TY  - JOUR
T1  - INtegrating neurobiological markers of depression
AU  - Hahn T, Marquand AF, Ehlis A, et al
Y1  - 2010/12/06
N1  - 10.1001/archgenpsychiatry.2010.178
JO  - Archives of General Psychiatry
SP  - 361
EP  - 368
VL  - 68
IS  - 4
N2  - 
Simultaneously, the development and application of powerful whole-brain pattern classification algorithms has brought single-subject classification based on neurobiological markers within reach. These procedures furnish predictions based on spatial or spatiotemporal patterns within the data while also making use of information encoded by correlations between brain regions.2 It is this multivariate nature of pattern-recognition algorithms that leads to increased sensitivity over univariate methods.3- 4 Generally, pattern recognition is a field within the area of machine learning that is concerned with the automatic discovery of regularities in data through the use of computer algorithms. Using these regularities, a computer can classify data into different categories.5 In the context of neuroimaging, brain images are treated as spatial patterns and pattern-recognition approaches are used to identify statistical properties of the data that discriminate between 2 groups of subjects (eg, patients and controls) or 2 cognitive tasks. A classifier based on pattern recognition is trained by providing examples of the form < x, c>, where x represents a spatial pattern and c is the class label (eg, c = +1 for patients and c = −1 for controls). Each spatial pattern (eg, whole-brain image) corresponds to a point in the input space, and each voxel in the brain image represents 1 dimension of this space. During the training phase, the pattern-recognition algorithm finds a decision function that separates the examples in the input space according to the class label. Once the decision function is determined from the training data, it can be used to predict the class label of a new test example. There are different approaches to determine the decision function depending on the learning method used. Generally, it is important to have a decision function that classifies both the training data and the test data correctly. In this regard, gaussian process (GP) classifiers, recently introduced in the field of neuroimaging, have consistently shown high levels of performance.3

SN  - 0003-990X
M3  - doi: 10.1001/archgenpsychiatry.2010.178
UR  - http://dx.doi.org/10.1001/archgenpsychiatry.2010.178
ER  -