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%)

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

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

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

Telomere length and bipolar disorder

Variation in telomere length is heritable and is currently considered a promising biomarker of susceptibility for neuropsychiatric disorders, particularly because of its association with memory function and hippocampal morphology. Here, we investigate telomere length in connection to familial risk and disease expression in bipolar disorder (BD). We used quantitative polymerase chain reactions and a telomere-sequence to single-copy-gene-sequence ratio method to determine telomere length in genomic DNA extracted from buccal smears from 63 patients with BD, 74 first-degree relatives (49 relatives had no lifetime psychopathology and 25 had a non-BD mood disorder) and 80 unrelated healthy individuals. Participants also underwent magnetic resonance imaging to determine hippocampal volumes and cognitive assessment to evaluate episodic memory using the verbal paired associates test. Telomere length was shorter in psychiatrically-well relatives (p=0.007) compared to unrelated healthy participants. Telomere length was also shorter in relatives (regardless of psychiatric status; p<0.01) and patients with BD not on lithium (p=0.02) compared to lithium-treated patients with BD. In the entire sample, telomere length was positively associated with left and right hippocampal volume and with delayed recall. This study provides evidence that shortened telomere length is associated with familial risk for BD. Lithium may have neuroprotective properties that require further investigation using prospective designs

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

Memory for emotional words in aMCI and depression

Objectives: 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. 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