Circuit to construct mapping: a mathematical tool for assisting the diagnosis and treatment in major depressive disorder

Turning data into better mental health: Past, present, and future

In this mini-review, we discuss the fundamentals of using technology in mental health diagnosis and tracking. We highlight those principles using two clinical concepts: (1) cravings and relapse in the context of addictive disorders and (2) anhedonia in the context of depression. This manuscript is useful for both clinicians wanting to understand the scope of technology use in psychiatry and for computer scientists and engineers wishing to assess psychiatric frameworks useful for diagnosis and treatment. The increase in smartphone ownership and internet connectivity, as well as the accelerated development of wearable devices, have made the observation and analysis of human behavior patterns possible. This has, in turn, paved the way to understand mental health conditions better. These technologies have immense potential in facilitating the diagnosis and tracking of mental health conditions; they also allow the implementation of existing behavioral treatments in new contexts (e.g., remotely, online, and in rural/underserved areas), and the possibility to develop new treatments based on new understanding of behavior patterns. The path to understand how to best use technology in mental health includes the need to match interdisciplinary frameworks from engineering/computer sciences and psychiatry. Thus, we start our review by introducing bio-behavioral sensing, the types of information available, and what behavioral patterns they may reflect and be related to in psychiatric diagnostic frameworks. This information is linked to the use of functional imaging, highlighting how imaging modalities can be considered "ground truth" for mental health/psychiatric dimensions, given the heterogeneity of clinical presentations, and the difficulty of determining what symptom corresponds to what disease. We then discuss how mental health/psychiatric dimensions overlap, yet differ from, psychiatric diagnoses. Using two clinical examples, we highlight the potential agreement areas in assessment/management of anhedonia and cravings. These two dimensions were chosen because of their link to two very prevalent diseases worldwide: depression and addiction. Anhedonia is a core symptom of depression, which is one of the leading causes of disability worldwide. Cravings, the urge to use a substance or perform an action (e.g., shopping, internet), is the leading step before relapse. Lastly, through the manuscript, we discuss potential mental health dimensions.

Increasing robustness of pairwise methods for effective connectivity in magnetic resonance imaging by using fractional moment series of BOLD signal distributions

Estimating causal interactions in the brain from functional magnetic resonance imaging (fMRI) data remains a challenging task. Multiple studies have demonstrated that all current approaches to determine direction of connectivity perform poorly when applied to synthetic fMRI datasets. Recent advances in this field include methods for pairwise inference, which involve creating a sparse connectome in the first step, and then using a classifier in order to determine the directionality of connection between every pair of nodes in the second step. In this work, we introduce an advance to the second step of this procedure, by building a classifier based on fractional moments of the BOLD distribution combined into cumulants. The classifier is trained on datasets generated under the dynamic causal modeling (DCM) generative model. The directionality is inferred based on statistical dependencies between the two-node time series, for example, by assigning a causal link from time series of low variance to time series of high variance. Our approach outperforms or performs as well as other methods for effective connectivity when applied to the benchmark datasets. Crucially, it is also more resilient to confounding effects such as differential noise level across different areas of the connectome.

The impact of hemodynamic variability and signal mixing on the identifiability of effective connectivity structures in BOLD fMRI

PURPOSE

Multiple computational studies have demonstrated that essentially all current analytical approaches to determine effective connectivity perform poorly when applied to synthetic functional Magnetic Resonance Imaging (fMRI) datasets. In this study, we take a theoretical approach to investigate the potential factors facilitating and hindering effective connectivity research in fMRI.

MATERIALS AND METHODS

In this work, we perform a simulation study with use of Dynamic Causal Modeling generative model in order to gain new insights on the influence of factors such as the slow hemodynamic response, mixed signals in the network and short time series, on the effective connectivity estimation in fMRI studies.

RESULTS

First, we perform a Linear Discriminant Analysis study and find that not the hemodynamics itself but mixed signals in the neuronal networks are detrimental to the signatures of distinct connectivity patterns. This result suggests that for statistical methods (which do not involve lagged signals), deconvolving the BOLD responses is not necessary, but at the same time, functional parcellation into Regions of Interest (ROIs) is essential. Second, we study the impact of hemodynamic variability on the inference with use of lagged methods. We find that the local hemodynamic variability provide with an upper bound on the success rate of the lagged methods. Furthermore, we demonstrate that upsampling the data to TRs lower than the TRs in state-of-the-art datasets does not influence the performance of the lagged methods.

CONCLUSIONS

Factors such as background scale-free noise and hemodynamic variability have a major impact on the performance of methods for effective connectivity research in functional Magnetic Resonance Imaging.

Latent variable analysis of positive and negative valence processing focused on symptom and behavioral units of analysis in mood and anxiety disorders

BACKGROUND

Mood and anxiety disorders are highly heterogeneous and their underlying pathology is complex. The Research Domain Criteria (RDoC) approach seeks to establish dimensionally and neuroscience-based descriptions of psychopathology that may inform better classification and treatment approaches. The current investigation sought to determine the latent variables underlying positive and negative valence processing in terms of symptoms and behavioral units of analysis.

