Ambulatory assessment as a means of longitudinal phenotypes characterization in psychiatric disorders

Reciprocal Prospective Effects of Momentary Cognitions and Affect in Daily Life and Mood Reactivity Toward Daily Events in Remitted Recurrent Depression

Major depressive disorder is a recurrent condition. Potential risk factors for future episodes are maladaptive cognitions, such as rumination and unfavorable reactivity toward negative daily events. Positive thoughts and positive daily events, in contrast, could act as a buffer against mood deterioration. The aim of the present study is to (a) examine differences in daily affect and cognitions in remitted depressed patients with a history of recurrent episodes (rMDD) and healthy controls, (b) analyze reciprocal prospective effects of momentary cognitions and affect, and (c) investigate effects of daily events on affect and cognitions in both groups. A sample of N = 102 participants underwent an ecological momentary assessment (EMA) phase of 5 consecutive days, where rMDD patients (n = 51) and healthy controls (n = 51) indicated their momentary rumination, positive thoughts, affect, and the occurrence of daily events 10 times per day. Via multilevel lag models, we found higher rumination to predict a decrease of positive affect (PA) in the rMDD group, but no effect of rumination on subsequent negative affect (NA) in either group. Higher positive thoughts predicted an increase in PA and a decrease in NA, similarly strong in both groups. Regarding daily events, rMDD patients reported a stronger increase in NA and rumination following negative daily events compared to controls, whereas an observed subsequent decrease of PA and positive thoughts was not moderated by group. Following positive daily events, rMDD patients showed a stronger increase in PA and positive thoughts and a stronger decrease in NA and rumination than controls. For interventions targeting relapse prevention, our results indicate the implementation of strategies fostering the responsiveness to positive events and the up-regulation of positive affect.

From Maps to Multi-dimensional Network Mechanisms of Mental Disorders

The development of advanced neuroimaging techniques and their deployment in large cohorts has enabled an assessment of functional and structural brain network architecture at an unprecedented level of detail. Across many temporal and spatial scales, network neuroscience has emerged as a central focus of intellectual efforts, seeking meaningful descriptions of brain networks and explanatory sets of network features that underlie circuit function in health and dysfunction in disease. However, the tools of network science commonly deployed provide insight into brain function at a fundamentally descriptive level, often failing to identify (patho-)physiological mechanisms that link system-level phenomena to the multiple hierarchies of brain function. Here we describe recently developed techniques stemming from advances in complex systems and network science that have the potential to overcome this limitation, thereby contributing mechanistic insights into neuroanatomy, functional dynamics, and pathology. Finally, we build on the Research Domain Criteria framework, highlighting the notion that mental illnesses can be conceptualized as dysfunctions of neural circuitry present across conventional diagnostic boundaries, to sketch how network-based methods can be combined with pharmacological, intermediate phenotype, genetic, and magnetic stimulation studies to probe mechanisms of psychopathology.

Cognitive and affective trait and state factors influencing the long-term symptom course in remitted depressed patients

Background

Major depressive disorder (MDD) is characterized by a high risk for relapses and chronic developments. Clinical characteristics such as residual symptoms have been shown to negatively affect the long-term course of MDD. However, it is unclear so far how trait repetitive negative thinking (RNT) as well as cognitive and affective momentary states, the latter experienced during daily-life, affect the long-term course of MDD.

Method

We followed up 57 remitted depressed (rMDD) individuals six (T2) and 36 (T3) months after baseline. Clinical outcomes were time to relapse, time spent with significant symptoms as a marker of chronicity, and levels of depressive symptoms at T2 and T3. Predictors assessed at baseline included residual symptoms and trait RNT. Furthermore, momentary daily life affect and momentary rumination, and their variation over the day were assessed at baseline using ambulatory assessment (AA).

Results

In multiple models, residual symptoms and instability of daily-life affect at baseline independently predicted a faster time to relapse, while chronicity was significantly predicted by trait RNT. Multilevel models revealed that depressive symptom levels during follow-up were predicted by baseline residual symptom levels and by instability of daily-life rumination. Both instability features were linked to a higher number of anamnestic MDD episodes.

Conclusions

Our findings indicate that trait RNT, but also affective and cognitive processes during daily life impact the longer-term course of MDD. Future longitudinal research on the role of respective AA-phenotypes as potential transdiagnostic course-modifiers is warranted.

Digital footprints: facilitating large-scale environmental psychiatric research in naturalistic settings through data from everyday technologies

Digital footprints, the automatically accumulated by-products of our technology-saturated lives, offer an exciting opportunity for psychiatric research. The commercial sector has already embraced the electronic trails of customers as an enabling tool for guiding consumer behaviour, and analogous efforts are ongoing to monitor and improve the mental health of psychiatric patients. The untargeted collection of digital footprints that may or may not be health orientated comprises a large untapped information resource for epidemiological scale research into psychiatric disorders. Real-time monitoring of mood, sleep and physical and social activity in a substantial portion of the affected population in a naturalistic setting is unprecedented in psychiatry. We propose that digital footprints can provide these measurements from real world setting unobtrusively and in a longitudinal fashion. In this perspective article, we outline the concept of digital footprints and the services and devices that create them, and present examples where digital footprints have been successfully used in research. We then critically discuss the opportunities and fundamental challenges associated digital footprints in psychiatric research, such as collecting data from different sources, analysis, ethical and research design challenges.