Resting-state connectivity predictors of response to psychotherapy in major depressive disorder

Linked patterns of symptoms and cognitive covariation with functional brain controllability in major depressive disorder

BACKGROUND

Controllability analysis is an approach developed for evaluating the ability of a brain region to modulate function in other regions, which has been found to be altered in major depressive disorder (MDD). Both depressive symptoms and cognitive impairments are prominent features of MDD, but the case-control differences of controllability between MDD and controls can not fully interpret the contribution of both clinical symptoms and cognition to brain controllability and linked patterns among them in MDD.

METHODS

Sparse canonical correlation analysis was used to investigate the associations between resting-state functional brain controllability at the network level and clinical symptoms and cognition in 99 first-episode medication-naïve patients with MDD.

FINDINGS

Average controllability was significantly correlated with clinical features. The average controllability of the dorsal attention network (DAN) and visual network had the highest correlations with clinical variables. Among clinical variables, depressed mood, suicidal ideation and behaviour, impaired work and activities, and gastrointestinal symptoms were significantly negatively associated with average controllability, and reduced cognitive flexibility was associated with reduced average controllability.

INTERPRETATION

These findings highlight the importance of brain regions in modulating activity across brain networks in MDD, given their associations with symptoms and cognitive impairments observed in our study. Disrupted control of brain reconfiguration of DAN and visual network during their state transitions may represent a core brain mechanism for the behavioural impairments observed in MDD.

FUNDING

National Natural Science Foundation of China (82001795 and 82027808), National Key R&D Program (2022YFC2009900), and Sichuan Science and Technology Program (2024NSFSC0653).

Neural and behavioral markers of inhibitory control predict symptom improvement during internet-delivered cognitive behavioral therapy for depression

Poor inhibitory control contributes to deficits in emotion regulation, which are often targeted by treatments for major depressive disorder (MDD), including cognitive behavioral therapy (CBT). Brain regions that contribute to inhibitory control and emotion regulation overlap; thus, inhibitory control might relate to response to CBT. In this study, we examined whether baseline inhibitory control and resting state functional connectivity (rsFC) within overlapping emotion regulation-inhibitory control regions predicted treatment response to internet-based CBT (iCBT). Participants with MDD were randomly assigned to iCBT (N = 30) or a monitored attention control (MAC) condition (N = 30). Elastic net regression was used to predict post-treatment Patient Health Questionnaire-9 (PHQ-9) scores from baseline variables, including demographic variables, PHQ-9 scores, Flanker effects (interference, sequential dependency, post-error slowing), and rsFC between the dorsal anterior cingulate cortex, bilateral anterior insula (AI), and right temporoparietal junction (TPJ). Essential prognostic predictor variables retained in the elastic net regression included treatment group, gender, Flanker interference response time (RT), right AI-TPJ rsFC, and left AI-right AI rsFC. Prescriptive predictor variables retained included interactions between treatment group and baseline PHQ-9 scores, age, gender, Flanker RT, sequential dependency effects on accuracy, post-error accuracy, right AI-TPJ rsFC, and left AI-right AI rsFC. Inhibitory control and rsFC within inhibitory control-emotion regulation regions predicted reduced symptom severity following iCBT, and these effects were stronger in the iCBT group than in the MAC group. These findings contribute to a growing literature indicating that stronger inhibitory control at baseline predicts better outcomes to psychotherapy, including iCBT.

Emotion brain network topology in healthy subjects following passive listening to different auditory stimuli

A large body of research establishes the efficacy of musical intervention in many aspects of physical, cognitive, communication, social, and emotional rehabilitation. However, the underlying neural mechanisms for musical therapy remain elusive. This study aimed to investigate the potential neural correlates of musical therapy, focusing on the changes in the topology of emotion brain network. To this end, a Bayesian statistical approach and a cross-over experimental design were employed together with two resting-state magnetoencephalography (MEG) as controls. MEG recordings of 30 healthy subjects were acquired while listening to five auditory stimuli in random order. Two resting-state MEG recordings of each subject were obtained, one prior to the first stimulus (pre) and one after the final stimulus (post). Time series at the level of brain regions were estimated using depth-weighted minimum norm estimation (wMNE) source reconstruction method and the functional connectivity between these regions were computed. The resultant connectivity matrices were used to derive two topological network measures: transitivity and global efficiency which are important in gauging the functional segregation and integration of brain network respectively. The differences in these measures between pre- and post-stimuli resting MEG were set as the equivalence regions. We found that the network measures under all auditory stimuli were equivalent to the resting state network measures in all frequency bands, indicating that the topology of the functional brain network associated with emotional regulation in healthy subjects remains unchanged following these auditory stimuli. This suggests that changes in the emotion network topology may not be the underlying neural mechanism of musical therapy. Nonetheless, further studies are required to explore the neural mechanisms of musical interventions especially in the populations with neuropsychiatric disorders.

Breakthroughs and challenges for generating brain network-based biomarkers of treatment response in depression

Treatment outcomes widely vary for individuals diagnosed with major depressive disorder, implicating a need for deeper understanding of the biological mechanisms conferring a greater likelihood of response to a particular treatment. Our improved understanding of intrinsic brain networks underlying depression psychopathology via magnetic resonance imaging and other neuroimaging modalities has helped reveal novel and potentially clinically meaningful biological markers of response. And while we have made considerable progress in identifying such biomarkers over the last decade, particularly with larger, multisite trials, there are significant methodological and practical obstacles that need to be overcome to translate these markers into the clinic. The aim of this review is to review current literature on brain network structural and functional biomarkers of treatment response or selection in depression, with a specific focus on recent large, multisite trials reporting predictive accuracy of candidate biomarkers. Regarding pharmaco- and psychotherapy, we discuss candidate biomarkers, reporting that while we have identified candidate biomarkers of response to a single intervention, we need more trials that distinguish biomarkers between first-line treatments. Further, we discuss the ways prognostic neuroimaging may help to improve treatment outcomes to neuromodulation-based therapies, such as transcranial magnetic stimulation and deep brain stimulation. Lastly, we highlight obstacles and technical developments that may help to address the knowledge gaps in this area of research. Ultimately, integrating neuroimaging-derived biomarkers into clinical practice holds promise for enhancing treatment outcomes and advancing precision psychiatry strategies for depression management. By elucidating the neural predictors of treatment response and selection, we can move towards more individualized and effective depression interventions, ultimately improving patient outcomes and quality of life.

Individualized Functional Brain System Topologies and Major Depression: Relationships Among Patch Sizes and Clinical Profiles and Behavior

BACKGROUND

Neuroimaging studies of major depression have typically been conducted using group-level approaches. However, given interindividual differences in brain systems, there is a need for individualized approaches to brain systems mapping and putative links toward diagnosis, symptoms, and behavior.

METHODS

We used an iterative parcellation approach to map individualized brain systems in 328 participants from a multisite, placebo-controlled clinical trial. We hypothesized that participants with depression would show abnormalities in salience, control, default, and affective systems, which would be associated with higher levels of self-reported anhedonia, anxious arousal, and worse cognitive performance. Within hypothesized brain systems, we compared patch sizes (number of vertices) between depressed and healthy control groups. Within depressed groups, abnormal patches were correlated with hypothesized clinical and behavioral measures.

RESULTS

Significant group differences emerged in hypothesized patches of 1) the lateral salience system (parietal operculum; t326 = -3.11, p = .002) and 2) the control system (left medial posterior prefrontal cortex region; z = -3.63, p < .001), with significantly smaller patches in these regions in participants with depression than in healthy control participants. Results suggest that participants with depression with significantly smaller patch sizes in the lateral salience system and control system regions experience greater anxious arousal and cognitive deficits.

CONCLUSIONS

The findings imply that neural features mapped at the individual level may relate meaningfully to diagnosis, symptoms, and behavior. There is strong clinical relevance in taking an individualized brain systems approach to mapping neural functional connectivity because these associated region patch sizes may help advance our understanding of neural features linked to psychopathology and foster future patient-specific clinical decision making.

Electroconvulsive Therapy Regulates Brain Connectome Dynamics in Patients With Major Depressive Disorder

BACKGROUND

Electroconvulsive therapy (ECT) is an effective treatment for patients with major depressive disorder (MDD), but its underlying neural mechanisms remain largely unknown. The aim of this study was to identify changes in brain connectome dynamics after ECT in MDD and to explore their associations with treatment outcome.

METHODS

We collected longitudinal resting-state functional magnetic resonance imaging data from 80 patients with MDD (50 with suicidal ideation [MDD-SI] and 30 without [MDD-NSI]) before and after ECT and 37 age- and sex-matched healthy control participants. A multilayer network model was used to assess modular switching over time in functional connectomes. Support vector regression was used to assess whether pre-ECT network dynamics could predict treatment response in terms of symptom severity.

