Resting-state hippocampal connectivity correlates with symptom severity in post-traumatic stress disorder

Altered long-range functional connectivity in PTSD: Role of the infraslow oscillations of cortical activity amplitude envelopes

OBJECTIVE

Coupling between the amplitude envelopes (AEs) of regional cortical activity reflects mechanisms that coordinate the excitability of large-scale cortical networks. We used resting-state MEG recordings to investigate the association between alterations in the coupling of cortical AEs and symptoms of post-traumatic stress disorder (PTSD).

METHODS

Participants (n = 96) were service members with combat exposure and various levels of post-traumatic stress severity (PTSS). We assessed the correlation between PTSS and (1) coupling of broadband cortical AEs of beta band activity, (2) coupling of the low- (<0.5 Hz) and high-frequency (>0.5 Hz) components of the AEs, and (3) their time-varying patterns.

RESULTS

PTSS was associated with widespread hypoconnectivity assessed from the broadband AE fluctuations, which correlated with subscores for the negative thoughts and feelings/emotional numbing (NTF/EN) and hyperarousal clusters of symptoms. Higher NTF/EN scores were also associated with smaller increases in resting-state functional connectivity (rsFC) with time during the recordings. The distinct patterns of rsFC in PTSD were primarily due to differences in the coupling of low-frequency (infraslow) fluctuations of the AEs of beta band activity.

CONCLUSIONS

Our findings implicate the mechanisms underlying the regulation/coupling of infraslow oscillations in the alterations of rsFC assessed from broadband AEs and in PTSD symptomatology.

SIGNIFICANCE

Altered coordination of infraslow amplitude fluctuations across large-scale cortical networks can contribute to network dysfunction and may provide a target for treatment in PTSD.

Comparison of three common inbred mouse strains reveals substantial differences in hippocampal GABAergic interneuron populations and in vitro network oscillations

A major challenge in neuroscience is to pinpoint neurobiological correlates of specific cognitive and neuropsychiatric traits. At the mesoscopic level, promising candidates for establishing such connections are brain oscillations that can be robustly recorded as local field potentials with varying frequencies in the hippocampus in vivo and in vitro. Inbred mouse strains show natural variation in hippocampal synaptic plasticity (e.g. long-term potentiation), a cellular correlate of learning and memory. However, their diversity in expression of different types of hippocampal network oscillations has not been fully explored. Here, we investigated hippocampal network oscillations in three widely used inbred mouse strains: C57BL/6J (B6J), C57BL/6NCrl (B6N) and 129S2/SvPasCrl (129) with the aim to identify common oscillatory characteristics in inbred mouse strains that show aberrant emotional/cognitive behaviour (B6N and 129) and compare them to "control" B6J strain. First, we detected higher gamma oscillation power in the hippocampal CA3 of both B6N and 129 strains. Second, higher incidence of hippocampal sharp wave-ripple (SPW-R) transients was evident in these strains. Third, we observed prominent differences in the densities of distinct interneuron types and CA3 associative network activity, which are indispensable for sustainment of mesoscopic network oscillations. Together, these results add further evidence to profound physiological differences among inbred mouse strains commonly used in neuroscience research.

Sex differences in anxiety and threat avoidance in GAD65 knock-out mice

Anxiety disorders have been linked to a disbalance of excitation and inhibition in a network of brain structures comprising frontal cortical regions, the amygdala and the hippocampus, among others. Recent imaging studies suggest sex differences in the activation of this anxiety network during the processing of emotional information. Rodent models with genetically altered ϒ-amino butyric acid (GABA) neurotransmission allow studying the neuronal basis of such activation shifts and their relation to anxiety endophenotypes, but to date sex effects have rarely been addressed. Using mice with a null mutation of the GABA synthetizing enzyme glutamate decarboxylase 65 (GAD65-/-), we started to compare anxiety-like behavior and avoidance in male vs. female GAD65-/- mice and their wildtype littermates. In an open field, female GAD65-/- mice displayed increased activity, while male GAD65-/- mice showed an increased adaptation of anxiety-like behavior over time. GAD65-/- mice of both sexes had a higher preference for social interaction partners, which was further heightened in male mice. In male mice higher escape responses were observed during an active avoidance task. Together, female mice showed more stable emotional responses despite GAD65 deficiency. To gain insights into interneuron function in network structures controlling anxiety and threat perception, fast oscillations (10-45 Hz) were measured in ex vivo slice preparations of the anterior cingulate cortex (ACC). GAD65-/- mice of both sexes displayed increased gamma power in the ACC and a higher density of PV-positive interneurons, which are crucial for generating such rhythmic activity. In addition, GAD65-/- mice had lower numbers of somatostatin-positive interneurons in the basolateral amygdala and in the dorsal dentate gyrus especially in male mice, two key regions important for anxiety and active avoidance responses. Our data suggest sex differences in the configuration of GABAergic interneurons in a cortico-amygdala-hippocampal network controlling network activity patterns, anxiety and threat avoidance behavior.

Preliminary Observations of Resting-State Magnetoencephalography in Nonmedicated Children with Obsessive-Compulsive Disorder

Background: Cortico-striato-thalamo-cortical (CSTC) network alterations are hypothesized to contribute to symptoms of obsessive-compulsive disorder (OCD). To date, very few studies have examined whether CSTC network alterations are present in children with OCD, who are medication naive. Medication-naive pediatric imaging samples may be optimal to study neural correlates of illness and identify brain-based markers, given the proximity to illness onset. Methods: Magnetoencephalography (MEG) data were analyzed at rest, in 18 medication-naive children with OCD (M = 12.1 years ±2.0 standard deviation [SD]; 10 M/8 F) and 13 typically developing children (M = 12.3 years ±2.2 SD; 6 M/7 F). Whole-brain MEG-derived resting-state functional connectivity (rs-fc), for alpha- and gamma-band frequencies were compared between OCD and typically developing (control) groups. Results: Increased MEG-derived rs-fc across alpha- and gamma-band frequencies was found in the OCD group compared to the control group. Increased MEG-derived rs-fc at alpha-band frequencies was evident across a number of regions within the CSTC circuitry and beyond, including the cerebellum and limbic regions. Increased MEG-derived rs-fc at gamma-band frequencies was restricted to the frontal and temporal cortices. Conclusions: This MEG study provides preliminary evidence of altered alpha and gamma networks, at rest, in medication-naive children with OCD. These results support prior findings pointing to the relevance of CSTC circuitry in pediatric OCD and further support accumulating evidence of altered connectivity between regions that extend beyond this network, including the cerebellum and limbic regions. Given the substantial portion of children and youth whose OCD symptoms do not respond to conventional treatments, our findings have implications for future treatment innovation research aiming to target and track whether brain patterns associated with having OCD may change with treatment and/or predict treatment response.

Resting-state EEG functional connectivity predicts post-traumatic stress disorder subtypes in veterans

Objective. Post-traumatic stress disorder (PTSD) is highly heterogeneous, and identification of quantifiable biomarkers that could pave the way for targeted treatment remains a challenge. Most previous electroencephalography (EEG) studies on PTSD have been limited to specific handpicked features, and their findings have been highly variable and inconsistent. Therefore, to disentangle the role of promising EEG biomarkers, we developed a machine learning framework to investigate a wide range of commonly used EEG biomarkers in order to identify which features or combinations of features are capable of characterizing PTSD and potential subtypes.Approach. We recorded 5 min of eyes-closed and 5 min of eyes-open resting-state EEG from 202 combat-exposed veterans (53% with probable PTSD and 47% combat-exposed controls). Multiple spectral, temporal, and connectivity features were computed and logistic regression, random forest, and support vector machines with feature selection methods were employed to classify PTSD. To obtain robust results, we performed repeated two-layer cross-validation to test on an entirely unseen test set.Main results. Our classifiers obtained a balanced test accuracy of up to 62.9% for predicting PTSD patients. In addition, we identified two subtypes within PTSD: one where EEG patterns were similar to those of the combat-exposed controls, and another that were characterized by increased global functional connectivity. Our classifier obtained a balanced test accuracy of 79.4% when classifying this PTSD subtype from controls, a clear improvement compared to predicting the whole PTSD group. Interestingly, alpha connectivity in the dorsal and ventral attention network was particularly important for the prediction, and these connections were positively correlated with arousal symptom scores, a central symptom cluster of PTSD.Significance. Taken together, the novel framework presented here demonstrates how unsupervised subtyping can delineate heterogeneity and improve machine learning prediction of PTSD, and may pave the way for better identification of quantifiable biomarkers.

