Computational Analysis of Developmental Disorders in Children

Early developmental disorders are common in children between the ages of 3 through 17. These developmental disorders begin at early ages and affect the day-to-day activities of children. These disorders also impact the growth and lifestyle of children. Most of the time these developmental disorders co-exist in children. The main focus of our research lies in Autism Spectrum Disorder, Attention-Deficit/Hyperactivity Disorder, Deletion syndrome (22q) and their co-occurrences. Most child psychologists and pediatricians diagnose these disorders in children through parent-based surveys. Our research uses three different parent-based reports: (1) Autism Diagnostic Interview (ADI), (2) Behavioral Assessment Schedule for Children (BASC), and (3) Vineland Adaptive Behavior Scales. These reports are questionnaires filled by parents under the inspection of certified professionals. These examinations require substantial amount of time and yield results after at least 13 months of wait time; hence, there is a pressing need to expedite the disorder detection process. Here, we address this challenge by utilizing machine learning techniques. We utilize Machine learning to parent-reviews to help understand the relevance and importance of parental assessments in diagnosing these disorders. Furthermore, we study the co-occurrence of these disorders and identify their indicators in parental-surveys using a variety of machine learning techniques. Our main objective is to determine whether one can accurately predict the occurrence of these disorders

Neurobiological markers for remission and persistence of childhood attention-deficit/hyperactivity disorder

Attention-deficit/hyperactivity disorder (ADHD) is one of the most prevalent neurodevelopmental disorders in children. Symptoms of childhood ADHD persist into adulthood in around 65% of patients, which elevates the risk for a number of adverse outcomes, resulting in substantial individual and societal burden. A neurodevelopmental double dissociation model is proposed based on existing studies in which the early onset of childhood ADHD is suggested to associate with dysfunctional subcortical structures that remain static throughout the lifetime; while diminution of symptoms over development could link to optimal development of prefrontal cortex. Current existing studies only assess basic measures including regional brain activation and connectivity, which have limited capacity to characterize the functional brain as a high performance parallel information processing system, the field lacks systems-level investigations of the structural and functional patterns that significantly contribute to the symptom remission and persistence in adults with childhood ADHD. Furthermore, traditional statistical methods estimate group differences only within a voxel or region of interest (ROI) at a time without having the capacity to explore how ROIs interact in linear and/or non-linear ways, as they quickly become overburdened when attempting to combine predictors and their interactions from high-dimensional imaging data set. This dissertation is the first study to apply ensemble learning techniques (ELT) in multimodal neuroimaging features from a sample of adults with childhood ADHD and controls, who have been clinically followed up since childhood. A total of 36 adult probands who were diagnosed with ADHD combined-type during childhood and 36 matched normal controls (NCs) are involved in this dissertation research. Thirty-six adult probands are further split into 18 remitters (ADHD-R) and 18 persisters (ADHD-P) based on the symptoms in their adulthood from DSM-IV ADHD criteria. Cued attention task-based fMRI, structural MRI, and diffusion tensor imaging data from each individual are analyzed. The high-dimensional neuroimaging features, including pair-wise regional connectivity and global/nodal topological properties of the functional brain network for cue-evoked attention process, regional cortical thickness and surface area, subcortical volume, volume and fractional anisotropy of major white matter fiber tract for each subject are calculated. In addition, all the currently available optimization strategies for ensemble learning techniques (i.e., voting, bagging, boosting and stacking techniques) are tested in a pool of semi-final classification results generated by seven basic classifiers, including K-Nearest Neighbors, support vector machine (SVM), logistic regression, Naïve Bayes, linear discriminant analysis, random forest, and multilayer perceptron. As hypothesized, results indicate that the features of nodal efficiency in right inferior frontal gyrus, right middle frontal (MFG)-inferior parietal (IPL) functional connectivity, and right amygdala volume significantly contributed to accurate discrimination between ADHD probands and controls; higher nodal efficiency of right MFG greatly contributed to inattentive and hyperactive/impulsive symptom remission, while higher right MFG-IPL functional connectivity strongly linked to symptom persistence in adults with childhood ADHD. The utilization of ELTs indicates that the bagging-based ELT with the base model of SVM achieves the best results, with the most significant improvement of the area under the receiver of operating characteristic curve (0.89 for ADHD probands vs. NCs, and 0.9 for ADHD-P vs. ADHD-R). The outcomes of this dissertation research have considerable value for the development of novel interventions that target mechanisms associated with recovery

Neural correlates of post-traumatic brain injury (TBI) attention deficits in children

