Using person-specific networks in psychotherapy: challenges, limitations, and how we could use them anyway.

BACKGROUND: The complexity of psychopathology is evident from its multifactorial etiology and diversity of symptom profiles and hampers effective treatment. In psychotherapy, therapists approach this complexity by using case conceptualization. During this process, patients and therapists closely collaborate on a personalized working theory of the patient's psychopathology. This is a challenging process and shows low reliability between therapists. With the experience sampling method (ESM), time-series data-valuable for case conceptualization-can be systematically gathered in a patient's normal daily life. These data can be analyzed and visualized in person-specific networks (PSNs). PSNs may support case conceptualization by providing a schematic representation of association patterns between affective, cognitive, behavioral, and context variables. MAIN TEXT: We adopt a clinical perspective in considering how PSNs might be implemented to serve case conceptualization and what their role could be in psychotherapy. We suggest PSNs to be based on personalized ESM assessment to capture the unique constellation of variables in each patient. We reflect on the lack of a gold standard for creating PSNs, which may result in substantially different PSNs and thereby disparate information for case conceptualization. Moreover, even if PSNs are created in a consistent manner, results remain ambiguous as they are subject to multiple interpretations. Therefore, associations in PSNs do not allow for firm conclusions about a patient's psychopathology, but they may nevertheless be valuable in the process of case conceptualization. PSNs are based on systematically gathered, ecologically valid ESM data and provide a unique personalized perspective. When used responsibly, PSNs may be able to support case conceptualization by generating questions that serve as a starting point for a dialog between therapists and patients. Well-targeted questions are an essential tool for therapists to gain insight into the patients' psychopathology patterns and improve the quality of case conceptualization. CONCLUSIONS: PSNs have limitations in terms of the reliability of the insights they provide directly. However, taking these challenges into account, we believe they have potential as a tool to help therapists and patients in their collaborative exploration of a patient's psychopathology. Clearly, this would need to be validated in future clinical research.

Impaired reward-related learning signals in remitted unmedicated patients with recurrent depression.

One of the core symptoms of major depressive disorder is anhedonia, an inability to experience pleasure. In patients with major depressive disorder, a dysfunctional reward-system may exist, with blunted temporal difference reward-related learning signals in the ventral striatum and increased temporal difference-related (dopaminergic) activation in the ventral tegmental area. Anhedonia often remains as residual symptom during remission; however, it remains largely unknown whether the abovementioned reward systems are still dysfunctional when patients are in remission. We used a Pavlovian classical conditioning functional MRI task to explore the relationship between anhedonia and the temporal difference-related response of the ventral tegmental area and ventral striatum in medication-free remitted recurrent depression patients (n = 36) versus healthy control subjects (n = 27). Computational modelling was used to obtain the expected temporal difference errors during this task. Patients, compared to healthy controls, showed significantly increased temporal difference reward learning activation in the ventral tegmental area (PFWE,SVC = 0.028). No differences were observed between groups for ventral striatum activity. A group × anhedonia interaction [t(57) = -2.29, P = 0.026] indicated that in patients, higher anhedonia was associated with lower temporal difference activation in the ventral tegmental area, while in healthy controls higher anhedonia was associated with higher ventral tegmental area activation. These findings suggest impaired reward-related learning signals in the ventral tegmental area during remission in patients with depression. This merits further investigation to identify impaired reward-related learning as an endophenotype for recurrent depression. Moreover, the inverse association between reinforcement learning and anhedonia in patients implies an additional disturbing influence of anhedonia on reward-related learning or vice versa, suggesting that the level of anhedonia should be considered in behavioural treatments.

Filling the gap: relationship between the serotonin-transporter-linked polymorphic region and amygdala activation.

The alleged association between the serotonin-transporter-linked polymorphic region (5-HTTLPR) and amygdala activation forms a cornerstone of the common view that carrying the short allele of this polymorphism is a potential risk factor for affective disorders. The authors of a recent meta-analysis showed that this association is statistically significant (Hedges's g = 0.35) but warned that estimates might be distorted because of publication bias. Here, we report a replication study of this relationship in 120 participants. We failed to find an association of 5-HTTLPR variation with amygdala activation during a widely used emotional-face-matching paradigm. Moreover, when we conducted a meta-analysis that included unpublished studies and data from the current study, the pooled meta-analytic effect size was no longer significant (g = 0.20, p = .06). These findings cast doubt on previously reported substantial effects, suggesting that the 5-HTTLPR-amygdala association is either much smaller than previously thought, conditional on other factors, or nonexistent.