A comparison of the functional connectome in mild traumatic brain injury and post-traumatic stress disorder

Post-traumatic stress disorder (PTSD) and mild traumatic brain injury (mTBI) often co-occur in the context of threat to one's life. These conditions also have an overlapping symptomatology and include symptoms of anxiety, poor concentration and memory problems. A major challenge has been articulating the underlying neurobiology of these overlapping conditions. The primary aim of this study was to compare intrinsic functional connectivity between mTBI (without PTSD) and PTSD (without mTBI). The study included functional MRI data from 176 participants: 42 participants with mTBI, 67 with PTSD and a comparison group of 66 age and sex-matched healthy controls. We used network-based statistical analyses for connectome-wide comparisons of intrinsic functional connectivity between mTBI relative to PTSD and controls. Our results showed no connectivity differences between mTBI and PTSD groups. However, we did find that mTBI had significantly reduced connectivity relative to healthy controls within an extensive network of regions including default mode, executive control, visual and auditory networks. The mTBI group also displayed hyperconnectivity between dorsal and ventral attention networks and perceptual regions. The PTSD group also demonstrated abnormal connectivity within these networks relative to controls. Connectivity alterations were not associated with severity of PTSD or post-concussive symptoms in either clinical group. Taken together, the similar profiles of intrinsic connectivity alterations in these two conditions provide neural evidence that can explain, in part, the overlapping symptomatology between mTBI and PTSD.

Neuroimaging Biomarkers of New-Onset Psychiatric Disorders Following Traumatic Brain Injury

Traumatic brain injury (TBI) has traditionally been associated with cognitive and behavioral changes during both the acute and chronic phases of injury. Because of its noninvasive nature, neuroimaging has the potential to provide unique information on underlying macroscopic and microscopic biological mechanisms that may serve as causative agents for these neuropsychiatric sequelae. This broad scoping review identifies at least 4 common macroscopic pathways that exist between TBI and new-onset psychiatric disorders, as well as several examples of how neuroimaging is currently being utilized in clinical research. The review then critically examines the strengths and limitations of neuroimaging for elucidating TBI-related microscopic pathology, such as microstructural changes, neuroinflammation, proteinopathies, blood-brain barrier damage, and disruptions in cellular signaling. A summary is then provided for how neuroimaging is currently being used to investigate TBI-related pathology in new-onset neurocognitive disorders, depression, and posttraumatic stress disorder. Identified gaps in the literature include a lack of prospective studies to definitively associate imaging findings with the development of new-onset psychiatric disorders, as well as antemortem imaging studies subsequently confirmed with postmortem correlates in the same study cohort. Although the spatial resolution and specificity of imaging biomarkers has greatly improved over the last 2 decades, we conclude that neuroimaging biomarkers do not yet exist for the definitive in vivo diagnosis of cellular pathology. This represents a necessary next step for further elucidating causal relationships between TBI and new-onset psychiatric disorders.

Neurophysiological dynamics for psychological resilience: A view from the temporal axis

When an individual is faced with adversity, the brain and body work cooperatively to adapt to it. This adaptive process is termed psychological resilience, and recent studies have identified several neurophysiological factors ("neurophysiological resilience"), such as monoamines, oscillatory brain activity, hemodynamics, autonomic activity, stress hormones, and immune systems. Each factor is activated in an interactive manner during specific time windows after exposure to stress. Thus, the differences in psychological resilience levels among individuals can be characterized by differences in the temporal dynamics of neurophysiological resilience. In this review, after briefly introducing the frequently used approaches in this research field and the well-known factors of neurophysiological resilience, we summarize the temporal dynamics of neurophysiological resilience. This viewpoint clarifies an important time window, the more-than-one-hour scale, but the neurophysiological dynamics during this window remain elusive. To address this issue, we propose exploring brain-wide oscillatory activities using concurrent functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG) techniques.

Alterations in the Topology of Functional Connectomes Are Associated with Post-Traumatic Stress Disorder and Blast-Related Mild Traumatic Brain Injury in Combat Veterans

Post-traumatic stress disorder (PTSD) is a common condition in post-deployment service members (SM). SMs of the conflicts in Iraq and Afghanistan also frequently experience traumatic brain injury (TBI) and exposure to blasts during deployments. This study evaluated the effect of these conditions and experiences on functional brain connectomes in post-deployment, combat-exposed veterans. Functional brain connectomes were created using 5-min resting-state magnetoencephalography data. Well-established clinical interviews determined current PTSD diagnosis, as well as deployment-acquired mild TBI and history of exposure to blast. Linear regression examined the effect of these conditions on functional brain connectomes beyond covariates. There were significant interactions between blast-related mild TBI and PTSD after correction for multiple comparisons including number of nodes (non-standardized parameter estimate [PE] = -12.47), average degree (PE = 0.05), and connection strength (PE = 0.05). A main effect of blast-related mild TBI was observed on the threshold level. These results demonstrate a distinct functional connectome presentation associated with the presence of both blast-related mild TBI and PTSD. These findings suggest the possibility that blast-related mild TBI alterations in functional brain connectomes affect the presentation or progression of recovery from PTSD. The current results offer mixed support for hyper-connectivity in the chronic phase of deployment TBI.

Depression in post-traumatic stress disorder

Major depressive disorder (MDD) symptoms commonly occur after trauma-exposure, both alone and in combination with post-traumatic stress disorder (PTSD). This article reviews recent research on comorbidity between these disorders, including its implications for symptom severity and response to treatment. Despite considerable symptom overlap, the two disorders represent distinct constructs and depend, at least in part, on separate biological mechanisms. Both, however, are also clearly related to stress psychopathology. We recommend that more research focus specifically on the study of individual differences in symptom expression in order to identify distinct subgroups of individuals and develop targeted treatments. However, a barrier to this line of inquiry is the trend of excluding particular patients from clinical trials of new interventions based on symptom severity or comorbidity. Another obstacle is the overreliance on self-report measures in human research. We argue that developing computer-based behavioral measures in order to supplement self-report can help address this challenge. Furthermore, we propose that these measures can help tie findings from human and non-human animal research. A number of paradigms have been used to model MDD-and PTSD-like behavior in animals. These models remain valuable for understanding the biological basis of these disorders in humans and for identifying potential interventions, but they have been underused for the study of comorbidity. Although the interpretation of animal behavior remains a concern, we propose that this can also be overcome through the development of close human analogs to animal paradigms.

Teasing apart trauma: neural oscillations differentiate individual cases of mild traumatic brain injury from post-traumatic stress disorder even when symptoms overlap

Post-traumatic stress disorder (PTSD) and mild traumatic brain injury (mTBI) are highly prevalent and closely related disorders. Affected individuals often exhibit substantially overlapping symptomatology - a major challenge for differential diagnosis in both military and civilian contexts. According to our symptom assessment, the PTSD group exhibited comparable levels of concussion symptoms and severity to the mTBI group. An objective and reliable system to uncover the key neural signatures differentiating these disorders would be an important step towards translational and applied clinical use. Here we explore use of MEG (magnetoencephalography)-multivariate statistical learning analysis in identifying the neural features for differential PTSD/mTBI characterisation. Resting state MEG-derived regional neural activity and coherence (or functional connectivity) across seven canonical neural oscillation frequencies (delta to high gamma) were used. The selected features were consistent and largely confirmatory with previously established neurophysiological markers for the two disorders. For regional power from theta, alpha and high gamma bands, the amygdala, hippocampus and temporal areas were identified. In line with regional activity, additional connections within the occipital, parietal and temporal regions were selected across a number of frequency bands. This study is the first to employ MEG-derived neural features to reliably and differentially stratify the two disorders in a multi-group context. The features from alpha and beta bands exhibited the best classification performance, even in cases where distinction by concussion symptom profiles alone were extremely difficult. We demonstrate the potential of using 'invisible' neural indices of brain functioning to understand and differentiate these debilitating conditions.

The functional connectome in posttraumatic stress disorder

BACKGROUND

Previous fMRI studies of posttraumatic stress disorder (PTSD) have investigated region-specific alterations in intrinsic connectivity but connectome-wide changes in connectivity are yet to be characterized. Understanding the neurobiology of this is important to develop novel treatment interventions for PTSD. This study aims to identify connectome-wide disruptions in PTSD to provide a more comprehensive analysis of nseural networks in this disorder.

