Validation strategies for subtypes in psychiatry: A systematic review of research on autism spectrum disorder

Comparison of network structures between autistic and non-autistic adults, and autism subgroups: A focus on demographic, psychological, and lifestyle factors

LAY ABSTRACT

There are large differences in the level of demographic, psychological, and lifestyle characteristics between autistic and non-autistic adults but also among autistic people. Our goal was to test whether these differences correspond to differences in underlying relationships between these characteristics-also referred to as network structure-to determine which characteristics (and relationships between them) are important. We tested differences in network structure in (1) autistic and non-autistic adults and (2) two previously identified subgroups of autistic adults. We showed that comparing networks of autistic and non-autistic adults provides subtle differences, whereas networks of the autism subgroups were similar. There were also no sex differences in the networks of the autism subgroups. Thus, the previously observed differences in the level of characteristics did not correspond to differences across subgroups in how these characteristics relate to one another (i.e. network structure). Consequently, a focus on differences in characteristics is not sufficient to determine which characteristics (and relationships between them) are of importance. Hence, network analysis provides a valuable tool beyond looking at (sub)group level differences. These results could provide hints for clinical practice, to eventually determine whether psychological distress, cognitive failures, and reduced quality of life in autistic adults can be addressed by tailored support. However, it is important that these results are first replicated before we move toward intervention or support.

Probing heterogeneity to identify individualized treatment approaches in autism: Specific clusters of executive function challenges link to distinct co-occurring mental health problems

LAY ABSTRACT

Many autistic people struggle with mental health problems like anxiety, depression, inattention, and aggression, which can be challenging to treat. Executive function challenges, which impact many autistic individuals, may serve as a risk factor for mental health problems or make treating mental health conditions more difficult. While some people respond well to medication or therapy, others do not. This study tried to understand if there are different subgroups of autistic young people who may have similar patterns of executive function strengths and challenges-like flexibility, planning, self-monitoring, and emotion regulation. Then, we investigated whether executive function subgroups were related to mental health problems in autistic youth. We found three different types of executive function subgroups in autistic youth, each with different patterns of mental health problems. This helps us identify specific profiles of executive function strengths and challenges that may be helpful with identifying personalized supports, services, and treatment strategies for mental health conditions.

Testing the Continuum/Spectrum Model in Russian-Speaking Children With and Without Developmental Language Disorder

PURPOSE

Previously, Lancaster and Camarata (2019) showed that the continuum/spectrum model of the developmental language disorder (DLD) best explained the high heterogeneity of symptoms in children with DLD. We hypothesize that the continuum/spectrum approach can include not only children with DLD but also typically developing (TD) children with different timelines and patterns of language acquisition. This model can explain individual language profiles and deficits in children.

METHOD

We assessed language abilities in a group of Russian-speaking children with DLD aged 4-7 years (n = 53) and their age- and gender-matched peers without speech and language diagnoses (n = 53, TD). We evaluated the children's performance at four language levels in production and comprehension domains, using 11 subtests of the standardized language assessment for Russian: Russian Child Language Assessment Battery (RuCLAB). Using the k-means cluster method and RuCLAB scores, we obtained two clusters of children and analyzed their language performance in individual subtests.

RESULTS

The analysis revealed that the two clusters of children both included DLD and TD participants: Group 1, with higher test scores (TD = 45, DLD = 24 children), and Group 2, with lower scores (TD = 8, DLD = 29). Children from Group 1 mostly had lower scores at one of the language levels, whereas those from Group 2 struggled at several language levels. Furthermore, children with DLD from both groups tended to be more sensitive to linguistic features such as word length, noun case, and sentence reversibility compared to TD children.

CONCLUSIONS

The presence of two mixed groups shows that children with diagnosed DLD could perform on par with TD children, whereas some younger TD children could perform similarly to children with DLD. Our findings support the continuum/spectrum model: Linguistic skills in preschool children are a continuum, varying from high to poor skills at all language levels in comprehension and production. To describe a child's language profile, the tasks assessing all language levels should be used.

SUPPLEMENTAL MATERIAL

https://doi.org/10.23641/asha.25521400.

The relationship between camouflaging and mental health: Are there differences among subgroups in autistic adults?

LAY ABSTRACT

When autistic people use strategies to hide their autistic characteristics, we call this camouflaging. Autistic adults suggested that camouflaging can result in mental health difficulties. That is, people who report to camouflage also report mental health difficulties. However, since there are many differences between autistic people, this relationship may also differ between subgroups. Therefore, in this study we investigated whether camouflaging and mental health difficulties are related and whether this relationship is equal for all autistic adults. For this study, 352 autistic adults aged 30-84 years filled in the Dutch Camouflaging Autistic Traits Questionnaire to measure camouflaging and the Symptom Checklist-90 Revised to measure mental health difficulties. We found that camouflaging was moderately related to mental health difficulties. This means that people who report more camouflaging also report more mental health difficulties. When we looked closer, we found that this relationship was strong for only a small subgroup of autistic adults. In most other autistic adults, there was a small or no relationship between camouflaging and mental health difficulties. Therefore, it is important that clinicians are aware of camouflaging and its possible relationship with mental health difficulties, but that they do not generalize the negative consequences to everyone.

