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At present, we do not have any biological tests which can contribute towards a diagnosis of depression. Neuroimaging measures have shown some potential as biomarkers for diagnosis. However, participants have generally been from the same ethnic background while the applicability of a biomarker would require replication in individuals of diverse ethnicities.
We sought to examine the diagnostic potential of the structural neuroanatomy of depression in a sample of a wide ethnic diversity.
Structural magnetic resonance imaging (MRI) scans were obtained from 23 patients with major depressive disorder in an acute depressive episode (mean age: 39.8 years) and 20 matched healthy volunteers (mean age: 38.8 years). Participants were of Asian, African and Caucasian ethnicity recruited from the general community.
Structural neuroanatomy combining white and grey matter distinguished patients from controls at the highest accuracy of 81% with the most stable pattern being at around 70%. A widespread network encompassing frontal, parietal, occipital and cerebellar regions contributed towards diagnostic classification.
These findings provide an important step in the development of potential neuroimaging-based tools for diagnosis as they demonstrate that the identification of depression is feasible within a multi-ethnic group from the community.
The widespread use of medical imaging as a means for diagnosis and monitoring of diseases, as well as for offering prognostic indicators, has enabled the collection of large imaging databases from healthy and diseased populations. Although earlier advanced imaging studies typically involved a few dozen subjects each, many current clinical research studies involve hundreds, even thousands of participants, often with multiple scans each. This has created a significant demand for quantitative and highly automated methods for population-based image analysis.
In order to be able to integrate images from different individuals, modalities, and time points, the concept of a statistical atlas (Figure 14.1) has been introduced and used extensively in the medical image analysis literature, especially in the fields of computational anatomy and statistical parametric mapping of functional brain images. An atlas reflects the spatiotemporal characteristics of certain types of images for specific populations. For example, a statistical atlas of the typical regional distribution of gray and white matter (GM, WM) in the brain can be constructed by spatially normalizing a number of brain images of healthy individuals into a common stereotaxic space, and measuring the mean and standard deviation of the amount of GM and WM in each brain region (Figure 14.2). This atlas can also be more specific, for example, as to age, gender, and other characteristics of the underlying population.
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