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Objectives To discover psychiatric subtypes, researchers are adopting a method called community detection. This method was not subjected to the same scrutiny in the psychiatric literature as traditional clustering methods. Furthermore, many community detection algorithms have been developed without psychiatric sample sizes and variable numbers in mind. We aim to provide clarity to researchers on the utility of this method. Methods We provide an introduction to community detection algorithms, specifically describing the crucial differences between correlation‐based and distance‐based community detection. We compare community detection results to results of traditional methods in a simulation study representing typical psychiatry settings, using three conceptualizations of how subtypes might differ. Results We discovered that the number of recovered subgroups was often incorrect with several community detection algorithms. Correlation‐based community detection fared better than distance‐based community detection, and performed relatively well with smaller sample sizes. Latent profile analysis was more consistent in recovering subtypes. Whether methods were successful depended on how differences were introduced. Conclusions Traditional methods like latent profile analysis remain reasonable choices. Furthermore, results depend on assumptions and theoretical choices underlying subtyping analyses, which researchers need to consider before drawing conclusions on subtypes. Employing multiple subtyping methods to establish method dependency is recommended.

In decision making, people may rely on their own information as well as on information from external sources, such as family members, peers, or experts. The current study investigated how these types of information are used by comparing four decision strategies: 1) an internal strategy that relies solely on own information; 2) an external strategy that relies solely on the information from an external source; 3) a sequential strategy that relies on information from an external source only after own information is deemed inadequate; 4) an integrative strategy that relies on an integration of both types of information. Of specific interest were individual and developmental differences in strategy use. Strategy use was examined via Bayesian hierarchical mixture model analysis. A visual decision task was administered to children and young adolescents (N=305, ages 9–14). Individual differences but no age-related changes were observed in either decision accuracy or strategy use. The internal strategy was dominant across ages, followed by the integrative and sequential strategy, respectively, while the external strategy was extremely rare. This suggests a reluctance to rely entirely on information provided by external sources. We conclude that there are individual differences but not developmental changes in strategy use pertaining to perceptual decision-making in 9- through 14-year-olds. Generalizability of these findings is discussed with regard to different forms of social influence and varying perceptions of the external source. This study provides stepping stones in better understanding and modeling decision making processes in the presence of both internal and external information.

In multivariate normative comparisons, a patient's profile of test scores is compared to those in a normative sample. Recently, it has been shown that these multivariate normative comparisons enhance the sensitivity of neuropsychological assessment. However, multivariate normative comparisons require multivariate normative data, which are often unavailable. In this paper, we show how a multivariate normative database can be constructed by combining healthy control group data from published neuropsychological studies. We show that three issues should be addressed to construct a multivariate normative database. First, the database may have a multilevel structure, with participants nested within studies. Second, not all tests are administered in every study, so many data may be missing. Third, a patient should be compared to controls of similar age, gender and educational background rather than to the entire normative sample. To address these issues, we propose a multilevel approach for multivariate normative comparisons that accounts for missing data and includes covariates for age, gender and educational background. Simulations show that this approach controls the number of false positives and has high sensitivity to detect genuine deviations from the norm. An empirical example is provided. Implications for other domains than neuropsychology are also discussed. To facilitate broader adoption of these methods, we provide code implementing the entire analysis in the open source software package R.

Children with attention-deficit/hyperactivity disorder (ADHD) are characterized by deficits in their executive functioning and motivation. In addition, these children are characterized by a decline in performance as time-on-task increases (i.e., time-on-task effects). However, it is unknown whether these time-on-task effects should be attributed to deficits in executive functioning or to deficits in motivation. Some studies in typically developing (TD) adults indicated that time-on-task effects should be interpreted as depletion of executive resources, but other studies suggested that they represent depletion of motivation. We, therefore, investigated, in children with and without ADHD, whether there were time-on-task effects on executive functions, such as inhibition and (in)attention, and whether these were best explained by depletion of executive resources or depletion of motivation. The stop-signal task (SST), which generates both indices of inhibition (stop-signal reaction time) and attention (reaction time variability and errors), was administered in 96 children (42 ADHD, 54 TD controls; aged 9–13). To differentiate between depletion of resources and depletion of motivation, the SST was administered twice. Half of the participants was reinforced during second task performance, potentially counteracting depletion of motivation. Multilevel analyses indicated that children with ADHD were more affected by time-on-task than controls on two measures of inattention, but not on inhibition. In the ADHD group, reinforcement only improved performance on one index of attention (i.e., reaction time variability). The current findings suggest that time-on-task effects in children with ADHD occur specifically in the attentional domain, and seem to originate in both depletion of executive resources and depletion of motivation. Clinical implications for diagnostics, psycho-education, and intervention are discussed.

