Introduction
Robust deficits in neurocognition are evident in the early stages of psychosis development among people at clinical high-risk for psychosis (CHR) (Catalan et al., Reference Catalan, Salazar de Pablo, Aymerich, Damiani, Sordi, Radua and Fusar-Poli2021; Hedges et al., Reference Hedges, Dickson, Tognin, Modinos, Antoniades, van der Gaag and Kempton2022; Seidman et al., Reference Seidman, Shapiro, Stone, Woodberry, Ronzio, Cornblatt and Woods2016). As these deficits are less pronounced than individuals with first-episode psychosis (FEP) compared to healthy control (HC) individuals (Sheffield, Karcher, & Barch, Reference Sheffield, Karcher and Barch2018), reviews indirectly comparing cross-sectional studies of FEP and CHR samples have suggested a potential neurocognitive decline prior to or over the transition to psychosis (Giuliano et al., Reference Giuliano, Li, Mesholam-Gately, Sorenson, Woodberry and Seidman2012; Mesholam-Gately, Giuliano, Goff, Faraone, & Seidman, Reference Mesholam-Gately, Giuliano, Goff, Faraone and Seidman2009). However, follow-up studies of CHR cohorts have shown inconsistent evidence for a decline (Bora & Murray, Reference Bora and Murray2014): some have reported a decline in visual memory, processing speed (Wood et al., Reference Wood, Brewer, Koutsouradis, Phillips, Francey, Proffitt and Pantelis2007) and verbal fluency (Lee et al., Reference Lee, Shin, Shin, Kim, Jang, Kang and Kwon2014), whereas others have observed stable cognitive deficits over time (Allott et al., Reference Allott, Wood, Yuen, Yung, Nelson, Brewer and Lin2019; Metzler et al., Reference Metzler, Dvorsky, Wyss, Müller, Gerstenberg, Traber-Walker and Heekeren2015). An improved understanding of longer-term cognitive changes in CHR populations, and particularly over illness onset for those who transition to psychosis (CHR-T), may provide insights for clinical research and inform early interventions targeting cognitive decline (Catalan et al., Reference Catalan, Salazar de Pablo, Aymerich, Damiani, Sordi, Radua and Fusar-Poli2021).
To date, only one systematic review and meta-analysis has examined longitudinal changes in neurocognitive function of CHR individuals (Bora & Murray, Reference Bora and Murray2014). Results indicated a general improvement in performance over time (i.e. stability of deficits), which did not significantly differ between CHR individuals and HCs with the exception of the verbal fluency domain. Here, the magnitude of improvement was significantly more pronounced in HC than in the CHR group. The main limitation of the meta-analysis was the small number of included studies, which meant that individual-task analysis was not always feasible. Instead, task performance was combined and analysed as global or domain-level cognition scores (Bora & Murray, Reference Bora and Murray2014). Since this meta-analysis, several large cohort studies have published results on longitudinal neurocognition in CHR samples and over longer follow-up times, including the North American Prodrome Longitudinal Study (NAPLS-2) (Addington et al., Reference Addington, Stowkowy, Liu, Cadenhead, Cannon, Cornblatt and Woods2019; Velikonja et al., Reference Velikonja, Velthorst, Zinberg, Cannon, Cornblatt, Perkins and McGlashan2021) and the Personal Assessment and Crisis Evaluation (PACE) Clinic (Allott et al., Reference Allott, Wood, Yuen, Yung, Nelson, Brewer and Lin2019). Given the increase in published studies examining longitudinal neurocognitive performance in CHR samples, an updated review is required.
