Laura E.M. Wisse

Hippocampal subfield volumes at 7T in early Alzheimer's disease and normal aging

We compared hippocampal subfield and entorhinal cortex (ERC) volumes between patients with mild cognitive impairment (MCI), Alzheimers disease (AD), and controls without cognitive impairment. Additionally, we investigated the relation between age and hippocampal subfields and ERC in controls. We performed ultra-high field 0.7 mm(3) 7Tesla magnetic resonance imaging in 16 patients with amnestic MCI, 9 with AD, and 29 controls. ERC, subiculum, cornu ammonis (CA)1, CA2, CA3, and dentate gyrus (DG)&CA4 were traced on T2-weighted images. Analyses of covariance, adjusted for age, sex, and intracranial volume showed that compared with controls and patients with MCI, patients with AD had significantly smaller ERC, subiculum, CA1, CA3, and DG&CA4 volumes. Trend analyses revealed similar associations between ERC and hippocampal subfields and diagnostic group. Older age was significantly associated with smaller CA1 and DG&CA4 volumes. In conclusion, almost all hippocampal subfields and ERC show volume reductions in patients with AD compared with controls and patients with MCI. Future, larger studies should determine which subfields are affected earliest in the disease process and what mechanisms underlie the volume loss.

A Critical Appraisal of the Hippocampal Subfield Segmentation Package in FreeSurfer

