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Jean Théberge

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13 papers

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YNICL Journal 2026 Journal Article

Bridging the self to the world: resting-state functional connectivity of the temporoparietal junction in post-traumatic stress disorder and its dissociative subtype

Sandhya Narikuzhy
Sherain Harricharan
Daniela Rabellino
Maria Densmore
Jean Théberge
Jonathan Lieberman
Margaret C. McKinnon
Andrew A. Nicholson

BACKGROUND: The temporoparietal junction (TPJ) is a cross-network hub involved in social cognition and attention, processes which are directly impacted by symptoms observed in clinical profiles of post-traumatic stress disorder (PTSD) and its dissociative subtype (PTSD + DS). METHODS: Using SPM12 and CONN, seed-based TPJ resting-state functional connectivity patterns were analyzed in individuals with PTSD (n = 81), PTSD + DS (n = 49), and healthy controls (n = 54) using four seeds [right anterior TPJ (raTPJ), left anterior TPJ (laTPJ), right posterior TPJ (rpTPJ), left posterior TPJ (lpTPJ)]. Post-hoc graph theoretical analyses were performed for raTPJ connectivity in PTSD + DS and healthy controls. RESULTS: As compared to healthy controls, PTSD + DS showed decreased raTPJ functional connectivity with critical anterior frontal lobe nodes involved in the ventral attention and social cognition networks (i.e., left ventrolateral and dorsomedial prefrontal cortices). PTSD showed decreased lpTPJ functional connectivity with the left superior parietal lobule as compared to healthy controls. When comparing PTSD to PTSD + DS, we observed increased bilateral TPJ functional connectivity with the cerebellum. Lastly, compared to healthy controls, both PTSD and PTSD + DS displayed decreased bilateral TPJ functional connectivity with the occipital lobe. Graph theoretical analyses revealed that PTSD + DS showed limited raTPJ involvement and instead more efficient neural communication between occipital lobe and frontal lobe structures as compared to healthy controls, suggesting a possible compensatory neural network in PTSD + DS. CONCLUSIONS: These findings reveal disruptions in TPJ neural circuitry in PTSD and PTSD + DS, which may carry cascading effects on intersecting neural networks involving the TPJ. Implications for psychotherapeutic treatments targeting disembodiment and social cognition are discussed.

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YNICL Journal 2026 Journal Article

Disrupted thalamocortical functional connectivity and canonical resting-state network integration in posttraumatic stress disorder

Nick Steele
Ahmed Hussain
Delin Sun
Courtney Russell
Ashley A. Huggins
Nicholas D. Davenport
Seth G. Disner
Scott R. Sponheim

The thalamus exhibits widespread connectivity to the entire cortical mantle, yet distinct thalamic subregions possess unique connectivity profiles and functional roles. While the thalamus has been consistently implicated in posttraumatic stress disorder (PTSD), fine-grained investigations examining thalamic subregions and nuclei remain sparse. We examined how resting-state functional connectivity (RSFC) of thalamic nuclei with the cortex and large-scale brain networks may contribute to PTSD using high-resolution functional magnetic resonance imaging (fMRI) data from a multi-site dataset of PTSD cases and controls (n = 397). We show that the pulvinar nuclei exhibit weaker RSFC with sensorimotor and salience regions, while the medial geniculate nucleus (MGN) exhibits stronger RSFC with the sensorimotor cortex in PTSD. Greater PTSD severity correlated with weaker RSFC between both the pulvinar and mediodorsal thalamus and cortical sensory/motor regions in the frontal, parietal, and occipital lobes. We identified that the default mode network of PTSD participants had stronger RSFC with the mediodorsal thalamus, while the salience and somatosensory networks exhibited stronger RSFC with somatomotor thalamic nuclei. Fine-grained thalamic mapping is important for uncovering thalamocortical disruptions in PTSD. Thalamic RSFC shows a shift toward heightened subcortical sensory responsivity and diminished voluntary control and cognitive regulation in PTSD.