RESULTS

At baseline, patients with MDD had lower global modularity and higher modular variability in functional connectomes than control participants. Network modularity increased and network variability decreased after ECT in patients with MDD, predominantly in the default mode and somatomotor networks. Moreover, ECT was associated with decreased modular variability in the left dorsal anterior cingulate cortex of MDD-SI but not MDD-NSI patients, and pre-ECT modular variability significantly predicted symptom improvement in the MDD-SI group but not in the MDD-NSI group.

CONCLUSIONS

We highlight ECT-induced changes in MDD brain network dynamics and their predictive value for treatment outcome, particularly in patients with SI. This study advances our understanding of the neural mechanisms of ECT from a dynamic brain network perspective and suggests potential prognostic biomarkers for predicting ECT efficacy in patients with MDD.

Using the Beck Depression Inventory to Assess Anhedonia: A Scale Validation Study

Anhedonia is central to several psychological disorders and a frequent target of psychosocial and pharmacological treatments. We evaluated the psychometric properties of two widely used anhedonia measures derived from the Beck Depression Inventory: a 3-item (BDI-Anh3) and a 4-item version (BDI-Anh4). We evaluated these measures in a large undergraduate sample, a community sample, and a clinical sample. Both the BDI-Anh3 and the BDI-Anh4 showed adequate internal consistency, with BDI-Anh4 performing somewhat better, across the three samples. Both measures showed good convergent and discriminant validity, even after controlling for shared variance with other items on the BDI. These findings indicate that both measures have sufficient reliability and validity to support their use by researchers and clinicians.

Default Mode Network Hypoalignment of Function to Structure Correlates With Depression and Rumination

BACKGROUND

Recent studies have begun to examine how signals in the brain correspond to the underlying white matter structure using tools from the field of graph signal processing to quantify brain function alignment to brain network topology. Here, we applied this framework for the first time toward a transdiagnostic cohort of individuals with internalizing psychopathologies, including mood and anxiety disorders, to uncover how such alignment within the default mode network (DMN) is related to depression and rumination symptoms.

METHODS

Both diffusion-weighted and resting-state functional magnetic resonance imaging were obtained from participants at baseline (n = 60 patients, n = 19 healthy control participants). Patients were randomized to 12 weeks of treatment with either a selective serotonin reuptake inhibitor or cognitive behavioral therapy, and symptom scales were readministered posttreatment (n = 46 patients at follow-up). Using graph signal processing methodology, we quantified the alignment of functional signals to their underlying white matter structural networks.

RESULTS

We found that signal alignment within the posterior DMN was decreased in patients with internalizing psychopathologies compared with healthy control participants and was inversely (negatively) correlated with baseline depression and rumination scales. Signal alignment within the posterior DMN was also correlated with the ratio of total within-DMN to extra-DMN functional connectivity for these regions.

CONCLUSIONS

These findings are consistent with previous literature regarding pathological promiscuity of posterior DMN connectivity and provide the first graph signal processing-based analyses in a transdiagnostic cohort of patients with internalizing psychopathologies.

Engineering Virtuous health habits using Emotion and Neurocognition: Flexibility for Lifestyle Optimization and Weight management (EVEN FLOW)

Interventions to preserve functional independence in older adults are critically needed to optimize 'successful aging' among the large and increasing population of older adults in the United States. For most aging adults, the management of chronic diseases is the most common and impactful risk factor for loss of functional independence. Chronic disease management inherently involves the learning and adaptation of new behaviors, such as adopting or modifying physical activity habits and managing weight. Despite the importance of chronic disease management in older adults, vanishingly few individuals optimally manage their health behavior in the service of chronic disease stabilization to preserve functional independence. Contemporary conceptual models of chronic disease management and health habit theory suggest that this lack of optimal management may result from an underappreciated distinction within the health behavior literature: the behavioral domains critical for initiation of new behaviors (Initiation Phase) are largely distinct from those that facilitate their maintenance (Maintenance Phase). Psychological factors, particularly experiential acceptance and trait levels of openness are critical to engagement with new health behaviors, willingness to make difficult lifestyle changes, and the ability to tolerate aversive affective responses in the process. Cognitive factors, particularly executive function, are critical to learning new skills, using them effectively across different areas of life and contextual demands, and updating of skills to facilitate behavioral maintenance. Emerging data therefore suggests that individuals with greater executive function are better able to sustain behavior changes, which in turn protects against cognitive decline. In addition, social and structural supports of behavior change serve a critical buffering role across phases of behavior change. The present review attempts to address these gaps by proposing a novel biobehavioral intervention framework that incorporates both individual-level and social support system-level variables for the purpose of treatment tailoring. Our intervention framework triangulates on the central importance of self-regulatory functioning, proposing that both cognitive and psychological mechanisms ultimately influence an individuals' ability to engage in different aspects of self-management (individual level) in the service of maintaining independence. Importantly, the proposed linkages of cognitive and affective functioning align with emerging individual difference frameworks, suggesting that lower levels of cognitive and/or psychological flexibility represent an intermediate phenotype of risk. Individuals exhibiting self-regulatory lapses either due to the inability to regulate their emotional responses or due to the presence of executive functioning impairments are therefore the most likely to require assistance to preserve functional independence. In addition, these vulnerabilities will be more easily observable for individuals requiring greater complexity of self-management behavioral demands (e.g. complexity of medication regimen) and/or with lesser social support. Our proposed framework also intuits several distinct intervention pathways based on the profile of self-regulatory behaviors: we propose that individuals with intact affect regulation and impaired executive function will preferentially respond to 'top-down' training approaches (e.g., strategy and process work). Individuals with intact executive function and impaired affect regulation will respond to 'bottom-up' approaches (e.g., graded exposure). And individuals with impairments in both may require treatments targeting caregiving or structural supports, particularly in the context of elevated behavioral demands.

Towards a neuroimaging biomarker for predicting cognitive behavioural therapy outcomes in treatment-naive depression: Preliminary findings

Clear prognostic indicators of cognitive behavioural therapy (CBT) are lacking for depression. This study aims to identify a biomarker that predicts CBT outcomes in depression. We developed a machine learning algorithm to predict post-CBT Hamilton Depression Rating Scale (HAMD) using pre-CBT regional homogeneity (ReHo). We examined transcriptomic signatures of regions with CBT-related ReHo changes. Twenty-five patients completed CBT and had increased ReHo in the dorsolateral prefrontal cortex (DLPFC) following CBT. Pre-CBT ReHo in left DLPFC was shown to be a predictor of post-HAMD scores. We identified left DLPFC ReHo as a neuroimaging biomarker for therapeutic effects of CBT in depression.

Brain connectivity in major depressive disorder: a precision component of treatment modalities?

Major depressive disorder (MDD) is a very prevalent mental disorder that imposes an enormous burden on individuals, society, and health care systems. Most patients benefit from commonly used treatment methods such as pharmacotherapy, psychotherapy, electroconvulsive therapy (ECT), and repetitive transcranial magnetic stimulation (rTMS). However, the clinical decision on which treatment method to use remains generally informed and the individual clinical response is difficult to predict. Most likely, a combination of neural variability and heterogeneity in MDD still impedes a full understanding of the disorder, as well as influences treatment success in many cases. With the help of neuroimaging methods like functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI), the brain can be understood as a modular set of functional and structural networks. In recent years, many studies have investigated baseline connectivity biomarkers of treatment response and the connectivity changes after successful treatment. Here, we systematically review the literature and summarize findings from longitudinal interventional studies investigating the functional and structural connectivity in MDD. By compiling and discussing these findings, we recommend the scientific and clinical community to deepen the systematization of findings to pave the way for future systems neuroscience roadmaps that include brain connectivity parameters as a possible precision component of the clinical evaluation and therapeutic decision.

Abnormal regional homogeneity and functional connectivity in major depressive disorder patients with long-term remission: An exploratory study

This study was the first to explore whether abnormal spontaneous neuronal activities exist in patients in the long-term remission stage of major depressive disorder (MDD). We recruited 34 MDD patients (PTs) and 30 sex- and age-matched healthy controls (HCs). Resting-state functional magnetic resonance imaging (rs-fMRI) was employed to scan all subjects' brain regions, and independent two-sample t-test was used for regional homogeneity (ReHo) and functional connectivity (FC) analysis. Compared with the HCs, the ReHo of PTs increased in the right superior frontal gyrus and left middle frontal gyrus, and decreased in the right anterior and collateral cingulate gyrus, right middle frontal gyrus, right inferior parietal lobule. The cingulate gyrus as a mask showed that FC of the cingulate gyrus with the bilateral lingual gyrus and the right middle temporal gyrus decreased, and FC with the left supper frontal gyrus increased. The correlation analysis revealed no significant correlation between the abnormal ReHo and HAMD-24 scores in PTs. The ReHo of inferior parietal lobule and the duration of remission were positively correlated. We concluded that the spontaneous neuronal activities might be disrupted in MDD patients in the long-term remission stage. Our findings provided new reasons for MDD relapse.

