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Bayes theorem, Magnetic resonance imaging, Hippocampus, Epilepsy, temporal lobe, What&,rsquo s Known The limitations of the usual brain analysis model were resolved by the spatial Bayesian variable selection (SBVS) method. A regression model that simultaneously considers SBVS and the grouping of brain voxels is termed the &,ldquo Ising and Dirichlet Process (Ising-DP)&,rdquo . What&,rsquo s New Ising-DP model appropriately detected active voxels in the hippocampal area with little evidence of false-positives in other areas. Ising-DP model, adjusted for between-subject factors, reduced shadow and false-positive areas, indicating a better fit of the model. IntroductionMesial temporal lobe epilepsy (mTLE) is a chronic disorder of the nervous system with a pathophysiological basis, namely hippocampal sclerosis. 1, There is evidence from both human and animal investigations, which indicated mTLE affects psychological capacities, particularly episodic memory. 2, Episodic memory is a type of long-term memory that serves as the collection of past personal experiences having occurred at a certain time and place. The surgical resection of the affected hippocampal area carries a 60% to 70% chance of seizure control, 3, but it has cognitive risks, particularly for memory, if the image of the lesion area fails to show the exact location. 4, Verbal and visual memories may be active in the left and right hippocampal regions, respectively. 5, Thus, a pre-surgical evaluation of cognitive functions such as memory in the case of temporal lobectomy in patients with refractory mTLE is critical.Functional magnetic resonance imaging (fMRI) has gained acceptance as a promising, noninvasive, and reproducible pre-surgical evaluation method with a high spatial resolution for imaging regional brain functions. fMRI uses endogenous blood-oxygen-level-dependent (BOLD) contrast to visualize task-specific changes in the regional cerebral blood flow and metabolism. The detection of brain activity is usually achieved based on a voxelwise regression model termed &,ldquo statistical parametric mapping (SPM)&,rdquo . 6, This approach relies on the parametric general linear model and calculates the t-value for each voxel within a slice. SPM has such limitations as multiple-comparison problems, caused by large numbers of voxels, and conservative Bonferroni correction results. Some limitations also arise because SPM is based on classical inference procedures. 7, The recent years have witnessed the introduction of a new approach, which utilizes a spatial Bayesian variable selection (SBVS) method, 8, , 9, whereby, for each voxel, the latent binary activation indicator explicitly parameterizes whether the voxel is active or inactive. The most important advantage of this approach is that it focuses on the estimation of activation probabilities, not activation amplitudes.Smith and colleagues introduced an Ising prior to the SBVS model for a set of binary indicator variables. 8, Improvement in edge preservation for the spatial smoothing of activation areas is one of the advantages of this prior. The external field of the Ising prior takes into account any prior knowledge of the likely areas of activation. Given that the sample size is smaller than the number of indicator variables, it is desirable to group the indictor variables so that regression coefficients with similar values can be congregated. A regression model that simultaneously considers an Ising prior for spatial correlation and the grouping of brain voxels is termed the &,ldquo Ising and Dirichlet Process (Ising-DP)&,rdquo . 10, In the present study, we aimed to identify brain areas involved in episodic memory processing in patients with right mTLE and the normal controls via the Ising-DP model. Between-subject differences prompted us to extend the model to include between-subject factors (BSFs). Moreover, we sought to ascertain whether this model would provide concordant results with other classic methods such as SPM. 11, Patients and MethodsParticipantsThis cross-sectional study was carried out in the Neuroimaging Center of Imam Khomeini Hospital, affiliated with Tehran University of Medical Sciences, Tehran, Iran, in 2018. The research protocol was approved by the Ethics Committee of Tehran University of Medical Sciences (Code, IR.TUMS.SPH.REC.1396.3979). The participants were 35 right-handed adults. As the experimental unit in brain image analyses is voxels, and&,nbsp one person might have about 150 000 or more voxels, the actual sample size in this study was about five 250 000 units. Fifteen patients suffering from mTLE with right hippocampal sclerosis (the experimental group), and 20 normal subjects with no psychiatric and neurological illnesses (the control group) were selected via the convenience sampling method. The exclusion criteria consisted of other neurological illnesses, psychiatric disorders, active medical diseases, and unwillingness to participate in the study. The normal subjects, chosen for the control group, were within the age range of the patients. The healthy status of the control group was confirmed by a neurologist. None of the participants in the control group was on psychotropic medications. A written informed consent form was completed by all the participants.Study ProtocolThe nonverbal face-encoding memory task was employed in this study. It incorporated 60 unfamiliar human faces selected from the Yale Face Database and the PICS Database. 