Cortical thickness estimation in individuals with cerebral small vessel disease, focal atrophy, and chronic stroke lesions

Abstract

BackgroundRegional changes to cortical thickness in individuals with neurodegenerative and cerebrovascular diseases can be estimated using specialised neuroimaging software. However, the presence of cerebral small vessel disease, focal atrophy, and cortico-subcortical stroke lesions, pose significant challenges that increase the likelihood of misclassification errors and segmentation failures.PurposeThe main goal of this study was to examine a correction procedure developed for enhancing FreeSurfer’s cortical thickness estimation tool, particularly when applied to the most challenging MRI obtained from participants with chronic stroke and cerebrovascular disease, with varying degrees of neuro-vascular lesions and brain atrophy.MethodsIn 155 cerebrovascular disease patients enrolled in the Ontario Neurodegenerative Disease Research Initiative (ONDRI), FreeSurfer outputs were compared between a fully automated, unmodified procedure and a corrected procedure that accounted for potential sources of error due to atrophy and neurovascular lesions. Quality control (QC) measures were obtained from both procedures. Association between cortical thickness and global cognitive status as assessed by the Montreal Cognitive Assessment (MoCA) score was also investigated from both procedures.ResultsCorrected procedures increased ‘Acceptable’ QC ratings from 18% to 76% for the cortical ribbon and from 38% to 92% for tissue segmentation. Corrected procedures reduced ‘Fail’ ratings from 11% to 0% for the cortical ribbon and 62% to 8% for tissue segmentation. FreeSurfer-based segmentation of T1-weighted white matter hypointensities were significantly greater in the corrected procedure (5.8mL vs. 15.9mL, p<0.001). The unmodified procedure yielded no significant associations with global cognitive status, whereas the corrected procedure yielded positive associations between MoCA total score and clusters of cortical thickness in the left superior parietal (p=0.018) and left insula (p=0.04) regions. Further analyses with the corrected cortical thickness results and MoCA subscores showed a positive association between left superior parietal cortical thickness and Attention (p<0.001).ConclusionsThese findings suggest that correction procedures that account for brain atrophy and neurovascular lesions can significantly improve FreeSurfer’s segmentation results, reduce failure rates, and potentially increase sensitivity to examine brain-behaviour relationships. Future work will examine relationships between cortical thickness, cerebral small vessel disease, and neurodegenerative disease in the ONDRI study.

Cortical thinning 3 years after ischaemic stroke is associated with cognitive impairment and APOE ε4

“…1 Effects of stroke lesion masking on FreeSurfer determination of pial surfaces. Small thickness values (yellow arrow) are still produced after ‘aseg’ modification based on recommendations from prior study, ( Ozzoude, 2020 ) thus requiring exclusion of infarcted vertices post-FreeSurfer processing (ID-1: pial surfaces identified by FreeSurfer for MRI of a participant with subcortical lesion (red); ID-2: pial surfaces for a second participant with both cortical and subcortical lesions with no modifications to ‘aseg’; ID-2 aseg masked: pial surfaces after ‘aseg’ modifications). …”

“…This is more so for cortical lesions and subcortical lesions stretching to cover portions of cortical grey matter causing distortions to the anatomy of the cortex. Corrective procedures have been proposed ( Ozzoude, 2020 ) to enhance FreeSurfer estimation of cortical thickness in stroke. The corrective procedures involved three modifications to FreeSurfer reconstruction stages: 1) replacing FreeSurfer skull stripped mask (brainmask.mgz) by an improved skull stripped mask during ‘-autorecon1′ run; 2) integration of lesion masks (assigned a 77 label in aseg.presurf.mgz) during ‘-autorecon2′ run; and 3) completing the final ‘-autorecon2-noaseg -autorecon3′ run using the modified brainmask.mgz and aseg.presurf.mgz as input.…”

Cognitive and Neuroimaging Profiles of Older Adults With Attention Deficit/Hyperactivity Disorder Presenting to a Memory Clinic

“…Pre-processing of T1-weighted scans included motion correction, skull-stripping, transformation to Talairach space, intensity normalization, hemispheric separation, and tissue segmentation and parcellation. Two additional stages improved segmentation accuracy and quality assessment based on the PD/T2 sequence ( Ozzoude et al, 2020 ): Stage 1 involved replacing the skull-stripped brain in FreeSurfer with one generated using a semi-automatic brain extraction (SABRE) pipeline ( Dade et al, 2004 ) which enhances the overall downstream processes in FreeSurfer. Stage 2 involved incorporating lesion masks from a PD and T2-based lesion segmentation pipeline to account for significant atrophy and small vessel disease ( Ramirez et al, 2011 ).…”

Amyloid-PET of the white matter: relationship to free water, fiber integrity, and cognition in patients with dementia and small vessel disease

“…We used the same MRI processing methodology as the one employed in a previous study 28 . Briefly, the T1-weighted MRI images were processed using FreeSurfer v6.0 using an in-house modified pipeline for subjects with SVD 31 . Subcortical lacunar infarcts were masked out using the SABRE-Lesion Explorer pipeline before WMH segmentation 32 .…”