Evaluation of Encephaloduroarteriosynangiosis Efficacy Using Probabilistic Independent Component Analysis Applied to Dynamic Susceptibility Contrast Perfusion MRI

Study Design

We performed a prospective study of adult patients (23–75 years of age) with TIA or nonsevere stroke attributed to 70%–99% intracranial stenosis of a major intracranial artery and confirmed by conventional angiography. All patients presented with persistent strokes or TIAs despite optimal medical management, as established in the SAMMPRIS trial.¹⁰ Patients with ICAS or MMD were included in this study, with the former enrolled in the ongoing EDAS Surgical Indirect Revascularization for Symptomatic Intracranial Arterial Stenosis (ERSIAS) trial (clinicaltrials.gov No. NCT01819597). Patients with MMD received the same imaging and therapy as those with ICAS through the ERSIAS protocol. All participants provided informed consent before taking part, and the study was conducted with institutional review board approval (No. 12-000439; University of California, Los Angeles).

Study Participants

Thirteen patients (female/male ratio: 11:2; mean age, 54.77 ± 15.66 years) who underwent unilateral EDAS surgery and DSC-MR imaging from 2013 to 2014 were included (see Table 1 for an overview of all enrolled patients). Eight of the patients were diagnosed with ICAS, and 5, with MMD. Of the 13 patients, 8 had DSC-MR imaging data at multiple time points, including 1 pre- and up to 4 postoperatively (ie, at approximately 3, 6, 9, and 12 months after the operation). The exact timing of the imaging studies was recorded in days after the operation, yielding negative numbers for preoperative scans (Table 1). For 12 of the 13 patients, postoperative DSA studies were performed to document the extent of newly formed vessels from the donor artery. These studies were used to compare potential perfusion changes with the actual observed sprouting of new vessels. One patient (patient 11) died of a myocardial infarction before these postoperative studies were performed.

Preoperative Evaluation

All patients underwent preoperative imaging that included DSA and/or MR imaging. DSA of all patients revealed severe stenosis in the intracranial cerebral vasculature that corresponded to their clinical symptoms. EDAS was considered for treatment on the basis of clinical need, including ischemic symptoms within 30 days and evidence of hypoperfusion and poor collateral flow (American Society of Interventional and Therapeutic Neuroradiology/Society of Interventional Radiology grades 0–2).

Surgical Technique

The EDAS surgical technique has been previously described.^5,7 In brief, the superficial temporal artery is identified and dissected with its surrounding galeal cuff. A craniotomy is performed, and the dura is opened in cruciate fashion, preserving the middle meningeal artery branches. The dural layers are separated, the inner avascular layer is removed, and the arachnoid is widely opened with microdissection under the surgical microscope. The superficial temporal artery is subsequently sutured to the edges. The burr-holes and inner table of the bone flap are trimmed to prevent kinking and compression of the superficial temporal artery on its replacement. Patients remain in intensive medical management during and after the operation with antiplatelets, high-dose statins, and strict control of blood pressure and glycemia.

MR Images

All MR imaging data were collected on a 1.5T (Avanto; Siemens, Erlangen, Germany) or 3T MR imaging system (Trio, Prisma, or Skyra; Siemens). Each patient underwent follow-up studies on the same MR imaging scanner that was used for their original preoperative baseline scan. Standard anatomic MR imaging sequences included a sagittal T1-weighted image, along with axial T2-weighted and FLAIR images. A 10- to 20-mL (0.1 mmol/kg body weight) dose of gadobenate dimeglumine contrast agent was administered during DSC-MR imaging (gradient-echo EPI: TE/TR = 23/1210 ms [1.5T], TE/TR = 32/1840 ms [3T], 35^° flip angle, 120 time points, bolus injection after 20–25 baseline images, 16–20 sections, 5-mm section thickness with no intersection gap, 128 × 128 matrix size, 24-cm FOV). rCBV, rCBF, and TTP were computed via the commercially available postprocessing software (IB Neuro, Version 2.0; Imaging Biometrics, Elm Grove, Wisconsin). Sagittal and axial postcontrast T1-weighted images were obtained following DSC perfusion images.