Traumatic brain injury (TBI) in children is a major public health concern worldwide. Attention deficits are among the most common neurocognitive and behavioral consequences in children post-TBI which have significant negative impacts on their educational and social outcomes and compromise the quality of their lives. However, there is a paucity of evidence to guide the optimal treatment strategies of attention deficit related symptoms in children post-TBI due to the lack of understanding regarding its neurobiological substrate. Thus, it is critical to understand the neural mechanisms associated with TBI-induced attention deficits in children so that more refined and tailored strategies can be developed for diagnoses and long-term treatments and interventions. This dissertation is the first study to investigate neurobiological substrates associated with post-TBI attention deficits in children using both anatomical and functional neuroimaging data. The goals of this project are to discover the quantitatively measurable markers utilizing diffusion tensor imaging (DTI), structural magnetic resonance imaging (MRI), and functional MRI (fMRI) techniques, and to further identify the most robust neuroimaging features in predicting severe post-TBI attention deficits in children, by utilizing machine learning and deep learning techniques. A total of 53 children with TBI and 55 controls from age 9 to 17 are recruited. The results show that the systems-level topological properties in left frontal regions, parietal regions, and medial occipitotemporal regions in structural and functional brain network are significantly associated with inattentive and/or hyperactive/impulsive symptoms in children post-TBI. Semi-supervised deep learning modeling further confirms the significant contributions of these brain features in the prediction of elevated attention deficits in children post-TBI. The findings of this project provide valuable foundations for future research on developing neural markers for TBI-induced attention deficits in children, which may significantly assist the development of more effective and individualized diagnostic and treatment strategies

Multimodal neuroimaging signatures of early cART-treated paediatric HIV - Distinguishing perinatally HIV-infected 7-year-old children from uninfected controls

Introduction: HIV-related brain alterations can be identified using neuroimaging modalities such as proton magnetic resonance spectroscopy (1H-MRS), structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI), and functional MRI (fMRI). However, few studies have combined multiple MRI measures/features to identify a multivariate neuroimaging signature that typifies HIV infection. Elastic net (EN) regularisation uses penalised regression to perform variable selection, shrinking the weighting of unimportant variables to zero. We chose to use the embedded feature selection of EN logistic regression to identify a set of neuroimaging features characteristic of paediatric HIV infection. We aimed to determine 1) the most useful features across MRI modalities to separate HIV+ children from HIV- controls and 2) whether better classification performance is obtained by combining multimodal MRI features rather than using features from a single modality. Methods: The study sample comprised 72 HIV+ 7-year-old children from the Children with HIV Early Antiretroviral Therapy (CHER) trial in Cape Town, who initiated combination antiretroviral therapy (cART) in infancy and had their viral loads suppressed from a young age, and 55 HIV- control children. Neuroimaging features were extracted to generate 7 MRI-derived sets. For sMRI, 42 regional brain volumes (1st set), mean cortical thickness and gyrification in 68 brain regions (2nd and 3rd set) were used. For DTI data: radial (RD), axial (AD), mean (MD) diffusivities, and fractional anisotropy (FA) in each of 20 atlas regions were extracted for a total of 80 DTI features (4th set). For 1H-MRS, concentrations of 14 metabolites and their ratios to creatine in the basal ganglia, peritrigonal white matter, and midfrontal gray matter voxels (5th, 6th and 7th set) were considered. A logistic EN regression model with repeated 10-fold cross validation (CV) was implemented in R, initially on each feature set separately. Sex, age and total intracranial volume (TIV) were included as confounders with no shrinkage penalty. For each model, the classification performance for HIV+ vs HIV- was assessed by computing accuracy, specificity, sensitivity, and mean area under the receiver operator characteristic curve (AUC) across 10 CV folds and 100 iterations. To combine feature sets, the best performing set was concatenated with each of the other sets and further EN regressions were run. The combination giving the largest AUC was combined with each of the remaining sets until there was no further increase in AUC. Two concatenation techniques were explored: nested and non-nested modelling. All models were assessed for their goodness of fit using χ 2 likelihood ratio tests for non-nested models and Akaike information criterion (AIC) for nested models. To identify features most useful in distinguishing HIV infection, the EN model was retrained on all the data, to find features with non-zero weights. Finally, multivariate imputation using chained equations (MICE) was explored to investigate the effect of increased sample size on classification and feature selection. Results: The best performing modality in the single modality analysis was sMRI volume

Supervised machine learning in psychiatry:towards application in clinical practice

