Recently, there has been a growing effort to analyze the morphometry of hippocampal subfields using both in vivo and postmortem magnetic resonance imaging (MRI). However, given that boundaries between subregions of the hippocampal formation (HF) are conventionally defined on the basis of microscopic features that often lack discernible signature in MRI, subfield delineation in MRI literature has largely relied on heuristic geometric rules, the validity of which with respect to the underlying anatomy is largely unknown. The development and evaluation of such rules are challenged by the limited availability of data linking MRI appearance to microscopic hippocampal anatomy, particularly in three dimensions (3D). The present paper, for the first time, demonstrates the feasibility of labeling hippocampal subfields in a high resolution volumetric MRI dataset based directly on microscopic features extracted from histology. It uses a combination of computational techniques and manual post-processing to map subfield boundaries from a stack of histology images (obtained with 200μm spacing and 5μm slice thickness; stained using the Kluver-Barrera method) onto a postmortem 9.4Tesla MRI scan of the intact, whole hippocampal formation acquired with 160μm isotropic resolution. The histology reconstruction procedure consists of sequential application of a graph-theoretic slice stacking algorithm that mitigates the effects of distorted slices, followed by iterative affine and diffeomorphic co-registration to postmortem MRI scans of approximately 1cm-thick tissue sub-blocks acquired with 200μm isotropic resolution. These 1cm blocks are subsequently co-registered to the MRI of the whole HF. Reconstruction accuracy is evaluated as the average displacement error between boundaries manually delineated in both the histology and MRI following the sequential stages of reconstruction. The methods presented and evaluated in this single-subject study can potentially be applied to multiple hippocampal tissue samples in order to construct a histologically informed MRI atlas of the hippocampal formation.

We propose a simple but generally applicable approach to improving the accuracy of automatic image segmentation algorithms relative to manual segmentations. The approach is based on the hypothesis that a large fraction of the errors produced by automatic segmentation are systematic, i.e., occur consistently from subject to subject, and serves as a wrapper method around a given host segmentation method. The wrapper method attempts to learn the intensity, spatial and contextual patterns associated with systematic segmentation errors produced by the host method on training data for which manual segmentations are available. The method then attempts to correct such errors in segmentations produced by the host method on new images. One practical use of the proposed wrapper method is to adapt existing segmentation tools, without explicit modification, to imaging data and segmentation protocols that are different from those on which the tools were trained and tuned. An open-source implementation of the proposed wrapper method is provided, and can be applied to a wide range of image segmentation problems. The wrapper method is evaluated with four host brain MRI segmentation methods: hippocampus segmentation using FreeSurfer (Fischl et al., 2002); hippocampus segmentation using multi-atlas label fusion (Artaechevarria et al., 2009); brain extraction using BET (Smith, 2002); and brain tissue segmentation using FAST (Zhang et al., 2001). The wrapper method generates 72%, 14%, 29% and 21% fewer erroneously segmented voxels than the respective host segmentation methods. In the hippocampus segmentation experiment with multi-atlas label fusion as the host method, the average Dice overlap between reference segmentations and segmentations produced by the wrapper method is 0.908 for normal controls and 0.893 for patients with mild cognitive impairment. Average Dice overlaps of 0.964, 0.905 and 0.951 are obtained for brain extraction, white matter segmentation and gray matter segmentation, respectively.

The median (MR) and dorsal raphe (DR) nuclei contain the majority of the 5-hydroxytryptamine (5-HT, serotonin) neurons that project to limbic forebrain regions, are important in regulating homeostatic functions and are implicated in the etiology and treatment of mood disorders and schizophrenia. The primary synaptic inputs within and to the raphe are glutamatergic and GABAergic. The DR is divided into three subfields, i.e., ventromedial (vmDR), lateral wings (lwDR) and dorsomedial (dmDR). Our previous work shows that cell characteristics of 5-HT neurons and the magnitude of the 5-HT(1A) and 5-HT(1B) receptor-mediated responses in the vmDR and MR are not the same. We extend these observations to examine the electrophysiological properties across all four raphe subfields in both 5-HT and non-5-HT neurons. The neurochemical topography of glutamatergic and GABAergic cell bodies and nerve terminals were identified using immunohistochemistry and the morphology of the 5-HT neurons was measured. Although 5-HT neurons possessed similar physiological properties, important differences existed between subfields. Non-5-HT neurons were indistinguishable from 5-HT neurons. GABA neurons were distributed throughout the raphe, usually in areas devoid of 5-HT neurons. Although GABAergic synaptic innervation was dense throughout the raphe (immunohistochemical analysis of the GABA transporters GAT1 and GAT3), their distributions differed. Glutamate neurons, as defined by vGlut3 anti-bodies, were intermixed and co-localized with 5-HT neurons within all raphe subfields. Finally, the dendritic arbor of the 5-HT neurons was distinct between subfields. Previous studies regard 5-HT neurons as a homogenous population. Our data support a model of the raphe as an area composed of functionally distinct subpopulations of 5-HT and non-5-HT neurons, in part delineated by subfield. Understanding the interaction of the cell properties of the neurons in concert with their morphology, local distribution of GABA and glutamate neurons and their synaptic input, reveals a more complicated and heterogeneous raphe. These results provide an important foundation for understanding how specific subfields modulate behavior and for defining which aspects of the circuitry are altered during the etiology of psychological disorders.