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ERP variables as a method for a biologically-based classification system in psychiatry
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QEEG/ERP and PET 2-DG profiles of 5 subtypes of schizophrenic patients John, E.R. 1'2, Prichep, L.S. 1'2, Almas, M. t, Zhang, Z. t'2 and Brodie, j.1 1New York University Medical Center, Dept of Psychiatry, New York, NY, U.S.A. 2Nathan S. Kline Psychiatric Research Institute, Orangeburg, NY, U.S.A.
QEEG data were obtained from 99 medicated schizophrenic patients, and 37 prior to medication. Evoked potentials were obtained in 43 of these patients after and from 18 prior to medication. Gluster analysis revealed 6 subtypes, with different neurometric (Nx) profiles. Positron emission tomography (PET) was used to study 2-1Sdeoxyglucose metabolism in 45 unmedicated schizophrenics. After size and shape normalization, these PET scans were subjected to pixel by pixel Ztransformation relative to our large normative PET database. A group of 6 judges separated these PET Z-images into 6 clusters. No clinical features distinguished these Nx or PET clusters from each other. Both Nx and PET data were available for 8 patients after medication, 5 of whom were also studied prior to medication. These patients fell into 5 of the six clusters. Multimodal data from these 5 clusters will be presented. Some salient f'mdings include: 1) Hyper-reactive 'spiky' evoked potentials were found in brain regions which showed marked excesses of relative power in the beta band. Such regions tended to be hyper metabolic. 2) Three of the PET clusters shared the characteristic of hypo-metabolism in the cortex combined with hyper-metabolism in the basal ganglia. Anomalously, schizophrenic patients in each of these clusters displayed a diffuse excess of absolute power in most or all frequency bands. Eiectrophysiological, metabolic and clinical responses to treatment of these patients will be described and the theoretical implications discussed.
Cluster analysis of QEEG/ERP variables as a method for a biologically-based classification system in psychiatry
C a n c r o , R.,
U.S.A.
The traditional distinction between nosology and classification of disease is useful to remember. A nosology is an organization of disease on the basis of some arbitrary set of criteria. A classification is a scientifically-based organization of disease that derives from the presence of some underlying causal factor. Clinical nosology based on phenomenology was helpful in the management of infectious disease at the end of the 19th century. With the advent of the era of microbiology, the nosology of infectious disease rapidly became displaced by a classification based on etiology and pathogenesis. The members of the groupings created by the nosology of infectious disease and the members of the groupings contained within the classification of infectious disease were overlapping but not identical. This movement, however, from nosology to classification was of vital importance for the progress of clinical medicine. Psychiatry at the end of the 20th century is at precisely the same point in its evolution as infectious disease was at the end of the 19th century. We do not have a classification of disease but merely a nosology based on phenomenology. It has
188
been shown by a number of groups that DSM-IIIR categories yield consistent and reproducible findings in the quantitative EEG. It has also been shown recently that a number of the DSM-IIIR groupings can be consistently subdivided into subtypes on the basis of their differing QEEG f'mdings. Furthermore, results are emerging that show these subtypes may have differential utility in therapeutics. The correlation between QEEG findings and DSM-IIIR nosology does not establish causality. Actually, such findings simply show that knowledge of a nosology category can improve our ability to predict the fmdings on the electroencephalogram. Conversely, a knowledge of the findings on the encephalogram can improve our prediction of the DSM-IIIR category. These findings establish that the nosology of psychiatry contains some features that can lead to a classification of psychiatric disease. While this is of great importance, it is of limited use. The modest proposal being offered is to move away from clinical nosology and to utilize physiologic measures, such as the QEEG, as the basis for a classification of psychiatric disease. An obvious method would be to take a large population of patients who are studied electrophysiologicallyand clinically before the administration of routine treatments. One can then use these data in cluster analysis to construct groupings independent of clinical diagnosis. These patients should be followed over a period of time so that outcome data become available. One can seek the clinical correlations to these groupings. Such an endeavor must yield a classification system that has both scientific and phenomenologic utility. Cleady, a procedure that bases the classification of disease more closely upon its physiologic markers will improve the ability to treat selectively and appropriately. Our laboraties have developed promising evidence that the proposed approach may provide worthwhile contributions to a biologically based classification alternative to our present nosological dependence.
