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Background and Objectives: Brain MRI abnormalities and increases in neurofilament light chain (NfL) have mostly been observed in cross-sectional studies before ataxia onset in polyglutamine spinocerebellar ataxias. Our study aimed to identify longitudinal changes in biological, clinical, and/or imaging biomarkers in spinocerebellar ataxia (SCA) 2 and SCA7 carriers over 1 year., Methods: We studied SCA2 and SCA7 carriers and controls (expansion-negative relatives) at the Paris Brain Institute. Inclusion criteria included Scale for the Assessment and Rating of Ataxia (SARA) scores between 0 and 15. Assessments at baseline, 6 months, and 12 months comprised neurologic, quality of life, orofacial motor, neuropsychological, and ophthalmologic examinations, along with gait and oculomotor recordings, brain MRI, CSF, and blood sampling. The primary outcome was the longitudinal change in these assessments over 1 year., Results: We included 15 SCA2 carriers, 15 SCA7 carriers, and 10 controls between May 2020 and April 2021. At baseline, the ages were similar (41 [37, 46] for SCA2, 38 [28.5, 39.8] for SCA7, and 39.5 [31, 54.5] for controls, p = 0.78), as well the sex ( p = 0.61); SARA scores were low but different (4 [1.25, 6.5] in SCA2, 2 [0, 11.5] in SCA7, and 0 in controls, p < 0.01). Pons and medulla volumes were smaller in SCAs ( p < 0.05) and cerebellum volume only in SCA2 ( p = 0.01). Plasma NfL levels were higher in SCA participants (SCA2: 14.2 pg/mL [11.52, 15.89], SCA7: 15.53 [13.27, 23.23]) than in controls (4.88 [3.56, 6.17], p < 0.001). After 1-year follow-up, in SCA2, there was significant pons (-144 ± 60 mm 3 ) and cerebellum (-1,508 ± 580 mm 3 ) volume loss and a worsening of gait assessment; in SCA7, SARA score significantly increased (+1.3 ± 0.4) and outer retinal nuclear layer thickness decreased (-15.4 ± 1.6 μm); for both SCA groups, the orofacial motor assessment significantly worsened. For preataxic and early ataxic carriers, the strongest longitudinal deterioration on outcome measures was orofacial motility in SCA2 and retinal thickness in SCA7., Discussion: Despite the limitation of the small sample size, we detected annual changes in preataxic and early ataxic SCA individuals across brain MRI imaging, clinical scores, gait parameters, and retinal thickness. These parameters could serve as potential end points for future therapeutic trials in the preataxic phase., Trial Registration Information: ClinicalTrials.gov NCT04288128.

Data on 2,045 non-demented individuals with memory complaints were drawn from the Memento cohort study to examine the association between Apolipoprotein E ε4 allele (APOE4) and regional brain gray matter volumes. Linear regression was used to examine the association of APOE4 and measures of regional gray matter volumes in cross-sectional analysis and change therein using longitudinal analyses based on two brain MRI performed at baseline and at two-year follow-up. Overall, in analyses adjusted for age, sex, and intracranial volume, the presence of APOE4 was associated with lower total gray matter volume at baseline and with a higher atrophy rate over the follow-up. The hippocampus and entorhinal cortex were the two gray matter regions most associated with APOE4. Further adjustment for cardiovascular risk factors had little impact on these associations. There was an interaction between age, APOE4 status and total brain volume atrophy rate, with evidence of an earlier age at onset of atrophy in hippocampal volume in APOE4 carriers compared to non-carriers. Those results are in accordance with the role of medial temporal structures in the greater risk of dementia observed in people carrying the APOE4 allele., (Copyright © 2022. Published by Elsevier Inc.)

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. © 2016 Wiley Periodicals, Inc., (© 2016 Wiley Periodicals, Inc.)

