Automated MRI segmentation for individualized modeling of current flow in the human head

Abstract

Objective High-definition transcranial direct current stimulation (HD-tDCS) and high-density electroencephalography (HD-EEG) require accurate models of current flow for precise targeting and current source reconstruction. At a minimum, such modeling must capture the idiosyncratic anatomy of brain, cerebrospinal fluid (CSF) and skull for each individual subject. Currently, the process to build such high-resolution individualized models from structural magnetic resonance images (MRI) requires labor-intensive manual segmentation, even when leveraging available automated segmentation tools. Also, accurate placement of many high-density electrodes on individual scalp is a tedious procedure. The goal was to develop fully automated techniques to reduce the manual effort in such a modeling process. Approach A fully automated segmentation technique based on Statical Parametric Mapping 8 (SPM8), including an improved tissue probability map (TPM) and an automated correction routine for segmentation errors, was developed, along with an automated electrode placement tool for high-density arrays. The performance of these automated routines was evaluated against results from manual segmentation on 4 healthy subjects and 7 stroke patients. The criteria include segmentation accuracy, the difference of current flow distributions in resulting HD-tDCS models and the optimized current flow intensities on cortical targets. Main results The segmentation tool can segment out not just the brain but also provide accurate results for CSF, skull and other soft tissues with a field of view (FOV) extending to the neck. Compared to manual results, automated segmentation deviates by only 7% and 18% for normal and stroke subjects, respectively. The predicted electric fields in the brain deviate by 12% and 29% respectively, which is well within the variability observed for various modeling choices. Finally, optimized current flow intensities on cortical targets do not differ significantly. Significance Fully automated individualized modeling may now be feasible for large-sample EEG research studies and tDCS clinical trials.

Measurements and models of electric fields in the in vivo human brain during transcranial electric stimulation

“…Individualized modeling implies the need to consider the anatomy of individual subjects , in particular for patient populations that may have abnormal brain anatomy Dmochowski et al, 2013). Various teams have also worked on automating segmentation and modeling for this purpose (Acar and Makeig, 2010;Windhoff et al, 2013;Dannhauer et al, 2012;Huang et al, 2013;Huang and Parra, 2015;Thielscher et al, 2015). We confirmed on the present data that modeling individualized anatomy indeed is beneficial in correctly predicting electric field distribution across space.…”

Slow-theta power decreases during item-place encoding predict spatial accuracy of subsequent context recall

“…Participants' T1 images were bias corrected, co registered to MNI space and resliced to 1 mm voxel size with SPM8. Resliced MRI images were automatically segmented by apply ing the method described in (Huang et al, 2013), which is based on the algorithm implemented in SPM8's "New Segment" toolbox (Ashburner and Friston, 2005). This segmentation is combined with an improved tissue probability map (TPM) developed at the Center for Advanced Brain Imaging at Georgia State University.…”

“…Resulting tissue probabilities are smoothed and binarized. To fill CSF discontinuities, all gray matter voxels adjacent within 3 mm distance to the skull are reinterpreted as CSF (for more details, see procedure and code provided by Huang et al, 2013). The output consisted of tissues binary masks, which were modified to be compatible with BEM computation.…”

Transcranial direct current stimulation in obsessive–compulsive disorder: emerging clinical evidence and considerations for optimal montage of electrodes

“…The models were built following previously described protocols [35]. Briefly, the MRI was automatically segmented into six tissue types in Statistical Parametric Mapping 8 (SPM8; Welcome Trust Centre for Neuroimaging, London, UK).…”