Elizabeth Shephard

Simple viewing tests can detect eye movement abnormalities that distinguish schizophrenia cases from controls with exceptional accuracy

BACKGROUND We have investigated which eye-movement tests alone and combined can best discriminate schizophrenia cases from control subjects and their predictive validity. METHODS A training set of 88 schizophrenia cases and 88 controls had a range of eye movements recorded; the predictive validity of the tests was then examined on eye-movement data from 34 9-month retest cases and controls, and from 36 novel schizophrenia cases and 52 control subjects. Eye movements were recorded during smooth pursuit, fixation stability, and free-viewing tasks. Group differences on performance measures were examined by univariate and multivariate analyses. Model fitting was used to compare regression, boosted tree, and probabilistic neural network approaches. RESULTS As a group, schizophrenia cases differed from control subjects on almost all eye-movement tests, including horizontal and Lissajous pursuit, visual scanpath, and fixation stability; fixation dispersal during free viewing was the best single discriminator. Effects were stable over time, and independent of sex, medication, or cigarette smoking. A boosted tree model achieved perfect separation of the 88 training cases from 88 control subjects; its predictive validity on retest assessments and novel cases and control subjects was 87.8%. However, when we examined the whole data set of 298 assessments, a cross-validated probabilistic neural network model was superior and could discriminate all cases from controls with near perfect accuracy at 98.3%. CONCLUSIONS Simple viewing patterns can detect eye-movement abnormalities that can discriminate schizophrenia cases from control subjects with exceptional accuracy.

Mid-childhood outcomes of infant siblings at familial high-risk of Autism Spectrum Disorder

Almost 20% of infants with an older sibling with autism spectrum disorder (ASD) exhibit ASD themselves by age 3 years. The longer‐term outcomes of high‐risk infants are less clear. We examined symptoms of ASD, attention‐deficit/hyperactivity disorder (ADHD) and anxiety, language, IQ, and adaptive behaviour at age 7 years in high‐ and low‐risk children prospectively studied since the first year of life. Clinical outcomes were compared between high‐risk children who met diagnostic criteria for ASD at age 7 (HR‐ASD‐7 group, n = 15), high‐risk children without ASD (HR‐Non‐ASD‐7 group, n = 24), and low‐risk control children (LR group, n = 37). Diagnostic stability between age 3 and 7 years was moderate, with five children who did not meet diagnostic criteria for ASD at age 3 years being assigned the diagnosis at age 7, and three children showing the opposite pattern. The HR‐ASD‐7 group showed elevated ADHD and anxiety symptoms and had lower adaptive behaviour scores than LR controls. The HR‐Non‐ASD‐7 group had higher repetitive behaviour, lower adaptive functioning and elevated scores on one anxiety subscale (Separation Anxiety) compared to LR controls, but evidence for subclinical ASD symptoms (the broader autism phenotype, BAP) was limited in the group as a whole, although we identified a subgroup with elevated ASD traits. The difficulties experienced by high‐risk siblings at school‐age extend beyond ASD symptoms. The pattern of difficulties exhibited by the HR‐ASD‐7 group may inform our understanding of developmental trajectories of co‐occurring psychopathology in ASD. Autism Res 2017, 10: 546–557.

Electrophysiological correlates of reinforcement learning in young people with Tourette syndrome with and without co-occurring ADHD symptoms

Altered reinforcement learning is implicated in the causes of Tourette syndrome (TS) and attention‐deficit/hyperactivity disorder (ADHD). TS and ADHD frequently co‐occur but how this affects reinforcement learning has not been investigated. We examined the ability of young people with TS (n = 18), TS + ADHD (N = 17), ADHD (n = 13) and typically developing controls (n = 20) to learn and reverse stimulus‐response (S‐R) associations based on positive and negative reinforcement feedback. We used a 2 (TS‐yes, TS‐no) × 2 (ADHD‐yes, ADHD‐no) factorial design to assess the effects of TS, ADHD, and their interaction on behavioural (accuracy, RT) and event‐related potential (stimulus‐locked P3, feedback‐locked P2, feedback‐related negativity, FRN) indices of learning and reversing the S‐R associations. TS was associated with intact learning and reversal performance and largely typical ERP amplitudes. ADHD was associated with lower accuracy during S‐R learning and impaired reversal learning (significantly reduced accuracy and a trend for smaller P3 amplitude). The results indicate that co‐occurring ADHD symptoms impair reversal learning in TS + ADHD. The implications of these findings for behavioural tic therapies are discussed.

Learning and altering behaviours by reinforcement: neurocognitive differences between children and adults.

