Schizophrenia as a Network Disease: Disruption of Emergent Brain Function in Patients with Auditory Hallucinations

<div><p>Schizophrenia is a psychiatric disorder that has eluded characterization in terms of local abnormalities of brain activity, and is hypothesized to affect the collective, “emergent” working of the brain. Indeed, several recent publications have demonstrated that functional networks in the schizophrenic brain display disrupted topological properties. However, is it possible to explain such abnormalities just by alteration of local activation patterns? This work suggests a negative answer to this question, demonstrating that significant disruption of the topological and spatial structure of functional MRI networks in schizophrenia (a) cannot be explained by a disruption to area-based task-dependent responses, i.e. indeed relates to the emergent properties, (b) is global in nature, affecting most dramatically long-distance correlations, and (c) can be leveraged to achieve high classification accuracy (93%) when discriminating between schizophrenic vs control subjects based just on a single fMRI experiment using a simple auditory task. While the prior work on schizophrenia networks has been primarily focused on discovering statistically significant differences in network properties, this work extends the prior art by exploring the generalization (prediction) ability of network models for schizophrenia, which is not necessarily captured by such significance tests.</p></div

Stability of feature subset selection over cross-validation (CV) folds.

<p>Stability is measured as the percent of voxels in common among the subsets of <i>k</i> top variables selected at all CV folds: (a) activations and degrees; (b,c) edge weights (correlations), clustering coefficients, strength, absolute strength, positive strength, and local efficiency: (b) linear scale on x-axis, (c) log-scale on x-axis (focusing on small number of features selected.</p

Area-to-area functional connections determined by the 9 more stable links.

<p>Area-to-area functional connections determined by the 9 more stable links.</p

Two-sample t-test results for different features: voxels surviving FDR correction.

<p>(a) Normalized degree maps; (b) strength (red-yellow), absolute strength (blue-light blue) and positive strength (black-white); (c) clustering coefficient and local efficiency maps. Here the null hypothesis at each voxel assumes no difference between the schizophrenic vs normal groups. Colored areas denotes low p-values passing FDR correction at level (i.e., 5% false-positive rate). Note that the mean (normalized) degree at highlighted voxels was always (significantly) <i>higher</i> for normals than for schizophrenics. Coordinates of the center of the image: (a) and (c) X = 26,Y = 30,Z = 16, (b) X = 26,Y = 30,Z = 18.xl.</p

Experimental paradigm.

<p>Experimental paradigm.</p

Areas corresponding to the nodes on the 9 most stable links.

<p>Areas corresponding to the nodes on the 9 most stable links.</p

Functional connectivity disruption in schizophrenic subjects vs controls.

<p>(a) Probability of finding a network link as a function of the Euclidean distance between the nodes (in millimeters): schizophrenics (red) show reduced connectivity than controls (blue) for distances in the middle range (10 to 150 mm). (b) Disruption of <i>global</i> inter-hemispheric connectivity. For each subject, we compute the fraction of links spanning both hemispheres over the total number of links, and plot a normalized histogram over all subjects in each group (normal - blue, schizophrenic - red). (c) Disruption of <i>task-dependent</i> inter-hemispheric connectivity between specific ROIs (Brodmann Area 22 selected bilaterally). The ROIs were defined by a 9 mm radius ball centered at [x = −42, y = −24, z = 3] and [x = 42, y = −24, z = 3]. For each subject, we compute the fraction of links connecting the bilateral ROIs over all links, and show a histogram of this connectivity measure over all subjects in each group. The histograms are similarly normalized.</p

9 stable edges common to all subsets of 30 top-ranked (lowest-pvalue) edges that survived Bonferroni correction, over 22 different cross-validation folds (leave-subject-out data subsets).

<p>(a) All views and (b) enlarged saggital view. Edge density is proportional to their absolute value. The network includes several areas not picked up by the degree maps, i.e. other than BA 22 and BA 21, mainly the cerebellum (declive) and the occipital cortex (BA 19).</p

Classification results: degree vs. activation features.

<p>Three classifiers, Gaussian Naive Bayes (GNB) in panel (a), SVM in panel (b) and sparse MRF in panel (c) are compared on two types of features, degrees and activation contrasts; (d) all three classifiers compared on long-distance degree maps (best-performing for MRF).</p

Classification errors using (a) global features and (b) activation and degree maps (using SVM on the complete set of voxels (i.e., without voxel subset selection).

<p>Classification errors using (a) global features and (b) activation and degree maps (using SVM on the complete set of voxels (i.e., without voxel subset selection).</p