Synopsis
Temporal lobe epilepsy with unilateral hippocampal sclerosis patients
benefit from the medial temporal lobectomy. Since the hippocampus is
involved in many cognitive tasks, we hypothesized that
resting-state(rs) network alterations would occur in these patients
following temporal lobectomy. All patients had pre- and
post-operative neurocognitive tests, rs-fMRI and structural
T1-weighted imaging . Post-operative studies were performed at
1-year-follow-up. Following temporal lobectomy, left- and right-HS
patients showed significantly decreased and increased activations in
default-mode-network and fronto-parietal-network. A pre-operative
extent of tissue damage or dominancy of the epileptic hemisphere may
be responsible for the different patterns of adaptation/change of
brain networks after lobectomy.Target
audience
Researchers and
physicians who work in the field of neurology,
neuroscience, specifically epilepsy.
Purpose
The most frequent pathologic finding in temporal lobe epilepsy (TLE) is hippocampal sclerosis (HS).
1,2 TLE with HS has also evolved towards the view that this syndrome affects widespread brain networks and in terms of lateralization, left and right TLE seem to show a different pattern of network disease. These patients with hippocampal sclerosis (HS) also benefit from medial temporal lobectomy. Since the hippocampus is involved in many cognitive tasks, we hypothesized that resting state network (RSN) alterations would occur in these patients with network disease following medial temporal lobectomy.
Material
and Methods
IRB was obtained for
this study and all the participants gave signed consent form.
Subjects:
We studied 20 right-handed patients (11 left HS, 9 right HS) with
diagnosis of isolated unilateral HS on basis of clinical,
electrophysiological and MRI findings. These patients had medial
temporal lobectomy. They were categorized into left HS (F/M:
4/7; 28.6±5.56)
and right HS (F/M: 4/5; 29±5.86
years).
Image
Acquisition:
Imaging of the brain was performed on a 3T MR scanner (Magnetom, Trio
TIM system, Siemens, Germany) equipped with a 32-channel phase-array
head coil. Resting-state (rs) fMRI imaging applied T2* weighted
gradient echo spiral pulse sequence (TR/TE: 2000/35 msec, FA 75°,
FOV: 230 mm, matrix: 64 x 64, in-plane spatial resolution of 3.6 mm)
while the subjects kept their eyes closed without a specific
concentration. Structural 3D T1-weighted high resolution
(magnetization prepared rapid gradient echo-MPRAGE) (TR/TE: 1900/3.4
msec; FA: 90; FOV: 256mm; matrix: 224x256; distance factor: %50)
sequence was also obtained.
All
patients had pre- and post-operative neurocognitive tests, and imaging
with the same protocol on the same scanner. Post-operative studies
were performed at 1-year follow-up.
Data Processing
and Analysis
Preprocessing
of the rs-fMRI data:
The rs-fMRI scans were preprocessed using SPM8 3 . Preprocessing of
the rs-fMRI data included realignment, slice-timing correction,
co-registration and normalization (to Montreal Neurological Institute
(MNI) template), and spatial smoothing with an 6 mm3
isotropic Gaussian kernel.
Independent
components analysis (ICA):
Melodic ICA version 3.14 (Beckmann and Smith, 2004) 4 with a
multisession
temporal concatenation tool
was used to perform groupICA analysis. Preprocessed data were used as
input for ICA. 30 spatiotemporal component were yielded by using a
dimensionality estimation using the Laplace approximation to the
Bayesian evidence of the model. We used 17 popular Resting State
Network (RSN)s (Yeo et al., 2011) 5 to compare the spatial map of each
ICA component. We used “fslcc” (tool of FSL) to do the reference
network correlations (Pearson's r > .207) . This procedure was
repeated for each group. For pre- and post- operative comparisons
within left and right HS groups, 10 and 8 reference network
correlated components were extracted respectively (Fig.1a: left HS,
1b: right HS).