Highlights • Developmental differences in acquiring and adapting behaviours by reinforcement were examined.• Children and adults acquired simple new behaviours by feedback comparably.• Childrens performance was more disrupted than adults’ when adapting behaviours.• P3 ERP changes indicated children consolidated adapted behaviours less than adults.• FRN ERP changes showed children relied more on feedback than adults in adaptation.

Resting-State Neurophysiological Activity Patterns in Young People with ASD, ADHD, and ASD + ADHD.

Altered power of resting-state neurophysiological activity has been associated with autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD), which commonly co-occur. We compared resting-state neurophysiological power in children with ASD, ADHD, co-occurring ASD + ADHD, and typically developing controls. Children with ASD (ASD/ASD + ADHD) showed reduced theta and alpha power compared to children without ASD (controls/ADHD). Children with ADHD (ADHD/ASD + ADHD) displayed decreased delta power compared to children without ADHD (ASD/controls). Children with ASD + ADHD largely presented as an additive co-occurrence with deficits of both disorders, although reduced theta compared to ADHD-only and reduced delta compared to controls suggested some unique markers. Identifying specific neurophysiological profiles in ASD and ADHD may assist in characterising more homogeneous subgroups to inform treatment approaches and aetiological investigations.

Early developmental pathways to childhood symptoms of attention-deficit hyperactivity disorder, anxiety and autism spectrum disorder

Background Children with autism spectrum disorder (ASD) often have co‐occurring symptoms of attention‐deficit/hyperactivity disorder (ADHD) and/or anxiety. It is unclear whether these disorders arise from shared or distinct developmental pathways. We explored this question by testing the specificity of early‐life (infant and toddler) predictors of mid‐childhood ADHD and anxiety symptoms compared to ASD symptoms. Methods Infants (n = 104) at high and low familial risk for ASD took part in research assessments at 7, 14, 24 and 38 months, and 7 years of age. Symptoms of ASD, ADHD and anxiety were measured by parent report at age 7. Activity levels and inhibitory control, also measured by parent report, in infancy and toddlerhood were used as early‐life predictors of ADHD symptoms. Fearfulness and shyness measured in infancy and toddlerhood were used as early‐life predictors of anxiety symptoms. Correlations and path analysis models tested associations between early‐life predictors and mid‐childhood ADHD and anxiety symptoms compared to mid‐childhood ASD symptoms, and the influence of controlling for ASD symptoms on those associations. Results Increased activity levels and poor inhibitory control were correlated with ADHD symptoms and not ASD or anxiety; these associations were unchanged in path models controlling for risk‐group and ASD symptoms. Increased fearfulness and shyness were correlated with anxiety symptoms, but also ASD symptoms. When controlling for risk‐group in path analysis, the association between shyness and anxiety became nonsignificant, and when further controlling for ASD symptoms the association between fearfulness and anxiety became marginal. Conclusions The specificity of early‐life predictors to ADHD symptoms suggests early developmental pathways to ADHD might be distinct from ASD. The overlap in early‐life predictors of anxiety and ASD suggests that these disorders are difficult to differentiate early in life, which could reflect the presence of common developmental pathways or convergence in early behavioural manifestations of these disorders.

Diagnosis of Autism Spectrum Disorder after Age 5 in Children Evaluated Longitudinally Since Infancy

OBJECTIVE The diagnosis of autism spectrum disorder (ASD) has been found to be remarkably stable but few studies have followed children not initially diagnosed with ASD beyond 3 years of age to examine late or delayed diagnoses. The present study used a prospective familial-risk design to identify children who had undergone multiple comprehensive assessments in preschool and were determined to be negative for ASD only to meet criteria for ASD when tested in middle childhood. METHOD Data were pooled across 3 research teams studying later-born siblings of children with ASD. Fourteen children met inclusion criteria for the late-diagnosed group and were compared with a large sample of high- and low-risk siblings from the same sites who had ASD or typical development (TD) outcomes at 3 years of age. RESULTS As a group, the late-diagnosed children scored between the TD and ASD groups on most measures administered at 3 years and differed significantly from the ASD group on most measures. However, there was significant heterogeneity among the late-diagnosed cases. Seven showed very little evidence of ASD in preschool, whereas 7 demonstrated subtle, subthreshold symptomatology. CONCLUSION Some children with ASD might present with a subtle phenotype early in life or show a prolonged time course of symptom development. This emphasizes the need for screening and surveillance schedules that extend past 36 months and continued evaluation of any child who presents with atypical early development and/or high-risk status. The findings also shed light on reasons why the mean age of ASD diagnosis remains older than 4 years.

Anxiety and Attentional Bias to Threat in Children at Increased Familial Risk for Autism Spectrum Disorder.