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YNICL Journal 2025 Journal Article

Contributions of the default mode and central executive networks during posterior cingulate cortex-targeted fMRI neurofeedback in PTSD

Jonathan M. Lieberman
Ruth A. Lanius
Maria Densmore
Jean Théberge
Daniela Rabellino
Paul A. Frewen
Frank Scharnowski
Rakesh Jetly

INTRODUCTION: Functional magnetic resonance imaging-based neurofeedback (fMRI-NFB) enables individuals to regulate brain activity implicated in psychopathology, including post-traumatic stress disorder (PTSD). While most fMRI-NFB studies in PTSD target the amygdala, which engages the central executive network (CEN) for top-down regulation, the posterior cingulate cortex (PCC), a core node of the default mode network (DMN), has recently emerged as a promising therapeutic target. However, the relative contributions of intra-network DMN mechanisms versus inter-network CEN engagement during PCC downregulation remain unclear. METHODS: We used independent component analysis (ICA) to examine DMN and CEN functional connectivity during PCC-targeted fMRI-NFB in individuals with PTSD (n = 14) and healthy controls (n = 15) while viewing trauma-related/distressing words. Mixed-design repeated measures ANOVAs assessed within- and between-group connectivity changes, and clinical correlations were explored. RESULTS: During PCC downregulation, PTSD participants exhibited greater DMN connectivity than controls with trauma-related regions, including the precentral gyrus and anterior insula, which correlated with PTSD severity and emotion regulation difficulties. Conversely, CEN connectivity decreased in both groups, with PTSD participants showing progressively reduced connectivity across training. Direct comparisons revealed that DMN connectivity exceeded CEN connectivity with several brain regions, particularly among PTSD participants. CONCLUSION: These findings highlight the predominant role of DMN mechanisms in PCC downregulation in PTSD. The reliance on intra-network DMN processes over CEN-driven regulation underscores distinct network dynamics that may be unique to PCC-targeted fMRI-NFB. These neural mechanistic insights may inform targeted fMRI-NFB protocols to recalibrate altered DMN connectivity and enhance emotion regulation in PTSD.

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YNIMG Journal 2025 Journal Article

Image-based meta- and mega-analysis (IBMMA): A unified framework for large-scale, multi-site, neuroimaging data analysis

Nick Steele
Ashley A. Huggins
Rajendra A. Morey
Ahmed Hussain
Courtney Russell
Benjamin Suarez-Jimenez
Elena Pozzi
Hadis Jameei

The increasing scale and complexity of neuroimaging datasets aggregated from multiple study sites present substantial analytic challenges, as existing statistical analysis tools struggle to handle missing voxel-data, suffer from limited computational speed and inefficient memory allocation, and are restricted in the types of statistical designs they are able to model. We introduce Image-Based Meta- & Mega-Analysis (IBMMA), a novel software package implemented in R and Python that provides a unified framework for analyzing diverse neuroimaging features, efficiently handles large-scale datasets through parallel processing, offers flexible statistical modeling options, and properly manages missing voxel-data commonly encountered in multi-site studies. IBMMA successfully analyzed a large-n dataset of several thousand participants and revealed findings in brain regions that some traditional software overlooked due to missing voxel-data resulting in gaps in brain coverage. IBMMA has the potential to accelerate discoveries in neuroscience and enhance the clinical utility of neuroimaging findings.