The aim of the present study was to meta-analytically examine changes in neurocognitive functioning in specific tasks over two assessments among (1) CHR compared to HCs, as well as (2) CHR-T compared to CHR-NT individuals. In the current paper, we sought to address some of the limitations in the design of the earlier meta-analysis. First, we aimed to conduct analyses of performance in individual tasks, which may be a more effective approach for identifying longitudinal changes in specific cognitive processes, some of which may be differentially impaired (Brewer et al., Reference Brewer, Wood, Phillips, Francey, Pantelis, Yung and McGorry2006; Szöke et al., Reference Szöke, Trandafir, Dupont, Meary, Schürhoff and Leboyer2008). Second, we extended the analysis to examine whether changes in specific neurocognition were associated with transition to psychosis among CHR, which may help to characterise CHR-T individuals (Fusar-Poli et al., Reference Fusar-Poli, de Pablo, Correll, Meyer-Lindenberg, Millan, Borgwardt and Arango2020). Third, we have applied a robust method for calculating effect sizes from data collected at multiple time points recommended by Morris (Reference Morris2008), who has comprehensively assessed the precision and stability of effect sizes from repeated measures designs.
Methods
Selection procedure
The systematic review protocol was registered on PROSPERO (CRD42020207568) and followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (Moher, Liberati, Tetzlaff, & Altman, Reference Moher, Liberati, Tetzlaff and Altman2009). Three independent authors (E.H., S.S., C.S.) carried out systematic literature searches in Medline, Embase and PsycINFO databases until 31 December 2021. Identified articles were screened first by title and abstract for possible inclusion. Full text of relevant papers was then reviewed for eligibility. A manual search of the reference lists of included articles was also conducted.
Search strategy and eligibility criteria
Literature searches were implemented using the following key terms: (‘at risk mental state’ OR ‘ultra high risk’ OR ‘UHR’ OR ‘clinical high risk’ OR ‘psychosis risk’ OR ‘prodrome’ OR ‘psychosis’ OR ‘basic symptoms’) AND (‘neurocognit*’ OR ‘cognit*’ OR ‘neuropsych*’) AND (‘retest’ OR ‘longitudinal’ OR ‘chang*’ OR ‘follow-up’ OR ‘course’).
Studies were included if they (1) were original research articles published in English; (2) included individuals who met CHR criteria, as defined by any validated scale including the Comprehensive Assessment of At-Risk Mental States (CAARMS) (Yung et al., Reference Yung, Yung, Pan Yuen, Mcgorry, Phillips, Kelly and Buckby2005) and Structured Interview for Psychosis-risk Syndromes (SIPS) (McGlashan, Walsh, & Woods, Reference McGlashan, Walsh and Woods2010); (3) included a comparison group of HCs or provided data separately for CHR-T and CHR-NT groups; (4) reported raw neurocognitive test scores from two assessments and (5) administered the same cognitive test at both assessments. Studies were excluded if they: (1) were unpublished studies, reviews, conference abstracts or case reports; (2) had overlapping samples on the same cognitive measure; (3) only examined cognitive performance in FEP, schizophrenia or bipolar disorder samples (no CHR sample); (4) included intervention therapies to improve cognition between assessments and (5) reported only composite cognition or standardised z-scores in the original article and could not provide the raw data upon request. For example, when studies only reported composite or standardised scores, corresponding authors were contacted by email to obtain the raw group data on individual task performance. For overlapping samples, the study with the largest sample size was chosen.
Data extraction and risk of bias
Three researchers independently extracted data from included studies using a structured coding form (E.H., C.S., S.S.). Sample characteristics (e.g. number of subjects at first and second assessment, age at baseline, follow-up months) and details of neurocognitive measures [e.g. task used, domain, means and standard deviations (s.d.s) of results at both assessments] were extracted for CHR, HC, CHR-T and CHR-NT groups. The means and s.d.s of subgroups (i.e. CHR-T and CHR-NT) were pooled together using Equations 23.2 and 23.3 (Borenstein, Hedges, Higgins, & Rothstein, Reference Borenstein, Hedges, Higgins and Rothstein2009) to calculate performance for an overall group (i.e. CHR), if it was not reported. Data extraction forms were compared to verify accuracy. Any inconsistencies were resolved under supervision of senior researchers (M.K., H.D.). Study risk of bias was assessed using a modified version of the Newcastle–Ottawa Scale (NOS) for cohort studies, which rates study quality from 0 to 8 stars across three categories: selection, comparability and exposure/outcome (Wells et al., Reference Wells, Shea, O'Connell, Petersen, Welch, Losos and Tugwell2011) (online Supplementary Table S1). Although there is no threshold for determining ‘good’ quality studies, accumulating stars index reduced risk of bias. This tool has been validated for longitudinal observational studies and has been used in previous meta-analyses of CHR samples (Catalan et al., Reference Catalan, Salazar de Pablo, Aymerich, Damiani, Sordi, Radua and Fusar-Poli2021; Fusar-Poli et al., Reference Fusar-Poli, Deste, Smieskova, Barlati, Yung, Howes and Borgwardt2012; Salazar de Pablo, Catalan, & Fusar-Poli, Reference Salazar de Pablo, Catalan and Fusar-Poli2020).
