High-Frequency and Other Pathological Network Hemodynamics Observed in Epilepsy Patients Imaged With Multi-Band Multi-Echo BOLD Functional MRI at 7T
Prantik Kundu1,2, Lara V. Marcuse3, Bradley Delman1, Rebecca Feldman1, Madeline C. Fields3, and Priti Balchandani1

1Department of Radiology, Icahn School of Medicine at Mt. Sinai, New York, NY, United States, 2Department of Psychiatry, Icahn School of Medicine at Mt. Sinai, New York, NY, United States, 3Department of Neurology, Icahn School of Medicine at Mt. Sinai, New York, NY, United States

Synopsis

Clinical assessment of epilepsy based on extra-cranial EEG electrophysiology has moderate diagnostic sensitivity (40%), poor spatial specificity (1-5 cm), and no prognostic value. We seek to utilize MRI for more effective non-invasive characterization of epilepsy than currently established. We implemented multi-echo multi-band (MEMB) BOLD fMRI at 7T to map the hemodynamic signatures of seizure zones and networks in spontaneous brain activity of focal epilepsy patients versus matched controls. We mapped seizure networks in patients at millimeter-resolution, and observed epileptiform BOLD to have significantly amplified infra-slow and high-frequency temporal oscillations, analogous to characteristic epileptiform activity from EEG.

Purpose

Clinical epilepsy assessment involves localizing seizure onset zones (SOZs) and networks (SNs) non-invasively based on extra-cranial EEG and identifying focal slowing, high-frequency oscillations, and various epileptiform discharges1-3. However, EEG has moderate diagnostic sensitivity (40%) and poor spatial specificity (1-5 cm)4. Towards more effective non-invasive characterization of SOZs and SNs than currently established, we implemented multi-echo multi-band (ME-MB) BOLD fMRI at 7T to map seizure-related networks at millimeter-resolution and characterize epileptiform hemodynamics across BOLD frequencies.

Methods

Three patients with focal non-lesional (1 female, mean age 26y) and two with focal lesional epilepsy (1 female, mean age 24y) and matched controls participated in this 7T MRI study, which was approved by the Mt. Sinai IRB. Lesion diagnosis was by a board certified and experienced radiologist, and epilepsy diagnosis was by certified epileptologists at the Mt. Sinai Epilepsy Center. All participants were scanned on a Siemens Magnetom 7T MRI scanner (Siemens, Erlangen, Germany) with a birdcage-transmit/32-channel-receive head coil. Scanning sequences included MP2RAGE5 (0.8mm iso., TR/TE=6s/5ms, TI=1050ms;3000ms), T2-FLAIR (0.7x0.7x3mm, TR/TE/TI=9000/123/2600ms), and 8 minutes of "resting state" MEMB-fMRI (whole-brain 2.5mm iso.; TR=1.85s, TEs=8.5,23.2,37.8, 52.5ms; MB=3; GRAPPA=3)6-7. MP2RAGE “UNI” images homogenized T1 contrast and attenuated intensity non-uniformity, and were used for brain extraction and masking T2-FLAIR images co-registered with T1 images. MEMB-fMRI analysis involved multi-echo independent components analysis (ME-ICA) with AFNI meica.py8 with default settings, which implemented: slice time and motion correction; co-registration to masked T1; T2* mapping and weighted "optimal combination" of echoes9; dimensionality estimation with multi-echo PCA (ΔT2* fitting of PC amplitude vs. TE); spatial FastICA decomposition; BOLD/non-BOLD IC selection (ΔT2* fitting of IC amplitude vs. TE), and time series denoising by non-BOLD component removal – without arbitrary noise models for head and cardiopulmonary motion, temporal bandpass filtering, spatial smoothing, etc.10-12 BOLD IC (i.e. functional network) time series were compared of patients and controls to determine differences in 1) frequency spectra and 2) statistical moments reflecting sparsity to infer “bursting.” Power spectra were computed for all BOLD component time courses (temporally Z-normalized), collapsed within control and patient groups, and then amplitudes per frequency bin were compared between patients and controls using a 2 independent-samples T-test. Sparsity was assessed for each IC time course as skewness (σ3) and kurtosis (σ4), and groups were compared with Mann-Whitney U-tests. Individual functional network IC maps were manually examined for the lesional epilepsy patients, respectively with hippocampal cavernoma (venous abnormality) and temporal lobe dysembryonic neuroepithelial tumor (DNET, development abnormality). Networks were examined to elucidate if BOLD networks would co-localize with anatomical abnormalities (putative SOZs) and suggest SNs.

Results

7T MEMB time series after T2* weighted combination compensated orbitofrontal susceptibility artifact without specialized shim/hardware (Figure 1a, patient). The default mode network was found in all patients and controls (Figure 1b). BOLD network ICs totaled 165 and 115 for [all] patients and matched controls, respectively. BOLD network ICs of patients had significantly higher spectral power in f<0.01 and f>0.1 Hz frequency ranges versus controls (p<10-5, p<10-10, respectively, Figure 2a-b). Notably, the average difference of BOLD spectral power in the canonical 0.01-0.1 Hz range was not significant. Additionally, network IC time course sparsity was significantly higher for patients than controls, in both skewness and kurtosis (both p<10-6; Figure 2c-d), suggesting greater hemodynamic bursting activity in patients. Patient functional network maps showed: 1) hippocampal cavernoma associated with a unilateral hippocampal-temporal lobe functional network not resembling a canonical13 network (Figure 3a) but consistent with clinically determined seizure network and showing non-stationary time course transitions between high-frequency/low-amplitude and low-frequency/high-amplitude states; 2) DNET (dark in T1, bright in T2) was associated with a bilateral auditory cortex network (Figure 3b), notable since patient heard buzzing during seizure aura.

