Human ethics

The study and consent procedure were approved by the University of Sydney Human Research Ethics Committee (project numbers 2012/1626 and 2012/1631) and conducted in accordance with the revised Declaration of Helsinki. All participants aged 16 and older provided written informed consent and parental or guardian consent was obtained for participants aged under 16 years.

Participants

Participants were drawn from a cohort of 6743 consecutive referrals (aged 12–30) presenting to youth mental health clinics at the Brain and Mind Center in Sydney, Australia, who were recruited to a research register of adolescents and young adults with mental disorders between 2008–2018. These clinics (e.g., headspace) aim to provide youth-friendly and highly accessible early intervention services for young people with emerging mental and substance use disorders29. Headspace consists of an integrated mix of primary-level services and more specialized services (e.g., drug and alcohol) and primarily attracts young people with a wide range of mental health problems (typically anxiety, mood and/or psychotic syndromes). All participants were receiving ongoing clinician-based case management and relevant social, psychological and/or medical treatments as part of standard care, which may have involved contact with a psychiatrist, psychologist, occupational therapist, support worker or hospitalization for those whose need exceeded the capacity of the primary care services.

Eligibility criteria

Inclusion criteria for this study were: (a) a baseline neurocognitive assessment with the majority of test scores available; (b) aged 12–30 at the neurocognitive assessment; (c) an available proforma assessment (see below) within three-months of the neurocognitive assessment; and (d) willing and able to provide written informed consent (or parental/guardian consent was obtained). Exclusion criteria were: (i) history of neurological disease; (ii) medical illness known to impact brain function (e.g., cancer, epilepsy); (iii) electroconvulsive therapy in three-months prior to neurocognitive assessment; (iv) clinically-evident intellectual disability; and/or (v) insufficient understanding of the English language to allow participation in verbal assessments or testing.

Data collection (baseline)

A subset of the wider cohort participated in detailed clinical and neurocognitive assessments between 2008–2015. A board-certified neuropsychologist, research psychologist or supervised doctoral student administered a neurocognitive battery with the domains chosen on the basis of sound validity and reliability30, relevance to the diagnoses under study9,11,31, and overlap with instruments used in the Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) initiative32. The following domains were assessed: processing speed (Trail Making Test, part A)33, cognitive flexibility (Trail Making Test, part B)33, verbal learning (sum of trials 1–5 of the Rey Auditory Verbal Learning Test; RAVLT)34, verbal memory (20-min delayed recall of the RAVLT)34, sustained attention (A’ Prime subtest of the Rapid Visual Information Processing Test)35, set-shifting (Intra-Extra Dimensional Set Shift)35, visuospatial learning (Paired Associates Learning Task)35 and working memory (Spatial Span Task)35. Premorbid IQ was estimated using the Wechsler Test of Adult Reading36 or the Wide Range Achievement Test37 (for participants younger than 16). Neurocognitive scores were standardized to age-matched and sex-matched norms (z-scores) using established norms38,39. To limit the impact of extreme scores and minimize data transformation, z-scores were curtailed at a maximum of ±5.0, with fewer than 3% of scores curtailed for each test. Symptom type and severity were determined using the Brief Psychiatric Rating Scale, with four dimensions empirically derived (depressive, negative, positive, and manic)40.

Data collection (longitudinal)

A standardized clinical proforma was used to retrospectively extract demographic, clinical, and functioning data from clinical and research files across eight predetermined timepoints (baseline, 3 months, 6 months, 1 year, 2 years, 3 years, 4 years, and 5 years)41. A “time-last-seen” entry was also recorded; however, this was not included in the current study. The proforma captures information at each timepoint regarding the current presentation and illness course, including: (a) demographics; (b) socio-occupational functioning; (c) clinical presentation (including clinical diagnosis according to DSM-542); (d) self-harm and suicidal thoughts and behaviors; (e) alcohol and other substance use; and (f) physical health comorbidities.

