Abstract
Clinical research employing functional magnetic resonance imaging (fMRI) is often conducted within the connectionist paradigm, focusing on patterns of connectivity between voxels, regions of interest (ROIs) or spatially distributed functional networks. Connectivity-based analyses are concerned with pairwise correlations of the temporal activation associated with restrictions of the whole-brain hemodynamic signal to locations of a priori interest. There is a more abstract question however that such spatially granular correlation-based approaches do not elucidate: Are the broad spatiotemporal organizing principles of brains in certain populations distinguishable from those of others? Global patterns (in space and time) of hemodynamic activation are rarely scrutinized for features that might characterize complex psychiatric conditions, aging effects or gender—among other variables of potential interest to researchers. We introduce a canonical, transparent technique for characterizing the role in overall brain activation of spatially scaled periodic patterns with given temporal recurrence rates. A core feature of our technique is the spatiotemporal spectral profile (STSP), a readily interpretable 2D reduction of the native four-dimensional brain × time frequency domain that is still “big enough” to capture important group differences in globally patterned brain activation. Its power to distinguish populations of interest is demonstrated on a large balanced multi-site resting fMRI dataset with nearly equal numbers of schizophrenia patients and healthy controls. Our analysis reveals striking differences in the spatiotemporal organization of brain activity that correlate with the presence of diagnosed schizophrenia, as well as with gender and age. To the best of our knowledge, this is the first demonstration that a 4D frequency domain analysis of full volume fMRI data exposes clinically or demographically relevant differences in resting-state brain function.
Highlights
Much fMRI research focuses onestimates offunctional connectivity between fixed parcellations or weightings of voxel space (van den Heuvel and Pol, 2010; Erhardt et al, 2011a; Biswal, 2012; Calhoun and Adali, 2012; Smith, 2012)
The spatiotemporal spectral profile (STSP) we report here does succeed in highlighting important features of brain activation that are obscured in conventional functional network connectivity (FNC) analyses
The approach we propose is intended to capture information that speaks to the full range of neurophysiological mechanisms governing how information distributes over the brain in space and in time
Summary
Much fMRI research focuses onestimates offunctional connectivity between fixed parcellations or weightings of voxel space (van den Heuvel and Pol, 2010; Erhardt et al, 2011a; Biswal, 2012; Calhoun and Adali, 2012; Smith, 2012). While a correlation-driven network connectivity framework is optimal for certain questions, the brain is operating on many scales simultaneously and we can miss useful information or even bypass interesting questions by structuring so much analysis around the assumptions that:. (2) Popular methods of collapsing space to a small number signal-carrying nodes generally produce networks that preserve temporal variability at the most salient spatial scales. (3) Spatiotemporal properties of information flow through inter-node tissue, i.e., through the often substantial gray matter spatial complement of the ROIs or networks under explicit consideration, can be safely ignored. We investigate the relative contributions of 3D spatial intensity patterns of roughly homogeneous directional periodlengths (from small to large) moving at different temporal frequencies through the 4D fMRI signal. In the present investigation it proves entirely sufficient to expose significant group differences in spatiotemporal hemodynamic activation patterns
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