Frequency domains of resting state default mode network activity in schizophrenia

Introduction:

Recent studies have demonstrated altered low-frequency BOLD signal fluctuations during resting state (RS) in schizophrenia. It is unclear whether this alteration relates to DMN dysfunction. Here, we analyzed the power for different frequency bands from DMN time series extracted using a probabilistic independent component analysis (pICA) of fMRI data, in order to test the hypothesis of altered frequency power in the DMN under RS conditions.

Methods:

We obtained RS fMRI series (3T, 3 × 3x3 mm resolution, 45 slices, TR 2.55 s, 210 volumes) in 25 schizophrenia patients (mean age 30a ± 7.3), on stable antipsychotic medication and 25 matched healthy controls (30.3a ± 8.6). Subjects were asked to lie in the scanner keeping eyes closed with no further specific instructions. Data were pre-processed using SPM5 (motion correction, co-registration, normalization and smoothing). Band pass (0.009 – 0.18 Hz) frequency filters were applied. We applied FSL MELODIC (pICA) yielding 30 IC, and an automated routine to select for each subject the component matching the anatomical DMN definition. We then analyzed the frequency domains for this extracted DMN, estimating the power of a signal at different frequencies. The time course associated with each individual's DMN component was transformed from the time domain to the frequency domain using Welch's method. For this purpose we used pwelch, a Matlab signal processing toolbox. Results: We found a significant diagnosis x frequency interaction (F(11, 38)= 2.484, p = 0.019). Comparison of the frequency bins between groups showed that the schizophrenia group exhibited significantly higher spectral power than controls at frequencies around 0.0784 Hz (F = 5.938, p = 0.019) and 0.1725 Hz (F = 5.463, p = 0.024).

Conclusions:

Our results demonstrate that at least a part of the low-frequency alterations found in schizophrenia can be specifically attributed to DMN dysfunction, unrelated to cardiac or breathing artefacts, and task-driven cognitive activity. While the power differences in our results appeared to be rather specific to particular frequency bands, the role of these remains unclear and will need further investigations.

Fig. 1: Bar diagrams of power in different frequency ranges (“bins”) based on the DMN component of the BOLD signal extracted from a resting state fMRI series.

References:

Fransson, P., Metsaranta, M., Blennow, M., Aden, U., Lagercrantz, H., Vanhatalo, S. (2012), 'Early Development of Spatial Patterns of Power-Law Frequency Scaling in fMRI Resting-State and EEG Data in theNewborn Brain', Cerebral cortex.

Mingoia, G., Wagner, G., Langbein, K., Maitra, R., Smesny, S., Dietzek, M., Burmeister, H.P., Reichenbach, J.R., Schlösser, R.G.M., Gaser, C., Sauer, H., Nenadic, I. (2012), 'Default mode network activity in schizophrenia studied at resting state using probabilistic ICA', Schizophr Research, vol. 138, no. 2 – 3, pp. 143 – 149.

Raichle, M.E., Snyder, A.Z. (2007), 'A default mode of brain function: a brief history of an evolving idea', Neuroimage, vol 37, pp. 1083 – 1090; discussion pp. 1097 – 1089.

Welch, P.D., (1967), 'The Use of Fast Fourier Transform for the Estimation of Power Spectra: A Method Based on Time Averaging Over Short, Modified Periodograms', IEEE Transactions on Audio Electroacoustics, vol. AU-15, pp. 70 – 73.