Corrigendum to: "FreeSurfer subcortical normative data" [Data in Brief 9 (2016) 732-736].

[This corrects the article DOI: 10.1016/j.dib.2016.10.001.].

FreeSurfer subcortical normative data.

This article contains a spreadsheet computing estimates of the expected subcortical regional volumes of an individual based on its characteristics and the scanner characteristics, in addition to supplementary results related to the article "Normative data for subcortical regional volumes over the lifetime of the adult human brain" (O. Potvin, A. Mouiha, L. Dieumegarde, S. Duchesne, 2016) [1] on normative data for subcortical volumes. Data used to produce normative values was obtained by anatomical magnetic resonance imaging from 2790 healthy individuals aged 18-94 years using 23 samples provided by 21 independent research groups. The segmentation was conducted using FreeSurfer. The spreadsheet includes formulas in order to compute for a new individual, significance test for volume abnormality, effect size and estimated percentage of the normative population with a smaller volume while taking into account age, sex, estimated intracranial volume (eTIV), and scanner characteristics. Detailed R-squares of each predictor for all formula are also reported as well as the difference of subcortical volumes segmented by FreeSurfer on two different computer hardware setups.

Normative data for subcortical regional volumes over the lifetime of the adult human brain.

Normative data for volumetric estimates of brain structures are necessary to adequately assess brain volume alterations in individuals with suspected neurological or psychiatric conditions. Although many studies have described age and sex effects in healthy individuals for brain morphometry assessed via magnetic resonance imaging, proper normative values allowing to quantify potential brain abnormalities are needed. We developed norms for volumetric estimates of subcortical brain regions based on cross-sectional magnetic resonance scans from 2790 healthy individuals aged 18 to 94years using 23 samples provided by 21 independent research groups. The segmentation was conducted using FreeSurfer, a widely used and freely available automated segmentation software. Models predicting subcortical regional volumes of each hemisphere were produced including age, sex, estimated total intracranial volume (eTIV), scanner manufacturer, magnetic field strength, and interactions as predictors. The mean explained variance by the models was 48%. For most regions, age, sex and eTIV predicted most of the explained variance while manufacturer, magnetic field strength and interactions predicted a limited amount. Estimates of the expected volumes of an individual based on its characteristics and the scanner characteristics can be obtained using derived formulas. For a new individual, significance test for volume abnormality, effect size and estimated percentage of the normative population with a smaller volume can be obtained. Normative values were validated in independent samples of healthy adults and in adults with Alzheimer's disease and schizophrenia.

Impact of Depressive Symptoms on Memory for Emotional Words in Mild Cognitive Impairment and Late-Life Depression.

BACKGROUND: Amnestic mild cognitive impairment (aMCI) and late-life depression (LLD) are associated with increased risk of Alzheimer's disease (AD). This is also true for aMCI with concomitant depressive symptoms (aMCI/D+), but few studies have investigated this syndrome. OBJECTIVES: We aimed to clarify the association between cognitive and depressive symptoms in individuals at risk for AD by examining episodic memory for emotional stimuli in aMCI, aMCI/D+, and LLD. METHODS: Participants were 34 patients with aMCI, 20 patients with aMCI/D+, 19 patients with LLD, and 28 healthy elderly adults. In an implicit encoding task, participants rated the emotional valence of 12 positive, 12 negative, and 12 neutral words. Immediately and 20 minutes later, participants recalled as many words as possible. They were also asked to identify previously presented words during a yes/no recognition trial. RESULTS: At immediate recall, aMCI participants displayed better recall of emotional words, particularly positive words. aMCI/D+ and control participants displayed better recall of positive and negative words compared to neutral words. LLD participants recalled more negative than neutral words. At delayed recall, emotional words were generally better-remembered than neutral words by all groups. At recognition, all subjects responded more liberally to emotional than to neutral words. CONCLUSION: We find that the type of emotional information remembered by aMCI patients at immediate recall depends on the presence or absence of depressive symptoms. These findings contribute to identifying sources of heterogeneity in individuals at risk for AD, and suggest that the cognitive profile of aMCI/D+ is different from that of aMCI and LLD. Future studies should systematically consider the presence of depressive symptoms in elderly at-risk individuals.

White Matter Hyperintensities in Mild Cognitive Impairment and Lower Risk of Cognitive Decline.

