With the increasing life expectancy in developed countries, the incidence of Alzheimer's disease (AD) and thus its socioeconomic impact are growing. Increasing knowledge over the last years about the pathomechanisms involved in AD allow for the development of specific treatment strategies aimed at slowing down or even preventing neuronal death in AD. However, this requires also that (1) AD can be diagnosed with high accuracy, because non-AD dementias would not benefit from an AD-specific treatment; (2) AD can be diagnosed in very early stages when any intervention would be most effective; and (3) treatment efficacy can be reliably and meaningfully monitored. Although there currently is no ideal biomarker that would fulfill all these requirements, there is increasing evidence that a combination of currently existing neuroimaging and cerebrospinal fluid (CSF) and blood biomarkers can provide important complementary information and thus contribute to a more accurate and earlier diagnosis of AD. The Alzheimer's Disease Neuroimaging Initiative (ADNI) is exploring which combinations of these biomarkers are the most powerful for diagnosis of AD and monitoring of treatment effects.

Alzheimer's disease: the way to an effective treatmentWith increasing life expectancy in developed countries, diseases typically associated with old age are becoming more frequent and, thus, increasingly gain in socioeconomic importance. Age is a major risk factor for almost all neurodegenerative diseases, in particular dementia. Currently, more than 4 million people in the United States suffer from dementia, the majority of them from Alzheimer's disease (AD). The cost of dementia to the United States economy is now well over $100 billion per year. It is expected, however, that the incidence of dementia will double during the next 20 years [1] and that its cost will exceed $380 billion per year. Therefore, there is considerable effort to unravel the pathophysiologic mechanisms of AD [2], allowing for the development of effective treatment strategies. Pathologic studies show that neurodegeneration in AD starts in the entorhinal cortex but in later stages also involves the hippocampus, the limbic system, and neocortical regions. It is characterized by accumulations of β-amyloid plaques and neurofibrillary tangles [2-6], which exert direct and indirect neurotoxic effects by promoting oxidative stress [7,8] and inflammation. In the rare forms of early-onset familial AD, mutations of the amyloid precursor protein and the presenilin genes are identified, which are associated with increased amyloid production and deposition, whereas in late-onset AD, intensive research has led to the identification of several risk factors associated with increased amyloid deposition (eg, allele producing the ε4 type of apolipoprotein E [APOE*E4], hyper-homocysteinemia, hyperlipidemia, and disturbances of the neuronal insulin signal transduction pathway) [9]. This increasing knowledge about the mechanisms in AD facilitates the development of treatments aimed at modifying the disease process [10,11] (eg, anti-inflammatory drugs, statins, antioxidants, acetylcho-linesterase inhibitors, γ-and β-

Background-Neuroimaging in mild cognitive impairment (MCI) and Alzheimer disease (AD) generally shows medial temporal lobe atrophy and diminished glucose metabolism and cerebral blood flow in the posterior cingulate gyrus. However, it is unclear whether these abnormalities also impact the cingulum fibers, which connect the medial temporal lobe and the posterior cingulate regions.

OBJECTIVE An increasing number of human in vivo magnetic resonance imaging (MRI) studies have focused on examining the structure and function of the subfields of the hippocampal formation (the dentate gyrus, CA fields 1–3, and the subiculum) and subregions of the parahippocampal gyrus (entorhinal, perirhinal, and parahippocampal cortices). The ability to interpret the results of such studies and to relate them to each other would be improved if a common standard existed for labeling hippocampal subfields and parahippocampal subregions. Currently, research groups label different subsets of structures and use different rules, landmarks, and cues to define their anatomical extents. This paper characterizes, both qualitatively and quantitatively, the variability in the existing manual segmentation protocols for labeling hippocampal and parahippocampal substructures in MRI, with the goal of guiding subsequent work on developing a harmonized substructure segmentation protocol. METHOD MRI scans of a single healthy adult human subject were acquired both at 3 Tesla and 7 Tesla. Representatives from 21 research groups applied their respective manual segmentation protocols to the MRI modalities of their choice. The resulting set of 21 segmentations was analyzed in a common anatomical space to quantify similarity and identify areas of agreement. RESULTS The differences between the 21 protocols include the region within which segmentation is performed, the set of anatomical labels used, and the extents of specific anatomical labels. The greatest overall disagreement among the protocols is at the CA1/subiculum boundary, and disagreement across all structures is greatest in the anterior portion of the hippocampal formation relative to the body and tail. CONCLUSIONS The combined examination of the 21 protocols in the same dataset suggests possible strategies towards developing a harmonized subfield segmentation protocol and facilitates comparison between published studies.

