Smartphone-based ecological momentary assessment reveals an incremental association between natural diversity and mental wellbeing.

Using smartphone-based ecological momentary assessment, this study investigated an association between natural diversity on mental wellbeing. A sample of 1,998 participants completed 41,448 assessments between April 2018 and September 2023. Environments which included a larger range of natural features, such as trees, plants and birdlife (high natural diversity) were associated with greater mental wellbeing than environments including a smaller range of natural features (low natural diversity). There was evidence of a mediating effect of natural diversity on the association between natural environments and mental wellbeing. These results highlight the importance of policies and practices that support richness of biodiversity for public mental health.

Brain morphometric features predict medication response in youth with bipolar disorder: a prospective randomized clinical trial.

BACKGROUND: Identification of treatment-specific predictors of drug therapies for bipolar disorder (BD) is important because only about half of individuals respond to any specific medication. However, medication response in pediatric BD is variable and not well predicted by clinical characteristics. METHODS: A total of 121 youth with early course BD (acute manic/mixed episode) were prospectively recruited and randomized to 6 weeks of double-blind treatment with quetiapine (n = 71) or lithium (n = 50). Participants completed structural magnetic resonance imaging (MRI) at baseline before treatment and 1 week after treatment initiation, and brain morphometric features were extracted for each individual based on MRI scans. Positive antimanic treatment response at week 6 was defined as an over 50% reduction of Young Mania Rating Scale scores from baseline. Two-stage deep learning prediction model was established to distinguish responders and non-responders based on different feature sets. RESULTS: Pre-treatment morphometry and morphometric changes occurring during the first week can both independently predict treatment outcome of quetiapine and lithium with balanced accuracy over 75% (all p < 0.05). Combining brain morphometry at baseline and week 1 allows prediction with the highest balanced accuracy (quetiapine: 83.2% and lithium: 83.5%). Predictions in the quetiapine and lithium group were found to be driven by different morphometric patterns. CONCLUSIONS: These findings demonstrate that pre-treatment morphometric measures and acute brain morphometric changes can serve as medication response predictors in pediatric BD. Brain morphometric features may provide promising biomarkers for developing biologically-informed treatment outcome prediction and patient stratification tools for BD treatment development.

Smartphone-based ecological momentary assessment reveals mental health benefits of birdlife.

The mental health benefits of everyday encounters with birdlife for mental health are poorly understood. Previous studies have typically relied on retrospective questionnaires or artificial set-ups with little ecological validity. In the present study, we used the Urban Mind smartphone application to examine the impact of seeing or hearing birds on self-reported mental wellbeing in real-life contexts. A sample of 1292 participants completed a total of 26,856 ecological momentary assessments between April 2018 and October 2021. Everyday encounters with birdlife were associated with time-lasting improvements in mental wellbeing. These improvements were evident not only in healthy people but also in those with a diagnosis of depression, the most common mental illness across the world. These findings have potential implications for both environmental and wildlife protection and mental healthcare policies. Specific measures, aimed at preserving and increasing everyday encounters with birdlife in urban areas, should be implemented.

The mental health benefits of visiting canals and rivers: An ecological momentary assessment study.

Existing evidence shows positive effects of being in nature on wellbeing, but we know little about the mental health benefits of spending time near canals and rivers specifically. This study investigates the association between visits to canals and rivers and mental wellbeing. We addressed the following questions: Are visits to canals and rivers associated with higher levels of mental wellbeing? Does this association depend on age and gender? Does this association vary between people with and without a diagnosis on mental illness? We used Urban Mind, a flexible smartphone application for examining the impact of different aspects of the built and social environment on mental wellbeing, a strong predictor of mental health. Participants were invited to complete an ecological momentary assessment three times a day for fourteen days. Each assessment included questions about their surrounding environment and mental wellbeing. A total of 7,975 assessments were completed by 299 participants including 87 with a diagnosis of mental illness. Multilevel regression models were used to analyse the data. We found positive associations between visits to canals and rivers and mental wellbeing (p < .05) when compared to being anywhere else and when compared to being in green spaces. Increases in mental wellbeing were still evident after the visit had taken place. These effects remained significant after adjusting for age, gender, ethnicity and education, and were consistent in people with and without a diagnosis of mental illness. Spending time near canals and rivers is associated with better mental wellbeing. These findings have potential implications for mental health as well as urban planning and policy. Visits to canals and rivers could become part of social prescribing schemes, playing a role in preventing mental health difficulties and complementing more traditional interventions.

Can we predict who will benefit from cognitive-behavioural therapy? A systematic review and meta-analysis of machine learning studies.