Autism Spectrum Disorder: Brain Areas Involved, Neurobiological Mechanisms, Diagnoses and Therapies

Autism spectrum disorder (ASD), affecting over 2% of the pre-school children population, includes an important fraction of the conditions accounting for the heterogeneity of autism. The disease was discovered 75 years ago, and the present review, based on critical evaluations of the recognized ASD studies from the beginning of 1990, has been further developed by the comparative analyses of the research and clinical reports, which have grown progressively in recent years up to late 2023. The tools necessary for the identification of the ASD disease and its related clinical pathologies are genetic and epigenetic mutations affected by the specific interaction with transcription factors and chromatin remodeling processes occurring within specific complexes of brain neurons. Most often, the ensuing effects induce the inhibition/excitation of synaptic structures sustained primarily, at dendritic fibers, by alterations of flat and spine response sites. These effects are relevant because synapses, established by specific interactions of neurons with glial cells, operate as early and key targets of ASD. The pathology of children is often suspected by parents and communities and then confirmed by ensuing experiences. The final diagnoses of children and mature patients are then completed by the combination of neuropsychological (cognitive) tests and electro-/magneto-encephalography studies developed in specialized centers. ASD comorbidities, induced by processes such as anxieties, depressions, hyperactivities, and sleep defects, interact with and reinforce other brain diseases, especially schizophrenia. Advanced therapies, prescribed to children and adult patients for the control of ASD symptoms and disease, are based on the combination of well-known brain drugs with classical tools of neurologic and psychiatric practice. Overall, this review reports and discusses the advanced knowledge about the biological and medical properties of ASD.

Robustly uncovering the heterogeneity of neurodegenerative disease by using data-driven subtyping in neuroimaging: A review

Neurodegenerative diseases are associated with heterogeneity in genetics, pathology, and clinical manifestation. Understanding this heterogeneity is particularly relevant for clinical prognosis and stratifying patients for disease modifying treatments. Recently, data-driven methods based on neuroimaging have been applied to investigate the subtyping of neurodegenerative disease, helping to disentangle this heterogeneity. We reviewed brain-based subtyping studies in aging and representative neurodegenerative diseases, including Alzheimer's disease, mild cognitive impairment, frontotemporal dementia, and Lewy body dementia, from January 2000 to November 2022. We summarized clustering methods, validation, robustness, reproducibility, and clinical relevance of 71 eligible studies in the present study. We found vast variations in approaches between studies, including ten neuroimaging modalities, 24 cluster algorithms, and 41 methods of cluster number determination. The clinical relevance of subtyping studies was evaluated by summarizing the analysis method of clinical measurements, showing a relatively low clinical utility in the current studies. Finally, we conclude that future studies of heterogeneity in neurodegenerative disease should focus on validation, comparison between subtyping approaches, and prioritise clinical utility.

Assessing cortical features in early stage ASD children

Autism Spectrum Disorder (ASD) is defined as a neurodevelopmental disorder largely investigated in the neurologic field. Recently, neuroimaging studies have been conducted in order to investigate cerebral morphologic alterations in ASD patients, demonstrating an atypical brain development before the clinical manifestations of the disorder. Cortical Thickness (CT) and Local Gyrification Index (LGI) distribution for ASD children were investigated in this study, with the aim to evaluate possible relationship between brain measures and individual characteristics (i.e., IQ and verbal ability). 3D T1-w sequences from 129 ASD and 58 age-matched Healthy Controls (HC) were acquired and processed in order to assess CT and LGI for each subject. Intergroup differences between ASD and HC were investigated, including analyses of 2 ASD subgroups, split according to patient verbal ability and IQ. When compared to HC, ASD showed increased CT and LGI within several brain areas, both as an overall group and as verbal ability an IQ subgroups. Moreover, when comparing language characteristics of the ASD subjects, those patients with verbal ability exhibit significant CT and LGI increase was found within the occipital lobe of right hemisphere. No significant results occurred when comparing ASD patients according to their IQ value. These results support the hypothesis of abnormal brain maturation in ASD since early childhood with differences among clinical subgroups suggesting different anatomical substrates underlying an aberrant connectivity.

Heterogeneity of dynamic synergetic configurations of salience network in children with autism spectrum disorder

Atypical functional connectivity (FC) patterns have been identified in autism spectrum disorders (ASD), especially within salience network (SN) and between SN and default mode network (DMN) and central executive network (CEN). But whether the dynamic configuration of intra-SN and inter-SN (SN with DMN and CEN) FC in ASD is also heterogeneous remains unknown. Based on the resting-state functional magnetic resonance imaging data from 105 ASD and 102 typically-developing controls (TC), we calculated the time-varying FC of intra-SN and inter-SN (SN with DMN and CEN). Then, the joint recurrence features for the time-varying FC were calculated to assess how the SN dynamically recruits different configurations of network segregation and integration in ASD, that is, synergies, from the dynamical systems perspective. We analyzed the differences in synergetic patterns between ASD subtypes obtained by k-means clustering algorithm based on the synergy of SN and TC, and investigated the relationships between synergy of SN and severity of clinical symptoms of ASD for ASD subtypes. Two ASD subtypes were revealed, where the synergy of SN in ASD subtype 1 has lower stability and periodicity compared to the TC, and ASD subtype 2 exhibits the opposite alteration. Synergy of SN for ASD subtype 1 and 2 was found to predict the severity of communication impairments and restricted and repetitive behaviors in ASD, respectively. These results suggest the existence of subtypes with distinct patterns of the synergy of SN in ASD, and provide insight into the complex pathophysiological mechanism of clinical manifestations of ASD.

