Neuropsychological profile of executive functions in autism spectrum disorder and schizophrenia spectrum disorders: a comparative group study in adults

Autism spectrum disorder (ASD) and schizophrenia spectrum disorder (SSD) are two conditions recognized by classification systems, such as the diagnostic and statistical manual of mental disorders—DSM-5 [1], as two distinct entities. In ASD, core symptoms include severe difficulties in social communication and restricted or repetitive patterns of behavior and interests. These symptoms usually manifest themselves in early childhood and are maintained throughout the individual’s life [1]. In SSD, however, symptoms usually appear in late adolescence or, more frequently, early adulthood. The symptoms of SSD are grouped into two categories: positive symptoms and negative symptoms [1]. Positive symptoms include hallucinations and/or delusions, whereas negative symptoms refer to the absence or reduction of behaviors such as emotional expression or the scarcity of communicative gestures.

In general, a person who displays typical clinical psychotic symptoms, such as hallucinations and delusions, would be diagnosed with SSD. However, many of the symptoms presented by people with ASD can be mistaken for SSD symptoms. For example, difficulties in social communication and social-emotional reciprocity, sensory disturbances, and rigidity in thinking are features shared by the two disorders [2,3,4]. Thus, the overlap in symptoms can lead to diagnostic confusion [4]. On a biological level, the two conditions share similar genetic modifications in DNA sequence (copy-number variations, CNV) and specific rare alleles [5]. On the neurological level, some magnetic resonance imaging (MRI) studies in ASD and SSD have shown similar alterations in the morphology of the posterior lobe of the cerebellum [6], and others demonstrated the same type of grey matter volume abnormalities in regions of the frontal and parietal lobes for the two conditions [7,8,9]. These neurobiological similarities, added to the similarities in behavioral symptomatology, generally make differential diagnosis difficult, especially in adulthood and if the individual did not receive a diagnosis of ASD in childhood. The present study aims to advance research on similarities and differences in the neuropsychological profiles of the two conditions. The long-term goal is to facilitate differential diagnosis and treatment strategies.

Executive Functions (EFs) are one of the aspects in which ASD and SSD present strikingly similar characteristics, which may influence the difficulties in differential diagnosis [10, 11]. EFs are known to be fundamental for learning, academic performance, mental health, adaptive behaviors [12, 13], and goal-directed behaviors [14]. Past research has demonstrated that the poor outcomes in personal, academic, vocational, or everyday functioning displayed by individuals with ASD or SSD have, indeed, been attributed to impairments in EF abilities [9]. Although scarce, the existing evidence suggests that, while in ASD the EF difficulties persist through adulthood [15], in SSD, there is a visible decline in EF in aging and after psychotic episodes [16].

Despite the lack of consensus on how to best assess EF, researchers agree that it is not a unitary domain but rather encapsulates a series of domains and abilities. Although there are different EF models, many of them suggest Inhibition, Updating and Shifting [12, 17] as the core components of EFs. This three-component conceptualization was first introduced by Miyake et al. [17] as the Unity and Diversity theory of executive functioning. The Unity and Diversity framework is a well-recognized and science-based assessment approach, wherein each EF component is measured using three different tasks. Miyake et al. [17] suggested that EF components work, both independently and interactively with one another. A confirmatory factor analysis (CFA) demonstrated that these three components are statistically separable into clusters, but since they are not perfectly correlated, they could still share a great portion of features between components [18]. Thus, the novelty of our study is to assess ASD and SSD EFs using Miyake and Friedman’s framework, which offers a task-based approach [17,18,19,20] that covers the core components of EFs found to be affected in the two disorders. We describe each component as follows.

Inhibition

Inhibition is fundamental when it comes to suppressing unwanted responses to minimize the processing of irrelevant information and for selecting useful or relevant information to respond appropriately to a given situation [21], or to successfully complete a task [19, 22]. To date, literature on Inhibition in adults with ASD shows mixed results, with some studies showing spared functioning of this component [23], while others find impairments [24]. For example, in a study with adults with ASD, deficits were observed using a random-motor-generation task where participants were asked to inhibit motor-prepotent responses [25]. Individuals with SSD were also found to make more errors in inhibiting motor responses in tasks like the Stroop [21]. Furthermore, Ettinger et al., [21] showed that individuals with SSD make more mistakes when the task requires them to, not only inhibit a prepotent response, but also to produce an alternative response (e.g., in Antisaccade and Stroop tasks). However, in tasks where these individuals were required to only suppress an unwanted response (e.g., in the Stop-Signal task), their performance was intact.