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YNICL Journal 2023 Journal Article

How the body remembers: Examining the default mode and sensorimotor networks during moral injury autobiographical memory retrieval in PTSD

Breanne E. Kearney
Braeden A. Terpou
Maria Densmore
Saurabh B. Shaw
Jean Théberge
Rakesh Jetly
Margaret C. McKinnon
Ruth A. Lanius

Neural representations of sensory percepts and motor responses constitute key elements of autobiographical memory. However, these representations may remain as unintegrated sensory and motor fragments in traumatic memory, thus contributing toward re-experiencing and reliving symptoms in trauma-related conditions such as post-traumatic stress disorder (PTSD). Here, we investigated the sensorimotor network (SMN) and posterior default mode network (pDMN) using a group independent component analysis (ICA) by examining their functional connectivity during a script-driven memory retrieval paradigm of (potentially) morally injurious events in individuals with PTSD and healthy controls. Moral injury (MI), where an individual acts or fails to act in a morally aligned manner, is examined given its inherent ties to disrupted motor planning and thus sensorimotor mechanisms. Our findings revealed significant differences in functional network connectivity across the SMN and pDMN during MI retrieval in participants with PTSD (n = 65) as compared to healthy controls (n = 25). No such significant group-wise differences emerged during retrieval of a neutral memory. PTSD-related alterations included hyperconnectivity between the SMN and pDMN, enhanced within-network connectivity of the SMN with premotor areas, and increased recruitment of the supramarginal gyrus into both the SMN and the pDMN during MI retrieval. In parallel with these neuroimaging findings, a positive correlation was found between PTSD severity and subjective re-experiencing intensity ratings after MI retrieval. These results suggest a neural basis for traumatic re-experiencing, where reliving and/or re-enacting a past morally injurious event in the form of sensory and motor fragments occurs in place of retrieving a complete, past-contextualized narrative as put forth by Brewin and colleagues (1996) and Conway and Pleydell-Pearce (2000). These findings have implications for bottom-up treatments targeting directly the sensory and motoric elements of traumatic experiences.

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YNIMG Journal 2023 Journal Article

Neuroimaging-based classification of PTSD using data-driven computational approaches: A multisite big data study from the ENIGMA-PGC PTSD consortium

Xi Zhu
Yoojean Kim
Orren Ravid
Xiaofu He
Benjamin Suarez-Jimenez
Sigal Zilcha-Mano
Amit Lazarov
Seonjoo Lee

BACKGROUND: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. METHODS: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. RESULTS: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60 % test AUC for s-MRI, 59 % for rs-fMRI and 56 % for d-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75 % AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. CONCLUSION: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable.

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YNICL Journal 2022 Journal Article

Spectral decomposition of EEG microstates in post-traumatic stress disorder

Braeden A. Terpou
Saurabh B. Shaw
Jean Théberge
Victor Férat
Christoph M. Michel
Margaret C. McKinnon
Ruth A. Lanius
Tomas Ros

Microstates offer a promising framework to study fast-scale brain dynamics in the resting-state electroencephalogram (EEG). However, microstate dynamics have yet to be investigated in post-traumatic stress disorder (PTSD), despite research demonstrating resting-state alterations in PTSD. We performed microstate-based segmentation of resting-state EEG in a clinical population of participants with PTSD (N = 61) and a non-traumatized, healthy control group (N = 61). Microstate-based measures (i.e., occurrence, mean duration, time coverage) were compared group-wise using broadband (1-30 Hz) and frequency-specific (i.e., delta, theta, alpha, beta bands) decompositions. In the broadband comparisons, the centro-posterior maximum microstate (map E) occurred significantly less frequently (d = -0.64, pFWE = 0.03) and had a significantly shorter mean duration in participants with PTSD as compared to controls (d = -0.71, pFWE < 0.01). These differences were reflected in the narrow frequency bands as well, with lower frequency bands like delta (d = -0.78, pFWE < 0.01), theta (d = -0.74, pFWE = 0.01), and alpha (d = -0.65, pFWE = 0.02) repeating these group-level trends, only with larger effect sizes. Interestingly, a support vector machine classification analysis comparing broadband and frequency-specific measures revealed that models containing only alpha band features significantly out-perform broadband models. When classifying PTSD, the classification accuracy was 76 % and 65 % for the alpha band and the broadband model, respectively (p = 0.03). Taken together, we provide original evidence supporting the clinical utility of microstates as diagnostic markers of PTSD and demonstrate that filtering EEG into distinct frequency bands significantly improves microstate-based classification of a psychiatric disorder.

