Autism, spectrum or clusters? An EEG coherence study

Dimensional Neuroimaging Endophenotypes: Neurobiological Representations of Disease Heterogeneity Through Machine Learning

Machine learning has been increasingly used to obtain individualized neuroimaging signatures for disease diagnosis, prognosis, and response to treatment in neuropsychiatric and neurodegenerative disorders. Therefore, it has contributed to a better understanding of disease heterogeneity by identifying disease subtypes with different brain phenotypic measures. In this Review, we first present a systematic literature overview of studies using machine learning and multimodal MRI to unravel disease heterogeneity in various neuropsychiatric and neurodegenerative disorders, including Alzheimer's disease, schizophrenia, major depressive disorder, autism spectrum disorder, multiple sclerosis, as well as their potential in a transdiagnostic framework, where neuroanatomical and neurobiological commonalities were assessed across diagnostic boundaries. Subsequently, we summarize relevant machine learning methodologies and their clinical interpretability. We discuss the potential clinical implications of the current findings and envision future research avenues. Finally, we discuss an emerging paradigm called dimensional neuroimaging endophenotypes (DNE). DNE dissects the neurobiological heterogeneity of neuropsychiatric and neurodegenerative disorders into low-dimensional yet informative, quantitative brain phenotypic representations, serving as robust intermediate phenotypes (i.e., endophenotypes) presumably reflecting the interplay of underlying genetic, lifestyle, and environmental processes associated with the disease etiology.

Body mass index is an overlooked confounding factor in existing clustering studies of 3D facial scans of children with autism spectrum disorder

Cluster analyzes of facial models of autistic patients aim to clarify whether it is possible to diagnose autism on the basis of facial features and further to stratify the autism spectrum disorder. We performed a cluster analysis of sets of 3D scans of ASD patients (116) and controls (157) using Euclidean and geodesic distances in order to recapitulate the published results on the Czech population. In the presented work, we show that the major factor determining the clustering structure and consequently also the correlation of resulting clusters with autism severity degree is body mass index corrected for age (BMIFA). After removing the BMIFA effect from the data in two independent ways, both the cluster structure and autism severity correlations disappeared. Despite the fact that the influence of body mass index (BMI) on facial dimensions was studied many times, this is the first time to our knowledge when BMI was incorporated into the faces clustering study and it thereby casts doubt on previous results. We also performed correlation analysis which showed that the only correction used in the existing clustering studies-dividing the facial distance by the average value within the face-is not eliminating correlation between facial distances and BMIFA within the facial cohort.

Dimensional Neuroimaging Endophenotypes: Neurobiological Representations of Disease Heterogeneity Through Machine Learning

Machine learning has been increasingly used to obtain individualized neuroimaging signatures for disease diagnosis, prognosis, and response to treatment in neuropsychiatric and neurodegenerative disorders. Therefore, it has contributed to a better understanding of disease heterogeneity by identifying disease subtypes that present significant differences in various brain phenotypic measures. In this review, we first present a systematic literature overview of studies using machine learning and multimodal MRI to unravel disease heterogeneity in various neuropsychiatric and neurodegenerative disorders, including Alzheimer's disease, schizophrenia, major depressive disorder, autism spectrum disorder, multiple sclerosis, as well as their potential in transdiagnostic settings. Subsequently, we summarize relevant machine learning methodologies and discuss an emerging paradigm which we call dimensional neuroimaging endophenotype (DNE). DNE dissects the neurobiological heterogeneity of neuropsychiatric and neurodegenerative disorders into a low-dimensional yet informative, quantitative brain phenotypic representation, serving as a robust intermediate phenotype (i.e., endophenotype) largely reflecting underlying genetics and etiology. Finally, we discuss the potential clinical implications of the current findings and envision future research avenues.

Differentiated Approach to Pharmacotherapy of Autism Spectrum Disorders: Biochemical Aspects