Anxiety and threat bias were examined in 6-8-year-old children at familial-risk for Autism Spectrum Disorder (ASD) and low-risk (LR, n = 37) controls. The high-risk (HR) group was divided into those who met diagnostic criteria for ASD (HR-ASD, n = 15) and those who did not (HR-non ASD, n = 24). The HR-ASD group had highest levels of parent-reported anxiety. The HR-non ASD group exhibited increased threat bias on a spatial-cueing task, while the HR-ASD group did not. Anxiety symptoms were associated with both threat bias and ASD severity. These findings suggest that the mechanisms underlying anxiety in HR siblings without ASD are similar to those in non-ASD populations. However, among children with ASD, hypersensitivity to threat may not underlie anxiety symptoms.

P-513 - Specificity and characteristics of eye movement dysfunction in adult major depressive disorder

Major depressive disorder (MDD) affects at some point in their lives a tenth of the worlds population with a higher incidence in females than males. Like all clinical disorders encountered in adult psychiatry, a diagnosis of MDD is symptom-based and has not been externally validated. Eye movement dysfunctions (EMDs) in the functional psychoses have been extensively reported and their potential as biomarkers highlighted but it is unclear whether there are patterns of EMDs specific to MDD. Abnormal EMs in bipolar affective cases have been observed during face and picture viewing, saccadic control and smooth pursuit tasks. However most studies reporting EMs in affective disorders, have not distinguished between unipolar/MDD and bipolar cases. To address this problem we have compared performance on a broad range of EM tests in patients meeting DSM-IV criteria for MDD with identical measures made in a large sample of bipolar, schizophrenia and undiagnosed individuals. Remarkably a network classifier was able to delineate controls and each patient group using EM performance measures with exceptional sensitivity (94%) and specificity (98%). What is more, probability of illness category was not associated with demographic, symptom, neuropsychological or medication variables. It therefore appears that a unique multivariate eye movement phenotype may be associated with MDD. If verified in further MDD cases these findings could be an enormous advance in helping to assess and/or diagnose individuals with symptoms of MDD or at risk of developing MDD.

Perseverative eye movements in obsessive-compulsive disorder and schizophrenia

0 15 30 Time (s) leaf M2 M3 M4 M5 fly Detecting animate entities in the environment is necessary to identify and interact with predators, pray or mates (1). The percept of animacy (aliveness) can be evoked from impoverished visual displays of “biological motion”: moving objects appearing self-propelled (e.g.: point-light walkers (2), animated squares and triangles (3)). Most studies of biological motion use multi-dot displays which contain structural information (shapefrom-motion); thus, discounting the role of structural information is difficult. Use a single dot?..................................................................................................................................... 8 1. Introduction ........................................................................................................................ 9 1.1. Mirror Neurons in Monkeys ...................................................................................... 10 1.2. The Human Mirror Neuron System .......................................................................... 12 1.3. Critique on the Human Mirror Neuron System ......................................................... 13 1.4. fMRI Adaptation as a Method to Investigate Mirror Neurons .................................. 15 1.5. Aim of the Present Study .......................................................................................... 18 2. fMRI Experiment ............................................................................................................. 19 2.1. Methods ..................................................................................................................... 19 2.1.1. Participants ......................................................................................................... 19 2.1.2. Visual Stimulation ............................................................................................. 20 2.1.3. Stimuli ................................................................................................................ 20 2.1.4. Procedure and Design ........................................................................................ 22 2.1.5. Data Acquisition ................................................................................................ 23 2.1.6. Data Analysis ..................................................................................................... 23 2.2. Results ....................................................................................................................... 26 2.2.1. Localizer Runs ................................................................................................... 26 2.2.2. Experimental Runs ............................................................................................. 27 2.2.3. Cluster Analysis ................................................................................................. 28 2.2.4. Analysis of Time Courses and Parameter Estimates ......................................... 29 2.3. Discussion ................................................................................................................. 34 Cross-modal adaptation of human MNs 5 3. Psychophysical Experiment ............................................................................................. 38 3.1. Introduction ............................................................................................................... 38 3.2. Methods ..................................................................................................................... 40 3.2.1. Participants ......................................................................................................... 40 3.2.2. Visual Stimulation ............................................................................................. 40 3.2.3. Stimuli ................................................................................................................ 40 3.2.4. Procedure and Design ........................................................................................ 42 3.2.5. Data Analysis ..................................................................................................... 42 3.3. Results ....................................................................................................................... 42 3.4. Discussion ................................................................................................................. 43 4. Conclusion ....................................................................................................................... 45 5. References ........................................................................................................................ 46 6. Supplementary Figures .................................................................................................... 51 Cross-modal adaptation of human MNs 6