Outcome measures
Across studies, neurocognitive data were grouped by task and group comparisons (CHR v. HC; CHR-T v. CHR-NT). Each task was separately meta-analysed. To ensure analyses were sufficiently powered, tasks with less than three independent studies were excluded from the meta-analyses. Individual tasks that were analysed included Trail Making Test A (TMT-A) and B (TMT-B), Brief Assessment of Cognition Scale (BACS) symbol coding, semantic fluency, letter fluency, Continuous Performance Task – Identical Pairs (CPT-IP), Rey Auditory Verbal Learning Test (RAVLT) immediate recall, California Verbal Learning Test (CVLT) immediate recall, Wechsler Adult Intelligence Scale (WAIS) block design and digit span. For consistency of interpretations, task outcome measures were categorised into neurocognitive domains based on the Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) criteria (Kern et al., Reference Kern, Nuechterlein, Green, Baade, Fenton, Gold and Marder2008; Nuechterlein et al., Reference Nuechterlein, Green, Kern, Baade, Barch, Cohen and Marder2008) and in line with two published meta-analyses examining baseline cognition in CHR populations (Catalan et al., Reference Catalan, Salazar de Pablo, Aymerich, Damiani, Sordi, Radua and Fusar-Poli2021; Fusar-Poli et al., Reference Fusar-Poli, Deste, Smieskova, Barlati, Yung, Howes and Borgwardt2012). These included: (1) processing speed, (2) attention/vigilance, (3) verbal learning and memory, (4) visuospatial ability, (5) executive functioning and (5) working memory (see online Supplementary Methods 1 for individual tasks involved).
Statistical analyses
Methods used by researchers to calculate effect sizes from repeated measures designs have been examined in terms of precision, robustness and bias. Morris (Reference Morris2008) proposed an optimal methodology which calculates the effect size using the pre- and post-condition means, s.d.s and sample sizes of two independent groups. Therefore, for each study, we calculated the Hedges' g effect size (which is the Cohen's effect size corrected for small sample bias; Lakens, Reference Lakens2013) and its variance from Equations 8 and 25 provided by Morris (Reference Morris2008). If participants were lost to follow-up, sample sizes at the second assessment were used in the meta-analyses. The effect size variance requires an estimation of the correlation coefficient, rho, between first and second neurocognitive measures. Although rho is not usually reported in publications, it can be calculated from study data if the mean (and s.d.) pre, post and change values are reported. These data were provided by Allott et al. (Reference Allott, Wood, Yuen, Yung, Nelson, Brewer and Lin2019) and the mean weighted rho across neurocognitive tasks was determined at 0.64 [95% confidence interval (CI) 0.58–0.70]. Therefore, rho was set to 0.65 for each meta-analysis but was adjusted from 0.65 to 0.58 and 0.70 in sensitivity analyses, consistent with the CIs from Allott et al. (Reference Allott, Wood, Yuen, Yung, Nelson, Brewer and Lin2019), to examine the strength of results. To enable direct comparisons of effect sizes, we used the same methodological approach across all the neurocognitive tasks. However, this methodology for calculating effect size estimates assumes homogeneity of variance between the comparison groups (Morris, Reference Morris2008). We used Bartlett's (Reference Bartlett1937) test to assess the assumption of equal variances. For studies where this assumption of homogeneity did not hold, we conducted a second sensitivity analysis to verify our findings. In the sensitivity analysis, Hedges' g study effect sizes were recalculated from equations provided by Morris and DeShon (Reference Morris and DeShon2002) which do not rely on the assumption of equal variances (Equation 6 and the corresponding sampling variance in Table 2).
