Differential Associations of IL-4 With Hippocampal Subfields in Mild Cognitive Impairment and Alzheimer's Disease.

Background/Aims: We aimed to assess the association between in volumetric measures of hippocampal sub-regions - in healthy older controls (HC), subjects with mild cognitive impairment (MCI) and AD- with circulating levels of IL-4. Methods: From AddNeuroMed Project 113 HC, 101 stable MCI (sMCI), 22 converter MCI (cMCI) and 119 AD were included. Hippocampal subfield volumes were analyzed using Freesurfer 6.0.0 on high-resolution sagittal 3D-T1W MP-RAGE acquisitions. Plasmatic IL-4 was measured using ELISA assay. Results: IL-4 was found to be (a) positively associate with left subiculum volume (ß = 0.226, p = 0.037) in sMCI and (b) negatively associate with left subiculum volume (ß = -0.253, p = 0.011) and left presubiculum volume (ß = -0.257, p = 0.011) in AD. Conclusion: Our results indicate a potential neuroprotective effect of IL-4 on the areas of the hippocampus more vulnerable to aging and neurodegeneration.

A Multi-Cohort Study of ApoE ɛ4 and Amyloid-ß Effects on the Hippocampus in Alzheimer's Disease.

The apolipoprotein E (APOE) gene has been consistently shown to modulate the risk of Alzheimer's disease (AD). Here, using an AD and normal aging dataset primarily consisting of three AD multi-center studies (n = 1,781), we compared the effect of APOE and amyloid-ß (Aß) on baseline hippocampal volumes in AD patients, mild cognitive impairment (MCI) subjects, and healthy controls. A large sample of healthy adolescents (n = 1,387) was also used to compared hippocampal volumes between APOE groups. Subjects had undergone a magnetic resonance imaging (MRI) scan and APOE genotyping. Hippocampal volumes were processed using FreeSurfer. In the AD and normal aging dataset, hippocampal comparisons were performed in each APOE group and in ɛ4 carriers with positron emission tomography Aß who were dichotomized (Aß+/Aß-) using previous cut-offs. We found a linear reduction in hippocampal volumes with ɛ4 carriers possessing the smallest volumes, ɛ3 carriers possessing intermediate volumes, and ɛ2 carriers possessing the largest volumes. Moreover, AD and MCI ɛ4 carriers possessed the smallest hippocampal volumes and control ɛ2 carriers possessed the largest hippocampal volumes. Subjects with both APOE ɛ4 and Aß+ had the lowest hippocampal volumes when compared to Aß- ɛ4 carriers, suggesting a synergistic relationship between APOE ɛ4 and Aß. However, we found no hippocampal volume differences between APOE groups in healthy 14-year-old adolescents. Our findings suggest that the strongest neuroanatomic effect of APOE ɛ4 on the hippocampus is observed in AD and groups most at risk of developing the disease, whereas hippocampi of old and young healthy individuals remain unaffected.

Disrupted Network Topology in Patients with Stable and Progressive Mild Cognitive Impairment and Alzheimer's Disease.

Recent findings suggest that Alzheimer's disease (AD) is a disconnection syndrome characterized by abnormalities in large-scale networks. However, the alterations that occur in network topology during the prodromal stages of AD, particularly in patients with stable mild cognitive impairment (MCI) and those that show a slow or faster progression to dementia, are still poorly understood. In this study, we used graph theory to assess the organization of structural MRI networks in stable MCI (sMCI) subjects, late MCI converters (lMCIc), early MCI converters (eMCIc), and AD patients from 2 large multicenter cohorts: ADNI and AddNeuroMed. Our findings showed an abnormal global network organization in all patient groups, as reflected by an increased path length, reduced transitivity, and increased modularity compared with controls. In addition, lMCIc, eMCIc, and AD patients showed a decreased path length and mean clustering compared with the sMCI group. At the local level, there were nodal clustering decreases mostly in AD patients, while the nodal closeness centrality detected abnormalities across all patient groups, showing overlapping changes in the hippocampi and amygdala and nonoverlapping changes in parietal, entorhinal, and orbitofrontal regions. These findings suggest that the prodromal and clinical stages of AD are associated with an abnormal network topology.

Automated Hippocampal Subfield Measures as Predictors of Conversion from Mild Cognitive Impairment to Alzheimer's Disease in Two Independent Cohorts.