METHODS

A functional MRI scan was completed by 138 individuals (67 PTSD and 71 non-trauma-exposed healthy controls [HC]). For every individual, inter-regional intrinsic functional connectivity was estimated between 436 brain regions, comprising intra and inter-network connectivity of eight large-scale brain networks. Group-wise differences between PTSD and HC were investigated using network-based statistics at a family-wise error rate of p < 0.05. Significant network differences were then further investigated in 27 individuals with trauma exposure but no PTSD [TC]).

RESULTS

Compared to HC, PTSD displayed lower intrinsic functional connectivity in a network of 203 connections between 420 regions within and between mid-posterior default mode, central executive, limbic, visual and somatomotor regions. Additionally, PTSD displayed higher connectivity across a network of 50 connections from thalamic and limbic to sensory and default-mode regions. Connectivity in TC in both these networks was intermediate and significantly different to PTSD and HC.

CONCLUSION

A large-scale imbalance between hypoconnectivity of higher-order cortical networks and hyperconnectivity of emotional and arousal response systems seems to occur on a sliding scale from trauma exposure to clinical manifestation as PTSD. Novel interventions that target this systemic functional imbalance could provide potential mitigation of PTSD.

Diffusion Tensor Imaging Reveals White Matter Differences in Military Personnel Exposed to Trauma with and without Post-traumatic Stress Disorder

BACKGROUND

Post-traumatic stress disorder (PTSD) is a debilitating mental health condition that develops in response to exposure to a traumatic event. The purpose of this study was to investigate white matter differences using diffusion tensor imaging (DTI) in trauma exposed military personnel with and without PTSD.

METHODS

Data were acquired in compliance with the Hospital for Sick Children and Canadian Armed Forces Research Ethics Boards for the following groups: military personnel with PTSD (PTSD, n = 23), trauma exposed military personnel with no PTSD diagnosis (TE, n = 25) and civilian controls (CC, n =13) . All participants were male. DTI was acquired on a Siemens Trio 3T MRI. Maps of Fractional Anisotropy (FA), Mean Diffusivity (MD), Axial Diffusivity (AD), and Radial Diffusivity (RD) were analyzed using Tract-Based Spatial Statistics (TBSS).

RESULTS

In the PTSD and TE groups, FA was significantly greater within the hippocampus, corpus callosum, cingulum, and several associated white matter tracts. Elevated FA was shown to be largely due to reduced RD suggesting a possible structural substrate that underscores neurophysiological connectivity.

CONCLUSIONS

This study reinforces previous findings showing differences in DTI metrics within the limbic system in military personnel exposed to trauma with and without PTSD.

Magnetoencephalography: physics, techniques, and applications in the basic and clinical neurosciences

Magnetoencephalography (MEG) is a technique used to measure the magnetic fields generated from neuronal activity in the brain. MEG has a high temporal resolution on the order of milliseconds and provides a more direct measure of brain activity when compared with hemodynamic-based neuroimaging methods such as magnetic resonance imaging and positron emission tomography. The current review focuses on basic features of MEG such as the instrumentation and the physics that are integral to the signals that can be measured, and the principles of source localization techniques, particularly the physics of beamforming and the techniques that are used to localize the signal of interest. In addition, we review several metrics that can be used to assess functional coupling in MEG and describe the advantages and disadvantages of each approach. Lastly, we discuss the current and future applications of MEG.

Compassion As an Intervention to Attune to Universal Suffering of Self and Others in Conflicts: A Translational Framework

As interpersonal, racial, social, and international conflicts intensify in the world, it is important to safeguard the mental health of individuals affected by them. According to a Buddhist notion "if you want others to be happy, practice compassion; if you want to be happy, practice compassion," compassion practice is an intervention to cultivate conflict-proof well-being. Here, compassion practice refers to a form of concentrated meditation wherein a practitioner attunes to friend, enemy, and someone in between, thinking, "I'm going to help them (equally)." The compassion meditation is based on Buddhist philosophy that mental suffering is rooted in conceptual thoughts that give rise to generic mental images of self and others and subsequent biases to preserve one's egoism, blocking the ultimate nature of mind. To contextualize compassion meditation scientifically, we adopted a Bayesian active inference framework to incorporate relevant Buddhist concepts, including mind (buddhi), compassion (karuna), aggregates (skandhas), suffering (duhkha), reification (samaropa), conceptual thoughts (vikalpa), and superimposition (prapañca). In this framework, a person is considered a Bayesian Engine that actively constructs phenomena based on the aggregates of forms, sensations, discriminations, actions, and consciousness. When the person embodies rigid beliefs about self and others' identities (identity-grasping beliefs) and the resulting ego-preserving bias, the person's Bayesian Engine malfunctions, failing to use prediction errors to update prior beliefs. To counter this problem, after recognizing the causes of sufferings, a practitioner of the compassion meditation aims to attune to all others equally, friends and enemies alike, suspend identity-based conceptual thoughts, and eventually let go of any identity-grasping belief and ego-preserving bias that obscure reality. We present a brain model for the Bayesian Engine of three components: (a) Relation-Modeling, (b) Reality-Checking, and (c) Conflict-Alarming, which are subserved by (a) the Default-Mode Network (DMN), (b) Frontoparietal Network (FPN) and Ventral Attention Network (VAN), and (c) Salience Network (SN), respectively. Upon perceiving conflicts, the strengthening or weakening of ego-preserving bias will critically depend on whether the SN up-regulates the DMN or FPN/VAN, respectively. We propose that compassion meditation can strengthen brain regions that are conducive for suspending prior beliefs and enhancing the attunements to the counterparts in conflicts.

Supervised machine learning for diagnostic classification from large-scale neuroimaging datasets

There are growing concerns about the generalizability of machine learning classifiers in neuroimaging. In order to evaluate this aspect across relatively large heterogeneous populations, we investigated four disorders: Autism spectrum disorder (N = 988), Attention deficit hyperactivity disorder (N = 930), Post-traumatic stress disorder (N = 87) and Alzheimer's disease (N = 132). We applied 18 different machine learning classifiers (based on diverse principles) wherein the training/validation and the hold-out test data belonged to samples with the same diagnosis but differing in either the age range or the acquisition site. Our results indicate that overfitting can be a huge problem in heterogeneous datasets, especially with fewer samples, leading to inflated measures of accuracy that fail to generalize well to the general clinical population. Further, different classifiers tended to perform well on different datasets. In order to address this, we propose a consensus-classifier by combining the predictive power of all 18 classifiers. The consensus-classifier was less sensitive to unmatched training/validation and holdout test data. Finally, we combined feature importance scores obtained from all classifiers to infer the discriminative ability of connectivity features. The functional connectivity patterns thus identified were robust to the classification algorithm used, age and acquisition site differences, and had diagnostic predictive ability in addition to univariate statistically significant group differences between the groups. A MATLAB toolbox called Machine Learning in NeuroImaging (MALINI), which implements all the 18 different classifiers along with the consensus classifier is available from Lanka et al. (2019) The toolbox can also be found at the following URL: https://github.com/pradlanka/malini .

Predicting PTSD severity using longitudinal magnetoencephalography with a multi-step learning framework

Objective The present study explores the effectiveness of incorporating temporal information in predicting Post-Traumatic Stress Disorder (PTSD) severity using magnetoencephalography (MEG) imaging data. The main objective was to assess the relationship between longitudinal MEG functional connectome data, measured across a variety of neural oscillatory frequencies and collected at two-timepoints (Phase I & II), against PTSD severity captured at the later time point. Approach We used an in-house developed informatics solution, featuring a two-step process featuring pre-learn feature selection (CV-SVR-rRF-FS, cross-validation with support vector regression and recursive random forest feature selection) and deep learning (long-short term memory recurrent neural network, LSTM-RNN) techniques. Main results The pre-learn step selected a small number of functional connections (or edges) from Phase I MEG data associated with Phase II PTSD severity, indexed using the PTSD CheckList (PCL) score. This strategy identified the functional edges affected by traumatic exposure and indexed disease severity, either permanently or evolving dynamically over time, for optimal predictive performance. Using the selected functional edges, LSTM modelling was used to incorporate the Phase II MEG data into longitudinal regression models. Single timepoint (Phase I and Phase II MEG data) SVR models were generated for comparison. Assessed with holdout test data, alpha and high gamma bands showed enhanced predictive performance with the longitudinal models comparing to the Phase I single timepoint models. The best predictive performance was observed for lower frequency ranges compared to the higher frequencies (low gamma), for both model types. Significance This study identified the neural oscillatory signatures that benefited from additional temporal information when estimating the outcome of PTSD severity using MEG functional connectome data. Crucially, this approach can similarly be applied to any other mental health challenge, using this effective informatics foundation for longitudinal tracking of pathological brain states and predicting outcome with a MEG-based neurophysiology imaging system.