In recent years, the field of machine learning (often named with the more general term artificial intelligence) has literally exploded and its application has been proposed in basically all fields, including psychiatry and mental health. This has been motivated by the promise of using machine learning to develop new clinical tools that could help perform personalized predictions and recommendations, ultimately improving the results achievable in the psychiatric clinical practice that still faces only a limited success in the fight against mental diseases. However, despite this huge interest, there is still a substantial lack of tools in psychiatry that are based on machine learning algorithms. Massimiliano Grassi, in his Ph.D. thesis, investigates the challenges of translating machine learning algorithms into clinical practice and proposes innovative solutions to these challenges. The thesis presents the development and validation of new algorithms for the prediction of the onset of Alzheimer’s disease, the remission of obsessive-compulsive disorder, and the automatization of sleep staging in polysomnography, a method to diagnose sleep disorders. The results from these studies demonstrate that the use of machine learning in psychiatric clinical practice is not just a promise, and it is possible to develop machine learning algorithms that achieve clinically relevant performance even if based solely on information that can be easily accessible in the daily clinical routine

Genetic implications of individual intervention and neuronal dysfunction in neurodevelopmental disorders

Neurodevelopmental disorders (NDDs) are a group of conditions appearing in childhood, with developmental deficits that produce impairments of functioning. Autism spectrum disorder (ASD) is a common NDD with a high heritability affected by complex genetic factors, including both common and rare variants. Behavior interventions such as social skills group training (SSGT) have been widely used in school-aged autistic individuals to relieve social communication difficulties in a group setting. Studies have confirmed that intervention outcomes can be influenced by sex and age, but how the genetic risk contributes to the outcome variability remains elusive. Furthermore, although large population cohorts have been well studied and have found numerous genes associated with ASD and NDDs, the molecular and neuronal outcomes of risk variants and genes are unclear. Therefore, this thesis included four studies in which the effects of genetic factors on intervention outcomes and cellular level neuronal functions were investigated. Results from this thesis may provide a genetic perspective for further studies to explore potential individualized treatments for ASD and other NDDs. Specifically, In STUDY 1-3, exome sequencing and microarray were performed on individuals from a randomized controlled trial of SSGT (KONTAKT®). Common and rare variants, including copy number variations (CNVs) and exome variants, were tested for association effects with SSGT and standard care intervention outcomes. Polygenic risk scores (PRSs) were calculated from common variants, and clinically significant rare CNVs and rare exome variants were prioritized. Molecular diagnoses were identified in 12.6% of the autistic participants. PRSs and carrier status of clinically significant rare variants were associated with intervention outcomes, although with varied effects on both SSGT and standard care. In addition, genetic scores representing variant loads in specific gene sets were obtained from rare and common variants in ASD-related pathways. Outcomes of interventions were differentially associated with genetic scores for ASD-related gene sets including synaptic transmission and transcription regulation from RNA polymerase II. After combining genetic information and behavior measures, a machine learning model was able to select important features and confirm that the intervention outcomes were predictable. In STUDY 4, genetic variants affecting Calcium/Calmodulin Dependent Serine Protein Kinase (CASK) gene, a risk gene for NDDs, were examined using human induced pluripotent stem cell-derived neuronal models to identify the cellular effects of these mutation consequences. CASK protein was reduced in maturing neurons from mutation carriers. Bulk RNA sequencing results revealed that the global expression of genes from presynaptic development and CASK network were downregulated in CASK-deficient neurons compared to controls. Neuronal cells influenced by CASK mutations showed a decrease of inhibitory presynapse size and changed excitatory-inhibitory (E/I) balance in developing neural circuitries. In summary, this is the first study to investigate the association of genome-wide rare and common variants with ASD intervention outcomes. Differential variant effects were found for individuals receiving SSGT or standard care. Future studies should include genetic information at different levels to improve molecular genetic testing for diagnoses and intervention plans. Presynapses and E/I imbalance could be an option to be developed for the treatment of CASK-related disorders

Autism Spectrum Disorders: from clinical identification to neuroimaging detection of brain abnormalities.