Probabilistic Independent Component Analysis

Technical and mathematic details regarding the probabilistic ICA algorithm are described in Beckmann and Smith.²⁸ In the current study, we implemented the probabilistic ICA algorithm by using the MELODIC program from the FMRIB Software Library (FSL; http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/MELODIC). Briefly, probabilistic ICA was applied to the raw DSC-MR imaging data throughout the entire brain to identify 3 statistically independent temporal components plus noise.²² These 3 independent components were then manually identified as “arterial,” “venous,” and “capillary” components based on their temporal hemodynamic profiles, in which the arterial component had the shortest TTP, the venous component had the longest TTP, and the capillary component had the smallest signal amplitude and an intermediate TTP. For verification of this empiric categorization, the components were checked via their respective spatial patterns to ensure that the arterial component covered the circle of Willis and the venous component contained the choroidal plexus. Next, the posterior probability of the DSC time signal for each independent component compared with noise was calculated for each image voxel. This calculation resulted in 3 unique brain maps reflecting the relative probability of a voxel having a DSC time-series consistent with arterial, venous, or capillary hemodynamic profiles. These resulting probability maps were then used for subsequent analyses.

Image Registration

All conventional and perfusion images for each subject were registered to the axial, postcontrast T1-weighted images at the first postoperative time point by using a 12-df affine transformation with a mutual information cost function (in FSL). If required, 9-df manual alignment was subsequently performed (tkregister2, FreeSurfer; surfer.nmr.mgh.harvard.edu). Three investigators (A.N.L., B.M.E., K.L.) verified adequate alignment of the images by consensus.

ROIs

ROIs were manually drawn on baseline postcontrast T1-weighted images around the surgical graft site and on a mirrored region within the contralateral hemisphere. Because all images were aligned between baseline and postsurgical follow-up, a single set of ROIs was used for comparison. The mean values of all 6 measures (arterial probability, capillary probability, venous probability, rCBV, rCBF, and TTP) within these 2 ROIs (surgical and contralateral) were calculated for each patient and evaluation time point.

Statistical Analysis

The calculated ROI-wise values (probabilities for arterial, capillary, and venous components, as well as rCBV, rCBF, and TTP) were used as the dependent measures in a statistical model. The association between time after the operation (independent variable) and the imaging measures of interest (dependent variable) was calculated by using a general linear model and permutation testing. This approach allowed accounting for interindividual differences among the participants by including “subject” as a factor of no interest and assessing interactions between surgical and control hemispheres by modeling appropriate interactions. Correlation coefficients were calculated from the t values on the basis of the effective dfs. Because none of the measures of interest can be assumed to be normally distributed in this small sample, significance was established by using a Monte Carlo simulation with 10,000 permutations.²⁹ Specifically, the null hypothesis was that there is no association between the time after the operation and the measures of interest (dependent variables), so all observed differences were explained by interindividual variability and noise. This entails that no systematic changes occur in either measurement with time, which means that under the null hypothesis it is inconsequential at which time relative to surgery the measure was taken. While we controlled for interindividual differences, the time after the operation can thus be interchanged at random, without yielding a less significant correlation or interaction under the null hypothesis. The null hypothesis can be rejected at P ≤ .05, however, if random permutations of the time after the operation yield less significant results in at least 95% of all cases. With 6 different measures of interest, we controlled the type I error by applying a Bonferroni correction for multiple comparisons (ie, significance was determined at P < .0083). Seeing that previous analyses focused on MMD, and not ICAS, and that the underlying disease may be a modifying factor for the results, we repeated the statistical analysis for each disease group as a post hoc analysis and additionally calculated the interaction between disease group and postsurgical changes. The analysis was performed in Matlab (Version 7.12; MathWorks, Natick, Massachusetts).

In addition, standard plots of the probability of arterial, venous, and capillary components within the prescribed ROIs with time in the complete sample and descriptive statistics were generated for demographic and clinical assessments to characterize the study population. Additional plots were created for rCBF, rCBV, and TTP. For comparison with the individual DSA studies, plots of the changes within the arterial and venous component with time for each individual patient were created. These analyses were conducted by using JMP (Version 11; SAS Institute, Cary, North Carolina).

Clinical Analysis

Patient data were reviewed up to 24 months following EDAS surgery to evaluate TIA, stroke, and death.

Role of the Funding Source

None of the funding sources had any role in the collection or interpretation of data or any influence on the writing or decision to submit the manuscript.