Integrating personalized experience sampling in psychotherapy: A case illustration of the Therap-i module.

Background: The experience sampling methodology (ESM) is increasingly being suggested as a clinical tool in mental health care, as it offers ecologically valid, microlevel information on psychopathological processes. Patients and clinicians have recommended that applications of ESM should be personalized and integrated into the existing clinical process, but there is still much uncertainty about how implementation may look like. Objective: To provide an example of personalized ESM assessment and feedback being integrated into psychotherapy for depression, specifically looking at the collaborative use of ESM in case conceptualization. Methods: George, a 27-year-old man diagnosed with depression, and his therapist participated in the Therap-i randomized controlled trial, which investigates the efficacy of a personalized ESM module added to psychotherapy. Together, they created a personalized ESM questionnaire, aiming to capture their hypotheses and questions regarding George's case conceptualization. George then filled out his ESM questionnaire five times per day, for 8 weeks. During this period, ESM data were discussed and interpreted by George, his therapist, and a researcher, in three feedback sessions. In these sessions, data were visualized in a flexible feedback interface that allowed for collaborative exploration of George's data. Both patient and therapist evaluated the module through questionnaires and George also participated in a semi-structured evaluation interview. Results: George's ESM questionnaire included personalized items on the topics of self-esteem and open versus withdrawn behavior. He completed 241 (89.3%) assessments. Discussions during the feedback sessions focused on two core themes: First, George's low energy level, which was further explored with regard to his sleep, medication, and activity patterns. Second, his low sense of self-esteem, which led to an in-depth exploration of his thinking patterns and social interactions. The ESM module was seen as useful and insightful by both George and therapist. Conclusions: This case shows how ESM and ESM-based feedback can stimulate the collaborative exploration of the patient's complaints, and how it can provide useful insights for treatment. We discuss how our personalized ESM module relates to current clinical principles and practices, and make suggestions for further implementation.

Increased affective reactivity among depressed individuals can be explained by floor effects: An experience sampling study.

Experience sampling studies into daily-life affective reactivity indicate that depressed individuals react more strongly to both positive and negative stimuli than non-depressed individuals, particularly on negative affect (NA). Given the different mean levels of both positive affect (PA) and NA between patients and controls, such findings may be influenced by floor/ceiling effects, leading to violations of the normality and homoscedasticity assumptions underlying the used statistical models. Affect distributions in prior studies suggest that this may have particularly influenced NA-reactivity findings. Here, we investigated the influence of floor/ceiling effects on the observed PA- and NA-reactivity to both positive and negative events. Data came from 346 depressed, non-depressed, and remitted participants from the Netherlands Study of Depression and Anxiety (NESDA). In PA-reactivity analyses, no floor/ceiling effects and assumption violations were observed, and PA-reactivity to positive events, but not negative events, was significantly increased in the depressed and remitted groups versus the non-depressed group. However, NA-scores exhibited a floor effect in the non-depressed group and naively estimated models violated model assumptions. When these violations were accounted for in subsequent analyses, group differences in NA-reactivity that had been present in the naive models were no longer observed. In conclusion, we found increased PA-reactivity to positive events but no evidence of increased NA-reactivity in depressed individuals when accounting for violations of assumptions. The results indicate that affective-reactivity results are very sensitive to modeling choices and that previously observed increased NA-reactivity in depressed individuals may (partially) reflect unaddressed assumption violations resulting from floor effects in NA.

Feedback based on experience sampling data: Examples of current approaches and considerations for future research.

Methodologies such as the Experience Sampling Method (ESM) or Ecological Momentary Assessment allow the gathering of fine-graded, dynamic, personal data within a patient's daily life. Currently, it is studied whether feedback based on experience sampling data (ESM-based feedback) can be used as a clinical tool to inform shared decision-making in clinical practice. Although the potential of feedback is recognized, little is known on how to generate, use, and implement it. This article (i) presents n = 15 ongoing ESM projects within the Belgian-Dutch network for ESM research wherein ESM-based feedback is provided to various patient populations, and (ii) summarizes qualitative data on experiences with ESM-based feedback of researchers (n = 8) with extensive expertise with ESM (average of 10 years) involved in these ongoing studies. The following aspects appear to be of relevance when providing ESM-based feedback: training for healthcare professionals and researchers, the use of online interfaces and graphical visualizations to present data, and interacting with patients in a face-to-face setting when discussing the contextual relevance and potential implications. Prospectively, research may build on these aspects and create coherent consensus-based guidelines for the use of ESM-based feedback.