Conceptualization of the latent structure of autism: further evidence and discussion of dimensional and hybrid models

Autism spectrum disorder (ASD) might be conceptualized as an essentially dimensional, categorical, or hybrid model. Yet, current empirical studies are inconclusive and the latent structure of ASD has explicitly been examined only in a few studies. The aim of our study was to identify and discuss the latent model structure of behavioral symptoms related to ASD and to address the question of whether categories and/or dimensions best represent ASD symptoms. We included data of 2920 participants (1-72 years of age), evaluated with the Autism Diagnostic Observation Schedule (Modules 1-4). We applied latent class analysis, confirmatory factor analysis, and factor mixture modeling and evaluated the model fit by a combination of criteria. Based on the model selection criteria, the model fits, the interpretability as well as the clinical utility we conclude that the hybrid model serves best for conceptualization and assessment of ASD symptoms. It is both grounded in empirical evidence and in clinical usefulness, is in line with the current classification system (DSM-5) and has the potential of being more specific than the dimensional approach (decreasing false positive diagnoses).

Persistence and clinical consequences of post-traumatic and dissociative symptoms in people with depressive symptoms: a one-year follow-up study

BACKGROUND

Recent studies found that post-traumatic and dissociative symptoms are common in people with depressive symptoms. Although a trauma-related subtype of depression has been proposed, little is known about the persistence and clinical consequences of these symptoms.

OBJECTIVE

This one-year follow-up study investigated the persistence and clinical consequences of post-traumatic and dissociative symptoms in people with depressive symptoms.

METHODS

We analyzed longitudinal data from an international sample of people self-reporting depressive emotions (N = 152) (mean Patient Health Questionnaire-9 score = 17.27; SD = 6.31).

RESULTS

More than half (58.4%) of participants with baseline post-traumatic stress disorder (PTSD) still met the criteria for PTSD after one year. Participants with dissociative symptoms at baseline were significantly more likely to report lifetime psychiatric hospitalization (31.2% vs 14.7%), past-year use of psychiatric hospitalization (10.4% vs 0%) and emergency services (16.9% vs 4%) than those without dissociative symptoms. All post-traumatic and dissociative symptom clusters were cross-sectionally (r = .286 to .528, p < .001) and longitudinally (r = .181 to .462, p < .001) correlated with depressive symptoms. A sense of current threat (β = .146, p < .05) and negative self-concept (β = .173, p < .05) at baseline significantly predicted depressive symptoms after one year.

CONCLUSIONS

These findings contribute to the increasing body of knowledge regarding the PTSD/dissociation-depression comorbidity. Given their persistence and clinical consequences, we recommend that post-traumatic and dissociative symptoms be regularly screened for in clinical settings. The existence of a possible trauma-related subtype of depression should receive more attention in both research and clinical practice.

Examining Phenotypical Heterogeneity and its Underlying Factors in Gesture Skills of Chinese Autistic Children: Clustering Analysis

PURPOSE

The heterogeneity of autism is well documented, but few studies have studied the heterogeneity of gesture production ability in autistic children. The present study aimed to identify subgroups of autistic children who displayed heterogeneous gesture production abilities and explore the underlying factors, including autism characteristics, intellectual ability, and language ability, that were associated with the heterogeneity.

METHODS

A total of 65 Chinese autistic children (mean age = 5;3) participated. Their autism characteristics and intellectual ability were assessed by standardized measurements. Language output and gesture production were captured from a parent-child interaction task.

RESULTS

We conducted a hierarchical cluster analysis and identified four distinct clusters. Cluster 1 and Cluster 2 both had low gesture production whereas Cluster 3 and Cluster 4 had high gesture production. Both Clusters 1 and 2 had relatively strong autism characteristics, in comparison to Clusters 3 and 4.

CONCLUSIONS

Our findings revealed that children with stronger autism characteristics may gesture less often than those with weaker characteristics. However, the relationship between language ability and intellectual ability and gesture production was not clear. These findings shed light on the directions of intervention on gesture production for autistic children, especially those with stronger autism characteristics.

Finding Similarities in Differences Between Autistic Adults: Two Replicated Subgroups

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_COMP = 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.

Stratifying ASD and characterizing the functional connectivity of subtypes in resting-state fMRI

BACKGROUND

Although stratifying autism spectrum disorder (ASD) into different subtypes is a common effort in the research field, few papers have characterized the functional connectivity alterations of ASD subgroups classified by their clinical presentations.

METHODS

This is a case-control rs-fMRI study, based on large samples of open database (Autism Brain Imaging Data Exchange, ABIDE). The rs-MRI data from n=415 ASD patients (males n=357), and n=574 typical development (TD) controls (males n=410) were included. Clinical features of ASD were extracted and classified using data from each patient's Autism Diagnostic Interview-Revised (ADI-R) evaluation. Each subtype of ASD was characterized by local functional connectivity using regional homogeneity (ReHo) for assessment, remote functional connectivity using voxel-mirrored homotopic connectivity (VMHC) for assessment, the whole-brain functional connectivity, and graph theoretical features. These identified imaging properties from each subtype were integrated to create a machine learning model for classifying ASD patients into the subtypes based on their rs-fMRI data, and an independent dataset was used to validate the model.