Preclinical evidence in support of repurposing sub-anesthetic ketamine as a treatment for L-DOPA-induced dyskinesia

Parkinson's disease (PD) is the second most common neurodegenerative disease. Pharmacotherapy with L-DOPA remains the gold-standard therapy for PD, but is often limited by the development of the common side effect of L-DOPA-induced dyskinesia (LID), which can become debilitating. The only effective treatment for disabling dyskinesia is surgical therapy (neuromodulation or lesioning), therefore effective pharmacological treatment of LID is a critical unmet need. Here, we show that sub-anesthetic doses of ketamine attenuate the development of LID in a rodent model, while also having acute anti-parkinsonian activity. The long-term anti-dyskinetic effect is mediated by brain-derived neurotrophic factor-release in the striatum, followed by activation of ERK1/2 and mTOR pathway signaling. This ultimately leads to morphological changes in dendritic spines on striatal medium spiny neurons that correlate with the behavioral effects, specifically a reduction in the density of mushroom spines, a dendritic spine phenotype that shows a high correlation with LID. These molecular and cellular changes match those occurring in hippocampus and cortex after effective sub-anesthetic ketamine treatment in preclinical models of depression, and point to common mechanisms underlying the therapeutic efficacy of ketamine for these two disorders. These preclinical mechanistic studies complement current ongoing clinical testing of sub-anesthetic ketamine for the treatment of LID by our group, and provide further evidence in support of repurposing ketamine to treat individuals with PD. Given its clinically proven therapeutic benefit for both treatment-resistant depression and several pain states, very common co-morbidities in PD, sub-anesthetic ketamine could provide multiple therapeutic benefits for PD in the future.

Circuit-Based Biomarkers for Mood and Anxiety Disorders

Mood and anxiety disorders are complex heterogeneous syndromes that manifest in dysfunctions across multiple brain regions, cell types, and circuits. Biomarkers using brain-wide activity patterns in humans have proven useful in distinguishing between disorder subtypes and identifying effective treatments. In order to improve biomarker identification, it is crucial to understand the basic circuitry underpinning brain-wide activity patterns. Leveraging a large repertoire of techniques, animal studies have examined roles of specific cell types and circuits in driving maladaptive behavior. Recent advances in multiregion recording techniques, data-driven analysis approaches, and machine-learning-based behavioral analysis tools can further push the boundary of animal studies and bridge the gap with human studies, to assess how brain-wide activity patterns encode and drive emotional behavior. Together, these efforts will allow identifying more precise biomarkers to enhance diagnosis and treatment.

Local and large-scale beta oscillatory dysfunction in males with mild traumatic brain injury

Mild traumatic brain injury (mTBI) is impossible to detect with standard neuroradiological assessment such as structural magnetic resonance imaging (MRI). Injury does, however, disrupt the dynamic repertoire of neural activity indexed by neural oscillations. In particular, beta oscillations are reliable predictors of cognitive, perceptual, and motor system functioning, as well as correlating highly with underlying myelin architecture and brain connectivity-all factors particularly susceptible to dysregulation after mTBI. We measured local and large-scale neural circuit function by magnetoencephalography (MEG) with a data-driven model fit approach using the fitting oscillations and one-over f algorithm in a group of young adult men with mTBI and a matched healthy control group. We quantified band-limited regional power and functional connectivity between brain regions. We found reduced regional power and deficits in functional connectivity across brain areas, which pointed to the well-characterized thalamocortical dysconnectivity associated with mTBI. Furthermore, our results suggested that beta functional connectivity data reached the best mTBI classification performance compared with regional power and symptom severity [measured with Sport Concussion Assessment Tool 2 (SCAT2)]. The present study reveals the relevance of beta oscillations as a window into neurophysiological dysfunction in mTBI and also highlights the reliability of neural synchrony biomarkers in disorder classification.NEW & NOTEWORTHY Mild traumatic brain injury (mTBI) disrupts the dynamic repertoire of neural oscillations, but so far beta activity has not been studied. In mTBI, we found reductions in frontal beta and large-scale beta networks, indicative of thalamocortical dysconnectivity and disrupted information flow through cortico-basal ganglia-thalamic circuits. Relatively, connectivity more accurately classifies individual mTBI cases compared with regional power. We show the relevance of beta oscillations in mTBI and the reliability of these markers in classification.

Rethinking post-traumatic stress disorder - A predictive processing perspective

Predictive processing has become a popular framework in neuroscience and computational psychiatry, where it has provided a new understanding of various mental disorders. Here, we apply the predictive processing account to post-traumatic stress disorder (PTSD). We argue that the experience of a traumatic event in Bayesian terms can be understood as a perceptual hypothesis that is subsequently given a very high a-priori likelihood due to its (life-) threatening significance; thus, this hypothesis is re-selected although it does not fit the actual sensory input. Based on this account, we re-conceptualise the symptom clusters of PTSD through the lens of a predictive processing model. We particularly focus on re-experiencing symptoms as the hallmark symptoms of PTSD, and discuss the occurrence of flashbacks in terms of perceptual and interoceptive inference. This account provides not only a new understanding of the clinical profile of PTSD, but also a unifying framework for the corresponding pathologies at the neurobiological level. Finally, we derive directions for future research and discuss implications for psychological and pharmacological interventions.

Classifying post-traumatic stress disorder using the magnetoencephalographic connectome and machine learning

Given the subjective nature of conventional diagnostic methods for post-traumatic stress disorder (PTSD), an objectively measurable biomarker is highly desirable; especially to clinicians and researchers. Macroscopic neural circuits measured using magnetoencephalography (MEG) has previously been shown to be indicative of the PTSD phenotype and severity. In the present study, we employed a machine learning-based classification framework using MEG neural synchrony to distinguish combat-related PTSD from trauma-exposed controls. Support vector machine (SVM) was used as the core classification algorithm. A recursive random forest feature selection step was directly incorporated in the nested SVM cross validation process (CV-SVM-rRF-FS) for identifying the most important features for PTSD classification. For the five frequency bands tested, the CV-SVM-rRF-FS analysis selected the minimum numbers of edges per frequency that could serve as a PTSD signature and be used as the basis for SVM modelling. Many of the selected edges have been reported previously to be core in PTSD pathophysiology, with frequency-specific patterns also observed. Furthermore, the independent partial least squares discriminant analysis suggested low bias in the machine learning process. The final SVM models built with selected features showed excellent PTSD classification performance (area-under-curve value up to 0.9). Testament to its robustness when distinguishing individuals from a heavily traumatised control group, these developments for a classification model for PTSD also provide a comprehensive machine learning-based computational framework for classifying other mental health challenges using MEG connectome profiles.

New perspectives on the neurobiology of PTSD: High-resolution imaging of neural circuit (dys)function with magnetoencephalography

Introduction: Combat-related posttraumatic stress disorder (PTSD) is increasingly conceptualized in psychiatry as a disorder of dysfunctional neural circuits. Advances in neuroimaging have enabled the study of those networks non-invasively. PTSD is currently assessed using subjective self-reporting to inform crucial decisions, such as fitness to deploy, but objective markers would aid in diagnosis and return-to-deployment decisions. Methods: Magnetoencephalography (MEG) allows investigation of neural circuit function via imaging of brain waves (known as neural oscillations) that index information processing in the brain and would prove a reliable, objective, biomarker. These measures of brain function establish how regions communicate to form brain circuits that support thinking and behaviour. Results: Studies into intrinsic brain function, both during rest and when engaged in a task designed to tap into cognitive dysfunction, have found these neurobiological mechanisms are disrupted in PTSD and are a reliable objective marker of illness. We now know that these alterations in brain function are directly related to core symptoms of PTSD and comorbid cognitive-behavioural challenges. Discussion: Continued characterization of neural function using MEG and related methods will advance understanding of the neurobiology underlying PTSD; allow for the identification of biomarkers that, coupled with machine learning, will aid in diagnoses; provide individualized therapeutic targets for neurostimulation; predict treatment outcomes; and track disorder remission in military personnel and Veterans who are disproportionately affected by this devastating illness.

