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Fibromyalgia, a condition characterized by chronic pain, is frequently accompanied by emotional disturbances. Here we aimed to study brain activation and functional connectivity (FC) during processing of emotional stimuli in fibromyalgia. Thirty female patients with fibromyalgia and 31 female healthy controls (HC) were included. Psychometric tests were administered to measure alexithymia, affective state, and severity of depressive and anxiety symptoms. Next, participants performed an emotion processing and regulation task during functional magnetic resonance imaging (fMRI). We performed a 2 × 2 ANCOVA to analyze main effects and interactions of the stimuli valence (positive or negative) and group (fibromyalgia or HC) on brain activation. Generalized psychophysiological interaction analysis was used to assess task-dependent FC of brain regions previously associated with emotion processing and fibromyalgia (i.e., hippocampus, amygdala, anterior insula, and pregenual anterior cingulate cortex [pACC]). The left superior lateral occipital cortex showed more activation in fibromyalgia during emotion processing than in HC, irrespective of valence. Moreover, we found an interaction effect (valence x group) in the FC between the left pACC and the precentral and postcentral cortex, and central operculum, and premotor cortex. These results suggest abnormal brain activation and connectivity underlying emotion processing in fibromyalgia, which could help explain the high prevalence of psychopathological symptoms in this condition.

Contains fulltext : 288062.pdf (Publisher’s version ) (Closed access) There is limited convergence in neuroimaging investigations into volumes of subcortical brain regions in social anxiety disorder (SAD). The inconsistent findings may arise from variations in methodological approaches across studies, including sample selection based on age and clinical characteristics. The ENIGMA-Anxiety Working Group initiated a global mega-analysis to determine whether differences in subcortical volumes can be detected in adults and adolescents with SAD relative to healthy controls. Volumetric data from 37 international samples with 1115 SAD patients and 2775 controls were obtained from ENIGMA-standardized protocols for image segmentation and quality assurance. Linear mixed-effects analyses were adjusted for comparisons across seven subcortical regions in each hemisphere using family-wise error (FWE)-correction. Mixed-effects d effect sizes were calculated. In the full sample, SAD patients showed smaller bilateral putamen volume than controls (left: d = -0.077, pFWE = 0.037; right: d = -0.104, pFWE = 0.001), and a significant interaction between SAD and age was found for the left putamen (r = -0.034, pFWE = 0.045). Smaller bilateral putamen volumes (left: d = -0.141, pFWE

Mind-wandering, and specifically the frequency and content of mind-wandering, plays an important role in the psychological well-being of individuals. Repetitive negative thinking has been associated with a high risk to develop and maintain Major Depressive Disorder. We combined forces between psychiatry and cognitive sciences to investigate the adjustability of the content and characteristics of mind-wandering in individuals scoring high (n=42) or low (n=40) on their vulnerability for negative affect and depression. To assess the impact of mood induction on mind-wandering, participants performed a Sustained Attention to Response Task (SART) after a single session of positive fantasizing and a single session of stress induction in a cross-over design. Affective states were measured before and after the interventions. Results showed increased negative affect after stress, and increased positive and reduced negative affect after fantasizing. Thoughts were more on-task and future-related, and less negative after fantasizing compared to after stress. Individuals more susceptible to negative affect showed more off-task thinking after stress than after fantasizing compared to individuals low on this. These results suggest that stress-induced negative thinking underlying vulnerability for depression could be partially countered by fantasizing, which could inform the development of treatments for depression.

Background: Comorbid anxiety disorders and anxious distress are highly prevalent in major depressive disorder (MDD). The presence of the DSM-5 anxious distress specifier (ADS) has been associated with worse treatment outcomes and chronic disease course. However, little is known about the neurobiological correlates of anxious distress in MDD.Methods: We probed the relation between the DSM-5 ADS and task-related reactivity to emotional faces, as well as resting-state functional connectivity patterns of intrinsic salience and basal ganglia networks in unmedicated MDD patients with (MDD/ADS+, N = 24) and without ADS (MDD/ADS−, N = 48) and healthy controls (HC, N = 59). Both categorical and dimensional measures of ADS were investigated.Results: MDD/ADS+ patients had higher left amygdala responses to emotional faces compared to MDD/ADS− patients (p =.015)—part of a larger striato-limbic cluster. MDD/ADS+ did not differ from MDD/ADS− or controls in resting-state functional connectivity of the salience or basal ganglia networks.Conclusions: Current findings suggest that amygdala and striato-limbic hyperactivity to emotional faces may be a neurobiological hallmark specific to MDD with anxious distress, relative to MDD without anxious distress. This may provide preliminary indications of the underlying mechanisms of anxious distress in depression, and underline the importance to account for heterogeneity in depression research.

Objective: Cognitive alterations putatively contribute to the risk for suicide in individuals with psychosis. Yet, a comprehensive assessment of social-and general-cognitive abilities in a large sample is lacking. Methods: Seven-hundred -fifteen individuals diagnosed with a psychotic disorder performed tasks of facial emotion recognition, Theory of Mind, and general cognitive functioning (sustained attention, set-shifting, IQ-tests and verbal learning) as part of the Genetic-Risk-and-Outcome-of-Psychosis (GROUP) study. Presence of past suicide attempt/s and/or current suicidal ideation was reported by 261 individuals and 454 individuals reported no suicide attempt or ideation. We used general linear models to investigate group differences in task performance. All analysis were controlled for age, sex, education, and psychotic symptom severity. Results: Individuals with suicide attempt and/or ideation showed better performance on the facial emotion recognition task and lower performance on tasks of sustained attention and verbal learning, compared to individuals without suicide attempt and/or ideation, without a clear effect of attempt or ideation. Theory of Mind performance was also better for individuals with suicide attempt and/or ideation, with largest differences between individuals who reported both attempts and ideation compared to individuals without suicide attempt and/or ideation. No effect of suicide attempt and/or ideation was found on misperception of facial emotions, IQ and set-shifting. Overall, effect sizes were small. Conclusion: Higher sensitivity to social-emotional cues together with weakened attentional control and learning capacity was observed in individuals with psychosis and suicide attempt and/or ideation. This may suggest that insufficient capacity for regulating perceived social stress contributes to suicidal thoughts and behavior. (c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/).