Altered functional connectivity in common resting-state networks in patients with major depressive disorder: A resting-state functional connectivity study

The neural correlates of major depressive disorder (MDD) remain disputed. In the absence of reliable biological markers, the dysfunction and interaction of neural networks have been proposed as pathophysiological neural mechanisms in depression. Here, we examined the functional connectivity (FC) of brain networks. 51 healthy volunteers (mean age 33.57 ± 7.80) and 55 individuals diagnosed with MDD (mean age 33.89 ± 11.00) participated by performing a resting-state (rs) fMRI scan. Seed to voxel FC analyses were performed. Compared to healthy control (HC), MDD patients showed higher connectivity between the hippocampus and the anterior cingulate cortex (ACC) and lower connectivity between the insula and the ACC. The MDD group displayed lower connectivity between the inferior parietal lobule (IPL) and the superior frontal gyrus (SFG). The current data replicate previous findings regarding the cortico-limbic network (hippocampus - ACC connection) and the salience network (insula - ACC connection) and provide novel insight into altered rsFC in MDD, in particular involving the hippocampus - ACC and the insula - ACC connection. Furthermore, altered connectivity between the IPL and SFG indicates that the processing in higher cognitive processes such as attention and working memory is affected in MDD. These data further support dysfunctional neuronal networks as an interesting pathophysiological marker in depression.

Dynamic Resting-State Network Biomarkers of Antidepressant Treatment Response

BACKGROUND

Delivery of effective antidepressant treatment has been hampered by a lack of objective tools for predicting or monitoring treatment response. This study aimed to address this gap by testing novel dynamic resting-state functional network markers of antidepressant response.

METHODS

The Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study randomized adults with major depressive disorder to 8 weeks of either sertraline or placebo, and depression severity was evaluated longitudinally. Participants completed resting-state neuroimaging pretreatment and again after 1 week of treatment (n = 259 eligible for analyses). Coactivation pattern analyses identified recurrent whole-brain states of spatial coactivation, and computed time spent in each state for each participant was the main dynamic measure. Multilevel modeling estimated the associations between pretreatment network dynamics and sertraline response and between early (pretreatment to 1 week) changes in network dynamics and sertraline response.

RESULTS

Dynamic network markers of early sertraline response included increased time in network states consistent with canonical default and salience networks, together with decreased time in network states characterized by coactivation of cingulate and ventral limbic or temporal regions. The effect of sertraline on depression recovery was mediated by these dynamic network changes. In contrast, early changes in dynamic functioning of corticolimbic and frontoinsular-default networks were related to patterns of symptom recovery common across treatment groups.

CONCLUSIONS

Dynamic resting-state markers of early antidepressant response or general recovery may assist development of clinical tools for monitoring and predicting effective intervention.

Effects of escitalopram therapy on functional brain controllability in major depressive disorder

Antidepressant drugs are the mainstay of treatment for patients with major depressive disorders (MDD). Given the critical role of the underlying neural control mechanism in the physiopathology of depression, this study aims to investigate the effects of escitalopram, a type of antidepressant drug, on the changes of functional brain controllability throughout the escitalopram treatment for MDD. We collected resting-state functional magnetic resonance imaging data from 20 unmedicated major depressive patients at baseline (visit 1, pre-treatment), one week (visit 2, 1-week after the onset of the treatment) and six weeks (visit 3, after the 6-week escitalopram treatment). Our results revealed that the global average and modal controllability of MDD patients were significantly larger and smaller, respectively, compared to healthy subjects (P < 0.01). Furthermore, the modal controllability rank of the frontoparietal network in depression patients was also significantly smaller than the healthy subjects (P < 0.01). However, throughout the escitalopram treatment, the global average and modal controllability, and the controllability of the default mode network and frontoparietal network of MDD patients were consistently changed to the healthy subjects' level. Our results also showed that the changes of global average and modal controllability measures can predict the improvements of clinical scores of the MDD patients as the escitalopram treatment advanced (P < 0.05). In conclusion, this study reveals promising brain controllability-based biomarkers to mechanistically understand and predict the effects of the escitalopram treatment for depression and maybe extended to predict and understand the effects of other interventions for other neurological and psychiatric diseases.

Abnormal Brain Topological Structure of Mild Depression During Visual Search Processing Based on EEG Signals

Studies have shown that attention bias can affect behavioral indicators in patients with depression, but it is still unclear how this bias affects the brain network topology of patients with mild depression (MD). Therefore, a novel functional brain network analysis and hierarchical clustering methods were used to explore the abnormal brain topology of MD patients based on EEG signals during the visual search paradigm. The behavior results showed that the reaction time of MD group was significantly higher than that of normal group. The results of functional brain network indicated significant differences in functional connections between the two groups, the amount of inter-hemispheric long-distance connections are much larger than intra-hemispheric short-distance connections. Patients with MD showed significantly lower local efficiency and clustering coefficient, destroyed community structure of frontal lobe and parietal-occipital lobe, frontal asymmetry, especially in beta band. In addition, the average value of long-distance connections between left frontal and right parietal-occipital lobes presented significant correlation with depressive symptoms. Our results suggested that MD patients achieved long-distance connections between the frontal and parietal-occipital regions by sacrificing the connections within the regions, which might provide new insights into the abnormal cognitive processing mechanism of depression.

Tonic pain alters functional connectivity of the descending pain modulatory network involving amygdala, periaqueductal gray, parabrachial nucleus and anterior cingulate cortex

INTRODUCTION

Resting state functional connectivity (FC) is widely used to assess functional brain alterations in patients with chronic pain. However, reports of FC accompanying tonic pain in pain-free persons are rare. A network we term the Descending Pain Modulatory Network (DPMN) is implicated in healthy and pathologic pain modulation. Here, we evaluate the effect of tonic pain on FC of specific nodes of this network: anterior cingulate cortex (ACC), amygdala (AMYG), periaqueductal gray (PAG), and parabrachial nuclei (PBN).

METHODS

In 50 pain-free participants (30F), we induced tonic pain using a capsaicin-heat pain model. functional MRI measured resting BOLD signal during pain-free rest with a 32°C thermode and then tonic pain where participants experienced a previously warm temperature combined with capsaicin. We evaluated FC from ACC, AMYG, PAG, and PBN with correlation of self-report pain intensity during both states. We hypothesized tonic pain would diminish FC dyads within the DPMN.

RESULTS

Of all hypothesized FC dyads, only PAG and subgenual ACC was weakly altered during pain (F=3.34; p=0.074; pain-free>pain d=0.25). After pain induction sACC-PAG FC became positively correlated with pain intensity (R=0.38; t=2.81; p=0.007). Right PBN-PAG FC during pain-free rest positively correlated with subsequently experienced pain (R=0.44; t=3.43; p=0.001). During pain, this connection's FC was diminished (paired t=-3.17; p=0.0026). In whole-brain analyses, during pain-free rest, FC between left AMYG and right superior parietal lobule and caudate nucleus were positively correlated with subsequent pain. During pain, FC between left AMYG and right inferior temporal gyrus negatively correlated with pain. Subsequent pain positively correlated with right AMYG FC with right claustrum; right primary visual cortex and right temporo-occipitoparietal junction Conclusion: We demonstrate sACC-PAG tonic pain FC positively correlates with experienced pain and resting right PBN-PAG FC correlates with subsequent pain and is diminished during tonic pain. Finally, we reveal PAG- and right AMYG-anchored networks which correlate with subsequently experienced pain intensity. Our findings suggest specific connectivity patterns within the DPMN at rest are associated with subsequently experienced pain and modulated by tonic pain. These nodes and their functional modulation may reveal new therapeutic targets for neuromodulation or biomarkers to guide interventions.

Returning to basic principles to develop more effective treatments for central nervous system disorders

Development of new treatments for diseases of the central nervous system (CNS) is stalled. Of candidate drugs developed through costly preclinical research, 93% fail clinical trials. Hoped-for improvements in diagnosis or treatment from decades of positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) imaging have yet to materialize. To understand what we are doing wrong, I begin with recognition that all aspects of life, including the brain and mind, are physical phenomena consistent with processes described by physicists. Two processes, emergence and entropy, are of particular relevance in complex arrangements of matter that constitute life in general and the brain in particular. The human brain functions through dynamically reconfiguring and hierarchically organized neural functional systems with emergent properties of cognition, emotion, and conscious experience. These systems are shaped and maintained by negentropic environmental input transformed by sensory receptors into neural signals that trigger epigenetic neuroplastic processes. CNS diseases produce clinical disorders by disrupting these systems. As researchers seek appropriate levels of system organization at which to characterize and treat illness, focus has been on medications that impact processes at lower levels or transcranial electric or magnetic stimulation that impact broad contiguous swaths of tissue. Neither align with the brain’s neurosystem organization and therefore lack specificity necessary to be effective and to limit side effects. Digital neurotherapies (DNTs), in contrast, align with neurosystem organization and achieve the needed specificity using the same input pathways and neuroplastic processes that created the neural systems organization to repair it. The omission of DNTs from major systems-based initiatives represents powerful residua of dualist thinking. Interventions based on perceptual and cognitive processes are not thought of as being as physical as drugs or electric or magnetic stimulation through the skull. In fact, they are examples of the most basic processes that create and support life itself.