Hippocampal Functional Connectivity Mediates the Impact of Acceptance on Posttraumatic Stress Symptom Severity

Investigation in posttraumatic stress disorder (PTSD) shows a negative association between patients' degrees of acceptance (the willingness to face unwanted private experiences while pursuing one's values and goals) and those of clinical symptom severity, suggesting that experiential acceptance is a protective factor of symptoms or an early indicator of resilience after trauma. However, neural mechanisms involved in the relationship between these two variables have yet to be elucidated. Thus, we here investigate whether there are neural mechanisms mediating such relationship using whole-brain voxel-level mediation analysis with seed-based resting-state functional connectivity (RSFC) maps generated by hippocampal subregion seeds in accident survivors (n = 33). We found that the correlation between patients' acceptance and symptom severity was mediated by the RSFC strength between left hippocampal body and left lateral occipital cortex adjacent to superior parietal cortex, the areas related to flashbacks. Our result provides novel evidence that hippocampal RSFC mediates the effect of experiential acceptance on posttraumatic stress symptom severity. If further refined and validated, the finding may aid to the identification of biomarkers to intervention and prevention programs for patients with PTSD.

Disrupted Visual Cortex Neurophysiology Following Very Preterm Birth

BACKGROUND

Visual regions develop rapidly in utero and throughout early childhood, but very preterm (VPT) birth can disrupt the typical maturation of primary cortices, with VPT children exhibiting mild visual impairments in early life and throughout development. This is thought to be due to dysfunctional maturation of occipital cortices. A way to readily index brain function is to examine neural oscillations; these mechanisms play a central role in the modeling and pruning of connections, providing an intrinsic temporal structure that refines the precise alignment of spiking, processing information in the brain, and coordinating networks.

METHODS

Using magnetoencephalography, we examined regional oscillatory patterns and functional coupling in VPT and full-term children. Five minutes of eyes-open resting-state data were acquired from 27 VPT and 32 full-term children at 8 years of age.

RESULTS

As hypothesized, the VPT group, when compared with control children, had elevated theta-band power, while alpha amplitude envelope coupling, a marker of connectivity, was found to be decreased.

CONCLUSIONS

These results support the hypothesis of spectral slowing in VPT children and more broadly suggest that the developmental arc of visual neurophysiology is disrupted by VPT birth. We conclude that these deficits underlie difficulties in complex visual perceptual processing evident during childhood and beyond.

Hippocampal connectivity in the aftermath of acute social stress

The hippocampus is a core brain region that responds to stress. Previous studies have found a dysconnectivity between hippocampus and other brain regions under acute and chronic stress. However, whether and how acute social stress influences the directed connectivity patterns from and to the hippocampus remains unclear. In this study, using a within-subject design and Granger causal analysis (GCA), we investigated the alterations of resting state effective connectivity from and to hippocampal subregions after an acute social stressor (the Trier Social Stress Test). Participants were engaged in stress and control conditions spaced approximately one month apart. Our findings showed that stress altered the information flows in the thalamus-hippocampus-insula/midbrain circuit. The changes in this circuit could also predict with high accuracy the stress and control conditions at the subject level. These hippocampus-related brain networks have been documented to be involved in emotional information processing and storage, as well as habitual responses. We speculate that alterations of the effective connectivity between these brain regions may be associated with the registering and encoding of threatening stimuli under stress. Our investigation of hippocampal functional connectivity at a subregional level may help elucidate the functional neurobiology of stress-related psychiatric disorders.

Reduced electrophysiological habituation to novelty after trauma reflects heightened salience network detection

BACKGROUND

Event-Related Potential (ERP) studies of PTSD have reported enhanced P3 amplitudes in response to trauma-related stimuli that are less likely to habituate over time.

METHODS

In the present study, we compared ERPs to the first and last half of an auditory novelty oddball task using neutral (trauma-unrelated) stimuli. Participants were 59 young students who were: trauma-exposed with "Probable PTSD", trauma-exposed without PTSD, or non-traumatized controls.

RESULTS

Reduced P3 amplitudes were observed for the last half of the trials for the entire sample, but this habituation was less profound for both trauma-exposed groups, demonstrating reduced habituation over time. Arousal symptom severity and trauma history negatively correlated with P3 amplitude habituation across the entire sample. Reduced N1 amplitudes for the last half of the trials were found in both trauma-exposed groups, but not among controls.

CONCLUSIONS

Our findings suggest that trauma-exposed individuals exhibit information processing alterations in response to neutral environmental stimuli that may be related to a general pattern of heightened activity of the Salience Network. Implications for the neurobiological model of PTSD and PTSD psychotherapy are discussed.

Functional reorganization of neural networks involved in emotion regulation following trauma therapy for complex trauma disorders

OBJECTIVES

We investigated whether patients with complex interpersonal trauma engage neural networks that are commonly activated during cognitive reappraisal and responding naturally to affect-laden images. In this naturalistic study, we examined whether trauma treatment not only reduces symptoms but also changes neural networks involved in emotional control.

METHODS

Before and after eight weeks of phase-oriented inpatient trauma treatment, patients (n = 28) with complex posttraumatic stress disorder (cPTSD) and complex dissociative disorders (CDD) performed a cognitive reappraisal task while electroencephalography (EEG) was registered. Patients were measured as a prototypical dissociative part that aims to fulfill daily life goals while avoiding traumatic memories and associated dissociative parts. Matched healthy controls (n = 38) were measured twice as well. We examined task-related functional connectivity and assessed self-reports of clinical symptoms and emotion regulation skills.

RESULTS

Prior to treatment and compared to controls, patients showed hypoconnectivity within neural networks involved in emotional downregulation while reappraising affect-eliciting pictures as well as viewing neutral and affect-eliciting pictures. Following treatment, connectivity became normalized in these networks comprising regions associated with cognitive control and memory. Additionally, patients showed a treatment-related reduction of negative but not of positive dissociative symptoms.

CONCLUSIONS

This is the first study demonstrating that trauma-focused treatment was associated with favorable changes in neural networks involved in emotional control. Emotional overregulation manifesting as negative dissociative symptoms was reduced but not emotional underregulation, manifesting as positive dissociative symptoms.

Characterizing hippocampal dynamics with MEG: A systematic review and evidence-based guidelines

The hippocampus, a hub of activity for a variety of important cognitive processes, is a target of increasing interest for researchers and clinicians. Magnetoencephalography (MEG) is an attractive technique for imaging spectro-temporal aspects of function, for example, neural oscillations and network timing, especially in shallow cortical structures. However, the decrease in MEG signal-to-noise ratio as a function of source depth implies that the utility of MEG for investigations of deeper brain structures, including the hippocampus, is less clear. To determine whether MEG can be used to detect and localize activity from the hippocampus, we executed a systematic review of the existing literature and found successful detection of oscillatory neural activity originating in the hippocampus with MEG. Prerequisites are the use of established experimental paradigms, adequate coregistration, forward modeling, analysis methods, optimization of signal-to-noise ratios, and protocol trial designs that maximize contrast for hippocampal activity while minimizing those from other brain regions. While localizing activity to specific sub-structures within the hippocampus has not been achieved, we provide recommendations for improving the reliability of such endeavors.

Altered default mode network connectivity in adolescents with post-traumatic stress disorder

Post-traumatic stress disorder (PTSD) is characterized by intrusions, re-experiencing, avoidance and hyperarousal. These symptoms might be linked to dysfunction in core neurocognitive networks subserving self-referential mental processing (default mode network, DMN), detection of salient stimuli (salience network, SN) and cognitive dysfunction (central executive network, CEN). Resting state studies in adolescent PTSD are scarce and findings are inconsistent, probably due to differences in patient symptom severity. Resting state brain activity was measured in 14 adolescents with severe PTSD and 24 age-matched controls. Seed-based connectivity analyses were used to examine connectivity between the DMN and the whole brain, including regions from other networks (SN and CEN). The relationships of network properties with symptom dimensions (severity, anxiety and depression) and episodic memory were also examined. Analyses revealed decreased within-DMN connectivity (between PCC and occipital cortex) in patients compared to controls. Furthermore, within-DMN connectivity (between PCC and hippocampus) correlated negatively with symptom dimensions (severity and anxiety), while increased connectivity (DMN-SN and DMN-CEN) correlated positively with episodic memory measures. These abnormal network properties found in adolescent PTSD corroborate those previously reported in adult PTSD. Decreased within-DMN connectivity and disrupted DMN-SN and DMN-CEN coupling could form the basis for intrusive trauma recollection and impaired episodic autobiographical recall in PTSD.

