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Universal and Selective Interventions to Prevent Poor Mental Health Outcomes in Young People: Systematic Review and Meta-analysis
Background Much is not known about the efficacy of interventions to prevent poor mental health outcomes in young people by targeting either the general population (universal prevention) or asymptomatic individuals with high risk of developing a mental disorder (selective prevention). Methods We conducted a PRISMA/MOOSE-compliant systematic review and meta-analysis of Web of Science to identify studies comparing post-test efficacy (effect size [ES]; Hedges' g) of universal or selective interventions for poor mental health outcomes versus control groups, in samples with mean age <35 years (PROSPERO: CRD42018102143). Measurements included random-effects models, I2 statistics, publication bias, meta-regression, sensitivity analyses, quality assessments, number needed to treat, and population impact number. Results 295 articles (447,206 individuals; mean age = 15.4) appraising 17 poor mental health outcomes were included. Compared to control conditions, universal and selective interventions improved (in descending magnitude order) interpersonal violence, general psychological distress, alcohol use, anxiety features, affective symptoms, other emotional and behavioral problems, consequences of alcohol use, posttraumatic stress disorder features, conduct problems, tobacco use, externalizing behaviors, attention-deficit/hyperactivity disorder features, and cannabis use, but not eating-related problems, impaired functioning, internalizing behavior, or sleep-related problems. Psychoeducation had the highest effect size for ADHD features, affective symptoms, and interpersonal violence. Psychotherapy had the highest effect size for anxiety features. Conclusion Universal and selective preventive interventions for young individuals are feasible and can improve poor mental health outcomes.
Using Natural Language Processing on Electronic Health Records to Enhance Detection and Prediction of Psychosis Risk
Background: Using novel data mining methods such as natural language processing (NLP) on electronic health records (EHRs) for screening and detecting individuals at risk for psychosis. Method: The study included all patients receiving a first index diagnosis of nonorganic and nonpsychotic mental disorder within the South London and Maudsley (SLaM) NHS Foundation Trust between January 1, 2008, and July 28, 2018. Least Absolute Shrinkage and Selection Operator (LASSO)-regularized Cox regression was used to refine and externally validate a refined version of a five-item individualized, transdiagnostic, clinically based risk calculator previously developed (Harrell's C = 0.79) and piloted for implementation. The refined version included 14 additional NLP-predictors: tearfulness, poor appetite, weight loss, insomnia, cannabis, cocaine, guilt, irritability, delusions, hopelessness, disturbed sleep, poor insight, agitation, and paranoia. Results: A total of 92 151 patients with a first index diagnosis of nonorganic and nonpsychotic mental disorder within the SLaM Trust were included in the derivation (n = 28 297) or external validation (n = 63 854) data sets. Mean age was 33.6 years, 50.7% were women, and 67.0% were of white race/ethnicity. Mean follow-up was 1590 days. The overall 6-year risk of psychosis in secondary mental health care was 3.4 (95% CI, 3.3-3.6). External validation indicated strong performance on unseen data (Harrell's C 0.85, 95% CI 0.84-0.86), an increase of 0.06 from the original model. Conclusions: Using NLP on EHRs can considerably enhance the prognostic accuracy of psychosis risk calculators. This can help identify patients at risk of psychosis who require assessment and specialized care, facilitating earlier detection and potentially improving patient outcomes.
Third external replication of an individualised transdiagnostic prediction model for the automatic detection of individuals at risk of psychosis using electronic health records.