12, Samples of the human faces are presented in figure 1,. During fMRI, six faces were presented in 10 blocks, which were started in the rest state (figure 2,). Before fMRI, the subjects were instructed to remember the faces for a later test. The recognition memory test, comprised of 30 new faces and the 60 faces presented during fMRI, was done approximately 20 minutes after scanning. During the test, the subjects were asked whether the pictures were new or identical to the one previously shown. Two states were assumed for the 60 target faces, a subject can recall faces (R response) or not (F response). Figure 1. Samples of the stimuli presented to the subjects during the encoding condition are shown herein.Figure 2. Schematic diagram of the experimental design in the present study is illustrated.Magnetic Resonance Imaging AcquisitionImages were acquired on a Siemens 3 T Trio scanner with a 12-channel head coil. Functional T2*-weighted images were collected with an echo time (TE) of 30 ms, repetition time (TR) of 3000 ms, field of view (FOV) of 192 mm2, flip angle of 90&,deg , voxel size of 3&,times 3&,times 3 mm3, matrix size of 64&,times 64, slice gap of 0 mm, and slice thickness of 3 mm.Statistical AnalysisBSFs were compared between the study groups through the use of the independent t test for the continuous variables, and the chi-square/Fisher exact test for the categorical variables. The association between the number of activated voxels in the right hippocampal region and the number of hits in the recognition memory test was checked using a correlation test. The BSF analysis and the correlation test were done using IBM SPSS, version 20. A probability of less than 0.05 was considered statistically significant.Pre-statistical processing and single-subject analyses were performed using the SPM12 software (version 12.0, London, UK). First, the slice-timing of the images was corrected, and the images were realigned (motion corrected). Afterwards, they were normalized to a template in the Montreal Neurological Institute and Hospital (MNI) standard. 13, Finally, spatial smoothing with a Gaussian kernel of full-width half-maximum (FWHM) of 5.0 mm was used to smooth the images. Trial-specific responses were modeled by convolving a delta function with the canonical hemodynamic response function. In the first-level analysis, a voxelwise regression was employed to obtain the contrast signals for all the voxels of each subject corresponding to the memory effect. In this regression, the hemodynamic response function was considered to be the response variable, and a dummy variable was utilized to define the act or rest period. Contrast signals were obtained based on the beta coefficients (act mines rest) of the models. For the second-level analysis, a model was defined as follows, Y=X&,eta +&,epsilon where Y (n&,times 1) was the response variable in the recognition memory test score, and X=(X1, . . . , Xp) was the n&,times p matrix of the covariates. The covariates comprised contrast signals, which were obtained from the first-level analysis, and/or BSFs, which were considered to be age, sex, marital status, area of residence, and years of schooling. The error term (&,#603 ) was assumed to have an N (0, &,sigma 2I&,eta ) distribution. 10, The SBVS approach was employed to indicate which covariates were included in the model. In this approach, for each voxel, a latent indicator (&,gamma i) was defined to select the voxels, that were significantly predictive of the response. An Ising prior was imposed on the latent binary indicators to incorporate the spatial information between the voxels, and, in parallel, a DP prior was imposed on &,eta to achieve the grouping of the regression coefficients. 10, The posterior inference of the proposed models was conducted using a Gibbs sampler with data augmentation. For each model, 8000 iterations of the Markov chain Monte Carlo (MCMC) were performed, with the first 1000 discarded as burn-ins. The convergent property was assessed by trace plots. For the calculation of the posterior inclusion probabilities P(&,gamma i=1,Y), the number of iterations, where &,gamma i was equal to 1, was divided by the total number of iterations excluding the burn-in period. The number of activated voxels based on SPM was counted by considering the critical value for P(&,gamma i=1,Y) to be 0.8772. 8, The second-level analysis was done using the MATLAB software (version R2014a, USA). The MATLAB code of the Ising-DP model for the second-level analysis, which was written by Li and colleagues, is available. 10, For the use of the first-level output (i.e., contrast signals as an input of this code), its nifti (.nii) format was converted into the text (.txt) using the fsl2sacii command of the FSL software (version, 5.0.6, Germany).ResultsThe participants were 35 right-handed adults, 15 patients with right mTLE at a mean age of 25.46&,plusmn 5.38 years, and 20 normal controls at a mean age of 26.71&,plusmn 6.12 years. There was a significant difference between the mean years of schooling (P=0.032), and the area of residency (P=0.040) between the mTLE and control groups. The proportions of females, unmarried individuals, and urban residents were higher in both mTLE and control groups. The posterior inclusion probabilities of the latent indicator (&,gamma ) of age, sex, and years of schooling exceeded the critical value of 0.8772 (table 1,).BSFTotal (N=35) Patient Group (n=15)Control Group (n=20)P valueMean&,plusmn SDMean&,plusmn SDP (&,gamma )P (&,gamma )Mean&,plusmn SDP (&,gamma )Age (years)26.21&,plusmn 5.5925.46&,plusmn 5.380.91126.71&,plusmn 6.120.9320.468aYears of schooling15.13&,plusmn 2.2914.10&,plusmn 2.290.98415.80&,plusmn 2.1409120.032a N (%)N (%)P (&,gamma )N (%)P (&,gamma ) Sex |