681.06: Automatic cortical labeling system for neuroimaging studies of normal and disordered speech Jason A. Tourville Frank H. Guenther Department of Speech, Language, and Hearing Sciences, Department of Biomedical Engineering, Center for Computational Neuroscience and Neural Technology (CompNet) Background The SpeechLabel Cortical Labeling System Volume-based inter-subject averaging methods fail to account for cortical variability, resulting co-regis- Regions are plotted on the Freesurfer fsaverage surface template for the left hemisphere. Each cortical hemisphere is divided into 63 regions of interest based on individual anatomical landmarks. Superior temporal, in- tration of functionally distinct regions and a loss of statistical power. This is particularly problematic for ferior frontal, precentral, and medial frontal regions are divided into functionally meaningful subunits for neuroimaging studies of speech. See table 1 for region abbreviation key. neuroimaging studies of speech because functionally distinct regions in the peri-Sylvian region that are distant along cortical surface are proximal in the volume. Individual region of interest (ROI)-based analy- Inated Surface Pial Surface sis analysis of cortical responses signicantly improves statistical power (Nieto-Castanon et al., 2003) but Lateral Medial pdPMC requires lengthy, tedious labeling by an expert. dMC Lateral dSC Medial mdPMC dMC pdPMC dSC SPL SMA adPMC preSMA pMFg midMC SFg midPMC aSMg pSMg PCN pCGg midCGg SFg aMFg vSC Ag pIFs vMC vPMC PO pCO aIFs PT dIFovIFo aCO pFO OC OC dIFt Hg aCG FP pINS pSTg FP vIFt aFO pdSTs MTO aINS PP pvSTs Lg FOC ITO SCC FMC aSTg pMTg pPHg adSTsavSTs TP pITg Dorsal Ventral aMTg aPHg aITg pITg ITO aITg aFO pFO TP aTFg pTFg aINS TOFg Ventral pPHg aPHg FOC FP OC Anatomical variability after volume-based normalization and co-registration. Left: Mean Lg voxel overlap of superior temporal ROIs at various group sizes. Little of no overlap is FMC SCC seen with groups of 9 subjects. Right: The alignment of major sulci. Sulci were drawn on 2 individual surfaces (top) and then overlaid on the same surface (bottom), aligned to the Sylvian ssure. Overlap of automatic and manually edited labels Anatomical landmarks PFS: Mean PO: 88% +/- 10%, Range: 46-100; PWS: Mean PO: 88% +/- 9%, Range: 52-100 Freesurfer’s (http://surfer.nmr.mgh.harvard.edu/) individual cortical surface-based inter-subject averag- Bounding sulci are shown on the mean surface curvature of the inated fsaverage surface template for Good overall overlap b/w auto and edited labels and no dierence b/w groups (2-tailed paired t-test of mean over- ing (Fischl et al., 1999) improves upon volume-based methods (eg., Ghosh et al, 2010) but packaged cor- the left hemisphere. Red (positive curvature) indicates a sulcal region; green (negative curvature) indi- laps for each region: p > .41). But are specic regions of low overlap, especially subdivisions of the inferior frontal tical labeling atlases fail to distinguish putative functionally distinct areas that underlie speech process- cates a gyral regions. Additional dividing landmarks include sulcal intersections and the anterior com- gyrus region (see plot below). es. Here, we describe a cortical labeling system tailored for neuroimaging studies of speech that was missures . White stars indicate landmarks that are note easily viewed on the average template. See used to generate a Freesurfer-based labeling atlas. We applied the atlas to T1 volumes of 18 uent Table 2 for landmark abbreviation key. PO of speech regions with PO < 75% in one of the groups subject groups. speakers and 20 persons who stutter to assess the performance of the automatic atlas. 80 PWS Methods cs sfrs itps cgs PFS pocs * sbps 70 SpeechLabel System: Each hemisphere is divided into 63 cortical regions based on individual anatom- prcs cas pos ical landmarks. Superior temporal, inferior frontal, precentral regions are divided into multiple gyral csts1 ifrs pals and sulcal components. ls phls csts2 60 aals csts3 aocs ccs crs hs SLaparc Atlas Generation: Image segmentation, cortical surface reconstruction, surface-based ahls % Overlap (Dice) 50 ftts sts sros co-registration, and initial surface labeling was performed on T1 MRI volumes from 17 neurologically crs cos AC its normal subjects (9 Female, Age: 20-43, Mean: 29) using Freesurfer v5.0. T1 data aqcuisition: Siemens 40 TIM Trio 3T scanner, MPRAGE sequence, 1mm voxel size. its rhs aals ots ** Left vIFt Left aIFs Surface labels were edited to conform to the SpeechLabel system (editing was overseen by a trained ahls crs Left vIFo Left SMA Left dIFo Left dIFt Left pIFs rhs cos Right vIFo Right dIFo Right vIFtRight aFO Right dIFt Right aIFsRight pIFs neuroanatomical expert). The labeled surfaces were used as a training set to generate the SLaparc atlas Left pdPMC using standard Freesurfer tools (Fischl et al. 2004). olfs Conclusions Atlas Application: The atlas was used to automatically label 18 additional uent speakers (PFS; 4 The SpeechLabel system divides the cortex into regions that are tailored for neuroimaging studies of Female, Age 19-35, Age Range: 19-35) and 20 matched persons who stutter (PWS; 5 Female, Age: speech production. 