Sara A. Beedie

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.

Smooth pursuit and visual scanpaths: Independence of two candidate oculomotor risk markers for schizophrenia

Abstract Objectives. Smooth pursuit and visual scanpath deficits are candidate trait markers for schizophrenia. It is not clear whether eye tracking dysfunction (ETD) and atypical scanpath behaviour are the product of the same underlying neurobiological processes. We have examined co-occurrence of ETD and scanpath disturbance in individuals with schizophrenia and healthy volunteers. Methods. Eye movements of individuals with schizophrenia (N = 96) and non-clinical age-matched comparison participants (N = 100) were recorded using non-invasive infrared oculography during smooth pursuit in both predictable (horizontal sinusoid) and less predictable (Lissajous sinusoid) conditions and a free viewing scanpath task. Results. Individuals with schizophrenia demonstrated scanning deficits in both tasks. There was no association between performance measures of smooth pursuit and scene scanpaths in patient or control groups. Odds ratios comparing the likelihood of scanpath dysfunction when ETD was present, and the likelihood of finding scanpath dysfunction when ETD was absent were not significant in patients or controls in either pursuit variant, suggesting that ETD and scanpath dysfunction are independent anomalies in schizophrenia. Conclusion. ETD and scanpath disturbance appear to reflect independent oculomotor or neurocognitive deficits in schizophrenia. Each task may confer unique information about the pathophysiology of psychosis.

P03-124 - Frontal brain function and visual exploration of natural scenes in schizophrenia

Scanpaths are the patterns of ocular fixations and saccades produced during visual exploration of a scene. Schizophrenia is associated with a restricted style of visual scanning, characterised by fewer fixations and saccades and shorter scanpath lengths compared with well viewers. Such patterns are reflective of chronic dysfunction in real-world visual processing in schizophrenia. Scanpath measures are also emerging as strong discriminatory tools in schizophrenia trait marker research. However, little is understood about the neurocognitive mechanisms underlying atypical viewing patterns. We conducted an exploratory study of the relationships between patterns of visual exploration and neuropsychological test performance in individuals with schizophrenia to further understand the neural substrates of scanpath abnormalities in this group. Fifty-one individuals meeting DSM-IV criteria for schizophrenia completed a range of neuropsychological tests and viewed a battery of static natural scenes while eye movements were recorded using non-invasive infra-red oculography. Restricted scanpath behaviours were unrelated to measures of sustained attention, visual memory, cognitive interference or general cognitive decline. Restricted scanpath patterns were most strongly associated with short term verbal memory, manipulation of information in working memory and verbal fluency performance. Such functions are unlikely to directly impinge on visual exploration in schizophrenia but may be commensurate with dysfunction of the dorsolateral prefrontal cortex, a region known to play a number of key roles in oculomotor control. The results support a role of frontal brain dysfunction in the formation and execution of viewing behaviours in schizophrenia.

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.

P-1213 - The continuum hypothesis: multidimensional eye movement phenotypes in nosology and taxonomy

Clinical diagnosis currently requires fitting symptoms to DSM, ICD or RDC criteria and assumes face validity of the presentation and medical history. Clinical signs and symptoms can overlap illness categories, and the recent genetic evidence of biological overlap between major illnesses such as schizophrenia and bipolar disorder lends significant support to the case for refinement of classification systems. We recruited 222 individuals who had DSM-IV schizophrenia or mood disorders (bipolar, unipolar/MDD, schizo-affective) confirmed with OpCrit, and 208 diagnosis-free volunteers as controls. Eye movement function was assessed using pursuit, fixation and static picture viewing tests; extensive demographic, neuropsychological and state measures were also obtained for each person. The natural boundaries between illness types were probed using supervised and unsupervised machine learning using eye movement information alone. Respectively, a probabilistic network delineated case groups and controls according to current nosological practice with exceptional accuracy while a Bayesian approach described a taxonomic geometry consistent with a trait continuum. Taxonomies derived from eye movement measures reveal a multidimensional space spanning naturally occurring clusters of ‘normal’, subclinical and abnormal clinical traits. Dimensionality reduction to simplify interpretation of cluster shape, orientation and fuzziness provides an index of phenotype heterogeneity. These results let us understand various psychiatric eye movement phenotypes hierarchically and at multiple scales and support the idea of a psychometric continuum along non-linear dimensions populated by many subtypes of people. We now need to study how clinical symptoms map onto this space.

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

P01-398 - A classification model of schizophrenia using a multivariate eye movement phenotype

Eye movements are used as an index of neural pathways and mental processes active during visual tasks. The atypical or dysfunctional eye movements observed in the schizophrenia spectrum of disorders are manifest because of abnormal neurodevelopment of particular features of those processes. Schizophrenia patients’ eye movements often also mirror the inflexible or perseverative cognitive style associated with the illness. So far no single test of oculomotor function has emerged as a marker for schizophrenia. For example, not all patients show deficits in sustained smooth pursuit ability, and a significant proportion of unaffected controls are prone to the same errors made by patients on identical tasks. The same is true during pro- and anti-saccade tasks. This poses a significant challenge for statistical models. We assessed the power of combined tests in discriminating cases from controls. Eye tracking was recorded in 95 out-patients with a diagnosis of paranoid schizophrenia and 88 unaffected, age-matched controls. Participants performed smooth pursuit, visual exploration and fixation tasks. A decision tree was trained using only eye movement measures from each task to account for performance variation in both groups. The model perfectly distinguished between cases and controls. Additional data from 31 re-test sessions and 70 new observers were also scored. Overall accuracy was 89%. It was possible to unambiguously account for individuals who were misclassified. The discriminatory power of the multivariate model is very impressive. Covariates did not improve classification. The model can be extended to include other illnesses to examine the relationship between psychophysiology and nosology.