Introduction/purpose: In vivo detection of cerebral microbleeds (CMBs) from T2* gradient recalled echo (GRE) magnitude image suffers from low specificity, modest inter-rater reproducibility and is biased by its sensitivity to acquisition parameters. New methods were proposed for improving this identification, but they mostly rely on 3D acquisitions, not always feasible in clinical practice. A fast 2D phase processing technique for computing internal field maps (IFM) has been shown to make it possible to characterize CMBs through their magnetic signature in routine clinical setting, based on 2D multi-slice acquisitions. However, its clinical interest for CMBs identification with respect to more common images remained to be assessed. To do so, systematic experiments were undertaken to compare the ratings obtained by trained observers with several image types, T2* magnitude, Susceptibility Weighted Imaging reconstructions (SWI) and IFM built from the same T2*-weighted acquisition., Materials/methods: 15 participants from the MEMENTO multi-center cohort were selected: six subjects with numerous CMBs (20 ± 6 CMBs), five subjects with a few CMBs (2 ± 1 CMBs) and four subjects without CMB. 2D multi-slice T2* GRE sequences were acquired on Philips and Siemens 3T systems. After pilot experiments, T2* magnitude, Susceptibility Weighted Imaging (SWI) minimum intensity projection (mIP) on three slices and IFM were considered for the rating experiments. A graphical user interface (GUI) was designed in order to consistently display images in random order. Six raters of various background and expertise independently selected "definite" or "possible" CMBs. Rating results were compared with respect to a specific consensus reference, on both lesion and subject type points of view., Results: IFM yielded increased sensitivity and decreased false positives rate (FPR) for CMBs identification compared to T2* magnitude and SWI-mIP images. Inter-rater variability was decreased with IFM when identifying subjects with numerous lesions, with only a limited increase in rating time. IFM thus appears as an interesting candidate to improve CMBs identification in clinical setting.

Objective: Motion-robust multi-slab imaging of hippocampal inner structure in vivo at 7T., Materials and Methods: Motion is a crucial issue for ultra-high resolution imaging, such as can be achieved with 7T MRI. An acquisition protocol was designed for imaging hippocampal inner structure at 7T. It relies on a compromise between anatomical details visibility and robustness to motion. In order to reduce acquisition time and motion artifacts, the full slab covering the hippocampus was split into separate slabs with lower acquisition time. A robust registration approach was implemented to combine the acquired slabs within a final 3D-consistent high-resolution slab covering the whole hippocampus. Evaluation was performed on 50 subjects overall, made of three groups of subjects acquired using three acquisition settings; it focused on three issues: visibility of hippocampal inner structure, robustness to motion artifacts and registration procedure performance., Results: Overall, T2-weighted acquisitions with interleaved slabs proved robust. Multi-slab registration yielded high quality datasets in 96 % of the subjects, thus compatible with further analyses of hippocampal inner structure., Conclusion: Multi-slab acquisition and registration setting is efficient for reducing acquisition time and consequently motion artifacts for ultra-high resolution imaging of the inner structure of the hippocampus.

Background: Despite known relationship between hippocampal volumes and major depressive episodes (MDE) and the increased suicidality in MDE, the links between hippocampal volumes and suicidality remain unclear in major depressive disorders (MDD). If the hippocampus could be a biomarker of suicide attempts in depression, it could be useful for prevention matters. This study assessed the association between hippocampal volumes and suicide attempts in MDD., Methods: Hippocampal volumes assessed with automatic segmentation were compared in 63 patients with MDD, with (n = 24) or without (n = 39) suicide attempts. Acute (one month) suicide attempts were studied., Results: Although not different in terms of socio-demographic, MDD and MDE clinical features, suicide attempters had lower total hippocampus volumes than non-attempters (4.61 (± 1.15) cm(3) vs 5.22 (± 0.99) cm(3); w = 625.5; p = 0.03), especially for acute suicide attempts (4.19 (± 0.81) cm(3) vs 5.22 (± 0.99) cm(3); w = 334; p = 0.005), even after adjustment on brain volumes, sex, age, Hamilton Depression Rating Scale (HDRS) scores and MDD duration. A ROC analysis showed that a total hippocampal volume threshold of 5.00 cm(3) had a 98.2% negative predictive value for acute suicide attempts., Conclusion: Depressed suicide attempters have smaller hippocampus than depressed patients without suicide attempts, independently from socio-demographics and MDD characteristics. This difference is related to acute suicide attempts but neither to past suicide attempts nor to duration since the first suicide attempt, suggesting that hippocampal volume could be a suicidal state marker in MDE. Further studies are required to better understand this association., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