Previous studies have shown that hippocampal subfields may be differentially affected by Alzheimer's disease (AD). This study used an automated analysis technique and two large cohorts to (1) investigate patterns of subfield volume loss in mild cognitive impairment (MCI) and AD, (2) determine the pattern of subfield volume loss due to age, gender, education, APOE ε4 genotype, and neuropsychological test scores, (3) compare combined subfield volumes to hippocampal volume alone at discriminating between AD and healthy controls (HC), and predicting future MCI conversion to AD at 12 months. 1,069 subjects were selected from the AddNeuroMed and Alzheimer's disease neuroimaging initiative (ADNI) cohorts. Freesurfer was used for automated segmentation of the hippocampus and hippocampal subfields. Orthogonal partial least squares to latent structures (OPLS) was used to train models on AD and HC subjects using one cohort for training and the other for testing and the combined cohort was used to predict MCI conversion. MANCOVA and linear regression analyses showed multiple subfield volumes including Cornu Ammonis 1 (CA1), subiculum and presubiculum were atrophied in AD and MCI and were related to age, gender, education, APOE ε4 genotype, and neuropsychological test scores. For classifying AD from HC, combined subfield volumes achieved comparable classification accuracy (81.7%) to total hippocampal (80.7%), subiculum (81.2%) and presubiculum (80.6%) volume. For predicting MCI conversion to AD combined subfield volumes and presubiculum volume were more accurate (81.1%) than total hippocampal volume. (76.7%).

Different multivariate techniques for automated classification of MRI data in Alzheimer's disease and mild cognitive impairment.

Automated structural magnetic resonance imaging (MRI) processing pipelines and different multivariate techniques are gaining popularity for Alzheimer's disease (AD) research. We used four supervised learning methods to classify AD patients and controls (CTL) and to prospectively predict the conversion of mild cognitive impairment (MCI) to AD from baseline MRI data. A total of 345 participants from the AddNeuroMed cohort were included in this study; 116 AD patients, 119 MCI patients and 110 CTL individuals. High resolution sagittal 3D MP-RAGE datasets were acquired and MRI data were processed using FreeSurfer. We explored the classification ability of orthogonal projections to latent structures (OPLS), decision trees (Trees), artificial neural networks (ANN) and support vector machines (SVM). Applying 10-fold cross-validation demonstrated that SVM and OPLS were slightly superior to Trees and ANN, although not statistically significant for distinguishing between AD and CTL. The classification experiments resulted in up to 83% sensitivity and 87% specificity for the best techniques. For the prediction of conversion of MCI patients at baseline to AD at 1-year follow-up, we obtained an accuracy of up to 86%. The value of the multivariate models derived from the classification of AD vs. CTL was shown to be robust and efficient in the identification of MCI converters.

Entorhinal cortex thickness predicts cognitive decline in Alzheimer's disease.

Biomarkers for Alzheimer's disease (AD) based on non-invasive methods are highly desirable for diagnosis, disease progression, and monitoring therapeutics. We aimed to study the use of hippocampal volume, entorhinal cortex (ERC) thickness, and whole brain volume (WBV) as predictors of cognitive change in patients with AD. 120 AD subjects, 106 mild cognitive impairment (MCI), and 99 non demented controls (NDC) from the multi-center pan-European AddNeuroMed study underwent MRI scanning at baseline and clinical evaluations at quarterly follow-up up to 1 year. The rate of cognitive decline was estimated using cognitive outcomes, Mini-Mental State Examination (MMSE) and Alzheimer disease assessment scale-cognitive (ADAS-cog) by fitting a random intercept and slope model. AD subjects had smaller ERC thickness and hippocampal and WBV volumes compared to MCI and NDC subjects. Within the AD group, ERC > WBV was significantly associated with baseline cognition (MMSE, ADAS-cog) and disease severity (Clinical Dementia Rating). Baseline ERC thickness was associated with both longitudinal MMSE and ADAS-cog score changes and WBV with ADAS-cog decline. These data indicate that AD subjects with thinner ERC had lower baseline cognitive scores, higher disease severity, and predicted greater subsequent cognitive decline at one year follow up. ERC is a region known to be affected early in the disease. Therefore, the rate of atrophy in this structure is expected to be higher since neurodegeneration begins earlier. Focusing on structural analyses that predict decline can identify those individuals at greatest risk for future cognitive loss. This may have potential for increasing the efficacy of early intervention.