Background: Impaired cognition can be a late effect after treatment in long-term testicular cancer (TC) survivors, negatively affecting their daily life. However, little data is available beyond 20 years post-treatment. We assessed cognitive impairment in very long-term TC survivors after CT or RT and compared the results with stage I TC survivors and controls. Methods: In this cross-sectional multicenter cohort study, we enrolled TC survivors (treated with orchiectomy followed by CT or RT or orchiectomy only)—with a follow-up duration ≥ 20 years—and age-matched healthy controls. Cognitive testing included the Auditory Verbal Learning Test, Letter Fluency Test, Category Fluency Test, and Trail Making Test. We used fasting blood samples to assess the presence of hypogonadism and measured cardiovascular aging parameters, including carotid pulse wave velocity (c-PWV) and advanced glycation end products (AGEs). Results: We included 184 TC survivors (66 CT patients, 53 RT patients, and 65 orchiectomy-only patients) and 70 healthy controls. The median follow-up was 26 years (range: 20–42). TC survivors had a lower combined score of the cognitive tests (mean cumulative Z-score −0.85; 95% CI −1.39 to −0.33) compared to controls (mean 0.67; 95% CI −0.21 to 1.57, p < 0.01). In univariate analysis, the presence of hypogonadism (β −1.50, p < 0.01), high c-PWV (β −0.35, p = 0.09), and high AGEs (β −1.27, p = 0.02) were associated with lower cognitive scores, while only AGEs (β −1.17, p = 0.03) remained a significant predictor in multivariate analysis (Model R2 0.31, p < 0.01). Conclusions: Long-term TC survivors performed worse on cognitive tests compared to controls. Physicians and patients should be informed about timely cardiovascular risk management and testosterone supplementation therapy during follow-up to reduce the risk of cognitive impairment. Trial Registration: NCT02572934.

Autism is heterogeneous, which complicates providing tailored support and future prospects. We aim to identify subgroups in autistic adults with average to high intelligence, to clarify if certain subgroups might need support. We included 14 questionnaire variables related to aging and/or autism (e.g., demographic, psychological, and lifestyle). Community detection analysis was used for subgroup identification in an original sample of 114 autistic adults with an adulthood diagnosis (autism) and 58 non-autistic adults as comparison group (COMP), and a replication sample ( N Autism  = 261; N COM P  = 287), both aged 30–89 years. Next, we identified subgroups and assessed external validity (for cognitive and psychological difficulties, and quality of life [QoL]) in the autism samples. To test specificity, we repeated the analysis after adding 123 adults with ADHD, aged 30–80 years. As expected, the autism and COMP groups formed distinct subgroups. Among autistic adults, we identified three subgroups of which two were replicated. One of these subgroups seemed most vulnerable on the cluster variables; this subgroup also reported the most cognitive and psychological difficulties, and lowest QoL. Adding the ADHD group did not alter results. Within autistic adults, one subgroup could especially benefit from support and specialized care, although this must be tested in future studies.

Many neuropsychologists are of the opinion that the multitude of cognitive tests may be grouped into a much smaller number of cognitive domains. However, there is little consensus on how many domains exist, what these domains are, nor on which cognitive tests belong to which domain. This incertitude can be solved by factor analysis, provided that the analysis includes a broad range of cognitive tests that have been administered to a very large number of people. In this article, two such factor analyses were performed, each combining multiple studies. However, because it was not possible to obtain complete multivariate data on more than the most common test variables in the field, not all possible domains were examined here. The first analysis was a factor meta-analysis of correlation matrices combining data of 60,398 healthy participants from 52 studies. Several models from the literature were fitted, of which a version based on the Cattell-Horn-Carroll (CHC) model was found to describe the correlations better than the others. The second analysis was a factor analysis of the Advanced Neuropsychological Diagnostics Infrastructure (ANDI) database, combining scores of 11,881 participants from 54 Dutch and Belgian studies not included in the first meta-analysis. Again, the model fit was better for the CHC model than for other models. Therefore, we conclude that the CHC model best characterizes both cognitive domains and which test belongs to each domain. Therefore, although originally developed in the intelligence literature, the CHC model deserves more attention in neuropsychology.