Neuroimaging Mechanism of Cognitive Behavioral Therapy in Pain Management

Purpose. To review the recent neuroimaging studies on cognitive-behavioral therapy (CBT) for pain management, with the aim of exploring possible mechanisms of CBT. Recent Findings. Current studies can be divided into four categories, mixed pain, fibromyalgia, migraine, and experimental pain, based on the type of disease included, with the same or different changes of brain regions after CBT intervention. According to structural and functional MRI analyses, changes of brain gray matter volume, activation and deactivation of brain regions, and intrinsic connectivity between brain regions were observed after CBT sessions. The brain regions involved mainly included some areas related to cognitive and emotional regulation. After comparison, the DLPFC, OFC, VLPFC, PCC and amygdala were found to be recurrent in multiple studies and may be key regions for CBT intervention in pain management. In the treatment of mixed chronic pain, CBT may decrease the gray matter volume of DLPFC, reduce ICN connection of OFC within the DAN network, and increase fALFF of the PCC. For FM intervention, CBT may activate the bilateral OFC and VLPFC, while in migraine, only the right OFC, VLPFC, and DLPFC were found to be more activated after CBT. In addition, the differential action of the left and right amygdala has also been shown in the latest study of migraine. In heat-evoked pain, CBT may increase the deactivation of the PCC, the connectivity between the DMN and right VLPFC, while diminishing the deactivation of VLPFC. Summary. After CBT, the brain showed stronger top-down pain control, cognitive reassessment, and altered perception of stimulus signals (chronic pain and repeated acute pain). The DLPFC, OFC, VLPFC, PCC, and amygdala may be the key brain regions in CBT intervention of pain.

Vortioxetine Modulates the Regional Signal in First-Episode Drug-Free Major Depressive Disorder at Rest

BACKGROUND

Previous studies on brain functional alterations associated with antidepressants for major depressive disorder (MDD) have produced conflicting results because they involved short treatment periods and a variety of compounds.

METHODS

Resting-state functional magnetic resonance imaging scans were obtained from 25 first-episode drug-free patients with MDD and 25 healthy controls. The patients, who were treated with vortioxetine for 8 weeks, were scanned at two-time points (baseline and week 8 of treatment). The amplitude of low-frequency fluctuation (ALFF) in the imaging data was used to analyze local brain signal alterations associated with antidepressant treatment.

RESULTS

Compared with the controls, the patients at baseline showed decreased ALFF values in the right inferior temporal gyrus and increased ALFF values in the left inferior cerebellum, right cingulate gyrus and postcentral gyrus. After 8 weeks of vortioxetine treatment, patients showed increased ALFF values in the bilateral cingulate gyrus, middle temporal gyrus, medial superior frontal gyrus, and inferior cerebellum.

CONCLUSION

This study provided evidence that vortioxetine modulates brain signals in MDD sufferers. These findings contribute to the understanding of how antidepressants effect brain function.

Differential patterns of dynamic functional connectivity variability in major depressive disorder treated with cognitive behavioral therapy

BACKGROUND

Numerous studies have shown that major depressive disorder (MDD) is characterized by a range of impairments in emotional and cognitive functions that are closely related to abnormalities in brain structure and function. Cognitive behavioral therapy (CBT) can be used as treatment for mild to moderate MDD, which can assist with ameliorating the symptoms. Previous studies have assumed that the internal fluctuations throughout the entire scan are static. However, it has recently been suggested that the brain connectivity is dynamic and relative to continuous rhythmic activity. The effect of dynamic changes in CBT on MDD patients is unknown.

METHODS

Nineteen first-episode, unmedicated MDD patients and twenty-two healthy controls (HC) participated in the study. The patients received early CBT treatment once a week for 6 weeks. Symptom examinations and magnetic resonance imaging (MRI) scans were performed pre and post treatment. Degree centrality (DC) was used to investigate the whole-brain connectivity differences between patients with MDD and healthy controls, and sliding window correlation analysis was applied to investigate the dynamic changes of functional connectivity among MDD patients treated with CBT. The variance of dynamic functional connectivity (dFC) was calculated to evaluate the temporal variability along the time.

RESULTS

Patients with MDD showed abnormal DC in dorsolateral prefrontal cortex (dlPFC), insula and postcentral gyrus. Correlation analysis revealed that degree centrality of dlPFC was negatively correlated with the course of disease in patients with MDD. Results of dynamic functional connectivity showed that, compared to HC, MDD patients-remained excessively stable in dlPFC and precuneus connectivity, which is associated with emotional cognitive symptoms. After CBT, patients showed increased dFC variability in dlPFC and precuneus (p < 0.01, GRF corrected).

CONCLUSION

DLPFC plays an important role in pathophysiological mechanism of MDD. CBT helped patients suppress redundant thoughts and negative self-focus. As a connecting node, dlPFC participates in the mechanism of action of CBT.

A lateralized model of the pain-depression dyad

Chronic pain and depression are two frequently co-occurring and debilitating conditions. Even though the former is treated as a physical affliction, and the latter as a mental illness, both disorders closely share neural substrates. Here, we review the association of pain with depression, especially when symptoms are lateralized on either side of the body. We also explore the overlapping regions in the forebrain implicated in these conditions. Finally, we synthesize these findings into a model, which addresses gaps in our understanding of comorbid pain and depression. Our lateralized pain-depression dyad model suggests that individuals diagnosed with depression should be closely monitored for pain symptoms in the left hemibody. Conversely, for patients in pain, with the exception of acute pain with a known source, referrals in today's pain centers for psychological evaluation should be part of standard practice, within the framework of an interdisciplinary approach to pain treatment.

Latent Profiles of Comorbid Depression as Predictors of PTSD Treatment Outcome

Posttraumatic stress disorder (PTSD) frequently co-occurs with major depressive disorder, and empirically supported PTSD treatments consistently improve depression. However, both diagnoses are heterogeneous and specific patterns of symptom overlap may be related to worse treatment outcome. Two hundred individuals with chronic PTSD participated in a doubly randomized preference trial comparing prolonged exposure and sertraline. Latent Profile Analysis was used to identify classes based on PTSD and depression symptoms prior to starting treatment. A three-class model best fit the data, with a high depression and PTSD severity class (distressed), a moderate depression and low PTSD avoidance class (depressive), and a low depression and high PTSD avoidance class (avoidant). The avoidant class showed the lowest rates of major depressive disorder diagnosis and transdiagnostic vulnerabilities to depression. Patients in the distressed class experienced more robust PTSD treatment response, with no differences between prolonged exposure and sertraline. These findings highlight the role of avoidance in nondepressed PTSD presentations while also demonstrating that co-occurring depression is not contraindicated in evidence-based PTSD treatment.

Dynamic functional connectivity in adolescence-onset major depression: relationships with severity and symptom dimensions

BACKGROUND

The spatial functional chronnectome is an innovative mathematical model designed to capture dynamic features in the organization of brain function derived from resting-state functional magnetic resonance imaging (rs-fMRI) data. Measurements of dynamic functional connectivity (dFC) have been developed from this model to quantify the brain dynamical self-reconfigurations at different spatial and temporal scales. This study examined whether two spatiotemporal dFC quantifications were linked to late adolescence-onset major depressive disorder (AO-MDD), and scaled with depression and symptom severity measured with the Montgomery-Asberg depression rating scale (MADRS) Methods: Thirty-five AO-MDD patients (21±6y) and fifty-three age- and gender-matched healthy young participants (20±3y) underwent 3T MRI structural and rs-fMRI acquisitions. The chronnectome here comprised seven individualized functional networks portrayed along 132 temporal overlapping windows, each framing 110s of resting brain activity Results: Based on voxelwise analyses, AO-MDD patients demonstrated significantly reduced temporal variability within the bilateral prefrontal cortex in five functional networks including the limbic network, the default-mode network (DMN) and frontoparietal network (FPN). Furthermore, the limbic network appeared to be particularly involved in this sample, and was associated with MADRS scores, and its progressive dynamic inflexibility was linked to sadness. DMN and FPN dynamics scaled with negative thoughts and neurovegetative symptoms, respectively Conclusions: This triple-network imbalance could delay spatiotemporal integration, while across-subject symptom variability would be network-specific. Therefore, the present approach supports that brain network dynamics underlie patients' symptom heterogeneity in AO-MDD.