Cerebral microbleeds (CMBs) have emerged as a new imaging marker of small vessel disease. Composed of hemosiderin, CMBs are paramagnetic and can be detected with MRI sequences sensitive to magnetic susceptibility (typically, gradient recalled echo T2* weighted images). Nevertheless, their identification remains challenging on T2* magnitude images because of confounding structures and lesions. In this context, T2* phase image may play a key role in better characterizing CMBs because of its direct relationship with local magnetic field variations due to magnetic susceptibility difference. To address this issue, susceptibility-based imaging techniques were proposed, such as Susceptibility Weighted Imaging (SWI) and Quantitative Susceptibility Mapping (QSM). But these techniques have not yet been validated for 2D clinical data in multicenter settings. Here, we introduce 2DHF, a fast 2D phase processing technique embedding both unwrapping and harmonic filtering designed for data acquired in 2D, even with slice-to-slice inconsistencies. This method results in internal field maps which reveal local field details due to magnetic inhomogeneity within the region of interest only. This technique is based on the physical properties of the induced magnetic field and should yield consistent results. A synthetic phantom was created for numerical simulations. It simulates paramagnetic and diamagnetic lesions within a 'brain-like' tissue, within a background. The method was evaluated on both this synthetic phantom and multicenter 2D datasets acquired in standardized clinical setting, and compared with two state-of-the-art methods. It proved to yield consistent results on synthetic images and to be applicable and robust on patient data. As a proof-of-concept, we finally illustrate that it is possible to find a magnetic signature of CMBs and CMCs on internal field maps generated with 2DHF on 2D clinical datasets that give consistent results with CT-scans in a subsample of 10 subjects acquired with both modalities., (Copyright © 2014. Published by Elsevier Inc.)

In Alzheimer's disease (AD), the hippocampus is an early site of tau pathology and neurodegeneration. Histological studies have shown that lesions are not uniformly distributed within the hippocampus. Moreover, alterations of different hippocampal layers may reflect distinct pathological processes. 7 T MRI dramatically improves the visualization of hippocampal subregions and layers. In this study, we aimed to assess whether 7 T MRI can detect volumetric changes in hippocampal layers in vivo in patients with AD. We studied four AD patients and seven control subjects. MR images were acquired using a whole-body 7 T scanner with an eight channel transmit-receive coil. Hippocampal subregions were manually segmented from coronal T2*-weighted gradient echo images with 0.3 × 0.3 × 1.2 mm3 resolution using a protocol that distinguishes between layers richer or poorer in neuronal bodies. Five subregions were segmented in the region of the hippocampal body: alveus, strata radiatum, lacunosum and moleculare (SRLM) of the cornu Ammonis (CA), hilum, stratum pyramidale of CA and stratum pyramidale of the subiculum. We found strong bilateral reductions in the SRLM of the cornu Ammonis and in the stratum pyramidale of the subiculum (p < 0.05), with average cross-sectional area reductions ranging from -29% to -49%. These results show that it is possible to detect volume loss in distinct hippocampal layers using segmentation of 7 T MRI. 7 T MRI-based segmentation is a promising tool for AD research.

Introduction: Hippocampus volumetry is a useful surrogate marker for the diagnosis of Alzheimer's disease (AD). Our purpose was to compare visual assessment of medial temporal lobe atrophy made by radiologists with automatic hippocampal volume and to compare their performances for the classification of AD, mild cognitive impairment (MCI) and cognitively normal (CN)., Methods: We studied 30 CN, 30 MCI and 30 AD subjects. Six radiologists with two levels of expertise performed two readings of medial temporal lobe atrophy. Medial temporal lobe atrophy was evaluated on coronal three-dimensional T1-weighted images using Scheltens scale and compared with hippocampal volume obtained using a fully automatic segmentation method (Spearman's rank coefficient)., Results: Visual assessment of medial temporal lobe atrophy was correlated with hippocampal volume (p < 0.01). Classification performances between MCI converter and CN was better using volumetry than visual assessment of non-expert readers whereas classification of AD and CN did not differ between visual assessment and volumetry except for the first reading of one non-expert (p = 0.03)., Conclusions: Visual assessment of medial temporal lobe atrophy by radiologists was well correlated with hippocampal volume. Radiological assessment is as good as computer-based volumetry for the classification of AD, MCI non-converter and CN and less good for the classification of MCI converter versus CN. Use of Scheltens scale for assessing hippocampal atrophy in AD seems thus justified in clinical routine.

