An exploratory analysis of the use of cognitive, adaptive and behavioural indices for cluster analysis of ASD subgroups

Prevalence, Symptom Profiles, and Correlates of Mixed Anxiety-Depression in Male and Female Autistic Youth

Relatively little attention has been given to mixed anxiety and depression in autistic youth, particularly how this differs between males and females. This study investigated sex-based differences in the prevalence and correlates of mixed anxiety and depression in a sample of 51 autistic males (M age = 10.16 yr, SD = 2.81 yr, and range = 6 yr to 17 yr) and 51 autistic females (M age = - 10.07 yr, SD = 2.76 yr, and range = 6 yr to 17 yr), matched for age, IQ, and autism severity. Self-reports on generalised anxiety disorder and major depressive disorder, morning salivary cortisol, ADOS-2 scores, and WASI-II full-scale scores were collected from these autistic youth, and data on the ASD-related symptoms of these youth were collected from their parents. The data were analysed for total anxiety-depression score levels, for the underlying components of this scale, and for the individual items used in the scale. The results indicate no significant sex differences for the prevalence of mixed anxiety and depression total scores or the underlying components of anxiety and depression or for the individual items of the mixed anxiety-depression scale. There were sex differences in the significant correlates of mixed anxiety and depression: morning cortisol and ASD-related difficulties in social interaction for females, and ASD-related behaviour for males. Males' feelings of being restless or edgy were correlated with their social interaction and repetitive and restricted behaviour. Females' difficulties in social interaction were correlated with their concerns about their abilities and their sleeping problems. Females' sleeping problems, their tendency to talk about dying, and feeling worthless, were correlated with their morning cortisol. These findings suggest that, while mixed anxiety and depression is experienced similarly by autistic males and females at the global, component, and individual item levels, specific aspects of the symptomatology of mixed anxiety and depression are differently associated with aspects of their ASD-related symptomatology and their levels of chronic physiological stress for males and females.

Mental Health Symptom Profiles of Adolescents in Foster Care

The article describes an investigation of the nature, patterns and complexity of carer-reported mental health symptoms for a population sample (N = 230) of adolescents (age 12-17) placed in long-term foster and kinship care following chronic and severe maltreatment. Two cluster analyses of Child Behaviour Checklist DSM-oriented (CBCL-DSM) and Assessment Checklist for Adolescents sub-scale scores of clinical cases were performed. The first yielded 8 profiles of attachment- and trauma-related symptoms as measured across eight ACA scales (N = 113 cases). The second yielded 11 profiles of a broader range of symptoms, as measured across five CBCL-DSM and five ACA sub-scales (N = 141 cases). The symptom profiles derived from both cluster analyses are differentiated more by symptom severity and complexity, than by symptom specificity - suggesting that trauma- and attachment-related symptomatology does not conform to a taxonomy of discrete disorders. Five of the 11 CBCL-DSM/ACA profiles describe severe and complex symptomatology that does not correspond to discrete DSM-5 or ICD-11 diagnoses. Accurate measurement and formulation of clinical phenomena is an essential component of evidence-based psychological and psychiatric practice. Clinicians who carry out mental health assessments of children and adolescents in care should be aware of the limits of the diagnostic classification systems for formulating complex attachment- and trauma-related symptomatology.

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.