RESULTS

All ASD participants were classified into Cluster-1 (patients with more severe impairment) and Cluster-2 (patients with moderate impairment) according to the dimensional scores of ADI-R. When compared to the TD group, Cluster-1 demonstrated increased local connection and decreased remote connectivity, and widespread hyper- and hypo-connectivity variations in the whole-brain functional connectivity. Cluster-2 was quite similar to the TD group in both local and remote connectivity. But at the level of whole-brain functional connectivity, the MCC-related connections were specifically impaired in Cluster-2. These properties of functional connectivity were fused to build a machine learning model, which achieved ~75% for identifying ASD subtypes (Cluster-1 accuracy = 81.75%; Cluster-2 accuracy = 76.48%).

CONCLUSIONS

The stratification of ASD by clinical presentations can help to minimize disease heterogeneity and highlight the distinguished properties of brain connectivity in ASD subtypes.

Genesis, modelling and methodological remedies to autism heterogeneity

Diagnostic criteria used in autism research have undergone a shift towards the inclusion of a larger population, paralleled by increasing, but variable, estimates of autism prevalence across clinical settings and continents. A categorical diagnosis of autism spectrum disorder is now consistent with large variations in language, intelligence, comorbidity, and severity, leading to a heterogeneous sample of individuals, increasingly distant from the initial prototypical descriptions. We review the history of autism diagnosis and subtyping, and the evidence of heterogeneity in autism at the cognitive, neurological, and genetic levels. We describe two strategies to address the problem of heterogeneity: clustering, and truncated-compartmentalized enrollment strategy based on prototype recognition. The advances made using clustering methods have been modest. We present an alternative, new strategy for dissecting autism heterogeneity, emphasizing incorporation of prototypical samples in research cohorts, comparison of subgroups defined by specific ranges of values for the clinical specifiers, and retesting the generality of neurobiological results considered to be acquired from the entire autism spectrum on prototypical cohorts defined by narrow specifiers values.

Current Approaches in Computational Psychiatry for the Data-Driven Identification of Brain-Based Subtypes

The ability of our current psychiatric nosology to accurately delineate clinical populations and inform effective treatment plans has reached a critical point with only moderately successful interventions and high relapse rates. These challenges continue to motivate the search for approaches to better stratify clinical populations into more homogeneous delineations, to better inform diagnosis and disease evaluation, and prescribe and develop more precise treatment plans. The promise of brain-based subtyping based on neuroimaging data is that finding subgroups of individuals with a common biological signature will facilitate the development of biologically grounded, targeted treatments. This review provides a snapshot of the current state of the field in empirical brain-based subtyping studies in child, adolescent, and adult psychiatric populations published between 2019 and March 2022. We found that there is vast methodological exploration and a surprising number of new methods being created for the specific purpose of brain-based subtyping. However, this methodological exploration and advancement is not being met with rigorous validation approaches that assess both reproducibility and clinical utility of the discovered brain-based subtypes. We also found evidence for a collaboration crisis, in which methodological exploration and advancements are not clearly grounded in clinical goals. We propose several steps that we believe are crucial to address these shortcomings in the field. We conclude, and agree with the authors of the reviewed studies, that the discovery of biologically grounded subtypes would be a significant advancement for treatment development in psychiatry.

Continuity of temperament subgroup classifications from infancy to toddlerhood in the context of early autism traits

Our previous cross-sectional investigation (Chetcuti et al., 2020) showed that infants with autism traits could be divided into distinct subgroups based on temperament. This longitudinal study builds on this existing work by exploring the continuity of temperament subgroup classifications and their associations with behavioral/clinical phenotypic features from infancy to toddlerhood. 103 infants (68% male) showing early signs of autism were referred to the study by community healthcare professionals and seen for assessments when aged around 12-months (Time 1), 18-months (Time 2), and 24-months (Time 3). Latent profile analysis revealed inhibited/low positive, active/negative reactive, and sociable/well-regulated subgroups at each timepoint, and a unique reactive/regulated subgroup at Time 3. Cross-tabulations indicated a significant likelihood of children having a recurrent subgroup classification from one timepoint to the next, and no apparent patterns to the movement of children who did change from one subgroup to another over time. Temperament subgroups were associated with concurrent child social-emotional functioning and autism traits, but unrelated to child age, sex, or developmental level. These findings suggest that temperament subgroup classifications might represent a reliable and very early indicator of autism characteristics and social-emotional functioning among infants/toddlers with autism traits.

Identification of subgroups of children in the Australian Autism Biobank using latent class analysis

BACKGROUND

The identification of reproducible subtypes within autistic populations is a priority research area in the context of neurodevelopment, to pave the way for identification of biomarkers and targeted treatment recommendations. Few previous studies have considered medical comorbidity alongside behavioural, cognitive, and psychiatric data in subgrouping analyses. This study sought to determine whether differing behavioural, cognitive, medical, and psychiatric profiles could be used to distinguish subgroups of children on the autism spectrum in the Australian Autism Biobank (AAB).

METHODS

Latent profile analysis was used to identify subgroups of children on the autism spectrum within the AAB (n = 1151), utilising data on social communication profiles and restricted, repetitive, and stereotyped behaviours (RRBs), in addition to their cognitive, medical, and psychiatric profiles.

