Affective Computing in the Area of Autism

The prevalence rate of Autism Spectrum Disorders (ASD) is increasing at an alarming rate (1 in 68 children). With this increase comes the need of early diagnosis of ASD, timely intervention, and understanding the conditions that could be comorbid to ASD. Understanding co-morbid anxiety and its interaction with emotion comprehension and production in ASD is a growing and multifaceted area of research. Recognizing and producing contingent emotional expressions is a complex task, which is even more difficult for individuals with ASD. First, I investigate the arousal experienced by adolescents with ASD in a group therapy setting. In this study I identify the instances in which the physiological arousal is experienced by adolescents with ASD ( have-it ), see if the facial expressions of these adolescents indicate their arousal ( show-it ), and determine if the adolescents are self-aware of this arousal or not ( know-it ). In order to establish a relationship across these three components of emotion expression and recognition, a multi-modal approach for data collection is utilized. Machine learning techniques are used to determine whether still video images of facial expressions could be used to predict Electrodermal Activity (EDA) data. Implications for the understanding of emotion and social communication difficulties in ASD, as well as future targets for intervention, are discussed. Second, it is hypothesized that a well-designed intervention technique helps in the overall development of children with ASD by improving their level of functioning. I designed and validated a mobile-based intervention designed for teaching social skills to children with ASD. I also evaluated the social skill intervention. Last, I present the research goals behind an mHealth-based screening tool for early diagnosis of ASD in toddlers. The design purpose of this tool is to help people from low-income group, who have limited access to resources. This goal is achieved without burdening the physicians, their staff, and the insurance companies

Investigating Gaze of Children with ASD in Naturalistic Settings.

BACKGROUND: Visual behavior is known to be atypical in Autism Spectrum Disorders (ASD). Monitor-based eye-tracking studies have measured several of these atypicalities in individuals with Autism. While atypical behaviors are known to be accentuated during natural interactions, few studies have been made on gaze behavior in natural interactions. In this study we focused on i) whether the findings done in laboratory settings are also visible in a naturalistic interaction; ii) whether new atypical elements appear when studying visual behavior across the whole field of view. METHODOLOGY/PRINCIPAL FINDINGS: Ten children with ASD and ten typically developing children participated in a dyadic interaction with an experimenter administering items from the Early Social Communication Scale (ESCS). The children wore a novel head-mounted eye-tracker, measuring gaze direction and presence of faces across the child's field of view. The analysis of gaze episodes to faces revealed that children with ASD looked significantly less and for shorter lapses of time at the experimenter. The analysis of gaze patterns across the child's field of view revealed that children with ASD looked downwards and made more extensive use of their lateral field of view when exploring the environment. CONCLUSIONS/SIGNIFICANCE: The data gathered in naturalistic settings confirm findings previously obtained only in monitor-based studies. Moreover, the study allowed to observe a generalized strategy of lateral gaze in children with ASD when they were looking at the objects in their environment

The morphology of the intraparietal sulcus in children prenatally exposed to alcohol in a sample of children from the Western Cape, South Africa and its potential relationship with number processing

The intraparietal sulcus (IPS) is a prominent feature in the parietal lobe and extends posteriorly from the postcentral sulcus through the parietal lobe to end in the occipital. It is involved in visuospatial functions and is known to play a critical role in number processing. Fetal alcohol spectrum disorders (FASD) result from prenatal exposure to alcohol and are particularly prevalent in the Western Cape region of South Africa. Arithmetic is a domain of cognitive function that is particularly sensitive to prenatal alcohol exposure, and effects on arithmetic remain significant after controlling for lower IQ. Magnetic resonance imaging (MRI) was used to investigate the morphology of the IPS and whether this morphology had a relation to the number processing abilities of children prenatally exposed to alcohol in a Western Cape community. Participants were 9 to 14-year-old children from the same community in Cape Town, South Africa, who formed part of a study aimed at investigating the effects of prenatal alcohol exposure (PAE) on brain structure and function particularly during number processing. Mothers were interviewed regarding alcohol consumption during pregnancy using a timeline follow-back approach. The first analysis included designing a protocol for manually parcellating the IPS into two regions of interest (ROI): the medial wall (MIPS) and the lateral wall (LIPS) respectively. The neuroimaging program MultiTracer was used for the manual tracing and to calculate the volume of the cortex of both the MIPS and LIPS. The purpose of this first analysis was to examine the effects of PAE on IPS volume and asymmetry using manual tracing, the relation between IPS volume and number processing performance, and potential moderation by PAE of the relation between IPS volume and number processing performance. Results indicated that when comparing the FAS/PFAS (Fetal Alcohol Syndrome/Partial FAS) children to the controls, PAE had an effect on the left LIPS and higher arithmetic scores were associated with larger bilateral MIPS volumes suggesting that the effect of PAE on math may not be moderated by IPS volume. The left LIPS was significantly smaller in FAS/PFAS individuals when compared by FASD diagnosis, and this remained a trend after controlling for potential confounders. In the second analysis, the automated neuroimaging software program FreeSurfer was used to parcellate the IPS. These volumes were then compared with our previously manually traced volumes. Intra-rater reliability testing was statistically significant for consistency and absolute agreement indicating good retraceability of the designed protocol for manual tracing. Both left and right IPS volumes were significantly larger with the manually traced method compared to automated tracing. The manually traced left IPS yielded stronger results when comparing volumes by diagnostic groups, conversely the automated volumes showed stronger associations with alcohol measures. A possible explanation is that FreeSurfer parcellated the IPS differently to our protocol and does not take into account the extensive variability of the morphology of the sulcus. BrainVoyager QX, another neuroimaging software program was used in the third analysis when looking at the BOLD fMRI data of the participants. For this analysis, the manually traced MIPS and LIPS were subdivided into five ROI's for the left and right hemispheres respectively: (1) the superior MIPS, (2) the medial branch of the MIPS, (3) the inferior MIPS, (4) the superior LIPS, and (5) the inferior LIPS. The percent signal change were calculated for each participant for the proximity judgement (PJ) tasks they performed inside the scanner. Associations of the percent signal change of the ROI's of the PAE children with absolute alcohol per occasion (oz) were all significant even after controlling for IQ except the left inferior LIPS, supporting what is found in the literature. The current findings, in agreement with previous studies, demonstrate that PAE is associated with both structural and functional changes in the brain. While the morphology of the IPS may not moderate the effects of PAE on arithmetic function, some cortical volumes within the IPS were sensitive to PAE. Moreover, altered activation of the IPS in the performance of magnitude comparison tasks was strongly associated with PAE. The IPS is an extremely variable structure whose anatomy is often misunderstood, which emphasises the importance of anatomical knowledge for imaging studies. Future research will refine the protocol for manual tracing of the IPS, which may lead to greater understanding of the functions of the different areas. It is to be hoped that these findings will give more insight into understanding the functioning of children and adults with FASDs and contribute to more effective therapeutic interventions for these individuals