Identification of Homogeneous Subgroups from Resting-State fMRI Data

The identification of homogeneous subgroups of patients with psychiatric disorders can play an important role in achieving personalized medicine and is essential to provide insights for understanding neuropsychological mechanisms of various mental disorders. The functional connectivity profiles obtained from functional magnetic resonance imaging (fMRI) data have been shown to be unique to each individual, similar to fingerprints; however, their use in characterizing psychiatric disorders in a clinically useful way is still being studied. In this work, we propose a framework that makes use of functional activity maps for subgroup identification using the Gershgorin disc theorem. The proposed pipeline is designed to analyze a large-scale multi-subject fMRI dataset with a fully data-driven method, a new constrained independent component analysis algorithm based on entropy bound minimization (c-EBM), followed by an eigenspectrum analysis approach. A set of resting-state network (RSN) templates is generated from an independent dataset and used as constraints for c-EBM. The constraints present a foundation for subgroup identification by establishing a connection across the subjects and aligning subject-wise separate ICA analyses. The proposed pipeline was applied to a dataset comprising 464 psychiatric patients and discovered meaningful subgroups. Subjects within the identified subgroups share similar activation patterns in certain brain areas. The identified subgroups show significant group differences in multiple meaningful brain areas including dorsolateral prefrontal cortex and anterior cingulate cortex. Three sets of cognitive test scores were used to verify the identified subgroups, and most of them showed significant differences across subgroups, which provides further confirmation of the identified subgroups. In summary, this work represents an important step forward in using neuroimaging data to characterize mental disorders.

Sensitivity to interaural level and time differences in individuals with autism spectrum disorder

Individuals with autism spectrum disorders (ASD) are reported to exhibit degraded performance in sound localization. This study investigated whether the sensitivity to the interaural level differences (ILDs) and interaural time differences (ITDs), major cues for horizontal sound localization, are affected in ASD. Thresholds for discriminating the ILD and ITD were measured for adults with ASD and age- and IQ-matched controls in a lateralization experiment. Results show that the ASD group exhibited higher ILD and ITD thresholds than the control group. Moreover, there was a significant diversity of ITD sensitivity in the ASD group, and it contained a larger proportion of participants with poor ITD sensitivity than the control group. The current study suggests that deficits in relatively low-level processes in the auditory pathway are implicated in degraded performance of sound localization in individuals with ASD. The results are consistent with the structural abnormalities and great variability in the morphology in the brainstem reported by neuroanatomical studies of ASD.

Applications of Unsupervised Machine Learning in Autism Spectrum Disorder Research: a Review

Large amounts of autism spectrum disorder (ASD) data is created through hospitals, therapy centers, and mobile applications; however, much of this rich data does not have pre-existing classes or labels. Large amounts of data—both genetic and behavioral—that are collected as part of scientific studies or a part of treatment can provide a deeper, more nuanced insight into both diagnosis and treatment of ASD. This paper reviews 43 papers using unsupervised machine learning in ASD, including k-means clustering, hierarchical clustering, model-based clustering, and self-organizing maps. The aim of this review is to provide a survey of the current uses of unsupervised machine learning in ASD research and provide insight into the types of questions being answered with these methods.

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

Heterogeneity within autism spectrum disorder (ASD) is recognized as a challenge to both biological and psychological research, as well as clinical practice. To reduce unexplained heterogeneity, subtyping techniques are often used to establish more homogeneous subtypes based on metrics of similarity and dissimilarity between people. We review the ASD literature to create a systematic overview of the subtyping procedures and subtype validation techniques that are used in this field. We conducted a systematic review of 156 articles (2001-June 2020) that subtyped participants (range N of studies = 17-20,658), of which some or all had an ASD diagnosis. We found a large diversity in (parametric and non-parametric) methods and (biological, psychological, demographic) variables used to establish subtypes. The majority of studies validated their subtype results using variables that were measured concurrently, but were not included in the subtyping procedure. Other investigations into subtypes' validity were rarer. In order to advance clinical research and the theoretical and clinical usefulness of identified subtypes, we propose a structured approach and present the SUbtyping VAlidation Checklist (SUVAC), a checklist for validating subtyping results.