METHOD

As part of an ongoing study, individuals with mood and anxiety problems were recruited largely from primary care clinics at UCLA (n=62) and UCSD (n=58). These participants underwent a comprehensive symptomatic and behavioral assessment. An implicit approach avoidance task and a modified dot probe detection task were used to measure positive and negative valence processing.

RESULTS

Principal components analysis with varimax rotation identified four symptom components, three behavioral components for the dot probe task, and two behavioral components for the implicit approach avoidance task. These components yielded two meta-components consisting of: negative valence symptoms, negative approach bias, and high sustained, selective attention; and positive valence symptoms, positive approach bias, and slow selective or sustained attention. The components did not differ between males and females, nor by age or medication status.

LIMITATIONS

The limitations are: (1) relatively small sample, (2) exploratory analysis strategy, (3) no test/re-test data, (4) no neural circuit analysis, and (5) limited reliability of behavioral data.

CONCLUSIONS

These preliminary data show that positive and negative valence processing domains load on independent dimensions. Taken together, multi-level assessment approaches combined with advanced statistical analyses may help to identify distinct positive and negative valence processes within a clinical population that cut across traditional diagnostic categories.

Rapid antidepressant effect of ketamine correlates with astroglial plasticity in the hippocampus

BACKGROUND AND PURPOSE

Astroglia contribute to the pathophysiology of major depression and antidepressant drugs act by modulating synaptic plasticity; therefore, the present study investigated whether the fast antidepressant action of ketamine is reflected in a rapid alteration of the astrocytes' morphology in a genetic animal model of depression.

EXPERIMENTAL APPROACH

S-Ketamine (15 mg·kg-1 ) or saline was administered as a single injection to Flinders Line (FSL/ FRL) rats. Twenty-four hours after the treatment, perfusion fixation was carried out and the morphology of glial fibrillary acid protein (GFAP)-positive astrocytes in the CA1 stratum radiatum (CA1.SR) and the molecular layer of the dentate gyrus (GCL) of the hippocampus was investigated by applying stereological techniques and analysis with Imaris software. The depressive-like behaviour of animals was also evaluated using forced swim test.

KEY RESULTS

FSL rats treated with ketamine exhibited a significant reduction in immobility time in comparison with the FSL-vehicle group. The volumes of the hippocampal CA1.SR and GCL regions were significantly increased 1 day after ketamine treatment in the FSL rats. The size of astrocytes in the ketamine-treated FSL rats was larger than those in the FSL-vehicle group. Additionally, the number and length of the astrocytic processes in the CA1.SR region were significantly increased 1 day following ketamine treatment.

CONCLUSIONS AND IMPLICATIONS

Our results support the hypothesis that astroglial atrophy contributes to the pathophysiology of depression and a morphological modification of astrocytes could be one mechanism by which ketamine rapidly improves depressive behaviour.

Mindfulness-Based Cognitive Therapy in Major depressive disorder - systematic review and metanalysis

Abstract Introduction: MBCT practices increases the ability of concentration and attention, as well is particularly effective for people with current and treatment-resistant depression. Objective: To analyze the effects of the application of MBCT in symptoms of MDD. Methods: systematic review and meta-analysis. To find suitable studies, we searched PubMed/MEDLINE's database using the keywords mindfulness and major depressive disorder. Studies in English published between 2003 and 2015 were selected. The studies were evaluated according to their methodological quality by PEDro scale (score greater than 3), studies that showed empirical evidence, had an experimental study design (randomized and non-randomized), and whose full text was available. For the meta-analysis, we used a random-effects model with standardized mean differences and 95% confidence intervals. Results: Fourteen es were included, of which three were non-randomized, with only one group with intervention of MBCT, and 11 were randomized studies, divided into two-group samples and three-group samples. The non-randomized studies showed a PEDro score of 5, while the two-group and three-group randomized studies showed PEDro scores of 5-10 and 6-9, respectively. In the meta-analysis, the four randomized studies selected revealed a moderate effect of MBCT on the outcome of depression symptoms, with a mean difference of -0.52 (95% CI: -1.050 to -0.002; p = 0.04). Conclusion: The MBCT presented as a promising alternative for the treatment of this disorder.

Dysfunction of the cingulo-opercular network in first-episode medication-naive patients with major depressive disorder

BACKGROUND

Major depressive disorder (MDD) is a common psychiatric disorder that may be associated with abnormal cognitive control and emotion regulation. Previous studies have found that network disconnection within the cingulo-opercular network (CON) plays an important role in psychiatric disorders and that the CON may be relevant to the psychopathology of MDD. We thus used the resting-state functional connectivity method in patients with MDD and healthy controls to examine CON neural circuit abnormalities in MDD.

METHODS

Using resting-state functional magnetic resonance imaging (fMRI), we investigated the resting state functional connectivity of the CON using the dorsal anterior cingulate cortex (dACC) as the seed region of interest. The resulting functional connectivity (FC) correlation maps were employed to investigate between-group differences. Additionally, we examined the association between depression symptom severity and functional connectivity results. The participants were patients with MDD (n=19) and healthy controls (n=19).