Histological studies suggest that hippocampal subfields are differently affected by aging and Alzheimer's disease (AD). The aims of this study were: (1) To test if hippocampal subfields can be identified and marked using anatomical landmarks on high resolution MR images obtained on a 4T magnet. (2) To test if age-specific volume changes of subfields can be detected. Forty-two healthy controls (21-85 years) and three AD subjects (76-86 years) were studied with a high resolution T2 weighted fast spin echo sequence. The entorhinal cortex (ERC), subiculum, CA1, CA2 and CA3/4 and dentate were marked. A significant correlation between age and CA1 (r=-0.51, p=0.0002) which was most pronounced in the seventh decade of life was found in healthy controls. In AD subjects, CA1 and subiculum were smaller than in age-matched controls. These preliminary findings suggest that measurement of hippocampal subfields may be helpful to distinguish between normal aging and AD.

Histopathological studies and animal models suggest that different physiological and pathophysiological processes exert different subfield specific effects on the hippocampus. High-resolution images at 4T depict details of the internal structure of the hippocampus allowing for in vivo volumetry of hippocampal subfields. The aims of this study were (1) to determine patterns of hippocampal subfield volume loss due to normal aging and Apo e4 carrier state, (2) to determine subfield specific volume losses due to preclinical (MCI) and clinical Alzheimer’s disease (AD) and their modification due to age and Apo e4 carrier state. One hundred fifty seven subjects (119 cognitively healthy elderly controls, 20 MCI and 18 AD) were studied with a high resolution T2 weighted imaging sequence obtained at 4T aimed at the hippocampus. Apo e4 carrier state was known in 95 subjects (66 controls, 14 MCI, 15 AD). Subiculum (SUB), CA1, CA1–CA2 transition zone (CA1–2 transition), CA3- dentate gyrus (CA3&DG) were manually marked. Multiple linear regression analysis was used to test for effects of age, Apo e4 carrier state and effects of MCI and AD on different hippocampal subfields. Age had a significant negative effect on CA1 and CA3&DG volumes in controls (P < 0.05). AD had significantly smaller volumes of SUB, CA1, CA1–2 transition, and MCI had smaller CA1–2 transition volumes than controls (P < 0.05). Apo e4 carrier state was associated with volume loss in CA3&DG compared to non-Apo e4 carriers in healthy controls and AD. Based on these findings, we conclude that subfield volumetry provides regional selective information that allows to distinguish between different normal and pathological processes affecting the hippocampus and thus for an improved differential diagnosis of neurodegenerative diseases affecting the hippocampus.

Background Histopathological studies and animal models suggest that hippocampal subfields may be differently affected by aging, Alzheimer’s disease (AD), and other diseases. High-resolution images at 4 Tesla depict details of the internal structure of the hippocampus allowing for in vivo volumetry of different subfields. The aims of this study were as follows: (1) to determine patterns of volume loss in hippocampal subfields in normal aging, AD, and amnestic mild cognitive impairment (MCI). (2) To determine if measurements of hippocampal subfields provide advantages over total hippocampal volume for differentiation between groups. Methods Ninety-one subjects (53 controls (mean age: 69.3 ± 7.3), 20 MCI (mean age: 73.6 ± 7.1), and 18 AD (mean age: 69.1 ± 9.5) were studied with a high-resolution T2 weighted imaging sequence aimed at the hippocampus. Entorhinal cortex (ERC), subiculum, CA1, CA1–CA2 transition zone (CA1-2), CA3 & dentate gyrus (CA3&DG) were manually marked in the anterior third of the hippocampal body. Hippocampal volume was obtained from the Freesurfer and manually edited. Results Compared to controls, AD had smaller volumes of ERC, subiculum, CA1, CA1-2, and total hippocampal volumes. MCI had smaller CA1-2 volumes. Discriminant analysis and power analysis showed that CA1-2 was superior to total hippocampal volume for distinction between controls and MCI. Conclusion The patterns of subfield atrophy in AD and MCI were consistent with patterns of neuronal cell loss/reduced synaptic density described by histopathology. These preliminary findings suggest that hippocampal subfield volumetry might be a better measure for diagnosis of early AD and for detection of other disease effects than measurement of total hippocampus.

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