Prediction of Autopsy Verified Neuropathological Change of Alzheimer’s Disease Using Machine Learning and MRI

Background: Alzheimer’s disease (AD) is the most common form of dementia. While neuropathological changes pathognomonic for AD have been defined, early detection of AD prior to cognitive impairment in the clinical setting is still lacking. Pioneer studies applying machine learning to magnetic-resonance imaging (MRI) data to predict mild cognitive impairment (MCI) or AD have yielded high accuracies, however, an algorithm predicting neuropathological change is still lacking. The objective of this study was to compute a prediction model supporting a more distinct diagnostic criterium for AD compared to clinical presentation, allowing identification of hallmark changes even before symptoms occur.Methods: Autopsy verified neuropathological changes attributed to AD, as described by a combined score for Aβ-peptides, neurofibrillary tangles and neuritic plaques issued by the National Institute on Aging – Alzheimer’s Association (NIAA), the ABC score for AD, were predicted from structural MRI data with RandomForest (RF). MRI scans were performed at least 2 years prior to death. All subjects derive from the prospective Vienna Trans-Danube Aging (VITA) study that targeted all 1750 inhabitants of the age of 75 in the starting year of 2000 in two districts of Vienna and included irregular follow-ups until death, irrespective of clinical symptoms or diagnoses. For 68 subjects MRI as well as neuropathological data were available and 49 subjects (mean age at death: 82.8 ± 2.9, 29 female) with sufficient MRI data quality were enrolled for further statistical analysis using nested cross-validation (CV). The decoding data of the inner loop was used for variable selection and parameter optimization with a fivefold CV design, the new data of the outer loop was used for model validation with optimal settings in a fivefold CV design. The whole procedure was performed ten times and average accuracies with standard deviations were reported.Results: The most informative ROIs included caudal and rostral anterior cingulate gyrus, entorhinal, fusiform and insular cortex and the subcortical ROIs anterior corpus callosum and the left vessel, a ROI comprising lacunar alterations in inferior putamen and pallidum. The resulting prediction models achieved an average accuracy for a three leveled NIAA AD score of 0.62 within the decoding sets and of 0.61 for validation sets. Higher accuracies of 0.77 for both sets, respectively, were achieved when predicting presence or absence of neuropathological change.Conclusion: Computer-aided prediction of neuropathological change according to the categorical NIAA score in AD, that currently can only be assessed post-mortem, may facilitate a more distinct and definite categorization of AD dementia. Reliable detection of neuropathological hallmarks of AD would enable risk stratification at an earlier level than prediction of MCI or clinical AD symptoms and advance precision medicine in neuropsychiatry

Automated ROI-Based Labeling for Multi-Voxel Magnetic Resonance Spectroscopy Data Using FreeSurfer

Purpose: Advanced analysis methods for multi-voxel magnetic resonance spectroscopy (MRS) are crucial for neurotransmitter quantification, especially for neurotransmitters showing different distributions across tissue types. So far, only a handful of studies have used region of interest (ROI)-based labeling approaches for multi-voxel MRS data. Hence, this study aims to provide an automated ROI-based labeling tool for 3D-multi-voxel MRS data.Methods: MRS data, for automated ROI-based labeling, was acquired in two different spatial resolutions using a spiral-encoded, LASER-localized 3D-MRS imaging sequence with and without MEGA-editing. To calculate the mean metabolite distribution within selected ROIs, masks of individual brain regions were extracted from structural T1-weighted images using FreeSurfer. For reliability testing of automated labeling a comparison to manual labeling and single voxel selection approaches was performed for six different subcortical regions.Results: Automated ROI-based labeling showed high consistency [intra-class correlation coefficient (ICC) > 0.8] for all regions compared to manual labeling. Higher variation was shown when selected voxels, chosen from a multi-voxel grid, uncorrected for voxel composition, were compared to labeling methods using spatial averaging based on anatomical features within gray matter (GM) volumes.Conclusion: We provide an automated ROI-based analysis approach for various types of 3D-multi-voxel MRS data, which dramatically reduces hands-on time compared to manual labeling without any possible inter-rater bias

Assessing modulatory effects of sex-steroid hormones on the human brain with ultra-high field MR and multimodal neuroimaging