Dual
Regression:
To investigate
spatial and intensity differences in those RSNs as a function of
within-group difference between pre- and post- operative studies of
left and right HS, we used FSL dual regression technique that allows
for voxel-wise comparisons of rs- fMRI (Filippini et al., 2009;
Littow et al., 2010;
Veer et al., 2010;
Abou Elseoud et al., 2011). 6-9
Results
Dual regression
results showed alterations in default-mode network (DMN) and
fronto-parietal network (FPN) in both right and left HS groups with
temporal lobectomy.
Post-operatively,
left HS group showed decreased activation in posterior cingulate
cortex (PCC) (Fig. 2a) with an increased activation in anteror cingulate
cortex (ACC) (Fig. 2b) in DMN (p
corrected
= 0.029). In FPN, subtle reduction in activation of right sub-gyral
frontal (Fig. 3a) with marked increased activation in right superior
frontal gyrus (Fig. 3b) (p
corrected
= 0.001) .
In
comparison of pre- and post-operative imaging of right HS group,
there was significantly reduced activation in medial prefrontal
cortex (p
corrected
= 0.023) without increased activation in DMN (Fig. 4). On the
contrary, right inferior frontal gyrus showed increased
activation (p
corrected
= 0.023) (Fig. 5) with no significant reduction in activation in FPN in this group.
Discussion
Following
temporal lobectomy, left and right HS patients showed different
patterns of BOLD alterations on rs-fmri obtained at one-year
follow-up. Significant decreased and increased activations occurred
in DMN and FPN. A pre-operative extent of tissue damage or dominancy
of the epileptic hemisphere may be responsible for the different
patterns of adaptation/change of brain networks after temporal
lobectomy.
Acknowledgements
This
study has been granted by Turkish Scientific Council (TUBITAK) (1001
project; number 112S150).References
1. Schevon CA, Cappell J, Emerson R, et al. Cortical
abnormalities in epilepsy revealed by local EEG synchrony.
NeuroImage. 2007;35(1):140–148.
2. Ortega GJ, Menendez de la Prida L, Sola RG, and Pastor J . Synchronization clusters of interictal activity in the lateral
temporal cortex of epileptic patients: Intraoperative
electrocorticographic analysis. Epilepsia. 2008;49(2):269–280.
3. http://www.fil.ion.ucl.ac.uk/spm/software/spm8/
4. Beckmann CF, Smith SM. Probabilistic independent component analysis for functional magnetic resonance imaging. IEEE Trans. Med. Imaging. 2004;23:137–152
5. Yeo BTT, Krienen FM, Sepulcre J, Sabuncu MR, Lashkari D, Hollinshead M, Buckner RL. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J. Neurophysiol. 2011;106(3):1125–1165.
6. Filippini N, MacIntosh BJ, Hough MG, Goodwin GM, Frisoni GB, Smith SM, et al. Distinct patterns of brain activity in young carriers of the APOE-epsilon4 allele. Proc. Natl. Acad. Sci. U.S.A. 2009;106: 7209–7214.
7.
Littow H, Abou Elseoud A, Haapea M, Isohanni M, Moilanen I
,Mankinen K, et al. (2010) Age-related
differences in functional nodes of the brain cortex - a high model order
group ICA study. Front. Syst. Neurosci. 4:32. doi: 10.3389/fnsys.2010.00032
8. Veer IM, Beckmann CF, van Tol MJ, Ferrarini L, Milles J, Veltman DJ, et al. (2010). Whole brain resting-state analysis reveals decreased functional connectivity in major depression. Front. Syst. Neurosci. 4:41
10.3389/fnsys.2010.00041
9. Abou Elseoud A, Littow H, Remes J, Starck T, Nikkinen J, Nissila J, et al. (2011). Group-ICA model order highlights patterns of functional brain connectivity. Front. Syst. Neurosci. 5:37
10.3389/fnsys.2011.00037