RESULTS

Our study identified four subgroups of children on the autism spectrum with differing profiles of autism traits and associated comorbidities. Two subgroups had more severe clinical and cognitive phenotype, suggesting higher support needs. For the 'Higher Support Needs with Prominent Language and Cognitive Challenges' subgroup, social communication, language and cognitive challenges were prominent, with prominent sensory seeking behaviours. The 'Higher Support Needs with Prominent Medical and Psychiatric and Comorbidity' subgroup had the highest mean scores of challenges relating to social communication and RRBs, with the highest probability of medical and psychiatric comorbidity, and cognitive scores similar to the overall group mean. Individuals within the 'Moderate Support Needs with Emotional Challenges' subgroup, had moderate mean scores of core traits of autism, and the highest probability of depression and/or suicidality. A fourth subgroup contained individuals with fewer challenges across domains (the 'Fewer Support Needs Group').

LIMITATIONS

Data utilised to identify subgroups within this study was cross-sectional as longitudinal data was not available.

CONCLUSIONS

Our findings support the holistic appraisal of support needs for children on the autism spectrum, with assessment of the impact of co-occurring medical and psychiatric conditions in addition to core autism traits, adaptive functioning, and cognitive functioning. Replication of our analysis in other cohorts of children on the autism spectrum is warranted, to assess whether the subgroup structure we identified is applicable in a broader context beyond our specific dataset.

Profiles of circumscribed interests in autistic youth

Circumscribed interests (CI) encompass a range of different interests and related behaviors that can be characterized by either a high intensity but otherwise usual topic [referred to as restricted interests (RI)] or by a focus on topics that are not salient outside of autism [referred to as unusual interests (UI)]. Previous research has suggested that there is pronounced variability across individuals in terms of the endorsement of different interests, however, this variability has not been quantified using formal subtyping approaches. Therefore, using Latent Profile Analysis in a sample of 1,892 autistic youth (M_age = 10.82, SD_age = 4.14; 420 females), this study aimed to identify subgroups based on the RU and UI profiles. Three profiles of autistic individuals were identified. They were characterized as Low CI, Predominantly RI, and Predominantly UI. Importantly, profiles differed on several key demographic and clinical variables, including age, sex composition, IQ, language level, social and communication abilities, anxiety, and obsessive-compulsive behaviors. Although replication across other samples is needed, the profiles identified in this study are potentially promising for future research given their distinct profiles of RI and UI and unique patterns of associations with key cognitive and clinical variables. Therefore, this study represents an important initial step towards more individualized assessment and support for diverse presentations of CI in autistic youth.

A genetics-first approach to understanding autism and schizophrenia spectrum disorders: the 22q11.2 deletion syndrome

Recently, increasing numbers of rare pathogenic genetic variants have been identified that are associated with variably elevated risks of a range of neurodevelopmental outcomes, notably including Autism Spectrum Disorders (ASD), Schizophrenia Spectrum Disorders (SSD), and Intellectual Disability (ID). This review is organized along three main questions: First, how can we unify the exclusively descriptive basis of our current psychiatric diagnostic classification system with the recognition of an identifiable, highly penetrant genetic risk factor in an increasing proportion of patients with ASD or SSD? Second, what can be learned from studies of individuals with ASD or SSD who share a common genetic basis? And third, what accounts for the observed variable penetrance and pleiotropy of neuropsychiatric phenotypes in individuals with the same pathogenic variant? In this review, we focus on findings of clinical and preclinical studies of the 22q11.2 deletion syndrome (22q11DS). This particular variant is not only one of the most common among the increasing list of known rare pathogenic variants, but also one that benefits from a relatively long research history. Consequently, 22q11DS is an appealing model as it allows us to: (1) elucidate specific genotype-phenotype associations, (2) prospectively study behaviorally defined classifications, such as ASD or SSD, in the context of a known, well-characterized genetic basis, and (3) elucidate mechanisms underpinning variable penetrance and pleiotropy, phenomena with far-reaching ramifications for research and clinical practice. We discuss how findings from animal and in vitro studies relate to observations in human studies and can help elucidate factors, including genetic, environmental, and stochastic, that impact the expression of neuropsychiatric phenotypes in 22q11DS, and how this may inform mechanisms underlying neurodevelopmental expression in the general population. We conclude with research priorities for the field, which may pave the way for novel therapeutics.

Inter-individual heterogeneity of functional brain networks in children with autism spectrum disorder

BACKGROUND

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with considerable clinical heterogeneity. This study aimed to explore the heterogeneity of ASD based on inter-individual heterogeneity of functional brain networks.

METHODS

Resting-state functional magnetic resonance imaging data from the Autism Brain Imaging Data Exchange database were used in this study for 105 children with ASD and 102 demographically matched typical controls (TC) children. Functional connectivity (FC) networks were first obtained for ASD and TC groups, and inter-individual deviation of functional connectivity (IDFC) from the TC group was then calculated for each individual with ASD. A k-means clustering algorithm was used to obtain ASD subtypes based on IDFC patterns. The FC patterns were further compared between ASD subtypes and the TC group from the brain region, network, and whole-brain levels. The relationship between IDFC and the severity of clinical symptoms of ASD for ASD subtypes was also analyzed using a support vector regression model.