In the last decade, the in vivo assessment of hippocampal subfields has received increasing attention because of the differential role of hippocampal subfields in several neuropsychiatric diseases (Geuze et al., 2005). Several manual segmentation protocols have been developed for 3–7 T MRI (Mueller et al., 2007; Van Leemput et al., 2008; La Joie et al., 2010; Wisse et al., 2012), some of which are automated (Van Leemput et al., 2008; Yushkevich et al., 2009). One of these automated protocols (Van Leemput et al., 2008, 2009) has recently been implemented in FreeSurfer (Fischl, 2012), a freely available easy-to-use set of automated brain MRI analysis tools. This has made hippocampal subfield segmentation available to everyone with 1.5–3 T MRI data and the method is being used in an increasing number of studies (Teicher et al., 2012; Li et al., 2013; Pereira et al., 2014). In this commentary, we express our concern with the hippocampal subfield segmentation package in FreeSurfer. In particular, we address issues concerning (1) image acquisition, (2) the parcelation scheme, and (3) validation of this automated segmentation. The first concern with the hippocampal subfield segmentation package in FreeSurfer is that it requires low resolution (1 mm3) T1 images (whole-brain). Most other manual or automated segmentation methods are developed for high-resolution T2 images (in-plane: 0.20–0.70 mm2, often with partial-brain coverage) (Mueller et al., 2007; Kerchner et al., 2010; La Joie et al., 2010; Wisse et al., 2012). On high-resolution T2 images, contrast between white and gray matter is sufficient to visualize the white matter bands between the dentate gyrus and the cornu ammonis (CA) that are generally used as a boundary between these subfields. The low resolution T1 images on which the FreeSurfer segmentation is applied do not contain this amount of detail. See Figure ​Figure11 for a comparison of low resolution T1 and high-resolution T2 images. Figure 1 Coronal images of the head (A), body (B) and tail (C) and a sagittal cross-section of the hippocampus (D) on low resolution 1mm3 T1 1.5 T images (A–D) and on high resolution 0.7 mm3 T2 7 T images (E–H). Note the ... The second concern is the parcelation scheme used for the FreeSurfer segmentation, which is based on the subfield distribution in one coronal section in the body of the hippocampus (Van Leemput et al., 2008, 2009) and then used to segment subfields along the complete long axis of the hippocampus. However, the presence and position of the subfields differ along the long axis (Duvernoy et al., 2005; Mai et al., 2008; Insausti and Amaral, 2012). Consequently, the locations of the boundaries between subfields in this segmentation protocol are in mismatch with the anatomical atlases in a large part of the long axis. For example, in FreeSurfer, the dentate gyrus is segmented from the anterior pole of the hippocampus, while it only becomes visible 6 mm after the anterior pole of the hippocampus (Insausti and Amaral, 2012). Several segmentation methods exist also for T2 images, manual (La Joie et al., 2010; Wisse et al., 2012) as well as automated (Yushkevich et al., 2009). Because of the complex anatomy of the hippocampal head and tail, these methods either limit the segmentation of subfields to the hippocampal body (Mueller et al., 2007; Yushkevich et al., 2009) or developed a separate segmentation scheme for the head and/or tail (La Joie et al., 2010; Wisse et al., 2012; Winterburn et al., 2013). As a consequence of the placement of the subfield boundaries in FreeSurfer, large parts of subfields are assigned to neighboring subfields. For example, large parts of CA1 are included in the subiculum and CA2&3. This generates volume estimates that are in contrast with anatomical studies. In studies using the FreeSurfer segmentation package (e.g., Teicher et al., 2012; Boen et al., 2014), CA2&3 is the largest subfield, while CA1 is the smallest. According to anatomical studies, CA1 is the largest and CA2&3 is the smallest subfield (Simic et al., 1997; Rossler et al., 2002). In general, subfield boundaries are difficult to discern in vivo and part of subfields are counted toward neighboring subfields in all segmentation protocols. However, other manual or automated methods generate subfield estimates that are more in line with those of anatomical studies (e.g., Wisse et al., 2012; Winterburn et al., 2013). See Table S1 in Supplementary Material for a comparison of subfield volumes and their percentage distribution within the hippocampus according to several segmentation protocols. Studies using this FreeSurfer segmentation package to investigate hippocampal subfield volumes in mild cognitive impairment (MCI) and Alzheimer disease (AD) reported results that differ from anatomical studies. Several studies using the FreeSurfer package reported that MCI and AD were mainly related to CA2&3 atrophy (Hanseeuw et al., 2011; Lim et al., 2012). These latter results stand in contrast to the anatomical studies that reported the greatest atrophy in CA1 (Simic et al., 1997; Rossler et al., 2002). Perhaps, CA2&3 atrophy in MCI or AD in studies using FreeSurfer actually represents CA1 atrophy, as a large part of CA1 is counted toward CA2&3 in FreeSurfer. Studies using other manual or automated segmentation methods reported subfield atrophy in AD that more closely matched the results of anatomical studies (Mueller and Weiner, 2009; Pluta et al., 2012; La Joie et al., 2013). A third concern is that the automated segmentation in FreeSurfer was developed on high-resolution (0.19 mm × 0.19 mm × 0.80 mm) 3 T images and is now applied on low resolution (1 mm3) images. To the best of our knowledge, the protocol was not validated against a manual segmentation on these lower resolutions 1.5–3 T MR images (see also Lim et al., 2012; Pluta et al., 2012). Moreover, it should be noted that the intra-rater reliability of the manual segmentation used for the FreeSurfer package was based on repeated segmentation of two coronal slices rather than on segmentation of the complete long axis of the hippocampus (Van Leemput et al., 2009). In conclusion, though FreeSurfer provides a useful, broad set of automated brain MRI analysis tools, we have concerns about the current package for automated hippocampal subfield segmentation. The boundaries of the parcelation scheme are in mismatch with known anatomical boundaries. This will impact the reliability of studies using FreeSurfer to investigate subfield atrophy in neuropsychiatric diseases.

A harmonized segmentation protocol for hippocampal and parahippocampal subregions : why do we need one and what are the key goals?

The advent of high‐resolution magnetic resonance imaging (MRI) has enabled in vivo research in a variety of populations and diseases on the structure and function of hippocampal subfields and subdivisions of the parahippocampal gyrus. Because of the many extant and highly discrepant segmentation protocols, comparing results across studies is difficult. To overcome this barrier, the Hippocampal Subfields Group was formed as an international collaboration with the aim of developing a harmonized protocol for manual segmentation of hippocampal and parahippocampal subregions on high‐resolution MRI. In this commentary we discuss the goals for this protocol and the associated key challenges involved in its development. These include differences among existing anatomical reference materials, striking the right balance between reliability of measurements and anatomical validity, and the development of a versatile protocol that can be adopted for the study of populations varying in age and health. The commentary outlines these key challenges, as well as the proposed solution of each, with concrete examples from our working plan. Finally, with two examples, we illustrate how the harmonized protocol, once completed, is expected to impact the field by producing measurements that are quantitatively comparable across labs and by facilitating the synthesis of findings across different studies.

