The use of older data and references is becoming increasingly disfavored for publication. A myopic focus on newer research risks losing sight of important research questions already addressed by now-invisible older studies. This creates a ‘Groundhog Day’ effect as illustrated by the 1993 movie of this name in which the protagonist has to relive the same day (Groundhog Day) over and over and over within a world with no memory of it. This article examines the consequences of the recent preference for newer data and references in current publication practices and is intended to stimulate new consideration of the utility of selected older data and references for the advancement of scientific knowledge.

Examples from the literature are used to exemplify the value of older data and older references. To illustrate the recency of references published in original medical research articles in a selected sample of recent academic medical journals, original research articles were examined in recent issues in selected psychiatry, medicine, and surgery journals.

The literature examined reflected this article's initial assertion that journals are emphasizing the publication of research with newer data and more recent references.

The current valuation of newer data above older data fails to appreciate the fact that new data eventually become old, and that old data were once new. The bias demonstrated in arbitrary policies pertaining to older data and older references can be addressed by instituting comparable treatment of older and newer data and references.

In 2013, the diagnosis of somatic symptom disorder (SSD) was introduced into the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). This review aims to comprehensively synthesize contemporary evidence related to SSD.

A scoping review was conducted using PubMed, PsycINFO, and Cochrane Library. The main inclusion criteria were SSD and publication in the English language between 01/2009 and 05/2020. Systematic search terms also included subheadings for the DSM-5 text sections; i.e., diagnostic features, prevalence, development and course, risk and prognostic factors, culture, gender, suicide risk, functional consequences, differential diagnosis, and comorbidity.

Eight hundred and eighty-two articles were identified, of which 59 full texts were included for analysis. Empirical evidence supports the reliability, validity, and clinical utility of SSD diagnostic criteria, but the further specification of the psychological SSD B-criteria criteria seems necessary. General population studies using self-report questionnaires reported mean frequencies for SSD of 12.9% [95% confidence interval (CI) 12.5–13.3%], while prevalence studies based on criterion standard interviews are lacking. SSD was associated with increased functional impairment, decreased quality of life, and high comorbidity with anxiety and depressive disorders. Relevant research gaps remain regarding developmental aspects, risk and prognostic factors, suicide risk as well as culture- and gender-associated issues.

Strengths of the SSD diagnosis are its good reliability, validity, and clinical utility, which substantially improved on its predecessors. SSD characterizes a specific patient population that is significantly impaired both physically and psychologically. However, substantial research gaps exist, e.g., regarding SSD prevalence assessed with criterion standard diagnostic interviews.

As self-injurious thoughts and behaviors (SITB) remain a pressing public health concern, research continues to focus on risk factors, such as posttraumatic stress disorder (PTSD). Network analysis provides a novel approach to examining the PTSD-SITB relationship. This study utilized the network approach to elucidate how individual PTSD symptoms may drive and maintain SITB.

We estimated cross-sectional networks in two samples of trauma-exposed adults (Sample 1: N = 349 adults; Sample 2: N = 1307 Veterans) to identify PTSD symptoms that may act as bridges to SITB. Additionally, we conducted a cross-lagged panel network in Sample 2 to further clarify the temporal relationship between PTSD symptoms and SITB during a 2-year follow-up. Finally, in both samples, we conducted logistic regressions to examine the utility of PTSD symptoms in prospectively predicting SITB, over a 15-day period (Sample 1) and over a 2-year period (Sample 2), allowing us to examine both short- and long-term prediction.

Two PTSD symptoms (i.e. negative beliefs and risky behaviors) emerged as highly influential on SITB in both cross-sectional networks. In the cross-lagged panel network, distorted blame emerged as highly influential on SITB over time. Finally, risky behaviors, unwanted memories, and psychological distress served as the strongest predictors of SITB across the two samples.

Overall, our results suggest that treatments targeting negative beliefs and risky behaviors may prevent SITB in community and Veteran populations, whereas treatments targeting distorted blame and unwanted memories may help reduce SITB for individuals with a history of combat trauma.

The effect of working memory training (WM-T) has been found to transfer to emotional wellbeing, despite some debate on whether an affective component in training is necessary to achieve specific emotion-related benefits. These novel cognitive trainings have not yet been tested in highly anxious individuals, who have deficits in implicit and explicit emotional regulation and should be the potential beneficiaries of these trainings.

We designed two types of mobile phone-based training applications: (1) WMT and (2) an emotional working memory training (EWM-T) that comprised negative face distraction. Ninety-eight participants (33, WM-T; 35, EWM-T; 30, Control group) with high trait anxiety completed the 21-day intervention or placebo program and conducted pre- and post-test procedures, including questionnaires, emotional regulation and emotional Stroop tasks alongside electroencephalogram recording. Late positive potential (LPP) in emotion regulation task and P3 in the emotional Stroop task were adopted as neutral indicators for the explicit and implicit affective regulation/control processing.