BACKGROUND: White matter hyperintensities (WMH) may have a different impact on cognitive decline depending on strategic localization. OBJECTIVE: The goal of this study is to assess the impact of global and cholinergic WMH on cognitive decline of mild cognitive impairment (MCI) patients in the ADNI-1 dataset. METHODS: This is a retrospective analysis of data from a natural history study. MRI scans (T2 and PD sequences) were assessed with two visual scales: 1) The Cholinergic Pathways HyperIntensities Scale (CHIPS) score, designed to assess WMH in the cholinergic tracts, and 2) the Age-Related White Matter Changes Scale (ARWMC), a scale to assess the global WMH burden. All subjects underwent standardized neuropsychological testing. RESULTS: Subjects included 310 individuals with MCI. Analysis showed no association between WMH at baseline and conversion from MCI to Alzheimer's disease (AD), either for the global WMH burden or WMH within the cholinergic pathways. However, ARWMC scores had a significant confounding effect (p = 0.03) on conversion to dementia (hazard ratio of 0.37) among MCI subjects with low executive functions. CONCLUSION: We found no association between the burden of WMH at baseline in MCI and conversion to AD over 3 years. However, a higher global WMH burden appears to reduce the risk of conversion to AD in subjects with low executive functions. These results suggest that higher WMH burden in MCI individuals may be associated with a more gradual cognitive decline or stabilization, compared to a low WMH burden.

Manual segmentation qualification platform for the EADC-ADNI harmonized protocol for hippocampal segmentation project.

BACKGROUND: The use of hippocampal volumetry as a biomarker for Alzheimer's disease (AD) requires that tracers from different laboratories comply with the same segmentation method. Here we present a platform for training and qualifying new tracers to perform the manual segmentation of the hippocampus on magnetic resonance images (MRI) following the European Alzheimer's Disease Consortium and Alzheimer's Disease Neuroimaging Initiative (EADC-ADNI) Harmonized Protocol (HarP). Our objective was to demonstrate that the training process embedded in the platform leads to increased compliance and qualification with the HarP. METHOD: Thirteen new tracers' segmentations were compared with benchmark images with respect to: (a) absolute segmentation volume; (b) spatial overlap of contour with the reference using the Jaccard similarity index; and (c) spatial distance of contour with the reference. Point by point visual feedback was provided through three training phases on 10 MRI. Tracers were then tested on 10 different MRIs in the qualification phase. RESULTS: Statistical testing of training over three phases showed a significant increase of Jaccard (i.e. mean Jaccard overlap P < .001) between phases on average for all raters, demonstrating that training positively increased compliance with the HarP. Based on these results we defined qualification thresholds which all tracers were able to meet. CONCLUSIONS: This platform is an adequate infrastructure allowing standardized training and evaluation of tracers' compliance with the HarP. This is a necessary step allowing the use of hippocampal volumetry as a biomarker for AD in clinical and research centers.

Single time point high-dimensional morphometry in Alzheimer's disease: group statistics on longitudinally acquired data.

Quantitative assessment of medial temporal lobe atrophy has been proposed as a biomarker for Alzheimer's disease (AD) diagnostic and prognostic in mild cognitive impairment (MCI) due to AD. We present the first results of our high-dimensional morphometry technique, tracking tissue composition, and atrophy changes on T1-weighted magnetic resonance imaging at various time points. We selected 187 control subjects, 17 control subjects having progressed to MCI and/or AD, 178 subjects with stable MCI, 165 subjects with MCI having progressed to AD, and 147 AD subjects from the Alzheimer's Disease Neuroimaging Initiative study. Results show statistically significant differences between almost every diagnostic and time point comparison pairs (0-12, 12-24, and 24-36 months), including controls having progressed to either MCI or AD and trajectory dynamics that demonstrate the algorithm's ability at tracking specific pathology-related neurodegeneration.

High-Dimensional Medial Lobe Morphometry: An Automated MRI Biomarker for the New AD Diagnostic Criteria.