BackgroundMajor depressive disorder (MDD) is associated with an increased risk of brain atrophy, aging-related diseases, and mortality. We examined potential advanced brain aging in MDD patients, and whether this process is associated with clinical characteristics in a large multi-center international dataset.MethodsWe performed a mega-analysis by pooling brain measures derived from T1-weighted MRI scans from 29 samples worldwide. Normative brain aging was estimated by predicting chronological age (10-75 years) from 7 subcortical volumes, 34 cortical thickness and 34 surface area, lateral ventricles and total intracranial volume measures separately in 1,147 male and 1,386 female controls from the ENIGMA MDD working group. The learned model parameters were applied to 1,089 male controls and 1,167 depressed males, and 1,326 female controls and 2,044 depressed females to obtain independent unbiased brain-based age predictions. The difference between predicted “brain age” and chronological age was calculated to indicate brain predicted age difference (brain-PAD).FindingsOn average, MDD patients showed a higher brain-PAD of +0.90 (SE 0.21) years (Cohen’s d=0.12, 95% CI 0.06-0.17) compared to controls. Relative to controls, first-episode and currently depressed patients showed higher brain-PAD (+1.2 [0.3] years), and the largest effect was observed in those with late-onset depression (+1.7 [0.7] years). In addition, higher brain-PAD was associated with higher self-reported depressive symptomatology (b=0.05, p=0.004).InterpretationThis highly powered collaborative effort showed subtle patterns of abnormal structural brain aging in MDD. Substantial within-group variance and overlap between groups were observed. Longitudinal studies of MDD and somatic health outcomes are needed to further assess the predictive value of these brain-PAD estimates.FundingThis work was supported, in part, by NIH grants U54 EB020403 and R01 MH116147.

BACKGROUND Worldwide, insomnia remains a highly prevalent public health problem. eHealth presents a novel opportunity to deliver effective, accessible, and affordable insomnia treatments on a population-wide scale. However, there is no quantitative integration of evidence regarding the effectiveness of eHealth-based psychosocial interventions on insomnia. OBJECTIVE We aimed to evaluate the effectiveness of eHealth-based psychosocial interventions for insomnia and investigate the influence of specific study characteristics and intervention features on these effects. METHODS We searched PubMed, Embase, Web of Science, PsycINFO, and the Cochrane Central Register of Controlled Trials from database inception to February 16, 2021, for publications investigating eHealth-based psychosocial interventions targeting insomnia and updated the search of PubMed to December 6, 2021. We also screened gray literature for unpublished data. Eligible studies were randomized controlled trials of eHealth-based psychosocial interventions targeting adults with insomnia. Random-effects meta-analysis models were used to assess primary and secondary outcomes. Primary outcomes were insomnia severity and sleep quality. Meta-analyses were performed by pooling the effects of eHealth-based psychosocial interventions on insomnia compared with inactive and in-person conditions. We performed subgroup analyses and metaregressions to explore specific factors that affected the effectiveness. Secondary outcomes included sleep diary parameters and mental health–related outcomes. RESULTS Of the 19,980 identified records, 37 randomized controlled trials (13,227 participants) were included. eHealth-based psychosocial interventions significantly reduced insomnia severity (Hedges g=−1.01, 95% CI −1.12 to −0.89; Pg=−0.58, 95% CI −0.75 to −0.41; Pg=0.41, 95% CI −0.02 to 0.85; P=.06) and a significant advantage for in-person treatment in enhancing sleep quality (Hedges g=0.56, 95% CI 0.24-0.88; PP=.01) on alleviating insomnia severity in clinical samples than in subclinical samples. eHealth-based psychosocial interventions that incorporated guidance from trained therapists had a significantly greater effect on insomnia severity (P=.05) and sleep quality (P=.02) than those with guidance from animated therapists or no guidance. Higher baseline insomnia severity and longer intervention duration were associated with a larger reduction in insomnia severity (P=.004). eHealth-based psychosocial interventions significantly improved each secondary outcome. CONCLUSIONS eHealth interventions for insomnia are effective in improving sleep and mental health and can be considered a promising treatment for insomnia. Our findings support the wider dissemination of eHealth interventions and their further promotion in a stepped-care model. Offering blended care could improve treatment effectiveness. Future research needs to elucidate which specific intervention components are most important to achieve intervention effectiveness. Blended eHealth interventions may be tailored to benefit people with low socioeconomic status, limited access to health care, or lack of eHealth literacy.

Individuals speaking multiple language have been asserted to have a cognitive advantage, perhaps specifically in the domain of selective attention, although this claim has recently been challenged. The diversity of multilingual experiences and use seems of great importance here, and suggestions have been made that advantages especially emerge for individuals with higher ‘multilingual load’, referring to language experience and use factors including duration of multilingualism, number of languages mastered, and use of multiple languages in daily life. We captured multilingual language diversity using a language entropy measure, which encompasses several language use factors into one metric. We related individual differences in language entropy to selective attention as measured with an attentional blink (AB) task in 53 diverse multilingual individuals. During task performance, brain activity in the lateral prefrontal cortex was measured using fNIRS. We found no support for the claim that language diversity, or other individual factors related to language experience and use, influence AB magnitude. However, relations with T1 identification accuracy were observed and brain activity in the DLPFC during the attentional blink task also related to higher language diversity, jointly suggesting that language diversity may promote alertness and attention. This study is the first to relate simultaneous behavioral and brain attentional blink data to the language entropy measure.

