Detection of cortical thickness correlates of cognitive performance: Reliability across MRI scan sessions, scanners, and field strengths
Introduction
Remarkably specific cognitive deficits can be present in patients with focal cortical lesions (Caramazza and Hillis, 1991, Damasio et al., 1996, Stuss et al., 2001a, Rosenbaum et al., 2005). Yet the relationships, in normal persons, between individual differences in performance on neuropsychologic tests and individual variability in measures of cortical structure have received relatively little study. The lack of data on this fundamental brain–behavior correlate is in part a reflection of the paucity of tools with which to perform such measurements. Manual operator-derived region-of-interest (ROI) measurements from magnetic resonance imaging (MRI) data have demonstrated, for example, that hippocampal and entorhinal volume in patients with Alzheimer’s disease (AD) correlates with performance on particular neuropsychological tests of memory (de Toledo-Morrell et al., 2000a, de Toledo-Morrell et al., 2000b, Kramer et al., 2005). However, manual ROI-based approaches are limited in that they are quite laborious and thus are typically restricted to a few brain regions. Furthermore, this procedure enables the measurement of only cortical volume, not cortical thickness, because cortical thickness is a property that can only be properly measured if the location and orientation of both the gray/white and pial cortical surfaces are known. In addition, cortical volumetric approaches typically require an a priori definition of ROIs, limiting the possibility of exploratory analyses of other cortical regions or of subregions within ROIs.
Voxel-based methods have been developed that enable the exploratory analysis of MRI data with respect to clinical diagnosis, cognitive performance, or other variables, and have demonstrated relationships between the grey matter density of particular brain regions and cognitive performance measures in patients with traumatic brain injury, for example (Gale et al., 2005). Yet given the range of normal individual variance in cortical morphologic features, such as gyral and sulcal patterns, the use of voxel-based tools that transform and smooth individual MRI data into common coordinate spaces may remove the precise features of interest for studies investigating within-group correlations between cortical morphometry and cognitive performance and reduce the ability to specifically localize findings. Furthermore, the measure typically analyzed by voxel-based techniques – ‘grey matter density’ (Thompson et al., 2001) – is difficult to interpret quantitatively with respect to a particular morphometric property of cerebral tissue (i.e., volume, thickness, surface area).
To enable the study of morphometric properties of the human cerebral cortex and their relationship to cognitive function, disease state, or other behavioral variables, automated methods have been developed for segmenting and measuring the cerebral cortex from MRI data (Dale et al., 1999, Joshi et al., 1999, MacDonald et al., 1999, Xu et al., 1999, Zeng et al., 1999, van Essen et al., 2001, Shattuck and Leahy, 2002, Sowell et al., 2003, Barta et al., 2005, Han et al., 2005). Using such tools, relationships have been identified between regional cortical thickness and intelligence quotient (Narr et al., 2006, Shaw et al., 2006), personality measures (Wright et al., 2006, Wright et al., 2007), and memory (Walhovd et al., 2006). Although the validation of MRI-derived cortical thickness measurements has been performed against manual measurements derived from both in vivo and post-mortem MRI brain scans (Rosas et al., 2002b, Kuperberg et al., 2003a, Salat et al., 2004), the reliability of measures of this fundamental morphometric property of the brain has received relatively little systematic investigation (Fischl and Dale, 2000, Rosas et al., 2002a, Kuperberg et al., 2003b, Sowell et al., 2004, Lerch and Evans, 2005, Han et al., 2006). Most studies have investigated reliability by comparing thickness measurements across different subjects, or by performing repeated scans on a few subjects acquired within the same scan session or within very short scan intervals (e.g., the subjects were removed from the scanner and then scanned again in 5 min (Sowell et al., 2004)). This approach may greatly underestimate the sources of variability relevant for longitudinal studies (e.g., subject-related factors, such as hydration status, or instrument-related factors, such as scanner drift). Furthermore, the level of reliability that is needed for the detection of effects of interest is not clear, as none of these studies have evaluated the reliability of cortical morphometric methods for the detection of specific effects of interest, such as correlation of thickness with behavioral measures or group differences in thickness between normal and diseased populations. Thus, the feasibility of the pooling of MRI data across multiple centers for the study of cortical thickness correlates of behavioral performance, disease state, or other purposes is unknown.
We undertook this study to extend our previous investigation of the reliability of a cortical thickness measurement method both within and across different scanner platforms and field strengths (Han et al., 2006). In the previous investigation, we analyzed the test–retest and cross-platform and cross-field strength reliability of cortical thickness measurements across the entire cerebral cortex.
The goal of the present analysis, which makes use of the same MRI data set used previously (Han et al., 2006), was to investigate the reliability of detection of correlates of interest between cortical thickness and cognitive task performance. Fifteen healthy older subjects were scanned four times at two-week intervals on three different scanner platforms (test scan on Siemens 1.5 T, retest scan on Siemens 1.5 T, cross-site/manufacturer scan on GE 1.5 T located at a different clinical site, and cross-field-strength scan on Siemens 3 T). Older participants were studied so that anatomical variability related to atrophy and age-related signal changes was represented. The 2-week interval was chosen so that elements of variability related to subject hydration status and minor instrument drift would be included, which may be artificially minimized when the test–retest interval is several minutes to ∼ 1 day. We initially treated each of the four MRI data sets independently to investigate the reliability of the spatial localization of findings from exploratory whole-cortex analyses of cortical thickness–cognitive performance correlates. Next, we used the first of the four data sets to define cortical ROIs based on the results of the exploratory analysis that could then be applied to the remaining three data sets, in an unbiased manner, to determine how well the magnitude measures of absolute cortical thickness within these regions corresponded across the data sets, and whether the relationships (slopes of regression lines) between cognitive performance and regional cortical thickness were comparable across different scanner platforms and field strengths.
