Proceedings of the 29th Annual International Conference of the IEEE EMBS Cité Internationale, Lyon, France August 23-26, 2007.
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Application of fractal dimension method of functional MRI timeseries to limbic dysregulation in anxiety study Elżbieta Olejarczyk, Institute of Biocybernetics and Biomedical Engineering, PAS Abstract—Functional magnetic resonance imaging (fMRI) allows to investigate the amplitude of activation in neural networks of brain. In this work we present the results of fMRI time-series analysis performed to identify the process of dysregulation of dynamic interaction between different limbic system regions in healthy adults in state of increased anxiety. The results obtain for 65 healthy adults using nonlinear dynamics methods like fractal dimension confirm the key roles of the bilateral amygdala, bilateral hippocampus, BA9 (dorsolateral prefrontal cortex), and BA45 (ventromedial prefrontal cortex) in modulating emotional response in healthy adults. For different regions of interest (ROIs) significant correlations were found not only for the neutral respective rest but also for fear and angry contrasts.
I. METHODS
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MRI time series, each 33 points long, were analyzed for 65 healthy subjects in 6 regions of brain (BA45, BA9; left and right amygdale - LA, RA; left and right hippocampus - LH, RH) for 5 contrasts (NR – Neutral vs. Rest, FN – Fear vs. Neutral, AN - Angry vs. Neutral, FR – Fear vs. Rest, AR – Angry vs. Rest). Subjects were scanned on a 1.5T Philips Intera MRI scanner at the Stony Brook Hospital using a SENSE head coil. A lower field strength, rather than the more standard 3T, was used to minimize susceptibility artifacts in the amygdale. Time series were acquired using two blocks (one for each fMRI run) of 136 T2* - weighted echoplanar single-shot images covering the frontal and limbic areas of the brain, with TR=2500ms, SENSE factor =2, TE=45ms, Flip angle = 90º. Matrix: 64x64, 3.9x3.9x4 mm3 voxels, and 30 contiguous oblique coronal slices. In addition to the functional scan, an anatomical scan to match the slice orientation of the functional scan was obtained. The acquisition parameters for this sequence were: TR=15ms, TE=450ms, Matrix=256x256, FOV=250 and 30 contiguous oblique coronal slices with 4mm slice thickness and no gap between the slices. The anatomical data were used to generate a customized EPI template to normalize EPI scans to the standard frame of reference. The subject’s head was secured with tape to minimize head movements during the scans. In the fMRI scanner the subjects underwent two runs of a UNCTIONAL
This work was supported in part by Institute of Biocybernetics and Biomedical Engineering Polish Academy of Sciences under Grant St/18/07. Dr E. Olejarczyk is with Institute of Biocybernetics and Biomedical Engineering Polish Academy of Sciences, 4 Trojdena str., 02-109 Warsaw, Poland (e-mail:
[email protected]).
1-4244-0788-5/07/$20.00 ©2007 IEEE
blocked design task. The emotionally-valent facial stimuli consisted of black and white pictures of male and female faces depicting anger, fear, happy and neutral emotions. The fMRI task consisted of blocked presentations of faces alternating with a 20s fixation cross block, during which a white cross-hairs was presented on a black background (“Rest” block). Each fMRI run lasted for 5min and 40s and included 2 blocks of angry, neutral, happy and fearful faces. Each face block consisted of 9 different faces of the same emotion type, displayed for 2.2s each for total block duration of 20s. The maximally-activated voxel for each cluster (two clusters centered in the ventrolateral (Brodmann area 45) and the dorsolateral (Brodmann area 9) area of the prefrontal cortex and bilateral activation in the amygdala and hippocampus) was selected and used to extract each individual subject’s time-series. For every time series Higuchi’s fractal dimension, Df [1] was calculated. It was calculated directly from the time series, without embedding the data in a phase-space like it is in the case of correlation dimension. Higuchi’s fractal dimension should not be confused with fractal dimension of an attractor in the system’s phase space. It is fractal dimension of the curve representing the signal and has the values between 1 and 2, since a simple curve has dimension equal 1 and a plane has dimension equal 2. Fractal dimension is a measure of the signal complexity.
Fig.1 Choice of parameter kmax. Higuchi’s fractal dimension method was previously applied to EEG-signal analysis in sleep [4,5,6], anesthesia [7-8], epilepsy [6] and evoked EEG by magneto- and photostimulation [2,3,6]. fMRI time series are quite different signals than EEG and they require a different treatment. In
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case of EEG-signals Df was calculated often in 1 sec window (100, 128, 250 points depending on the sampling frequency) and appropriate kmax parameter was used. Parameter kmax is the only parameter of Df algorithm. For EEG signals kmax was 8 for 128 Hz and kmax 16 for 250 Hz. The utility of Higuchi’s fractal dimension method to fMRI time series analysis was tested. Fractal dimension was calculated in very short window (33 points only). It was found that kmax equal 14 was a good value for this purpose. II. RESULTS Higuchi’s fractal dimension, Df was calculated for 65 subjects in 6 regions for 5 contrasts. For each of 33-points time series one Df value was received, so as a result we have 65 x 30 matrices of Df values. Pearson correlation coefficient matrices was calculated for every pair of 30 conditions (6 regions x 5 contrasts). It was found that the analyzed activity of brain regions manifested significant correlations, p