Objectives Life experiences that are complex, sustained, and intense, such as active participation in music and speaking multiple languages, have been suggested to contribute to maintaining or improving cognitive performance and mental health. The current study focuses on whether lifetime musical and multilingual experiences differentially relate to cognition and well-being in older adults, and tests whether there is a cumulative effect of both experiences. Methods A total of 11,335 older adults from the population-based Lifelines Cohort Study completed a musical and multilingual background and experience questionnaire. Latent class analysis was used to categorize individuals into subgroups according to their various musical and multilingual experiences resulting in a (1) nonmusical, low-multilingual group; (2) nonmusical, high-multilingual group; (3) musical, low-multilingual group; and (4) musical high-multilingual group. To determine whether the groups differed in terms of cognition or emotional affect, differences in Ruff Figural Fluency Test (RFFT) and Positive and Negative Affect Schedule scores were investigated by means of multinomial logistic regression analysis. Results Having high-multilingual, and not musical, experience was related to better RFFT performance compared to no experience, but not to more positive affect. Having both musical and high-multilingual experiences is related to better RFFT performance and more positive affect in advanced age compared to having only one experience or none. Importantly, these results were found independently of age, level of education, and socioeconomic status. Discussion Musical and multilingual experiences are related to healthy aging, especially when combined, which supports the suggestion that a broader spectrum of lifetime experiences relates to cognitive reserve.

Major Depressive Disorder (MDD) is a prevalent psychiatric disorder, characterized by high relapse risk. With every new episode, risk for relapse increases. This makes preventing relapse an important clinical target in limiting the personal and societal burden of MDD. Preventive Cognitive Therapy (PCT) is a protocolized psychological therapy which has shown to lower relapse risk. How PCT attains its effects needs further elucidation. Understanding the treatment mechanisms provides a window to identify critical target points to prevent depressive relapse. In this randomized controlled trial, 50 patients remitted from at least two depressive episodes in the past five years were randomized to eight sessions of PCT (n=25) or to a waiting list condition (n=25) in the context of the NEWPRIDE trial. Primary outcome measures were changes in brain activation during effortful emotion regulation and in biased processing, covering both negative and positive valence dimensions. All patients were assessed twice (baseline and three-month follow-up) for these outcome measures, as well as their diagnosis, symptomatology, cognitive and affective reactivity, and emotion regulation styles. Linear Mixed Models and Repeated Measures ANOVAs were conducted to objectify the immediate changes induced by the therapy in brain reactivity, and clinical and cognitive measures. Following PCT, patients showed decreased recruitment of dorsomedial prefrontal regions during upregulation of positive affect and stable recruitment of the pregenual anterior cingulate cortex during regulation of emotions over valences, compared to the waiting list. No effects on biased processing of emotional information were observed. Furthermore, PCT resulted in a lower increase of depressive symptomatology over three months as compared to the waiting list condition. Finally, PCT resulted in increased activation of positive thoughts following reading positive self-related scenarios, lower responsivity of negative affect to negative stimuli and increased successful application of cognitive reappraisal to modify affective states. These results suggest that PCT obtains its relapse preventing effects by targeting mechanisms that underpin regulation of mood. More specifically, changes in regulation of positive affect and content of positive cognitions may decrease negative mood and affect. This supports cross-valence compensatory models of cognitive therapy and suggests that strengthening and shifting cognition and affect to more positive content may guard against the activation of negative cognitions and affect in the face of daily hassles and life events.

Depression and anxiety are common and often comorbid mental health disorders that represent risk factors for aging-related conditions. Brain aging has shown to be more advanced in patients with major depressive disorder (MDD). Here, we extend prior work by investigating multivariate brain aging in patients with MDD, anxiety disorders, or both, and examine which factors contribute to older-appearing brains. Adults aged 18–57 years from the Netherlands Study of Depression and Anxiety underwent structural MRI. A pretrained brain-age prediction model based on >2000 samples from the ENIGMA consortium was applied to obtain brain-predicted age differences (brain PAD, predicted brain age minus chronological age) in 65 controls and 220 patients with current MDD and/or anxiety. Brain-PAD estimates were associated with clinical, somatic, lifestyle, and biological factors. After correcting for antidepressant use, brain PAD was significantly higher in MDD (+2.78 years, Cohen’s d = 0.25, 95% CI −0.10-0.60) and anxiety patients (+2.91 years, Cohen’s d = 0.27, 95% CI −0.08-0.61), compared with controls. There were no significant associations with lifestyle or biological stress systems. A multivariable model indicated unique contributions of higher severity of somatic depression symptoms (b = 4.21 years per unit increase on average sum score) and antidepressant use (−2.53 years) to brain PAD. Advanced brain aging in patients with MDD and anxiety was most strongly associated with somatic depressive symptomatology. We also present clinically relevant evidence for a potential neuroprotective antidepressant effect on the brain-PAD metric that requires follow-up in future research.

