Functional analysis and treatment of OCD-related behaviour in a child with Autism Spectrum Disorder

Research indicates that Obsessive-Compulsive Disorder (OCD; DSM-IV-TR, American Psychiatric Association, 2000) is the second most frequent disorder to coincide with Autism Spectrum Disorder (ASD; Leyfer et aI., 2006). Excessive collecting and hoarding are also frequently reported in children with ASD (Berjerot, 2007). Although functional analysis (Iwata, Dorsey, Slifer, Bauman, & Richman, 1982/1994) has successfully identified maintaining variables for repetitive behaviours such as of bizarre vocalizations (e.g., Wilder, Masuda, O'Connor, & Baham, 2001), tics (e.g., Scotti, Schulman, & Hojnacki, 1994), and habit disorders (e.g., Woods & Miltenberger, 1996), extant literature ofOCD and functional analysis methodology is scarce (May et aI., 2008). The current studies utilized functional analysis methodology to identify the types of operant functions associated with the OCD-related hoarding behaviour of a child with ASD and examined the efficacy of function-based intervention. Results supported hypotheses of automatic and socially mediated positive reinforcement. A corresponding function-based treatment plan incorporated antecedent strategies and differential reinforcement (Deitz, 1977; Lindberg, Iwata, Kahng, and DeLeon, 1999; Reynolds, 1961). Reductions in problem behaviour were evidenced through use of a multiple baseline across behaviours design and maintained during two-month follow-up. Decreases in symptom severity were also discerned through subjective measures of treatment effectiveness.

Electrophysiological constituents of the P100 and N170 ERP complex: De-constructing of face processing using independent component analysis and robust estimation.

The initial timing of face-specific effects in event-related potentials (ERPs) is a point of contention in face processing research. Although effects during the time of the N170 are robust in the literature, inconsistent effects during the time of the P100 challenge the interpretation of the N170 as being the initial face-specific ERP effect. The interpretation of the early P100 effects are often attributed to low-level differences between face stimuli and a host of other image categories. Research using sophisticated controls for low-level stimulus characteristics (Rousselet, Husk, Bennett, & Sekuler, 2008) report robust face effects starting at around 130 ms following stimulus onset. The present study examines the independent components (ICs) of the P100 and N170 complex in the context of a minimally controlled low-level stimulus set and a clear P100 effect for faces versus houses at the scalp. Results indicate that four ICs account for the ERPs to faces and houses in the first 200ms following stimulus onset. The IC that accounts for the majority of the scalp N170 (icNla) begins dissociating stimulus conditions at approximately 130 ms, closely replicating the scalp results of Rousselet et al. (2008). The scalp effects at the time of the P100 are accounted for by two constituent ICs (icP1a and icP1b). The IC that projects the greatest voltage at the scalp during the P100 (icP1a) shows a face-minus-house effect over the period of the P100 that is less robust than the N 170 effect of icN 1 a when measured as the average of single subject differential activation robustness. The second constituent process of the P100 (icP1b), although projecting a smaller voltage to the scalp than icP1a, shows a more robust effect for the face-minus-house contrast starting prior to 100 ms following stimulus onset. Further, the effect expressed by icP1 b takes the form of a larger negative projection to medial occipital sites for houses over faces partially canceling the larger projection of icP1a, thereby enhancing the face positivity at this time. These findings have three main implications for ERP research on face processing: First, the ICs that constitute the face-minus-house P100 effect are independent from the ICs that constitute the N170 effect. This suggests that the P100 effect and the N170 effect are anatomically independent. Second, the timing of the N170 effect can be recovered from scalp ERPs that have spatio-temporally overlapping effects possibly associated with low-level stimulus characteristics. This unmixing of the EEG signals may reduce the need for highly constrained stimulus sets, a characteristic that is not always desirable for a topic that is highly coupled to ecological validity. Third, by unmixing the constituent processes of the EEG signals new analysis strategies are made available. In particular the exploration of the relationship between cortical processes over the period of the P100 and N170 ERP complex (and beyond) may provide previously unaccessible answers to questions such as: Is the face effect a special relationship between low-level and high-level processes along the visual stream?