White matter hyperintensities (WMH) on T2 or FLAIR sequences have been commonly observed on MR images of elderly people. They have been associated with various disorders and have been shown to be a strong risk factor for stroke and dementia. WMH studies usually required visual evaluation of WMH load or time-consuming manual delineation. This paper introduced WHASA (White matter Hyperintensities Automated Segmentation Algorithm), a new method for automatically segmenting WMH from FLAIR and T1 images in multicentre studies. Contrary to previous approaches that were based on intensities, this method relied on contrast: non linear diffusion filtering alternated with watershed segmentation to obtain piecewise constant images with increased contrast between WMH and surroundings tissues. WMH were then selected based on subject dependant automatically computed threshold and anatomical information. WHASA was evaluated on 67 patients from two studies, acquired on six different MRI scanners and displaying a wide range of lesion load. Accuracy of the segmentation was assessed through volume and spatial agreement measures with respect to manual segmentation; an intraclass correlation coefficient (ICC) of 0.96 and a mean similarity index (SI) of 0.72 were obtained. WHASA was compared to four other approaches: Freesurfer and a thresholding approach as unsupervised methods; k-nearest neighbours (kNN) and support vector machines (SVM) as supervised ones. For these latter, influence of the training set was also investigated. WHASA clearly outperformed both unsupervised methods, while performing at least as good as supervised approaches (ICC range: 0.87-0.91 for kNN; 0.89-0.94 for SVM. Mean SI: 0.63-0.71 for kNN, 0.67-0.72 for SVM), and did not need any training set.

Purpose: To determine if ultrahigh-field-strength magnetic resonance (MR) imaging can be used to detect subregional hippocampal alterations., Materials and Methods: Subjects provided written consent to participate in this prospective institutional review board-approved HIPAA-compliant study. T1- and T2-weighted 7-T brain MR images were acquired in 11 healthy subjects and eight patients with temporal lobe epilepsy (TLE). In all subjects, images were qualitatively examined for evidence of hippocampal atrophy, signal change, and malrotation with the Bernasconi definition, and digitations of the hippocampal heads were counted (agreement was measured with the κ statistic). Data were analyzed quantitatively with manual subregional hippocampal body segmentation. Subregional data in individual subjects with TLE were compared with data in control subjects to detect deviation from the control range for volume measures on each side and with asymmetry indexes., Results: All eight patients with TLE had hippocampal abnormalities on the epileptogenic side. Subregional analysis revealed selective lateral Ammon horn atrophy in six patients and diffuse Ammon horn and dentate gyrus atrophy in one patient. Paucity of hippocampal digitations occurred on the epileptogenic side in all patients with TLE and also on the contralateral side in three patients (interrater κ value, 0.80). Hippocampal malrotation was observed in three patients with TLE and four control subjects., Conclusion: Ultrahigh-field-strength MR imaging permitted detection of selectively greater Ammon horn atrophy in patients with TLE and hippocampal sclerosis. Paucity of digitations is a deformity of the hippocampal head that was detected independent of hippocampal atrophy in patients with mesial TLE., (© RSNA, 2011.)

