Resting state brain dynamics: Associations with childhood sexual abuse and major depressive disorder

Brain-based graph-theoretical predictive modeling to map the trajectory of anhedonia, impulsivity, and hypomania from the human functional connectome

Clinical assessments often fail to discriminate between unipolar and bipolar depression and identify individuals who will develop future (hypo)manic episodes. To address this challenge, we developed a brain-based graph-theoretical predictive model (GPM) to prospectively map symptoms of anhedonia, impulsivity, and (hypo)mania. Individuals seeking treatment for mood disorders (n = 80) underwent an fMRI scan, including (i) resting-state and (ii) a reinforcement-learning (RL) task. Symptoms were assessed at baseline as well as at 3- and 6-month follow-ups. A whole-brain functional connectome was computed for each fMRI task, and the GPM was applied for symptom prediction using cross-validation. Prediction performance was evaluated by comparing the GPM to a corresponding null model. In addition, the GPM was compared to the connectome-based predictive modeling (CPM). Cross-sectionally, the GPM predicted anhedonia from the global efficiency (a graph theory metric that quantifies information transfer across the connectome) during the RL task, and impulsivity from the centrality (a metric that captures the importance of a region) of the left anterior cingulate cortex during resting-state. At 6-month follow-up, the GPM predicted (hypo)manic symptoms from the local efficiency of the left nucleus accumbens during the RL task and anhedonia from the centrality of the left caudate during resting-state. Notably, the GPM outperformed the CPM, and GPM derived from individuals with unipolar disorders predicted anhedonia and impulsivity symptoms for individuals with bipolar disorders. Importantly, the generalizability of cross-sectional models was demonstrated in an external validation sample. Taken together, across DSM mood diagnoses, efficiency and centrality of the reward circuit predicted symptoms of anhedonia, impulsivity, and (hypo)mania, cross-sectionally and prospectively. The GPM is an innovative modeling approach that may ultimately inform clinical prediction at the individual level.

Sex-specific resting state brain network dynamics in patients with major depressive disorder

Sex-specific neurobiological changes have been implicated in Major Depressive Disorder (MDD). Dysfunctions of the default mode network (DMN), salience network (SN) and frontoparietal network (FPN) are critical neural characteristics of MDD, however, the potential moderating role of sex on resting-state network dynamics in MDD has not been sufficiently evaluated. Thus, resting-state functional magnetic resonance imaging (fMRI) data were collected from 138 unmedicated patients with first-episode MDD (55 males) and 243 healthy controls (HCs; 106 males). Recurring functional network co-activation patterns (CAPs) were extracted, and time spent in each CAP (the total amount of volumes associated to a CAP), persistence (the average number of consecutive volumes linked to a CAP), and transitions across CAPs involving the SN, DMN and FPN were quantified. Relative to HCs, MDD patients exhibited greater persistence in a CAP involving activation of the DMN and deactivation of the FPN (DMN + FPN-). In addition, relative to the sex-matched HCs, the male MDD group spent more time in two CAPs involving the SN and DMN (i.e., DMN + SN- and DMN-SN + ) and transitioned more frequently from the DMN + FPN- CAP to the DMN + SN- CAP relative to the male HC group. Conversely, the female MDD group showed less persistence in the DMN + SN- CAP relative to the female HC group. Our findings highlight that the imbalance between SN and DMN could be a neurobiological marker supporting sex differences in MDD. Moreover, the dominance of the DMN accompanied by the deactivation of the FPN could be a sex-independent neurobiological correlate related to depression.

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

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

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

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

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

Importance

Temporal dynamic measures provide insight into the neurobiological properties of nicotine use. It is critical to determine whether brain-based measures are associated with substance use risk factors, such as childhood trauma-related emotion dysregulation.

Objective

To assess temporal dynamic differences based on smoking status and examine the associations between childhood trauma, alexithymia, nicotine smoking, and default mode network (DMN) states.

Design, Setting, and Participants

This cross-sectional study was conducted in the Baltimore, Maryland, area at the National Institute on Drug Abuse. Participants included individuals aged 18 to 65 years who smoked nicotine long term and matched controls with no co-occurring substance use or psychiatric disorders. Participants were enrolled from August 8, 2013, to August 9, 2022. Analysis was conducted from August 2022 to July 2023.

Exposure

Long-term nicotine smoking.

Main Outcomes and Measures

The main outcome was temporal dynamic differences based on smoking status. Coactivation pattern analysis was conducted based on 16-minute resting-state functional magnetic resonance imaging; total time in, persistence of, and frequency of transitions into states were evaluated. The associations between childhood trauma (Childhood Trauma Questionnaire), alexithymia (20-item Toronto Alexithymia Scale), and DMN temporal dynamics were assessed.

Results

The sample included 204 participants (102 individuals who smoked nicotine and 102 control individuals) with a mean (SD) age of 37.53 (10.64) years (109 [53.4%] male). Compared with controls, individuals who smoked nicotine spent more time in the frontoinsular DMN (FI-DMN) state (mean difference, 25.63 seconds; 95% CI, 8.05-43.20 seconds; η2p = 0.04; P = .004 after Bonferroni correction). In those who smoked nicotine, greater alexithymia was associated with less time spent in the FI-DMN state (r, -0.26; 95% CI, -0.44 to -0.07; P = .007). In a moderated mediation analysis, alexithymia mediated the association between childhood trauma and time spent in the FI-DMN state only in individuals who smoked nicotine (c' = -0.24; 95% CI, -0.58 to -0.03; P = .02).