Keeping weight off: Mindfulness-Based Stress Reduction alters amygdala functional connectivity during weight loss maintenance in a randomized control trial

Obesity is associated with significant comorbidities and financial costs. While behavioral interventions produce clinically meaningful weight loss, weight loss maintenance is challenging. The objective was to improve understanding of the neural and psychological mechanisms modified by mindfulness that may predict clinical outcomes. Individuals who intentionally recently lost weight were randomized to Mindfulness-Based Stress Reduction (MBSR) or a control healthy living course. Anthropometric and psychological factors were measured at baseline, 8 weeks and 6 months. Functional connectivity (FC) analysis was performed at baseline and 8 weeks to examine FC changes between regions of interest selected a priori, and independent components identified by independent component analysis. The association of pre-post FC changes with 6-month weight and psychometric outcomes was then analyzed. Significant group x time interaction was found for FC between the amygdala and ventromedial prefrontal cortex, such that FC increased in the MBSR group and decreased in controls. Non-significant changes in weight were observed at 6 months, where the mindfulness group maintained their weight while the controls showed a weight increase of 3.4% in BMI. Change in FC at 8-weeks between ventromedial prefrontal cortex and several ROIs was associated with change in depression symptoms but not weight at 6 months. This pilot study provides preliminary evidence of neural mechanisms that may be involved in MBSR’s impact on weight loss maintenance that may be useful for designing future clinical trials and mechanistic studies.

The Function and Structure of Precuneus Is Associated With Subjective Sleep Quality in Major Depression

BACKGROUND

Poor sleep quality is related to depression. However, the investigation of the neural basis for poor sleep quality in individuals with major depression (MD) is limited.

METHODS

Resting state functional and structural MRI data were derived from 114 MD individuals and 74 normal controls (NCs). Fractional amplitude of low-frequency fluctuation (fALFF) and gray matter volume (GMV) were used to measure function and structure of the brain. Pittsburgh Sleep Quality Index (PSQI) was performed to evaluate subjective sleep quality. Correlations were carried out to investigate links of PSQI score with brain imaging indices in MD and NCs, separately. We also examined the differences in fALFF and GMV of brain regions related to PSQI score between MD and NCs.

RESULTS

In contrast to NCs, MD individuals had higher PSQI score. The higher PSQI score was associated with lower fALFF and lower GMV in bilateral precuneus in MD individuals. Moreover, the MD individuals exhibited increased fALFF in bilateral precuneus compared with NCs. However, the correlation between subjective sleep quality and neuroimaging parameters was not significant in NCs.

CONCLUSION

The implication of these findings is that the function and structure of precuneus provides a neural basis for subjective poor sleep quality in MD. Understanding this may lead to better intervention of depression and associated sleep complaints.

Resting State Functional Connectivity Biomarkers of Treatment Response in Mood Disorders: A Review

There are currently no validated treatment biomarkers in psychiatry. Resting State Functional Connectivity (RSFC) is a popular method for investigating the neural correlates of mood disorders, but the breadth of the field makes it difficult to assess progress toward treatment response biomarkers. In this review, we followed general PRISMA guidelines to evaluate the evidence base for mood disorder treatment biomarkers across diagnoses, brain network models, and treatment modalities. We hypothesized that no treatment biomarker would be validated across these domains or with independent datasets. Results are organized, interpreted, and discussed in the context of four popular analytic techniques: (1) reference region (seed-based) analysis, (2) independent component analysis, (3) graph theory analysis, and (4) other methods. Cortico-limbic connectivity is implicated across studies, but there is no single biomarker that spans analyses or that has been replicated in multiple independent datasets. We discuss RSFC limitations and future directions in biomarker development.

Predicting response to psychiatric surgery: a systematic review of neuroimaging findings

BACKGROUND

Psychiatric surgery, including deep brain stimulation and stereotactic ablation, is an important treatment option in severe refractory psychiatric illness. Several large trials have demonstrated response rates of approximately 50%, underscoring the need to identify and select responders preoperatively. Recent advances in neuroimaging have brought this possibility into focus. We systematically reviewed the psychiatric surgery neuroimaging literature to assess the current state of evidence for preoperative imaging predictors of response.

METHODS

We performed this study in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and Meta-analysis of Observational Studies in Epidemiology (MOOSE) frameworks, and preregistered it using PROSPERO. We systematically searched the Medline, Embase and Cochrane databases for studies reporting preoperative neuroimaging analyses correlated with clinical outcomes in patients who underwent psychiatric surgery. We recorded and synthesized the methodological details, imaging results and clinical correlations from these studies.

RESULTS

After removing duplicates, the search yielded 8388 unique articles, of which 7 met the inclusion criteria. The included articles were published between 2001 and 2018 and reported on the outcomes of 101 unique patients. Of the 6 studies that reported significant findings, all identified clusters of hypermetabolism, hyperconnectivity or increased size in the frontostriatal limbic circuitry.

LIMITATIONS

The included studies were few and highly varied, spanning 2 decades.

CONCLUSION

Although few studies have analyzed preoperative imaging for predictors of response to psychiatric surgery, we found consistency among the reported results: most studies implicated overactivity in the frontostriatal limbic network as being correlated with clinical response. Larger prospective studies are needed.

REGISTRATION

www.crd.york.ac.uk/prospero/display_record.php?RecordID=131151.

Predicting Antidepressant Citalopram Treatment Response via Changes in Brain Functional Connectivity After Acute Intravenous Challenge

Introduction: The early and therapy-specific prediction of treatment success in major depressive disorder is of paramount importance due to high lifetime prevalence, and heterogeneity of response to standard medication and symptom expression. Hence, this study assessed the predictability of long-term antidepressant effects of escitalopram based on the short-term influence of citalopram on functional connectivity. Methods: Twenty nine subjects suffering from major depression were scanned twice with resting-state functional magnetic resonance imaging under the influence of intravenous citalopram and placebo in a randomized, double-blinded cross-over fashion. Symptom factors were identified for the Hamilton depression rating scale (HAM-D) and Beck's depression inventory (BDI) taken before and after a median of seven weeks of escitalopram therapy. Predictors were calculated from whole-brain functional connectivity, fed into robust regression models, and cross-validated. Results: Significant predictive power could be demonstrated for one HAM-D factor describing insomnia and the total score (r = 0.45-0.55). Remission and response could furthermore be predicted with an area under the receiver operating characteristic curve of 0.73 and 0.68, respectively. Functional regions with high influence on the predictor were located especially in the ventral attention, fronto-parietal, and default mode networks. Conclusion: It was shown that medication-specific antidepressant symptom improvements can be predicted using functional connectivity measured during acute pharmacological challenge as an easily assessable imaging marker. The regions with high influence have previously been related to major depression as well as the response to selective serotonin reuptake inhibitors, corroborating the advantages of the current approach of focusing on treatment-specific symptom improvements.

Neuroanatomical Abnormalities in Older Depressed Adults With Apathy: A Systematic Review

OBJECTIVE

Apathy is a common phenomenon in late-life depression and is associated with poor outcomes. Apathy is often unrecognized in older depressed adults, and efficacious treatment options are lacking. This review provides a systematic review of the neuroanatomical abnormalities associated with apathy in late-life depression. In addition, the review summarizes the neuroimaging findings from studies of neurodegenerative and focal brain injury conditions that frequently present with apathy. The goal is to elucidate cerebral network abnormalities that give rise to apathy in older adults with mood disturbances and to inform future treatment targets.

METHOD

Systematic literature review.

RESULTS

The few studies that have directly examined the neuroanatomical abnormalities of apathy in late-life depression suggest disturbances in the anterior cingulate cortex, insula, orbital and dorsal prefrontal cortex, striatum, and limbic structures (ie, amygdala, thalamus, and hippocampus). Studies examining the neuroanatomical correlates of apathy in other aging populations are consistent with the pattern observed in late-life depression.

CONCLUSIONS

Apathy in late-life depression appears to be accompanied by neuroanatomical abnormalities in the salience and reward networks. These network findings are consistent with that observed in individuals presenting with apathy in other aging-related conditions. These findings may inform future treatments that target apathy.

Causes and Consequences of Diagnostic Heterogeneity in Depression: Paths to Discovering Novel Biological Depression Subtypes

Depression is a highly heterogeneous syndrome that bears only modest correlations with its biological substrates, motivating a renewed interest in rethinking our approach to diagnosing depression for research purposes and new efforts to discover subtypes of depression anchored in biology. Here, we review the major causes of diagnostic heterogeneity in depression, with consideration of both clinical symptoms and behaviors (symptomatology and trajectory of depressive episodes) and biology (genetics and sexually dimorphic factors). Next, we discuss the promise of using data-driven strategies to discover novel subtypes of depression based on functional neuroimaging measures, including dimensional, categorical, and hybrid approaches to parsing diagnostic heterogeneity and understanding its biological basis. The merits of using resting-state functional magnetic resonance imaging functional connectivity techniques for subtyping are considered along with a set of technical challenges and potential solutions. We conclude by identifying promising future directions for defining neurobiologically informed depression subtypes and leveraging them in the future for predicting treatment outcomes and informing clinical decision making.