The Free and Cued Selective Reminding Test (FCSRT) is a verbal episodic memory test used to identify patients with mild Alzheimer's disease (AD). The present study investigates the relationships between performance on FCSRT and grey matter atrophy assessed with structural MRI in patients with AD. Three complementary MRI-based analyses (VBM analysis, ROI-based analysis, and three-dimensional hippocampal surface-based shape analysis) were performed in 35 patients with AD to analyze correlations between regional atrophy and their scores for episodic memory using the FCSRT. With VBM analysis, the total score on the FCSRT was correlated with left medial temporal lobe atrophy including the left hippocampus but also the thalami. In addition, using ROI-based analysis, the total recall score on the FCSRT was correlated with the left hippocampal volume. With three-dimensional hippocampal surface-based shape analysis, both free recall and total recall scores were correlated with regions corresponding approximately to the CA1 field. No correlation was found with short term memory scores using any of these methods of analysis. In AD, the FCSRT may be considered as a useful clinical marker of memory disorders due to medial temporal damage, specially the CA1 field of the hippocampus.

Introduction and Aim: Region of interest (ROI)-based functional magnetic resonance imaging (fMRI) data analysis relies on extracting signals from a specific area which is presumed to be involved in the brain activity being studied. The hippocampus is of interest in many functional connectivity studies for example in epilepsy as it plays an important role in epileptogenesis. In this context, ROI may be defined using different techniques. Our study aims at evaluating the spatial correspondence of hippocampal ROIs obtained using three brain atlases with hippocampal ROI obtained using an automatic segmentation algorithm dedicated to the hippocampus., Material and Methods: High-resolution volumetric T1-weighted MR images of 18 healthy volunteers (five females) were acquired on a 3T scanner. Individual ROIs for both hippocampi of each subject were segmented from the MR images using an automatic hippocampus and amygdala segmentation software called SACHA providing the gold standard ROI for comparison with the atlas-derived results. For each subject, hippocampal ROIs were also obtained using three brain atlases: PickAtlas available as a commonly used software toolbox; automated anatomical labeling (AAL) atlas included as a subset of ROI into PickAtlas toolbox and a frequency-based brain atlas by Hammers et al. The levels of agreement between the SACHA results and those obtained using the atlases were assessed based on quantitative indices measuring volume differences and spatial overlap. The comparison was performed in standard Montreal Neurological Institute space, the registration being obtained with SPM5 (http://www.fil.ion.ucl.ac.uk/spm/)., Results: The mean volumetric error across all subjects was 73% for hippocampal ROIs derived from AAL atlas; 20% in case of ROIs derived from the Hammers atlas and 107% for ROIs derived from PickAtlas. The mean false-positive and false-negative classification rates were 60% and 10% respectively for the AAL atlas; 16% and 32% for the Hammers atlas and 6% and 72% for the PickAtlas., Conclusion: Though atlas-based ROI definition may be convenient, the resulting ROIs may be poor representations of the hippocampus in some studies critical to under- or oversampling. Performance of the AAL atlas was inferior to that of the Hammers atlas. Hippocampal ROIs derived from PickAtlas are highly significantly smaller, and this results in the worst performance out of three atlases. It is advisable that the defined ROIs should be verified with knowledge of neuroanatomy before using it for further data analysis.

We describe a new method to automatically discriminate between patients with Alzheimer's disease (AD) or mild cognitive impairment (MCI) and elderly controls, based on multidimensional classification of hippocampal shape features. This approach uses spherical harmonics (SPHARM) coefficients to model the shape of the hippocampi, which are segmented from magnetic resonance images (MRI) using a fully automatic method that we previously developed. SPHARM coefficients are used as features in a classification procedure based on support vector machines (SVM). The most relevant features for classification are selected using a bagging strategy. We evaluate the accuracy of our method in a group of 23 patients with AD (10 males, 13 females, age+/-standard-deviation (SD)=73+/-6 years, mini-mental score (MMS)=24.4+/-2.8), 23 patients with amnestic MCI (10 males, 13 females, age+/-SD=74+/-8 years, MMS=27.3+/-1.4) and 25 elderly healthy controls (13 males, 12 females, age+/-SD=64+/-8 years), using leave-one-out cross-validation. For AD vs controls, we obtain a correct classification rate of 94%, a sensitivity of 96%, and a specificity of 92%. For MCI vs controls, we obtain a classification rate of 83%, a sensitivity of 83%, and a specificity of 84%. This accuracy is superior to that of hippocampal volumetry and is comparable to recently published SVM-based whole-brain classification methods, which relied on a different strategy. This new method may become a useful tool to assist in the diagnosis of Alzheimer's disease.