For each neurocognitive task, study effect sizes were combined using a random-effects inverse-weighted variance model (DerSimonian & Laird, Reference DerSimonian and Laird1986) for (1) CHR participants v. HCs and (2) CHR-NT v. CHR-T participants. Meta-analyses were conducted in Microsoft Excel using standard meta-analytical equations taken from the Major Depressive Disorder Neuroimaging Database (Kempton et al., Reference Kempton, Salvador, Munafò, Geddes, Simmons, Frangou and Williams2011), which are identical to the METAN command (Llamas-Velasco, Contador, Villarejo-Galende, Lora-Pablos, & Bermejo-Pareja, Reference Llamas-Velasco, Contador, Villarejo-Galende, Lora-Pablos and Bermejo-Pareja2015) in STATA (StataCorp, 2017). In terms of validation, previous meta-analyses have used this method in parallel with STATA and produced the same results (Bromis, Calem, Reinders, Williams, & Kempton, Reference Bromis, Calem, Reinders, Williams and Kempton2018; Kempton et al., Reference Kempton, Salvador, Munafò, Geddes, Simmons, Frangou and Williams2011). In the meta-analyses, where changes in neurocognition from baseline to follow-up significantly differed between groups, estimated mean scores were plotted to visualise these changes in performance. To note, as our analyses examine change over time, we are not able to comment on statistically significant group differences at individual time points. Between-study heterogeneity was estimated using the Cochran Q test (χ2 and p value) and the degree of heterogeneity was measured by the I 2 statistic. I 2 values above 75% indicate high heterogeneity (Higgins, Thompson, Deeks, & Altman, Reference Higgins, Thompson, Deeks and Altman2003). Potential effect size moderators can be explored using meta-regression when at least 10 studies are available (Sharp, Reference Sharp1998). Publication bias was assessed using the Egger's test (Egger, Smith, Schneider, & Minder, Reference Egger, Smith, Schneider and Minder1997) when at least six studies were included to ensure the test was adequately powered (Sutton, Duval, Tweedie, Abrams, & Jones, Reference Sutton, Duval, Tweedie, Abrams and Jones2000). Tests were two-sided and statistical significance was set at p < 0.05.
Results
Study characteristics
Of 9804 unique articles that were identified in the literature search, 76 full-text articles were assessed for possible inclusion (see online Supplementary Fig. S1 for the study selection procedure). Seven authors were successfully contacted to provide additional neurocognitive data required for the meta-analysis (Addington et al., Reference Addington, Stowkowy, Liu, Cadenhead, Cannon, Cornblatt and Woods2019; Allott et al., Reference Allott, Wood, Yuen, Yung, Nelson, Brewer and Lin2019; Barbato et al., Reference Barbato, Colijn, Keefe, Perkins, Woods, Hawkins and Addington2013; Fujioka et al., Reference Fujioka, Kirihara, Koshiyama, Tada, Nagai, Usui and Kasai2020; Lam et al., Reference Lam, Lee, Rapisarda, See, Yang, Lee and Keefe2018; Lee et al., Reference Lee, Shin, Shin, Kim, Jang, Kang and Kwon2014; Liu et al., Reference Liu, Hua, Hwang, Chiu, Liu, Hsieh and Hwu2015). Thirteen studies met inclusion criteria for the meta-analyses (Addington et al., Reference Addington, Stowkowy, Liu, Cadenhead, Cannon, Cornblatt and Woods2019; Allott