BACKGROUND: Primary indicated prevention is a key target for reducing the incidence and burden of schizophrenia and related psychotic disorders. An individualised, clinically-based transdiagnostic model for the detection of individuals at risk of psychosis has been developed and validated in two large, urban healthcare providers. We tested its external validity in a geographically and demographically different non-urban population. METHOD: Retrospective EHR cohort study. All individuals accessing secondary healthcare provided by Oxford Health NHS Foundation Trust between 1st January 2011 and 30th November 2019 and receiving a primary index diagnosis of a non-psychotic or non-organic mental disorder were considered eligible. The previously developed model was applied to this database and its external prognostic accuracy was measured with Harrell's C. FINDINGS: The study included n = 33,710 eligible individuals, with an average age of 27.7 years (SD = 19.8), mostly white (92.0%) and female (57.3%). The mean follow-up was 1863.9 days (SD = 948.9), with 868 transitions to psychosis and a cumulative incidence of psychosis at 6 years of 2.9% (95%CI: 2.7-3.1). Compared to the urban development database, Oxford Health was characterised by a relevant case mix, lower incidence of psychosis, different distribution of baseline predictors, higher proportion of white females, and a lack of specialised clinical services for at risk individuals. Despite these differences the model retained an adequate prognostic performance (Harrell's C = 0.79, 95%CI: 0.78-0.81), with no major miscalibration. INTERPRETATION: The transdiagnostic, individualised, clinically-based risk calculator is transportable outside urban healthcare providers. Further research should test transportability of this risk prediction model in an international setting.
The case for improved transdiagnostic detection of first-episode psychosis: Electronic health record cohort study
Background: Improving outcomes of a First Episode of Psychosis (FEP) relies on the ability to detect most individuals with emerging psychosis and treat them in specialised Early Intervention (EI) services. Efficacy of current detection strategies is undetermined. Methods: RECORD-compliant clinical, 6-year, retrospective, transdiagnostic, lifespan-inclusive, Electronic Health Record (EHR) cohort study, representing real-world secondary mental healthcare in South London and Maudsley (SLaM) NHS. All individuals accessing SLaM in the period 2007–2017 and receiving any ICD-10 diagnosis other than persistent psychosis were included. Descriptive statistics, Kaplan-Meier curves, logistic regression, epidemiological incidence of psychosis in the general population were used to address pathways to care and detection power of EI services for FEP. Results: A total of 106,706 individuals underwent the 6-year follow-up: they were mostly single (72.57%) males (50.51%) of white ethnicity (60.01%), aged on average 32.96 years, with an average Health Of the Nation Outcome Scale score of 11.12 and mostly affected with F40–48 Neurotic/stress-related/somatoform disorders (27.46%). Their transdiagnostic risk of developing a FEP cumulated to 0.072 (95%CI 0.067–0.077) at 6 years. Those individuals who developed a FEP (n = 1841) entered healthcare mostly (79.02%) through inpatient mental health services (29.76%), community mental health services (29.54%) or accident and emergency departments (19.50%); at the time of FEP onset, most of them (46.43%) were under the acute care pathway. Individuals contacting accident and emergency departments had an increased risk of FEP (OR 2.301, 95%CI 2.095–2.534, P < 0.001). The proportion of SLaM FEP cases that were eligible and under the care of EI services was 0.456 at any time. The epidemiological proportion of FEP cases in the sociodemographically-matched general population that was detected by EI service was 0.373. Conclusions: More than half of individuals who develop a FEP remain undetected by current pathways to care and EI services. Improving detection strategies should become a mainstream area in the future generation of early psychosis research.