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YNICL Journal 2021 Journal Article

Emotion regulation in emerging adults with major depressive disorder and frequent cannabis use

Emily S. Nichols
Jacob Penner
Kristen A. Ford
Michael Wammes
Richard W.J. Neufeld
Derek G.V. Mitchell
Steven G. Greening
Jean Théberge

In people with mental health issues, approximately 20% have co-occurring substance use, often involving cannabis. Although emotion regulation can be affected both by major depressive disorder (MDD) and by cannabis use, the relationship among all three factors is unknown. In this study, we used fMRI to evaluate the effect that cannabis use and MDD have on brain activation during an emotion regulation task. Differences were assessed in 74 emerging adults aged 16-23 with and without MDD who either used or did not use cannabis. Severity of depressive symptoms, emotion regulation style, and age of cannabis use onset were also measured. Both MDD and cannabis use interacted with the emotion regulation task in the left temporal lobe, however the location of the interaction differed for each factor. Specifically, MDD showed an interaction with emotion regulation in the middle temporal gyrus, whereas cannabis use showed an interaction in the superior temporal gyrus. Emotion regulation style predicted activity in the right superior frontal gyrus, however, this did not interact with MDD or cannabis use. Severity of depressive symptoms interacted with the emotion regulation task in the left middle temporal gyrus. The results highlight the influence of cannabis use and MDD on emotion regulation processing, suggesting that both may have a broader impact on the brain than previously thought.

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YNICL Journal 2020 Journal Article

A randomized, controlled trial of alpha-rhythm EEG neurofeedback in posttraumatic stress disorder: A preliminary investigation showing evidence of decreased PTSD symptoms and restored default mode and salience network connectivity using fMRI

Andrew A. Nicholson
Tomas Ros
Maria Densmore
Paul A. Frewen
Richard W.J. Neufeld
Jean Théberge
Rakesh Jetly
Ruth A. Lanius

OBJECTIVE: The default-mode network (DMN) and salience network (SN) have been shown to display altered connectivity in posttraumatic stress disorder (PTSD). Restoring aberrant connectivity within these networks with electroencephalogram neurofeedback (EEG-NFB) has been shown previously to be associated with acute decreases in symptoms. Here, we conducted a double-blind, sham-controlled randomized trial of alpha-rhythm EEG-NFB in participants with PTSD (n = 36) over 20-weeks. Our aim was to provide mechanistic evidence underlying clinical improvements by examining changes in network connectivity via fMRI. METHODS: We randomly assigned participants with a primary diagnosis of PTSD to either the experimental group (n = 18) or sham-control group (n = 18). We collected resting-state fMRI scans pre- and post-NFB intervention, for both the experimental and sham-control PTSD groups. We further compared baseline brain connectivity measures pre-NFB to age-matched healthy controls (n = 36). RESULTS: With regard to the primary outcome measure of PTSD severity, we found a significant main effect of time in the absence of a group × time interaction. Nevertheless, we found significantly decreased PTSD severity scores in the experimental NFB group only, when comparing post-NFB (dz = 0.71) and 3-month follow-up scores (dz = 0.77) to baseline measures. Interestingly, we found evidence to suggest a shift towards normalization of DMN and SN connectivity post-NFB in the experimental group only. Both decreases in PTSD severity and NFB performance were correlated to DMN and SN connectivity post-NFB in the experimental group. Critically, remission rates of PTSD were significant higher in the experimental group (61.1%) as compared to the sham-control group (33.3%). CONCLUSION: The current study shows mechanistic evidence for therapeutic changes in DMN and SN connectivity that are known to be associated with PTSD psychopathology with no patient dropouts. This preliminary investigation merits further research to demonstrate fully the clinical efficacy of EEG-NFB as an adjunctive therapy for PTSD.