Sexualhormone wirken in umfangreicher Weise auf das menschliche Gehirn und haben modulierende Eigenschaften, die Kognition und Emotion beeinflussen. Die Untersuchung von transgender Personen, die sich einer gegengeschlechtlichen Hormontherapie unterziehen, bietet eine einzigartige Möglichkeit, die Wirkung von Sexualhormonen auf das Gehirn zu erforschen. Ein multimodaler Ansatz wurde gewählt, um (1) strukturelle Veränderungen mittels voxel-basierter Morphometrie (VBM) und kortikaler Dickenmessungen (2) Veränderungen in den Diffusionseigenschaften mit diffusions-gewichteter Bildgebung (DWI) und (3) Ruhenetzwerkmessungen mit funktioneller MRT bei 3 und 7 Tesla (Ultra)-Hochfeld MR zu untersuchen. Dadurch besteht die Möglichkeit Änderungen in der grauen und weißen Substanz sowie in der funktionellen Konnektivität aufgrund der Hormontherapie festzustellen. Weiters wurde ein methodischer Vergleich zwischen 3 T und 7 T VBM durchgeführt um Messungen bei ultra-hohen Magnetfeldern zu validieren. Die methodologische Untersuchung von 3 T und 7 T VBM Daten zeigte Unterschiede zwischen den beiden Feldstärken, wobei die Messungen bei 3 T bessere Test-Retest-Werte bei den von uns verwendeten Sequenzen aufwiesen. Obwohl mit 7 T vor allem höher-aufgelöste MR-Aufnahmen bei gleicher Messzeit möglich sind, konnte die Analyse zeigen, dass hohe Intensitätsvariationen vorhanden sind und 3 T verlässlichere Resultate lieferte und bei klinischen Populationen Vorteile aufweist. Verbesserungen der Sequenzen sowie der Softwareapplikationen bei 7 T sollten diesen Problemen in Zukunft entgegenwirken, um das volle Potential bei ultra-hohen Magnetfeldern voll ausschöpfen zu können. Die anschließende Analyse der grauen Substanz zeigte Veränderungen in der Mann-zu-Frau (MzF) Kohorte nach 4-monatiger Einnahme von Östradiol und Antiandrogenen. Es wurden Volumenreduktionen im Hippocampus beobachtet, wobei es zu einer gleichzeitigen Zunahme in den Ventrikelvolumina kam. Die Veränderungen in der grauen Substanz korrelierten ferner mit den Progesteronspiegeln. Die Untersuchung der weißen Substanz zeigte, dass sowohl männliche als auch weibliche Geschlechtshormone in gegensätzlicher Weise auf die Nervenfaserbündel wirken. In der Frau-zu-Mann (FzM) Gruppe kam es zu einer Abnahme der mittleren Diffusivität (MD) und zu einer Zunahme der fraktionellen Anisotropie (FA). Bei der MzF-Kohorte konnte das gegenteilige Ergebnis beobachtet werden. Zusätzlich korrelierten die Hormonschwankungen mit den MD- und FA-Werten. In der multimodalen Untersuchung wurden Messungen der grauen und weißen Substanz kombiniert und durch die Analyse der funktionellen Konnektivität ergänzt. Die VBM-Analyse zeigte eine Abnahme der grauen Substanz im Broca- und Wernicke-Areal, die mit einer Zunahme der Testosteronwerte einherging. Die Analyse der Traktographie in dem verbindenden Trakt der weißen Substanz zeigte eine negative Korrelation mit der MD, die als Verstärkung der Verbindung der beiden Areale gedeutet werden kann. Die Messung der funktionellen Konnektivität zwischen den beiden Arealen zeigte eine Zunahme mit steigenden Testosteronwerten. Mit dieser Arbeit konnte gezeigt werden, dass Sexualhormone einen Einfluss auf die graue und weiße Substanz als auch auf die Konnektivität des Gehirns von transgender Personen ausüben. Die Ergebnisse legen somit wesentliche modulierende Eigenschaften der Sexualsteroide im adulten Gehirn nahe.Sex-steroid hormones have widespread effects on the human brain and are involved in numerous physiological processes throughout life. In addition, they have a strong influence on cognition and emotion. Transgender persons undergoing cross-sex hormonal treatment offer the unique opportunity to study these effects in vivo. Here, we utilized a multimodal, MR-based neuroimaging approach using (1) structural MR for voxel-based morphometry (VBM) and cortical thickness assessment, (2) diffusion-weighted imaging (DWI) for diffusion changes and (3) resting-state fMRI for functional connectivity metrics with 3 and 7 Tesla (ultra-)high field MR to assess structural changes (gray matter and white matter) and alterations in functional connectivity due to hormonal treatment. In addition, a methodological comparison between structural 3 T and 7 T VBM data was conducted in order to validate these measurements at ultra-high fields. The methodological investigation of VBM data yielded differences between the magnetic field strengths with better test-retest reproducibility for 3 T data. This was likely due to signal intensity variations at 7 T with the sequences we used. Although 7 T assessments deliver several benefits including higher resolution, our results motivated us to use the 3 T data for the subsequent structural analysis. Nevertheless, improvement of sequences and adapted software for higher fields will help to get rid of the most common artefacts. Gray matter volume (GMV) analysis after 4-months hormonal treatment predominantly revealed changes for Male-to-Females (MtF) after anti-androgen and estradiol treatment. Significant volumetric decreases in the hippocampus were observed, while the volume of the ventricles increased. Furthermore, progesterone levels were associated with these volumetric changes. The white matter assessments using diffusion tensor imaging (DTI) were carried out 4 weeks and 4 months following the baseline scan. Opposing effects of both treatment regimens were found. In the Female-to-Male (FtM) cohort, increased fractional anisotropy (FA) values and reduced mean diffusivity (MD) were observed, while in the MtF group, the opposite effects were found. In addition, hormonal fluctuations correlated with MD and FA parameters. Furthermore, we combined gray matter (GM) morphometry and white matter (WM) structural connectivity measurements in a multimodal fashion. FtMs were analyzed before testosterone treatment and after one month of continuous administration. The VBM analysis revealed reduced GMV with increasing levels of testosterone in Brocas as well as in the Wernickes area. Probabilistic tractography in the path of the extreme capsule, connecting both areas above, indicated a negative association between testosterone and MD values. This could be interpreted as a strengthening of this fiber tract. Finally, resting-state functional connectivity showed increased connectivity metrics between these two brain areas, correlated with increasing levels of testosterone. In this thesis we could demonstrate that sex-steroid hormones have widespread effects on the living human brain by altering GM, WM and functional connectivity metrics in transgender persons.submitted by Rene SeigerAbweichender Titel laut Übersetzung der Verfasserin/des VerfassersMedizinische Universität Wien, Dissertation, 2017OeBB(VLID)246524