Dysfunction of the Auditory Brainstem as a Neurophysiology Subtype of Autism Spectrum Disorder

Autism spectrum disorder (ASD) is very heterogeneous, particularly in language. Studies have suggested that language impairment is linked to auditory-brainstem dysfunction in ASD. However, not all ASD children have these deficits, which suggests potential subtypes of ASD. We classified ASD children into two subtypes according to their speech-evoked auditory brainstem response (speech-ABR) and explored the neural substrates for possible subtypes. Twenty-nine children with ASD and 25 typically developing (TD) peers were enrolled to undergo speech-ABR testing and structural magnetic resonance imaging (sMRI). There were significant differences between the ASD group and TD group in surface area, cortical volume and cortical thickness. According to speech-ABR results, ASD participants were divided into the ASD-typical (ASD-T) group and ASD-atypical (ASD-A) group. Compared with the ASD-T group, the ASD-A group had a lower score in language of the Gesell Developmental Diagnosis Scale (GDDS), increased left rostral middle frontal gyrus (lRMFG) area and decreased local gyrification index of the right superior temporal gyrus. GDDS-language and surface area of lRMFG were correlated to the wave-A amplitude in ASD. Surface area of lRMFG had an indirect effect on language performance via alteration of the wave-V amplitude. Thus, cortical deficits may impair language ability in children with ASD by causing subcortical dysfunction at preschool age. These evidences support dysfunction of the auditory brainstem as a potential subtype of ASD. Besides, this subtype-based method may be useful for various clinical applications.

Identifying Subgroups of Toddlers with DSM-5 Autism Spectrum Disorder Based on Core Symptoms

The objective of this study was to identify subgroups of toddlers with DSM-5 ASD based on core ASD symptoms using a person-based analytical framework. This is a retrospective study of 500 toddlers (mean age 26 months, 79% male) with DSM-5 ASD. Data were analyzed using latent class analyses in which profiles were formed based on ASD symptomatology. Social communication (SC) symptoms favored a three-class solution, while restricted/repetitive behaviors (RRBs) favored a two-class solution. Classes with higher consistency of SC deficits were younger, with lower developmental functioning. The class with more RRBs was older, with higher functioning. If confirmed in other populations, these classes may more precisely characterize subgroups within the heterogeneous group of toddlers at time of ASD diagnosis.

Fractionating autism based on neuroanatomical normative modeling

Autism is a complex neurodevelopmental condition with substantial phenotypic, biological, and etiologic heterogeneity. It remains a challenge to identify biomarkers to stratify autism into replicable cognitive or biological subtypes. Here, we aim to introduce a novel methodological framework for parsing neuroanatomical subtypes within a large cohort of individuals with autism. We used cortical thickness (CT) in a large and well-characterized sample of 316 participants with autism (88 female, age mean: 17.2 ± 5.7) and 206 with neurotypical development (79 female, age mean: 17.5 ± 6.1) aged 6-31 years across six sites from the EU-AIMS multi-center Longitudinal European Autism Project. Five biologically based putative subtypes were derived using normative modeling of CT and spectral clustering. Three of these clusters showed relatively widespread decreased CT and two showed relatively increased CT. These subtypes showed morphometric differences from one another, providing a potential explanation for inconsistent case-control findings in autism, and loaded differentially and more strongly onto symptoms and polygenic risk, indicating a dilution of clinical effects across heterogeneous cohorts. Our results provide an important step towards parsing the heterogeneous neurobiology of autism.

Autism, spectrum or clusters? An EEG coherence study

BACKGROUND

Autism prevalence continues to grow, yet a universally agreed upon etiology is lacking despite manifold evidence of abnormalities especially in terms of genetics and epigenetics. The authors postulate that the broad definition of an omnibus 'spectrum disorder' may inhibit delineation of meaningful clinical correlations. This paper presents evidence that an objectively defined, EEG based brain measure may be helpful in illuminating the autism spectrum versus subgroups (clusters) question.

METHODS

Forty objectively defined EEG coherence factors created in prior studies demonstrated reliable separation of neuro-typical controls from subjects with autism, and reliable separation of subjects with Asperger's syndrome from all other subjects within the autism spectrum and from neurotypical controls. In the current study, these forty previously defined EEG coherence factors were used prospectively within a large (N = 430) population of subjects with autism in order to determine quantitatively the potential existence of separate clusters within this population.

RESULTS

By use of a recently published software package, NbClust, the current investigation determined that the 40 EEG coherence factors reliably identified two distinct clusters within the larger population of subjects with autism. These two clusters demonstrated highly significant differences. Of interest, many more subjects with Asperger's syndrome fell into one rather than the other cluster.