Cognitive-behavioural therapy (CBT) is the first line of treatment for several mental health disorders. However, not all patients show clinical improvements after receiving CBT. Machine learning allows inferences at the individual level and therefore is a promising approach for predicting who will and will not benefit from CBT. A comprehensive literature search was conducted to identify all studies that used machine learning to predict clinical response to CBT. A random-effects meta-analysis of proportions was used to estimate an overall performance accuracy across all studies. Twenty-four studies (N = 7497) were identified, covering five diagnostic groups: Major Depressive Disorder (k = 4), Obsessive-Compulsive Disorder (OCD, k = 5), Post-Traumatic Stress Disorder (k = 2), Anxiety Disorders (AD, k = 7), Substance Use Disorders (k = 4) and two transdiagnostic models. Studies used clinical, neuroimaging, cognitive and genetic data, or a combination of these, as predictors. The overall performance accuracy across studies was 74.0% [70.0-77.8]. Accuracies differed significantly between diagnostic groups and was highest in PTSD (78.7%, 69.1-87.0), AD (77.6%, 67.5-86.4) and OCD (76.1%, 67.3-84.0). Some studies were at a high risk of bias due to how the outcome was operationalised and/or how the analyses were conducted/reported. There are many challenges to overcome before these promising results can be applied to real-world clinical practice.

Graph Convolutional Networks Reveal Network-Level Functional Dysconnectivity in Schizophrenia.

BACKGROUND AND HYPOTHESIS: Schizophrenia is increasingly understood as a disorder of brain dysconnectivity. Recently, graph-based approaches such as graph convolutional network (GCN) have been leveraged to explore complex pairwise similarities in imaging features among brain regions, which can reveal abstract and complex relationships within brain networks. STUDY DESIGN: We used GCN to investigate topological abnormalities of functional brain networks in schizophrenia. Resting-state functional magnetic resonance imaging data were acquired from 505 individuals with schizophrenia and 907 controls across 6 sites. Whole-brain functional connectivity matrix was extracted for each individual. We examined the performance of GCN relative to support vector machine (SVM), extracted the most salient regions contributing to both classification models, investigated the topological profiles of identified salient regions, and explored correlation between nodal topological properties of each salient region and severity of symptom. STUDY RESULTS: GCN enabled nominally higher classification accuracy (85.8%) compared with SVM (80.9%). Based on the saliency map, the most discriminative brain regions were located in a distributed network including striatal areas (ie, putamen, pallidum, and caudate) and the amygdala. Significant differences in the nodal efficiency of bilateral putamen and pallidum between patients and controls and its correlations with negative symptoms were detected in post hoc analysis. CONCLUSIONS: The present study demonstrates that GCN allows classification of schizophrenia at the individual level with high accuracy, indicating a promising direction for detection of individual patients with schizophrenia. Functional topological deficits of striatal areas may represent a focal neural deficit of negative symptomatology in schizophrenia.

Using graph convolutional network to characterize individuals with major depressive disorder across multiple imaging sites.

BACKGROUND: Establishing objective and quantitative neuroimaging biomarkers at individual level can assist in early and accurate diagnosis of major depressive disorder (MDD). However, most previous studies using machine learning to identify MDD were based on small sample size and did not account for the brain connectome that is associated with the pathophysiology of MDD. Here, we addressed these limitations by applying graph convolutional network (GCN) in a large multi-site MDD dataset. METHODS: Resting-state functional MRI scans of 1586 participants (821 MDD vs. 765 controls) across 16 sites of Rest-meta-MDD consortium were collected. GCN model was trained with individual whole-brain functional network to identify MDD patients from controls, characterize the most salient regions contributing to classification, and explore the relationship between topological characteristics of salient regions and clinical measures. FINDINGS: GCN achieved an accuracy of 81·5% (95%CI: 80·5-82·5%, AUC: 0·865), which was higher than other common machine learning classifiers. The most salient regions contributing to classification were primarily identified within the default mode, fronto-parietal, and cingulo-opercular networks. Nodal topologies of the left inferior parietal lobule and left dorsolateral prefrontal cortex were associated with depressive severity and illness duration, respectively. INTERPRETATION: These findings based on a large, multi-site dataset support the feasibility and effectiveness of GCN in characterizing MDD, and also illustrate the potential utility of GCN for enhancing understanding of the neurobiology of MDD by detecting clinically-relevant disruption in functional network topology. FUNDING: This study was supported by the National Natural Science Foundation of China (Grant Nos. 81621003, 82027808, 81820108018).

