Classifying patients and controls using multi-dimensional scaling and exploring the metric of semantic space

Classifying patients and controls using multi-dimensional scaling and exploring the metric of semantic space Steven Andrew Chance (steven.chance@clneuro.ox.ac.uk) Neuroanatomy& Cognition Group (University of Oxford),Neuropathology, Level 1, West Wing, John Radcliffe Hospital, Oxford, OX3 9DU, UK Anthony C.D. James (tony.james@obmh.nhs.uk) Highfield family and adolescent unit, Warneford Hospital, Oxford, UK Rebecca Peet (rebecca.peet@hertford.ox.ac.uk) Department of Statistics, University of Oxford, Oxford, UK Geoffrey Nicholls (nicholls@stats.ox.ac.uk) Department of Statistics, University of Oxford, Oxford, UK Abstract Multi-dimensional scaling (MDS) has been used to visualize the organization of semantic memory in normal control subjects and in psychiatric conditions such as schizophrenia and Alzheimer’s disease. However, the potential for such techniques to classify subjects into diagnostic groups has not been realized. This study attempted to tackle this by developing classification statistics and by exploring the dimensional organization of semantic space using models with different underlying metrics. The test data were from controls and patients with early onset schizophrenia. The results indicated subtly altered semantic organization in schizophrenia, sufficient for novel classification statistics to correctly classify subjects as either patient or control with >80% accuracy. Keywords: language; semantics; schizophrenia; verbal fluency; multi-dimensional-scaling; classification. Introduction Certain conditions, such as schizophrenia and Alzheimer’s disease, are associated with disordered semantic memory (Huff et al, 1986; Phillips et al, 2004). The underlying alterations in memory organisation can be investigated using word generation tasks. Deficits include slower, disorganized generation of fewer exemplars from a semantic category. This data can then be analyzed with multidimensional scaling (MDS) methods (Rips et al, 1973) to visualize semantic networks and assess differences between patients and healthy control subjects (Chan et al, 1993). However, the refinement of these methods and application of additional data classification tools have not been sufficiently explored. The present study emulates previous studies (Chan et al, 1993; Prescott et al, 2006) by analysing animal category fluency lists from schizophrenia patients and controls. We used MDS to identify axes in the data which represent the characteristics, or dimensions, people use to categorise animals (Chan et al, 1993). Typically, the two dominant dimensions characterizing semantic space for animals have been labeled as “size” and “domesticity” (a third dimension labeled “danger” may also be useful). A goal of this study was to identify aspects of the data which could be used to classify the individual as a patient or control. A further issue to explore with MDS analysis is a possible diagnostic difference in the underlying metric of semantic space. Different metrics can be employed in MDS, probing differences in conceptual organisation (Shepard, 1987, Gardenfors, 2000). For example, various psychological spaces are usually better represented by the ‘city-block’ metric (Shepard, 1987) rather than the familiar Euclidean metric that has been used typically in MDS analyses (eg. Paulsen et al 1996). This is illustrated by a normal developmental shift. Older children and adults perceive dimensions such as high and tall, or big and bright, to be separable, whereas young children tend to confuse these concepts (Carey, 1978). This is seen as a developmental shift from a more Euclidean cognitive metric to the rigidly grid-like, separated dimensions of the city-block metric (Gardenfors, 2000). Such a shift may be disrupted in developmental disorders, particularly in early onset schizophrenia. The evidence that category boundaries are less clear in schizophrenia (beginning with Cameron, 1938) raises the prospect that the city-block metric may not provide a better fit for patients. Semantic memory abnormalities appear to be worse in patients who had an earlier onset of illness (Paulsen et al, 1996) but adolescent patients with the early form of illness have not been adequately studied. The present study considers a group of adolescent subjects who have reductions in semantic fluency (as reported in Phillips et al, 2004). It was hypothesized that schizophrenia patients would exhibit differences in network organization and word