The segmentation from MRI of macroscopically ill-defined and highly variable structures, such as the hippocampus (Hc) and the amygdala (Am), requires the use of specific constraints. Here, we describe and evaluate a fast fully automatic hybrid segmentation that uses knowledge derived from probabilistic atlases and anatomical landmarks, adapted from a semi-automatic method. The algorithm was designed at the outset for application on images from healthy subjects and patients with hippocampal sclerosis. Probabilistic atlases were built from 16 healthy subjects, registered using SPM5. Local mismatch in the atlas registration step was automatically detected and corrected. Quantitative evaluation with respect to manual segmentations was performed on the 16 young subjects, with a leave-one-out strategy, a mixed cohort of 8 controls and 15 patients with epilepsy with variable degrees of hippocampal sclerosis, and 8 healthy subjects acquired on a 3 T scanner. Seven performance indices were computed, among which error on volumes RV and Dice overlap K. The method proved to be fast, robust and accurate. For Hc, results with the new method were: 16 young subjects {RV=5%, K=87%}; mixed cohort {RV=8%, K=84%}; 3 T cohort {RV=9%, K=85%}. Results were better than with atlas-based (thresholded probability map) or semi-automatic segmentations. Atlas mismatch detection and correction proved efficient for the most sclerotic Hc. For Am, results were: 16 young controls {RV=7%, K=85%}; mixed cohort {RV=19%, K=78%}; 3 T cohort {RV=10%, K=77%}. Results were better than with the semi-automatic segmentation, and were also better than atlas-based segmentations for the 16 young subjects.

Context: According to meta-analyses, depression is associated with a smaller hippocampus. Most magnetic resonance imaging (MRI) studies among middle aged acute depressed patients are based on manual segmentation of the hippocampus. Few studies used automated methods such as voxel-based morphometry (VBM) or automated segmentation that can overcome certain drawbacks of manual segmentation (essentially intra- and inter-rater variability and operator time consumption)., Objective: The aim of our study was to compare the sensitivity of manual segmentation, automated segmentation and VBM to detect hippocampal structural changes in middle aged acute depressed population., Method: Twenty-one middle aged depressed inpatients and 21 matched controls were compared regarding their hippocampal structure using VBM with SPM5, manual segmentation and an automated segmentation algorithm. The VBM-ROI analysis was performed using two different normalization methods: the standard approach implemented in SPM5 and the most recent DARTEL algorithm., Results: Using VBM-DARTEL, when corrected for multiple comparisons, significant volume differences were detected between groups in different regions and more specifically in hippocampus with ROI analyses. Whereas using standard VBM (without DARTEL), ROI analyses did not show bilateral volume between group differences. Significant hippocampal volume reductions between patients and controls were also detected using manual segmentation (-11.6% volume reduction, p<0.05) and automated segmentation (-9.7% volume reduction, p<0.05). VBM-DARTEL and automated segmentation show equal sensitivity in detecting hippocampal differences in depressed patients, while standard VBM was unable to detect hippocampal changes. Both VBM-DARTEL and automated segmentation could be used to perform large scale volumetric studies in humans. The new automated segmentation technique could further explore and detect hippocampal subpart differences that could be very useful for clarifying physiopathology of psychiatric disorders.