Machine Learning Approaches for Fine-Grained Symptom Estimation in Schizophrenia: A Comprehensive Review

Schizophrenia is a severe yet treatable mental disorder, it is diagnosed using a multitude of primary and secondary symptoms. Diagnosis and treatment for each individual depends on the severity of the symptoms, therefore there is a need for accurate, personalised assessments. However, the process can be both time-consuming and subjective; hence, there is a motivation to explore automated methods that can offer consistent diagnosis and precise symptom assessments, thereby complementing the work of healthcare practitioners. Machine Learning has demonstrated impressive capabilities across numerous domains, including medicine; the use of Machine Learning in patient assessment holds great promise for healthcare professionals and patients alike, as it can lead to more consistent and accurate symptom estimation.This survey aims to review methodologies that utilise Machine Learning for diagnosis and assessment of schizophrenia. Contrary to previous reviews that primarily focused on binary classification, this work recognises the complexity of the condition and instead, offers an overview of Machine Learning methods designed for fine-grained symptom estimation. We cover multiple modalities, namely Medical Imaging, Electroencephalograms and Audio-Visual, as the illness symptoms can manifest themselves both in a patient's pathology and behaviour. Finally, we analyse the datasets and methodologies used in the studies and identify trends, gaps as well as opportunities for future research.Comment: 19 pages, 5 figure

Current and Future Advances in Surgical Therapy for Pituitary Adenoma

The vital physiological role of the pituitary gland, alongside its proximal critical neurovascular structures means pituitary adenomas cause significant morbidity or mortality. Whilst enormous advancements have been made in the surgical care of pituitary adenomas, treatment failure and recurrence remain challenges. To meet these clinical challenges, there has been an enormous expansion of novel medical technologies (e.g. endoscopy, advanced imaging, artificial intelligence). These innovations have the potential to benefit each step of the patient journey, and ultimately, drive improved outcomes. Earlier and more accurate diagnosis addresses this in part. Analysis of novel patient data sets, such as automated facial analysis or natural language processing of medical records holds potential in achieving an earlier diagnosis. After diagnosis, treatment decision-making and planning will benefit from radiomics and multimodal machine learning models. Surgical safety and effectiveness will be transformed by smart simulation methods for trainees. Next-generation imaging techniques and augmented reality will enhance surgical planning and intraoperative navigation. Similarly, the future armamentarium of pituitary surgeons, including advanced optical devices, smart instruments and surgical robotics, will augment the surgeon's abilities. Intraoperative support to team members will benefit from a surgical data science approach, utilising machine learning analysis of operative videos to improve patient safety and orientate team members to a common workflow. Postoperatively, early detection of individuals at risk of complications and prediction of treatment failure through neural networks of multimodal datasets will support earlier intervention, safer hospital discharge, guide follow-up and adjuvant treatment decisions. Whilst advancements in pituitary surgery hold promise to enhance the quality of care, clinicians must be the gatekeepers of technological translation, ensuring systematic assessment of risk and benefit. In doing so, the synergy between these innovations can be leveraged to drive improved outcomes for patients of the future