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The thalamus is a centrally-located brain structure responsible for controlling motor activity, sleep and the awake state of consciousness. However, its most important function is relaying information from the sensory receptors to the relevant cortical areas. Damage to this part of the brain results in incorrect processing of sensory information.
Thalamic abnormalities have been found in many psychiatric diseases such as schizophrenia, obsessive-compulsive disorders, depression, Alzheimer’s and dementia. Investigating thalamic shape and volume in these and other neuropsychiatric disorders may provide valuable information regarding the nature of these illnesses. Furthermore, being able to scrutinize thalamic morphology may allow the study, when disease-modifying agents are available, of how various neuropsychiatric conditions respond to treatment.
Manual segmentation of the thalamus on MRI scans is a challenging task, due to the presence of several structures, such as the hypothalamus, associated with its inferior boundary. In order to achieve effective boundary definition, scientists from the University of Notre Dame Australia designed a new protocol using Analyze software. Through the optimization of intensity settings, for instance, enhancing the contrast between gray and white matter, or inverting them so that white matter appears dark, the investigators critically improved and facilitated thalamic segmentation and volume measurement. This advanced protocol proved to have high intra-rater reliability (similar results when repeated by the same person) and inter-rater reliability (comparable values between independent raters).
Overcoming the challenge of boundary definition related to manual segmentation is an important achievement in neuroimaging research. Results from this study indicate that by following this newly developed protocol, researchers will be able to compare thalamic morphologies and how they relate to neuropsychiatric disorders. In fact, understanding how and if shape and volume of the thalamus are correlated with clinical features is an essential piece of information. The validation of this protocol may, therefore, lead to the identification of biomarkers specific for each condition, which will lead to more efficient treatments.
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Segmentation and Volume Measurement