A stochastic model for studying the laminar structure of cortex from MRI

Morphometric differences in planum temporale in schizophrenia and bipolar disorder revealed by statistical analysis of labeled cortical depth maps

Differences in cortical thickness in the lateral temporal lobe, including the planum temporale (PT), have been reported in MRI studies of schizophrenia (SCZ) and bipolar disorder (BPD) patients. Most of these studies have used a single-valued global or local measure for thickness. However, additional and complementary information can be obtained by generating labeled cortical distance maps (LCDMs), which are distances of labeled gray matter (GM) voxels from the nearest point on the GM/white matter (WM) (inner) cortical surface. Statistical analyses of pooled and censored LCDM distances reveal subtle differences in PT between SCZ and BPD groups from data generated by Ratnanather et al. (Schizophrenia Research, http://dx.doi.org/10.1016/j.schres.2013.08.014). These results confirm that the left planum temporale (LPT) is more sensitive than the right PT in distinguishing between SCZ, BPD, and healthy controls. Also confirmed is a strong gender effect, with a thicker PT seen in males than in females. The differences between groups at smaller distances in the LPT revealed by pooled and censored LCDM analysis suggest that SCZ and BPD have different effects on the cortical mantle close to the GM/WM surface. This is consistent with reported subtle changes in the cortical mantle observed in post-mortem studies.

Censoring distances based on labeled cortical distance maps in cortical morphometry

It has been demonstrated that shape differences in cortical structures may be manifested in neuropsychiatric disorders. Such morphometric differences can be measured by labeled cortical distance mapping (LCDM) which characterizes the morphometry of the laminar cortical mantle of cortical structures. LCDM data consist of signed/labeled distances of gray matter (GM) voxels with respect to GM/white matter (WM) surface. Volumes and other summary measures for each subject and the pooled distances can help determine the morphometric differences between diagnostic groups, however they do not reveal all the morphometric information contained in LCDM distances. To extract more information from LCDM data, censoring of the pooled distances is introduced for each diagnostic group where the range of LCDM distances is partitioned at a fixed increment size; and at each censoring step, the distances not exceeding the censoring distance are kept. Censored LCDM distances inherit the advantages of the pooled distances but also provide information about the location of morphometric differences which cannot be obtained from the pooled distances. However, at each step, the censored distances aggregate, which might confound the results. The influence of data aggregation is investigated with an extensive Monte Carlo simulation analysis and it is demonstrated that this influence is negligible. As an illustrative example, GM of ventral medial prefrontal cortices (VMPFCs) of subjects with major depressive disorder (MDD), subjects at high risk (HR) of MDD, and healthy control (Ctrl) subjects are used. A significant reduction in laminar thickness of the VMPFC in MDD and HR subjects is observed compared to Ctrl subjects. Moreover, the GM LCDM distances (i.e., locations with respect to the GM/WM surface) for which these differences start to occur are determined. The methodology is also applicable to LCDM-based morphometric measures of other cortical structures affected by disease.

Thickness profile generation for the corpus callosum using Laplace's equation

The corpus callosum facilitates communication between the cerebral hemispheres. Morphological abnormalities of the corpus callosum have been identified in numerous psychiatric and neurological disorders. To quantitatively analyze the thickness profile of the corpus callosum, we adapted an automatic thickness measurement method, which was originally used on magnetic resonance (MR) images of the cerebral cortex (Hutton et al. [2008]: NeuroImage 40:1701-10; Jones et al. [2002]: Hum Brain Mapp 11:12-32; Schmitt and Böhme [2002]: NeuroImage 16:1103-9; Yezzi and Prince [2003]: IEEE Trans Med Imaging 22:1332-9), to MR images of the corpus callosum. The thickness model was derived by computing a solution to Laplace's equation evaluated on callosal voxels. The streamlines from this solution form non-overlapping, cross-sectional contours the lengths of which are modeled as the callosal thickness. Apart from the semi-automated segmentation and endpoint selection procedures, the method is fully automated, robust, and reproducible. We compared the Laplace method with the orthogonal projection technique previously published (Walterfang et al. [2009a]: Psych Res Neuroimaging 173:77-82; Walterfang et al. [2008a]: Br J Psychiatry 192:429-34; Walterfang et al. [2008b]: Schizophr Res 103:1-10) on a cohort of 296 subjects, composed of 86 patients with chronic schizophrenia (CSZ), 110 individuals with first-episode psychosis, 100 individuals at ultra-high risk for psychosis (UHR; 27 of whom later developed psychosis, UHR-P, and 73 who did not, UHR-NP), and 55 control subjects (CTL). We report similar patterns of statistically significant differences in regional callosal thickness with respect to the comparisons CSZ vs. CTL, UHR vs. CTL, UHR-P vs. UHR-NP, and UHR vs. CTL.

