Spatial heterogeneity analysis of brain activation in fMRI

Abstract

In many brain diseases it can be qualitatively observed that spatial patterns in blood oxygenation level dependent (BOLD) activation maps appear more (diffusively) distributed than in healthy controls. However, measures that can quantitatively characterize this spatial distributiveness in individual subjects are lacking. In this study, we propose a number of spatial heterogeneity measures to characterize brain activation maps. The proposed methods focus on different aspects of heterogeneity, including the shape (compactness), complexity in the distribution of activated regions (fractal dimension and co-occurrence matrix), and gappiness between activated regions (lacunarity). To this end, functional MRI derived activation maps of a language and a motor task were obtained in language impaired children with (Rolandic) epilepsy and compared to age-matched healthy controls. Group analysis of the activation maps revealed no significant differences between patients and controls for both tasks. However, for the language task the activation maps in patients appeared more heterogeneous than in controls. Lacunarity was the best measure to discriminate activation patterns of patients from controls (sensitivity 74%, specificity 70%) and illustrates the increased irregularity of gaps between activated regions in patients. The combination of heterogeneity measures and a support vector machine approach yielded further increase in sensitivity and specificity to 78% and 80%, respectively. This illustrates that activation distributions in impaired brains can be complex and more heterogeneous than in normal brains and cannot be captured fully by a single quantity. In conclusion, heterogeneity analysis has potential to robustly characterize the increased distributiveness of brain activation in individual patients.

Influence of White and Gray Matter Connections on Endogenous Human Cortical Oscillations

“…Amzica et al showed that disconnection of intracortical synaptic linkages in anesthetized cats disrupts synchronization of slow oscillations (Amzica and Steriade, 1995 ). Gupta et al showed that long range connectivity as measured by functional magnetic resonance imaging was changed after diseases and this altered spatial heterogeneity (Gupta et al, 2014 ). Ibrahim et al found that ipsilateral functional connectivity was increased after a functional hemispherotomy in which one hemisphere was disconnected from the other (Ibrahim et al, 2015 ).…”

Exploiting Complexity Information for Brain Activation Detection

“…Nevertheless, while one surveys fMRI signals, a detail must not be ignored that these signals, like many other physiological time series, commonly exhibit extremely inhomogeneous and non-stationary fluctuations in an irregular and complex manner [ 4 , 5 ]. We hypothesize that these complexity/regularity could be modulated in pertinent cognitive tasks, and they may change through experimental paradigms.…”

Wavelet entropy of BOLD time series: An application to Rolandic epilepsy

“…Previously applied functional connectivity methods also showed significant differences between patients with Rolandic epilepsy and controls in these regions (Rolandic strip, language regions, and default mode network regions) in the literature . However, it needs to be remarked that a spatial overlap (SPM) analysis, as applied in this study, only reveals brain areas that coherently over the patients show abnormalities in the time series signal, while more heterogeneously (spatially) distributed abnormalities may remain undetected …”