Lower speech connectedness linked to incidence of psychosis in people at clinical high risk
Background: Formal thought disorder is a cardinal feature of psychotic disorders, and is also evident in subtle forms before psychosis onset in individuals at clinical high-risk for psychosis (CHR-P). Assessing speech output or assessing expressive language with speech as the medium at this stage may be particularly useful in predicting later transition to psychosis. Method: Speech samples were acquired through administration of the Thought and Language Index (TLI) in 24 CHR-P participants, 16 people with first-episode psychosis (FEP) and 13 healthy controls. The CHR-P individuals were then followed clinically for a mean of 7 years (s.d. = 1.5) to determine if they transitioned to psychosis. Non-semantic speech graph analysis was used to assess the connectedness of transcribed speech in all groups. Results: Speech was significantly more disconnected in the FEP group than in both healthy controls (p
Suicide, self-harm and thoughts of suicide or self-harm in infectious disease epidemics: a systematic review and meta-analysis
Aims. Suicide accounts for 2.2% of all years of life lost worldwide. We aimed to establish whether infectious epidemics are associated with any changes in the incidence of suicide or the period prevalence of self-harm, or thoughts of suicide or self-harm, with a secondary objective of establishing the frequency of these outcomes. Methods. In this systematic review and meta-analysis, MEDLINE, Embase, PsycINFO and AMED were searched from inception to 9 September 2020. Studies of infectious epidemics reporting outcomes of (a) death by suicide, (b) self-harm or (c) thoughts of suicide or self-harm were identified. A random-effects model meta-analysis for the period prevalence of thoughts of suicide or self-harm was conducted. Results. In total, 1354 studies were screened with 57 meeting eligibility criteria, of which 7 described death by suicide, 9 by self-harm, and 45 thoughts of suicide or self-harm. The observation period ranged from 1910 to 2020 and included epidemics of Spanish Flu, severe acute respiratory syndrome, human monkeypox, Ebola virus disease and coronavirus disease 2019 (COVID-19). Regarding death by suicide, data with a clear longitudinal comparison group were available for only two epidemics: SARS in Hong Kong, finding an increase in suicides among the elderly, and COVID-19 in Japan, finding no change in suicides among children and adolescents. In terms of self-harm, five studies examined emergency department attendances in epidemic and non-epidemic periods, of which four found no difference and one showed a reduction during the epidemic. In studies of thoughts of suicide or self-harm, one large survey showed a substantial increase in period prevalence compared to non-epidemic periods, but smaller studies showed no difference. As a secondary objective, a meta-analysis of thoughts of suicide and self-harm found that the pooled prevalence was 8.0% overall (95% confidence interval (CI) 5.2–12.0%; 14 820 of 99 238 cases in 24 studies) over a time period of between seven days and six months. The quality assessment found 42 studies were of low quality, nine of moderate quality and six of high quality. Conclusions. There is little robust evidence on the association of infectious epidemics with suicide, self-harm and thoughts of suicide or self-harm. There was an increase in suicides among the elderly in Hong Kong during SARS and no change in suicides among young people in Japan during COVID-19, but it is unclear how far these findings may be generalised. The development of up-to-date self-harm and suicide statistics to monitor the effect of the current pandemic is an urgent priority.
Implementing Precision Psychiatry: A Systematic Review of Individualized Prediction Models for Clinical Practice.
BACKGROUND: The impact of precision psychiatry for clinical practice has not been systematically appraised. This study aims to provide a comprehensive review of validated prediction models to estimate the individual risk of being affected with a condition (diagnostic), developing outcomes (prognostic), or responding to treatments (predictive) in mental disorders. METHODS: PRISMA/RIGHT/CHARMS-compliant systematic review of the Web of Science, Cochrane Central Register of Reviews, and Ovid/PsycINFO databases from inception until July 21, 2019 (PROSPERO CRD42019155713) to identify diagnostic/prognostic/predictive prediction studies that reported individualized estimates in psychiatry and that were internally or externally validated or implemented. Random effect meta-regression analyses addressed the impact of several factors on the accuracy of prediction models. FINDINGS: Literature search identified 584 prediction modeling studies, of which 89 were included. 10.4% of the total studies included prediction models internally validated (n = 61), 4.6% models externally validated (n = 27), and 0.2% (n = 1) models considered for implementation. Across validated prediction modeling studies (n = 88), 18.2% were diagnostic, 68.2% prognostic, and 13.6% predictive. The most frequently investigated condition was psychosis (36.4%), and the most frequently employed predictors clinical (69.5%). Unimodal compared to multimodal models (β = .29, P = .03) and diagnostic compared to prognostic (β = .84, p < .0001) and predictive (β = .87, P = .002) models were associated with increased accuracy. INTERPRETATION: To date, several validated prediction models are available to support the diagnosis and prognosis of psychiatric conditions, in particular, psychosis, or to predict treatment response. Advancements of knowledge are limited by the lack of implementation research in real-world clinical practice. A new generation of implementation research is required to address this translational gap.
Physical Health and Transition to Psychosis in People at Clinical High Risk.