CONCLUSIONS

EEG coherence factors provide evidence of two highly significant separate clusters within the subject population with autism. The establishment of a unitary "Autism Spectrum Disorder" does a disservice to patients and clinicians, hinders much needed scientific exploration, and likely leads to less than optimal educational and/or interventional efforts.

Review of cluster analysis of phenotypic data in Autism Spectrum Disorders: distinct subtypes or a severity gradient model?

Background: Individuals with autism spectrum disorder (ASD) form a heterogeneous group, posing a challenge for clinical definition. Additional problems regarding the diverse clinical presentation arise from changes in diagnostic criteria according to the latest Diagnostic and Statistical Manual of Mental Disorders (DSM-5), with exclusion of individuals who met earlier criteria or inclusion of more than previously. Objectives: To investigate studies that have attempted to reduce the heterogeneity of ASD based on cluster analysis of phenotypic data and to clarify whether ASD should be interpreted as 'a unitary spectrum,' with a severity gradient, or defined by distinct subtypes. This will allow better understanding of the disorder with implications for its treatment and prognosis. Methods: A literature search was made through PubMed, Researchgate and Google Scholar for studies of ASD populations. In addition, reference lists from identified studies were reviewed. Results: Only 10 studies were found that dealt with the heterogeneity of ASD and its different subtypes, based on the review prerequisites. Most of the studies appear to support the existence of subtypes within ASD, but it remains unclear whether these are considered as different specific subtypes with characteristic profiles of symptoms or as a part of a severity gradient across symptom domains. Conclusions: Drawing definitive conclusions from the published studies about the nature of ASD is difficult, due to the fundamental methodological differences among the studies and their inconsistent findings. This review shed light on a number of discrepancies regarding the current classification of ASD. However, future research will be necessary to provide a more definite answer on the question of a definition based on separate diagnostic subtypes or on a severity gradient by including larger samples that are followed longitudinal and by applying better diagnostic system and choosing the appropriate variables.

Identifying Needs-Based Groupings Among People Accessing Intellectual Disability Services

There is increasing emphasis on needs-led service provision for people with intellectual disability (ID). This study outlines the statistical cluster analysis of clinical data from 1,692 individuals accessing secondary care ID services in the United Kingdom (U.K.) Using objective needs assessment data from a newly developed ID assessment tool, six clusters were identified. These had clinical face validity and were validated using six concurrently (but independently) rated tools. In keeping with previous studies, the clusters varied in terms of overall level of need as well as specific clinical features (autism spectrum disorder, mental health problems, challenging behaviors and physical health conditions). More work is now needed to further develop these clusters and explore their utility for planning, commissioning and optimizing needs-led services.

Cognitive and Adaptive Skills in Toddlers Who Meet Criteria for Autism in DSM-IV but not DSM-5

The current study compared adaptive and cognitive skills, and autism severity of toddlers with an autism spectrum disorder (ASD) diagnosis under DSM-IV but not DSM-5 criteria (DSM-IV only group) to those who met autism criteria under both diagnostic systems (DSM-5 group) and to those without ASD (non-ASD group). The toddlers in the DSM-IV only group were less delayed on various domains of adaptive (Communication, Socialization) and cognitive (Expressive and Receptive language, Fine Motor, Visual Reception) skills, and had less severe symptoms of ASD than the DSM-5 group. Thus, they might have the best potential for successful intervention. The DSM-IV only group did not differ from the non-ASD group in any adaptive or cognitive skills except for socialization skills, the hallmark of ASD.