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YNICL Journal 2020 Journal Article

Classifying heterogeneous presentations of PTSD via the default mode, central executive, and salience networks with machine learning

Andrew A. Nicholson
Sherain Harricharan
Maria Densmore
Richard W.J. Neufeld
Tomas Ros
Margaret C. McKinnon
Paul A. Frewen
Jean Théberge

Intrinsic connectivity networks (ICNs), including the default mode network (DMN), the central executive network (CEN), and the salience network (SN) have been shown to be aberrant in patients with posttraumatic stress disorder (PTSD). The purpose of the current study was to a) compare ICN functional connectivity between PTSD, dissociative subtype PTSD (PTSD+DS) and healthy individuals; and b) to examine the use of multivariate machine learning algorithms in classifying PTSD, PTSD+DS, and healthy individuals based on ICN functional activation. Our neuroimaging dataset consisted of resting-state fMRI scans from 186 participants [PTSD (n = 81); PTSD + DS (n = 49); and healthy controls (n = 56)]. We performed group-level independent component analyses to evaluate functional connectivity differences within each ICN. Multiclass Gaussian Process Classification algorithms within PRoNTo software were then used to predict the diagnosis of PTSD, PTSD+DS, and healthy individuals based on ICN functional activation. When comparing the functional connectivity of ICNs between PTSD, PTSD+DS and healthy controls, we found differential patterns of connectivity to brain regions involved in emotion regulation, in addition to limbic structures and areas involved in self-referential processing, interoception, bodily self-consciousness, and depersonalization/derealization. Machine learning algorithms were able to predict with high accuracy the classification of PTSD, PTSD+DS, and healthy individuals based on ICN functional activation. Our results suggest that alterations within intrinsic connectivity networks may underlie unique psychopathology and symptom presentation among PTSD subtypes. Furthermore, the current findings substantiate the use of machine learning algorithms for classifying subtypes of PTSD illness based on ICNs.

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YNICL Journal 2020 Journal Article

The hijacked self: Disrupted functional connectivity between the periaqueductal gray and the default mode network in posttraumatic stress disorder using dynamic causal modeling

Braeden A. Terpou
Maria Densmore
Jean Théberge
Paul Frewen
Margaret C. McKinnon
Andrew A. Nicholson
Ruth A. Lanius

Self-related processes define assorted self-relevant or social-cognitive functions that allow us to gather insight and to draw inferences related to our own mental conditions. Self-related processes are mediated by the default mode network (DMN), which, critically, shows altered functionality in individuals with posttraumatic stress disorder (PTSD). In PTSD, the midbrain periaqueductal gray (PAG) demonstrates stronger functional connectivity with the DMN [i.e., precuneus (PCN), medial prefrontal cortex (mPFC)] as compared to healthy individuals during subliminal, trauma-related stimulus processing. Here, we analyzed the directed functional connectivity between the PAG and the PCN, as well as between the PAG and the mPFC to more explicitly characterize the functional connectivity we have observed previously on the corresponding sample and paradigm. We evaluated three models varying with regard to context-dependent modulatory directions (i.e., bi-directional, bottom-up, top-down) among individuals with PTSD (n = 26) and healthy participants (n = 20), where Bayesian model selection was used to identify the most optimal model for each group. We then compared the effective connectivity strength for each parameter across the models and between our groups using Bayesian model averaging. Bi-directional models were found to be favoured across both groups. In PTSD, we revealed the PAG to show stronger excitatory effective connectivity to the PCN, as well as to the mPFC as compared to controls. In PTSD, we further demonstrated that PAG-mediated effective connectivity to the PCN, as well as to the mPFC were modulated more strongly during subliminal, trauma-related stimulus conditions as compared to controls. Clinical disturbances towards self-related processes are reported widely by participants with PTSD during trauma-related stimulus processing, where altered functional connectivity directed by the PAG to the DMN may elucidate experiential links between self- and trauma-related processing in traumatized individuals.