Journal of Neuroimaging / Cortical Thickness Estimations of FreeSurfer and the CAT12 Toolbox in Patients with Alzheimers Disease and Healthy Controls

BACKGROUND AND PURPOSE Automated cortical thickness (CT) measurements are often used to assess gray matter changes in the healthy and diseased human brain. The FreeSurfer software is frequently applied for this type of analysis. The computational anatomy toolbox (CAT12) for SPM, which offers a fast and easytouse alternative approach, was recently made available. METHODS In this study, we compared region of interest (ROI)wise CT estimations of the surfacebased FreeSurfer 6 (FS6) software and the volumebased CAT12 toolbox for SPM using 44 elderly healthy female control subjects (HC). In addition, these 44 HCs from the crosssectional analysis and 34 age and sexmatched patients with Alzheimer's disease (AD) were used to assess the potential of detecting group differences for each method. Finally, a testretest analysis was conducted using 19 HC subjects. All data were taken from the OASIS database and MRI scans were recorded at 1.5 Tesla. RESULTS A strong correlation was observed between both methods in terms of ROI mean CT estimates (R2 = .83). However, CAT12 delivered significantly higher CT estimations in 32 of the 34 ROIs, indicating a systematic difference between both approaches. Furthermore, both methods were able to reliably detect atrophic brain areas in AD subjects, with the highest decreases in temporal areas. Finally, FS6 as well as CAT12 showed excellent testretest variability scores. CONCLUSION Although CT estimations were systematically higher for CAT12, this study provides evidence that this new toolbox delivers accurate and robust CT estimates and can be considered a fast and reliable alternative to FreeSurfer.(VLID)342385