Depression-related anterior cingulate prefrontal resting state connectivity normalizes following cognitive behavioral therapy

Background.

Aberrant activity of the subcallosal cingulate (SCC) is a common theme across pharmacologic treatment efficacy prediction studies. The functioning of the SCC in psychotherapeutic interventions is relatively understudied, as are functional differences among SCC subdivisions. We conducted functional connectivity analyses (rsFC) on resting-state functional magnetic resonance imaging (fMRI) data, collected before and after a course of cognitive behavioral therapy (CBT) in patients with major depressive disorder (MDD), using seeds from three SCC subdivisions.

Methods.

Resting-state data were collected from unmedicated patients with current MDD (Hamilton Depression Rating Scale-17 > 16) before and after 14-sessions of CBT monotherapy. Treatment outcome was assessed using the Beck Depression Inventory (BDI). Rostral anterior cingulate (rACC), anterior subcallosal cingulate (aSCC), and Brodmann’s area 25 (BA25) masks were used as seeds in connectivity analyses that assessed baseline rsFC and symptom severity, changes in connectivity related to symptom improvement after CBT, and prediction of treatment outcomes using whole-brain baseline connectivity.

Results.

Pretreatment BDI negatively correlated with pretreatment rACC ~ dorsolateral prefrontal cortex and aSCC ~ lateral prefrontal cortex rsFC. In a region-of-interest longitudinal analysis, rsFC between these regions increased post-treatment (p < 0.05_FDR). In whole-brain analyses, BA25 ~ paracentral lobule and rACC ~ paracentral lobule connectivities decreased post-treatment. Whole-brain baseline rsFC with SCC did not predict clinical improvement.

Conclusions.

rsFC features of rACC and aSCC, but not BA25, correlated inversely with baseline depression severity, and increased following CBT. Subdivisions of SCC involved in top-down emotion regulation may be more involved in cognitive interventions, while BA25 may be more informative for interventions targeting bottom-up processing. Results emphasize the importance of subdividing the SCC in connectivity analyses.

Predicting individual clinical trajectories of depression with generative embedding

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Patients with major depressive disorder (MDD) show variable clinical trajectories.

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Generative embedding (GE) is used to predict clinical trajectories in MDD patients.

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GE classifies patients with chronic depression vs. fast remission with 79% accuracy.

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GE provides mechanistic interpretability and outperforms conventional measures.

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Proof-of-concept that illustrates the potential of GE for clinical prediction.

Patients with major depressive disorder (MDD) show heterogeneous treatment response and highly variable clinical trajectories: while some patients experience swift recovery, others show relapsing-remitting or chronic courses. Predicting individual clinical trajectories at an early stage is a key challenge for psychiatry and might facilitate individually tailored interventions. So far, however, reliable predictors at the single-patient level are absent. Here, we evaluated the utility of a machine learning strategy – generative embedding (GE) – which combines interpretable generative models with discriminative classifiers. Specifically, we used functional magnetic resonance imaging (fMRI) data of emotional face perception in 85 MDD patients from the NEtherlands Study of Depression and Anxiety (NESDA) who had been followed up over two years and classified into three subgroups with distinct clinical trajectories. Combining a generative model of effective (directed) connectivity with support vector machines (SVMs), we could predict whether a given patient would experience chronic depression vs. fast remission with a balanced accuracy of 79%. Gradual improvement vs. fast remission could still be predicted above-chance, but less convincingly, with a balanced accuracy of 61%. Generative embedding outperformed classification based on conventional (descriptive) features, such as functional connectivity or local activation estimates, which were obtained from the same data and did not allow for above-chance classification accuracy. Furthermore, predictive performance of GE could be assigned to a specific network property: the trial-by-trial modulation of connections by emotional content. Given the limited sample size of our study, the present results are preliminary but may serve as proof-of-concept, illustrating the potential of GE for obtaining clinical predictions that are interpretable in terms of network mechanisms. Our findings suggest that abnormal dynamic changes of connections involved in emotional face processing might be associated with higher risk of developing a less favorable clinical course.

Intrinsic connectomes are a predictive biomarker of remission in major depressive disorder

Although major depressive disorder (MDD) is associated with altered functional coupling between disparate neural networks, the degree to which such measures are ameliorated by antidepressant treatment is unclear. It is also unclear whether functional connectivity can be used as a predictive biomarker of treatment response. Here, we used whole-brain functional connectivity analysis to identify neural signatures of remission following antidepressant treatment, and to identify connectomic predictors of treatment response. 163 MDD and 62 healthy individuals underwent functional MRI during pre-treatment baseline and 8-week follow-up sessions. Patients were randomized to escitalopram, sertraline or venlafaxine-XR antidepressants and assessed at follow-up for remission. Baseline measures of intrinsic functional connectivity between each pair of 333 regions were analyzed to identify pre-treatment connectomic features that distinguish remitters from non-remitters. We then interrogated these connectomic differences to determine if they changed post-treatment, distinguished patients from controls, and were modulated by medication type. Irrespective of medication type, remitters were distinguished from non-remitters by greater connectivity within the default mode network (DMN); specifically, between the DMN, fronto-parietal and somatomotor networks, the DMN and visual, limbic, auditory and ventral attention networks, and between the fronto-parietal and somatomotor networks with cingulo-opercular and dorsal attention networks. This baseline hypo-connectivity for non-remitters also distinguished them from controls and increased following treatment. In contrast, connectivity for remitters was higher than controls at baseline and also following remission, suggesting a trait-like connectomic characteristic. Increased functional connectivity within and between large-scale intrinsic brain networks may characterize acute recovery with antidepressants in depression.

Neurocognitive working mechanisms of the prevention of relapse in remitted recurrent depression (NEWPRIDE): protocol of a randomized controlled neuroimaging trial of preventive cognitive therapy

BACKGROUND

Major Depressive Disorder (MDD) is a psychiatric disorder with a highly recurrent character, making prevention of relapse an important clinical goal. Preventive Cognitive Therapy (PCT) has been proven effective in preventing relapse, though not for every patient. A better understanding of relapse vulnerability and working mechanisms of preventive treatment may inform effective personalized intervention strategies. Neurocognitive models of MDD suggest that abnormalities in prefrontal control over limbic emotion-processing areas during emotional processing and regulation are important in understanding relapse vulnerability. Whether changes in these neurocognitive abnormalities are induced by PCT and thus play an important role in mediating the risk for recurrent depression, is currently unclear. In the Neurocognitive Working Mechanisms of the Prevention of Relapse In Depression (NEWPRIDE) study, we aim to 1) study neurocognitive factors underpinning the vulnerability for relapse, 2) understand the neurocognitive working mechanisms of PCT, 3) predict longitudinal treatment effects based on pre-treatment neurocognitive characteristics, and 4) validate the pupil dilation response as a marker for prefrontal activity, reflecting emotion regulation capacity and therapy success.

METHODS

In this randomized controlled trial, 75 remitted recurrent MDD (rrMDD) patients will be included. Detailed clinical and cognitive measurements, fMRI scanning and pupillometry will be performed at baseline and three-month follow-up. In the interval, 50 rrMDD patients will be randomized to eight sessions of PCT and 25 rrMDD patients to a waiting list. At baseline, 25 healthy control participants will be additionally included to objectify cross-sectional residual neurocognitive abnormalities in rrMDD. After 18 months, clinical assessments of relapse status are performed to investigate which therapy induced changes predict relapse in the 50 patients allocated to PCT.

DISCUSSION

The present trial is the first to study the neurocognitive vulnerability factors underlying relapse and mediating relapse prevention, their value for predicting PCT success and whether pupil dilation acts as a valuable marker in this regard. Ultimately, a deeper understanding of relapse prevention could contribute to the development of better targeted preventive interventions.

TRIAL REGISTRATION

Trial registration: Netherlands Trial Register, August 18, 2015, trial number NL5219.

EEG cross-frequency correlations as a marker of predisposition to affective disorders

EEG cross-frequency amplitude-amplitude correlation (CF-AAC) has been considered as a potential marker of social anxiety and other affective disturbances. Functional significance of this phenomenon remains unclear, partly because the majority of studies used channel-level analysis, which precluded the spatial localization of observed effects. It is not also clear whether CF-AAC may serve as a marker of specific pathological conditions and specific states, or a more general predisposition to affective disturbances. We used source-level analysis of EEG data obtained in resting conditions in a nonclinical sample and patients with major depressive disorder (MDD) and investigated associations of CF-AAC measures with a broad range of known risk factors for affective disorders, including age, gender, genotype, stress exposure, personality, and self-reported ‘neurotic’ symptomatology. A consistent pattern of associations showed that all investigated risk factors were associated with an enhancement of CF-AAC in cortical regions associated with emotional and self-referential processing. It could be concluded that CF-AAC is a promising candidate marker of a general predisposition to affective disorders at preclinical stages.