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YNICL Journal 2016 Journal Article

Alpha oscillation neurofeedback modulates amygdala complex connectivity and arousal in posttraumatic stress disorder

Andrew A. Nicholson
Tomas Ros
Paul A. Frewen
Maria Densmore
Jean Théberge
Rosemarie C. Kluetsch
Rakesh Jetly
Ruth A. Lanius

OBJECTIVE: Electroencephalogram (EEG) neurofeedback aimed at reducing the amplitude of the alpha-rhythm has been shown to alter neural networks associated with posttraumatic stress disorder (PTSD), leading to symptom alleviation. Critically, the amygdala is thought to be one of the central brain regions mediating PTSD symptoms. In the current study, we compare directly patterns of amygdala complex connectivity using fMRI, before and after EEG neurofeedback, in order to observe subcortical mechanisms associated with behavioural and alpha oscillatory changes among patients. METHOD: We examined basolateral (BLA), centromedial (CMA), and superficial (SFA) amygdala complex resting-state functional connectivity using a seed-based approach via SPM Anatomy Toolbox. Amygdala complex connectivity was measured in twenty-one individuals with PTSD, before and after a 30-minute session of EEG neurofeedback targeting alpha desynchronization. RESULTS: EEG neurofeedback was associated with a shift in amygdala complex connectivity from areas implicated in defensive, emotional, and fear processing/memory retrieval (left BLA and left SFA to the periaqueductal gray, and left SFA to the left hippocampus) to prefrontal areas implicated in emotion regulation/modulation (right CMA to the medial prefrontal cortex). This shift in amygdala complex connectivity was associated with reduced arousal, greater resting alpha synchronization, and was negatively correlated to PTSD symptom severity. CONCLUSION: These findings have significant implications for developing targeted non-invasive treatment interventions for PTSD patients that utilize alpha oscillatory neurofeedback, showing evidence of neuronal reconfiguration between areas highly implicated in the disorder, in addition to acute symptom alleviation.

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YNIMG Journal 2013 Journal Article

Mind over chatter: Plastic up-regulation of the fMRI salience network directly after EEG neurofeedback

Tomas Ros
Jean Théberge
Paul A. Frewen
Rosemarie Kluetsch
Maria Densmore
Vince D. Calhoun
Ruth A. Lanius

Neurofeedback (NFB) involves a brain–computer interface that allows users to learn to voluntarily control their cortical oscillations, reflected in the electroencephalogram (EEG). Although NFB is being pioneered as a noninvasive tool for treating brain disorders, there is insufficient evidence on the mechanism of its impact on brain function. Furthermore, the dominant rhythm of the human brain is the alpha oscillation (8–12Hz), yet its behavioral significance remains multifaceted and largely correlative. In this study with 34 healthy participants, we examined whether during the performance of an attentional task, the functional connectivity of distinct fMRI networks would be plastically altered after a 30-min session of voluntary reduction of alpha rhythm (n=17) versus a sham-feedback condition (n=17). We reveal that compared to sham-feedback, NFB induced an increase of connectivity within regions of the salience network involved in intrinsic alertness (dorsal anterior cingulate), which was detectable 30min after termination of training. The increase in salience network (default-mode network) connectivity was negatively (positively) correlated with changes in ‘on task’ mind-wandering as well as resting state alpha rhythm. Crucially, we observed a causal dependence between alpha rhythm synchronization during NFB and its subsequent change at resting state, not exhibited by the SHAM group. Our findings provide neurobehavioral evidence for the brain's exquisite functional plasticity, and for a temporally direct impact of NFB on a key cognitive control network, suggesting a promising basis for its use to treat cognitive disorders under physiological conditions.

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