QEEG/ERP and PET 2-DG profiles of 5 subtypes of schizophrenic patients John, E.R. 1'2, Prichep, L.S. 1'2, Almas, M. t, Zhang, Z. t'2 and Brodie, j.1 1New York University Medical Center, Dept of Psychiatry, New York, NY, U.S.A. 2Nathan S. Kline Psychiatric Research Institute, Orangeburg, NY, U.S.A.
QEEG data were obtained from 99 medicated schizophrenic patients, and 37 prior to medication. Evoked potentials were obtained in 43 of these patients after and from 18 prior to medication. Gluster analysis revealed 6 subtypes, with different neurometric (Nx) profiles. Positron emission tomography (PET) was used to study 2-1Sdeoxyglucose metabolism in 45 unmedicated schizophrenics. After size and shape normalization, these PET scans were subjected to pixel by pixel Ztransformation relative to our large normative PET database. A group of 6 judges separated these PET Z-images into 6 clusters. No clinical features distinguished these Nx or PET clusters from each other. Both Nx and PET data were available for 8 patients after medication, 5 of whom were also studied prior to medication. These patients fell into 5 of the six clusters. Multimodal data from these 5 clusters will be presented. Some salient f'mdings include: 1) Hyper-reactive 'spiky' evoked potentials were found in brain regions which showed marked excesses of relative power in the beta band. Such regions tended to be hyper metabolic. 2) Three of the PET clusters shared the characteristic of hypo-metabolism in the cortex combined with hyper-metabolism in the basal ganglia. Anomalously, schizophrenic patients in each of these clusters displayed a diffuse excess of absolute power in most or all frequency bands. Eiectrophysiological, metabolic and clinical responses to treatment of these patients will be described and the theoretical implications discussed.
Cluster analysis of QEEG/ERP variables as a method for a biologically-based classification system in psychiatry
C a n c r o , R.,
U.S.A.
The traditional distinction between nosology and classification of disease is useful to remember. A nosology is an organization of disease on the basis of some arbitrary set of criteria. A classification is a scientifically-based organization of disease that derives from the presence of some underlying causal factor. Clinical nosology based on phenomenology was helpful in the management of infectious disease at the end of the 19th century. With the advent of the era of microbiology, the nosology of infectious disease rapidly became displaced by a classification based on etiology and pathogenesis. The members of the groupings created by the nosology of infectious disease and the members of the groupings contained within the classification of infectious disease were overlapping but not identical. This movement, however, from nosology to classification was of vital importance for the progress of clinical medicine. Psychiatry at the end of the 20th century is at precisely the same point in its evolution as infectious disease was at the end of the 19th century. We do not have a classification of disease but merely a nosology based on phenomenology. It has
188
been shown by a number of groups that DSM-IIIR categories yield consistent and reproducible findings in the quantitative EEG. It has also been shown recently that a number of the DSM-IIIR groupings can be consistently subdivided into subtypes on the basis of their differing QEEG f'mdings. Furthermore, results are emerging that show these subtypes may have differential utility in therapeutics. The correlation between QEEG findings and DSM-IIIR nosology does not establish causality. Actually, such findings simply show that knowledge of a nosology category can improve our ability to predict the fmdings on the electroencephalogram. Conversely, a knowledge of the findings on the encephalogram can improve our prediction of the DSM-IIIR category. These findings establish that the nosology of psychiatry contains some features that can lead to a classification of psychiatric disease. While this is of great importance, it is of limited use. The modest proposal being offered is to move away from clinical nosology and to utilize physiologic measures, such as the QEEG, as the basis for a classification of psychiatric disease. An obvious method would be to take a large population of patients who are studied electrophysiologicallyand clinically before the administration of routine treatments. One can then use these data in cluster analysis to construct groupings independent of clinical diagnosis. These patients should be followed over a period of time so that outcome data become available. One can seek the clinical correlations to these groupings. Such an endeavor must yield a classification system that has both scientific and phenomenologic utility. Cleady, a procedure that bases the classification of disease more closely upon its physiologic markers will improve the ability to treat selectively and appropriately. Our laboraties have developed promising evidence that the proposed approach may provide worthwhile contributions to a biologically based classification alternative to our present nosological dependence.