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Adolescent PTSD is linked to dysfunction in core neurocognitive networks.

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Results show decreased within-DMN connectivity in patients compared to controls.

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Within-DMN connectivity correlates negatively with severity and anxiety.

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Increased DMN-SN connectivity correlates positively with episodic memory.

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Disrupted connectivity may form the basis for intrusive trauma recollection in PTSD.

Ten-Hour Exposure to Low-Dose Ketamine Enhances Corticostriatal Cross-Frequency Coupling and Hippocampal Broad-Band Gamma Oscillations

Introduction: Treatment-resistant depression, post-traumatic stress disorder, chronic pain, and L-DOPA-induced dyskinesia in Parkinson’s disease are characterized by hypersynchronous neural oscillations. Sub-anesthetic ketamine is effective at treating these conditions, and this may relate to ketamine’s capacity to reorganize oscillatory activity throughout the brain. For example, a single ketamine injection increases gamma (∼40 Hz) and high-frequency oscillations (HFOs, 120–160 Hz) in the cortex, hippocampus, and striatum. While the effects of single injections have been investigated, clinical ketamine treatments can involve 5-h up to 3-day sub-anesthetic infusions. Little is known about the effects of such prolonged exposure on neural synchrony. We hypothesized that hours-long exposure entrains circuits that generate HFOs so that HFOs become sustained after ketamine’s direct effects on receptors subside.

Methods: Local-field recordings were acquired from motor cortex (M1), striatum, and hippocampus of behaving rats (n = 8), and neural responses were measured while rats received 5 ketamine injections (20 mg/kg, i.p., every 2 h, 10-h exposure). In a second experiment, the same animals received injections of D1-receptor antagonist (SCH-23390, 1 mg/kg, i.p.) prior to ketamine injection to determine if D1 receptors were involved in producing HFOs.

Results: Although HFOs remained stable throughout extended ketamine exposure, broad-band high-frequency activity (40–140 Hz) in the hippocampus and delta-HFO cross-frequency coupling (CFC) in dorsal striatum increased with the duration of exposure. Furthermore, while ketamine-triggered HFOs were not affected by D1 receptor blockade, ketamine-associated gamma in motor cortex was suppressed, suggesting involvement of D1 receptors in ketamine-mediated gamma activity in motor cortex.

Conclusion: Prolonged ketamine exposure does not enhance HFOs in corticostriatal circuits, but, instead, enhances coordination between low and high frequencies in the striatum and reduces synchrony in the hippocampus. Increased striatal CFC may facilitate spike-timing dependent plasticity, resulting in lasting changes in motor activity. In contrast, the observed wide-band high-frequency “noise” in the hippocampus suggests that ketamine disrupts action-potential timing and reorganizes connectivity in this region. Differential restructuring of corticostriatal and limbic circuits may contribute to ketamine’s clinical benefits.

Reduced local segregation of single-subject gray matter networks in adult PTSD

To psychoradiologically investigate the topological organization of single-subject gray matter networks in patients with PTSD. Eighty-nine adult PTSD patients and 88 trauma-exposed controls (TEC) underwent a structural T1 magnetic resonance imaging scan. The single-subject brain structural networks were constructed based on gray matter similarity of 90 brain regions. The area under the curve (AUC) of each network metric was calculated and both global and nodal network properties were measured in graph theory analysis. We used nonparametric permutation tests to identify group differences in topological metrics. Relationships between brain network measures and clinical symptom severity were analyzed in the PTSD group. Compared with TEC, brain networks of PTSD patients were characterized by decreased clustering coefficient (C_p ) (p = .04) and local efficiency (E_loc ) (p = .04). Locally, patients with PTSD exhibited altered nodal centrality involving medial superior frontal (mSFG), inferior orbital frontal (iOFG), superior parietal (SPG), middle frontal (MFG), angular, and para-hippocampal gyri (p < .05, corrected). A negative correlation between the segregation (C_p ) of gray matter and functional networks was found in PTSD patients but not the TEC group. Analyses of topological brain gray matter networks indicate a more randomly organized brain network in PTSD. The reduced segregation in gray matter networks and its negative relation with increased segregation in the functional network indicate an inverse relation between gray matter and functional changes. The present psychoradiological findings may reflect a compensatory increase in functional network segregation following a loss of segregation in gray matter networks.

Post-traumatic stress disorder and chronic hyperconnectivity in emotional processing

Post-traumatic stress disorder (PTSD) is associated with heightened responses to threatening stimuli, particularly aggression-related emotional facial expressions. The stability over time of this neurophysiological 'hyperactive' threat response has not been determined. We studied implicit emotional face processing in soldiers with and without PTSD at two time-points (roughly 2 years apart) using magnetoencephalography to determine the response of oscillations and synchrony to happy and angry faces, and the reliability of this marker for PTSD over time. At the initial time-point we had 20 soldiers with and 25 without PTSD; 35 returned for follow-up testing 2 years later, and included 13 with and 22 without PTSD. A mixed-effects analysis was used. There were no significant differences (albeit a slight reduction) in the severity of PTSD between the two time-points. MEG contrasts of the neurophysiological networks involved in the processing of angry vs. happy faces showed that the PTSD group had elevated oscillatory connectivity for angry faces. Maladaptive hypersynchrony in PTSD for threatening faces was seen in subcortical regions, including the thalamus, as well as the ventromedial prefrontal cortex, cingulum gyri, inferior temporal and parietal regions. These results are generally consistent with prior studies and our own, and we demonstrate that this hyperconnectivity was stable over a two year period, in line with essentially stable symptomatology. Together, these results are consistent with the theory that hypervigilance in PTSD is driven by bottom-up, rapid processing of threat-related stimuli that engage a widespread network working in synchrony.

Visual Working Memory Encoding and Recognition in Good Outcome Aneurysmal Subarachnoid Patients

Objectives: Aneurysmal subarachnoid hemorrhage (aSAH) accounts for less than 5% of strokes but is associated with significant morbidity and mortality. Amongst survivors, neurocognitive complaints are common, often despite normal imaging. We used magnetoencephalography (MEG) to investigate neurophysiological function during a visual working memory task in aSAH survivors with good recovery and normal structural imaging.

Methods: Patients with aSAH treated with coiling and exhibiting good outcome measured by Glasgow Outcome Scale (GOS) and without related parenchymal structural lesions in post-treatment MRI, were recruited and compared to age- and sex-matched controls. All participants underwent intelligence and cognitive screening, structural MRI, and MEG testing in conjunction with a 1-back visual working memory task. Sensor-level global field power and virtual electrode source analysis of neuronal activity and connectivity in aSAH were assessed.

Results: Thirteen patients and 13 matched controls were enrolled (age: 56 ± 11 years, 19 female). The 1-back task was completed with similar accuracy despite a trend for a longer reaction time in aSAH patients (p = 0.054). During encoding and recognition phases, aSAH patients showed significantly increased neuronal activation and hyperconnectivity in periventricular areas, specifically the anterior and posterior cingulate gyri.

Conclusions: Increased posterior and anterior cingulate gyri neuronal activity is demonstrated in aSAH patients during visual working memory tasks, in the absence of structural lesions. These areas work mainly as a hub to “organize” memory storage and retrieval. Increased activity in these areas might be compensatory due to injury and consequently loss of neuronal response in connected areas in the working memory networks.

Eye movement desensitization and reprocessing as a treatment for PTSD: current neurobiological theories and a new hypothesis

Eye movement desensitization and reprocessing (EMDR), a form of psychotherapy for individuals with post-traumatic stress disorder (PTSD), has long been a controversial topic, hampered in part by a lack of understanding of the neural mechanisms that contribute to its remedial effect. Here, we review current theories describing EMDR's potential neurobiological mechanisms of action involving working memory, interhemispheric communication, de-arousal, and memory reconsolidation. We then discuss recent studies describing the temporal and spatial aspects of smooth pursuit and predictive saccades, which resemble those made during EMDR, and their neural correlates within the default mode network (DMN) and cerebellum. We hypothesize that if the production of bilateral predictive eye movements is supportive of DMN and cerebellum activation, then therapies that shift the brain towards this state correspondingly would benefit the processes regulated by these structures (i.e., memory retrieval, relaxation, and associative learning), all of which are essential components for PTSD recovery. We propose that the timing of sensory stimulation may be relevant to treatment effect and could be adapted across different patients depending on their baseline saccade metrics. Empirical data in support of this model are reviewed and experimental predictions are discussed.