Discussion

7T MEMB-fMRI with ME-ICA processing elucidated patient functional networks at millimeter-resolution and mitigated artifacts from magnetic susceptibility as well as subject motion, in a comprehensive but unbiased way, critically reducing processing-related analysis confounds. This approach specially supported using MB-fMRI at 7T to find SNs as non-canonical functional networks co-localized with lesion/SOZs and observing temporal BOLD hemodynamics of significantly higher infra-slow (<0.01Hz) and high-frequency (>0.1Hz) amplitude and bursting in patients than controls. With future development and application, these techniques may lead to rapid high-resolution SN and SOZ mapping based on MRI, more effectively and earlier in medical or surgical treatment planning than currently achievable with EEG.

Conclusion

Our 7T MEMB-fMRI approach is promising towards achieving high sensitivity and specificity for ultra-high-field functional imaging of epilepsy and other neuropsychiatric patients - at individual level - with the multi-echo component enabling identification of networks localized to abnormal tissue and/or exhibiting aberrant hemodynamics potentially coupled to pathological electrophysiology.

Acknowledgements

We acknowledge support from NIH-NINDS R00 NS070821, Icahn School of Medicine Capital Campaign, Translational and Molecular Imaging Institute and Department of Radiology, Siemens Healthcare. We also acknowledge Dr. Benedikt Poser (Maastricht University, Netherlands) and Dr. Essa Yacoub (Center for Magnetic Resonance Research, Minnesota, USA) for supporting sequence development of multi-echo multi-band EPI, collaborators Drs. Junxian (Gordon) Xu and Rafael O'Halloran in sequence implementation Mt. Sinai, and Dr. Jiyeoun Yoo of the Mt. Sinai Epilepsy Center in patient recruitment.

References

[1] Tran, T. A., Spencer, S. S., Javidan, M., Pacia, S., Marks, D., & Spencer, D. D. (1997). Significance of Spikes Recorded on Intraoperative Electrocorticography in Patients with Brain Tumor and Epilepsy. Epilepsia, 38(10), 1132–1139. [2] Englot, D. J., Yang, L., Hamid, H., Danielson, N., Bai, X., Marfeo, A., … Blumenfeld, H. (2010). Impaired consciousness in temporal lobe seizures: role of cortical slow activity. Brain, 133(12), 3764–3777. [3] D. Bautista, R. E., Spencer, D. D., & Spencer, S. S. (1998). EEG findings in frontal lobe epilepsies. Neurology, 50(6), 1765–1771. [4] Plummer, Chris, A. Simon Harvey, and Mark Cook. "EEG source localization in focal epilepsy: where are we now?." Epilepsia 49.2 (2008): 201-218. [5] Marques, José P., et al. "MP2RAGE, a self bias-field corrected sequence for improved segmentation and T 1-mapping at high field." Neuroimage 49.2 (2010): 1271-1281. [6] Olafsson, Valur, et al. "Enhanced identification of BOLD-like components with multi-echo simultaneous multi-slice (MESMS) fMRI and multi-echo ICA." NeuroImage 112 (2015): 43-51. [7] Boyacioglu, Rasim, et al. "Whole brain, high resolution multiband spin-echo EPI fMRI at 7T: a comparison with gradient-echo EPI using a color-word Stroop task." Neuroimage 97 (2014): 142-150. [8] Kundu, Prantik, et al. "Integrated strategy for improving functional connectivity mapping using multiecho fMRI." Proceedings of the National Academy of Sciences 110.40 (2013): 16187-16192. [9] Evans, Jennifer W., et al. "Separating slow BOLD from non-BOLD baseline drifts using multi-echo fMRI." NeuroImage 105 (2015): 189-197. [9] Posse, Stefan, et al. "Enhancement of BOLD-contrast sensitivity by single-shot multi-echo functional MR imaging." Magnetic Resonance in Medicine 42.1 (1999): 87-97. [10] Kundu, Prantik, et al. "Differentiating BOLD and non-BOLD signals in fMRI time series using multi-echo EPI." Neuroimage 60.3 (2012): 1759-1770. [11] Kundu, Prantik, et al. "Robust resting state fMRI processing for studies on typical brain development based on multi-echo EPI acquisition." Brain imaging and behavior 9.1 (2015): 56-73. [12] Smith, Stephen M., et al. "Correspondence of the brain's functional architecture during activation and rest." Proceedings of the National Academy of Sciences106.31 (2009): 13040-13045.

Figures

(a) Preprocessed image from multi-echo multi-band fMRI after T2* weighted "optimal combination" of echoes to compensate susceptibility artifact in orbitofrontal, brainstem, temporal, and cerebellar brain regions. (b) DMN and time series for non-lesional patient and control from ME-ICA. Note localization precision and low noise, from ME-ICA analysis and not explicitly smoothing data for noise reduction.


(a) Comparison of mean BOLD IC power spectra of patients vs. controls. (b) Plot of difference significance vs. frequency from T-test. Patients show clearly significant increases in high-frequency (0.1Hz-0.2Hz) and infra-slow (f<0.01 Hz) BOLD fluctuations (p<<10-5), which may reflect epileptiform focal slowing and fast oscillations. (c-d) Network timecourse kurtosis and skewness significantly higher (p<10-6) in patient BOLD network activity than controls, based on U-test.

(a) BOLD IC on MP2RAGE-T1 of cavernoma patient shows involvement of left hippocampus and temporal lobe corresponding to clinical seizure network. Bilateral insula suggests propagation path of focal-to-general epilepsy. Time course shows more non-stationary activity than DMN from same imaging period. (b) From DNET patient, bilateral auditory cortex activity identified across healthy (dorsal, left) and pathological (ventral, right) tissues in T1 (top) and T2-FLAIR (bottom)



Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)
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