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Approach to biological nosology
64S
Quantitative electrophysiology in psychiatry
BIOL. PSYCHIATRY 1997;42:15-2975
as novel analysis (chaotic dimensionality. Low Resolution Electromagnetic Tomography LORETA [A.D. Pasqual-Marquij. microstate analysis) of the multichannel brain electric field data. The power spectral results suggest an ectropic, state-andlor age-inadequate update of wor1
122-41 Quantitative EEG (QEEG) and psychiatric classification LS. Prichep. E.A. John. 1New York University Medical Center, Brain Research Laboratories, Dept Psychiatry. New York. NY, USA, 2 Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA The use of quantitative EEG (OEEG) has greatly enhanced the clinical utility of the EEG in psychiatric and neurological patients. Studies In large cohorts of psychiatric/neurological patients have reported significant deviations from predicted normal OEEG values. and demonstrated distinctive profiles of deviation corresponding to different disorders. The statistical significance of such abnormalities has been also been reported to Increase with clinical severity. The sensitivity and specificity of these OEEG measures has been further demonstrated through the exploration of the existence of SUbtypes within clinically homogeneous diagnostic categories. and the relationship between subtype membership and treatment outcome. Using cluster analysis we have reported the existence of subtypes within several populations of patients with DSM III-R diagnoses including: Obsessive Compulsive Disorder (OCD). Attention Deficit Hyperactivity Disorder (ADHD), Cocaine Dependence. and Schizophrenia In each of these disorders we have also demonstrated that subtype membership could be used to predict subsequent treatment outcome with high accuracy. Current developments In imaging technology which enables representation of the generators within the brain which most plausibly account for the distribution of voltages on the surface of the scalp will be used to explore the pathophysiology In such SUbtypes with differential treatment response.
122-51 Approach to biological nosology E.R. John. LS. Prichep. P. Valdes-Sosa. New York University Medical Center, Brain Research Laboratories. Dept Psychiatry. New York, NY, USA, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA Using selected quantitative EEG (OEEG) measures. evaluated relative to an age regressed normative database ("neurometrics"), multiple stepwise discriminant functions have been constructed which accurately classify many different psychiatric disorders upon Independent replications. In many of these disorders, cluster analysis based upon such measures has revealed multiple pathophysiological subtypes with differential treatment response. A large sample of patients [n = 800] in many different diagnostic categories
was assembled. Members of each category were divided into two ranctom• ized split-halves and encoded without any diagnostic Identification. USing a selected subset of neurometric OEEG variables. cluster analysis was performed in the first split-half combined sample [n =400]. Twelve clusters were Identified. The equations defining these 12 clusters were then used to classify the second split-half combined sample [n 4001. The diagnostic classifications of the patients In the two split-halves were then decoded. The correlation between the distributions of the two independent sample across the 12 clusters was r = 0.88. Members of each diagnostic category were classified into several clusters. Indicating multiple pathophysiological profiles within each symptomatically defined disorder. Each cluster contained mem• bers of multiple diagnostic categories. Indicating that different symptomaJogy could be caused by the same underlying pathophysiology. _; Low resolution electrophysiological tomography (l<>RETA) combined with spatial principal component analysis (SPCA). has been applied to these OEEG cluster profiles. In an attempt to Identify the underlying functional neuroanatomical systems.
