On these pages, the anatomy of the human hippocampal formation, parahippocampal region and retrosplenial cortex will be detailed. This is a work in progress. For now, we recommend to have a look at a video comparing the mouse and human brain in size, a Nissl stained section of hippocampus tissue and looking into the articles below:
Hum Brain Mapp. 2012 Aug 30. doi: 10.1002/hbm.22170. [Epub ahead of print]
Identification of the human medial temporal lobe regions on magnetic resonance images.
INSERM, U1075 COMETE, 14032 Caen, France; University of Caen, U1075 COMETE, 14032 Caen, France; CHU de Caen, Department of Functional Explorations, 14032 Caen, France.
The medial temporal lobe (MTL) plays a key role in learning, memory, spatial navigation, emotion, and social behavior. The improvement of noninvasive neuroimaging techniques, especially magnetic resonance imaging, has increased the knowledge about this region and its involvement in cognitive functions and behavior in healthy subjects and in patients with various neuropsychiatric and neurodegenerative disorders. However, cytoarchitectonic boundaries are not visible on magnetic resonance images (MRI), which makes it difficult to identify precisely the different parts of the MTL (hippocampus, amygdala, temporopolar, perirhinal, entorhinal, and posterior parahippocampal cortices) with imaging techniques, and thus to determine their involvement in normal and pathological functions. Our aim in this study was to define neuroanatomical landmarks visible on MRI, which can facilitate the examination of this region. We examined the boundaries of the MTL regions in 50 post-mortem brains. In eight cases, we also obtained post-mortem MRI on which the MTL boundaries were compared with histological examination before applying them to 26 in vivo MRI of healthy adults. We then defined the most relevant neuroanatomical landmarks that set the rostro-caudal limits of the MTL structures, and we describe a protocol to identify each of these structures on coronal T1-weighted MRI. This will help the structural and functional imaging investigations of the MTL in various neuropsychiatric and neurodegenerative disorders affecting this region. Hum Brain Mapp, 2012. © 2012 Wiley Periodicals, Inc.
Copyright © 2012 Wiley Periodicals, Inc.
Imaging hippocampal subregions with in vivo MRI: advances and limitations.
Comment onA pathophysiological framework of hippocampal dysfunction in ageing and disease. [Nat Rev Neurosci. 2011]
In their recent Review (A pathophysiological framework of hippocampal dysfunction in ageing and disease), Small et al. present a compelling framework for differentiating hippocampal disorders based on the selective vulnerability of hippocampal subregions, using recent neuroimaging findings. This framework stimulates thoughts about how (dys)function of distinct hippocampal subregions relates to disease and can be assessed in future, using high-resolution structural and functional magnetic resonance imaging (MRI). The impact this pathophysiological framework has on clinical practice depends to a great extent on the availability, quality and reliability of methods to discern hippocampal subregions in-vivo. In many hospitals, 3 Tesla MRI scanners have become the standard for obtaining high-resolution in-vivo brain images and the introduction of 7 Tesla MRI may lead to a revolutionary increase in image detail. Nonetheless, accurately measuring hippocampal subregions with in-vivo MRI has proved to be challenging . In this comment, we illustrate how human in-vivo MRI acquisition and image post-processing methods need to advance, in order to reliably measure and differentiate hippocampal subregions.
In neuroanatomy, hippocampal subregions are discerned on the basis of transitions in cytoarchitecture  (e.g. the number of cortical layers or the density of a cell-layer), made visible in for example Nissl-stained tissue (Figure 1A). Using high-field strength ex-vivo MRI on small hippocampus samples, images that approach microscopic quality (e.g. 0.1 mm isotropic voxels) can be obtained [3, 4]. However, even then the level of spatial detail is often too limited to reliably discern hippocampal subregions (Figure 1B, 1C). This is even more challenging in in-vivo 3 and 7 Tesla images. With a spatial resolution of 1.5 mm isotropic voxels in functional MRI images and sub-millimeter (> 0.4 mm) in structural images (Figure 1E, 1D), the size of the smallest measuring unit (voxel) substantially exceeds the thickness of a cortical layer, resulting in an inability to reliably see cortical layers or layer transitions in such images.