Community violence exposure in early adolescence: Longitudinal associations with hippocampal and amygdala volume and resting state connectivity

Community violence exposure is a common stressor, known to compromise youth cognitive and emotional development. In a diverse, urban sample of 22 adolescents, participants reported on community violence exposure (witnessing a beating or illegal drug use, hearing gun shots, or other forms of community violence) in early adolescence (average age 12.99), and underwent a neuroimaging scan 3-5 years later (average age 16.92). Community violence exposure in early adolescence predicted smaller manually traced left and right hippocampal and amygdala volumes in a model controlling for age, gender, and concurrent community violence exposure, measured in late adolescence. Community violence continued to predict hippocampus (but not amygdala) volumes after we also controlled for family aggression exposure in early adolescence. Community violence exposure was also associated with stronger resting state connectivity between the right hippocampus (using the manually traced structure as a seed region) and bilateral frontotemporal regions including the superior temporal gyrus and insula. These resting state connectivity results held after controlling for concurrent community violence exposure, SES, and family aggression. Although this is the first study focusing on community violence in conjunction with brain structure and function, these results dovetail with other research linking childhood adversity with smaller subcortical volumes in adolescence and adulthood, and with altered frontolimbic resting state connectivity. Our findings suggest that even community-level exposure to neighborhood violence can have detectable neural correlates in adolescents.

Default Mode Network Oscillatory Coupling Is Increased Following Concussion

Concussion is a common form of mild traumatic brain injury. Despite the descriptor "mild," a single injury can leave long-lasting and sustained alterations to brain function, including changes to localized activity and large-scale interregional communication. Cognitive complaints are thought to arise from such functional deficits. We investigated the impact of injury on neurophysiological and functionally specialized resting networks, known as intrinsic connectivity networks (ICNs), using magnetoencephalography. We assessed neurophysiological connectivity in 40 males, 20 with concussion and 20 without. Regions-of-interest that comprise nodes of ICNs were defined, and their time courses derived using a beamformer approach. Pairwise fluctuations and covariations in band-limited amplitude envelopes were computed reflecting measures of functional connectivity. Intra-network connectivity was compared between groups using permutation testing and correlated with symptoms. We observed increased resting spectral connectivity in the default mode network (DMN) and motor networks (MOTs) in our concussion group when compared with controls, across alpha through gamma ranges. Moreover, these differences were not explained by power spectrum density within the ICNs. Furthermore, this increased coupling was significantly associated with symptoms in the DMN and MOTs-but once accounting for comorbidities (including, depression, anxiety, and ADHD) only the DMN continued to be associated with symptoms. The DMN plays a critical role in shifting between cognitive tasks. These data suggest even a single concussion can perturb the intrinsic coupling of this functionally specialized network in the brain, and may explain persistent and wide-ranging symptomatology.

Attention training modulates resting-state neurophysiological abnormalities in posttraumatic stress disorder

Recent research indicates the relative benefits of computerized attention control treatment (ACT) and attention bias modification treatment (ABMT) for posttraumatic stress disorder (PTSD); however, neural changes underlying these therapeutic effects remain unknown. This study examines how these two types of attention training modulate neurological dysfunction in veterans with PTSD. A community sample of 46 combat veterans with PTSD participated in a randomized double-blinded clinical trial of ACT versus ABMT and 32 of those veterans also agreed to undergo resting-state magnetoencephalography (MEG) recordings. Twenty-four veterans completed psychological and MEG assessments at pre- and post-training to evaluate treatment effects. MEG data were imaged using an advanced Bayesian reconstruction method and examined using statistical parametric mapping. In this report, we focus on the neural correlates and the differential treatment effects observed using MEG; the results of the full clinical trial have been described elsewhere. Our results indicated that ACT modulated occipital and ABMT modulated medial temporal activity more strongly than the comparative treatment. PTSD symptoms decreased significantly from pre- to post-test. These initial neurophysiological outcome data suggest that ACT modulates visual pathways, while ABMT modulates threat-processing regions, but that both are associated with normalizing aberrant neural activity in veterans with PTSD.

Single-Prolonged Stress: A Review of Two Decades of Progress in a Rodent Model of Post-traumatic Stress Disorder

Post-traumatic stress disorder (PTSD) is a common, costly, and often debilitating psychiatric condition. However, the biological mechanisms underlying this disease are still largely unknown or poorly understood. Considerable evidence indicates that PTSD results from dysfunction in highly-conserved brain systems involved in stress, anxiety, fear, and reward. Pre-clinical models of traumatic stress exposure are critical in defining the neurobiological mechanisms of PTSD, which will ultimately aid in the development of new treatments for PTSD. Single prolonged stress (SPS) is a pre-clinical model that displays behavioral, molecular, and physiological alterations that recapitulate many of the same alterations observed in PTSD, illustrating its validity and giving it utility as a model for investigating post-traumatic adaptations and pre-trauma risk and protective factors. In this manuscript, we review the present state of research using the SPS model, with the goals of (1) describing the utility of the SPS model as a tool for investigating post-trauma adaptations, (2) relating findings using the SPS model to findings in patients with PTSD, and (3) indicating research gaps and strategies to address them in order to improve our understanding of the pathophysiology of PTSD.

Default mode network connectivity of fear- and anxiety-related cue and context conditioning

Classical fear conditioning is an important mechanism to adequately respond and adapt to environmental threats and has been related to the development of fear and anxiety. Both cue and context conditioning have been studied but little is known about their relation to relevant resting state networks. The default mode network (DMN) has been reported to be involved in affective learning and described as facilitating a state of readiness in responding to environmental changes. We examined resting state brain connectivity patterns of the default mode network (DMN) in 119 healthy volunteers. Specifically, we carried out correlation analyses between the DMN and skin conductance responses (SCRs) as well as arousal, valence and contingency ratings during learning. In addition, we examined the role of trait anxiety. Two different DMN patterns were identified in which stronger connectivity was linked to lower differential SCRs during fear and anxiety learning. One was related to cue conditioning and involved the amygdala and the medial prefrontal cortex, and one was associated with context conditioning and included the hippocampal formation and sensorimotor areas. These results were replicated in an independent sample. Functional connectivity of the DMN with these key regions at rest was also predictive of trait anxiety but this association could not be replicated in the second sample. We showed that DMN connectivity is differently associated with cued versus contextual learning mechanisms. Uncovering individual differences in baseline network connectivity of the DMN with these key regions might lead to a better understanding of fear and anxiety. Such findings could indeed help to identify vulnerability factors linked to network alterations at rest with dysregulation of learning processes involved in the pathophysiology of stress and anxiety disorders.

Disrupted cortical brain network in post-traumatic stress disorder patients: a resting-state electroencephalographic study

This study aimed to examine the source-level cortical brain networks of post-traumatic stress disorder (PTSD) based on the graph theory using electroencephalography (EEG). Sixty-six cortical source signals were estimated from 78 PTSD and 58 healthy controls (HCs) of resting-state EEG. Four global indices (strength, clustering coefficient (CC), path length (PL) and efficiency) and one nodal index (CC) were evaluated in six frequency bands (delta, theta, alpha, low beta, high beta and gamma). PTSD showed decreased global strength, CC and efficiency, in delta, theta, and low beta band and enhanced PL in theta and low beta band. In low beta band, the strength and CC correlated positively with the anxiety scores, while PL had a negative correlation. In addition, nodal CCs were reduced in PTSD in delta, theta and low beta band. Nodal CCs of theta band correlated negatively with rumination and re-experience symptom scores; while, nodal CCs in low beta band correlated positively with anxiety and pain severity. Inefficiently altered and symptom-dependent changes in cortical networks were seen in PTSD. Our source-level cortical network indices might be promising biomarkers for evaluating PTSD.