Background: Impaired clinical and cognitive insight are prevalent in schizophrenia and relate to poorer outcome. Good insight has been suggested to depend on social cognitive and metacognitive abilities requiring global integration of brain signals. Impaired insight has been related to numerous focal gray matter (GM) abnormalities distributed across the brain suggesting dysconnectivity at the global level. In this study, we test whether global integration deficiencies reflected in gray matter network connectivity underlie individual variations in insight.Methods: We used graph theory to examine whether individual GM-network metrics relate to insight in patients with a psychotic disorder (n = 114). Clinical insight was measured with the Schedule for the Assessment of Insight & ndash;Expanded and item G12 of the Positive and Negative Syndrome Scale, and cognitive insight with the Beck Cognitive Insight Scale. Individual GM-similarity networks were created from GM-segmentations of T1weighted MRI-scans. Graph metrics were calculated using the Brain Connectivity Toolbox.Results: Networks of schizophrenia patients with poorer clinical insight showed less segregation (i.e. clustering coefficient) into specialized subnetworks at the global level. Schizophrenia patients with poorer cognitive insight showed both less segregation and higher connectedness (i.e. lower path length) of their brain networks, making their network topology more & ldquo;random & rdquo;.Conclusions: Our findings suggest less segregated processing of information in patients with poorer cognitive and clinical insight, in addition to higher connectedness in patients with poorer cognitive insight. The ability to take a critical perspective on one & rsquo;s symptoms (clinical insight) or views (cognitive insight) might depend especially on segregated specialized processing within distinct subnetworks.

Background: Social amotivation is a core element of the negative symptoms of schizophrenia. However, it is still largely unknown which neural substrates underpin social amotivation in people with schizophrenia, though deficiencies in the mesocorticolimbic dopamine system have been proposed.Methods: We examined the association between social amotivation and substantia nigra/ventral tegmental area-seeded intrinsic connectivity in 84 people with schizophrenia using resting state functional magnetic resonance imaging.Results: Spontaneous fluctuations of midbrain dopaminergic regions were positively associated with striatal and prefrontal fluctuations in people with schizophrenia. Most importantly, social amotivation was negatively associated with functional connectivity between the midbrain's substantia nigra/ventral tegmental area and medial-and lateral prefrontal cortex, the temporoparietal junction, and dorsal and ventral striatum. These associations were observed independently of depressive and positive symptoms.Conclusions: Our findings suggest that social amotivation in people with schizophrenia is associated with altered intrinsic connectivity of mesocorticolimbic pathways linked to cognitive control and reward processing. Dysconnectivity of dopaminergic neuronal ensembles that are fundamental to approach behavior and motivation may help explain the lack of initiative social behavior in people with social amotivation. (C) 2019 Elsevier B.V. All rights reserved.

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.

Background: Depression has been associated with decreased regional grey matter volume, which might partly be explained by an unhealthier lifestyle in depressed individuals which has been ignored by most earlier studies. Also, the longitudinal nature of depression, lifestyle and brain structure associations is largely unknown. This study investigates the relationship of depression and lifestyle with brain structure cross-sectionally and longitudinally over up to 9 years. Methods: We used longitudinal structural MRI data of persons with depression and/or anxiety disorders and controls (Nunique participants = 347, Nobservations = 609). Cortical thickness of medial orbitofrontal cortex (mOFC), rostral anterior cingulate cortex (rACC) and hippocampal volume were derived using FreeSurfer. Using Generalized Estimating Equations, we investigated associations of depression and lifestyle (Body mass index (BMI), smoking, alcohol consumption, physical activity and sleep duration) with brain structure and change in brain structure over 2 (n = 179) and 9 years (n = 82). Results: Depression status (B = -.053, p = .002) and severity (B = -.002, p = .002) were negatively associated with rACC thickness. mOFC thickness was negatively associated with BMI (B = -.004, p < .001) and positively with moderate alcohol consumption (B = .030, p = .009). All associations were independent of each other. No associations were observed between (change in) depression, disease burden or lifestyle factors with brain change over time. Conclusions: Depressive symptoms and diagnosis were independently associated with thinner rACC, BMI with thinner mOFC, and moderate alcohol consumption with thicker mOFC. No longitudinal associations were observed, suggesting that regional grey matter alterations are a long-term consequence or vulnerability indicator for depression but not dynamically or progressively related to depression course trajectory.

BackgroundThe importance of the hippocampus and amygdala for disrupted emotional memory formation in depression is well-recognized, but it remains unclear whether functional abnormalities are state-dependent and whether they are affected by the persistence of depressive symptoms.MethodsThirty-nine patients with major depressive disorder and 28 healthy controls were included from the longitudinal functional magnetic resonance imaging (fMRI) sub-study of the Netherlands Study of Depression and Anxiety. Participants performed an emotional word-encoding and -recognition task during fMRI at baseline and 2-year follow-up measurement. At baseline, all patients were in a depressed state. We investigated state-dependency by relating changes in brain activation over time to changes in symptom severity. Furthermore, the effect of time spent with depressive symptoms in the 2-year interval was investigated.ResultsSymptom change was linearly associated with higher activation over time of the left anterior hippocampus extending to the amygdala during positive and negative word-encoding. Especially during positive word encoding, this effect was driven by symptomatic improvement. There was no effect of time spent with depression in the 2-year interval on change in brain activation. Results were independent of medication- and psychotherapy-use.ConclusionUsing a longitudinal within-subjects design, we showed that hippocampal–amygdalar activation during emotional memory formation is related to depressive symptom severity but not persistence (i.e. time spent with depression or ‘load’), suggesting functional activation patterns in depression are not subject to functional ‘scarring’ although this hypothesis awaits future replication.

BackgroundChildhood maltreatment (CM) plays an important role in the development of major depressive disorder (MDD). The aim of this study was to examine whether CM severity and type are associated with MDD-related brain alterations, and how they interact with sex and age.MethodsWithin the ENIGMA-MDD network, severity and subtypes of CM using the Childhood Trauma Questionnaire were assessed and structural magnetic resonance imaging data from patients with MDD and healthy controls were analyzed in a mega-analysis comprising a total of 3872 participants aged between 13 and 89 years. Cortical thickness and surface area were extracted at each site using FreeSurfer.ResultsCM severity was associated with reduced cortical thickness in the banks of the superior temporal sulcus and supramarginal gyrus as well as with reduced surface area of the middle temporal lobe. Participants reporting both childhood neglect and abuse had a lower cortical thickness in the inferior parietal lobe, middle temporal lobe, and precuneus compared to participants not exposed to CM. In males only, regardless of diagnosis, CM severity was associated with higher cortical thickness of the rostral anterior cingulate cortex. Finally, a significant interaction between CM and age in predicting thickness was seen across several prefrontal, temporal, and temporo-parietal regions.ConclusionsSeverity and type of CM may impact cortical thickness and surface area. Importantly, CM may influence age-dependent brain maturation, particularly in regions related to the default mode network, perception, and theory of mind.