Connectome gradient dysfunction in major depression and its association with gene expression profiles and treatment outcomes.

Patients with major depressive disorder (MDD) exhibit concurrent deficits in both sensory and higher-order cognitive processing. Connectome studies have suggested a principal primary-to-transmodal gradient in functional brain networks, supporting the spectrum from sensation to cognition. However, whether this gradient structure is disrupted in patients with MDD and how this disruption associates with gene expression profiles and treatment outcome remain unknown. Using a large cohort of resting-state fMRI data from 2227 participants (1148 MDD patients and 1079 healthy controls) recruited at nine sites, we investigated MDD-related alterations in the principal connectome gradient. We further used Neurosynth, postmortem gene expression, and an 8-week antidepressant treatment (20 MDD patients) data to assess the meta-analytic cognitive functions, transcriptional profiles, and treatment outcomes related to MDD gradient alterations, respectively. Relative to the controls, MDD patients exhibited global topographic alterations in the principal primary-to-transmodal gradient, including reduced explanation ratio, gradient range, and gradient variation (Cohen's d = 0.16-0.21), and focal alterations mainly in the primary and transmodal systems (d = 0.18-0.25). These gradient alterations were significantly correlated with meta-analytic terms involving sensory processing and higher-order cognition. The transcriptional profiles explained 53.9% variance of the altered gradient pattern, with the most correlated genes enriched in transsynaptic signaling and calcium ion binding. The baseline gradient maps of patients significantly predicted symptomatic improvement after treatment. These results highlight the connectome gradient dysfunction in MDD and its linkage with gene expression profiles and clinical management, providing insight into the neurobiological underpinnings and potential biomarkers for treatment evaluation in this disorder.

Subtyping Schizophrenia Patients Based on Patterns of Structural Brain Alterations.

Schizophrenia is a complex and heterogeneous syndrome. Whether quantitative imaging biomarkers can identify discrete subgroups of patients as might be used to foster personalized medicine approaches for patient care remains unclear. Cross-sectional structural MR images of 163 never-treated first-episode schizophrenia patients (FES) and 133 chronically ill patients with midcourse schizophrenia from the Bipolar and Schizophrenia Network for Intermediate Phenotypes (B-SNIP) consortium and a total of 403 healthy controls were recruited. Morphometric measures (cortical thickness, surface area, and subcortical structures) were extracted for each subject and then the optimized subtyping results were obtained with nonsupervised cluster analysis. Three subgroups of patients defined by distinct patterns of regional cortical and subcortical morphometric features were identified in FES. A similar three subgroup pattern was identified in the independent dataset of patients from the multi-site B-SNIP consortium. Similarities of classification patterns across these two patient cohorts suggest that the 3-group typology is relatively stable over the course of illness. Cognitive functions were worse in subgroup 1 with midcourse schizophrenia than those in subgroup 3. These findings provide novel insight into distinct subgroups of patients with schizophrenia based on structural brain features. Findings of different cognitive functions among the subgroups support clinical differences in the MRI-defined illness subtypes. Regardless of clinical presentation and stage of illness, anatomic MR subgrouping biomarkers can separate neurobiologically distinct subgroups of schizophrenia patients, which represent an important and meaningful step forward in differentiating subtypes of patients for studies of illness neurobiology and potentially for clinical trials.

Lonely in a crowd: investigating the association between overcrowding and loneliness using smartphone technologies.

Loneliness is a major public health concern with links to social and environmental factors. Previous studies have typically investigated loneliness as a stable emotional state using retrospective cross-sectional designs. Yet people experience different levels of loneliness throughout the day depending on their surrounding environment. In the present study, we investigated the associations between loneliness and social and environmental factors (i.e. overcrowding, population density, social inclusivity and contact with nature) in real-time. Ecological momentary assessment data was collected from participants using the Urban Mind smartphone application. Data from 756 participants who completed 16,602 assessments between April 2018 and March 2020 were used in order to investigate associations between momentary feeling of loneliness, the social environment (i.e. overcrowding, social inclusivity, population density) and the built environment (i.e. contact with nature) using multilevel modelling. Increased overcrowding and population density were associated with higher levels of loneliness; in contrast, social inclusivity and contact with nature were associated with lower levels of loneliness. These associations remained significant after adjusting for age, gender, ethnicity, education and occupation. The positive association between social inclusivity and lower levels of loneliness was more pronounced when participants were in contact with nature, indicating an interaction between the social and built environment on loneliness. The feeling of loneliness changes in relation to both social and environmental factors. Our findings have potential implications for public health strategies and interventions aimed at reducing the burden of loneliness on society. Specific measures, which would increase social inclusion and contact with nature while reducing overcrowding, should be implemented, especially in densely populated cities.