RESULTS

Patients with MDD showed abnormalities in the connectivity of the CON. We found abnormal connectivity in MDD patients between the dACC and the bilateral middle frontal gyrus (MFG) and left angular gyrus (LAG) and precentral gyrus. Moreover, regression analysis showed that depression symptom severity (measured with the Hamilton Depression Rating Scale (HDRS), Hamilton Anxiety Rating Scale (HARS) and Automatic Thoughts Questionnaire scores (ATQ)) was significantly correlated with the FC values of the CON.

LIMITATIONS

First, our study consisted of a relatively small sample size that may have limited statistical power. Second, the current study design cannot conclusively specify the role of the CON in the neuropathology of depression.

CONCLUSIONS

Our findings suggest that MDD is associated with disrupted FC of the CON, which plays an important role in the pathophysiological mechanisms of MDD.

The clinical effectiveness of cognitive behavior therapy and an alternative medicine approach in reducing symptoms of depression in adolescents

The main aim of the study was to investigate the effectiveness of two psychotherapeutic approaches, cognitive behavioral therapy (CBT) and a complementary medicine method Reiki, in reducing depression scores in adolescents. We recruited 188 adolescent patients who were 12-17 years old. Participants were randomly assigned to CBT, Reiki or wait-list. Depression scores were assessed before and after the 12 week interventions or wait-list. CBT showed a significantly greater decrease in Child Depression Inventory (CDI) scores across treatment than both Reiki (p<.001) and the wait-list control (p<.001). Reiki also showed greater decreases in CDI scores across treatment relative to the wait-list control condition (p=.031). The analyses indicated a significant interaction between gender, condition and change in CDI scores, such that male participants showed a smaller treatment effect for Reiki than did female participants. Both CBT and Reiki were effective in reducing the symptoms of depression over the treatment period, with effect for CBT greater than Reiki. These findings highlight the importance of early intervention for treatment of depression using both cognitive and complementary medicine approaches. However, research that tests complementary therapies over a follow-up period and against a placebo treatment is required.

Modulation of the antioxidant nuclear factor (erythroid 2-derived)-like 2 pathway by antidepressants in rats

Patients with major depression who are otherwise medically healthy have activated inflammatory pathways in their organism. It has been described that depression is not only escorted by inflammation but also by induction of multiple oxidative/nitrosative stress pathways. Nevertheless, there are finely regulated mechanisms involved in preserving cells from damage, such as the antioxidant nuclear transcription factor Nrf2. We aim to explore in a depression-like model the Nrf2 pathway in the prefrontal cortex (PFC) and the hippocampus of rats and to analyze whether antidepressants affect the antioxidant activity of the Nrf2 pathway. Male Wistar rats were exposed to chronic mild stress (CMS) and some of them were treated with desipramine, escitalopram or duloxetine. We studied the expression of upstream and downstream elements of the Nrf2 pathway and the oxidative damage induced by the CMS. After CMS, there is an inhibition of upstream and downstream elements of the Nrf2 pathway in the PFC (e.g. PI3K/Akt, GPx…). Moreover, antidepressant treatments, particularly desipramine and duloxetine, are able to recover some of these elements and to reduce the oxidative damage induced by the CMS. However, in the hippocampus, Nrf2 pathways are not that affected and antidepressants do not have many actions. In conclusion, Nrf2 pathway is differentially regulated by antidepressants in the PFC and hippocampus. The Nrf2 pathway is involved in the oxidative/nitrosative damage detected in the PFC and antidepressants have a therapeutic action through this pathway. However, it seems that Nrf2 is not involved in the effects caused by CMS in the hippocampus.

Causality in Psychiatry: A Hybrid Symptom Network Construct Model

Causality or etiology in psychiatry is marked by standard biomedical, reductionistic models (symptoms reflect the construct involved) that inform approaches to nosology, or classification, such as in the DSM-5 [Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition; (1)]. However, network approaches to symptom interaction [i.e., symptoms are formative of the construct; e.g., (2), for posttraumatic stress disorder (PTSD)] are being developed that speak to bottom-up processes in mental disorder, in contrast to the typical top-down psychological construct approach. The present article presents a hybrid top-down, bottom-up model of the relationship between symptoms and mental disorder, viewing symptom expression and their causal complex as a reciprocally dynamic system with multiple levels, from lower-order symptoms in interaction to higher-order constructs affecting them. The hybrid model hinges on good understanding of systems theory in which it is embedded, so that the article reviews in depth non-linear dynamical systems theory (NLDST). The article applies the concept of emergent circular causality (3) to symptom development, as well. Conclusions consider that symptoms vary over several dimensions, including: subjectivity; objectivity; conscious motivation effort; and unconscious influences, and the degree to which individual (e.g., meaning) and universal (e.g., causal) processes are involved. The opposition between science and skepticism is a complex one that the article addresses in final comments.