Section snippets
Subjects: recruitment and evaluation
Healthy individuals older than 65 were recruited from the Boston metropolitan community via newspaper advertisements specifically for this reliability study. Respondents were pre-screened extensively using a standardized telephone interview (Go et al., 1997) and excluded based on evidence of significant medical, psychiatric, or neurologic conditions, the use of psychoactive medications, contraindications to MRI, or evidence of memory impairment. Respondents who were not excluded based on
Subject characteristics and cognitive performance data
Seventeen older subjects were screened, 16 were enrolled, and 15 completed the study (age between 66 and 81 years; mean: 69.5 years; std: 4.8 years). (One subject was excluded based on a history of significant head trauma, and the other was unable to complete the scanning procedures due to excessive motion in the scanner.) Demographic and cognitive performance data are provided in Table 1. Although an informant was not interviewed in this study, all subjects received a Clinical Dementia Rating
Discussion
In this study, we demonstrate that an automated method for the measurement of cortical thickness from MRI data is remarkably reliable for the detection and quantification of regional cortical thickness correlates of cognitive performance in normal older adults. In a small sample of subjects, dissociated effects were detected between the performance of two different cognitive tests and cortical thickness in two different brain regions. Verbal memory performance was associated with left medial
Acknowledgments
This study was supported by grants from the NIA (K23-AG22509), the NCRR (P41-RR14075 R01 RR16594-01A1, the NCRR BIRN Morphometric Project BIRN002, U24 RR021382 and U24-RR021382), and the Mental Illness and Neuroscience Discovery (MIND) Institute. Additional support was provided by the National Institute for Biomedical Imaging and Bioengineering (R01 EB001550), the National Institute for Neurological Disorders and Stroke (R01 NS052585-01) and the National Alliance for Medical Image Computing
References (64)
- et al.
Cortical surface-based analysis. I. Segmentation and surface reconstruction
NeuroImage
(1999) - et al.
Neuroimaging biomarkers for clinical trials of disease-modifying therapies in Alzheimer’s disease
NeuroRx
(2005) - et al.
Cortical surface-based analysis. II: inflation, flattening, and a surface-based coordinate system
NeuroImage
(1999) - et al.
Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain
Neuron
(2002) - et al.
Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain
Neuron
(2002) - et al.
Single neuron activity in human hippocampus and amygdala during recognition of faces and objects
Neuron
(1997) - et al.
Reliability of MRI-derived measurements of human cerebral cortical thickness: the effects of field strength, scanner upgrade and manufacturer
NeuroImage
(2006) - et al.
Brain segmentation and the generation of cortical surfaces
NeuroImage
(1999) - et al.
Reliability in multi-site structural MRI studies: effects of gradient non-linearity correction on phantom and human data
NeuroImage
(2006) - et al.
Cortical thickness analysis examined through power analysis and a population simulation
NeuroImage
(2005)
The Alzheimer’s disease neuroimaging initiative
Neuroimaging Clin. N. Am.
The case of K.C.: contributions of a memory-impaired person to memory theory
Neuropsychologia
A hybrid approach to the skull stripping problem in MRI
NeuroImage
BrainSuite: an automated cortical surface identification tool
Med. Image Anal.
Stroop performance in focal lesion patients: dissociation of processes and frontal lobe lesion location
Neuropsychologia
On-line automatic slice positioning for brain MR imaging
NeuroImage
Regional cortical thickness matters in recall after months more than minutes
NeuroImage
Neuroanatomical correlates of personality in the elderly
NeuroImage
Preclinical prediction of AD using neuropsychological tests
J. Int. Neuropsychol. Soc.
Multiple components of lateral posterior parietal activation associated with cognitive set shifting
NeuroImage
A stochastic model for studying the laminar structure of cortex from MRI.
IEEE Trans. Med. Imag.
Offering to share: how to put heads together in autism neuroimaging
J. Autism Dev. Disord.
Comparison of manual and automatic section positioning of brain MR images
Radiology
Lexical organization of nouns and verbs in the brain
Nature
A neural basis for lexical retrieval
Nature
From healthy aging to early Alzheimer’s disease: in vivo detection of entorhinal cortex atrophy
Ann. N. Y. Acad. Sci.
Hemispheric differences in hippocampal volume predict verbal and spatial memory performance in patients with Alzheimer’s disease
Hippocampus
California Verbal Learning Test, Research Edition, Manual
Increased hippocampal activation in mild cognitive impairment compared to normal aging and AD
Neurology
Different regional patterns of cortical thinning in Alzheimer’s disease and frontotemporal dementia
Brain
Measuring the thickness of the human cerebral cortex from magnetic resonance images
Proc. Natl. Acad. Sci. U. S. A.
High-resolution intersubject averaging and a coordinate system for the cortical surface
Hum. Brain Mapp.
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