactivation Pattern Analysis (CAP) using resting-state functional magnetic resonance imaging data from 181 males and 181 females from the Human Connectome Project. Significant main effects of sex were observed across two independent imaging sessions. Relative to males, females spent more total time in two transient network states (TNSs) spatially overlapping with the dorsal attention network and occipital/sensory-motor network. Greater time spent in these TNSs was related to females making more frequent transitions into these TNSs compared to males. In contrast, males spent more total time in TNSs spatially overlapping with the salience network, which was related to males staying for longer periods once entering these TNSs compared to females. State-to-state transitions also significantly differed between sexes: females transitioned more frequently from default mode network (DMN) states to the dorsal attention network state, whereas males transitioned more frequently from DMN states to salience network states. Results show that males and females spend differing amounts of time at rest in two distinct attention-related networks and show sex-specific transition patterns from DMN states into these attention-related networks. This work lays the groundwork for future investigations into the cognitive and behavioral implications of these sex-specific network dynamics.

TbCAPs: A toolbox for co-activation pattern analysis

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).

Coactivation pattern analysis reveals altered salience network dynamics in children with autism spectrum disorder.

Brain connectivity studies of autism spectrum disorder (ASD) have historically relied on static measures of functional connectivity. Recent work has focused on identifying transient configurations of brain activity, yet several open questions remain regarding the nature of specific brain network dynamics in ASD. We used a dynamic coactivation pattern (CAP) approach to investigate the salience/midcingulo-insular (M-CIN) network, a locus of dysfunction in ASD, in a large multisite resting-state fMRI dataset collected from 172 children (ages 6–13 years; n = 75 ASD; n = 138 male). Following brain parcellation by using independent component analysis, dynamic CAP analyses were conducted and k-means clustering was used to determine transient activation patterns of the M-CIN. The frequency of occurrence of different dynamic CAP brain states was then compared between children with ASD and typically developing (TD) children. Dynamic brain configurations characterized by coactivation of the M-CIN with central executive/lateral fronto-parietal and default mode/medial fronto-parietal networks appeared less frequently in children with ASD compared with TD children. This study highlights the utility of time-varying approaches for studying altered M-CIN function in prevalent neurodevelopmental disorders. We speculate that altered M-CIN dynamics in ASD may underlie the inflexible behaviors commonly observed in children with the disorder.