Purpose: To prospectively evaluate the accuracy of automated hippocampal volumetry to help distinguish between patients with Alzheimer disease (AD), patients with mild cognitive impairment (MCI), and elderly controls, by using established criteria for patients with AD and MCI as the reference standard., Materials and Methods: The regional ethics committee approved the study and written informed consent was obtained from all participants. The study included 25 patients with AD (11 men, 14 women; mean age +/- standard deviation [SD], 73 years +/- 6; Mini-Mental State Examination (MMSE) score, 24.4 +/- 2.7), 24 patients with amnestic MCI (10 men, 14 women; mean age +/- SD, 74 years +/- 8; MMSE score, 27.2 +/- 1.4) and 25 elderly healthy controls (13 men, 12 women; mean age +/- SD, 64 years +/- 8). For each participant, the hippocampi were automatically segmented on three-dimensional T1-weighted magnetic resonance (MR) images with high spatial resolution. Segmentation was performed by using recently developed software that allows fast segmentation with minimal user input. Group differences in hippocampal volume were assessed by using Student t tests. To obtain robust estimates of P values, the correct classification rate, sensitivity, and specificity, bootstrap methods were used., Results: Significant hippocampal volume reductions were detected in all groups of patients (-32% in AD patients vs controls, P < .001; -19% in MCI patients vs controls, P < .001; and -15% in AD patients vs MCI patients, P < .01). Individual classification on the basis of hippocampal volume resulted in 84% correct classification (sensitivity, 84%; specificity, 84%) between AD patients and controls and 73% correct classification (sensitivity, 75%; specificity, 70%) between MCI patients and controls., Conclusion: This automated method can serve as an alternative to manual tracing and may thus prove useful in assisting with the diagnosis of AD., ((c) RSNA, 2008.)

We describe a new algorithm for the automated segmentation of the hippocampus (Hc) and the amygdala (Am) in clinical Magnetic Resonance Imaging (MRI) scans. Based on homotopically deforming regions, our iterative approach allows the simultaneous extraction of both structures, by means of dual competitive growth. One of the most original features of our approach is the deformation constraint based on prior knowledge of anatomical features that are automatically retrieved from the MRI data. The only manual intervention consists of the definition of a bounding box and positioning of two seeds; total execution time for the two structures is between 5 and 7 min including initialisation. The method is evaluated on 16 young healthy subjects and 8 patients with Alzheimer's disease (AD) for whom the atrophy ranged from limited to severe. Three aspects of the performances are characterised for validating the method: accuracy (automated vs. manual segmentations), reproducibility of the automated segmentation and reproducibility of the manual segmentation. For 16 young healthy subjects, accuracy is characterised by mean relative volume error/overlap/maximal boundary distance of 7%/84%/4.5 mm for Hc and 12%/81%/3.9 mm for Am; for 8 Alzheimer's disease patients, it is 9%/84%/6.5 mm for Hc and 15%/76%/4.5 mm for Am. We conclude that the performance of this new approach in data from healthy and diseased subjects in terms of segmentation quality, reproducibility and time efficiency compares favourably with that of previously published manual and automated segmentation methods. The proposed approach provides a new framework for further developments in quantitative analyses of the pathological hippocampus and amygdala in MRI scans.

The segmentation of macroscopically ill-defined and highly variable structures, such as the hippocampus Hc and the amygdala Am, from MRI requires specific constraints. Here, we describe and evaluate a hybrid segmentation method that uses knowledge derived from a probabilistic atlas and from anatomical landmarks based on stable anatomical characteristics of the structures. Combined in a previously published semi-automatic segmentation method, they lead to a fast, robust and accurate fully automatic segmentation of Hc and Am. The probabilistic atlas was built from 16 young controls and registered with the "unified segmentation" of SPM5. The algorithm was quantitatively evaluated with respect to manual segmentation on two MRI datasets: the 16 young controls, with a leave-one-out strategy, and a mixed cohort of 8 controls and 15 subjects with epilepsy with variable hippocampal sclerosis. The segmentation driven by hybrid knowledge leads to greatly improved results compared to that obtained by registration of the thresholded atlas alone: mean overlap for Hc on the 16 young controls increased from 78% to 87% (p < 0.001) and on the mixed cohort from 73% to 82% (p < 0.001) while the error on volumes decreased from 10% to 7% (p < 0.005) and from 18% to 8% (p < 0.001), respectively. Automatic results were better than the semi-automatic results: for the 16 young controls, average overlap increased from 84% to 87% (p < 0.001) for Hc and from 81% to 84% (p < 0.002) for Am, with equivalent improvements in volume error.

