Temporal lobe and "default" hemodynamic brain modes discriminate between schizophrenia and bipolar disorder

Abstract

Schizophrenia and bipolar disorder are currently diagnosed on the basis of psychiatric symptoms and longitudinal course. The determination of a reliable, biologically-based diagnostic indicator of these diseases (a biomarker) could provide the groundwork for developing more rigorous tools for differential diagnosis and treatment assignment. Recently, methods have been used to identify distinct sets of brain regions or "spatial modes" exhibiting temporally coherent brain activity. Using functional magnetic resonance imaging (fMRI) data and a multivariate analysis method, independent component analysis, we combined the temporal lobe and the default modes to discriminate subjects with bipolar disorder, chronic schizophrenia, and healthy controls. Temporal lobe and default mode networks were reliably identified in all participants. Classification results on an independent set of individuals revealed an average sensitivity and specificity of 90 and 95%, respectively. The use of coherent brain networks such as the temporal lobe and default mode networks may provide a more reliable measure of disease state than task-correlated fMRI activity. A combination of two such hemodynamic brain networks shows promise as a biomarker for schizophrenia and bipolar disorder.

Figure 1

Temporal lobe and default mode components. Group average temporal lobe (top) and default mode (bottom) features, extracted from fMRI data for controls, schizophrenia patients, and bipolar patients, thresholded at P < 0.001 (corrected).

Figure 2

Task‐relatedness of temporal lobe and default mode maps. Degree to which the temporal lobe (left) and default mode (right) component time courses were associated with the presented stimuli for schizophrenia (SZ), bipolar disorder (BP), or healthy controls (HC). The temporal lobe component was positively fluctuating with the task, with healthy controls the highest, then bipolar, then schizophrenia. The default mode was negatively fluctuating with the task and showed the same ordering of absolute magnitude for the groups.

Figure 3

Classification results. A priori decision regions for three‐way classification for (a) control (dark yellow) versus noncontrol (black), (b) schizophrenia (dark pink) versus nonschizophrenia (black), and (c) bipolar (dark green) versus nonbipolar (black). The actual diagnosis of a given individual is indicated by the color of the dot where controls are yellow, schizophrenia patients are pink, and bipolar patients are green. The classification was done on an independent data set each time using a leave‐one‐out approach. Sensitivity and specificity values were quite encouraging, with an average sensitivity of 90% and an average specificity of 95%.

Figure 4

Pair‐wise comparisons of the control, schizophrenia, and bipolar groups. Two‐sample t‐tests were performed to illustrate most significant differences for each pair‐wise comparison. Note that these maps are generated from all subjects and actual classification regions will be slightly different due to the leave‐1‐out approach.

Figure 5

Consistency of ICA and GLM maps. ICA temporal lobe component (top left), ICA default mode component (middle left), and GLM target‐related (bottom left) maps created using a subset of data ranging from 49 to 167 time points. The ICA maps appear to be more consistent even for a very small subset of the data. Spatial correlation of the “best” image (that resulting from 167 time points) with all the other images (right) reveals a higher consistency than the GLM in almost all cases.