Those who had received training (WM-T and EWM-T) showed enhanced explicit regulation (indexed by reduced LPP during reappraisal) compared with the control. Besides, individuals in EWM-T showed reduced behavioral attention bias and a decline of P3 in response to negative faces in an emotional Stroop task. The altered neural indicators were correlated with corresponding behavior indexes that contributed to the anxiety alleviation.

The general WM-T was effective in enhancing explicit emotional regulation, while training with emotional add-in further improved implicit emotional control. (E)WM-T shows potential as a beneficial intervention for the anxiety population.

Eating-disorder severity indicators should theoretically index symptom intensity, impairment, and level of needed treatment. Two severity indicators for binge-eating disorder (BED) have been proposed (categories of binge-eating frequency and shape/weight overvaluation) but have mixed empirical support including modest clinical utility. This project uses structural equation model (SEM) trees – a form of exploratory data mining – to empirically determine the precise levels of binge-eating frequency and/or shape/weight overvaluation that most significantly differentiate BED severities.

Participants were 788 adults with BED enrolled in BED treatment studies. Participants completed interviews and self-report measures assessing eating-disorder and comorbid symptoms. SEM Tree analyses were performed by specifying an outcome model of BED severity and then recursively partitioning the outcome model into subgroups. Subgroups were split based on empirically determined values of binge-eating frequency and/or shape/weight overvaluation. SEM Forests also quantified which variable contributed more improvement in model fit.

SEM Tree analyses yielded five subgroups, presented in ascending order of severity: overvaluation <1.25, overvaluation = 1.25–2.74, overvaluation = 2.75–4.24, overvaluation ⩾4.25 with weekly binge-eating frequency <4.875, and overvaluation ⩾4.25 with weekly binge-eating frequency ⩾4.875. SEM Forest analyses revealed that splits that occurred on shape/weight overvaluation resulted in much more improvement in model fit than splits that occurred on binge-eating frequency.

Shape/weight overvaluation differentiated BED severity more strongly than binge-eating frequency. Findings indicate a nuanced potential BED severity indicator scheme, based on a combination of cognitive and behavioral eating-disorder symptoms. These results inform BED classification and may allow for the provision of more specific and need-matched treatment formulations.

In research and clinical practice, familial risk for depression and anxiety is often constructed as a simple Yes/No dichotomous family history (FH) indicator. However, this measure may not fully capture the liability to these conditions. This study investigated whether a continuous familial loading score (FLS), incorporating family- and disorder-specific characteristics (e.g. family size, prevalence of depression/anxiety), (i) is associated with a polygenic risk score (PRS) for major depression and with clinical/psychosocial vulnerabilities and (ii) still captures variation in clinical/psychosocial vulnerabilities after information on FH has been taken into account.

Data came from 1425 participants with lifetime depression and/or anxiety from the Netherlands Study of Depression and Anxiety. The Family Tree Inventory was used to determine FLS/FH indicators for depression and/or anxiety.

Persons with higher FLS had higher PRS for major depression, more severe depression and anxiety symptoms, higher disease burden, younger age of onset, and more neuroticism, rumination, and childhood trauma. Among these variables, FH was not associated with PRS, severity of symptoms, and neuroticism. After regression out the effect of FH from the FLS, the resulting residualized measure of FLS was still associated with severity of symptoms of depression and anxiety, rumination, and childhood trauma.

Familial risk for depression and anxiety deserves clinical attention due to its associated genetic vulnerability and more unfavorable disease profile, and seems to be better captured by a continuous score that incorporates family- and disorder-specific characteristics than by a dichotomous FH measure.

A few previous studies suggest that a large number of individuals do not present at hospital following a suicide attempt, complicating recurrence prevention and prevalence estimation.

Data were extracted from a regular phone survey in representative samples of the French population aged 18–75 years old. Five surveys between 2000 and 2017 collected data about the occurrence of a previous suicide attempt and subsequent care contacts. A total of 102,729 individuals were surveyed. Among them, 6,500 (6.4%) reported a lifetime history of suicide attempt.

Following their last suicide attempt, 39.3% reported they did not present to hospital (53.4% in 18–24 year-olds), with limited changes in rates with time. Risk factors for non-presentation were being male [adjusted odds ratio = 1.3, 95% confidence interval (1.1–1.5)], living with someone [1.2 (1.0–1.4)], being a non-smoker [1.4 (1.2–1.6)], and being younger at time of attempt [0.97 (0.96–0.98) per year]. Of those who did not present to hospital, only 37.7% reported visiting a doctor or a psychiatrist/psychologist after their act v. 67.1% in those who presented to hospital (as a second health contact). In both cases, half disclosed their act to someone else. Prevalence rates of suicide attempts reported in community were 4.6 times higher than those in hospital administrative databases.

