Elsevier

NeuroImage

Volume 47, Issue 1, 1 August 2009, Pages 8-17
NeuroImage

Predicting the location of entorhinal cortex from MRI

https://doi.org/10.1016/j.neuroimage.2009.04.033Get rights and content

Abstract

Entorhinal cortex (EC) is a medial temporal lobe area critical to memory formation and spatial navigation that is among the earliest parts of the brain affected by Alzheimer's disease (AD). Accurate localization of EC would thus greatly facilitate early detection and diagnosis of AD. In this study, we used ultra-high resolution ex vivo MRI to directly visualize the architectonic features that define EC rostrocaudally and mediolaterally, then applied surface-based registration techniques to quantify the variability of EC with respect to cortical geometry, and made predictions of its location on in vivo scans. The results indicate that EC can be localized quite accurately based on cortical folding patterns, within 3 mm in vivo, a significant step forward in our ability to detect the earliest effects of AD when clinical intervention is most likely to be effective.

Introduction

In the medial temporal lobe, the hippocampal formation and in particular the entorhinal cortex (EC) – the anterior parahippocampal gyrus – show the most significant neurofibrillary tangle pathology and neuronal death early in Alzheimer's disease (Gomez-Isla et al., 1996a, Gomez-Isla et al., 1996b) and it is the most profoundly affected in the later stages of disease (Arnold et al., 1991a, Arnold et al., 1991b, Braak and Braak, 1991). It is widely accepted that neurofibrillary tangle pathology originates in specific layers of the multimodal areas, entorhinal and perirhinal cortex (Braak and Braak, 1985, Van Hoesen and Solodkin, 1993). The EC receives direct projections from frontal, occipital, parietal and other regions of temporal cortex, and provides the major convergent input from these areas to the hippocampus, serving as a gateway for neocortical information, in part for memory processing. Profound degeneration of EC layer II, the entorhinal output layer to the hippocampus through the perforant pathway, is found early in the disease and results in an isolation of the hippocampus from the neocortex (Hyman et al., 1984). Alzheimer's disease is the most common form of dementia and affects over four million people in the United States alone (Albert, 1996), highlighting the importance of being able to localize this cortical area in vivo.

Although definitive diagnosis of AD still rests on laborious pathological assessment, volumetric in vivo MRI has made notable contributions to reveal atrophy in mesocortical and allocortical areas early in the course of the disease, and has been shown to discriminate controls from AD (De Toledo-Morrell et al., 2000, Dickerson et al., 2001, Insausti et al., 1998, Killiany et al., 2000). Furthermore, in vivo volumetric studies have also shown that EC is atrophic in mild cognitive impairment patients, that decreased volume of the hippocampus can predict mild AD (Pennanen et al., 2004) and that clinical progression is correlated with gray matter volume reduction (Jack et al., 2000, Kaye et al., 1997). Nevertheless, it is not possible to observe and detect neuronal level resolutions in vivo.

Standard in vivo anatomical brain imaging protocols produce images that cannot resolve structures smaller than 1–2 mm in size. Achieving significantly higher resolution would be of fundamental clinical and neuroscientific value, as it would allow the in vivo detection and analysis of cytoarchitectural features of the cortex, as well as substructures of brain regions such as the hippocampus, thalamus and amygdala. Unfortunately, such resolution is extremely difficult to obtain in vivo, as the signal-to-noise ratio (SNR) goes down with the third power of the linear dimension of each voxel. While some recent studies have pushed this limit to under 1–2 mm, this is at the cost of extremely long scan sessions and specialized imaging hardware (Duyn et al., 2007), and even this is still a coarse resolution relative to what is required to visualize correlates of the cytoarchitecture with MRI. With the exception of the line of Gennari, which is one of the few histological features of the cortex that has been detected in vivo, at least for part of its extent (Barbier et al., 2002, Clare and Bridge, 2005, Clark et al., 1992), current in vivo methods cannot consistently resolve cytoarchitectural features of the cerebral cortex.

In contrast, ultra-high resolution ex vivo MRI can distinguish cytoarchitectural features. In a previous study, using ex vivo MRI and a 7 Tesla (7 T) scanner, we robustly distinguished the cell-dense layer II islands throughout the extent of EC (Augustinack et al., 2005), and the line of Gennari, a highly myelinated stripe in layer IV in primary visual cortex (Hinds et al., 2008), allowing us to delineate areas based on its microstructural properties. Moreover, EC recently has been mapped using quantitative architectonic techniques (Amunts et al., 2005) showing it to be less variable in volumetric coordinate than many previously mapped neocortical regions. In related work, we have recently shown that cortical folding patterns are excellent predictors for a wide array of histologically-defined architectonic regions (Fischl et al., 2008). In this study we found that the boundaries of primary and secondary sensory cortices could be localized to within 2–4 mm based on cortical folding patterns, although higher cortical areas such as BA 44/45 were more variable with respect to cortical geometry. The ability to detect cytoarchitecture with ex vivo MRI makes these images ideal for the construction of models for the location of cytoarchitectonically defined boundaries that can ultimately to be applied to in vivo data sets.