Updating

This component involves the ability to encode information in long-term memory, to retrieve it, and to subsequently use that information [12]. Moreover, it refers to the ability to monitor and control the contents of working memory and facilitates the access to relevant information [22]. In this fashion, Updating is very much linked to working memory capacity. Indeed, research suggests that for correct functioning, Updating requires working memory to incorporate new information of ongoing, planned behaviors and actions [12]. In their review, Gold et al. [26] found mixed results in SSD whereby some studies reported deficits and others report a spared Updating capacity. A great deal of research conducted in children and adolescents with ASD showed deficits in complex tasks that required both management of previous stored information and maintenance of immediate information, such as keeping track of stimuli. Furthermore, it seems that when the memory load is higher (e.g., keeping track of more than two objects), participants with ASD exhibit poorer performance [25]. A study assessing Updating in ASD and SSD revealed that both disorders have a lower working memory capacity as compared to healthy controls [11].

Shifting

Shifting ability allows the individual to disengage from one activity or mental set to another. Also, it involves switching flexibly from one thought, action, activity, or situation to another [15, 22, 27]. Arguably, problems in Shifting can account for some repetitive and restrictive behaviors observed in ASD [28]. For example, Albein-Urios et al. [28] argued that 69% of young adults with ASD showed important difficulties in performing Shifting tasks as expressed by the Shift index of the BRIEF informant-report [29]. Sarro et al. [15] further suggested that Shifting difficulties are reflected in the fact that people with ASD persevere in their responses even after receiving corrective feedback on their performance. Similar impairments have been found in Shifting in SSD using neuropsychological tests like the Trail Making Test, showcasing similar perseverance and rigidity [30], as these individuals continued with the same response style after receiving negative feedback on their performance.

The Miyake and Friedman three-component framework [17, 31] is appropriate for our study given that it has been used in many clinical groups [8, 13], including SSD [22], and across different age groups (children [32] and adults [17, 19]). To our knowledge, our study is the first one that attempts to understand the pattern of shared and independent deficits in EFs in both ASD and SSD. The motivation behind carrying out a comparative study is twofold. First, it is highly relevant to be able to make a better differentiation of the two disorders, because the similarities in symptomatology can lead to misdiagnosis. Despite the fact that comparative studies that look for differences in underlying mechanisms that contribute to symptoms can be useful for correct diagnosis, the research is rather scarce. Thus, the present study responds to this scarcity of research. Second, the misdiagnosis can lead to inadequate treatment. Thus, in a long run, differentiating between spared and impaired aspects of EF in each disorder can improve treatment in terms of designing more personalized intervention programs.

Our work is the first to use a computerized Spanish language adaptation of Miyake and Friedman’s [17, 31] task-based assessment to compare individuals with ASD and SSD. Specifically, we were interested in examining performance accuracy and the average reaction-time (RT). Although the nature of our study is exploratory, a previous study comparing the performance of ASD and SSD on some neuropsychological tasks found that both groups showed a lower performance in Inhibition-, Updating-, and Shifting-related tasks [11]. However, it is noteworthy that the results published so far are mixed, possibly due to methodological issues (small sample sizes, variety of EF tasks used, etc.). Also, to be able to draw firmer conclusions about the status of EF similarities and differences, we need to directly compare SSD and ASD individuals’ performance on EF tasks. In line with past research, we expected to observe deficits in performance across all three-core EF components compared to the control group, but our predictions about the pattern of results are rather exploratory in nature. That is, we are interested in looking for specific differences in the pattern of strengths and weaknesses on performance scores in the two clinical groups. In terms of RT and bearing in mind previous findings, we expected to find faster responses in the SSD group compared to the ASD group [33]. We used a self-paced task format which allowed us to examine the relationship between time spent on a given task and performance accuracy. Research emphasizes the importance of considering RT because of its relevance when assessing EFs [33]. For example, a study which compared EF in individuals with ASD and ADHD found their performance to improve significantly in tasks that had no time limit [34]. Therefore, we expected to see a positive correlation whereby spending more time on a task would allow participants in our study to reach greater accuracy in their performance.

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