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122-61 InQuantitative EEG reactivity to neuroleptic challenge first-eplsode and chronic schizophrenics S. Galderisi. A. Mucci. P. Bucci. M. Maj. Department of Psychiatry. First Medical School, University of Naples. Naples. Italy Ouantitative electroencephalographic (OEEG) studies have shown that schizophrenic patients with poor response to treatment with standard neu• roleptics show an increased alpha activity in their baseline OEEG recordings (Czobor and Volavka, 1991). However. according to our findings. baseline OEEG characteristics of responders and nonresponders to these dnugs show a large overlap and cannot be used to predict clinical response In a single patient (Galderisl et al.. 1994). We recently explored the possibility of discriminating responders from nonresponders by evaluating the QEEG reactiVity to a single dose of these drugs. Our results demonstrated that drug-induced changes in the slow alpha band predict clinical response to short-term treatment (Galderisl et aI.• 1994). These studies were carried out mainly in chronic schizophrenic patients. The present study was aimed at exploring OEEG reactivity to neuroleptic challenge in both chronic and first-episode patients with schizophrenia. Methods: The study has been carried out In 37 patients fulfilling DSM-IIIR criteria for schizophrenia. 13 patients were hospitaliZed during their first episode of the illness. In all subjects. at the end of a two-week wash-out period, OEEG recordings were obtained before and six hours after the ad• ministration of a single dose of a high-potency neuroleptic (either haloperidol or clopenthixol). Log transformed relative and absolute power values for eight frequency bands were evaluated. Patients were then treated with the same drug used for the OEEG challenge and prospectively followed-up. Psychopathological evaluation was carried out at baseline. as well as after one and six months (only for the first-episode patients) of treatment using Andreasen's seeles for positive and negative symptoms (SAPS and SANS respectively). A patient was considered responder when a reduction of least 50% of the total score on SAPS + SANS was observed. Results: The comparison of first-episode and chronic patients on base• line OEEG characteristics did not show any significant difference. Changes Induced by the drug In theta2 and beta2 bands showed a significant COr• relation with the duration of illness and were more diffuse In first-eplsOde patients. However, these changes did not show any relationship with clinical response. In both patient groups, responders and nonresponders presented opposite drug-induced changes In the slow alpha band. In fact, resPOnders presented a significant Increase of slow alpha while nonresponders shoWed a reduction of the same activity. Drug-induced changes of the slow alpha significantly correlated with changes of psychopathological scores, but not with duration of the illness. A discriminant analysis carned out on slow alpha changes only In chronic patients correctly classified the majority of first• episode cases as responders or nonresponders. We also found that QEEG reactivity In the slow alpha band predicts clinical response to long-term (six months) treatment.
at
References [1) Czobor P. Volavke J (1991): Pretreatment EEG predicts short-term nlSflOnae to haloperldol treatment. Biological Psychiatry 30; 927-942. (2) Galderlsl S, Mal M, Mucci A. BuccI P, and Kemall 0 (1994): QEEG alphal Change. alter asingle dose of high-potency neuroleptics as a predictor 01 short-term nl~ to treatment In schizophrenic patients. Biological Psychiatry 35: 367-374. [3] Hegarty JD, Baldessarinl RJ. Tohan M. Watemaux C, Oepen G(1994): On. hundred yeara schizophrenia: a rneta-analysls 01111. outcome IRerature. American Joum./
0'
ofPsychialry151: 1409-1416.
Quantitative electrophysiology in psychiatry
BIOL. PSYCHIATRY 1997;42:15-2975
as novel analysis (chaotic dimensionality. Low Resolution Electromagnetic Tomography LORETA [A.D. Pasqual-Marquij. microstate analysis) of the multichannel brain electric field data. The power spectral results suggest an ectropic, state-andlor age-inadequate update of wor1
122-41 Quantitative EEG (QEEG) and psychiatric classification LS. Prichep. E.A. John. 1New York University Medical Center, Brain Research Laboratories, Dept Psychiatry. New York. NY, USA, 2 Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA The use of quantitative EEG (OEEG) has greatly enhanced the clinical utility of the EEG in psychiatric and neurological patients. Studies In large cohorts of psychiatric/neurological patients have reported significant deviations from predicted normal OEEG values. and demonstrated distinctive profiles of deviation corresponding to different disorders. The statistical significance of such abnormalities has been also been reported to Increase with clinical severity. The sensitivity and specificity of these OEEG measures has been further demonstrated through the exploration of the existence of SUbtypes within clinically homogeneous diagnostic categories. and the relationship between subtype membership and treatment outcome. Using cluster analysis we have reported the existence of subtypes within several populations of patients with DSM III-R diagnoses including: Obsessive Compulsive Disorder (OCD). Attention Deficit Hyperactivity Disorder (ADHD), Cocaine Dependence. and Schizophrenia In each of these disorders we have also demonstrated that subtype membership could be used to predict subsequent treatment outcome with high accuracy. Current developments In imaging technology which enables representation of the generators within the brain which most plausibly account for the distribution of voltages on the surface of the scalp will be used to explore the pathophysiology In such SUbtypes with differential treatment response.
