Figure 3
The activity time of different states is significantly correlated with the flashback--like qualities of intrusive memories in the scanner. a) Correlation between time spent in the 'Medial temporal DMN + ', and quality of the intrusive memories. All variables related to quality of the memory are negatively correlated to time spent on this network, although only the relationship with how distressing the intrusive memories were remains significant after FWER correction. b) Matrix of correlation p--values for each of the network activity times with each of the memory variables (*p < 0.05 **FWER < 0.05). c) Aggregated p--values for the correlations of networks' activity times across memory variables (left) and memory variables across networks' activity times (right), FWER corrected. Time spent in the Visual ventral stream + (d) and Salience + networks (e) correlates with how distressing and how much the memory felt as if it was currently happening, respectively.
Source publication
Very little is known about the role of effective cognitive therapy in reversing imbalances in brain activity after trauma. We hypothesised that exaggerated threat perception characteristic of post-traumatic stress disorder (PTSD), and subsequent recovery from this disorder, are underpinned by changes in the dynamics of large-scale brain networks. H...
Contexts in source publication
Context 1
... positive correlations during presentation of trauma--related pictures were found between the activity time of the prefrontal --network and re--experiencing symptoms (p Analyses were carried out on the subsample of participants who experienced at least one intrusive memory during the fMRI task (n=72 (Figure 3d). ...
Context 2
... preprint (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for . http://dx.doi.org/10.1101/2020.01.07.891986 doi: bioRxiv preprint first posted online Jan. 7, 2020; the memory, and how much it made the participant feel as if it was happening again, could be predicted by a combination of the activity times of the estimated networks (Figure 3c, right increase in the activity times of the mtDMN+ as the task progresses than in participants with current PTSD. ...
Context 3
... previous study by St Jacques and colleagues (2013) Jan. 7, 2020; activity time of this network after CT--PTSD might be related to the work done during the psychological therapy. (Figure 2), and also flashback qualities of intrusive memories (Figure 3) Jan. 7, 2020; behaviour of large--scale brain networks, appropriate tools to address these hypotheses directly in human brain data were underdeveloped. The data--driven approach used here, by allowing for concurrent estimation of both large--scale brain networks and the specific time periods during which these dominate brain activity, was able to provide evidence in favour of difficulties in engaging DMN components in participants with PTSD. ...
Context 4
... positive correlations during presentation of trauma--related pictures were found between the activity time of the prefrontal --network and re--experiencing symptoms (p Analyses were carried out on the subsample of participants who experienced at least one intrusive memory during the fMRI task (n=72 (Figure 3d). ...
Context 5
... preprint (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for . http://dx.doi.org/10.1101/2020.01.07.891986 doi: bioRxiv preprint first posted online Jan. 7, 2020; the memory, and how much it made the participant feel as if it was happening again, could be predicted by a combination of the activity times of the estimated networks (Figure 3c, right increase in the activity times of the mtDMN+ as the task progresses than in participants with current PTSD. ...
Context 6
... previous study by St Jacques and colleagues (2013) Jan. 7, 2020; activity time of this network after CT--PTSD might be related to the work done during the psychological therapy. (Figure 2), and also flashback qualities of intrusive memories (Figure 3) Jan. 7, 2020; behaviour of large--scale brain networks, appropriate tools to address these hypotheses directly in human brain data were underdeveloped. The data--driven approach used here, by allowing for concurrent estimation of both large--scale brain networks and the specific time periods during which these dominate brain activity, was able to provide evidence in favour of difficulties in engaging DMN components in participants with PTSD. ...
Citations
... Besides, it is convenient for HMMs to generate state-wise mean activation maps in large-scale network via the multivariate observation model, as well as to observe the processing of visual perception, decisionmaking, and motor response by the sequential spatiotemporal activation maps. Recent study also showed that impaired brain dynamics could be characterized not only in limited targeted regions but also in the large-scale brain networks via HMMs (Charquero-Ballester et al., 2020). Furthermore, dynamic descriptors such as fractional occupancy (FO) inferred from HMMs were found to correlate with the symptoms of patients with schizophrenia, emerging the potential of the promotion to other psychosis, like MDD (Zhi et al., 2018). ...