Resilience and cross-network connectivity: A neural model for post-trauma survival

Literature on the neurobiological bases of Post-Traumatic Stress Disorder (PTSD) considers medial Prefrontal cortex (mPFC), a core region of the Default Mode Network (DMN), as a region involved in response regulation to stressors. Disrupted functioning of the DMN has been recognized at the basis of the pathophysiology of a number of mental disorders. Furthermore, in the evaluation of the protective factors to trauma consequence, an important role has been assigned to resilience. Our aim was to investigate the specific relation of resilience and PTSD symptoms severity with resting state brain connectivity in a traumatized population using magnetoencephalography (MEG), a non-invasive imaging technique with high temporal resolution and documented advantages in clinical applications. Nineteen Trauma Exposed non-PTSD (TENP) and 19 PTSD patients participated to a resting state MEG session. MEG functional connectivity of mPFC seed to the whole brain was calculated. Correlation between mPFC functional connectivity and Clinician Administered PTSD Scale (CAPS) or Connor-Davidson Resilience Scale (CD-RISC) total score was also assessed. In the whole group, it has been evidenced that the higher was the resilience, the lower was the cross-network connectivity between DMN and Salience Network (SN) nodes. Contrarily, in the TENP group, the negative correlation between resilience and DMN-SN cross-interaction disappeared, suggesting a protective role of resilience for brain functioning. Regarding our findings as a continuum between healthy and pathological after trauma outcomes, we could suggest a link between resilience and the good dialogue between the networks needed to face a traumatic event and its long-term consequence on individuals' lives.

Resting-State Neurophysiological Abnormalities in Posttraumatic Stress Disorder: A Magnetoencephalography Study

Posttraumatic stress disorder (PTSD) is a debilitating psychiatric condition that is common in veterans returning from combat operations. While the symptoms of PTSD have been extensively characterized, the neural mechanisms that underlie PTSD are only vaguely understood. In this study, we examined the neurophysiology of PTSD using magnetoencephalography (MEG) in a sample of veterans with and without PTSD. Our primary hypothesis was that veterans with PTSD would exhibit aberrant activity across multiple brain networks, especially those involving medial temporal and frontal regions. To this end, we examined a total of 51 USA combat veterans with a battery of clinical interviews and tests. Thirty-one of the combat veterans met diagnostic criteria for PTSD and the remaining 20 did not have PTSD. All participants then underwent high-density MEG during an eyes-closed resting-state task, and the resulting data were analyzed using a Bayesian image reconstruction method. Our results indicated that veterans with PTSD had significantly stronger neural activity in prefrontal, sensorimotor and temporal areas compared to those without PTSD. Veterans with PTSD also exhibited significantly stronger activity in the bilateral amygdalae, parahippocampal and hippocampal regions. Conversely, healthy veterans had stronger neural activity in the bilateral occipital cortices relative to veterans with PTSD. In conclusion, these data suggest that veterans with PTSD exhibit aberrant neural activation in multiple cortical areas, as well as medial temporal structures implicated in affective processing.

Disconnection from others in autism is more than just a feeling: whole-brain neural synchrony in adults during implicit processing of emotional faces

Background

Socio-emotional difficulties in autism spectrum disorder (ASD) are thought to reflect impaired functional connectivity within the “social brain”. Nonetheless, a whole-brain characterization of the fast responses in functional connectivity during implicit processing of emotional faces in adults with ASD is lacking.

Methods

The present study used magnetoencephalography to investigate early responses in functional connectivity, as measured by interregional phase synchronization, during implicit processing of angry, neutral and happy faces. The sample (n = 44) consisted of 22 young adults with ASD and 22 age- and sex-matched typically developed (TD) controls.

Results

Reduced phase-synchrony in the beta band around 300 ms emerged during processing of angry faces in the ASD compared to TD group, involving key areas of the social brain. In the same time window, de-synchronization in the beta band in the amygdala was reduced in the ASD group across conditions.

Conclusions

This is the first demonstration of atypical global and local synchrony patterns in the social brain in adults with ASD during implicit processing of emotional faces. The present results replicate and substantially extend previous findings on adolescents, highlighting that atypical brain synchrony during processing of socio-emotional stimuli is a hallmark of clinical sequelae in autism.

Electronic supplementary material

The online version of this article (doi:10.1186/s13229-017-0123-2) contains supplementary material, which is available to authorized users.

Rhythms dysregulation: A new perspective for understanding PTSD?

Post-traumatic stress disorder (PTSD) is a complex syndrome that may occur after exposure to one or more traumatic events. It associates physiological, emotional, and cognitive changes Brain and hormonal modifications contribute to some impairments in learning, memory, and emotion regulation. Some of these biological dysfunctions may be analyzed in terms of rhythms dysregulation that would be expressed through endocrine rhythmicity, sleep organization, and temporal synchrony in brain activity. In the first part of this article, we report studies on endocrine rhythmicity revealing that some rhythms abnormalities are frequently observed, although not constantly, for both cortisol and sympathetic nervous system (SNS) activity. The most typical changes are a flattening of the diurnal secretion of cortisol and the hyperactivation of the SNS. These results may explain why cognitive functioning, in particular consolidation of emotional memories, attention, learning, vigilance and arousal, is altered in patients with PTSD. The second part of this article focuses on sleep disturbances, one of the core features of PTSD. Abnormal REM sleep reported in various studies may have a pathophysiological role in PTSD and may exacerbate some symptoms such as emotional regulation and memory. In addition, sleep disorders, such as paradoxical insomnia, increase the risk of developing PTSD. We also discuss the potential impact of sleep disturbances on cognition. Finally, temporal synchrony of brain activity and functional connectivity, explored using electroencephalography and functional magnetic resonance imaging, are addressed. Several studies reported abnormalities in alpha, beta and gamma frequency bands that may affect both attentional and memory processes. Other studies confirmed abnormalities in connectivity and recent fMRI data suggest that this could limit top-down control and may be associated with flashback intrusive memories. These data illustrate that a better knowledge of the different patterns of biological rhythms contributes to explain the heterogeneity of PTSD and shed new light on the association with some frequent medical disorders.

Contrasting Effects of Posttraumatic Stress Disorder and Mild Traumatic Brain Injury on the Whole-Brain Resting-State Network: A Magnetoencephalography Study

The aim of this study was to evaluate alterations in whole-brain resting-state networks associated with posttraumatic stress disorder (PTSD) and mild traumatic brain injury (mTBI). Networks were constructed from locations of peak statistical power on an individual basis from magnetoencephalography (MEG) source series data by applying the weighted phase lag index and surrogate data thresholding procedures. Networks representing activity in the alpha bandwidth as well as wideband activity (DC-80 Hz) were created. Statistical comparisons were adjusted for age and education level. Alpha network results demonstrate reductions in network structure associated with PTSD, but no differences associated with mTBI. Wideband network results demonstrate a shift in connectivity from the alpha to theta bandwidth in both PTSD and mTBI. Also, contrasting alterations in network structure are noted, with increased randomness associated with PTSD and increased structure associated with mTBI. These results demonstrate the potential of the analysis of MEG resting-state networks to differentiate two highly comorbid conditions. The importance of the alpha bandwidth to resting-state connectivity is also highlighted, while demonstrating the necessity of considering activity in other bandwidths during network construction.

Detecting Mild Traumatic Brain Injury Using Resting State Magnetoencephalographic Connectivity

Accurate means to detect mild traumatic brain injury (mTBI) using objective and quantitative measures remain elusive. Conventional imaging typically detects no abnormalities despite post-concussive symptoms. In the present study, we recorded resting state magnetoencephalograms (MEG) from adults with mTBI and controls. Atlas-guided reconstruction of resting state activity was performed for 90 cortical and subcortical regions, and calculation of inter-regional oscillatory phase synchrony at various frequencies was performed. We demonstrate that mTBI is associated with reduced network connectivity in the delta and gamma frequency range (>30 Hz), together with increased connectivity in the slower alpha band (8–12 Hz). A similar temporal pattern was associated with correlations between network connectivity and the length of time between the injury and the MEG scan. Using such resting state MEG network synchrony we were able to detect mTBI with 88% accuracy. Classification confidence was also correlated with clinical symptom severity scores. These results provide the first evidence that imaging of MEG network connectivity, in combination with machine learning, has the potential to accurately detect and determine the severity of mTBI.

Detecting concussion is typically not possible using currently clinically used brain imaging, such as MRI and CT scans. Magnetoencephalographic (MEG) imaging is able to directly measure brain activity at fast time scales, and this can be used to map how various areas of the brain interact. We recorded MEG from individuals who had suffered a concussion, as well as control subjects who had not. We found characteristic alterations of inter-regional interactions associated with concussion. Moreover, using a machine learning approach, we were able to detect concussion with 88% accuracy from MEG connectivity, and confidence of classification correlated with symptom severity. This potentially provides new quantitative and objective methods for detecting and assessing the severity of concussion using neuroimaging.