Though social functioning is often hampered in Major Depressive Disorder (MDD), we lack a complete and integrated understanding of the underlying neurobiology. Connectional disturbances in the brain’s Default Mode Network (DMN) might be an associated factor, as they could relate to suboptimal social processing. DMN connectional integrity, however, has not been explicitly studied in relation to social dysfunctioning in MDD patients. Applying Independent Component Analysis and Dual Regression on resting-state fMRI data, we explored DMN intrinsic functional connectivity in relation to social dysfunctioning (i.e. composite of loneliness, social disability, small social network) among 74 MDD patients (66.2% female, Mean age = 36.9, SD = 11.9). Categorical analyses examined whether DMN connectivity differs between high and low social dysfunctioning MDD groups, dimensional analyses studied linear associations between social dysfunction and DMN connectivity across MDD patients. Threshold-free cluster enhancement (TFCE) with family-wise error (FWE) correction was used for statistical thresholding and multiple comparisons correction (P

A key objective in the field of translational psychiatry over the past few decades has been to identify the brain correlates of major depressive disorder (MDD). Identifying measurable indicators of brain processes associated with MDD could facilitate the detection of individuals at risk, and the development of novel treatments, the monitoring of treatment effects, and predicting who might benefit most from treatments that target specific brain mechanisms. However, despite intensive neuroimaging research towards this effort, underpowered studies and a lack of reproducible findings have hindered progress. Here, we discuss the work of the ENIGMA Major Depressive Disorder (MDD) Consortium, which was established to address issues of poor replication, unreliable results, and overestimation of effect sizes in previous studies. The ENIGMA MDD Consortium currently includes data from 45 MDD study cohorts from 14 countries across six continents. The primary aim of ENIGMA MDD is to identify structural and functional brain alterations associated with MDD that can be reliably detected and replicated across cohorts worldwide. A secondary goal is to investigate how demographic, genetic, clinical, psychological, and environmental factors affect these associations. In this review, we summarize findings of the ENIGMA MDD disease working group to date and discuss future directions. We also highlight the challenges and benefits of large-scale data sharing for mental health research. ENIGMA MDD work is supported by NIH grants U54 EB020403 (Thompson), R01 MH116147 (Thompson), and R01 MH117601 (Jahanshad & Schmaal). LS was supported by an NHMRC Career Development Fellowship (1140764). AFFDIS cohort: this study was funded by the University Medical Center Goettingen (UMG Startfoerderung) and the research team is supported by German Federal Ministry of Education and Research (Bundesministerium fuer Bildung und Forschung, BMBF: 01 ZX 1507, “PreNeSt - e:Med”). Barcelona cohort: MJP is funded by the Ministerio de Ciencia e Innovación of the Spanish Government and by the Instituto de Salud Carlos III through a ‘Miguel Servet’ research contract (CP16–0020); National Research Plan (Plan Estatal de I + D + I 2016–2019); and co-financed by the European Regional Development Fund (ERDF). BRC DeCC cohort: CHYF is supported by NIHR BRC. Calgary cohort: supported by Canadian Institutes for Health Research, Branch Out Neurological Foundation. Cardiff cohort: supported by the Medical Research Council (grant G 1100629) and the National Center for Mental Health (NCMH), funded by Health Research Wales (HS/14/20). CLING cohort: this study was partially supported by the Deutsche Forschungsgemeinschaft (DFG) via grants to OG (GR1950/5–1 and GR1950/10–1). CODE cohort: Henrik Walter is supported by a grant of the Deutsche Forschungsgemeinschaft (WA 1539/4–1). The CODE cohort was collected from studies funded by Lundbeck and the German Research Foundation (WA 1539/4–1, SCHN 1205/3–1, SCHR443/11–1). DIP-Groningen cohort: this study was supported by the Gratama Foundation, the Netherlands (2012/35 to NG). Edinburgh cohort: The research leading to these results was supported by IMAGEMEND, which received funding from the European Community’s Seventh Framework Programme (FP7/2007–2013) under grant agreement no. 602450. This paper reflects only the author’s views and the European Union is not liable for any use that may be made of the information contained therein. This work was also supported by a Wellcome Trust Strategic Award 104036/Z/14/Z. FOR2107-Marburg cohort: funded by the German Research Foundation (DFG, grant FOR2107 KR 3822/7–2 to AK; FOR2107 KI 588/14–2 to TK and FOR2107 JA 1890/7–2 to AJ). Houston cohorts: supported in part by NIMH grant R01 085667 and the Dunn Research Foundation. JCS is supported by the Pat Rutherford, Jr. Endowed Chair in Psychiatry. IMH Study cohort: supported by funding from NHG (SIG/15012) and NMRC CISSP (2018). Melbourne cohort: funded by National Health and Medical Research Council of Australia (NHMRC) Project Grants 1064643 (Principal Investigator BJH) and 1024570 (Principal Investigator CGD). Minnesota cohort: the study was funded by the National Institute of Mental Health (K23MH090421; Dr. Cullen) and Biotechnology Research Center (P41 RR008079; Center for Magnetic Resonance Research), the National Alliance for Research on Schizophrenia and Depression, the University of Minnesota Graduate School, and the Minnesota Medical Foundation. This work was carried out in part using computing resources at the University of Minnesota Supercomputing Institute. Münster cohort: funded by the German Research Foundation (DFG, grant FOR2107 DA1151/5–1 and DA1151/5–2 to UD; SFB-TRR58, Projects C09 and Z02 to UD) and the Interdisciplinary Center for Clinical Research (IZKF) of the medical faculty of Münster (grant Dan3/012/17 to UD). NESDA cohort: The infrastructure for the NESDA study (www.nesda.nl) is funded through the Geestkracht program of the Netherlands Organisation for Health Research and Development (Zon-Mw, grant number 10–000–1002) and is supported by participating universities (VU University Medical Center, GGZ inGeest, Arkin, Leiden University Medical Center, GGZ Rivierduinen, University Medical Center Groningen) and mental health care organizations, see www.nesda.nl. Pharmo cohort: supported by ERA-NET PRIOMEDCHILD FP 6 (EU) grant 11.32050.26. PSYABM-NORMENT: supported by the Research Council of Norway (project number 229135). The South East Norway Health Authority Research Funding (project number 2015052). The Department of Psychology, University of Oslo, Norway. San Francisco cohort: supported by NIH/NCCIH 1R61AT009864–01A1. NIMH R01MH085734. SHIP and SHIP-trend cohorts: SHIP is part of the Community Medicine Research net of the University of Greifswald, Germany, which is funded by the Federal Ministry of Education and Research (grants no. 01ZZ9603, 01ZZ0103, and 01ZZ0403), the Ministry of Cultural Affairs and the Social Ministry of the Federal State of Mecklenburg-West Pomerania. MRI scans in SHIP and SHIP-TREND have been supported by a joint grant from Siemens Healthineers, Erlangen, Germany and the Federal State of Mecklenburg-West Pomerania. Stanford cohorts: this work was supported by NIH grant R37 MH101495. The BiDirect Study was supported by grants from the German Federal Ministry of Education and Research (BMBF; grants FKZ-01ER0816 and FKZ-01ER1506). MDS is partially supported by an award funded by the Phyllis and Jerome Lyle Rappaport Foundation. TCH is supported by NIMH grant 5K01MH117442. EJWVS, JL, and TFB are supported by European Research Council grant no. ERC-ADG-2014–671084 INSOMNIA. TFB is supported by a VU University Amsterdam University Research Fellowship 2016–2017. JL is supported by a VU University Amsterdam University Research Fellowship 2017–2018.