White matter microstructure in transsexuals and controls investigated by diffusion tensor imaging

Biological causes underpinning the well known gender dimorphisms in human behavior, cognition, and emotion have received increased attention in recent years. The advent of diffusion-weighted magnetic resonance imaging has permitted the investigation of the white matter microstructure in unprecedented detail. Here, we aimed to study the potential influences of biological sex, gender identity, sex hormones, and sexual orientation on white matter microstructure by investigating transsexuals and healthy controls using diffusion tensor imaging (DTI). Twenty-three female-to-male (FtM) and 21 male-to-female (MtF) transsexuals, as well as 23 female (FC) and 22 male (MC) controls underwent DTI at 3 tesla. Fractional anisotropy, axial, radial, and mean diffusivity were calculated using tract-based spatial statistics (TBSS) and fiber tractography. Results showed widespread significant differences in mean diffusivity between groups in almost all white matter tracts. FCs had highest mean diffusivities, followed by FtM transsexuals with lower values, MtF transsexuals with further reduced values, and MCs with lowest values. Investigating axial and radial diffusivities showed that a transition in axial diffusivity accounted for mean diffusivity results. No significant differences in fractional anisotropy maps were found between groups. Plasma testosterone levels were strongly correlated with mean, axial, and radial diffusivities. However, controlling for individual estradiol, testosterone, or progesterone plasma levels or for subjects' sexual orientation did not change group differences. Our data harmonize with the hypothesis that fiber tract development is influenced by the hormonal environment during late prenatal and early postnatal brain development

Structural Connectivity Networks of Transgender People

Although previous investigations of transsexual people have focused on regional brain alterations, evaluations on a network level, especially those structural in nature, are largely missing. Therefore, we investigated the structural connectome of 23 female-to-male (FtM) and 21 male-to-female (MtF) transgender patients before hormone therapy as compared with 25 female and 25 male healthy controls. Graph theoretical analysis of whole-brain probabilistic tractography networks (adjusted for differences in intracranial volume) showed decreased hemispheric connectivity ratios of subcortical/limbic areas for both transgender groups. Subsequent analysis revealed that this finding was driven by increased interhemispheric lobar connectivity weights (LCWs) in MtF transsexuals and decreased intrahemispheric LCWs in FtM patients. This was further reflected on a regional level, where the MtF group showed mostly increased local efficiencies and FtM patients decreased values. Importantly, these parameters separated each patient group from the remaining subjects for the majority of significant findings. This work complements previously established regional alterations with important findings of structural connectivity. Specifically, our data suggest that network parameters may reflect unique characteristics of transgender patients, whereas local physiological aspects have been shown to represent the transition from the biological sex to the actual gender identity

Testosterone affects language areas of the adult human brain

Although the sex steroid hormone testosterone is integrally involved in the development of language processing, ethical considerations mostly limit investigations to single hormone administrations. To circumvent this issue we assessed the influence of continuous high-dose hormone application in adult female-to-male transsexuals. Subjects underwent magnetic resonance imaging before and after 4 weeks of testosterone treatment, with each scan including structural, diffusion weighted and functional imaging. Voxel-based morphometry analysis showed decreased gray matter volume with increasing levels of bioavailable testosterone exclusively in Broca's and Wernicke's areas. Particularly, this may link known sex differences in language performance to the influence of testosterone on relevant brain regions. Using probabilistic tractography, we further observed that longitudinal changes in testosterone negatively predicted changes in mean diffusivity of the corresponding structural connection passing through the extreme capsule. Considering a related increase in myelin staining in rodents, this potentially reflects a strengthening of the fiber tract particularly involved in language comprehension. Finally, functional images at resting-state were evaluated, showing increased functional connectivity between the two brain regions with increasing testosterone levels. These findings suggest testosterone-dependent neuroplastic adaptations in adulthood within language-specific brain regions and connections. Importantly, deteriorations in gray matter volume seem to be compensated by enhancement of corresponding structural and functional connectivity. Hum Brain Mapp 37:1738-1748, 2016. © 2016 Wiley Periodicals, Inc