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Concluding Remarks

  • Daniel R. Weinberger
Part of the Neurobiological Foundation of Aberrant Behaviors book series (NFAB, volume 4)

Abstract

Scavenging through dead tissue in an effort to understand how a normal mental life becomes a mental illness has challenged researchers for over a century. It has proved a daunting challenge. Postmortem brain research has progressed along with other major developments that characterized psychiatry research in its last half century. These include revolutions in descriptive psychopathology and diagnosis, in genetics, molecular biology and neuroscience, and in the development of industrial technologies for analyzing gene and protein expression. In many respects, the research results of the second half of the century echo the words of Ferraro who summed up postmortem research in the first half of the century as follows: “...we have (not) succeeded.in establishing the specific pathology of schizophrenia. We have only succeeded in establishing concomitant ...pathology in the course of the schizophrenic syndrome (Ferraro, 1952). ” There are many new findings, both anatomical and molecular, since the classic anatomical studies reviewed by Ferraro. There are still, however, no specific, diagnostic, or pathognomonic results. The traditional neuropathological definition of a brain disease, i.e. localization and characterization of “the lesion, ” has not been realized in primary psychiatric illness. It is reasonable to consider that the gross and cellular pathology of schizophrenia may never be characterizable in such classical neuropathological terms. The neuropathology of schizophrenia, rather than resulting from a specific histopathological process, may reflect combinatorial cellular responses to complex genetic programs and environmental stimuli across a lifetime.

Keywords

Mental Illness Prefrontal Cortex COMT Genotype Specific Cell Population Bioi Psychiatry 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 2002

Authors and Affiliations

  • Daniel R. Weinberger

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