Topography of brain signals in patients with obsessive compulsive disorder

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Abstracts / PsychiatryResearch: Neuroimaging 68 (1997) 155-184

inflammation which may be imaged with PET using the isotope 55-Cobalt (Co) as a Ca-tracer. Five patients with SD due to Alzheimer-disease were studied with MRI, FDG-PET, CoPET and NPT. Co-PET was performed in a dynamic mode (six frames of 10 rain) 20 _+4 hr after intravenous administration of 1 mCi sterile 55-COC12 using a Siemens ECAT-951/31 positron camera. Co-PET and MRI data were superimposed using feature-matching. Co-PET revealed evident focal uptake on the lesion-border of the hypometabolic (FDG) or atrophic (MRI) sites. Co-PET data and NPT did not correlate significantly. Severity of clinical signs correlates with Magnetic Resonance Imaging findings in Wilson's disease E. Kraft a, J. Schwarz b, C. Trenkwalder a, T. Vogl c, G. Arnold b, W.M. Oertel b

aMax-Planck-lnstitute of Psychiatry, Clinical Institute, Ne,lroradiology and Neurology, Munich. Germany. bDepartment of Neurology, Klinikum Groflhadem. L,tdwig-Maximilians-Universit'ht. Manich, Germany. CDepartment of Radiology, Klinikum lnnenstadt, Ludwig-Maximilians-Universitht, M,mich, Germany Wilson's disease is an uncommon autosomal-recessive inherited neuropsychiatric disorder. Conflicting results have been reported concerning the clinico-morphological relationship between neurologic symptoms and brain abnormalities seen on magnetic resonance imaging (MRI). Therefore, we performed clinical and MRI (1.5 T, T2-weighted images) investigations in twenty patients (mean age: 32 years, 11/9 m / f ) with Wilson's disease. We correlated the severity of clinical signs with lesions seen on MRI. Pathologic findings on MRI were recorded using a rating scale ranging from zero (absent) to three (severe). Seven criteria including atrophic changes, white matter lesions and signal changes in putamen, nucleus caudatus, pallidum, thalamus and hrainstem were assessed. Five clinical signs (dysarthria, tremor, rigidity/hypokinesia, ataxia, dystonia/chorea) were graded from zero (absent) to three (severe). Using Spearman rank correlation coefficients a significant correlation ( P < 0.001, r = 0.7923) was found between clinical status and MRI morphology. Furthermore pathologic changes within putamen, which were seen in 15 (75%) of the 20 patients, correlated significantly ( P < 0 . 0 5 ) with hypokinesia/ridigity as well as dysarthria and tremor. Our data suggest that MRI morphology is indeed closely related to clinical status. Especially basal ganglia lesions may correlate with specific neurologic deficits. Topography of brain signals in patients with obsessive compulsive disorder S. KriegeP', S. Lis", J. Timmer b, G. Winkelmann b, F. Hohagen ~

~'Centre for Paychiatn.', Justus-Liebig University, Giessen, Germany. ~Department of Psychiato', University of Freibu~, Ger-

Insight to attentional control is considered to be a precondition Ioz the understanding of cognitive disturbances in obses-

sive-compulsive disorder (OCD). In order to evaluate these processes an acoustical oddball paradigm was performed by 22 medication-free OCD patients and 22 healthy controls. EEG was measured at 21 electrode positions while stimuli were processed. From these measurements scalp potential and surface-spline-laplacian brain maps were computed. Group differences were tested by means of significance probability mapping. Patients showed greater amplitudes in the latency range of the N100 and P300 components of ERP which are considered as a sign of attentional overfocussing. Further, correlations between psychopathometric ratings and bioelectrical brain signals were calculated for the patient group. Psychopathometry consisted of the assessment of obsessivecompulsive sy~nptoms as well as depressed mood and anxiety. Topographic analysis of covariations revealed distinct relationships between all three psychopathological features as mentioned above and scalp distribution of bioelectrical brain signals in time. Spatial analysis of brain electrical activity D. Lehmann

The KEY lnstitme for Brain-Mind Research, University Hospital of Psychiatly, Zwqch, Switzerland

Analyses for disease diagnostics and treatment classifications must be distinguished from those used to elucidate brain mechanisms. Classifications need methods and parameters selected for optimal distinction between the specific target groups; on the contrary, understanding of functional mechanisms requires non-selected data and comprehensive, unbiased analyses. Functional interpretations based on successful classifiers at least need careful consideration of the involved physics. For example, locations of maximal ERP amplitude or EEG power do not directly indicate source locations, because (1) power (just as waveshape, coherency and latency) depends on the chosen reference, i.e. on two sites; and (2) electric fields possess orientations. (Orientation is not necessarily orthogonal to the scalp surface; this is the basis of MEG. However, the fact that maximal EEG power vs. the ears [a virtual reference at skull center] often is over source sites indicates that many sources indeed are orthogonal to the surface.) Momentary potential map landscapes are not affected by the reference choice, contrary to waveforms and thereby power maps. Unique values for each site are produced by spatial DC reJection ('Average Ref.') and current source density recomputation (spatial high pass, which alters the map landscapes), but these do not solve the orientation problem. Centroid locations of Av Ref power and absolute potential distributions, and maximal local gradient values (and maximal power of gradient waveshapes) are conservative indicators of mean source sites. Computational source site modeling in the time and frequency domain involves acceptance of various assumptions. Whereas classifications depend on rigid methods and statistics, functional interpretations should prefer datadriven, minimum theory approaches (example: different maps