Alzheimer's disease biomarker discovery using in silico literature mining and clinical validation.

BACKGROUND: Alzheimer's Disease (AD) is the most widespread form of dementia in the elderly but despite progress made in recent years towards a mechanistic understanding, there is still an urgent need for disease modification therapy and for early diagnostic tests. Substantial international efforts are being made to discover and validate biomarkers for AD using candidate analytes and various data-driven 'omics' approaches. Cerebrospinal fluid is in many ways the tissue of choice for biomarkers of brain disease but is limited by patient and clinician acceptability, and increasing attention is being paid to the search for blood-based biomarkers. The aim of this study was to use a novel in silico approach to discover a set of candidate biomarkers for AD. METHODS: We used an in silico literature mining approach to identify potential biomarkers by creating a summarized set of assertional metadata derived from relevant legacy information. We then assessed the validity of this approach using direct assays of the identified biomarkers in plasma by immunodetection methods. RESULTS: Using this in silico approach, we identified 25 biomarker candidates, at least three of which have subsequently been reported to be altered in blood or CSF from AD patients. Two further candidate biomarkers, indicated from the in silico approach, were choline acetyltransferase and urokinase-type plasminogen activator receptor. Using immunodetection, we showed that, in a large sample set, these markers are either altered in disease or correlate with MRI markers of atrophy. CONCLUSIONS: These data support as a proof of concept the use of data mining and in silico analyses to derive valid biomarker candidates for AD and, by extension, for other disorders.

Development of a small D-enantiomeric Alzheimer's amyloid-ß binding peptide ligand for future in vivo imaging applications.

Alzheimer's disease (AD) is a devastating disease affecting predominantly the aging population. One of the characteristic pathological hallmarks of AD are neuritic plaques, consisting of amyloid-ß peptide (Aß). While there has been some advancement in diagnostic classification of AD patients according to their clinical severity, no fully reliable method for pre-symptomatic diagnosis of AD is available. To enable such early diagnosis, which will allow the initiation of treatments early in the disease progress, neuroimaging tools are under development, making use of Aß-binding ligands that can visualize amyloid plaques in the living brain. Here we investigate the properties of a newly designed series of D-enantiomeric peptides which are derivatives of ACI-80, formerly called D1, which was developed to specifically bind aggregated Aß1-42. We describe ACI-80 derivatives with increased stability and Aß binding properties, which were characterized using surface plasmon resonance and enzyme-linked immunosorbent assays. The specific interactions of the lead compounds with amyloid plaques were validated by ex vivo immunochemistry in transgenic mouse models of AD. The novel compounds showed increased binding affinity and are promising candidates for further development into in vivo imaging compounds.

Education increases reserve against Alzheimer's disease--evidence from structural MRI analysis.

INTRODUCTION: The aim of this study was to determine whether years of schooling influences regional cortical thicknesses and volumes in Alzheimer's disease (AD), mild cognitive impairment (MCI), and healthy age-matched controls. METHODS: Using an automated image analysis pipeline, 33 regional cortical thickness and 15 regional volumes measures from MRI images were determined in 121 subjects with MCI, 121 patients with AD, and 113 controls from AddNeuroMed study. Correlations with years of schooling were determined and more highly and less highly educated subjects compared, controlling for intracranial volume, age, gender, country of origin, cognitive status, and multiple testing. RESULTS: After controlling for confounding factors and multiple testing, in the control group, subjects with more education had larger regional cortical thickness in transverse temporal cortex, insula, and isthmus of cingulate cortex than subjects with less education. However, in the AD group, the subjects with more education had smaller regional cortical thickness in temporal gyrus, inferior and superior parietal gyri, and lateral occipital cortex than the subjects with less education. No significant difference was found in the MCI group. CONCLUSION: Education may increase regional cortical thickness in healthy controls, leading to increased brain reserve, as well as helping AD patients to cope better with the effects of brain atrophy by increasing cognitive reserve.

Fluorine-18 labeling of three novel D-peptides by conjugation with N-succinimidyl-4-[18F]fluorobenzoate and preliminary examination by postmortem whole-hemisphere human brain autoradiography.