Personality Neuroscience: An Emerging Field with Bright Prospects.

Personality neuroscience is the study of persistent psychological individual differences, typically in the general population, using neuroscientific methods. It has the potential to shed light on the neurobiological mechanisms underlying individual differences and their manifestation in ongoing behavior and experience. The field was inaugurated many decades ago, yet has only really gained momentum in the last two, as suitable technologies have become widely available. Personality neuroscience employs a broad range of methods, including molecular genetics, pharmacological assays or manipulations, electroencephalography, and various neuroimaging modalities, such as magnetic resonance imaging and positron emission tomography. Although exciting progress is being made in this young field, much remains unknown. In this brief review, we discuss discoveries that have been made, methodological challenges and advances, and important questions that remain to be answered. We also discuss best practices for personality neuroscience research and promising future directions for the field.

Time to get personal? The impact of researchers choices on the selection of treatment targets using the experience sampling methodology.

OBJECTIVE: One of the promises of the experience sampling methodology (ESM) is that a statistical analysis of an individual's emotions, cognitions and behaviors in everyday-life could be used to identify relevant treatment targets. A requisite for clinical implementation is that outcomes of such person-specific time-series analyses are not wholly contingent on the researcher performing them. METHODS: To evaluate this, we crowdsourced the analysis of one individual patient's ESM data to 12 prominent research teams, asking them what symptom(s) they would advise the treating clinician to target in subsequent treatment. RESULTS: Variation was evident at different stages of the analysis, from preprocessing steps (e.g., variable selection, clustering, handling of missing data) to the type of statistics and rationale for selecting targets. Most teams did include a type of vector autoregressive model, examining relations between symptoms over time. Although most teams were confident their selected targets would provide useful information to the clinician, not one recommendation was similar: both the number (0-16) and nature of selected targets varied widely. CONCLUSION: This study makes transparent that the selection of treatment targets based on personalized models using ESM data is currently highly conditional on subjective analytical choices and highlights key conceptual and methodological issues that need to be addressed in moving towards clinical implementation.

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.

Personalized Network Modeling in Psychopathology: The Importance of Contemporaneous and Temporal Connections.

Recent literature has introduced (a) the network perspective to psychology and (b) collection of time series data to capture symptom fluctuations and other time varying factors in daily life. Combining these trends allows for the estimation of intraindividual network structures. We argue that these networks can be directly applied in clinical research and practice as hypothesis generating structures. Two networks can be computed: a temporal network, in which one investigates if symptoms (or other relevant variables) predict one another over time, and a contemporaneous network, in which one investigates if symptoms predict one another in the same window of measurement. The contemporaneous network is a partial correlation network, which is emerging in the analysis of cross-sectional data but is not yet utilized in the analysis of time series data. We explain the importance of partial correlation networks and exemplify the network structures on time series data of a psychiatric patient.

Personalized Feedback on Symptom Dynamics of Psychopathology: A Proof-of-Principle Study.

BACKGROUND AND OBJECTIVES: In the proposed symptom network approach to psychopathology, psychiatric disorders are assumed to result from the (causal) interplay between symptoms. By implementing this approach we explored whether individual feedback on symptom dynamics complements current categorical classification and treatment. The aim of this proof-of-principle case-study was to explore the feasibility, acceptability and usability of this transdiagnostic approach. METHODS: A female patient, aged 67, suffering from treatment resistant anxious and depressive symptoms was treated in our tertiary outpatient clinic for old age psychiatry. She participated in ecological momentary assessments (EMA), which involved intensive repeated measurements of mood and context-related items during two weeks. Visualizations of the interplay between the items were provided by network graphs and were discussed with the patient. RESULTS: Network graphs were discussed with the patient. For example, it was hypothesized and discussed with the patient that feeling relaxed increased physical activity, causing physical discomfort in the following hours. Physical discomfort caused stress as its symptoms resembled her feared somatic anxiety symptoms. This increased the patient's insight that stress, expressed as somatic symptoms, played a central role in her panic disorder. This started a dialogue on how to cope with stress caused by somatic (anxiety) symptoms and provided a rationale for the patient to start an interoceptive exposure intervention she had repeatedly refused before. LIMITATIONS: The observed symptom dynamics may not be generalizable to any other random two weeks. CONCLUSIONS: Personalized diagnosis of psychopathology incorporating complex symptom dynamics is feasible and a promising addition to current categorical diagnostic systems and could guide intervention selection. This merits further exploration.