In current in-vivo neuroimaging studies in which hippocampal subfields are discerned, either a manual or automatized segmentation procedure is applied that is based on visual or calculated similarity between the obtained MR image and a detailed anatomical atlas of the hippocampal subdivisions. However, no reliable, quantifiable relationship between macroscopic anatomical landmarks (e.g. folding patterns of gyri) and the exact location of hippocampal subregions is known to exist , despite reports that in other cortical regions macroscopic features can be predictive for the underlying cytoarchitecture . In healthy individuals, a probabilistic atlas may be used to determine the probability of the location of hippocampal subregions . However, this atlas is based on healthy individuals and is likely to be invalid for determining subregions in individuals suffering from hippocampus related pathology, since the pathology distorts the geometry of the hippocampal subregions differentially . The Review by Small et al. provides an excellent starting point to advance knowledge about the relationship between high resolution in-vivo and ex-vivo MRI and histopathological images, yet clinical relevance will increase only if methods become available that allow accurate localization of hippocampal subfields using in-vivo MRI.
Predicting the location of human perirhinal cortex, Brodmann's area 35, from MRI.
The perirhinal cortex (Brodmann's area 35) is a multimodal area that is important for normal memory function. Specifically, perirhinal cortex is involved in the detection of novel objects and manifests neurofibrillary tangles in Alzheimer's disease very early in disease progression. We scanned ex vivo brain hemispheres at standard resolution (1mm×1mm×1mm) to construct pial/white matter surfaces in FreeSurfer and scanned again at high resolution (120µm×120µm×120µm) to determine cortical architectural boundaries. After labeling perirhinal area 35 in the high resolution images, we mapped the high resolution labels to the surface models to localize area 35 in fourteen cases. We validated the area boundaries determined using histological Nissl staining. To test the accuracy of the probabilistic mapping, we measured the Hausdorff distance between the predicted and true labels and found that the median Hausdorff distance was 4.0mm for the left hemispheres (n=7) and 3.2mm for the right hemispheres (n=7) across subjects. To show the utility of perirhinal localization, we mapped our labels to a subset of the Alzheimer's Disease Neuroimaging Initiative dataset and found decreased cortical thickness measures in mild cognitive impairment and Alzheimer's disease compared to controls in the predicted perirhinal area 35. Our ex vivo probabilistic mapping of the perirhinal cortex provides histologically validated, automated and accurate labeling of architectonic regions in the medial temporal lobe, and facilitates the analysis of atrophic changes in a large dataset for earlier detection and diagnosis.
Copyright © 2012 Elsevier Inc. All rights reserved.
Nearly automatic segmentation of hippocampal subfields in in vivo focal T2-weighted MRI.
We present and evaluate a new method for automatically labeling the subfields of the hippocampal formation in focal 0.4 × 0.5 × 2.0mm(3) resolution T2-weighted magnetic resonance images that can be acquired in the routine clinical setting with under 5 min scan time. The method combines multi-atlas segmentation, similarity-weighted voting, and a novel learning-based bias correction technique to achieve excellent agreement with manual segmentation. Initial partitioning of MRI slices into hippocampal 'head', 'body' and 'tail' slices is the only input required from the user, necessitated by the nature of the underlying segmentation protocol. Dice overlap between manual and automatic segmentation is above 0.87 for the larger subfields, CA1 and dentate gyrus, and is competitive with the best results for whole-hippocampus segmentation in the literature. Intraclass correlation of volume measurements in CA1 and dentate gyrus is above 0.89. Overlap in smaller hippocampal subfields is lower in magnitude (0.54 for CA2, 0.62 for CA3, 0.77 for subiculum and 0.79 for entorhinal cortex) but comparable to overlap between manual segmentations by trained human raters. These results support the feasibility of subfield-specific hippocampal morphometry in clinical studies of memory and neurodegenerative disease.