BACKGROUND: The clinical high risk for psychosis (CHR-P) construct represents an opportunity for prevention and early intervention in young adults, but the relationship between risk for psychosis and physical health in these patients remains unclear. METHODS: We conducted a RECORD-compliant clinical register-based cohort study, selecting the long-term cumulative risk of developing a persistent psychotic disorder as the primary outcome. We investigated associations between primary outcome and physical health data with Electronic Health Records at the South London and Maudsley (SLaM) NHS Trust, UK (January 2013-October 2020). We performed survival analyses using Kaplan-Meier curves, log-rank tests, and Cox proportional hazard models. RESULTS: The database included 137 CHR-P subjects; 21 CHR-P developed psychosis during follow-up, and the cumulative incidence of psychosis risk was 4.9% at 1 year and 56.3% at 7 years. Log-rank tests suggested that psychosis risk might change between different levels of nicotine and alcohol dependence. Kaplan-Meier curve analyses indicated that non-hazardous drinkers may have a lower psychosis risk than non-drinkers. In the Cox proportional hazard model, nicotine dependence presented a hazard ratio of 1.34 (95% CI: 1.1-1.64) (p = 0.01), indicating a 34% increase in psychosis risk for every additional point on the Fagerström Test for Nicotine Dependence. CONCLUSIONS: Our findings suggest that a comprehensive assessment of tobacco and alcohol use, diet, and physical activity in CHR-P subjects is key to understanding how physical health contributes to psychosis risk.
Psychosis associated with cannabis withdrawal: systematic review and case series.
BACKGROUND: Abrupt cessation of heavy cannabis use can cause a withdrawal syndrome characterised by irritability, anxiety, insomnia, reduced appetite and restlessness. Recent reports have also described people in whom cannabis withdrawal immediately preceded the acute onset of psychosis. AIMS: To identify cases of psychosis associated with cannabis withdrawal. METHOD: We completed a systematic review of the literature, which comprised case reports, case series and other studies. We also searched a large electronic database of psychiatric healthcare records. RESULTS: The systematic review identified 44 individuals from 21 studies in whom cannabis withdrawal preceded the development of acute psychosis. In the health record study, we identified another 68 people, of whom 47 involved a first episode of psychosis and 21 represented further episodes of an existing psychotic disorder. Almost all people were daily cannabis users who had stopped using cannabis abruptly. Individuals who continued to use cannabis after the acute psychotic episode had a much higher risk of subsequent relapse than those who abstained (odds ratio 13.9 [95% CI: 4.1 to 56.9]; χ2 = 20.1, P < 0.00001). CONCLUSIONS: Abrupt cannabis withdrawal may act as a trigger for the first episode of psychosis and a relapse of an existing psychosis. Acute psychotic symptoms can emerge after the cessation, as well as following the use, of cannabis.
Longitudinal evolution of the transdiagnostic prodrome to severe mental disorders: a dynamic temporal network analysis informed by natural language processing and electronic health records.