Associations between genetic risk, functional brain network organization and neuroticism.

Neuroticism and genetic variation in the serotonin-transporter (SLC6A4) and catechol-O-methyltransferase (COMT) gene are risk factors for psychopathology. Alterations in the functional integration and segregation of neural circuits have recently been found in individuals scoring higher on neuroticism. The aim of the current study was to investigate how genetic risk factors impact functional network organization and whether genetic risk factors moderate the association between neuroticism and functional network organization. We applied graph theory analysis on resting-state fMRI data in a sample of 120 women selected based on their neuroticism score, and genotyped two polymorphisms: 5-HTTLPR (S-carriers and L-homozygotes) and COMT (rs4680-rs165599; COMT risk group and COMT non-risk group). For the 5-HTTLPR polymorphism, we found that subnetworks related to cognitive control show less connections with other subnetworks in S-carriers compared to L-homozygotes. The COMT polymorphism moderated the association between neuroticism and functional network organization. We found that neuroticism was associated with lower efficiency coefficients in visual and somatosensory-motor subnetworks in the COMT risk group compared to the COMT non-risk group. The findings of altered topology of specific subnetworks point to different cognitive-emotional processes that may be affected in relation to the genetic risk factors, concerning emotion regulation in S-carriers (5-HTTLPR) and emotional salience processing in COMT risk carriers.

Associations Between Daily Affective Instability and Connectomics in Functional Subnetworks in Remitted Patients with Recurrent Major Depressive Disorder.

Remitted patients with major depressive disorder (rMDD) often report more fluctuations in mood as residual symptomatology. It is unclear how this affective instability is associated with information processing related to the default mode (DMS), salience/reward (SRS), and frontoparietal (FPS) subnetworks in rMDD patients at high risk of recurrence (rrMDD). Sixty-two unipolar, drug-free rrMDD patients (⩾2 MDD episodes) and 41 healthy controls (HCs) were recruited. We used experience sampling methodology to monitor mood/cognitions (10 times a day for 6 days) and calculated affective instability using the mean adjusted absolute successive difference. Subsequently, we collected resting-state functional magnetic resonance imaging data and performed graph theory to obtain network metrics of integration within (local efficiency) the DMS, SRS, and FPS, and between (participation coefficient) these subnetworks and others. In rrMDD patients compared with HCs, we found that affective instability was increased in most negative mood/cognition variables and that the DMS had less connections with other subnetworks. Furthermore, we found that rrMDD patients, who showed more instability in feeling down and irritated, had less connections between the SRS and other subnetworks and higher local efficiency coefficients in the FPS, respectively. In conclusion, rrMDD patients, compared with HCs, are less stable in their negative mood and these dynamics are related to differences in information processing within- and between-specific functional subnetworks. These results are a first step to gain a better understanding of how mood fluctuations in real life are represented in the brain and provide insights into the vulnerability profile of MDD.

Vulnerability for new episodes in recurrent major depressive disorder: protocol for the longitudinal DELTA-neuroimaging cohort study.

INTRODUCTION: Major depressive disorder (MDD) is widely prevalent and severely disabling, mainly due to its recurrent nature. A better understanding of the mechanisms underlying MDD-recurrence may help to identify high-risk patients and to improve the preventive treatment they need. MDD-recurrence has been considered from various levels of perspective including symptomatology, affective neuropsychology, brain circuitry and endocrinology/metabolism. However, MDD-recurrence understanding is limited, because these perspectives have been studied mainly in isolation, cross-sectionally in depressed patients. Therefore, we aim at improving MDD-recurrence understanding by studying these four selected perspectives in combination and prospectively during remission. METHODS AND ANALYSIS: In a cohort design, we will include 60 remitted, unipolar, unmedicated, recurrent MDD-participants (35-65 years) with ≥ 2 MDD-episodes. At baseline, we will compare the MDD-participants with 40 matched controls. Subsequently, we will follow-up the MDD-participants for 2.5 years while monitoring recurrences. We will invite participants with a recurrence to repeat baseline measurements, together with matched remitted MDD-participants. Measurements include questionnaires, sad mood-induction, lifestyle/diet, 3 T structural (T1-weighted and diffusion tensor imaging) and blood-oxygen-level-dependent functional MRI (fMRI) and MR-spectroscopy. fMRI focusses on resting state, reward/aversive-related learning and emotion regulation. With affective neuropsychological tasks we will test emotional processing. Moreover, we will assess endocrinology (salivary hypothalamic-pituitary-adrenal-axis cortisol and dehydroepiandrosterone-sulfate) and metabolism (metabolomics including polyunsaturated fatty acids), and store blood for, for example, inflammation analyses, genomics and proteomics. Finally, we will perform repeated momentary daily assessments using experience sampling methods at baseline. We will integrate measures to test: (1) differences between MDD-participants and controls; (2) associations of baseline measures with retro/prospective recurrence-rates; and (3) repeated measures changes during follow-up recurrence. This data set will allow us to study different predictors of recurrence in combination. ETHICS AND DISSEMINATION: The local ethics committee approved this study (AMC-METC-Nr.:11/050). We will submit results for publication in peer-reviewed journals and presentation at (inter)national scientific meetings. TRIAL REGISTRATION NUMBER: NTR3768.