et al., Reference Allott, Wood, Yuen, Yung, Nelson, Brewer and Lin2019; Barbato et al., Reference Barbato, Colijn, Keefe, Perkins, Woods, Hawkins and Addington2013; Becker et al., Reference Becker, Nieman, Wiltink, Dingemans, Van de Fliert, Velthorst and Linszen2010; Fujioka et al., Reference Fujioka, Kirihara, Koshiyama, Tada, Nagai, Usui and Kasai2020; Jahshan et al., Reference Jahshan, Heaton, Golshan and Cadenhead2010; Lam et al., Reference Lam, Lee, Rapisarda, See, Yang, Lee and Keefe2018; Lee et al., Reference Lee, Shin, Shin, Kim, Jang, Kang and Kwon2014; Liu et al., Reference Liu, Hua, Hwang, Chiu, Liu, Hsieh and Hwu2015; Metzler et al., Reference Metzler, Dvorsky, Wyss, Müller, Gerstenberg, Traber-Walker and Heekeren2015; Shin et al., Reference Shin, Kim, Lee, Hur, Shin, Kim and Kwon2016; Wood et al., Reference Wood, Brewer, Koutsouradis, Phillips, Francey, Proffitt and Pantelis2007; Woodberry et al., 2013) (see online Supplementary Table S2 for characteristics of the study database). Although there were overlapping samples for certain tasks reported by (1) Allott et al. (Reference Allott, Wood, Yuen, Yung, Nelson, Brewer and Lin2019) and Wood et al. (Reference Wood, Brewer, Koutsouradis, Phillips, Francey, Proffitt and Pantelis2007), and (2) Lee et al. (Reference Lee, Shin, Shin, Kim, Jang, Kang and Kwon2014) and Shin et al. (Reference Shin, Kim, Lee, Hur, Shin, Kim and Kwon2016), only the latter of each pair included a HC group. Therefore, these two studies (Shin et al., Reference Shin, Kim, Lee, Hur, Shin, Kim and Kwon2016; Wood et al., Reference Wood, Brewer, Koutsouradis, Phillips, Francey, Proffitt and Pantelis2007) were included in CHR v. HC meta-analyses. Follow-up time of studies ranged from 6 months to 13.1 years (online Supplementary Table S2).
Longitudinal neurocognitive functioning in CHR compared to HC individuals
Eight studies were included in the CHR v. HC meta-analyses, comprising a total of 794 CHR and 787 HC individuals. Changes in neurocognitive performance significantly differed between CHR and HC individuals on letter fluency tests (g = −0.32; 95% CI −0.60 to −0.03; p = 0.029) and WAIS digit span (g = −0.30; 95% CI −0.53 to −0.07; p = 0.011) (online Supplementary Figs S2 and S4). For letter fluency, HCs improved significantly more than the CHR group (online Supplementary Fig. S3). For WAIS digit span, results indicated that there were little differences in performance at baseline, but HCs improved over time and CHR individuals did not (online Supplementary Fig. S5). There were no differences in TMT-A, semantic fluency, WAIS block design or TMT-B tasks (Fig. 1; Table 1), indicating that there was no significantly different improvement between CHR and HC groups.
Neurocognitive task-level functioning in CHR individuals compared to HC individuals. A negative effect size demonstrated an improvement in the HC compared to the CHR group. However, this is reversed for TMTs as higher scores indicate poorer performance on these tasks. Tasks highlighted in bold indicate significant results (p < 0.05).
Neurocognitive task-level functioning of individuals at CHR compared with HC individuals
Tasks highlighted in bold indicate significant results (p < 0.05).
a Estimated as the non-weighted mean performance from included studies.
*p < 0.05.