Copyright © 2010 Elsevier Inc. All rights reserved.
Automated segmentation of hippocampal subfields from ultra-high resolution in vivo MRI.
Recent developments in MRI data acquisition technology are starting to yield images that show anatomical features of the hippocampal formation at an unprecedented level of detail, providing the basis for hippocampal subfield measurement. However, a fundamental bottleneck in MRI studies of the hippocampus at the subfield level is that they currently depend on manual segmentation, a laborious process that severely limits the amount of data that can be analyzed. In this article, we present a computational method for segmenting the hippocampal subfields in ultra-high resolution MRI data in a fully automated fashion. Using Bayesian inference, we use a statistical model of image formation around the hippocampal area to obtain automated segmentations. We validate the proposed technique by comparing its segmentations to corresponding manual delineations in ultra-high resolution MRI scans of 10 individuals, and show that automated volume measurements of the larger subfields correlate well with manual volume estimates. Unlike manual segmentations, our automated technique is fully reproducible, and fast enough to enable routine analysis of the hippocampal subfields in large imaging studies.
2009 Wiley-Liss, Inc.
A high-resolution computational atlas of the human hippocampus from postmortem magnetic resonance imaging at 9.4 T.
This paper describes the construction of a computational anatomical atlas of the human hippocampus. The atlas is derived from high-resolution 9.4 Tesla MRI of postmortem samples. The main subfields of the hippocampus (cornu ammonis fields CA1, CA2/3; the dentate gyrus; and the vestigial hippocampal sulcus) are labeled in the images manually using a combination of distinguishable image features and geometrical features. A synthetic average image is derived from the MRI of the samples using shape and intensity averaging in the diffeomorphic non-linear registration framework, and a consensus labeling of the template is generated. The agreement of the consensus labeling with manual labeling of each sample is measured, and the effect of aiding registration with landmarks and manually generated mask images is evaluated. The atlas is provided as an online resource with the aim of supporting subfield segmentation in emerging hippocampus imaging and image analysis techniques. An example application examining subfield-level hippocampal atrophy in temporal lobe epilepsy demonstrates the application of the atlas to in vivo studies.
Cytoarchitectonic mapping of the human amygdala, hippocampal region and entorhinal cortex: intersubject variability and probability maps.
Research Center Jülich, IME, 52425 Jülich, Germany. k.amunts at(@) fz-juelich.de
Probabilistic maps of neocortical areas and subcortical fiber tracts, warped to a common reference brain, have been published using microscopic architectonic parcellations in ten human postmortem brains. The maps have been successfully applied as topographical references for the anatomical localization of activations observed in functional imaging studies. Here, for the first time, we present stereotaxic, probabilistic maps of the hippocampus, the amygdala and the entorhinal cortex and some of their subdivisions. Cytoarchitectonic mapping was performed in serial, cell-body stained histological sections. The positions and the extent of cytoarchitectonically defined structures were traced in digitized histological sections, 3-D reconstructed and warped to the reference space of the MNI single subject brain using both linear and non-linear elastic tools of alignment. The probability maps and volumes of all structures were calculated. The precise localization of the borders of the mapped regions cannot be predicted consistently by macroanatomical landmarks. Many borders, e.g. between the subiculum and entorhinal cortex, subiculum and Cornu ammonis, and amygdala and hippocampus, do not match sulcal landmarks such as the bottom of a sulcus. Only microscopic observation enables the precise localization of the borders of these brain regions. The superposition of the cytoarchitectonic maps in the common spatial reference system shows a considerably lower degree of intersubject variability in size and position of the allocortical structures and nuclei than the previously delineated neocortical areas. For the first time, the present observations provide cytoarchitectonically verified maps of the human amygdala, hippocampus and entorhinal cortex, which take into account the stereotaxic position of the brain structures as well as intersubject variability. We believe that these maps are efficient tools for the precise microstructural localization of fMRI, PET and anatomical MR data, both in healthy and pathologically altered brains.