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., NeuroImage: Clinical, 26, ISSN:2213-1582

BackgroundDisturbances in emotion regulation (ER) are characteristic of both patients with bipolar disorder (BD) and schizophrenia (SZ). We investigated the temporal dynamics of brain activation during cognitive ER in BD and SZ to understand the contribution of temporal characteristics of disturbed ER to their unique and shared symptomatology.MethodForty-six participants performed an ER-task (BD,n= 15; SZ,n= 16; controls,n= 15) during functional magnetic resonance imaging, in which they were instructed to use cognitive reappraisal techniques to regulate their emotional responses. Finite impulse response modeling was applied to estimate the temporal dynamics of brain responses during cognitive reappraisal (v.passive attending) of negative pictures. Group, time, and group × time effects were tested using multivariate modeling.ResultsWe observed a group × time interaction during ER in the ventrolateral prefrontal cortex (VLPFC), supplementary motor area (SMA) and inferior occipital gyrus. Patients with SZ demonstrated initial hyper-activation of the VLPFC and SMA activation that was not sustained in later regulatory phases. Response profiles in the inferior occipital gyrus in SZ showed abnormal activation in the later phases of regulation. BD-patients showed general blunted responsivity in these regions.ConclusionsThese results suggest that ER-disturbances in SZ are characterized by an inefficient initialization and failure to sustain regulatory control, whereas in BD, a failure to recruit regulatory resources may represent initial deficits in formulating adequate representations of the regulatory needs. This may help to further understand how ER-disturbances give rise to symptomatology of BD and SZ.

Suicidal behavior is highly prevalent in major depressive disorder (MDD), though not present in all patients. It is unclear whether the tendency for suicidal behavior is associated with a unique functional neuroanatomical signature identifiable through neuroimaging. In this study, we investigated brain activation in suicidal and non-suicidal patients with MDD during facial emotion processing and executive control. Functional magnetic resonance imaging (fMRI) data from the NESDA-fMRI study (MDD patients N = 103, healthy controls N = 26, HC) were analyzed. Patients were divided in a group of suicide attempters (N = 18, SA), suicide ideators (N = 31, SI) and a patient-control group (N = 73, PC). A gender discrimination task with emotional faces and the Tower of London executive planning task were investigated. An ANOVA was performed to compare brain activation among suicidal patients (SA + SI), PC and HC first and then among SI, SA, PC and HC. Significance was determined as meeting p

BackgroundMajor 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.MethodsIn 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.DiscussionThe 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 registrationTrial registration: Netherlands Trial Register, August 18, 2015, trial numberNL5219.

Neuroscience & biobehavioral reviews 87, 50-55 (2018). doi:10.1016/j.neubiorev.2018.01.004, Published by Elsevier Science, Amsterdam [u.a.]