This survey at a national level confirmed that a large proportion of individuals does not go to the hospital and does not meet any health care professionals following a suicidal act. Assessment of unmet needs is necessary.

Patients with obsessive-compulsive disorder (OCD) are at increased risk for suicide attempt (SA) compared to the general population. However, the significant risk factors for SA in this population remains unclear – whether these factors are associated with the disorder itself or related to extrinsic factors, such as comorbidities and sociodemographic variables. This study aimed to identify predictors of SA in OCD patients using a machine learning algorithm.

A total of 959 outpatients with OCD were included. An elastic net model was performed to recognize the predictors of SA among OCD patients, using clinical and sociodemographic variables.

The prevalence of SA in our sample was 10.8%. Relevant predictors of SA founded by the elastic net algorithm were the following: previous suicide planning, previous suicide thoughts, lifetime depressive episode, and intermittent explosive disorder. Our elastic net model had a good performance and found an area under the curve of 0.95.

This is the first study to evaluate risk factors for SA among OCD patients using machine learning algorithms. Our results demonstrate an accurate risk algorithm can be created using clinical and sociodemographic variables. All aspects of suicidal phenomena need to be carefully investigated by clinicians in every evaluation of OCD patients. Particular attention should be given to comorbidity with depressive symptoms.

Depression is a highly prevalent and heterogeneous disorder. This study aims to determine whether depression with atypical features shows different heritability and different degree of overlap with polygenic risk for psychiatric and immuno-metabolic traits than other depression subgroups.

Data included 30 069 European ancestry individuals from the UK Biobank who met criteria for lifetime major depression. Participants reporting both weight gain and hypersomnia were classified as ↑WS depression (N = 1854) and the others as non-↑WS depression (N = 28 215). Cases with non-↑WS depression were further classified as ↓WS depression (i.e. weight loss and insomnia; N = 10 142). Polygenic risk scores (PRS) for 22 traits were generated using genome-wide summary statistics (Bonferroni corrected p = 2.1 × 10−4). Single-nucleotide polymorphism (SNP)-based heritability of depression subgroups was estimated.

↑WS depression had a higher polygenic risk for BMI [OR = 1.20 (1.15–1.26), p = 2.37 × 10−14] and C-reactive protein [OR = 1.11 (1.06–1.17), p = 8.86 × 10−06] v. non-↑WS depression and ↓WS depression. Leptin PRS was close to the significance threshold (p = 2.99 × 10−04), but the effect disappeared when considering GWAS summary statistics of leptin adjusted for BMI. PRS for daily alcohol use was inversely associated with ↑WS depression [OR = 0.88 (0.83–0.93), p = 1.04 × 10−05] v. non-↑WS depression. SNP-based heritability was not significantly different between ↑WS depression and ↓WS depression (14.3% and 12.2%, respectively).

↑WS depression shows evidence of distinct genetic predisposition to immune-metabolic traits and alcohol consumption. These genetic signals suggest that biological targets including immune-cardio-metabolic pathways may be relevant to therapies in individuals with ↑WS depression.

Individual with internet gaming disorder (IGD) often experience a high level of loneliness, and neuroimaging studies have demonstrated that amygdala function is associated with both IGD and loneliness. However, the neurobiological basis underlying these relationships remains unclear.

In the current study, Granger causal analysis was performed to investigate amygdalar subdivision-based resting-state effective connectivity differences between 111 IGD subjects and 120 matched participants with recreational game use (RGUs). We further correlated neuroimaging findings with clinical measures. Mediation analysis was conducted to explore whether amygdalar subdivision-based effective connectivity mediated the relationship between IGD severity and loneliness.

Compared with RGUs, IGD subjects showed inhibitory effective connections from the left pregenual anterior cingulate cortex (pACC) to the left laterobasal amygdala (LBA) and from the right medial prefrontal cortex (mPFC) to the left LBA, as well as an excitatory effective connection from the left middle prefrontal gyrus (MFG) to the right superficial amygdala. Further analyses demonstrated that the left pACC-left LBA effective connection was negatively correlated with both Internet Addiction Test and UCLA Loneliness scores, and it mediated the relationship between the two.

IGD subjects and RGUs showed different connectivity patterns involving amygdalar subdivisions. These findings support a neurobiological mechanism for the relationship between IGD and loneliness, and suggest targets for therapeutic approaches that could be used to treat IGD.

Accumulating studies have found structural and functional abnormalities of the striatum in bipolar disorder (BD) and major depressive disorder (MDD). However, changes in intrinsic brain functional connectivity dynamics of striato-cortical circuitry have not been investigated in BD and MDD. This study aimed to investigate the shared and specific patterns of dynamic functional connectivity (dFC) variability of striato-cortical circuitry in BD and MDD.