Global brain atrophy but not hippocampal atrophy is related to type 2 diabetes.

AIMS It has been suggested that in patients with type 2 diabetes mellitus (T2DM), brain atrophy is most pronounced in the hippocampus, but this has not been investigated systematically. The present pooled analysis of three studies examined if hippocampal atrophy is more prominent than global brain atrophy in patients with T2DM relative to controls. METHODS Data were derived from a cohort study of patients with vascular disease (SMART-Medea (T2DM=120; no T2DM=502)), and from two case-control studies (UDES1 (T2DM=61; controls=30) and UDES2 (T2DM=54; controls=53)). In SMART-Medea and UDES1, hippocampal volume was obtained by manual tracing on 1.5 Tesla (T) MRI scans. Total brain and intracranial volume (ICV) were determined by an automated segmentation method. In UDES2, hippocampal and total brain volume were determined by FreeSurfer and ICV by manual segmentation on 3 T MRI scans. RESULTS The pooled analyses, adjusted for age and sex, showed a significant negative relation between T2DM and total brain-to-ICV ratio (standardized mean difference=-1.24%, 95% CI: -1.63; -0.86), but not between T2DM and hippocampal-to-ICV ratio (0.00%, 95% CI: -0.01; 0.00) or between T2DM and hippocampal-to-total brain volume ratio (0.01%, 95% CI: -0.01; 0.02). In patients with T2DM no associations were found between brain volume measures and HbA1c or memory. CONCLUSION Patients with T2DM had greater brain atrophy but not hippocampal atrophy, compared to controls. These findings do not support specific vulnerability of the hippocampus in patients with T2DM.

Automated Hippocampal Subfield Segmentation at 7T MRI

BACKGROUND AND PURPOSE: High resolution 7T MRI is increasingly used to investigate hippocampal subfields in vivo, but most studies rely on manual segmentation which is labor intensive. We aimed to evaluate an automated technique to segment hippocampal subfields and the entorhinal cortex at 7T MRI. MATERIALS AND METHODS: The cornu ammonis (CA)1, CA2, CA3, dentate gyrus, subiculum, and entorhinal cortex were manually segmented, covering most of the long axis of the hippocampus on 0.70-mm3 T2-weighted 7T images of 26 participants (59 ± 9 years, 46% men). The automated segmentation of hippocampal subfields approach was applied and evaluated by using leave-one-out cross-validation. RESULTS: Comparison of automated segmentations with corresponding manual segmentations yielded a Dice similarity coefficient of >0.75 for CA1, the dentate gyrus, subiculum, and entorhinal cortex and >0.54 for CA2 and CA3. Intraclass correlation coefficients were >0.74 for CA1, the dentate gyrus, and subiculum; and >0.43 for CA2, CA3, and the entorhinal cortex. Restricting the comparison of the entorhinal cortex segmentation to a smaller range along the anteroposterior axis improved both intraclass correlation coefficients (left: 0.71; right: 0.82) and Dice similarity coefficients (left: 0.78; right: 0.77). The accuracy of the automated segmentation versus a manual rater was lower, though only slightly for most subfields, than the intrarater reliability of an expert manual rater, but it was similar to or slightly higher than the accuracy of an expert-versus-manual rater with ∼170 hours of training for almost all subfields. CONCLUSIONS: This work demonstrates the feasibility of using a computational technique to automatically label hippocampal subfields and the entorhinal cortex at 7T MRI, with a high accuracy for most subfields that is competitive with the labor-intensive manual segmentation. The software and atlas are publicly available: http://www.nitrc.org/projects/ashs/.