122-51 Approach to biological nosology E.R. John. LS. Prichep. P. Valdes-Sosa. New York University Medical Center, Brain Research Laboratories. Dept Psychiatry. New York, NY, USA, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA Using selected quantitative EEG (OEEG) measures. evaluated relative to an age regressed normative database ("neurometrics"), multiple stepwise discriminant functions have been constructed which accurately classify many different psychiatric disorders upon Independent replications. In many of these disorders, cluster analysis based upon such measures has revealed multiple pathophysiological subtypes with differential treatment response. A large sample of patients [n = 800] in many different diagnostic categories
was assembled. Members of each category were divided into two ranctom• ized split-halves and encoded without any diagnostic Identification. USing a selected subset of neurometric OEEG variables. cluster analysis was performed in the first split-half combined sample [n =400]. Twelve clusters were Identified. The equations defining these 12 clusters were then used to classify the second split-half combined sample [n 4001. The diagnostic classifications of the patients In the two split-halves were then decoded. The correlation between the distributions of the two independent sample across the 12 clusters was r = 0.88. Members of each diagnostic category were classified into several clusters. Indicating multiple pathophysiological profiles within each symptomatically defined disorder. Each cluster contained mem• bers of multiple diagnostic categories. Indicating that different symptomaJogy could be caused by the same underlying pathophysiology. _; Low resolution electrophysiological tomography (l<>RETA) combined with spatial principal component analysis (SPCA). has been applied to these OEEG cluster profiles. In an attempt to Identify the underlying functional neuroanatomical systems.
=
122-61 InQuantitative EEG reactivity to neuroleptic challenge first-eplsode and chronic schizophrenics S. Galderisi. A. Mucci. P. Bucci. M. Maj. Department of Psychiatry. First Medical School, University of Naples. Naples. Italy Ouantitative electroencephalographic (OEEG) studies have shown that schizophrenic patients with poor response to treatment with standard neu• roleptics show an increased alpha activity in their baseline OEEG recordings (Czobor and Volavka, 1991). However. according to our findings. baseline OEEG characteristics of responders and nonresponders to these dnugs show a large overlap and cannot be used to predict clinical response In a single patient (Galderisl et al.. 1994). We recently explored the possibility of discriminating responders from nonresponders by evaluating the QEEG reactiVity to a single dose of these drugs. Our results demonstrated that drug-induced changes in the slow alpha band predict clinical response to short-term treatment (Galderisl et aI.• 1994). These studies were carried out mainly in chronic schizophrenic patients. The present study was aimed at exploring OEEG reactivity to neuroleptic challenge in both chronic and first-episode patients with schizophrenia. Methods: The study has been carried out In 37 patients fulfilling DSM-IIIR criteria for schizophrenia. 13 patients were hospitaliZed during their first episode of the illness. In all subjects. at the end of a two-week wash-out period, OEEG recordings were obtained before and six hours after the ad• ministration of a single dose of a high-potency neuroleptic (either haloperidol or clopenthixol). Log transformed relative and absolute power values for eight frequency bands were evaluated. Patients were then treated with the same drug used for the OEEG challenge and prospectively followed-up. Psychopathological evaluation was carried out at baseline. as well as after one and six months (only for the first-episode patients) of treatment using Andreasen's seeles for positive and negative symptoms (SAPS and SANS respectively). A patient was considered responder when a reduction of least 50% of the total score on SAPS + SANS was observed. Results: The comparison of first-episode and chronic patients on base• line OEEG characteristics did not show any significant difference. Changes Induced by the drug In theta2 and beta2 bands showed a significant COr• relation with the duration of illness and were more diffuse In first-eplsOde patients. However, these changes did not show any relationship with clinical response. In both patient groups, responders and nonresponders presented opposite drug-induced changes In the slow alpha band. In fact, resPOnders presented a significant Increase of slow alpha while nonresponders shoWed a reduction of the same activity. Drug-induced changes of the slow alpha significantly correlated with changes of psychopathological scores, but not with duration of the illness. A discriminant analysis carned out on slow alpha changes only In chronic patients correctly classified the majority of first• episode cases as responders or nonresponders. We also found that QEEG reactivity In the slow alpha band predicts clinical response to long-term (six months) treatment.
at
References [1) Czobor P. Volavke J (1991): Pretreatment EEG predicts short-term nlSflOnae to haloperldol treatment. Biological Psychiatry 30; 927-942. (2) Galderlsl S, Mal M, Mucci A. BuccI P, and Kemall 0 (1994): QEEG alphal Change. alter asingle dose of high-potency neuroleptics as a predictor 01 short-term nl~ to treatment In schizophrenic patients. Biological Psychiatry 35: 367-374. [3] Hegarty JD, Baldessarinl RJ. Tohan M. Watemaux C, Oepen G(1994): On. hundred yeara schizophrenia: a rneta-analysls 01111. outcome IRerature. American Joum./
0'
ofPsychialry151: 1409-1416.