Introduction. Medial temporal lobe atrophy assessment via magnetic resonance imaging (MRI) has been proposed in recent criteria as an in vivo diagnostic biomarker of Alzheimer's disease (AD). However, practical application of these criteria in a clinical setting will require automated MRI analysis techniques. To this end, we wished to validate our automated, high-dimensional morphometry technique to the hypothetical prediction of future clinical status from baseline data in a cohort of subjects in a large, multicentric setting, compared to currently known clinical status for these subjects. Materials and Methods. The study group consisted of 214 controls, 371 mild cognitive impairment (147 having progressed to probable AD and 224 stable), and 181 probable AD from the Alzheimer's Disease Neuroimaging Initiative, with data acquired on 58 different 1.5 T scanners. We measured the sensitivity and specificity of our technique in a hierarchical fashion, first testing the effect of intensity standardization, then between different volumes of interest, and finally its generalizability for a large, multicentric cohort. Results. We obtained 73.2% prediction accuracy with 79.5% sensitivity for the prediction of MCI progression to clinically probable AD. The positive predictive value was 81.6% for MCI progressing on average within 1.5 (0.3 s.d.) year. Conclusion. With high accuracy, the technique's ability to identify discriminant medial temporal lobe atrophy has been demonstrated in a large, multicentric environment. It is suitable as an aid for clinical diagnostic of AD.

Test-retest reliability of a new medial temporal atrophy morphological metric.

Clinicians and researchers alike are in need of quantitative and robust measurement tools to assess medial temporal lobe atrophy (MTA) due to Alzheimer's disease (AD). We recently proposed a morphological metric, extracted from T1-weighted magnetic resonance images (MRI), to track and estimate MTA in cohorts of controls, AD, and mild cognitive impairment subjects, at high-risk of progression to dementia. In this paper, we investigated its reliability through analysis of within-session scan/repeat images and scan/rescans from large multicenter studies. In total, we used MRI data from 1051 subjects recruited at over 60 centers. We processed the data identically and calculated our metric for each individual, based on the concept of distance in a high-dimensional space of intensity and shape characteristics. Over 759 subjects, the scan/repeat change in the mean was 1.97% (SD: 21.2%). Over three subjects, the scan/rescan change in the mean was 0.89% (SD: 22.1%). At this level, the minimum trial size required to detect this difference is 68 individuals for both samples. Our scan/repeat and scan/rescan results demonstrate that our MTA assessment metric shows high reliability, a necessary component of validity.

Tissue-based MRI intensity standardization: application to multicentric datasets.

Intensity standardization in MRI aims at correcting scanner-dependent intensity variations. Existing simple and robust techniques aim at matching the input image histogram onto a standard, while we think that standardization should aim at matching spatially corresponding tissue intensities. In this study, we present a novel automatic technique, called STI for STandardization of Intensities, which not only shares the simplicity and robustness of histogram-matching techniques, but also incorporates tissue spatial intensity information. STI uses joint intensity histograms to determine intensity correspondence in each tissue between the input and standard images. We compared STI to an existing histogram-matching technique on two multicentric datasets, Pilot E-ADNI and ADNI, by measuring the intensity error with respect to the standard image after performing nonlinear registration. The Pilot E-ADNI dataset consisted in 3 subjects each scanned in 7 different sites. The ADNI dataset consisted in 795 subjects scanned in more than 50 different sites. STI was superior to the histogram-matching technique, showing significantly better intensity matching for the brain white matter with respect to the standard image.

Toward a dynamic biomarker model in Alzheimer's disease.

Biomarkers, both biological and imaging, are indicators of specific changes that characterize Alzheimer's disease (AD) progression in vivo. Knowing the precise relationship between biomarkers and disease severity would allow for accurate disease staging and possible forecasting of decline. Jack et al. suggested as an initial hypothesis that this relationship be sigmoidal; the objective of this article is to determine, using large-scale population data from ADNI, the precise shape of this association. We considered six different models (linear; quadratic; robust quadratic; local quadratic regression; penalized B-spline; and sigmoid) and used the Akaike Information Criterion to gauge how well these models compare in conforming to the data. We included 576 subjects (229 controls, 193 AD, and 154 mild cognitive impairment subjects who converted to AD) from the ADNI study, for whom baseline data on cerebrospinal fluid amyloid-ß (Aß)42, phosphorylated tau (p-tau), and total-tau (t-tau), hippocampal volumes, and FDG-PET were available. Analysis of this cross-sectional dataset showed that a local quadratic regression model was 42% more likely than a sigmoid to be the best model for Aß42. This ratio augments to 22% and 73% for Penalized B-Spline in the case of p-tau and t-tau, respectively; to 3500% for the linear model for FDG-PET; and to 6700% for the Penalized B-Spline for hippocampal volumes. Preliminary, cross-sectional evidence therefore indicates that the shape of the association with disease severity is non-linear and differs between biomarkers.

Structural neuroimaging of concomitant depressive symptoms in amnestic mild cognitive impairment: a pilot study.