The current way that neuroimaging researchers localize a Brodmann area is problematic and the existing imaging localization methods have limited accuracy. Originally, Brodmann areas were described histologically by Korbinian Brodmann almost a century ago who defined these areas using a Nissl stain and categorized them based on the presence, absence or a combination of cortical layers in a particular region (Brodmann, 1909). Currently there are two common methods that neuroimagers use to localize a Brodmann area. First, neuroimaging researchers identify Brodmann areas based on an ad hoc assessment of the location of interest relative to surrounding folding patterns. This approach is problematic as there is no means to rigorously test the uncertainty of these localizations and the relationship between the Brodmann areas (BAs) and cortical folds is sometimes unclear. An alternative is to use volumetric registration to an atlas coordinate system (i.e. the Talairach coordinate system (Talairach et al., 1967, Talairach and Tournoux, 1988)), then map the BAs defined in the atlas onto individual subjects using the registration. Unfortunately, this technique has been shown to yield poor localization accuracy in a wide array of cortical areas (Amunts et al., 2000, Amunts et al., 1999, Geyer et al., 1997, Geyer et al., 2000, Malikovic et al., 2007, Rademacher et al., 2002, Rademacher et al., 2001). Thus, accurate in vivo identification and localization of cortical areas – specifically the EC – would be a significant step towards detecting AD pathology in the earliest stages and provide a critical MRI diagnostic tool.

Here we take a novel approach to identification and area localization, and image ex vivo tissue samples, in which exceedingly high resolution is obtainable, on the order of 100 μm isotropic, then apply the information derived from the ex vivo images to model the probabilistic relationship between cortical geometry and the underlying cytoarchitecture, allowing us to accurately predict the locations of cortical areas from standard in vivo imaging data. In the current study we extend beyond sulcal topography and use MR-cytoarchitecture to detect and focus on the localization of EC since it is known to be one of the earliest loci of Alzheimer's disease (AD), and critical for normal memory function. We show that surface-based registration aligns the borders of EC extremely well across subjects, within 2–3 mm, or less than the size of an average functional MRI voxel. The ability to more accurately localize this cortical region is a critical step in the early diagnosis of AD, and in the assessment of the efficacy of potential clinical interventions.

Section snippets

Participants and ex vivo samples

The in vivo AD participants (n = 61) had a mean and standard deviation of 77.7 ± 6.8 years of age with a range of 63–92 years old. Thirty-one women and thirty men participated. The Clinical Dementia Rating (CDR) was 0.5 for all and the Mini-Mental State Examination (MMSE) was 25.6 ± 3.3. The normal control participants (n = 73) had a mean age of 76.8 ± 6.9 years with a range of 63–95 years. Forty-seven women and twenty-six men were imaged. The control subjects were CDR = 0 and the MMSE for the control

Delineation of the borders of EC

We analyzed all rostrocaudal levels of the EC with special attention to the rostral, caudal, medial and lateral borders of the EC. In Fig. 3, the rostrocaudal extent of EC is illustrated showing the rostral most and caudal most boundaries in A and K, respectively. At the rostral end, primary olfactory cortex occupies the anterior most portion of EC (Fig. 3A) and some reports have described this cortex as subregion EO (Insausti et al., 1995) or prorhinal (Krimer et al., 1997). Although primary

Discussion

Sulcal patterns in the medial temporal lobe are notoriously variable in the human brain (Insausti, 1993, Insausti et al., 1998, Novak et al., 2002, Ono et al., 1990, Van Hoesen et al., 2000), making manual parcellation of these critical areas based on visual inspection of cortical geometry extremely difficult. The architectonic properties of EC however are much more well-characterized (e.g. (Amunts et al., 2005, Augustinack et al., 2004, Cajal, 1909, de No, 1933, Insausti et al., 1995, Krimer

Acknowledgments

Support for this research was provided in part by the National Center for Research Resources (P41-RR14075, and the NCRR BIRN Morphometric Project BIRN002, U24 RR021382), the National Institute for Biomedical Imaging and Bioengineering (R01 EB001550, R01EB006758), the National Institute for Neurological Disorders and Stroke (R01 NS052585-01) as well as the Mental Illness and Neuroscience Discovery (MIND) Institute, and is part of the National Alliance for Medical Image Computing (NAMIC), funded

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