RESULTS

Two ASD subtypes were identified based on the IDFC patterns. Compared with the TC group, the ASD subtype 1 group exhibited a hypoconnectivity pattern and the ASD subtype 2 group exhibited a hyperconnectivity pattern. IDFC for ASD subtype 1 and subtype 2 was found to predict the severity of social communication impairments and the severity of restricted and repetitive behaviors in ASD, respectively.

LIMITATIONS

Only male children were selected for this study, which limits the ability to study the effects of gender and development on ASD heterogeneity.

CONCLUSIONS

These results suggest the existence of subtypes with different FC patterns in ASD and provide insight into the complex pathophysiological mechanism of clinical manifestations of ASD.

Clinical subtyping using community detection: Limited utility?

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.

Functional Connectome-Based Predictive Modeling in Autism

Autism is a heterogeneous neurodevelopmental condition, and functional magnetic resonance imaging-based studies have helped advance our understanding of its effects on brain network activity. We review how predictive modeling, using measures of functional connectivity and symptoms, has helped reveal key insights into this condition. We discuss how different prediction frameworks can further our understanding of the brain-based features that underlie complex autism symptomatology and consider how predictive models may be used in clinical settings. Throughout, we highlight aspects of study interpretation, such as data decay and sampling biases, that require consideration within the context of this condition. We close by suggesting exciting future directions for predictive modeling in autism.

Re-Examining Labels in Neurocognitive Research: Evidence from Bilingualism and Autism as Spectrum-Trait Cases

Despite the fact that the urge to investigate bilingualism and neurodevelopmental disorders as continuous indices rather than categorical ones has been well-voiced among researchers with respect to research methodological approaches, in the recent literature, when it comes to examining language, cognitive skills and neurodivergent characteristics, it is still the case that the most prevalent view is the categorisation of adults or children into groups. In other words, there is a categorisation of individuals, e.g., monolingual vs. bilingual children or children with typical and atypical/non-typical/non-neurotypical development. We believe that this labelling is responsible for the conflicting results that we often come across in studies. The aim of this review is to bring to the surface the importance of individual differences through the study of relevant articles conducted in bilingual children and children with autism, who are ideal for this study. We concur with researchers who already do so, and we further suggest moving away from labels and instead shift towards the view that not everything is either white or black. We provide suggestions as to how this shift could be implemented in research, while mostly aiming at starting a discourse rather than offering a definite path.

Genetically modified mice for research on human diseases: A triumph for Biotechnology or a work in progress?

Genetically modified mice are engineered as models for human diseases. These mouse models include inbred strains, mutants, gene knockouts, gene knockins, and ‘humanized’ mice. Each mouse model is engineered to mimic a specific disease based on a theory of the genetic basis of that disease. For example, to test the amyloid theory of Alzheimer’s disease, mice with amyloid precursor protein genes are engineered, and to test the tau theory, mice with tau genes are engineered. This paper discusses the importance of mouse models in basic research, drug discovery, and translational research, and examines the question of how to define the “best” mouse model of a disease. The critiques of animal models and the caveats in translating the results from animal models to the treatment of human disease are discussed. Since many diseases are heritable, multigenic, age-related and experience-dependent, resulting from multiple gene-gene and gene-environment interactions, it will be essential to develop mouse models that reflect these genetic, epigenetic and environmental factors from a developmental perspective. Such models would provide further insight into disease emergence, progression and the ability to model two-hit and multi-hit theories of disease. The summary examines the biotechnology for creating genetically modified mice which reflect these factors and how they might be used to discover new treatments for complex human diseases such as cancers, neurodevelopmental and neurodegenerative diseases.

An Explorative Study of Atypical Social Interaction Styles in Adult Men with Autism Spectrum Disorder, Men with Personality Disorders and Men from the General Population

Different atypical social interaction styles (SISs) were defined and tested in children and adolescents with autism spectrum disorder (ASD). Whether these styles can also be distinguished in adults with ASD has not yet been explored. In men with ASD, men with personality disorder (PD), and men from the general population (N = 90), aged 18-65 years, we tested which SISs can be distinguished and how they relate to the presence of PD traits. We found a significant distinction in allocation to atypical SISs between the three groups. This study shows the presence of atypical SISs in adults with ASD, and complements previous SIS findings in children and adolescents with ASD.Trial registration The Netherlands National Trial Register NTR6391. Registered 04 May 2017.

Large scale validation of an early-age eye-tracking biomarker of an autism spectrum disorder subtype

Few clinically validated biomarkers of ASD exist which can rapidly, accurately, and objectively identify autism during the first years of life and be used to support optimized treatment outcomes and advances in precision medicine. As such, the goal of the present study was to leverage both simple and computationally-advanced approaches to validate an eye-tracking measure of social attention preference, the GeoPref Test, among 1,863 ASD, delayed, or typical toddlers (12-48 months) referred from the community or general population via a primary care universal screening program. Toddlers participated in diagnostic and psychometric evaluations and the GeoPref Test: a 1-min movie containing side-by-side dynamic social and geometric images. Following testing, diagnosis was denoted as ASD, ASD features, LD, GDD, Other, typical sibling of ASD proband, or typical. Relative to other diagnostic groups, ASD toddlers exhibited the highest levels of visual attention towards geometric images and those with especially high fixation levels exhibited poor clinical profiles. Using the 69% fixation threshold, the GeoPref Test had 98% specificity, 17% sensitivity, 81% PPV, and 65% NPV. Sensitivity increased to 33% when saccades were included, with comparable validity across sex, ethnicity, or race. The GeoPref Test was also highly reliable up to 24 months following the initial test. Finally, fixation levels among twins concordant for ASD were significantly correlated, indicating that GeoPref Test performance may be genetically driven. As the GeoPref Test yields few false positives (~ 2%) and is equally valid across demographic categories, the current findings highlight the ability of the GeoPref Test to rapidly and accurately detect autism before the 2nd birthday in a subset of children and serve as a biomarker for a unique ASD subtype in clinical trials.