Machine learning for brain age prediction: Introduction to methods and clinical applications.

The rise of machine learning has unlocked new ways of analysing structural neuroimaging data, including brain age prediction. In this state-of-the-art review, we provide an introduction to the methods and potential clinical applications of brain age prediction. Studies on brain age typically involve the creation of a regression machine learning model of age-related neuroanatomical changes in healthy people. This model is then applied to new subjects to predict their brain age. The difference between predicted brain age and chronological age in a given individual is known as 'brain-age gap'. This value is thought to reflect neuroanatomical abnormalities and may be a marker of overall brain health. It may aid early detection of brain-based disorders and support differential diagnosis, prognosis, and treatment choices. These applications could lead to more timely and more targeted interventions in age-related disorders.

Using normative modelling to detect disease progression in mild cognitive impairment and Alzheimer's disease in a cross-sectional multi-cohort study.

Normative modelling is an emerging method for quantifying how individuals deviate from the healthy populational pattern. Several machine learning models have been implemented to develop normative models to investigate brain disorders, including regression, support vector machines and Gaussian process models. With the advance of deep learning technology, the use of deep neural networks has also been proposed. In this study, we assessed normative models based on deep autoencoders using structural neuroimaging data from patients with Alzheimer's disease (n = 206) and mild cognitive impairment (n = 354). We first trained the autoencoder on an independent dataset (UK Biobank dataset) with 11,034 healthy controls. Then, we estimated how each patient deviated from this norm and established which brain regions were associated to this deviation. Finally, we compared the performance of our normative model against traditional classifiers. As expected, we found that patients exhibited deviations according to the severity of their clinical condition. The model identified medial temporal regions, including the hippocampus, and the ventricular system as critical regions for the calculation of the deviation score. Overall, the normative model had comparable cross-cohort generalizability to traditional classifiers. To promote open science, we are making all scripts and the trained models available to the wider research community.

Brain age prediction: A comparison between machine learning models using region- and voxel-based morphometric data.

Brain morphology varies across the ageing trajectory and the prediction of a person's age using brain features can aid the detection of abnormalities in the ageing process. Existing studies on such "brain age prediction" vary widely in terms of their methods and type of data, so at present the most accurate and generalisable methodological approach is unclear. Therefore, we used the UK Biobank data set (N = 10,824, age range 47-73) to compare the performance of the machine learning models support vector regression, relevance vector regression and Gaussian process regression on whole-brain region-based or voxel-based structural magnetic resonance imaging data with or without dimensionality reduction through principal component analysis. Performance was assessed in the validation set through cross-validation as well as an independent test set. The models achieved mean absolute errors between 3.7 and 4.7 years, with those trained on voxel-level data with principal component analysis performing best. Overall, we observed little difference in performance between models trained on the same data type, indicating that the type of input data had greater impact on performance than model choice. All code is provided online in the hope that this will aid future research.

Neuroanatomical abnormalities in first-episode psychosis across independent samples: a multi-centre mega-analysis.

BACKGROUND: Neuroanatomical abnormalities in first-episode psychosis (FEP) tend to be subtle and widespread. The vast majority of previous studies have used small samples, and therefore may have been underpowered. In addition, most studies have examined participants at a single research site, and therefore the results may be specific to the local sample investigated. Consequently, the findings reported in the existing literature are highly heterogeneous. This study aimed to overcome these issues by testing for neuroanatomical abnormalities in individuals with FEP that are expressed consistently across several independent samples. METHODS: Structural Magnetic Resonance Imaging data were acquired from a total of 572 FEP and 502 age and gender comparable healthy controls at five sites. Voxel-based morphometry was used to investigate differences in grey matter volume (GMV) between the two groups. Statistical inferences were made at p < 0.05 after family-wise error correction for multiple comparisons. RESULTS: FEP showed a widespread pattern of decreased GMV in fronto-temporal, insular and occipital regions bilaterally; these decreases were not dependent on anti-psychotic medication. The region with the most pronounced decrease - gyrus rectus - was negatively correlated with the severity of positive and negative symptoms. CONCLUSIONS: This study identified a consistent pattern of fronto-temporal, insular and occipital abnormalities in five independent FEP samples; furthermore, the extent of these alterations is dependent on the severity of symptoms and duration of illness. This provides evidence for reliable neuroanatomical alternations in FEP, expressed above and beyond site-related differences in anti-psychotic medication, scanning parameters and recruitment criteria.