Late-life depression (LLD) and amnestic mild cognitive impairment (aMCI) can both denote prodromal Alzheimer's disease. While the two concepts share common clinical features, differential diagnosis between them is crucial. The objective of this pilot study was to explore differences in terms of the hippocampal (HC) and entorhinal cortex (EC) volume reduction between LLD and aMCI patients with (aMCI/D+ group) or without (aMCI group) depressive symptoms. Six LLD, 6 aMCI, and 6 aMCI/D+ participants were assessed using a structural magnetic resonance imaging protocol. Manual segmentation of HC and EC was carried out. The results of volumetric comparisons suggest that the HC was larger in aMCI/D+ and LLD subjects compared to aMCI participants. The left EC mean volume was slightly lower in aMCI/D+ subjects. Power analyses revealed that 36 participants per group would suffice to confirm these findings. Overall, these pilot findings suggest that aMCI can be distinguished from LLD based on cerebral atrophy measures, and that HC and EC atrophy in aMCI varies according to the presence or absence of depressive symptoms.

Morphological Factor Estimation via High-Dimensional Reduction: Prediction of MCI Conversion to Probable AD.

We propose a novel morphological factor estimate from structural MRI for disease state evaluation. We tested this methodology in the context of Alzheimer's disease (AD) with 349 subjects. The method consisted in (a) creating a reference MRI feature eigenspace using intensity and local volume change data from 149 healthy, young subjects; (b) projecting MRI data from 75 probable AD, 76 controls (CTRL), and 49 Mild Cognitive Impairment (MCI) in that space; (c) extracting high-dimensional discriminant functions; (d) calculating a single morphological factor based on various models. We used this methodology in leave-one-out experiments to (1) confirm the superiority of an inverse-squared model over other approaches; (2) obtain accuracy estimates for the discrimination of probable AD from CTRL (90%) and the prediction of conversion of MCI subjects to probable AD (79.4%).

Multi-decade hippocampal and amygdala volume analysis: equal variability and limited age effect.

Hippocampal (HC) and amygdala (AG) variability throughout asymptomatic adulthood have not been often characterized. The prevailing assumption is that HC/AG variability is small in young adults, and widens with advancing age and pathology. More recent studies with samples at every decade have reported conflicting results. Our goal was to perform a precise investigation of the effects of Age, Sex and Hemisphere on HC/AG volumes throughout aging. Subjects - we included 422 subjects from the Italian Brain Normative Archive database. Subjects ranged in age from 20 to 84 years. Data - manual segmentation was performed on 422 individuals for the HC, and 228 for the AG, using the Pruessner protocol. Statistical analysis - we tested the influence of total intracranial volume normalization, and used a hierarchical regression model to determine the shape of the association for Age with HC/AG volumes, for both Sex and Hemisphere variables. We explored the distribution of HC/AG volume across age groups by dividing the data into six different strata by decades, and compared volume variability using ANOVA. The study revealed that HC or AG volumes were not significantly related to Age or Age(2), regardless of Sex, except in the right AG. There were no significant differences in variability across age strata. This study lends credence to counter-intuitive notions regarding HC/AG neurodegeneration. Further, researchers can use our HC/AG volumes, broken down by sex and age, as normative data in future fundamental and clinical research.

Hippocampal atrophy rates in Alzheimer's disease: automated segmentation variability analysis.

Hippocampal (HC) atrophy and atrophy rates are putative clinical markers of progression to Alzheimer's disease (AD). We compared results given by two different automated HC segmentation techniques in the Alzheimer's Disease Neuroimaging Initiative dataset between two time intervals. We used HC volumetric automated segmentation data for a total of 683 patients at baseline (198 controls, 331 with mild cognitive impairment (MCI) and 154 with AD), 684 at 6 months (198 controls, 332 with MCI and 154 with AD) and 587 at 12 months (176 controls, 280 with MCI and 131 with AD). Segmentation techniques included FreeSurfer and SNT. We calculated HC monthly atrophy rates between baseline and 6 months and between 6 and 12 months, and used a multiple-way ANOVA for repeated measures. Mean HC volumes decrease with time. The only significant (p<0.05) main effect was diagnosis. We measured strong interaction between technique and scan interval and weak interaction between diagnoses and scan interval. When compared to mean rates from largely manual segmentation, automated segmentation results show increased atrophy rates for both SNT and FreeSurfer techniques. While sensitive, there remains substantial technique variability, likely due to differences in methodological approaches and especially neuroanatomical HC definitions. These fundamental metrological problems need to be resolved before concluding with certainty on the accuracy and reliability of automated techniques.