Background: Motor capacity is crucial in amyotrophic lateral sclerosis (ALS) clinical trial design and patient care. However, few studies have explored the potential of multimodal MRI to predict motor capacity in ALS. This study aims to evaluate the predictive value of cervical spinal cord MRI parameters for motor capacity in ALS compared to clinical prognostic factors. Methods: Spinal multimodal MRI was performed shortly after diagnosis in 41 ALS patients and 12 healthy participants as part of a prospective multicenter cohort study, the PULSE study (NCT 2013-A00969-36). Motor capacity was assessed using ALSFRS-R scores. Multiple stepwise linear regression models were constructed to predict motor capacity at 3 and 6 months from diagnosis, based on clinical variables, structural MRI measurements, including spinal cord cross-sectional area (CSA), anterior–posterior, and left-to-right cross-section diameters at vertebral levels from C1 to T4, and diffusion parameters in the lateral corticospinal tracts (LCSTs) and dorsal columns. Results: Structural MRI measurements were significantly correlated with the ALSFRS-R score and its sub-scores. And as early as 3 months from diagnosis, structural MRI measurements fit the best multiple linear regression model to predict the total ALSFRS-R (R2 = 0.70, p value = 0.0001) and arm sub-score (R2 = 0.69, p value = 0.0002), and combined with DTI metric in the LCST and clinical factors fit the best multiple linear regression model to predict leg sub-score (R2 = 0.73, p value = 0.0002). Conclusions: Spinal multimodal MRI could be promising as a tool to enhance prognostic accuracy and serve as a motor function proxy in ALS. [ABSTRACT FROM AUTHOR]

Objectives: Since physical activity (PA) has demonstrated benefits for cardiovascular health, it is possible to hypothesize that higher or increasing PA slows the progression of white matter hyperintensities (WMH). We investigated the association between PA and the progression of WMH in non-demented older adults with memory complaints. Design: We included 152 participants (mean age 74.7±3.8 years; 63.8% women) in the analyses, in whom information on self-reported PA and MRI was available at both baseline and 3-year follow-up. From the PA questionnaire, the baseline metabolic equivalent of task (MET-minute/week) and changes in MET-minute/week over three years were separately calculated for overall, leisure-time, and non-leisure time PA. WMH volume at baseline and 3-year follow-up was obtained by using an automated segmentation algorithm. Results: Mixed-effect linear regression models showed that none of the baseline PA variables was associated with progression of WMH over time. People who had decreased their PA levels over three years tended to show greater progression of WMH compared with those who had maintained PA levels of ≥1200 MET-min/week (roughly equivalent to ≥300 minutes of brisk walking) in the unadjusted model (β±SE=4.85±2.42, p=0.045); however, this association was no longer significant after adjustment for confounders (β±SE =3.63±2.18, p=0.096). Conclusions: We did not find any significant association between PA and WMH in non-demented older adults with memory complaints. However, decrease over time in PA levels tended to be associated with progression of WMH. A larger longitudinal study with data on PA assessed using objective measures would provide important information in this field. [ABSTRACT FROM AUTHOR]

Objectives: The association between circulating biomarkers of red blood cells (RBC) omega-3 polyunsaturated fatty acids (PUFAs) and cerebral white matter hyperintensities (WMH) on the brain MRI remains yet unclear. We investigated the cross-sectional and prospective associations of RBC omega-3 PUFAs with WMH in dementia-free older adults with subjective memory complaints.Design: Participants were 234 older adults with assessments for both PUFA and MRI near to baseline; among them, 79 also had an MRI assessment at 3-year follow-up. The measurement of WMH volume was obtained by an automated segmentation algorithm. We related individual or combinational baseline RBC omega-3 PUFAs levels with baseline WMH volumes and WMH evolution over 3 years. We carried out multiple (cross-sectional) and mixed-effect (prospective analysis, with random effects at participant’s level) linear regressions with adjustment for age, sex, time interval between date of blood draw for measurement of fatty acids and date of brain MRI, the status of APOE e4 carrier, body mass index, and vascular risk factors. Associations were considered significant at p ≤ 0.006 to take into account multiplicity (8 comparisons).Results: None of the eight RBC omega-3 PUFAs tested was significantly associated with WMH at both cross-sectional and prospective analyses.Conclusions: We did not find any association between omega-3 PUFAs and WMH in non-demented older adults with memory complaints. A longer longitudinal study with data on omega-3 PUFAs and WMH would contribute important information to this field. [ABSTRACT FROM AUTHOR]