Longitudinal neurocognitive functioning in CHR-T compared to CHR-NT individuals
Eleven studies were included in the CHR-T v. CHR-NT meta-analyses, consisting of 227 CHR-T and 806 CHR-NT individuals. Changes in neurocognitive performance differed between CHR-T and CHR-NT individuals in TMT-A task (g = 0.24; 95% CI 0.05–0.43; p = 0.014) and BACS symbol coding subtest (g = −0.51; 95% CI −0.89 to −0.12; p = 0.011) (online Supplementary Figs S6 and S8). For TMT-A, CHR-NT improved significantly more than the CHR-T group (online Supplementary Fig. S7). For BACS symbol coding, CHR-T had higher scores than CHR-NT at baseline. However, CHR-T performance had worsened at follow-up, where CHR-NT had improved over time (online Supplementary Fig. S9). There were no significant differences in semantic or letter fluency, CPT-IP, RAVLT, CVLT, TMT-B or WAIS digit span tests (Fig. 2; Table 2). Results indicated that CHR-T and CHR-NT group performance on these tasks were both unchanged at follow-up, or had improved at a similar rate over time.
Neurocognitive task-level functioning in CHR individuals who developed psychosis (CHR-T) compared to those who did not develop psychosis (CHR-NT). A negative effect size demonstrated an improvement in the CHR-NT compared to the CHR-T group. However, this is reversed for TMTs as higher scores indicate poorer performance on these tasks. Tasks highlighted in bold indicate significant results (p < 0.05).
Neurocognitive task-level functioning of individuals at clinical high-risk for psychosis who did (CHR-T) and did not (CHR-NT) transition to psychosis
Tasks highlighted in bold indicate significant results (p < 0.05).
a Estimated as the non-weighted mean performance from included studies.
*p < 0.05.
Heterogeneity, study quality and publication bias
Heterogeneity across the studies was small to high (Tables 1 and 2). Potential effect size moderators could not be explored due to insufficient power to perform meta-regressions. Where publication bias could be assessed, we reported no significant evidence of bias (all p > 0.05) (Table 2). In terms of study risk of bias, NOS scores ranged from five to seven (mean = 5.88; median = 6.00) in the CHR v. HC meta-analysis and from two to seven (mean = 5.45; median = 6.00) in the CHR-T v. CHR-NT meta-analysis (online Supplementary Table S2).
Sensitivity analysis
By increasing rho (the correlation between baseline and follow-up neurocognitive measures) to 0.70, no change in significant results was observed. We did, however, detect an additional significant difference in longitudinal performance of CVLT among CHR-T and CHR-NT groups (g = −0.32; 95% CI −0.64 to −0.004; p = 0.047), where the CHR-NT had improved more than CHR-T. When rho was modified to 0.58, there was no change in significant results compared to rho at 0.65.
We conducted a second sensitivity analysis recalculating study effect sizes where we could not assume homogeneity of variance of comparison groups. In the sensitivity analysis, significant differences between CHR and HC in longitudinal performance of letter fluency (g = −0.33; 95% CI −0.62 to −0.04; p = 0.046) and WAIS digit span (g = −0.30; 95% CI −0.53 to −0.07; p = 0.011) remained. In keeping with our earlier findings, we also reported differences in longitudinal performance of TMT-A (g = 0.30; 95% CI 0.06 to 0.55; p = 0.016) and BACS symbol coding (g = −0.51; 95% CI −0.90 to −0.11; p = 0.012) among CHR-T and CHR-NT individuals. Therefore, the results of the second sensitivity analysis supported those of our main analysis.
Discussion
In this systematic review and meta-analysis, we first observed that longitudinal improvements in verbal fluency and digit span task performance were significantly more pronounced in HC compared to CHR individuals. Our second main finding was that performance over time in TMT-A and symbol coding tasks significantly differed between CHR-T and CHR-NT individuals. Whilst CHR-NT improved in performance on both tasks, CHR-T improved to a lesser degree in TMT-A and had worsened performance in symbol coding at follow-up. To our knowledge, this is the first comprehensive meta-analysis of longitudinal neurocognitive task performance in CHR-T and CHR-NT samples.