The Autism Phenome Project: Toward Identifying Clinically Meaningful Subgroups of Autism

One of the most universally accepted facts about autism is that it is heterogenous. Individuals diagnosed with autism spectrum disorder have a wide range of behavioral presentations and a variety of co-occurring medical and mental health conditions. The identification of more homogenous subgroups is likely to lead to a better understanding of etiologies as well as more targeted interventions and treatments. In 2006, we initiated the UC Davis MIND Institute Autism Phenome Project (APP) with the overarching goal of identifying clinically meaningful subtypes of autism. This ongoing longitudinal multidisciplinary study now includes over 400 children and involves comprehensive medical, behavioral, and neuroimaging assessments from early childhood through adolescence (2-19 years of age). We have employed several strategies to identify sub-populations within autistic individuals: subgrouping by neural, biological, behavioral or clinical characteristics as well as by developmental trajectories. In this Mini Review, we summarize findings to date from the APP cohort and describe progress made toward identifying meaningful subgroups of autism.

Future Prospects for Epigenetics in Autism Spectrum Disorder

Despite decades of investigation into the genetics of autism spectrum disorder (ASD), a current consensus in the field persists that ASD risk is too heterogeneous to be diagnosed by a single set of genetic variants. As such, ASD research has broadened to include assessment of other molecular biomarkers implicated in the condition that may be reflective of environmental exposures or gene by environment interactions. Epigenetic variance, and specifically differential DNA methylation, have emerged as areas of particularly high interest to ASD, as the epigenetic markers from specific chromatin loci collectively can reflect influences of multiple genetic and environmental factors and can also result in differential gene expression patterns. This review examines recent studies of the ASD epigenome, detailing common gene pathways found to be differentially methylated in people with ASD, and considers how these discoveries may inform our understanding of ASD etiology. We also consider future applications of epigenetics in ASD research and clinical practice, focusing on substratification, biomarker development, and experimental preclinical models of ASD that test causality. In combination with other -omics approaches, epigenomics allows an improved conceptualization of the multifactorial nature of ASD, and opens future lines of inquiry for both basic research and clinical practice.

Heterogeneity thwarts autism explanatory power: A proposal for endophenotypes

Many researchers now believe that autism heterogeneity is likely to include many disorders, but most research is based on samples defined by the DSM-5 Autism Spectrum Disorder (ASD) criteria. However, individuals diagnosed with autism have complex and varied biological causes for their symptoms. Therefore, autism is not a unitary biological entity. And although autism is significantly different from typical development, autism is not a unitary clinical disorder because diagnosed individuals vary in symptom patterns, comorbidities, biomarkers, and gene variants. The DSM-5 ASD criteria were designed to reduce heterogeneity, and there have been many other efforts to reduce autism heterogeneity including using more stringent clinical criteria, dividing autism into low and high functioning groups, creating subgroups, and by studying larger samples. However, to date these efforts have not been successful. Heterogeneity is extensive and remains unexplained, and no autism pathophysiology has been discovered. Most importantly, heterogeneity has hindered the explanatory power of the autism diagnosis to discover drug regimens and effective behavioral treatments. The paper proposes that possible transdiagnostic endophenotypes may reduce autism heterogeneity. Searching for transdiagnostic endophenotypes requires exploring autism symptoms outside of the framework of the DSM-5 autism diagnosis. This paper proposes that researchers relax diagnostic criteria to increase the range of phenotypes to support the search for transdiagnostic endophenotypes. The paper proposes possible candidates for transdiagnostic endophenotypes. These candidates are taken from DSM-5 ASD criteria, from concepts that have resulted from researched theories, and from symptoms that are the result of subtyping. The paper then sketches a possible basis for a future transdiagnostic endophenotypes screening tool that includes symptoms of autism and other neurodevelopmental disorders.

Subgroups in Late Adulthood Are Associated With Cognition and Wellbeing Later in Life

Objectives: In this study, we aim to discover whether there are valid subgroups in aging that are defined by modifiable factors and are determinant of clinically relevant outcomes regarding healthy aging.

Method: Data from interviews were collected in the Longitudinal Aging Study Amsterdam at two measurement occasions with a 3-year interval. Input for the analyses were seven well-known vulnerability and protective factors of healthy aging. By means of community detection, we tested whether we could distinguish subgroups in a sample of 1478 participants (T1-sample, aged 61–101 years). We tested both the external validity (T1) and predictive validity (T2) for wellbeing and subjective cognitive decline. Moreover, replicability and long-term stability were determined in 1186 participants (T2-sample, aged 61–101 years).