Background: Immunometabolic dysregulation (low-grade inflammation and metabolic dysregulation) has been associated with the onset and more severe course of multiple psychiatric disorders, partly due to neuroanatomical changes and impaired neuroplasticity. We examined the effect of multiple markers of immunometabolic dysregulation on hippocampal and amygdala volume and anterior cingulate cortex thickness in a large sample of patients with depression and/or anxiety and healthy subjects (N = 283).Methods: Interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-a), c-reactive protein (CRP), triglyceride levels and HDL-cholesterol and genomic profile risk scores (GPRS) for immunometabolic dysregulation were determined in peripheral blood and T1 MRI scans were acquired at 3T. Regional brain volume and cortical thickness was assessed using FreeSurfer. Covariate-adjusted linear regression analyses were performed to examine the relationship between immunometabolic dysregulation and brain volume/ thickness across all subjects.Results: Multiple immunometabolic dysregulation markers (i.e. triglyceride levels and inflammation) were associated with lower rostral ACC thickness across all subjects. IL-6 was inversely associated with hippocampal and amygdala volume in healthy subjects only. GPRS for immunometabolic dysregulation were not associated with brain volume or cortical thickness.Conclusions: Multiple serum, but not genetic immunometabolic dysregulation markers were found to relate to rostral ACC structure, suggesting that inflammation and metabolic dysregulation may impact the ACC through similar mechanisms. (C) 2016 Elsevier Inc. All rights reserved.

Altres ajuts: The ENIGMA-Major Depressive Disorder working group gratefully acknowledges support from the NIH Big Data to Knowledge (BD2K) award (U54 EB020403 to PMT) and NIH grant R01 MH116147 (PMT). LS is supported by an NHMRC MRFF Career Development Fellowship (APP1140764). We wish to acknowledge the patients and control subjects that have particiaped int the study. We thank Rosa Schirmer, Elke Schreiter, Reinhold Borschke and Ines Eidner for image acquisition and data preparation, and Anna Oliynyk for quality checks. We thank Dorothee P. Auer and F. Holsboer for initiation of the RUD study. We wish to acknowledge the patients and control subjects that have particiaped int the study. We thank Rosa Schirmer, Elke Schreiter, Reinhold Borschke and Ines Eidner for image acquisition and data preparation, and Anna Oliynyk for quality checks. We thank Dorothee P. Auer and F. Holsboer for initiation of the RUD study. NESDA: The infrastructure for the NESDA study (www.nesda.nl) is funded through the Geestkracht program of the Netherlands Organisation for Health Research and Development (Zon-Mw, grant number 10-000-1002) and is supported by participating universities (VU University Medical Center, GGZ inGeest, Arkin, Leiden University Medical Center, GGZ Rivierduinen, University Medical Center Groningen) and mental health care organizations, see www.nesda.nl. M-JvT was supported by a VENI grant (NWO grant number 016.156.077). UCSF: This work was supported by the Brain and Behavior Research Foundation (formerly NARSAD) to TTY; the National Institute of Mental Health (R01MH085734 to TTY; K01MH117442 to TCH) and by the American Foundation for Suicide Prevention (PDF-1-064-13) to TCH. Stanford: This work was supported by NIMH Grants R01MH59259 and R37101495 to IHG. MS is partially supported by an award funded by the Phyllis and Jerome Lyle Rappaport Foundation. Muenster: This work was funded by the German Research Foundation (SFB-TRR58, Projects C09 and Z02 to UD) and the Interdisciplinary Center for Clinical Research (IZKF) of the medical faculty of Münster (grant Dan3/012/17 to UD). Marburg: This work was funded by the German Research Foundation (DFG, grant FOR2107 DA1151/5-1 and DA1151/5-2 to UD; KI 588/ 14-1, KI 588/14-2 to TK; KR 3822/7-1, KR 3822/7-2 to AK; JA 1890/ 7-1, JA 1890/7-2 to AJ). IMH-MDD: This work was supported by the National Healthcare Group Research Grant (SIG/15012) awarded to KS. Barcelona: This study was funded by two grants of the Fondo de Investigación Sanitaria from the Instituto de Salud Carlos III, by the Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM). The author is funded through 'Miguel Servet' research contract (CP16-0020), co-financed by the European Regional Development Fund (ERDF) (2016-2019). QTIM: We thank the twins and singleton siblings who gave generously of their time to participate in the QTIM study. We also thank the many research assistants, radiographers, and IT support staff for data acquisition and DNA sample preparation. This study was funded by White matter disturbances in major depressive disorder: a coordinated analysis across 20 international. . . 1521 the National Institute of Child Health & Human Development (RO1 HD050735); National Institute of Biomedical Imaging and Bioengineering (Award 1U54EB020403-01, Subaward 56929223); National Health and Medical Research Council, Australia (Project Grants 496682, 1009064). NIH ENIGMA-BD2K U54 EB020403 (Thompson); R01 MH117601 (Jahanshad/Schmaal). Magdeburg: M.L. and M.W. are funded by SFB 779. Bipolar Family Study: This study has received funding from the European Community's Seventh Framework Programme (FP7/2007-2013). This paper reflects only the author's views and the European Union is not liable for any use that may be made of the information contained therein. This work was also supported by a Wellcome Trust Strategic Award (104036/Z/14/Z). Minnesota Adolescent Depression Study: The study was funded by the National Institute of Mental Health (K23MH090421), the National Alliance for Research on Schizophrenia and Depression, the University of Minnesota Graduate School, the Minnesota Medical Foundation, and the Biotechnology Research Center (P41 RR008079 to the Center for Magnetic Resonance Research), University of Minnesota, and the Deborah E. Powell Center for Women's Health Seed Grant, University of Minnesota. Dublin: This study was supported by Science Foundation Ireland through a Stokes Professorhip grant to TF. MPIP: The MPIP Sample comprises patients included in the Recurrent Unipolar Depression (RUD) Case-Control study at the clinic of the Max Planck Institute of Psychiatry, Munich, German. The RUD study was supported by GlaxoSmithKline. Alterations in white matter (WM) microstructure have been implicated in the pathophysiology of major depressive disorder (MDD). However, previous findings have been inconsistent, partially due to low statistical power and the heterogeneity of depression. In the largest multi-site study to date, we examined WM anisotropy and diffusivity in 1305 MDD patients and 1602 healthy controls (age range 12-88 years) from 20 samples worldwide, which included both adults and adolescents, within the MDD Working Group of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) consortium. Processing of diffusion tensor imaging (DTI) data and statistical analyses were harmonized across sites and effects were meta-analyzed across studies. We observed subtle, but widespread, lower fractional anisotropy (FA) in adult MDD patients compared with controls in 16 out of 25 WM tracts of interest (Cohen's d between 0.12 and 0.26). The largest differences were observed in the corpus callosum and corona radiata. Widespread higher radial diffusivity (RD) was also observed (all Cohen's d between 0.12 and 0.18). Findings appeared to be driven by patients with recurrent MDD and an adult age of onset of depression. White matter microstructural differences in a smaller sample of adolescent MDD patients and controls did not survive correction for multiple testing. In this coordinated and harmonized multisite DTI study, we showed subtle, but widespread differences in WM microstructure in adult MDD, which may suggest structural disconnectivity in MDD.

