Chapter Eight - Opportunities for Bioinformatics in the Classification of Behavior and Psychiatric Disorders
Introduction
As the final chapter in this volume on the informatics of behavior, we here, expand on the historical challenges of behavioral neuroscience to define, characterize, and classify psychiatric disorders, and elaborate on ways in which the tools and analyses of bioinformatics are able to advance behavioral investigation (Fig. 8.1). In psychiatry, classification schemes have been largely based on clusters of symptoms of seemingly related overt phenotypes. Unfortunately, for many disorders, the resulting diagnostic criteria provide poor classification with limited implications for research and therapeutics. An alarming reduction in investments in behavioral science by industry is a telling indicator of the challenges that have been faced in psychopharmacology (Brunner, Balci, & Ludvig, 2012) and calls for a pharmacologically relevant nosology have been made (Ban, 2006). Each of the preceding chapters highlights the diverse technologies and methods for multilevel data integration and large-scale data analysis that can be brought to bear in the application of bioinformatics to the intersection of behavioral neuroscience and psychiatry.
Section snippets
Brief historical overview of psychiatric classification systems
Early psychiatric classifications, similar to other “medicalized” disorders, were based on broad aggregates of undesirable and maladaptive characteristics, or “habits.” These systems essentially attributed abnormal behavior to individual responsibility. Classifications and diagnosis of psychiatric and behavioral disorders originally stem from qualitative interpretation of the maladies of individuals, tanamount to “folk psychology” (Slavney, 1992) reliant on the psychiatrists own “theory of
Challenges of understanding psychiatric disorders
Classification of psychiatric and behavioral disorders has been challenging because of the complexity and heterogeneity of the disorders. Ultimately, this results in difficulty naming and identifying discrete entities of behavioral function and presents challenges for research, diagnostics, and therapeutics. These challenges are eloquently described in a recent review of translational studies of alcohol use disorders (Crabbe, 2012).
Transforming the approach to psychiatric classification from
Finding the biological correlates of behavior
The fundamental objective and challenge of biological psychology has been to reliably map behavioral states and traits onto biological mechanisms and processes. Early philosophers could merely ponder the connections between biology, cognition, and behavior from the scant evidence presumably provided by gross injury. These unfortunate explorations eventually led to the recognition that the seat of thought and emotion belonged in the brain, not the heart. Human consciousness was considered far
Data Intensive Methods for Mapping Biological Substrate to Behavioral Function
Genomics and bioinformatics present new technologies and experimental methods for the global mapping of biological substrates onto psychological functions and characteristics. Experimental technologies have rendered it feasible to measure the abundance of tens of thousands of biological molecules, image in situ the expression of transcripts in three-dimensional space, map large numbers of human functional images onto common coordinates, and enable integration of diverse experimental data types
Conclusion: The Promise of Reconstructing Behavior Through Biology
Bioinformatics and complementary advances in high-throughput assessment of brain and behavior have delivered technologies for rapidly identifying and characterizing the role of biological systems in behavioral processes. This has enabled the discovery of new molecular targets for investigation, diagnostics, and therapeutics. While much of this work is in early stages, compelling advances are being made and translation to practice is already occurring. A major opportunity enabled by the
Acknowledgments
The authors gratefully acknowledge helpful suggestions by M. H. Haendel and funding from NIH RO1 AA18776 and The Jackson Laboratory.
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