INTRODUCTION: ß-Amyloid (Aß) plaques and neurofibrillary tangles are the main characteristics of Alzheimer's disease (AD). Positron emission tomography (PET), a high-resolution, sensitive, and noninvasive imaging technique, has been widely utilized in visualizing the localization of plaques and tangles and thereby distinguishing between AD and healthy controls. A small 12-mer D-enantiomeric peptide (amino acid sequence=QSHYRHISPAQV), denoted as D1, has high binding affinity to Aß in vitro in the sub-micromolar range, and consequently, its radiolabeled analogues have a potential as radioligands for visualizing amyloid plaques in vivo by PET. AIM: The aims of the present work were to develop three different potent D1 derivative peptides labeled with fluorine-18 and to examine them in the AD and control postmortem human brain by autoradiography (ARG). METHODS: Three different D1 derivative peptides were radiolabeled with fluorine-18 ([(18)F]ACI-87, [(18)F]ACI-88, [(18)F]ACI-89) using the prosthetic group N-succinimidyl-4-[(18)F]fluorobenzoate ([(18)F]SFB) and purified by high performance liquid chromatography (HPLC). Preliminary ARG measurements were performed in AD and control brains. RESULTS: The three fluorine-18-labeled d-peptides were obtained in a total synthesis time of 140 min with radiochemical purity higher than 98%. The specific radioactivities of the three different D1 derivative peptides were between 9 and 113 GBq/µmol. ARG demonstrated a higher radioligand uptake in the cortical gray matter and the hippocampus in the AD brain as compared to age-matched control brain. CONCLUSIONS: Fluorine-18 labeling of the three novel D1 derivative peptides using [(18)F]SFB was successfully accomplished. Higher contrast between AD and control brain slices demonstrates their potential applicability for further use in vivo by PET.

Distribution and binding of 18F-labeled and 125I-labeled analogues of ACI-80, a prospective molecular imaging biomarker of disease: a whole hemisphere post mortem autoradiography study in human brains obtained from Alzheimer's disease patients.

One of the major pathological landmarks of Alzheimer's disease and other neurodegenerative diseases is the presence of amyloid deposits in the brain. The early non-invasive visualization of amyloid is a major objective of recent diagnostic neuroimaging approaches, including positron emission tomography (PET), with an eye on follow-up of disease progression and/or therapy efficacy. The development of molecular imaging biomarkers with binding affinity to amyloid in the brain is therefore in the forefront of imaging biomarker and radiochemistry research. Recently, a dodecamer peptide (amino acid sequence=QSHYRHISPAQV; denominated D1 or ACI-80) was identified as a prospective ligand candidate, binding with high ex vivo affinity to L-Aß-amyloid (K(d): 0.4 µM). In order to assess the ligand's capacity to visualize amyloid in Alzheimer's disease (AD), two (125)I labeled and three (18)F labeled analogues of the peptide were synthesized and tested in post mortem human autoradiography experiments using whole hemisphere brain slices obtained from deceased AD patients and age matched control subjects. The (18)F-labeled radioligands showed more promising visualization capacity of amyloid that the (125)I-labeled radioligands. In the case of each (18)F radioligands the grey matter uptake in the AD brains was significantly higher than that in control brains. Furthermore, the grey matter: white matter uptake ratio was over ~2, the difference being significant for each (18)F-radioligands. The regional distribution of the uptake of the various radioligands systematically shows a congruent pattern between the high uptake regions and spots in the autoradiographic images and the disease specific signals obtained in adjacent or identical brain slices labeled with histological, immunohistochemical or autoradiographic stains for amyloid deposits or activated astrocytes. The present data, using post mortem human brain autoradiography in whole hemisphere human brains obtained from deceased AD patients and age matched control subjects, support the visualization capacity of the radiolabeled ACI-80 analogues of amyloid deposits in the human brain. Further studies are warranted to explore the usefulness of the (18)F-labeled analogues as in vivo molecular imaging biomarkers in diagnostic PET studies.

Plasma biomarkers of brain atrophy in Alzheimer's disease.

Peripheral biomarkers of Alzheimer's disease (AD) reflecting early neuropathological change are critical to the development of treatments for this condition. The most widely used indicator of AD pathology in life at present is neuroimaging evidence of brain atrophy. We therefore performed a proteomic analysis of plasma to derive biomarkers associated with brain atrophy in AD. Using gel based proteomics we previously identified seven plasma proteins that were significantly associated with hippocampal volume in a combined cohort of subjects with AD (N = 27) and MCI (N = 17). In the current report, we validated this finding in a large independent cohort of AD (N = 79), MCI (N = 88) and control (N = 95) subjects using alternative complementary methods-quantitative immunoassays for protein concentrations and estimation of pathology by whole brain volume. We confirmed that plasma concentrations of five proteins, together with age and sex, explained more than 35% of variance in whole brain volume in AD patients. These proteins are complement components C3 and C3a, complement factor-I, γ-fibrinogen and alpha-1-microglobulin. Our findings suggest that these plasma proteins are strong predictors of in vivo AD pathology. Moreover, these proteins are involved in complement activation and coagulation, providing further evidence for an intrinsic role of these pathways in AD pathogenesis.