Background: Comorbid anxious distress is common in Major Depressive Disorder (MDD), and associated with significantly worse clinical course and treatment response. While DSM-5 recently introduced the Anxious Distress (AD) specifier as a potentially useful symptom-based subtyping scheme for MDD, its neurobiological underpinnings remain unclear. The current study hence uniquely probed whether MDD with co-occurring AD (MDD/AD+) relates to distinct perturbations in frontolimbic white matter (WM) pathways tentatively theorized in MDD/AD+ pathophysiology.Methods: Tract-based spatial statistics (TBSS) was therefore used to analyze diffusion tensor imaging data on WM microstructure, in MDD/AD+ patients (N = 20) relative to MDD patients without AD (MDD/AD-; N = 29) and healthy controls (HC; N = 39). Using TBSS, we probed fractional anisotropy and axial/radial/mean diffusivity as proxies for WM integrity. Categorical (between-groups) and dimensional (within-patients) analyses subsequently assessed how Anxious Distress in MDD impacts frontolimbic WM connectivity. Receiver-Operating Characteristics additionally assessed classification capabilities of between-groups WM effects.Results: Compared to MDD/AD- and HC participants, MDD/AD+ patients exhibited diminished integrity within the anterior thalamic radiation (ATR). Higher AD specifier scores within MDD patients additionally related to diminished integrity of the uncinate fasciculus and cingulum pathways. These effects were not confounded by key clinical (e.g., comorbid anxiety disorder) and sociodemographic (e.g., age/sex) factors, with altered ATR integrity moreover successfully classifying MDD/AD+ patients from MDD/AD- and HC participants (90% sensitivity vertical bar 73% specificity vertical bar 77% accuracy).Conclusions: These findings collectively link MDD/AD+ to distinct WM anomalies in frontolimbic tracts important to adaptive emotional functioning, and may as such provide relevant, yet preliminary, clues on MDD/AD+ pathophysiology.

BACKGROUND: Clinical insight is impaired in the majority of individuals with schizophrenia (Dam, 2006) and is associated with poorer outcome (Lincoln et al., 2007). Impaired insight cannot be pinpointed to abnormalities of isolated brain areas, as earlier studies on cortical structure and insight found abnormalities in a distributed network of brain regions (Pijnenborg et al., in prep). Getting a better understanding of the neural substrate of impaired insight might help in finding better treatment options to improve insight. Typically, brains are characterized by small-world topology, reflected by a balance between information segregation (i.e. short distances between nodes) and integration (i.e. high clustering of nodes). Several studies have shown less characteristics of small-world topology in patients with schizophrenia. In this study, we used tools of graph theory to investigate whether there are less characteristics of small-world topology of individual structural networks in patients with a psychotic disorder compared to healthy individuals, and whether this is related to interindividual differences in insight. METHODS: T1-weighted images of 114 patients with a psychotic disorder (76% males; mean age=33.67, SD=10.86) and 54 healthy controls (63% males; mean age=35.11, SD=10.76) were acquired with a 3T Philips Intera MRI-scanner. Clinical insight was measured with item G12 of the PANSS (Kay et al., 1987). In a subsample of 62 patients, clinical insight was also measured with the Schedule of Assessment of Insight – Expanded (SAI-E; Kemp & David, 1997), and cognitive insight with the Beck Cognitive Insight Scale (BCIS; Beck et al., 2004). Brain images were segmented using SPM12. Individual gray matter similarity networks were created from gray matter segmentations using a previously described method (Tijms et al., 2012). We calculated the following graph metrics: path length, clustering coefficient, betweenness centrality, lambda (i.e. normalized path length), gamma (i.e. normalized clustering coefficient) and small-world property (i.e. gamma/lambda) using the Brain Connectivity Toolbox.8 Differences in graph metrics between patients and healthy individuals were investigated with ANCOVA’s. The associations between insight and graph metrics were calculated with partial correlations. Education and total gray matter were entered as covariates in all analyses. RESULTS: We found significantly lower clustering coefficient (F(1,165)=17.90, pFDR

Apathy can be described as a loss of goal-directed purposeful behavior and is common in a variety of neurological and psychiatric disorders. Although previous studies investigated associations between abnormal brain functioning and apathy, it is unclear whether the neural basis of apathy is similar across different pathological conditions. The purpose of this systematic review was to provide an extensive overview of the neuroimaging literature on apathy including studies of various patient populations, and evaluate whether the current state of affairs suggest disorder specific or shared neural correlates of apathy. Results suggest that abnormalities within fronto-striatal circuits are most consistently associated with apathy across the different pathological conditions. Of note, abnormalities within the inferior parietal cortex were also linked to apathy, a region previously not included in neuroanatomical models of apathy. The variance in brain regions implicated in apathy may suggest that different routes towards apathy are possible. Future research should investigate possible alterations in different processes underlying goal-directed behavior, ranging from intention and goal-selection to action planning and execution. (C) 2016 Published by Elsevier Ltd.