Our meta-analysis of longitudinal neurocognition in 697 CHR and 761 HC individuals demonstrated that performance in both groups generally improved between baseline and follow-up assessments. This may reflect the magnitude of practice effects, particularly for meta-analyses that included studies with shorter follow-up intervals (Calamia, Markon, & Tranel, Reference Calamia, Markon and Tranel2012). We did, however, detect small effect size differences in longitudinal performance of digit span and letter fluency tasks, where improvements at follow-up were significantly more pronounced in HCs. An earlier meta-analysis of longitudinal cognition reported the same findings for letter fluency but did not have enough studies to analyse digit span performance in CHR (Bora & Murray, Reference Bora and Murray2014). However, deficits in digit span are well-established in FEP (Mesholam-Gately et al., Reference Mesholam-Gately, Giuliano, Goff, Faraone and Seidman2009) and schizophrenia patients (Fatouros-Bergman, Cervenka, Flyckt, Edman, & Farde, Reference Fatouros-Bergman, Cervenka, Flyckt, Edman and Farde2014). Furthermore, Bora and Murray (Reference Bora and Murray2014) did report that improvements over follow-up in the working memory domain, which comprises digit span performance, were significantly greater in HC than FEP. Findings are also in line with birth cohort studies that report developmental lags in cognitive performance from childhood at age 8 years among adults with psychotic disorder compared to HC individuals (Mollon, David, Zammit, Lewis, & Reichenberg, Reference Mollon, David, Zammit, Lewis and Reichenberg2018). Developmental lags in cognitive functioning have also been identified between ages 9 and 16 years among children at-risk who present with a triad of antecedent markers of schizophrenia compared to typically developing children (Dickson et al., Reference Dickson, Cullen, Jones, Reichenberg, Roberts, Hodgins and Laurens2018). Our results showed reduced cognitive improvement of CHR individuals between assessments, which may reflect underlying structural and functional brain abnormalities in the prefrontal and anterior cingulate cortex of CHR and FEP individuals; key regions of working memory and verbal fluency function (Fusar-Poli et al., Reference Fusar-Poli, Perez, Broome, Borgwardt, Placentino, Caverzasi and McGuire2007, Reference Fusar-Poli, Borgwardt, Crescini, Deste, Kempton, Lawrie and Sacchetti2011). Still, our results should be interpreted cautiously as we are limited by heterogeneity attributable to both the CHR phenotype and to primary studies, such as short follow-up times (up to 2 years).
Prior research has suggested that any potential decline in neurocognition may be specific to individuals who transition to psychosis (Bora & Murray, Reference Bora and Murray2014). As stated earlier, only one meta-analysis has examined the course of neurocognition in CHR across two assessments, but there was insufficient data to conduct task analysis for CHR-T and CHR-NT groups (Bora & Murray, Reference Bora and Murray2014). Of nine tasks analysed in the present review, we observed small to moderate effect sizes differences in longitudinal processing speed, indexed by performance on both TMT-A (g = 0.24) and symbol coding tasks (g = −0.51). Improvements in TMT-A were significantly more pronounced among CHR-NT than CHR-T individuals. For the symbol coding task, performance was in fact higher in the CHR-T group at baseline but there was evidence of worsening performance at the follow-up assessment, whereas the CHR-NT group had improved. Interestingly, processing speed, and specifically symbol coding, has been recognised as the largest deficit in schizophrenia (Dickinson, Ramsey, & Gold, Reference Dickinson, Ramsey and Gold2007), as well as in CHR samples (Seidman et al., Reference Seidman, Giuliano, Meyer, Addington, Cadenhead, Cannon and Cornblatt2010), relative to other common neurocognitive measures. Our results may indicate that some decline or lag in processing speed performance may occur later during the prodromal phase in those who develop psychosis (Seidman et al., Reference Seidman, Giuliano, Meyer, Addington, Cadenhead, Cannon and Cornblatt2010). This is of importance given the known relationship between poorer performance on trail making and symbol coding tasks and poorer social and role functioning among CHR individuals (Carrión et al., Reference Carrión, Goldberg, McLaughlin, Auther, Correll and Cornblatt2011) and highlights the need to develop interventions