The pathological mechanisms of major depressive disorders (MDDs) is associated with the overexpression of negative emotions, and the fast transient-activated patterns underlying overrepresentation in depression still remain to be revealed to date. We hypothesized that the aberrant spatiotemporal attributes of the process of sad expressions are related to the neuropathology of MDD and help to detect the depression severity. We enrolled a total of 96 subjects including 47 patients with MDD and 49 healthy controls (HCs), and recorded their magnetoencephalography data under a sad expression recognition task. A hidden Markov model (HMM) was applied to separate the whole neural activity into several brain states, then to characterize the dynamics. To find the disrupted temporal–spatial characteristics, power estimations and fractional occupancy (FO) of each state were estimated and contrasted between MDDs and HCs. Three states were found over the period of emotional stimuli processing procedure. The early visual stage (0–270 ms) was mainly manifested by state 1, and the emotional information processing stage (270–600 ms) was manifested by state 2, while the state 3 remained a steady proportion across the whole period. MDDs activated statistically more in limbic system during state 2 (p = 0.0045) and less in frontoparietal control network during state 3 (p = 5.38 × 10–5) relative to HCs. Hamilton Depression Rating Scale scores were significantly correlated with the predicted disorder severity using FO values (p = 0.0062, r = 0.3933). Relative to HCs, MDDs perceived the sad contents quickly and spent more time overexpressing the negative emotions. These phenomena indicated MDD patients might easily indulge in negative emotion and neglect other things. Furthermore, temporal descriptors built by HMM could be potential biomarkers for identifying the severity of depression disorders.
... Data-driven dynamical models are a promising and powerful tool for the analysis and prediction of the spatiotemporal organization of brain activity [26][27][28][29]. These models allow us to harness the vast amount of spurious information contained in large datasets [30][31][32], capture the hierarchical organization of brain activity [33], enhance brain-computer interfaces [34,35], and may even be employed in clinical settings [10,[36][37][38]. However, how the inference and identification of dynamical models is affected by different factors in multi-site data acquisition has yet to be investigated. ...
Context:
Large multi-site neuroimaging datasets have significantly advanced our quest to understand brain-behavior relationships and to develop biomarkers of psychiatric and neurodegenerative disorders. Yet, such data collections come at a cost, as the inevitable differences across samples may lead to biased or erroneous conclusions.
Objective:
We aim to validate the estimation of individual brain network dynamics fingerprints and appraise sources of variability in large resting-state functional magnetic resonance imaging (rs-fMRI) datasets by providing a novel point of view based on data-driven dynamical models.
Approach:
Previous work has investigated this critical issue in terms of effects on static measures, such as functional connectivity and brain parcellations. Here, we utilize dynamical models (Hidden Markov models - HMM) to examine how diverse scanning factors in multi-site fMRI recordings affect our ability to infer the brain's spatiotemporal wandering between large-scale networks of activity. Specifically, we leverage a stable HMM trained on the Human Connectome Project (homogeneous) dataset, which we then apply to an heterogeneous dataset of traveling subjects scanned under a multitude of conditions.
Main results:
Building upon this premise, we first replicate previous work on the emergence of non-random sequences of brain states. We next highlight how these time-varying brain activity patterns are robust subject-specific fingerprints. Finally, we suggest these fingerprints may be used to assess which scanning factors induce high variability in the data.
Significance:
These results demonstrate that we can i) use large scale dataset to train models that can be then used to interrogate subject-specific data, ii) recover the unique trajectories of brain activity changes in each individual, but also iii) urge caution as our ability to infer such patterns is affected by how, where and when we do so.
... Furthermore, mindfulness-based psychotherapy helped increase DMN connectivity in veterans suffering from war-related PTSD and the improvement correlated specifically with reduction in avoidant symptoms (13), consistent with our findings on the specific associations between DMN connectivity and avoidant symptoms. Similarly, PTSD patients exhibited shorter activations of the DMN after exposure to trauma reminders that correlated with the severity of symptoms (90). Trauma-focused cognitive behavior therapy rescued DMN activity duration of activation and brought it back to normality and other stressrelated psychopathologies linked with alterations in DMN activation patterns in adults (91,92). ...