IntroductionAutism spectrum conditions (ASC) develop early in life and are thought to last a lifetime. However, ASC research has two major knowledge gaps that hinder progression in understanding the concept of ASC and in providing proper support for autistic adults: (1) the majority of knowledge about ASC mainly stems from childhood studies so little is known about older autistic adults and (2) while it is broadly recognised that ASC is a heterogeneous condition, we do not yet understand the differences in trajectories leading to their future outcome. We aim to fill both knowledge gaps.Methods and analysisA multistage overlapping cohort design assessing (cognitive) ageing in ASC is designed to obtain an accelerated longitudinal data set. Data, including a multitude of questionnaires, diagnostics and cognitive tests, are collected over four waves within a 10-year time frame. This will provide information regarding actual changes in quality of life, co-occurring health conditions and cognition as well as the possibility to test external validity and temporal stability in newly formed behavioural subtypes. Participants consist of three groups of adults aged 20–90 years: (1) with a clinical diagnosis of ASC, (2) with a clinical diagnosis of attention deficit hyperactivity disorder (ADHD) but no ASC, (3) no ASC/ADHD (ie, comparison group). The sample size differs between waves and instruments. Detailed analysis plans will be preregistered in AsPredicted or at the Open Science Framework.Ethics and disseminationEthical approval for this study was obtained from the ethical review board of the Department of Psychology of the University of Amsterdam (wave 1 2011-PN-1952 and 2013-PN-2668, wave 2 2015-BC-4270, waves 3 and 4 2018-BC-9285). In line with the funding policies of the grant organisation funding this study, future papers will be published open access.

PurposeIt is assumed that a segment of breast cancer survivors are cognitively affected after chemotherapy. Our aim is to discover whether there is a qualitatively different cognitively affected subgroup of breast cancer survivors, or whether there are only quantitative differences between survivors in cognitive functioning.MethodsLatent profile analysis was applied to age-corrected neuropsychological data —measuring verbal memory, attention, speed, and executive functioning— from an existing sample of 62 breast cancer survivors treated with chemotherapy. Other clustering methods were applied as sensitivity analyses. Subgroup distinctness was established with posterior mean assignment probability and silhouette width. Simulations were used to calculate subgroup stability, posterior predictive checks to establish absolute fit of the subgrouping model. Subgrouping results were compared to traditional normative comparisons results.ResultsTwo subgroups were discovered. One had cognitive normal scores, the other —45%— had lower scores. Subgrouping results were consistent across clustering methods. The subgroups showed some overlap; 6% of survivors could fall in either. Subgroups were stable and described the data well. Results of the subgroup clustering model matched those of a traditional normative comparison method requiring small deviations on two cognitive domains.ConclusionsWe discovered that almost half of breast cancer survivors after chemotherapy form a cognitively affected subgroup, using a data-driven approach. This proportion is higher than previous studies using prespecified cutoffs observed.Implications for cancer survivorsA larger group of cancer survivors may be cognitively affected than previously recognized, and a less strict threshold for cognitive problems may be needed in this population.

Objective:Cognitive impairment is an often-overlooked issue that non-CNS cancer survivors face. Our current understanding of their issues is lacking, as traditional memory sum scores grant us little insight into the underlying cognitive processes of memory and its impairment. We can improve the informativity of memory impairment studies by isolating which cognitive processes are impaired.Participants and Methods:Participants were breast cancer survivors who received chemotherapy (n=68), and women controls (n=157). The participants completed the Amsterdam Cognition Scan (ACS), in which classical neuropsychological tests are digitally recreated for online at-home administration. Online administration reduces the burden on patients and allows for recording measurements with greater precision. The specific test used to illustrate the effectiveness of our computational modeling approach was the ACS equivalent of the Rey Auditory Verbal Learning Test, in which participants are tasked with recalling a list of 15 words five times. We formulated a Hierarchical Bayesian Cognitive Model to replace traditional sum scores and disentangle performance into the more theoretically meaningful concepts of 'memory storage’ and 'memory retrieval'.Results:A traditional analysis of the sum of trials 1-5 indicated no significant difference between patients and controls (t(223)=-0.99, p = 0.323), with a small effect size (Cohen’s d = -0.14).For the newly isolated cognitive process “memory storage”, a non-significant difference was found between patients and controls (d=0.10, 95% credible interval on Cohen’s d: [0.25, 0.43]). On the “memory retrieval” process, a medium significant difference was found between patients and controls (d = -0.57, 95% credible interval on Cohen’s d: [-1.00, -0.19]).Conclusions:The results indicate that the impaired memory processes in cancer patients are not a general impairment across all memory functions, but rather a selective impairment of memory retrieval. Our method of analysis revealed information that would have been left unnoticed had we relied on traditional sum over trials 1-5.