Functional brain alterations in acute sleep deprivation: An activation likelihood estimation meta-analysis

Sleep deprivation (SD) is a common problem in modern societies, which leads to cognitive dysfunctions including attention lapses, impaired working memory, hindering decision making, impaired emotional processing, and motor vehicle accidents. Numerous neuroimaging studies have investigated the neural correlates of SD, but these studies have reported inconsistent results. Thus, we aimed to identify convergent patterns of abnormal brain functions due to acute SD. Based on the preferred reporting for systematic reviews and meta-analyses statement, we searched the PubMed database and performed reference tracking and finally retrieved 31 eligible functional neuroimaging studies. Then, we applied activation estimation likelihood meta-analysis and found reduced activity mainly in the right intraparietal sulcus and superior parietal lobule. The functional decoding analysis using the BrainMap database indicated that this region is mostly related to visuospatial perception, memory and reasoning. The significant co-activation of this region using the BrainMap database were found in the left superior parietal lobule, intraparietal sulcus, bilateral occipital cortex, left fusiform gyrus and thalamus. This region also connected with the superior parietal lobule, intraparietal sulcus, insula, inferior frontal gyrus, precentral, occipital and cerebellum through resting-state functional connectivity in healthy subjects. Taken together, our findings highlight the role of superior parietal cortex in SD.

Rigidity in Motor Behavior and Brain Functioning in Patients With Schizophrenia and High Levels of Apathy

The aim of this study was to investigate whether apathy in schizophrenia is associated with rigidity in behavior and brain functioning. To this end, we studied associations between variability in dynamic functional connectivity (DFC) in relevant functional brain networks, apathy, and variability in physical activity in schizophrenia. Thirty-one patients with schizophrenia, scoring high on apathy, were included and wore an actigraph. Activity variability was calculated on the activity counts using the root of the Mean Squared Successive Difference (MSSD). Furthermore, we calculated DFC on resting-state data as phase interactions between blood oxygen-level dependent (BOLD) signals of 270 brain regions per volume. Variability (MSSD) in DFC was calculated for 3 networks, including the default-mode network (DMN), frontoparietal network, and salience-reward network (SRN). Finally, we calculated correlations between these DFC estimates and apathy and activity variability. First, lower activity variability was associated with higher levels of apathy. Second, higher levels of apathy were associated with lower variability in DFC in the DMN and SRN. Third, higher activity variability was associated with higher variability in DFC in the SRN. In conclusion, patients with schizophrenia and more severe levels of apathy showed less variability in their physical activity and more rigid functional brain network behavior in the DMN and SRN. These networks have been shown relevant for self-reflection, mental simulation, and reward processing, processes that are pivotal for self-initiated goal-directed behavior. Functional rigidity of these networks may therefore contribute to reduced goal-directed behavior, which is characteristic for these patients.

Resting-state functional connectivity and inflexibility of daily emotions in major depression

BACKGROUND

Major Depressive Disorder (MDD) is characterized by aberrant resting-state functional connectivity (FC) in anterior cingulate regions (e.g., subgenual anterior cingulate [sgACC]) and by negative emotional functioning that is inflexible or resistant to change.

METHODS

MDD (N = 33) and control (CTL; N = 31) adults completed a resting-state scan, followed by a smartphone-based Experience Sampling Methodology (ESM) protocol surveying 10 positive and negative emotions 5 times per day for 21 days. We used multilevel modeling to assess moment-to-moment emotional inflexibility (i.e., strong temporal connections between emotions). We examined group differences in whole-brain FC analysis of bilateral sgACC, and then examined associations between emotional experiences and the extracted FC values within each group.

RESULTS

As predicted, MDDs had inflexibility in sadness and avoidance (p < .001, FDR-corrected p < .05), indicating that these emotional experiences persist in depression. MDDs showed weaker FC between the right sgACC and pregenual/dorsal anterior cingulate (pg/dACC) than did CTLs (FWE-corrected, voxelwise p = .01). Importantly, sgACC-pg/dACC FC predicted sadness inflexibility in both MDDs (p = .046) and CTLs (p = .033), suggesting that sgACC FC is associated with day-to-day negative emotions.

LIMITATIONS

Other maladaptive behaviors likely also affect the flexibility of negative emotions. We cannot generalize our finding of a positive relation between sgACC FC and inflexibility of sadness to individuals with more chronic depression or who have recovered from depression.

CONCLUSIONS

Our preliminary findings suggest that connections between portions of the ACC contribute to the persistence of negative emotions and are important in identifying a brain mechanism that may underlie the maintenance of sadness in daily life.

Personality traits are related with dynamic functional connectivity in major depression disorder: A resting-state analysis

BACKGROUND

Major depressive disorder (MDD) is one of the most well-known psychiatric disorders, which can be destructive for its damage to people's normal cognitive, emotional and social functions. Personality refers to the unique and stable character of thinking and behavior style of an individual, which has long been thought as a key influence factor for MDD. Although some knowledge about the common neural basic between MDD and personality traits has been acquired, there are few studies exploring dynamic neural mechanism behind them, which changes brain connectivity pattern rapidly to adapt to the environment over time.

METHODS

In this study, the emerging dynamic functional network connectivity (DFNC) method was used in resting-state fMRI data to find the differences between healthy group (N = 107) and MDD group (N = 109) in state-based dynamic measures, and the correlations between these measures and personality traits (extraversion and neuroticism in Eysenck Personality Questionnaire, EPQ) were explored.

RESULTS

The results showed that MDD was significantly less than the health control group in dwell time and fraction time of state 4, which was positively correlated with extraversion score and negatively correlated with neuroticism score. Further exploration on state 4 showed that it had low modularity, hyper-connectedness of sensory-related regions and DMN, and weak connections between cortex and subcortical areas, which suggested that the absence of this state in MDD might represent a decrease in activity and positive emotions.

CONCLUSION

We found the dynamic functional connectivity mechanism underlying MDD, confirmed our hypothesis that there existed the interacted relationship between trait, disease and the brain's dynamic characteristic, and suggested some reference for treatment of depression.

Pretreatment brain connectivity during positive emotion upregulation predicts decreased anhedonia following behavioral activation therapy for depression

BACKGROUND

Neurobiological predictors of antidepressant response may help guide treatment selection and improve response rates to available treatments for major depressive disorder (MDD). Behavioral activation therapy for depression (BATD) is an evidence-based intervention designed to ameliorate core symptoms of MDD by promoting sustained engagement with value-guided, positively-reinforcing activities. The present study examined pre-treatment task-based functional brain connectivity as a predictor of antidepressant response to BATD.

METHODS

Thirty-three outpatients with MDD and 20 nondepressed controls completed a positive emotion regulation task during fMRI after which participants with MDD received up to 15 sessions of BATD. We used generalized psychophysiological interaction analyses to examine group differences in pre-treatment functional brain connectivity during intentional upregulation of positive emotion to positive images. Hierarchical linear models were used to examine whether group differences in functional connectivity predicted changes in depression and anhedonia over the course of BATD.

RESULTS

Compared to controls, participants with MDD exhibited decreased connectivity between the left middle frontal gyrus and right temporoparietal regions during upregulation of positive emotion. Within the MDD group, decreased connectivity of these regions predicted greater declines in anhedonia symptoms over treatment.

LIMITATIONS

Future studies should include comparison treatments and longitudinal follow-up to clarify the unique effects of BATD on neural function and antidepressant response.

CONCLUSIONS

Results are consistent with previous work showing BATD may be particularly effective for individuals with greater disturbances in brain reward network function, but extend these findings to highlight the importance of frontotemporoparietal connectivity in targeting symptoms of low motivation and engagement.

Sex Differences in Abnormal Intrinsic Functional Connectivity After Acute Mild Traumatic Brain Injury

Mild traumatic brain injury (TBI) is considered to induce abnormal intrinsic functional connectivity within resting-state networks (RSNs). The objective of this study was to estimate the role of sex in intrinsic functional connectivity after acute mild TBI. We recruited a cohort of 54 patients (27 males and 27 females with mild TBI within 7 days post-injury) from the emergency department (ED) and 34 age-, education-matched healthy controls (HCs; 17 males and 17 females). On the clinical scales, there were no statistically significant differences between males and females in either control group or mild TBI group. To detect whether there was abnormal sex difference on functional connectivity in RSNs, we performed independent component analysis (ICA) and a dual regression approach to investigate the between-subject voxel-wise comparisons of functional connectivity within seven selected RSNs. Compared to female patients, male patients showed increased intrinsic functional connectivity in motor network, ventral stream network, executive function network, cerebellum network and decreased connectivity in visual network. Further analysis demonstrated a positive correlation between the functional connectivity in executive function network and insomnia severity index (ISI) scores in male patients (r = 0.515, P = 0.006). The abnormality of the functional connectivity of RSNs in acute mild TBI showed the possibility of brain recombination after trauma, mainly concerning male-specific.