Registration based cortical thickness measurement

Cortical thickness is an important biomarker for image-based studies of the brain. A diffeomorphic registration based cortical thickness (DiReCT) measure is introduced where a continuous one-to-one correspondence between the gray matter-white matter interface and the estimated gray matter-cerebrospinal fluid interface is given by a diffeomorphic mapping in the image space. Thickness is then defined in terms of a distance measure between the interfaces of this sheet like structure. This technique also provides a natural way to compute continuous estimates of thickness within buried sulci by preventing opposing gray matter banks from intersecting. In addition, the proposed method incorporates neuroanatomical constraints on thickness values as part of the mapping process. Evaluation of this method is presented on synthetic images. As an application to brain images, a longitudinal study of thickness change in frontotemporal dementia (FTD) spectrum disorder is reported.

Voxel-based cortical thickness measurements in MRI

The thickness of the cerebral cortex can provide valuable information about normal and abnormal neuroanatomy. High resolution MRI together with powerful image processing techniques has made it possible to perform these measurements automatically over the whole brain. Here we present a method for automatically generating voxel-based cortical thickness (VBCT) maps. This technique results in maps where each voxel in the grey matter is assigned a thickness value. Sub-voxel measurements of thickness are possible using sub-sampling and interpolation of the image information. The method is applied to repeated MRI scans of a single subject from two MRI scanners to demonstrate its robustness and reproducibility. A simulated data set is used to show that small focal differences in thickness between two groups of subjects can be detected. We propose that the analysis of VBCT maps can provide results that are complementary to other anatomical analyses such as voxel-based morphometry.

Measuring structural complexity in brain images

An information theory based formalism for medical image analysis proposed in Young et al. [Young, K., Chen, Y., Kornak, J., Matson G. B., Schuff, N., 2005. Summarizing Complexity in High Dimensions, Phys. Rev. Lett. 94 098701-1] is described and used to estimate image complexity measures as a means of generating interpretable summary information. An analysis of anatomical brain MRI data exhibiting cortical thinning, currently considered to be a sensitive early biomarker for neurodegenerative diseases, is used to illustrate the method. Though requiring no previous assumptions about the detailed shape of the cortex or other brain structures, the method performed comparably (sensitivity=0.91) to direct cortical thickness estimation techniques (sensitivity=0.93) at separating populations in a data set designed specifically to test the cortical thickness estimation algorithms. The results illustrate that the complexity estimation method, though general, is capable of providing interpretable diagnostic information.

Detection of cortical thickness correlates of cognitive performance: Reliability across MRI scan sessions, scanners, and field strengths

In normal humans, relationships between cognitive test performance and cortical structure have received little study, in part, because of the paucity of tools for measuring cortical structure. Computational morphometric methods have recently been developed that enable the measurement of cortical thickness from MRI data, but little data exist on their reliability. We undertook this study to evaluate the reliability of an automated cortical thickness measurement method to detect correlates of interest between thickness and cognitive task performance. Fifteen healthy older participants were scanned four times at 2-week intervals on three different scanner platforms. The four MRI data sets were initially treated independently to investigate the reliability of the spatial localization of findings from exploratory whole-cortex analyses of cortical thickness-cognitive performance correlates. Next, the first data set was used to define cortical ROIs based on the exploratory results that were then applied to the remaining three data sets to determine whether the relationships between cognitive performance and regional cortical thickness were comparable across different scanner platforms and field strengths. Verbal memory performance was associated with medial temporal cortical thickness, while visuomotor speed/set shifting was associated with lateral parietal cortical thickness. These effects were highly reliable - in terms of both spatial localization and magnitude of absolute cortical thickness measurements - across the four scan sessions. Brain-behavior relationships between regional cortical thickness and cognitive task performance can be reliably identified using an automated data analysis system, suggesting that these measures may be useful as imaging biomarkers of disease or performance ability in multicenter studies in which MRI data are pooled.