Results: Three similar subgroups were identified at T1 and T2. Subgroup A was characterized by high levels of education with personal vulnerabilities, subgroup B by being physically active with low support and low levels of education, and subgroup C by high levels of support with low levels of education. Subgroup C showed the lowest wellbeing and memory profile, both at T1 and T2. On most measures of wellbeing and memory, subgroups A and B did not differ from each other. At T2, the same number of subgroups was identified and subgroup profiles at T1 and T2 were practically identical. Per T1 subgroup 47–62% retained their membership at T2.

Discussion: We identified valid subgroups that replicate over time and differ on external variables at current and later measurement occasions. Individual change in subgroup membership over time shows that transitions to subgroups with better outcomes are possible.

Common and Distinct Disruptions of Cortical Surface Morphology Between Autism Spectrum Disorder Children With and Without SHANK3 Deficiency

SH3 and Multiple Ankyrin Repeat Domains 3 (SHANK3)-caused autism spectrum disorder (ASD) may present a unique opportunity to clarify the heterogeneous neuropathological mechanisms of ASD. However, the specificity and commonality of disrupted large-scale brain organization in SHANK3-deficient children remain largely unknown. The present study combined genetic tests, neurobehavioral evaluations, and magnetic resonance imaging, aiming to explore the disruptions of both local and networked cortical structural organization in ASD children with and without SHANK3 deficiency. Multiple surface morphological parameters such as cortical thickness (CT) and sulcus depth were estimated, and the graph theory was adopted to characterize the topological properties of structural covariance networks (SCNs). Finally, a correlation analysis between the alterations in brain morphological features and the neurobehavioral evaluations was performed. Compared with typically developed children, increased CT and reduced nodal degree were found in both ASD children with and without SHANK3 defects mainly in the lateral temporal cortex, prefrontal cortex (PFC), temporo-parietal junction (TPJ), superior temporal gyrus (STG), and limbic/paralimbic regions. Besides commonality, our findings showed some distinct abnormalities in ASD children with SHANK3 defects compared to those without. Locally, more changes in the STG and orbitofrontal cortex were exhibited in ASD children with SHANK3 defects, while more changes in the TPJ and inferior parietal lobe (IPL) in those without SHANK3 defects were observed. For the SCNs, a trend toward regular network topology was observed in ASD children with SHANK3 defects, but not in those without. In addition, ASD children with SHANK3 defects showed more alterations of nodal degrees in the anterior and posterior cingulate cortices and right insular, while there were more disruptions in the sensorimotor areas and the left insular and dorsomedial PFC in ASD without SHANK3 defects. Our findings indicate dissociable disruptions of local and networked brain morphological features in ASD children with and without SHANK3 deficiency. Moreover, this monogenic study may provide a valuable path for parsing the heterogeneity of brain disturbances in ASD.

Exploring Sensory Subgroups in Typical Development and Autism Spectrum Development Using Factor Mixture Modelling

This study uses factor mixture modelling of the Short Sensory Profile (SSP) at two time points to describe subgroups of young autistic and typically-developing children. This approach allows separate SSP subscales to influence overall SSP performance differentially across subgroups. Three subgroups were described, one including almost all typically-developing participants plus many autistic participants. SSP performance of a second, largely-autistic subgroup was predominantly shaped by a subscale indexing behaviours of low energy/weakness. Finally, the third subgroup, again largely autistic, contained participants with low (or more “atypical”) SSP scores across most subscales. In this subgroup, autistic participants exhibited large P1 amplitudes to loud sounds. Autistic participants in subgroups with more atypical SSP scores had higher anxiety and more sleep disturbances.

The Monash Autism-ADHD genetics and neurodevelopment (MAGNET) project design and methodologies: a dimensional approach to understanding neurobiological and genetic aetiology

Background

ASD and ADHD are prevalent neurodevelopmental disorders that frequently co-occur and have strong evidence for a degree of shared genetic aetiology. Behavioural and neurocognitive heterogeneity in ASD and ADHD has hampered attempts to map the underlying genetics and neurobiology, predict intervention response, and improve diagnostic accuracy. Moving away from categorical conceptualisations of psychopathology to a dimensional approach is anticipated to facilitate discovery of data-driven clusters and enhance our understanding of the neurobiological and genetic aetiology of these conditions. The Monash Autism-ADHD genetics and neurodevelopment (MAGNET) project is one of the first large-scale, family-based studies to take a truly transdiagnostic approach to ASD and ADHD. Using a comprehensive phenotyping protocol capturing dimensional traits central to ASD and ADHD, the MAGNET project aims to identify data-driven clusters across ADHD-ASD spectra using deep phenotyping of symptoms and behaviours; investigate the degree of familiality for different dimensional ASD-ADHD phenotypes and clusters; and map the neurocognitive, brain imaging, and genetic correlates of these data-driven symptom-based clusters.

Methods

The MAGNET project will recruit 1,200 families with children who are either typically developing, or who display elevated ASD, ADHD, or ASD-ADHD traits, in addition to affected and unaffected biological siblings of probands, and parents. All children will be comprehensively phenotyped for behavioural symptoms, comorbidities, neurocognitive and neuroimaging traits and genetics.

Conclusion

The MAGNET project will be the first large-scale family study to take a transdiagnostic approach to ASD-ADHD, utilising deep phenotyping across behavioural, neurocognitive, brain imaging and genetic measures.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13229-021-00457-3.