Abstract This thesis collects doctoral studies about early autism spectrum disorders (ASD) clinical identification and early ASD detection of brain magnetic resonance imaging (MRI) abnormalities. The work has been organized in five parts. In particular, the first report regards a screening population program for early ASD identification, purposely suited by Prof. Muratori and its research group and carried out by pediatricians of ASL 1 in all children of Massa-Carrara district twice, i.e. at 12 and 18 months of age. The second work is a retrospective study about growth of head circumference (HC) during the first 14 months of age, in children with ASD compared to typical developing children. Respect to anthropometric measurement of control group, courtesy provided by pediatricians Dr. Becattini and Dr. Soldateschi, children subsequently diagnosed as ASD show in the first six months of life significantly excessive growth of HC. Nevertheless, the mechanism for ASD brain enlargement remains to be elucidated and it is unknown whether brain enlargement is a cause or consequence of ASD. The third report analyzes the capacity of CBCL parent-report questionnaire to discriminate between ASD patients, subjects with other psychiatric disorders and typical children and investigates on its possible use as a ASD screening instrument for children between 18 and 60 months of ages. The fourth research is implemented in cooperation with the Natbrainlab laboratory (Institute of Psychiatry, King’s College Hospital London), directed by Dr. Marco Catani, with the aim of detecting structural connectivity differences between ASD patients and control subjects by means diffusion tensor imaging (DTI) measurements. The fifth and last study stems from the strong collaboration with the Istituto Nazionale di Fisica Nucleare (INFN) and concerns a structural MRI investigation on female children with ASD, a population poorly investigated in ASD neuroimaging studies and, for this reason, considered as “research orphan”. Riassunto Questa tesi raccoglie gli studi effettuati nel corso del dottorato riguardanti il riconoscimento clinico precoce dei disturbi dello spettro autistico (DSA) e l’identificazione precoce tramite risonanza magnetica (RM), delle anomalie cerebrali nei pazienti DSA. Il lavoro è stato organizzato in cinque parti. In particolare, il primo resoconto riguarda un programma di screening per l’identificazione precoce dei DSA, messo a punto dal Prof. Muratori e dal suo gruppo di ricerca e condotto dai pediatri di libera scelta della ASL 1 con una duplice valutazione, effettuata a 12 e a 18 mesi di vita in tutti i bambini della provincia di Massa-Carrara. Il secondo lavoro è uno studio retrospettivo sulla crescita della circonferenza cranica (CC) nei primi 14 mesi di vita in bambini con DSA confrontati con bambini con uno sviluppo tipico. Rispetto alle misure antropometriche del gruppo di controllo, cortesemente fornite dai pediatri Dott.ssa Becattini e Dott. Soldateschi, i bambini successivamente diagnosticati come DSA mostrano nei primi sei mesi di vita una crescita significativamente maggiore della CC. Tuttavia, il meccanismo alla base dell’aumento cerebrale e il suo ruolo nell’eziopatogenesi dei DSA rimangono argomenti da chiarire. La terza ricerca analizza la capacità del questionario CBCL compilato dai genitori di discriminare tra pazienti con DSA, soggetti con altri disturbi psichiatrici e bambini con sviluppo tipico e indaga inoltre il suo possibile utilizzo come strumento di screening per i DSA nei bambini di età compresa tra i 18 e i 60 mesi. Il quarto lavoro è stato progettato in collaborazione con il laboratorio Natbrainlab (Institute of Psychiatry, King’s College Hospital London), diretto dal Dott. Marco Catani, ed ha lo scopo di individuare eventuali differenze nella connettività strutturale tra i pazienti DSA e i soggetti di controllo attraverso misure derivate dall’imaging del tensore di diffusione (DTI). Il quinto e ultimo studio nasce dalla forte collaborazione con l’Istituto Nazionale di Fisica Nucleare (INFN) e riguarda un’indagine di RM strutturale focalizzata sulle bambine con DSA, una popolazione scarsamente presa in considerazione dagli studi di neuroimmagine nei DSA e pertanto considerata “research orphan”

Augmenting Structure/Function Relationship Analysis with Deep Learning for the Classification of Psychoactive Drug Activity at Class A G Protein-Coupled Receptors

G protein-coupled receptors (GPCRs) initiate intracellular signaling pathways via interaction with external stimuli. [1-5] Despite sharing similar structure and cellular mechanism, GPCRs participate in a uniquely broad range of physiological functions. [6] Due to the size and functional diversity of the GPCR family, these receptors are a major focus for pharmacological applications. [1,7] Current state-of-the-art pharmacology and toxicology research strategies rely on computational methods to efficiently design highly selective, low toxicity compounds. [9], [10] GPCR-targeting therapeutics are associated with low selectivity resulting in increased risk of adverse effects and toxicity. Psychoactive drugs that are active at Class A GPCRs used in the treatment of schizophrenia and other psychiatric disorders display promiscuous binding behavior linked to chronic toxicity and high-risk adverse effects. [16-18] We hypothesized that using a combination of physiochemical feature engineering with a feedforward neural network, predictive models can be trained for these specific GPCR subgroups that are more efficient and accurate than current state-of-the-art methods.. We combined normal mode analysis with deep learning to create a novel framework for the prediction of Class A GPCR/psychoactive drug interaction activities. Our deep learning classifier results in high classification accuracy (5-HT F1-score = 0.78; DRD F1-score = 0.93) and achieves a 45% reduction in model training time when structure-based feature selection is applied via guidance from an anisotropic network model (ANM). Additionally, we demonstrate the interpretability and application potential of our framework via evaluation of highly clinically relevant Class A GPCR/psychoactive drug interactions guided by our ANM results and deep learning predictions. Our model offers an increased range of applicability as compared to other methods due to accessible data compatibility requirements and low model complexity. While this model can be applied to a multitude of clinical applications, we have presented strong evidence for the impact of machine learning in the development of novel psychiatric therapeutics with improved safety and tolerability