Brain resting-state functional magnetic resonance imaging data were acquired from 128 patients with unmedicated BD II (current episode depressed), 140 patients with unmedicated MDD, and 132 healthy controls (HCs). Six pairs of striatum seed regions were selected: the ventral striatum inferior (VSi) and the ventral striatum superior (VSs), the dorsal-caudal putamen (DCP), the dorsal-rostral putamen (DRP), and the dorsal caudate and the ventral-rostral putamen (VRP). The sliding-window analysis was used to evaluate dFC for each seed.

Both BD II and MDD exhibited increased dFC variability between the left DRP and the left supplementary motor area, and between the right VRP and the right inferior parietal lobule. The BD II had specific increased dFC variability between the right DCP and the left precentral gyrus compared with MDD and HCs. The MDD had increased dFC variability between the left VSi and the left medial prefrontal cortex compared with BD II and HCs.

The patients with BD and MDD shared common dFC alteration in the dorsal striatal-sensorimotor and ventral striatal-cognitive circuitries. The patients with MDD had specific dFC alteration in the ventral striatal-affective circuitry.

Alcohol use disorder (AUD) is associated with cognitive deficits but little is known to what degree this is caused by genetically influenced traits, i.e. endophenotypes, present before the onset of the disorder. The aim of the current study was to investigate to what degree family history (FH) of AUD is associated with cognitive functions.

Case-control cross-sectional study at an outpatient addiction research clinic. Treatment-seeking AUD patients (n = 106) were compared to healthy controls (HC; n = 90), matched for age and sex. The HC group was further subdivided into AUD FH positive (FH+; n = 47) or negative (FH−; n = 39) based on the Family Tree Questionnaire. Participants underwent psychiatric and substance use assessments, completed the Barratt Impulsiveness Scale and performed a comprehensive battery of neuropsychological tests assessing response inhibition, decision making, attention, working memory, and emotional recognition.

Compared to HC, AUD patients exhibited elevated self-rated impulsivity (p < 0.001; d = 0.62), as well as significantly poorer response inhibition (p = 0.001; d = 0.51), attention (p = 0.021; d = 0.38) and information gathering in decision making (p = 0.073; d = 0.34). Similar to AUD patients, FH+ individuals exhibited elevated self-rated impulsivity (p = 0.096; d = 0.46), and in addition significantly worse future planning capacity (p < 0.001; d = 0.76) and prolonged emotional recognition response time (p = 0.010; d = 0.60) compared to FH−, while no other significant differences were found between FH+ and FH−.

Elevated impulsivity, poor performance in future planning and emotional processing speed may be potential cognitive endophenotypes in AUD. These cognitive domains represent putative targets for prevention strategies and treatment of AUD.

A large body of research states that cognitive impairment in schizophrenia is static. Nevertheless, most previous studies lack a control group or have small study samples or short follow-up periods.

We aimed to address these limitations by studying a large epidemiological cohort of patients with first-episode schizophrenia spectrum disorders and a comparable control sample for a 10-year period.

Our results support the generalized stability of cognitive functions in schizophrenia spectrum disorders considering the entire group. However, the existence of a subgroup of patients characterized by deteriorating cognition and worse long-term clinical outcomes must be noted. Nevertheless, it was not possible to identify concomitant factors or predictors of deterioration (all Ps > 0.05).

Cognitive functions in schizophrenia spectrum disorder are stable; however, a subgroup of subjects that deteriorate can be characterized.

Only a few studies have analyzed the effects of physical and psychiatric conditions on the risk of chronic fatigue syndrome (CFS). Therefore, the goal of this exploratory case-control study was to investigate the associations of physical and psychiatric conditions with CFS in almost 19 800 adults from Germany.

This study included patients diagnosed for the first time with CFS in one of 1238 general practices in Germany between 2010 and 2017 (index date). Controls without CFS were matched (1:1) to cases with CFS by sex, age, index year, and practice. Physical and psychiatric conditions diagnosed in the year prior to the index date were included if they were present in at least 3% of patients with CFS. Associations between physical and psychiatric conditions (33 potential independent variables) and CFS (dependent variable) were analyzed in an adjusted conditional logistic regression model, and physical and psychiatric disorders were included in the model using forward stepwise selection.

This study included 9896 cases with CFS and 9896 controls without CFS [65.1% women; mean (standard deviation) age 49.5 (18.3) years]. Seven conditions were associated with CFS in the adjusted regression model. The disorders displaying the strongest relationship with CFS were cancer [odds ratio (OR) = 2.57, 95% confidence interval (CI) = 2.24–2.95], sleep disorders (OR = 1.88, 95% CI = 1.66–2.12) and depression (OR = 1.77, 95% CI = 1.61–1.95).

Cancer, sleep disorders, and depression were strongly and positively associated with CFS. Additional studies are needed to gain a better understanding of the mechanisms underlying these relationships.