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6 - Structural imaging of bipolar illness

from Section II - Mood Disorders

Published online by Cambridge University Press:  10 January 2011

Stephen M. Strakowski
Affiliation:
Department of Psychiatry University of Cincinnati Cincinnati, OH, USA
Martha E. Shenton
Affiliation:
VA Boston Healthcare System and Brigham and Women's Hospital, Harvard Medical School
Bruce I. Turetsky
Affiliation:
University of Pennsylvania
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Summary

Introduction

Bipolar disorder is a common psychiatric condition, affecting up to 3% of the world's population (Angst,1998; Narrow et al., 2002), and it is the sixth leading cause of disability worldwide (Murray and Lopez, 1996). Although bipolar disorder is defined by the occurrence of mania (type I disorder) or hypomania (type 2 disorder), in fact, the symptoms of bipolar disorder include affective instability, neurovegetative abnormalities, cognitive impairments, psychosis, and impulsivity. The likely neural basis of these symptoms, based on neuroimaging and other studies, has produced models of bipolar disorder that focus on dysfunction within so-called anterior limbic brain networks (Strakowski et al., 2004, 2005, 2007; Adler et al., 2006a; Brambilla et al., 2005). These networks consist of iterative prefrontal–striatal–pallidal–thalamic circuits that are modulated by amygdala and other limbic structures to direct social and emotional behaviors. An example of one such anterior limbic network model is provided in Figure 6.1 and is based on work that has been reviewed previously (Strakowski et al., 2005, 2007; Adler et al., 2006a). Indeed, recent advances in neuroimaging techniques, particularly those based on magnetic resonance imaging (MRI) and spectroscopy, have revolutionized the study of bipolar neurophysiology, leading to a proliferation of studies attempting to clarify the neural substrates of bipolar disorder.

One approach toward evaluating and extending functional neuroanatomic models of bipolar disorder is to use brain imaging to determine whether structural brain abnormalities within relevant networks can be identified.

Type
Chapter
Information
Understanding Neuropsychiatric Disorders
Insights from Neuroimaging
, pp. 93 - 108
Publisher: Cambridge University Press
Print publication year: 2010

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