Hippocampal Disconnection in Early Alzheimer's Disease: A 7 Tesla MRI Study

BACKGROUND In patients with Alzheimers disease (AD), atrophy of the entorhinal cortex (ERC) and hippocampal formation may induce degeneration of connecting white matter tracts. OBJECTIVE We examined the association of hippocampal subfield and ERC atrophy at 7 tesla MRI with fornix and parahippocampal cingulum (PHC) microstructure in patients with early AD. METHODS Twenty-five patients with amnestic mild cognitive impairment (aMCI) (n = 15) or early AD (n = 10) and 17 controls underwent 3 tesla diffusion MRI to obtain fractional anisotropy (FA) of the fornix and PHC and 7 tesla MRI to obtain ERC and hippocampal subfield volumes. Linear regression analyses were performed, adjusted for age, gender, and intracranial volume. RESULTS Fornix FA was significantly lower and subiculum, cornu ammonis (CA) 1, and dentate gyrus &CA4 volume were significantly smaller in patients with MCI or AD as compared to controls. In patients with MCI or AD, fornix FA was positively associated with subiculum volume (β = 0.53, 95% CI 0.10; 0.96), but not with ERC/other subfield volumes. PHC FA was not associated with ERC/subfield volumes. CONCLUSION These findings indicate that in early AD subiculum atrophy is associated with lower FA of the fornix, which primarily consists of axons originating in the subiculum. This suggests that degeneration of subicular cell bodies and their axons are related processes in early AD.

Childhood socioeconomic status and childhood maltreatment: Distinct associations with brain structure.

The present study examined the relationship between childhood socioeconomic status (SES), childhood maltreatment, and the volumes of the hippocampus and amygdala between the ages of 25 and 36 years. Previous work has linked both low SES and maltreatment with reduced hippocampal volume in childhood, an effect attributed to childhood stress. In 46 adult subjects, only childhood maltreatment, and not childhood SES, predicted hippocampal volume in regression analyses, with greater maltreatment associated with lower volume. Neither factor was related to amygdala volume. When current SES and recent interpersonal stressful events were also considered, recent interpersonal stressful events predicted smaller hippocampal volumes over and above childhood maltreatment. Finally, exploratory analyses revealed a significant sex by childhood SES interaction, with women’s childhood SES showing a significantly more positive relation (less negative) with hippocampus volume than men’s. The overall effect of childhood maltreatment but not SES, and the sex-specific effect of childhood SES, indicate that different forms of stressful childhood adversity affect brain development differently.

A protocol for manual segmentation of medial temporal lobe subregions in 7 Tesla MRI

Recent advances in MRI and increasing knowledge on the characterization and anatomical variability of medial temporal lobe (MTL) anatomy have paved the way for more specific subdivisions of the MTL in humans. In addition, recent studies suggest that early changes in many neurodegenerative and neuropsychiatric diseases are better detected in smaller subregions of the MTL rather than with whole structure analyses. Here, we developed a new protocol using 7 Tesla (T) MRI incorporating novel anatomical findings for the manual segmentation of entorhinal cortex (ErC), perirhinal cortex (PrC; divided into area 35 and 36), parahippocampal cortex (PhC), and hippocampus; which includes the subfields subiculum (Sub), CA1, CA2, as well as CA3 and dentate gyrus (DG) which are separated by the endfolial pathway covering most of the long axis of the hippocampus. We provide detailed instructions alongside slice-by-slice segmentations to ease learning for the untrained but also more experienced raters. Twenty-two subjects were scanned (19–32 yrs, mean age = 26 years, 12 females) with a turbo spin echo (TSE) T2-weighted MRI sequence with high-resolution oblique coronal slices oriented orthogonal to the long axis of the hippocampus (in-plane resolution 0.44 × 0.44 mm2) and 1.0 mm slice thickness. The scans were manually delineated by two experienced raters, to assess intra- and inter-rater reliability. The Dice Similarity Index (DSI) was above 0.78 for all regions and the Intraclass Correlation Coefficients (ICC) were between 0.76 to 0.99 both for intra- and inter-rater reliability. In conclusion, this study presents a fine-grained and comprehensive segmentation protocol for MTL structures at 7 T MRI that closely follows recent knowledge from anatomical studies. More specific subdivisions (e.g. area 35 and 36 in PrC, and the separation of DG and CA3) may pave the way for more precise delineations thereby enabling the detection of early volumetric changes in dementia and neuropsychiatric diseases.