Examining service-user perspectives for the development of a good outcome checklist for individuals at clinical high risk for psychosis.

AIMS: Around 15% of patients at clinical high risk for psychosis (CHR-P) experience symptomatic remission and functional recovery at follow-up, yet the definition of a good outcome (GO) in this population requires further development. Outcomes are typically designed and rated by clinicians rather than patients, to measure adverse as opposed to GOs. Here we investigate how CHR-P subjects define a GO, with the aim of developing a checklist that could be used to measure GO in this clinical group. METHODS: A set of GO-focused questions were designed in collaboration with a service-user. CHR-P patients (n = 48) were asked to rate the importance of items that could indicate short-term (1 year) and long-term (5 years) GO. These items were then ranked using the relative importance index (RII). RESULTS: Patients rated improvement in subjective wellbeing (RII = 0.829) and non-specific presenting symptoms (RII = 0.817) amongst the factors most important for indicating GO in the short-term, and improved resilience (RII = 0.879) and negative symptoms (RII = 0.858) as key items for indicating long-term GO. Patients regarded building resilience (RII = 0.842) and having support from mental health services (RII = 0.833) as being protective for their mental health. These measures were included in a preliminary 12-item GO checklist (GO-12) for assessing GO in CHR-P subjects. CONCLUSIONS: Patient-defined measures of GO included items that are not incorporated into conventional measures of outcomes in CHR-P subjects, such as subjective wellbeing and resilience. Integrating patient-defined metrics of GO may improve the assessment of outcomes in the CHR-P population.

Autobiographical memory as a latent vulnerability mechanism following childhood maltreatment: Association with future depression symptoms and prosocial behavior.

OBJECTIVES: Childhood maltreatment is associated with altered neural reactivity during autobiographical memory (ABM) recall and a pattern of overgeneral memory (OGM). Altered ABM and OGM have been linked with psychopathology and poorer social functioning. The present study investigated the association between altered ABM and subsequent socio-emotional functioning (measured two years later) in a sample of adolescents with (N = 20; maltreatment group, MT) and without (N = 17; non-MT group) documented childhood maltreatment histories. METHOD: At baseline, adolescents (aged 12.6 ± 1.45 years) were administered the Autobiographical Memory Test to measure OGM. Participants also recalled specific ABMs in response to emotionally valenced cue words during functional MRI. Adolescents in both groups underwent assessments measuring depressive symptoms and prosocial behavior at both timepoints. Regression analyses were carried out to predict outcome measures at follow-up controlling for baseline levels. RESULTS: In the MT group, greater OGM at baseline significantly predicted reduced prosocial behavior at follow-up and showed a trend level association with elevated depressive symptoms. Patterns of altered ABM-related brain activity did not significantly predict future psycho-social functioning. CONCLUSIONS: The current findings highlight the potential value of OGM as a cognitive mechanism that could be targeted to reduce risk of depression in adolescents with prior histories of maltreatment.

Functional Magnetic Resonance Imaging Connectivity Accurately Distinguishes Cases With Psychotic Disorders From Healthy Controls, Based on Cortical Features Associated With Brain Network Development.

BACKGROUND: Machine learning (ML) can distinguish cases with psychotic disorder from healthy controls based on magnetic resonance imaging (MRI) data, but it is not yet clear which MRI metrics are the most informative for case-control ML, or how ML algorithms relate to the underlying biology. METHODS: We analyzed multimodal MRI data from 2 independent case-control studies of psychotic disorders (cases, n = 65, 28; controls, n = 59, 80) and compared ML accuracy across 5 selected MRI metrics from 3 modalities. Cortical thickness, mean diffusivity, and fractional anisotropy were estimated at each of 308 cortical regions, as well as functional and structural connectivity between each pair of regions. Functional connectivity data were also used to classify nonpsychotic siblings of cases (n = 64) and to distinguish cases from controls in a third independent study (cases, n = 67; controls, n = 81). RESULTS: In both principal studies, the most informative metric was functional MRI connectivity: The areas under the receiver operating characteristic curve were 88% and 76%, respectively. The cortical map of diagnostic connectivity features (ML weights) was replicable between studies (r = 0.27, p < .001); correlated with replicable case-control differences in functional MRI degree centrality and with a prior cortical map of adolescent development of functional connectivity; predicted intermediate probabilities of psychosis in siblings; and was replicated in the third case-control study. CONCLUSIONS: ML most accurately distinguished cases from controls by a replicable pattern of functional MRI connectivity features, highlighting abnormal hubness of cortical nodes in an anatomical pattern consistent with the concept of psychosis as a disorder of network development.