to address these impairments prior to the onset of psychosis. Although few randomised controlled trials (RCTs) have examined the effectiveness of cognitive remediation therapies on neurocognition and functioning in CHR groups, some do provide evidence that cognitive remediation may improve performance in select cognitive domains, such as processing speed and verbal memory (Choi et al., Reference Choi, Corcoran, Fiszdon, Stevens, Javitt, Deasy and Pearlson2017; Loewy et al., Reference Loewy, Fisher, Schlosser, Biagianti, Stuart, Mathalon and Vinogradov2016), and social functioning (Friedman-Yakoobian, Parrish, Eack, & Keshavan, Reference Friedman-Yakoobian, Parrish, Eack and Keshavan2022; Piskulic, Barbato, Liu, & Addington, Reference Piskulic, Barbato, Liu and Addington2015). Of interest, in a double-blind RCT directly targeting processing speed deficits, CHR participants who underwent processing speed training had significantly improved scores on WAIS-III symbol coding task as well as enhanced social adjustment at follow-up compared to an active control group (Choi et al., Reference Choi, Corcoran, Fiszdon, Stevens, Javitt, Deasy and Pearlson2017).
To our knowledge, this is the largest comprehensive meta-analysis characterising longitudinal neurocognitive functioning in CHR individuals to date. We have extended previous research by Bora and Murray (Reference Bora and Murray2014) to compare changes in specific task performance of CHR-T and CHR-NT individuals. An additional strength of our review is that we applied a robust analytic approach to calculate effect sizes from repeated measures designs (Morris, Reference Morris2008) and our results did not change during sensitivity analyses. However, limitations of the current paper must also be noted. There were several tasks that could not be meta-analytically examined due to an insufficient number of included studies and our approach to analyse the data at the task level. Though heterogeneity was typically low, considerable heterogeneity was observed for TMT-B in the meta-analyses. However, due to limited studies, we could not perform meta-regression analyses to investigate heterogeneity, exploring potential moderator variables, such as changes in symptoms, medication use or length of follow-up (which may reflect practice effects). Lastly, although we were able to examine changes in neurocognition of CHR, our meta-analyses consisted of data collected from two assessments. As a result, the interpretation of our findings is limited. Of 13 articles included in the meta-analyses, two studies examined neurocognition at more than two assessments but had small sample sizes for the transition group at follow-up (Lam et al., Reference Lam, Lee, Rapisarda, See, Yang, Lee and Keefe2018; Lee et al., Reference Lee, Shin, Shin, Kim, Jang, Kang and Kwon2014). Therefore, we have limited insight into the nonlinear trajectories of neurocognition in CHR and over psychosis onset in CHR-T. Future research collecting repeated data at multiple time points in larger CHR cohorts is warranted.
To conclude, the current meta-analysis suggests that, despite general improvements in neurocognition among CHR, there are some differences in task performance over 2 years in CHR compared to HC as well as CHR-T relative to CHR-NT. These longitudinal differences were observed in processing speed and working memory domains. Taken together, these results suggest that tasks related to processing speed and working memory may be key targets for interventions aimed at improving neurocognitive deficits in clinical high-risk populations.
Supplementary material
The supplementary material for this article can be found at https://doi.org/10.1017/S0033291722001830.
Acknowledgements
The authors thank S. Morris for his advice on the statistical methods and J. Addington, L. Liu, K. Allott, M. Fujioka, J. Lee, K. Lim, T. Lee and C. Liu for providing the additional data necessary to complete the meta-analysis. C. See was supported by the UK Medical Research Council (MR/N013700/1). S. Si was supported by the China Scholarship Council. H. Dickson is affiliated with the National Institute for Health Research (NIHR) Specialist Biomedical Research Centre for Mental Health at the South London and Maudsley NHS Foundation Trust and Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom. M. Kempton was supported by a Medical Research Council Fellowship (grant MR/J008915/1).
Conflict of interest
The authors declare no conflict of interest.
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