Atypical physiological orienting to direct gaze in low-functioning children with autism spectrum disorder

Reduced use of eye contact is a prominent feature in individuals with autism spectrum disorder (ASD). It has been proposed that direct gaze does not capture the attention of individuals with ASD. Experimental evidence is, however, mainly restricted to relatively high-functioning school-aged children or adults with ASD. This study investigated whether 2-5-year-old low-functioning children with severe ASD differ from control children in orienting to gaze stimuli, as measured with the heart rate deceleration response. Responses were measured to computerized presentations of dynamic shifts of gaze direction either toward (direct) or away (averted) from the observing child. The results showed a significant group by gaze direction interaction effect on heart rate responses (permuted P = .004), reflecting a stronger orienting response to direct versus averted gaze in typically developing (N = 17) and developmentally delayed (N = 16) children but not in children with ASD (N = 12). The lack of enhanced orienting response to direct gaze in the ASD group was not caused by a lack of looking at the eye region, as confirmed by eye tracking. The results suggest that direct gaze is not a socially salient, attention-grabbing signal for low-functioning children with ASD. Autism Res 2017, 10: 810-820. © 2017 International Society for Autism Research, Wiley Periodicals, Inc.

Electronic Health Record Based Algorithm to Identify Patients with Autism Spectrum Disorder

Objective

Cohort selection is challenging for large-scale electronic health record (EHR) analyses, as International Classification of Diseases 9^th edition (ICD-9) diagnostic codes are notoriously unreliable disease predictors. Our objective was to develop, evaluate, and validate an automated algorithm for determining an Autism Spectrum Disorder (ASD) patient cohort from EHR. We demonstrate its utility via the largest investigation to date of the co-occurrence patterns of medical comorbidities in ASD.

Methods

We extracted ICD-9 codes and concepts derived from the clinical notes. A gold standard patient set was labeled by clinicians at Boston Children’s Hospital (BCH) (N = 150) and Cincinnati Children’s Hospital and Medical Center (CCHMC) (N = 152). Two algorithms were created: (1) rule-based implementing the ASD criteria from Diagnostic and Statistical Manual of Mental Diseases 4^th edition, (2) predictive classifier. The positive predictive values (PPV) achieved by these algorithms were compared to an ICD-9 code baseline. We clustered the patients based on grouped ICD-9 codes and evaluated subgroups.

Results

The rule-based algorithm produced the best PPV: (a) BCH: 0.885 vs. 0.273 (baseline); (b) CCHMC: 0.840 vs. 0.645 (baseline); (c) combined: 0.864 vs. 0.460 (baseline). A validation at Children’s Hospital of Philadelphia yielded 0.848 (PPV). Clustering analyses of comorbidities on the three-site large cohort (N = 20,658 ASD patients) identified psychiatric, developmental, and seizure disorder clusters.

Conclusions

In a large cross-institutional cohort, co-occurrence patterns of comorbidities in ASDs provide further hypothetical evidence for distinct courses in ASD. The proposed automated algorithms for cohort selection open avenues for other large-scale EHR studies and individualized treatment of ASD.

Prevalence, structure and correlates of anxiety-depression in boys with an autism spectrum disorder

BACKGROUND

Comorbidity of anxiety and depression predicts impaired treatment outcomes, poor quality of life and increased suicide risk. No study has reported on a combined measure of anxiety-depression in boys with an Autism Spectrum Disorder.

AIMS

To explore the prevalence, underlying factor structure and relationships between anxiety-depression, physiological stress and symptoms of Autism Spectrum Disorder (ASD).

METHODS

150 boys (aged 6-18 years; IQ M=94.9, range=73-132) with an ASD plus their parents (135 mothers, 15 fathers) completed scales about the boys' anxiety and depression, and the boys provided samples of their saliva in the morning and afternoon. Parents also completed the ASD Behaviour Checklist about the boys' ASD symptoms.

RESULTS

The two sources of ratings were not significantly different for prevalence of anxiety-depression but the factor structures varied between the parents' and boys' responses, with a four-factor solution for the boys' ratings and a three-factor solution for the parents' ratings. There were also differences in the correlations between cortisol and anxiety-depression and between ASD symptoms and anxiety depression across the boys' and parents' data.

CONCLUSIONS

Assessment of anxiety and depression comorbidity from parents and from children with an ASD themselves could provide a valuable adjunct datum when diagnosing ASD.