Background: Although the default mode network (DMN) is a core network essential for brain functioning, little is known about its developmental trajectory, particularly on factors associated with its coherence into a functional network. In light of adult studies indicating DMN's susceptibility to stress-related conditions, we examined links between variability on oxytocin-pathway genes and DMN connectivity in youth exposed to chronic war-related trauma Methods: Following a cohort of war-exposed children from early childhood, we imaged the brains of 74 preadolescents (age 11–13 years; 39 war-exposed) during rest using magnetoencephalography (MEG). A cumulative risk index on oxytocin-pathway genes was constructed by combining single nucleotide polymorphisms on five genes previously linked with social deficits and psychopathology; OXTR rs1042778, OXTR rs2254298, OXTRrs53576, CD38 rs3796863, and AVPR1A RS3. Avoidant response to trauma reminders in early childhood and anxiety disorders in late childhood were assessed as predictors of disruptions to DMN theta connectivity. Results: Higher vulnerability on oxytocin-pathway genes predicted greater disruptions to DMN theta connectivity. Avoidant symptoms in early childhood and generalized anxiety disorder in later childhood were related to impaired DMN connectivity. In combination, stress exposure, oxytocin-pathway genes, and stress-related symptoms explained 24.6% of the variance in DMN connectivity, highlighting the significant effect of stress on the maturing brain. Conclusions: Findings are the first to link the oxytocin system and maturation of the DMN, a core system sustaining autobiographical memories, alteration of intrinsic and extrinsic attention, mentalization, and sense of self. Results suggest that oxytocin may buffer the effects of chronic early stress on the DMN, particularly theta rhythms that typify the developing brain.
... Data-driven dynamical models are a promising and powerful tool for the analysis and prediction of the spatiotemporal organization of brain activity [26][27][28][29]. These models allow us to harness the vast amount of spurious information contained in large datasets [30][31][32], capture the hierarchical organization of brain activity [33], enhance brain-computer interfaces [34,35], and may even be employed in clinical settings [10,[36][37][38]. However, how the inference and identification of dynamical models is affected by different factors in multi-site data acquisition has yet to be investigated. ...
Large multi-site neuroimaging datasets have significantly advanced our quest to understand brain-behaviour relationships and to develop biomarkers of psychiatric and neurodegenerative disorders. Yet, such data collections come at a cost, as the inevitable differences across samples may lead to biased or erroneous conclusions. Previous work has investigated this critical issue in resting-state functional magnetic resonance imaging (rs-fMRI) data in terms of effects on static measures, such as functional connectivity and brain parcellations. Here, we depart from prior approaches and utilize dynamical models to examine how diverse scanning factors in multi-site fMRI recordings affect our ability to infer the brain’s spatiotemporal wandering between large-scale networks of activity. Building upon this premise, we first confirm the emergence of robust subject-specific dynamical patterns of brain activity. Next, we exploit these individual fingerprints to show that scanning sessions belonging to different sites and days tend to induce high variability, while other factors, such as the scanner manufacturer or the number of coils, affect the same metrics to a lesser extent. These results concurrently indicate that we can recover the unique trajectories of brain activity changes in each individual, but also that our ability to infer such patterns is affected by how, where and when we try to do so.
Author summary
We investigate the important issue of data heterogeneity in large multi-site data collections of brain activity recordings. At a time in which appraising the source of variability in large datasets is gaining increasing attention, this study provides a novel point of view based on data-driven dynamical models. By employing subject-specific signatures of brain network dynamics, we find that certain scanning factors significantly affect the quality of resting-state fMRI data. More specifically, we first validate the existence of subject-specific brain dynamics fingerprints. As a proof of concept, we show that dynamical states can be estimated reliably, even across different datasets. Finally, we assess which scanning factors, and to what extent, influence the variability of such fingerprints.