Modelling the prodrome to severe mental disorders (SMD), including unipolar mood disorders (UMD), bipolar mood disorders (BMD) and psychotic disorders (PSY), should consider both the evolution and interactions of symptoms and substance use (prodromal features) over time. Temporal network analysis can detect causal dependence between and within prodromal features by representing prodromal features as nodes, with their connections (edges) indicating the likelihood of one feature preceding the other. In SMD, node centrality could reveal insights into important prodromal features and potential intervention targets. Community analysis can identify commonly occurring feature groups to define SMD at-risk states. This retrospective (2-year) cohort study aimed to develop a global transdiagnostic SMD network of the temporal relationships between prodromal features and to examine within-group differences with sub-networks specific to UMD, BMD and PSY. Electronic health records (EHRs) from South London and Maudsley (SLaM) NHS Foundation Trust were included from 6462 individuals with SMD diagnoses (UMD:2066; BMD:740; PSY:3656). Validated natural language processing algorithms extracted the occurrence of 61 prodromal features every three months from two years to six months before SMD onset. Temporal networks of prodromal features were constructed using generalised vector autoregression panel analysis, adjusting for covariates. Edge weights (partial directed correlation coefficients, z) were reported in autocorrelative, unidirectional and bidirectional relationships. Centrality was calculated as the sum of (non-autoregressive) connections leaving (out-centrality, cout) or entering (in-centrality, cin) a node. The three sub-networks (UMD, BMD, PSY) were compared using permutation analysis, and community analysis was performed using Spinglass. The SMD network revealed strong autocorrelations (0.04 ≤ z ≤ 0.10), predominantly positive connections, and identified aggression (cout = 0.103) and tearfulness (cin = 0.134) as the most central features. Sub-networks for UMD, BMD, and PSY showed minimal differences, with 3.5% of edges differing between UMD and PSY, 0.8% between UMD and BMD, and 0.4% between BMD and PSY. Community analysis identified one positive psychotic community (delusional thinking-hallucinations-paranoia) and two behavioural communities (aggression-cannabis use-cocaine use-hostility, aggression-agitation-hostility) as the most common. This study represents the most extensive temporal network analysis conducted on the longitudinal interplay of SMD prodromal features. The findings provide further evidence to support transdiagnostic early detection services across SMD, refine assessments to detect individuals at risk and identify central features as potential intervention targets.
Connectome dysfunction in patients at clinical high risk for psychosis and modulation by oxytocin.
Abnormalities in functional brain networks (functional connectome) are increasingly implicated in people at Clinical High Risk for Psychosis (CHR-P). Intranasal oxytocin, a potential novel treatment for the CHR-P state, modulates network topology in healthy individuals. However, its connectomic effects in people at CHR-P remain unknown. Forty-seven men (30 CHR-P and 17 healthy controls) received acute challenges of both intranasal oxytocin 40 IU and placebo in two parallel randomised, double-blind, placebo-controlled cross-over studies which had similar but not identical designs. Multi-echo resting-state fMRI data was acquired at approximately 1 h post-dosing. Using a graph theoretical approach, the effects of group (CHR-P vs healthy control), treatment (oxytocin vs placebo) and respective interactions were tested on graph metrics describing the topology of the functional connectome. Group effects were observed in 12 regions (all pFDR
Familial coaggregation and shared familiality of functional and internalizing disorders in the Lifelines cohort.
BACKGROUND: Functional disorders (FDs) are characterized by persistent somatic symptoms and are highly comorbid with internalizing disorders (IDs). To provide much-needed insight into FD etiology, we evaluated FD and ID familial coaggregation and shared familiality. METHODS: Lifelines is a three-generation cohort study, which assessed three FDs (myalgic encephalomyelitis/chronic fatigue syndrome [ME/CFS], irritable bowel syndrome [IBS], and fibromyalgia [FM]) and six IDs (major depressive disorder [MDD], dysthymia [DYS], generalized anxiety disorder [GAD], agoraphobia [AGPH], social phobia [SPH], and panic disorder [PD]) according to diagnostic criteria. Based on 153,803 individuals, including 90,397 with a first-degree relative in Lifelines, we calculated recurrence risk ratios (λRs) and tetrachoric correlations to evaluate familial aggregation and coaggregation of these disorders in first-degree relatives. We then estimated their familiality and familial correlations. RESULTS: Familial aggregation was observed across disorders, with λR ranging from 1.45 to 2.23 within disorders and from 1.17 to 1.94 across disorders. Familiality estimates ranged from 22% (95% confidence interval [CI]: 16-29) for IBS to 42% (95% CI: 33-50) for ME/CFS. Familial correlations ranged from +0.37 (95% CI: 0.24-0.51) between FM and AGPH to +0.97 (95% CI: 0.80-1) between ME/CFS and FM. The highest familial correlation between an ID and FD was +0.83 (95% CI: 0.66-0.99) for MDD and ME/CFS. CONCLUSIONS: There is a clear familial component to FDs, which is partially shared with IDs. This suggests that IDs and FDs share both genetic and family-environmental risk factors. Of the FDs, ME/CFS is most closely related to IDs.
Truthful communication of mental science: pledge to our patients and profession.