Association of Aryl Hydrocarbon Receptor-Related Gene Variants with the Severity of Autism Spectrum Disorders

Exposure to environmental chemicals, such as dioxin, is known to have adverse effects on the homeostasis of gonadal steroids, thereby potentially altering the sexual differentiation of the brain to express autistic traits. Dioxin-like chemicals act on the aryl hydrocarbon receptor (AhR), polymorphisms, and mutations of AhR-related gene may exert pathological influences on sexual differentiation of the brain, causing autistic traits. To ascertain the relationship between AhR-related gene polymorphisms and autism susceptibility, we identified genotypes of them in patients and controls and determined whether there are different gene and genotype distributions between both groups. In addition, to clarify the relationships between the polymorphisms and the severity of autism, we compared the two genotypes of AhR-related genes (rs2066853, rs2228099) with the severity of autistic symptoms. Although no statistically significant difference was found between autism spectrum disorder (ASD) patients and control individuals for the genotypic distribution of any of the polymorphisms studied herein, a significant difference in the total score of severity was observed in rs2228099 polymorphism, suggesting that the polymorphism modifies the severity of ASD symptoms but not ASD susceptibility. Moreover, we found that a significant difference in the social communication score of severity was observed. These results suggest that the rs2228099 polymorphism is possibly associated with the severity of social communication impairment among the diverse ASD symptoms.

A genotype-first approach to defining the subtypes of a complex disease

Medical genetics typically entails the detailed characterization of a patient's phenotypes followed by genotyping to discover the responsible gene or mutation. Here, we propose that the systematic discovery of genetic variants associated with complex diseases such as autism are progressing to a point where a reverse strategy may be fruitful in assigning the pathogenic effects of many different genes and in determining whether particular genotypes manifest as clinically recognizable phenotypes. This "genotype-first" approach for complex disease necessitates the development of large, highly integrated networks of researchers, clinicians, and patient families, with the promise of improved therapies for subsets of patients.

Comorbidity clusters in autism spectrum disorders: an electronic health record time-series analysis

OBJECTIVE

The distinct trajectories of patients with autism spectrum disorders (ASDs) have not been extensively studied, particularly regarding clinical manifestations beyond the neurobehavioral criteria from the Diagnostic and Statistical Manual of Mental Disorders. The objective of this study was to investigate the patterns of co-occurrence of medical comorbidities in ASDs.

METHODS

International Classification of Diseases, Ninth Revision codes from patients aged at least 15 years and a diagnosis of ASD were obtained from electronic medical records. These codes were aggregated by using phenotype-wide association studies categories and processed into 1350-dimensional vectors describing the counts of the most common categories in 6-month blocks between the ages of 0 to 15. Hierarchical clustering was used to identify subgroups with distinct courses.

RESULTS

Four subgroups were identified. The first was characterized by seizures (n = 120, subgroup prevalence 77.5%). The second (n = 197) was characterized by multisystem disorders including gastrointestinal disorders (prevalence 24.3%) and auditory disorders and infections (prevalence 87.8%), and the third was characterized by psychiatric disorders (n = 212, prevalence 33.0%). The last group (n = 4316) could not be further resolved. The prevalence of psychiatric disorders was uncorrelated with seizure activity (P = .17), but a significant correlation existed between gastrointestinal disorders and seizures (P < .001). The correlation results were replicated by using a second sample of 496 individuals from a different geographic region.

CONCLUSIONS

Three distinct patterns of medical trajectories were identified by unsupervised clustering of electronic health record diagnoses. These may point to distinct etiologies with different genetic and environmental contributions. Additional clinical and molecular characterizations will be required to further delineate these subgroups.