Mapping the structural and functional network architecture of the medial temporal lobe using 7T MRI

Medial temporal lobe (MTL) subregions play integral roles in memory function and are differentially affected in various neurological and psychiatric disorders. The ability to structurally and functionally characterize these subregions may be important to understanding MTL physiology and diagnosing diseases involving the MTL. In this study, we characterized network architecture of the MTL in healthy subjects (n = 31) using both resting state functional MRI and MTL‐focused T2‐weighted structural MRI at 7 tesla. Ten MTL subregions per hemisphere, including hippocampal subfields and cortical regions of the parahippocampal gyrus, were segmented for each subject using a multi‐atlas algorithm. Both structural covariance matrices from correlations of subregion volumes across subjects, and functional connectivity matrices from correlations between subregion BOLD time series were generated. We found a moderate structural and strong functional inter‐hemispheric symmetry. Several bilateral hippocampal subregions (CA1, dentate gyrus, and subiculum) emerged as functional network hubs. We also observed that the structural and functional networks naturally separated into two modules closely corresponding to (a) bilateral hippocampal formations, and (b) bilateral extra‐hippocampal structures. Finally, we found a significant correlation in structural and functional connectivity (r = 0.25). Our findings represent a comprehensive analysis of network topology of the MTL at the subregion level. We share our data, methods, and findings as a reference for imaging methods and disease‐based research.

Multi-Template Analysis Of Human Perirhinal Cortex In Brain Mri: Explicitly Accounting For Anatomical Variability

Rational: The human perirhinal cortex (PRC) plays critical roles in episodic and semantic memory and visual perception. The PRC consists of Brodmann areas 35 and 36 (BA35, BA36). In Alzheimers disease (AD), BA35 is the first cortical site affected by neurofibrillary tangle pathology, which is closely linked to neural injury in AD. Large anatomical variability, manifested in the form of different cortical folding and branching patterns, makes it difficult to segment the PRC in MRI scans. Pathology studies have found that in ˜97% of specimens, the PRC falls into one of three discrete anatomical variants. However, current methods for PRC segmentation and morphometry in MRI are based on single‐template approaches, which may not be able to accurately model these discrete variants Methods: A multi‐template analysis pipeline that explicitly accounts for anatomical variability is used to automatically label the PRC and measure its thickness in T2‐weighted MRI scans. The pipeline uses multi‐atlas segmentation to automatically label medial temporal lobe cortices including entorhinal cortex, PRC and the parahippocampal cortex. Pairwise registration between label maps and clustering based on residual dissimilarity after registration are used to construct separate templates for the anatomical variants of the PRC. An optimal path of deformations linking these templates is used to establish correspondences between all the subjects. Experimental evaluation focuses on the ability of single‐template and multi‐template analyses to detect differences in the thickness of medial temporal lobe cortices between patients with amnestic mild cognitive impairment (aMCI, n=41) and age‐matched controls (n=44). Results: The proposed technique is able to generate templates that recover the three dominant discrete variants of PRC and establish more meaningful correspondences between subjects than a single‐template approach. The largest reduction in thickness associated with aMCI, in absolute terms, was found in left BA35 using both regional and summary thickness measures. Further, statistical maps of regional thickness difference between aMCI and controls revealed different patterns for the three anatomical variants. HIGHLIGHTSA multi‐template framework is proposed to quantify perirhinal cortex (PRC) using MRI.The framework explicitly models the 3 discrete anatomical variants of PRC.Better correspondences are established between subjects PRC anatomies.Regional and summary measures yield stronger power in discriminating aMCI.Spatial distributions of early AD pathology may vary among anatomical variants.