Brain functional localization: a survey of image registration techniques

Functional localization is a concept which involves the application of a sequence of geometrical and statistical image processing operations in order to define the location of brain activity or to produce functional/parametric maps with respect to the brain structure or anatomy. Considering that functional brain images do not normally convey detailed structural information and, thus, do not present an anatomically specific localization of functional activity, various image registration techniques are introduced in the literature for the purpose of mapping functional activity into an anatomical image or a brain atlas. The problems addressed by these techniques differ depending on the application and the type of analysis, i.e., single-subject versus group analysis. Functional to anatomical brain image registration is the core part of functional localization in most applications and is accompanied by intersubject and subject-to-atlas registration for group analysis studies. Cortical surface registration and automatic brain labeling are some of the other tools towards establishing a fully automatic functional localization procedure. While several previous survey papers have reviewed and classified general-purpose medical image registration techniques, this paper provides an overview of brain functional localization along with a survey and classification of the image registration techniques related to this problem.

Reliability of MRI-derived measurements of human cerebral cortical thickness: the effects of field strength, scanner upgrade and manufacturer

In vivo MRI-derived measurements of human cerebral cortex thickness are providing novel insights into normal and abnormal neuroanatomy, but little is known about their reliability. We investigated how the reliability of cortical thickness measurements is affected by MRI instrument-related factors, including scanner field strength, manufacturer, upgrade and pulse sequence. Several data processing factors were also studied. Two test-retest data sets were analyzed: 1) 15 healthy older subjects scanned four times at 2-week intervals on three scanners; 2) 5 subjects scanned before and after a major scanner upgrade. Within-scanner variability of global cortical thickness measurements was <0.03 mm, and the point-wise standard deviation of measurement error was approximately 0.12 mm. Variability was 0.15 mm and 0.17 mm in average, respectively, for cross-scanner (Siemens/GE) and cross-field strength (1.5 T/3 T) comparisons. Scanner upgrade did not increase variability nor introduce bias. Measurements across field strength, however, were slightly biased (thicker at 3 T). The number of (single vs. multiple averaged) acquisitions had a negligible effect on reliability, but the use of a different pulse sequence had a larger impact, as did different parameters employed in data processing. Sample size estimates indicate that regional cortical thickness difference of 0.2 mm between two different groups could be identified with as few as 7 subjects per group, and a difference of 0.1 mm could be detected with 26 subjects per group. These results demonstrate that MRI-derived cortical thickness measures are highly reliable when MRI instrument and data processing factors are controlled but that it is important to consider these factors in the design of multi-site or longitudinal studies, such as clinical drug trials.

Estimating linear cortical magnification in human primary visual cortex via dynamic programming

Human primary visual cortex is organized retinotopically, with adjacent locations in cortex representing adjacent locations on the retina. The spatial sampling in cortex is highly nonuniform: the amount of cortex devoted to a unit area of retina decreases with increasing retinal eccentricity. This sampling property can be quantified by the linear cortical magnification factor, which is expressed in terms of millimeters of cortex per degree of visual angle. In this paper, we present a new method using dynamic programming and fMRI retinotopic eccentricity mapping to estimate the linear cortical magnification factor in human primary visual cortex (V1). We localized cortical activity while subjects viewed each of seven stationary contrast- reversing radial checkerboard rings of equal thickness that tiled the visual field from 1.62 to 12.96 degrees of eccentricity. Imaging data from all epochs of each ring were contrasted with data from fixation epochs on a subject-by-subject basis. The resulting t statistic maps were then superimposed on a local coordinate system constructed from the gray/white matter boundary surface of each individual subject's occipital lobe, separately for each ring. Smoothed maps of functional activity on the cortical surface were constructed using orthonormal bases of the Laplace-Beltrami operator that incorporate the geometry of the cortical surface. This allowed us to stably track the ridge of maximum activation due to each ring via dynamic programming optimization over all possible paths on the cortical surface. We estimated the linear cortical magnification factor by calculating geodesic distances between activation ridges on the cortical surface in a population of five normal subjects. The reliability of these estimates was assessed by comparing results based on data from one quadrant to those based on data from the full hemifield along with a split-half reliability analysis.