Functional Connectivity of Brain Regions According to Resting State fMRI: Differences between Healthy and Depressed Subjects and Variability of the Results

In depressed patients, changes in spontaneous brain activity, in particular, the strength of functional connectivity between different regions are observed. The data on changes in the synchrony of different regions of interest in the brain can serve as markers of depressive symptoms and as the targets for the corresponding therapy. The study involved 21 patients with mild depression and 21 healthy volunteers; by the time of second fMRI scanning, 15 and 19 subjects, respectively). The subjects underwent two 4-min sessions of resting state fMRI with 2-4 months interval between the recordings; on the basis of these data, functional connectivity between regions of interest was assessed. During the first session, depressed patients demonstrated more pronounced connection between the right frontal eye field and cerebellar area III. When the sample was restricted to subjects who underwent both fMRI sessions, depressed patients demonstrated closer relations of the right parietal operculum and cerebellar vermis area VIII. During the second recording, healthy subjects showed stronger connectivity between more than 20 frontal, temporal, and subcortical regions of interest and cerebellum area II. In healthy participants, brainstem functional interactions increased from the first to the second fMRI-recording. In depressed subjects a number of cortical areas split from left intraparietal sulcus, but the left temporal cortex became more intra-connected. The results confirm the differences in functional connectivity between depressed and healthy subjects. At the same time, attention should be paid to the variability of the data obtained.

Reward Network Modulation as a Mechanism of Change in Behavioral Activation

Behavioral Activation (BA) is a contemporary third-wave psychosocial treatment approach that emphasizes helping individuals become more active in ways that are meaningful to them as a means of improving mood and quality of life. BA has been designated as a well-established, validated treatment for depression by the American Psychological Association following several decades of accumulated empirical support demonstrating that BA techniques successfully reduce depression symptoms and produce other desirable outcomes across a variety of populations and contexts. The purported mechanism of change underlying BA treatment lies in increasing activation, which in turn increases contact with positive reinforcement thereby reversing the cycle of depression. Current studies are further investigating how increasing activation and subsequent contact with mood reinforcers can influence mood and behavior. Specifically, there is growing evidence that BA modifies function of reward-related networks in the brain, and that these changes are associated with clinical improvement. Herein, we provide a brief history of BA, describe the primary components of BA treatment, and describe BA's purported mechanisms of change at behavioral, neural, and subjective activation levels. We present limitations as well as gaps in the current state of knowledge regarding mechanisms of action of BA.

A brain network model for depression: From symptom understanding to disease intervention

Understanding the neural substrates of depression is crucial for diagnosis and treatment. Here, we review recent studies of functional and effective connectivity in depression, in terms of functional integration in the brain. Findings from these studies, including our own, point to the involvement of at least four networks in patients with depression. Elevated connectivity of a ventral limbic affective network appears to be associated with excessive negative mood (dysphoria) in the patients; decreased connectivity of a frontal-striatal reward network has been suggested to account for loss of interest, motivation, and pleasure (anhedonia); enhanced default mode network connectivity seems to be associated with depressive rumination; and diminished connectivity of a dorsal cognitive control network is thought to underlie cognitive deficits especially ineffective top-down control of negative thoughts and emotions in depressed patients. Moreover, the restoration of connectivity of these networks-and corresponding symptom improvement-following antidepressant treatment (including medication, psychotherapy, and brain stimulation techniques) serves as evidence for the crucial role of these networks in the pathophysiology of depression.

Changes in resting-state brain networks after cognitive-behavioral therapy for chronic pain

BACKGROUND

Cognitive-behavioral therapy (CBT) is thought to be useful for chronic pain, with the pathology of the latter being closely associated with cognitive-emotional components. However, there are few resting-state functional magnetic resonance imaging (R-fMRI) studies. We used the independent component analysis method to examine neural changes after CBT and to assess whether brain regions predict treatment response.

METHODS

We performed R-fMRI on a group of 29 chronic pain (somatoform pain disorder) patients and 30 age-matched healthy controls (T1). Patients were enrolled in a weekly 12-session group CBT (T2). We assessed selected regions of interest that exhibited differences in intrinsic connectivity network (ICN) connectivity strength between the patients and controls at T1, and compared T1 and T2. We also examined the correlations between treatment effects and rs-fMRI data.

RESULTS

Abnormal ICN connectivity of the orbitofrontal cortex (OFC) and inferior parietal lobule within the dorsal attention network (DAN) and of the paracentral lobule within the sensorimotor network in patients with chronic pain normalized after CBT. Higher ICN connectivity strength in the OFC indicated greater improvements in pain intensity. Furthermore, ICN connectivity strength in the dorsal posterior cingulate cortex (PCC) within the DAN at T1 was negatively correlated with CBT-related clinical improvements.

CONCLUSIONS

We conclude that the OFC is crucial for CBT-related improvement of pain intensity, and that the dorsal PCC activation at pretreatment also plays an important role in improvement of clinical symptoms via CBT.

Neural Markers of Resilience in Adolescent Females at Familial Risk for Major Depressive Disorder

IMPORTANCE

Adolescence is a neurodevelopmental period during which experience-dependent plasticity in brain circuitry may confer vulnerability to depression as well as resilience to disorder. Little is known, however, about the neural mechanisms that underlie resilience during this critical period of brain development.

OBJECTIVE

To examine neural functional connectivity correlates of resilience in adolescent females at high and low familial risk for depression who did and did not develop the disorder.

DESIGN, SETTING, AND PARTICIPANTS

A longitudinal study was conducted at Stanford University from October 1, 2003, to January 31, 2017. Sixty-five female adolescents participated in the study: 20 at high risk in whom depression did not develop (resilient), 20 at high risk in whom depression developed (converted), and 25 at low risk with no history of psychopathology (control).

MAIN OUTCOMES AND MEASURES

We compared functional connectivity between resilient and converted, and between resilient and control, adolescent females using voxelwise 2-sided t tests to examine neural markers of resilience to depression as the main outcomes of interest. Specifically, we assessed differences in connectivity of the limbic (amygdala seed), salience (anterior insula seed), and executive control (dorsolateral prefrontal cortex seed) networks, implicated in emotion regulation. We also examined the association between functional connectivity and life events.

RESULTS

Of the 65 participants (mean [SD] age, 18.9 [2.5] years), adolescent females in the resilient group had greater connectivity between the amygdala and orbitofrontal cortex (z score = 0.23; P < .001) and between the dorsolateral prefrontal cortex and frontotemporal regions (z score = 0.24; P < .001) than did converted adolescent females. In adolescent females in the resilient group only, strength of amygdala-orbitofrontal cortex connectivity was correlated with positive life events (r18 = 0.48; P = .03). Resilient adolescent females had greater connectivity within frontal (z score = 0.07; P < .001) and limbic (z score = 0.21; P < .001) networks than did control individuals. Both high-risk groups had greater salience network connectivity: the converted group had greater intranetwork connectivity than did the resilient (z score = 0.13; P < .001) and control (z score = 0.10; P < .001) groups, and the adolescent females in the resilient group had greater salience network connectivity with the superior frontal gyrus than did the converted (z score = 0.24; P < .001) adolescent females.

CONCLUSIONS AND RELEVANCE

Resilient adolescent females have compensatory functional connectivity patterns in emotion regulatory networks that correlate with positive life events, suggesting that experience-dependent plasticity within these networks may confer resilience to depression. Further studies are warranted concerning connectivity-associated targets for promoting resilience in high-risk individuals.

Stimulated left DLPFC-nucleus accumbens functional connectivity predicts the anti-depression and anti-anxiety effects of rTMS for depression

Not all depression patients effectively respond to repeated transcranial magnetic stimulation (rTMS). We tested whether the intrinsic functional connectivity (FC) strength between the stimulated left dorsolateral prefrontal cortex (DLPFC) and left nucleus accumbens (NAcc) might predict effects of rTMS. Twenty-two medication-naïve depression patients received rTMS on left DLPFC for 2 weeks and underwent baseline functional magnetic resonance imaging (fMRI). We compared the amplitude of the low-frequency fluctuation (ALFF) and regional homogeneity (ReHo) in the stimulated target (the cortex region directly stimulated by rTMS) located in the left DLPFC, and the left NAcc, as well as the intrinsic FC of the DLPFC-NAcc between early improvers and non-improvers. We evaluated the association between the baseline brain imaging features (ALFF, ReHo, and FC) and improvements in depression and anxiety symptoms. We found that the pretreatment ALFF and ReHo in the stimulated DLPFC and left NAcc did not significantly differ between the subgroups. The early improvers displayed increased negative FC strength between the stimulated DLPFC and left NAcc with respect to non-improvers. The stimulated DLPFC-NAcc FC strength negatively correlated with improved depressive and anxious symptoms. This study is the first to demonstrate that the resting-state FC of the stimulated DLPFC-NAcc, rather than regional brain activity or local synchronization in the stimulated target, might predict the anti-depression and anti-anxiety effects of rTMS for depression.