Recent changes in US government priorities have serious negative implications for science that will compromise the integrity of mental health research, which focuses on vulnerable populations. Therefore, as editors of mental science journals and custodians of the academic record, we confirm with conviction our collective commitment to communicating the truth.
Multilevel irreversibility reveals higher-order organization of nonequilibrium interactions in human brain dynamics.
Information processing in the human brain can be modeled as a complex dynamical system operating out of equilibrium with multiple regions interacting nonlinearly. Yet, despite extensive study of the global level of nonequilibrium in the brain, quantifying the irreversibility of interactions among brain regions at multiple levels remains an unresolved challenge. Here, we present the Directed Multiplex Visibility Graph Irreversibility framework, a method for analyzing neural recordings using network analysis of time-series. Our approach constructs directed multilayer graphs from multivariate time-series where information about irreversibility can be decoded from the marginal degree distributions across the layers, which each represents a variable. This framework is able to quantify the irreversibility of every interaction in the complex system. Applying the method to magnetoencephalography recordings during a long-term memory recognition task, we quantify the multivariate irreversibility of interactions between brain regions and identify the combinations of regions which showed higher levels of nonequilibrium in their interactions. For individual regions, we find higher irreversibility in cognitive versus sensorial brain regions while for pairs, strong relationships are uncovered between cognitive and sensorial pairs in the same hemisphere. For triplets and quadruplets, the most nonequilibrium interactions are between cognitive-sensorial pairs alongside medial regions. Combining these results, we show that multilevel irreversibility offers unique insights into the higher-order, hierarchical organization of neural dynamics from the perspective of brain network dynamics.
FREQ-NESS Reveals the Dynamic Reconfiguration of Frequency-Resolved Brain Networks During Auditory Stimulation.
The brain is a dynamic system whose network organization is often studied by focusing on specific frequency bands or anatomical regions, leading to fragmented insights, or by employing complex and elaborate methods that hinder straightforward interpretations. To address this issue, a new analytical pipeline named FREQuency-resolved Network Estimation via Source Separation (FREQ-NESS) is introduced. This pipeline is designed to estimate the activation and spatial configuration of simultaneous brain networks across frequencies by analyzing the frequency-resolved multivariate covariance between whole-brain voxel time series. In this study, FREQ-NESS is applied to source-reconstructed magnetoencephalography (MEG) data during resting state and isochronous auditory stimulation. Our results reveal simultaneous, frequency-specific brain networks during resting state, such as the default mode, alpha-band, and motor-beta networks. During auditory stimulation, FREQ-NESS detects: 1) emergence of networks attuned to the stimulation frequency, 2) spatial reorganization of existing networks, such as alpha-band networks shifting from occipital to sensorimotor areas, 3) stability of networks unaffected by auditory stimuli. Furthermore, auditory stimulation significantly enhances cross-frequency coupling, with the phase of auditory networks attuned to the stimulation modulating gamma band amplitude in medial temporal lobe networks. In conclusion, FREQ-NESS effectively maps the brain's spatiotemporal dynamics, providing a comprehensive view of brain function by revealing a landscape of simultaneous, frequency-resolved networks and their interaction.
Aging Impacts Basic Auditory and Timing Processes.
Deterioration in the peripheral and central auditory systems is common in older adults and often leads to hearing and speech comprehension difficulties. Even when hearing remains intact, electrophysiological data of older adults frequently exhibit altered neural responses along the auditory pathway, reflected in variability in phase alignment of neural activity to speech sound onsets. However, it remains unclear whether challenges in speech processing in aging stem from more fundamental deficits in auditory and timing processes. Here, we investigated if and how aging individuals encoded temporal regularities in isochronous auditory sequences presented at 1.5Hz, and if they employed adaptive mechanisms of neural phase alignment in anticipation of next sound onsets. We recorded EEG in older and young individuals listening to simple isochronous tone sequences. We show that aging individuals displayed larger event-related neural responses, an increased 1/F slope, but reduced phase-coherence at the stimulation frequency (1.5Hz) and a reduced slope of phase-coherence over time in the delta and theta frequency-bands. These observations suggest altered top-down modulatory inhibition when processing repeated and predictable sounds in a sequence and altered mechanisms of continuous phase-alignment to expected sound onsets in aging. Given that deteriorations in these basic timing capacities may affect other higher-order cognitive processes (e.g., attention, perception, and action), these results underscore the need for future research examining the link between basic timing abilities and general cognition across the lifespan.