Autism Spectrum Disorders (ASD) are highly heterogeneous neurodevelopmental disorders caused by a complex interaction of numerous genetic and environmental factors and leading to deviations in the nervous system formation at the very early developmental stages. Currently, there are no accepted pharmacological treatments for the so-called core symptoms of ASD, such as social communication disorders and restricted and repetitive behavior patterns. Lack of knowledge about biological basis of ASD, absence of the clinically significant biochemical parameters reflecting abnormalities in the signaling cascades controlling the nervous system development and functioning, and lack of methods for selection of clinically and biologically homogeneous subgroups are considered as causes for the failure of clinical trials of ASD pharmacotherapy. This review considers the possibilities of applying differentiated clinical and biological approaches to the targeted search for ASD pharmacotherapy with emphasis on biochemical markers associated with ASD and attempts to stratify patients by biochemical parameters. The use of such approach as "the target-oriented therapy and assessment of the target status before and during the treatment to identify patients with a positive response to treatment" is discussed using the published results of clinical trials as examples. It is concluded that identification of biochemical parameters for selection of the distinct subgroups among the ASD patients requires research on large samples reflecting clinical and biological diversity of the patients with ASD, and use of unified approaches for such studies. An integrated approach, including clinical observation, clinical-psychological assessment of the patient behavior, study of medical history and description of individual molecular profiles should become a new strategy for stratifying patients with ASD for clinical pharmacotherapeutic trials, as well as for evaluating their efficiency.

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.

EMD-based analysis of complexity with dissociated EEG amplitude and frequency information: a data-driven robust tool -for Autism diagnosis- compared to multi-scale entropy approach

OBJECTIVE

Autism spectrum disorder (ASD) is usually characterised by altered social skills, repetitive behaviours, and difficulties in verbal/nonverbal communication. It has been reported that electroencephalograms (EEGs) in ASD are characterised by atypical complexity. The most commonly applied method in studies of ASD EEG complexity is multiscale entropy (MSE), where the sample entropy is evaluated across several scales. However, the accuracy of MSE-based classifications between ASD and neurotypical EEG activities is poor owing to several shortcomings in scale extraction and length, the overlap between amplitude and frequency information, and sensitivity to frequency. The present study proposes a novel, nonlinear, non-stationary, adaptive, data-driven, and accurate method for the classification of ASD and neurotypical groups based on EEG complexity and entropy without the shortcomings of MSE.

APPROACH

The proposed method is as follows: (a) each ASD and neurotypical EEG (122 subjects × 64 channels) is decomposed using empirical mode decomposition (EMD) to obtain the intrinsic components (intrinsic mode functions). (b) The extracted components are normalised through the direct quadrature procedure. (c) The Hilbert transforms of the components are computed. (d) The analytic counterparts of components (and normalised components) are found. (e) The instantaneous frequency function of each analytic normalised component is calculated. (f) The instantaneous amplitude function of each analytic component is calculated. (g) The Shannon entropy values of the instantaneous frequency and amplitude vectors are computed. (h) The entropy values are classified using a neural network (NN). (i) The achieved accuracy is compared to that obtained with MSE-based classification. (j) The consistency of the results of entropy 3D mapping with clinical data is assessed.

MAIN RESULTS

The results demonstrate that the proposed method outperforms MSE (accuracy: 66.4%), with an accuracy of 93.5%. Moreover, the entropy 3D mapping results are more consistent with the available clinical data regarding brain topography in ASD.

SIGNIFICANCE

This study presents a more robust alternative to MSE, which can be used for accurate classification of ASD/neurotypical as well as for the examination of EEG entropy across brain zones in 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.

Toward Neurosubtypes in Autism

There is a consensus that substantial heterogeneity underlies the neurobiology of autism spectrum disorder (ASD). As such, it has become increasingly clear that a dissection of variation at the molecular, cellular, and brain network domains is a prerequisite for identifying biomarkers. Neuroimaging has been widely used to characterize atypical brain patterns in ASD, although findings have varied across studies. This is due, at least in part, to a failure to account for neurobiological heterogeneity. Here, we summarize emerging data-driven efforts to delineate more homogeneous ASD subgroups at the level of brain structure and function-that is, neurosubtyping. We break this pursuit into key methodological steps: the selection of diagnostic samples, neuroimaging features, algorithms, and validation approaches. Although preliminary and methodologically diverse, current studies generally agree that at least 2 to 4 distinct ASD neurosubtypes may exist. Their identification improved symptom prediction and diagnostic label accuracy above and beyond group average comparisons. Yet, this nascent literature has shed light onto challenges and gaps. These include 1) the need for wider and more deeply transdiagnostic samples collected while minimizing artifacts (e.g., head motion), 2) quantitative and unbiased methods for feature selection and multimodal fusion, 3) greater emphasis on algorithms' ability to capture hybrid dimensional and categorical models of ASD, and 4) systematic independent replications and validations that integrate different units of analyses across multiple scales. Solutions aimed to address these challenges and gaps are discussed for future avenues leading toward a comprehensive understanding of the mechanisms underlying ASD heterogeneity.