Magnetic resonance imaging and magnetic resonance spectroscopy for detection of early Alzheimer's disease.

Alzheimer's disease is the most common form of neurodegenerative disorder and early detection is of great importance if new therapies are to be effectively administered. We have investigated whether the discrimination between early Alzheimer's disease (AD) and elderly healthy control subjects can be improved by adding magnetic resonance spectroscopy (MRS) measures to magnetic resonance imaging (MRI) measures. In this study 30 AD patients and 36 control subjects were included. High resolution T1-weighted axial magnetic resonance images were obtained from each subject. Automated regional volume segmentation and cortical thickness measures were determined for the images. 1H MRS was acquired from the hippocampus and LCModel was used for metabolic quantification. Altogether, this yielded 58 different volumetric, cortical thickness and metabolite ratio variables which were used for multivariate analysis to distinguish between subjects with AD and Healthy controls. Combining MRI and MRS measures resulted in a sensitivity of 97% and a specificity of 94% compared to using MRI or MRS measures alone (sensitivity: 87%, 76%, specificity: 86%, 83% respectively). Adding the MRS measures to the MRI measures more than doubled the positive likelihood ratio from 6 to 17. Adding MRS measures to a multivariate analysis of MRI measures resulted in significantly better classification than using MRI measures alone. The method shows strong potential for discriminating between Alzheimer's disease and controls.

Combinatorial markers of mild cognitive impairment conversion to Alzheimer's disease--cytokines and MRI measures together predict disease progression.

Progression of people presenting with Mild Cognitive Impairment (MCI) to dementia is not certain and it is not possible for clinicians to predict which people are most likely to convert. The inability of clinicians to predict progression limits the use of MCI as a syndrome for treatment in prevention trials and, as more people present with this syndrome in memory clinics, and as earlier diagnosis is a major goal of health services, this presents an important clinical problem. Some data suggest that CSF biomarkers and functional imaging using PET might act as markers to facilitate prediction of conversion. However, both techniques are costly and not universally available. The objective of our study was to investigate the potential added benefit of combining biomarkers that are more easily obtained in routine clinical practice to predict conversion from MCI to Alzheimer's disease. To explore this we combined automated regional analysis of structural MRI with analysis of plasma cytokines and chemokines and compared these to measures of APOE genotype and clinical assessment to assess which best predict progression. In a total of 205 people with MCI, 77 of whom subsequently converted to Alzheimer's disease, we find biochemical markers of inflammation to be better predictors of conversion than APOE genotype or clinical measures (Area under the curve (AUC) 0.65, 0.62, 0.59 respectively). In a subset of subjects who also had MRI scans the combination of serum markers of inflammation and MRI automated imaging analysis provided the best predictor of conversion (AUC 0.78). These results show that the combination of imaging and cytokine biomarkers provides an improvement in prediction of MCI to AD conversion compared to either datatype alone, APOE genotype or clinical data and an accuracy of prediction that would have clinical utility.

Sensitivity and specificity of medial temporal lobe visual ratings and multivariate regional MRI classification in Alzheimer's disease.