The major-minor mode dichotomy in music perception.
In Western tonal music, major and minor modes are recognized as the primary musical features in eliciting emotional responses. The underlying correlates of this dichotomy in music perception have been extensively investigated through decades of psychological and neuroscientific research, yielding plentiful yet often discordant results that highlight the complexity and individual differences in how these modes are perceived. This variability suggests that a deeper understanding of major-minor mode perception in music is still needed. We present the first comprehensive systematic review and meta-analysis, providing both qualitative and quantitative syntheses of major-minor mode perception and its behavioural and neural correlates. The qualitative synthesis includes 70 studies, revealing significant diversity in how the major-minor dichotomy has been empirically investigated. Most studies focused on adults, considered participants' expertise, used real-life musical stimuli, conducted behavioural evaluations, and were predominantly performed with Western listeners. Meta-analyses of behavioural, electroencephalography, and neuroimaging data (37 studies) consistently show that major and minor modes elicit distinct neural and emotional responses, though these differences are heavily influenced by subjective perception. Based on our findings, we propose a framework to describe a Major-Minor Mode(l) of music perception and its correlates, incorporating individual factors such as age, expertise, cultural background, and emotional disorders. Moreover, this work explores the cultural and historical implications of the major-minor dichotomy in music, examining its origins, universality, and emotional associations across both Western and non-Western contexts. By considering individual differences and acoustic characteristics, we contribute to a broader understanding of how musical frameworks develop across cultures. Limitations, implications, and suggestions for future research are discussed, including potential clinical applications for mood regulation and emotional disorders, alongside recommendations for experimental paradigms in investigating major-minor modes.
Decoding reveals the neural representation of perceived and imagined musical sounds.
Vividly imagining a song or a melody is a skill that many people accomplish with relatively little effort. However, we are only beginning to understand how the brain represents, holds, and manipulates these musical "thoughts." Here, we decoded perceived and imagined melodies from magnetoencephalography (MEG) brain data (N = 71) to characterize their neural representation. We found that, during perception, auditory regions represent the sensory properties of individual sounds. In contrast, a widespread network including fronto-parietal cortex, hippocampus, basal nuclei, and sensorimotor regions hold the melody as an abstract unit during both perception and imagination. Furthermore, the mental manipulation of a melody systematically changes its neural representation, reflecting volitional control of auditory images. Our work sheds light on the nature and dynamics of auditory representations, informing future research on neural decoding of auditory imagination.
Decoding the elite soccer player's psychological profile.
Soccer is arguably the most widely followed sport worldwide, and many dream of becoming soccer players. However, only a few manage to achieve this dream, which has cast a significant spotlight on elite soccer players who possess exceptional skills to rise above the rest. Originally, such attention was focused on their great physical abilities. However, recently, a new perspective has emerged, suggesting that being an elite soccer player requires a deep understanding of the game, rapid information processing, and decision-making. This growing attention has led to several studies suggesting higher executive functions in soccer players compared to the general population. Unfortunately, these studies often had small and nonelite samples, focusing mainly on executive functions alone without employing advanced machine learning techniques. In this study, we used artificial neural networks to comprehensively investigate the personality traits and cognitive abilities of a sample of 328 participants, including 204 elite soccer players from the top teams in Brazil and Sweden. Our findings indicate that elite soccer players demonstrate heightened planning and memory capacities, enhanced executive functions, especially cognitive flexibility, elevated levels of conscientiousness, extraversion, and openness to experience, coupled with reduced neuroticism and agreeableness. This research provides insights into the psychology of elite soccer players, holding significance for talent identification, development strategies in soccer, and understanding the psychological traits and cognitive abilities linked to success.