The proforma provided the primary outcome measure of socio-occupational functioning as assessed by a trained clinician using the Social and Occupational Functioning Assessment Scale (SOFAS). The SOFAS is a 100-point scale (with higher scores denoting better functioning) which improves on other measures of global functioning in its instruction to the rater to avoid confounding the rating with symptoms. A score of 60–70 is indicative of moderate difficulty in social, occupational, or school functioning. The SOFAS is widely used and has good construct validity43, inter-rater reliability43, and predictive validity 44.

As neurocognition was the primary baseline predictor for this study, we used the nearest proforma assessment occurring within a three-month interval of the neurocognitive assessment as the participants’ baseline timepoint (T1), with remaining proforma timepoints recoded if necessary. As we allowed a three-month interval for recoding, we subsequently excluded the three-month proforma timepoint from further analysis. The number of proforma assessments at each timepoint and the number of participants with one or more proforma assessments over time are presented in Supplementary Tables 1, 2, respectively.

Statistical analyses

Analyses were conducted in RStudio (version 1.0.143). Linear mixed-effects models with random-intercepts were constructed using the “lme4” package (version 1.1–18–145). Full-information maximum-likelihood estimation was used to handle missing follow-up data (as loss to follow-up was uncontrolled). The mixed-effects framework is recommended for longitudinal designs as it tolerates: (a) repeated-measures within participants (i.e., non-independence); (b) unbalanced assessment intervals; and (c) missing follow-up data. The continuous SOFAS rating at each timepoint represented the outcome variable, and participants could contribute one or multiple assessments over time (i.e., assessments nested within participants) (see Supplementary Table 2). To model associations with the rate of change in SOFAS over time, a “Time” variable was used which represented the timepoint of each assessment and was linearly coded. All baseline predictor variables were continuous (except for sex).

The literature describing relationships between neurocognitive test performance and aspects of functioning in major mental disorders report associations between a large variety of neurocognitive tests and various measures of global functioning and specific subdomains of functioning (e.g., relationship impairment, work impairment, and independent living)14,18,19,20,21,22. Relatedly, most studies have focussed on specific diagnostic groups (e.g., schizophrenia) and there is very limited research regarding specific neurocognition-functioning associations in early-phase, transdiagnostic cohorts. Accordingly, we chose to use a data-driven, backward elimination statistical approach to identify associations between individual neurocognitive test scores and social and occupational functioning in our cohort. Modeling proceeded in three stages. First, we examined unadjusted associations between all baseline predictors and variation in SOFAS scores at baseline as well as the rate of change in SOFAS over time. Second, we examined associations between all baseline predictors and variation in baseline SOFAS scores, using backward elimination to iteratively remove the least significant variable until only significant predictors remained (α = 0.05). Third, we examined associations between the rate of change in SOFAS longitudinally and all predictor variables that had significant associations with variation in SOFAS at baseline, using backward elimination to reduce the full model.

Normality of residuals was inspected with Q–Q plots, with an approximate normal distribution evident. Multicollinearity was assessed with the variation inflation factor (VIF), with no predictors exceeding a VIF of 3.0. Parameter-specific p-values were calculated using Satterthwaite’s approximation for degrees of freedom in the “lmerTest” package (version 1.046). Missing baseline neurocognitive and clinical data were imputed using multiple imputation by chained equations in the “mice” package (version 3.3.047). Missing data patterns were consistent with a missing-at-random mechanism and fewer than 10% of each neurocognitive domain and fewer than 12% of BPRS scores were missing (see Supplementary Table 3 for numbers and proportions of missing values for each predictor variable). Following recommendations, we multiply imputed 100 datasets using predictive mean matching (which makes use of all available data), modeled each imputed dataset separately, and pooled the coefficients, test statistics, and p-values47,48,49.

Role of the funding source

This study was partially funded by an Australian Government Research Training Program Scholarship (awarded to J.J.C.), a National Health & Medical Research Council Center of Research Excellence Grant (No. 1061043) and an Australia Fellowship (No. 511921) (awarded to I.B.H.). The funders of this study had no involvement in the: study design; collection, analysis and reporting of the data; writing of the report; or decision to submit the paper for publication.