BACKGROUND: Visual assessment rating scales for medial temporal lobe (MTL) atrophy have been used by neuroradiologists in clinical practice to aid the diagnosis of Alzheimer's disease (AD). Recently multivariate classification methods for magnetic resonance imaging (MRI) data have been suggested as alternative tools. If computerized methods are to be implemented in clinical practice they need to be as good as, or better than experienced neuroradiologists and carefully validated. The aims of this study were: (1) To compare the ability of MTL atrophy visual assessment rating scales, a multivariate MRI classification method and manually measured hippocampal volumes to distinguish between subjects with AD and healthy elderly controls (CTL). (2) To assess how well the three techniques perform when predicting future conversion from mild cognitive impairment (MCI) to AD. METHODS: High resolution sagittal 3D T1w MP-RAGE datasets were acquired from 75 AD patients, 101 subjects with MCI and 81 CTL from the multi-centre AddNeuroMed study. An automated analysis method was used to generate regional volume and regional cortical thickness measures, providing 57 variables for multivariate analysis (orthogonal partial least squares to latent structures using seven-fold cross-validation). Manual hippocampal measurements were also determined for each subject. Visual rating assessment of MTL atrophy was performed by an experienced neuroradiologist according to the approach of Scheltens et al. RESULTS: We found prediction accuracies for distinguishing between AD and CTL of 83% for multivariate classification, 81% for the visual rating assessments and 89% for manual measurements of total hippocampal volume. The three different techniques showed similar accuracy in predicting conversion from MCI to AD at one year follow-up. CONCLUSION: Visual rating assessment of the MTL gave similar prediction accuracy to multivariate classification and manual hippocampal volumes. This suggests a potential future role for computerized methods as a complement to clinical assessment of AD.

AddNeuroMed and ADNI: similar patterns of Alzheimer's atrophy and automated MRI classification accuracy in Europe and North America.

The European Union AddNeuroMed program and the US-based Alzheimer Disease Neuroimaging Initiative (ADNI) are two large multi-center initiatives designed to collect and validate biomarker data for Alzheimer's disease (AD). Both initiatives use the same MRI data acquisition scheme. The current study aims to compare and combine magnetic resonance imaging (MRI) data from the two study cohorts using an automated image analysis pipeline and a multivariate data analysis approach. We hypothesized that the two cohorts would show similar patterns of atrophy, despite demographic differences and could therefore be combined. MRI scans were analyzed from a total of 1074 subjects (AD=295, MCI=444 and controls=335) using Freesurfer, an automated segmentation scheme which generates regional volume and regional cortical thickness measures which were subsequently used for multivariate analysis (orthogonal partial least squares to latent structures (OPLS)). OPLS models were created for the individual cohorts and for the combined cohort to discriminate between AD patients and controls. The ADNI cohort was used as a replication dataset to validate the model created for the AddNeuroMed cohort and vice versa. The combined cohort model was used to predict conversion to AD at baseline of MCI subjects at 1 year follow-up. The AddNeuroMed, the ADNI and the combined cohort showed similar patterns of atrophy and the predictive power was similar (between 80 and 90%). The combined model also showed potential in predicting conversion from MCI to AD, resulting in 71% of the MCI converters (MCI-c) from both cohorts classified as AD-like and 60% of the stable MCI subjects (MCI-s) classified as control-like. This demonstrates that the methods used are robust and that large data sets can be combined if MRI imaging protocols are carefully aligned.

1H-MRS in spinal cord injury: acute and chronic metabolite alterations in rat brain and lumbar spinal cord.

A variety of tests of sensorimotor function are used to characterize outcome after experimental spinal cord injury (SCI). These tests typically do not provide information about chemical and metabolic processes in the injured CNS. Here, we used (1) H-magnetic resonance spectroscopy (MRS) to monitor long-term and short-term chemical changes in the CNS in vivo following SCI. The investigated areas were cortex, thalamus/striatum and the spinal cord distal to injury. In cortex, glutamate (Glu) decreased 1 day after SCI and slowly returned towards normal levels. The combined glutamine (Gln) and Glu signal was similarly decreased in cortex, but increased in the distal spinal cord, suggesting opposite changes of the Glu/Gln metabolites in cortex and distal spinal cord. In lumbar spinal cord, a marked increase of myo-inositol was found 3 days, 14 days and 4 months after SCI. Changes in metabolite concentrations in the spinal cord were also found for choline and N-acetylaspartate. No significant changes in metabolite concentrations were found in thalamus/striatum. Multivariate data analysis allowed separation between rats with SCI and controls for spectra acquired in cortex and spinal cord, but not in thalamus/striatum. Our findings suggest MRS could become a helpful tool to monitor spatial and temporal alterations of metabolic conditions in vivo in the brain and spinal cord after SCI. We provide evidence for dynamic temporal changes at both ends of the neuraxis, cortex cerebri and distal spinal cord, while deep brain areas appear less affected.

Automated hippocampal shape analysis predicts the onset of dementia in mild cognitive impairment.