An investigation of complex attachment- and trauma-related symptomatology among children in foster and kinship care

The paper reports an investigation into the nature, patterns and complexity of mental health symptomatology reported for a large (N = 347) population sample of children in foster and kinship care. Cluster analyses were performed on caregiver-reported Child Behavior Checklist (CBCL) and Assessment Checklist for Children (ACC) scores. The derived profile types are characterized more by symptom complexity than specificity, and are delineated more by elevation than shape. The analyses indicate that social and interpersonal relationship difficulties are hallmark features of clinical presentations of children in care; that anxiety is more often observed as a component of felt insecurity than as generalized or trauma-specific anxiety; and that attention-deficit hyperactivity is rarely manifested in isolation from other difficulties. Whereas 35 % of children had clinical difficulties that could plausibly be construed as discrete mental disorders or comorbidity, another 20 % displayed complex attachment- and trauma-related symptomatology that is not adequately conceptualized within DSM or ICD classifications.

Cluster analysis of autistic patients based on principal pathogenetic components

We have recently described four principal pathogenetic components in autism: (I) circadian and sensory dysfunction, (II) immune abnormalities, (III) neurodevelopmental delay, and (IV) stereotypic behaviors. Using hierarchical and k-means clustering, the same 245 patients assessed in our principal component analysis can be partitioned into four clusters: (a) 43 (17.6%) have prominent immune abnormalities accompanied by some circadian and sensory issues; (b) 44 (18.0%) display major circadian and sensory dysfunction, with little or no immune symptoms; (c) stereotypies predominate in 75 (31.0%); and (d) 83 (33.9%) show a mixture of all four components, with greater disruptive behaviors and mental retardation. The "immune" component provides the largest contributions to phenotypic variance (P = 2.7 x 10(-45)), followed by "stereotypic behaviors." These patient clusters may likely differ in genetic and immune underpinnings, developmental trajectories, and response to treatment.

Early behavioral changes and quantitative analysis of neuropathological features in murine prion disease: stereological analysis in the albino Swiss mice model

Behavioral and neuropathological changes have been widely investigated in murine prion disease but stereological based unbiased estimates of key neuropathological features have not been carried out. After injections of ME7 infected (ME7) or normal brain homogenates (NBH) into dorsal CA1 of albino Swiss mice and C57BL6, we assessed behavioral changes on hippocampal-dependent tasks. We also estimated by optical fractionator at 15 and 18 weeks post-injections (w.p.i.) the total number of neurons, reactive astrocytes, activated microglia and perineuronal nets (PN) in the polymorphic layer of dentate gyrus (PolDG), CA1 and septum in albino Swiss mice. On average, early behavioral changes in albino Swiss mice start four weeks later than in C57BL6. Cluster and discriminant analysis of behavioral data in albino Swiss mice revealed that four of nine subjects start to change their behavior at 12 w.p.i. and reach terminal stage at 22 w.p.i and the remaining subjects start at 22 w.p.i. and reach terminal stage at 26 w.p.i. Biotinylated dextran-amine BDA-tracer experiments in mossy fiber pathway confirmed axonal degeneration, and stereological data showed that early astrocytosis, microgliosis and reduction in the perineuronal nets are independent of a change in the number of neuronal cell bodies. Statistical analysis revealed that the septal region had greater levels of neuroinflammation and extracellular matrix damage than CA1. This stereological and multivariate analysis at early stages of disease in an outbred model of prion disease provided new insights connecting behavioral changes and neuroinflammation and seems to be important to understand the mechanisms of prion disease progression.

Trends in autism spectrum disorder diagnoses: 1994-2007

We analyzed predictors of parent-reported initial diagnosis (autistic disorder [AD], pervasive developmental disorder-not otherwise specified [PDD-NOS], pervasive developmental disorder ['PDD'] and autism spectrum disorder ['ASD'], and Asperger syndrome [AS]), among 6,176 individuals with autism spectrum disorders diagnosed from 1994 through 2007. Overall, distribution of diagnoses was influenced by a secular time trend factor; other significant factors included ethnicity, white race, geographic location, urbanicity, and initial evaluator. Since 2001, most initial diagnoses of AD and AS have remained steady while 'PDD' and PDD-NOS have decreased. 'ASD' diagnoses have increased, especially among school-based teams; AS diagnoses also increased uniquely among these evaluators. Findings from this study suggest that current diagnostic guidelines may not be meeting all community evaluator needs.