Conclusions and Relevance

Compared with controls, individuals who smoked nicotine spent more time in the FI-DMN state. Among those who smoked nicotine, childhood trauma-related alexithymia was associated with less time spent in the FI-DMN state, indicating that considering trauma-related factors may reveal alternative neurobiological underpinnings of substance use. These data may aid in reconciling contradictory findings in prior literature regarding the role of FI-DMN regions in substance use.

Stress and Epilepsy: Towards Understanding of Neurobiological Mechanisms for Better Management

Stress has been identified as a major contributor to human disease and is postulated to play a substantial role in epileptogenesis. In a significant proportion of individuals with epilepsy, sensitivity to stressful events contributes to dynamic symptomatic burden, notably seizure occurrence and frequency, and presence and severity of psychiatric comorbidities [anxiety, depression, posttraumatic stress disorder (PTSD)]. Here, we review this complex relationship between stress and epilepsy using clinical data and highlight key neurobiological mechanisms including the hypothalamic-pituitary-adrenal (HPA) axis dysfunction, altered neuroplasticity within limbic system structures, and alterations in neurochemical pathways such as brain-derived neurotrophic factor (BNDF) linking epilepsy and stress. We discuss current clinical management approaches of stress that help optimize seizure control and prevention, as well as psychiatric comorbidities associated with epilepsy. We propose that various shared mechanisms of stress and epilepsy present multiple avenues for the development of new symptomatic and preventative treatments, including disease modifying therapies aimed at reducing epileptogenesis. This would require close collaborations between clinicians and basic scientists to integrate data across multiple scales, from genetics to systems biology, from clinical observations to fundamental mechanistic insights. In future, advances in machine learning approaches and neuromodulation strategies will enable personalized and targeted interventions to manage and ultimately treat stress-related epileptogenesis.

Human brain state dynamics reflect individual neuro-phenotypes

Neural activity and behavior manifest state and trait dynamics, as well as variation within and between individuals. However, the mapping of state-trait neural variation to behavior is not well understood. To address this gap, we quantify moment-to-moment changes in brain-wide co-activation patterns derived from resting-state functional magnetic resonance imaging. In healthy young adults, we identify reproducible spatio-temporal features of co-activation patterns at the single subject level. We demonstrate that a joint analysis of state-trait neural variations and feature reduction reveal general motifs of individual differences, encompassing state-specific and general neural features that exhibit day-to-day variability. The principal neural variations co-vary with the principal variations of behavioral phenotypes, highlighting cognitive function, emotion regulation, alcohol and substance use. Person-specific probability of occupying a particular co-activation pattern is reproducible and associated with neural and behavioral features. This combined analysis of state-trait variations holds promise for developing reproducible neuroimaging markers of individual life functional outcome.

The influence of childhood maltreatment on trait depression in patients with major depressive disorder: A moderated mediation model of rumination and mindful attention awareness

Major depressive disorder (MDD) is one of the most prevalent psychiatric disorders. Individuals who were exposed to childhood maltreatment might be an especially vulnerable group and were more likely to meet the diagnostic criteria for depression than those who were not. Trait depression refers to a personality trait predisposition to depression, expressed as the frequency of symptoms rather than a transient depressive mood state. Clarifying the relationship between childhood maltreatment and trait depression in patients with MDD has therefore become an important field of research. Childhood Trauma Questionnaire-Short Form (CTQ-SF), Ruminative Responses Scale (RRS), State-Trait Depression Scale (ST-DEP), and Mindful Attention Awareness Scale (MAAS) were used as research instruments. SPSS 23.0 statistical software was used for statistical analysis and examined the moderated mediation models. A total of 288 patients with MDD were included in this study. After standardization of the variables, the model revealed childhood maltreatment was positively associated with trait depression (β = 0.215, p < 0.001) and that rumination partially mediated the effect between childhood trauma and trait depression. Mindfulness moderated the association between rumination and trait depression in depressed patients (β = 0.171, p < 0.001). Simple slope tests showed that rumination significantly predicted trait depression in patients with high levels of mindfulness (bsimple = 0.460, p < 0.001, 95%CI = [0.339, 0.581]), while this predictive effect was not significant in patients with low levels (bsimple = 0.119, p = 0.097, 95%CI = [-0.022, 0.261]). After adding mediating variables, we found that the negative impact of childhood maltreatment on trait depression was both directly and indirectly through the patients' own ruminative levels. However, mindfulness performed a critical moderating role in the overall mediating model, aggravating the negative impact of childhood maltreatment on trait depression. There are several limitations in this study: the history of childhood maltreatment was reviewed and reported; the MAAS was a single-dimensional questionnaire that fails to measure the content of other mindfulness factors; cross-sectional data could not be used to infer the causal relationship between variables.