The hippocampus is involved at the onset of the neuropathological pathways leading to Alzheimer's disease (AD). Individuals with mild cognitive impairment (MCI) are at increased risk of AD. Hippocampal volume has been shown to predict which MCI subjects will convert to AD. Our aim in the present study was to produce a fully automated prognostic procedure, scalable to high throughput clinical and research applications, for the prediction of MCI conversion to AD using 3D hippocampal morphology. We used an automated analysis for the extraction and mapping of the hippocampus from structural magnetic resonance scans to extract 3D hippocampal shape morphology, and we then applied machine learning classification to predict conversion from MCI to AD. We investigated the accuracy of prediction in 103 MCI subjects (mean age 74.1 years) from the longitudinal AddNeuroMed study. Our model correctly predicted MCI conversion to dementia within a year at an accuracy of 80% (sensitivity 77%, specificity 80%), a performance which is competitive with previous predictive models dependent on manual measurements. Categorization of MCI subjects based on hippocampal morphology revealed more rapid cognitive deterioration in MMSE scores (p<0.01) and CERAD verbal memory (p<0.01) in those subjects who were predicted to develop dementia relative to those predicted to remain stable. The pattern of atrophy associated with increased risk of conversion demonstrated initial degeneration in the anterior part of the cornus ammonis 1 (CA1) hippocampal subregion. We conclude that automated shape analysis generates sensitive measurements of early neurodegeneration which predates the onset of dementia and thus provides a prognostic biomarker for conversion of MCI to AD.

The AddNeuroMed framework for multi-centre MRI assessment of Alzheimer's disease: experience from the first 24 months.

OBJECTIVE: To describe the AddNeuroMed imaging framework for multi-centre magnetic resonance imaging (MRI) assessment of longitudinal changes in Alzheimer's disease and report on early results from the first 24 months of the project. METHODS: A multi-centre study similarly to a faux clinical trial has been established to assess longitudinal MRI changes in Alzheimer disease (AD), mild cognitive impairment (MCI) and healthy control subjects using an image acquisition protocol compatible with Alzheimer disease neuroimaging initiative (ADNI). Comprehensive quality control (QC) measures have been established throughout the study. An intelligent web-accessible database holds details on both the raw images and data processed using a sophisticated image analysis pipeline. RESULTS: A total of 378 subjects have been recruited (130 AD, 131 MCI, 117 healthy controls) of which a high percentage (97.3%) of the T1-weighted volumes passed the QC criteria. Measurements of normalized whole brain volume and whole brain cortical thickness showed significant differences between AD and controls, AD and MCI and MCI and controls. CONCLUSIONS: A framework for multi-centre MRI studies of Alzheimer's disease has been established consisting of a harmonized MRI acquisition protocol across centres, rigorous QC at both the sites and central data analysis hub and an automated image analysis pipeline. Early results demonstrate the high quality of the images acquired and the applicability of the automated image analysis techniques employed.

Multivariate analysis of MRI data for Alzheimer's disease, mild cognitive impairment and healthy controls.

We have used multivariate data analysis, more specifically orthogonal partial least squares to latent structures (OPLS) analysis, to discriminate between Alzheimer's disease (AD), mild cognitive impairment (MCI) and elderly control subjects combining both regional and global magnetic resonance imaging (MRI) volumetric measures. In this study, 117 AD patients, 122 MCI patients and 112 control subjects (from the AddNeuroMed study) were included. High-resolution sagittal 3D MP-RAGE datasets were acquired from each subject. Automated regional segmentation and manual outlining of the hippocampus were performed for each image. Altogether this yielded volumes of 24 different anatomically defined structures which were used for OPLS analysis. 17 randomly selected AD patients, 12 randomly selected control subjects and the 22 MCI subjects who converted to AD at 1-year follow up were excluded from the initial OPLS analysis to provide a small external test set for model validation. Comparing AD with controls we found a sensitivity of 87% and a specificity of 90% using hippocampal measures alone. Combining both global and regional measures resulted in a sensitivity of 90% and a specificity of 94%. This increase in sensitivity and specificity resulted in an increase of the positive likelihood ratio from 9 to 15. From the external test set, the model predicted 82% of the AD patients and 83% of the control subjects correctly. Finally, 73% of the MCI subjects which converted to AD at 1 year follow-up were shown to resemble AD patients more closely than controls. This method shows potential for distinguishing between different patient groups. Combining the different MRI measures together resulted in a significantly better classification than using them separately. OPLS also shows potential for predicting conversion from MCI to AD.