18-47, Mean: 27; SSI-4: 13-43, Median: 26 ). [See POSTER 681.12 for further analysis of these data] Table 2: Landmarks The SLaparc Freesurfer atlas automatically applies the system to T1 MRI volumes with relatively little To assess the performance of the automatic atlas, labels were corrected by a trained expert to conform aals anterior ascending ramus of the lateral sulcus user input/anatomical expertise, providing cortical ROIs at a huge time savings: Table 1: Regions ahls anterior horizontal ramus of the lateral sulcus to the SpeechLabel system and the automatic and manually corrected labels were compared in terms of aocs anterior occipital sulcus Fully manual operator editing time: 8-16 hours/brain percent overlap. Cortical regions were mapped to the each subjects T1 volume for these comparisons. aCGg anterior cingulate gyrus pCGg posterior cingulate gyrus cas callosal sulcus aCO anterior central operculum PCN precuneus cortex ccs calcarine sulcus Fully automatic operator editing time: .5-1 hours/brain adPMC anterior dorsal premotor cortex pCO posterior central operculum cgs cingulate sulcus 2 |X ∩ Y| adSTs anterior dorsal superior temporal sulcus pdPMC posterior dorsal premotor cortex cos collateral sulcus Auto+manual cleanup time: 1-2 hours/brain Region % overlap, PO, given by Dice coecent: PO = aFO anterior frontal operculum pdSTs posterior dorsal superior temporal sulcu crs circular insular sulcus |X|+|Y| cs central sulcus Ag angular gyrus pFO posterior frontal operculum aIFs anterior inferior frontal sulcus pIFs posterior inferior frontal sulcus csts1 caudal superior temporal sulcus, rst segment Freesurfer tools provide an easy means to create surface and volume-based cortical AND white Labeled fsaverage: The Freesurfer fsaverage surface template was labeled by SLaparc and manually aINS anterior insula pINS posterior insula csts2 caudal superior temporal sulcus, second segment aITg anterior inferior temporal gyrus pITg posterior inferior temporal gyrus csts3 caudal superior temporal sulcus, third segment matter regions of interest . edited as needed. aMFg anterior middle frontal gyrus pMFg posterior middle frontal gyrus ftts rst transverse temporal sulcus aMTg anterior middle temporal gyrus pMTg posterior middle temporal gyrus hs Heschl's sulcus aPHg anterior parahippocampal gyrus PO parietal operculum ifrs inferior frontal sulcus 2 Useful applications for neuroimaging research: Acknowledgments aSMg anterior supramarginal gyrus PP planum polare ihs interhemispheric sulcus i. individually derived ROIs for group comparative analyses, e.g., morphometric analyses, func- This research was supported by NIDCD R01 grants DC007683 and DC002852 (PI: FG). We would like to thank Bobbie Holland for editing aSTg anterior superior temporal gyrus pPHg posterior parahippocampal gyrus itps intraparietal sulcus its inferior temporal sulcus brain labels and Jennifer Segawa and Shanqing Cai for providing the T1 volumes. aTFg anterior temporal fusiform preSMA presupplementary motor area avSTs anterior ventral superior temporal sulcus pSMg posterior supramarginal gyrus locs lateral occipital sulcus tional analysis, diusion tensor analyses, that is not dependent upon inter-subject image volume dIFo dorsal inferior frontal gyrus, pars opercularis pSTg posterior superior temporal gyrus ls lateral sulcus References dIFt dorsal inferior frontal gyrus, pars triangularis PT planum temporale olfs olfactory sulcus averaging [See POSTER 681.12 and POSTER for examples] Fischl, B., Sereno, M.I., Dale, A.M., (1999). Cortical Surface-Based Analysis II: Ination, Flattening, and a Surface-Based Coordinate System. NeuroImage, dMC dorsal motor cortex pTFg posterior temporal fusiform ots occipitotemporal sulcus 9(2):195-207. dSC dorsal somatosensory cortex pvSTs superior temporal sulcu pals posterior ascending ramus of the lateral sulcus ii. provides a common substrate for localizing eects within a surface-based template Fischl, B., Salat, D.H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., van der Kouwe, A., Killiany, R., Kennedy, D., Klaveness, S., Montillo, A., FMC frontal medial cortex SCC subcallosal cortex pcs paracentral sulcus FOC frontal orbital cortex SFg superior frontal gyrus phls posterior horizontal ramus of the lateral sulcus But, accuracy of automatic classier needs improvement in some key