Connectomics and neuroticism: an altered functional network organization.

The personality trait neuroticism is a potent risk marker for psychopathology. Although the neurobiological basis remains unclear, studies have suggested that alterations in connectivity may underlie it. Therefore, the aim of the current study was to shed more light on the functional network organization in neuroticism. To this end, we applied graph theory on resting-state functional magnetic resonance imaging (fMRI) data in 120 women selected based on their neuroticism score. Binary and weighted brain-wide graphs were constructed to examine changes in the functional network structure and functional connectivity strength. Furthermore, graphs were partitioned into modules to specifically investigate connectivity within and between functional subnetworks related to emotion processing and cognitive control. Subsequently, complex network measures (ie, efficiency and modularity) were calculated on the brain-wide graphs and modules, and correlated with neuroticism scores. Compared with low neurotic individuals, high neurotic individuals exhibited a whole-brain network structure resembling more that of a random network and had overall weaker functional connections. Furthermore, in these high neurotic individuals, functional subnetworks could be delineated less clearly and the majority of these subnetworks showed lower efficiency, while the affective subnetwork showed higher efficiency. In addition, the cingulo-operculum subnetwork demonstrated more ties with other functional subnetworks in association with neuroticism. In conclusion, the 'neurotic brain' has a less than optimal functional network organization and shows signs of functional disconnectivity. Moreover, in high compared with low neurotic individuals, emotion and salience subnetworks have a more prominent role in the information exchange, while sensory(-motor) and cognitive control subnetworks have a less prominent role.

Neuroticism and the brain: a quantitative meta-analysis of neuroimaging studies investigating emotion processing.

Neuroticism is a robust personality trait that constitutes a risk factor for mood disorders. Neuroimaging findings related to neuroticism have been inconsistent across studies and hardly integrated in order to construct a model of the underlying neural correlates of neuroticism. The aim of the current meta-analysis was to provide a quantitative summary of the literature, using a parametric coordinate-based meta-analysis (PCM) approach. Data were pooled for emotion processing tasks investigating the contrasts (negative>neutral) and (positive>neutral) to identify brain regions that are consistently associated with neuroticism across studies. Significant negative and positive correlations with neuroticism were found only for the contrast (negative>neutral) after multiple comparisons correction. Differences in brain activation were found to be associated with neuroticism during fear learning, anticipation of aversive stimuli and the processing and regulation of emotion. The relationship between neuroticism and these three psychological processes and their corresponding neural correlates is discussed. Furthermore, the meta-analytic findings are incorporated into a general model of emotion processing in neuroticism.

Neural correlates of alexithymia: a meta-analysis of emotion processing studies.

Alexithymia is a personality trait characterized by difficulties in the experience and cognitive processing of emotions. It is considered a risk factor for a range of psychiatric and neurological disorders. Functional neuroimaging studies investigating the neural correlates of alexithymia have reported inconsistent results. To integrate previous findings, we conducted a parametric coordinate-based meta-analysis including fifteen neuroimaging studies on emotion processing in alexithymia. During the processing of negative emotional stimuli, alexithymia was associated with a diminished response of the amygdala, suggesting decreased attention to such stimuli. Negative stimuli additionally elicited decreased activation in supplementary motor and premotor brain areas and in the dorsomedial prefrontal cortex, possibly underlying poor empathic abilities and difficulties in emotion regulation associated with alexithymia. Positive stimuli elicited decreased activation in the right insula and precuneus, suggesting reduced emotional awareness in alexithymia regarding positive affect. Independent of valence, higher (presumably compensatory) activation was found in the dorsal anterior cingulate possibly indicating increased cognitive demand. These results suggest valence-specific as well as valence-independent effects of alexithymia on the neural processing of emotions.