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Janine Diehl-Schmid

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5 papers

1 author row

YNICL Journal 2023 Journal Article

Multiclass prediction of different dementia syndromes based on multi-centric volumetric MRI imaging

Leonie Lampe
Hans-Jürgen Huppertz
Sarah Anderl-Straub
Franziska Albrecht
Tommaso Ballarini
Sandrine Bisenius
Karsten Mueller
Sebastian Niehaus

INTRODUCTION: Dementia syndromes can be difficult to diagnose. We aimed at building a classifier for multiple dementia syndromes using magnetic resonance imaging (MRI). METHODS: Atlas-based volumetry was performed on T1-weighted MRI data of 426 patients and 51 controls from the multi-centric German Research Consortium of Frontotemporal Lobar Degeneration including patients with behavioral variant frontotemporal dementia, Alzheimer's disease, the three subtypes of primary progressive aphasia, i.e., semantic, logopenic and nonfluent-agrammatic variant, and the atypical parkinsonian syndromes progressive supranuclear palsy and corticobasal syndrome. Support vector machine classification was used to classify each patient group against controls (binary classification) and all seven diagnostic groups against each other in a multi-syndrome classifier (multiclass classification). RESULTS: The binary classification models reached high prediction accuracies between 71 and 95% with a chance level of 50%. Feature importance reflected disease-specific atrophy patterns. The multi-syndrome model reached accuracies of more than three times higher than chance level but was far from 100%. Multi-syndrome model performance was not homogenous across dementia syndromes, with better performance in syndromes characterized by regionally specific atrophy patterns. Whereas diseases generally could be classified vs controls more correctly with increasing severity and duration, differentiation between diseases was optimal in disease-specific windows of severity and duration. DISCUSSION: Results suggest that automated methods applied to MR imaging data can support physicians in diagnosis of dementia syndromes. It is particularly relevant for orphan diseases beside frequent syndromes such as Alzheimer's disease.

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YNICL Journal 2020 Journal Article

Disentangling brain functional network remodeling in corticobasal syndrome – A multimodal MRI study

Tommaso Ballarini
Franziska Albrecht
Karsten Mueller
Robert Jech
Janine Diehl-Schmid
Klaus Fliessbach
Jan Kassubek
Martin Lauer

OBJECTIVE: The clinical diagnosis of corticobasal syndrome (CBS) represents a challenge for physicians and reliable diagnostic imaging biomarkers would support the diagnostic work-up. We aimed to investigate the neural signatures of CBS using multimodal T1-weighted and resting-state functional magnetic resonance imaging (MRI). METHODS: Nineteen patients with CBS (age 67.0 ± 6.0 years; mean±SD) and 19 matched controls (66.5 ± 6.0) were enrolled from the German Frontotemporal Lobar Degeneration Consortium. Changes in functional connectivity and structure were respectively assessed with eigenvector centrality mapping complemented by seed-based analysis and with voxel-based morphometry. In addition to mass-univariate statistics, multivariate support vector machine (SVM) classification tested the potential of multimodal MRI to differentiate patients and controls. External validity of SVM was assessed on independent CBS data from the 4RTNI database. RESULTS: A decrease in brain interconnectedness was observed in the right central operculum, middle temporal gyrus and posterior insula, while widespread connectivity increases were found in the anterior cingulum, medial superior-frontal gyrus and in the bilateral caudate nuclei. Severe and diffuse gray matter volume reduction, especially in the bilateral insula, putamen and thalamus, characterized CBS. SVM classification revealed that both connectivity (area under the curve 0.81) and structural abnormalities (0.80) distinguished CBS from controls, while their combination led to statistically non-significant improvement in discrimination power, questioning the additional value of functional connectivity over atrophy. SVM analyses based on structural MRI generalized moderately well to new data, which was decisively improved when guided by meta-analytically derived disease-specific regions-of-interest. CONCLUSIONS: Our data-driven results show impairment of functional connectivity and brain structure in CBS and explore their potential as imaging biomarkers.

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YNICL Journal 2017 Journal Article

Predicting behavioral variant frontotemporal dementia with pattern classification in multi-center structural MRI data

Sebastian Meyer
Karsten Mueller
Katharina Stuke
Sandrine Bisenius
Janine Diehl-Schmid
Frank Jessen
Jan Kassubek
Johannes Kornhuber

PURPOSE: Frontotemporal lobar degeneration (FTLD) is a common cause of early onset dementia. Behavioral variant frontotemporal dementia (bvFTD), its most common subtype, is characterized by deep alterations in behavior and personality. In 2011, new diagnostic criteria were suggested that incorporate imaging criteria into diagnostic algorithms. The study aimed at validating the potential of imaging criteria to individually predict diagnosis with machine learning algorithms. MATERIALS & METHODS: Brain atrophy was measured with structural magnetic resonance imaging (MRI) at 3 Tesla in a multi-centric cohort of 52 bvFTD patients and 52 healthy control subjects from the German FTLD Consortium's Study. Beside group comparisons, diagnosis bvFTD vs. controls was individually predicted in each subject with support vector machine classification in MRI data across the whole brain or in frontotemporal, insular regions, and basal ganglia known to be mainly affected based on recent meta-analyses. Multi-center effects were controlled for with a new method, "leave one center out" conjunction analyses, i.e. repeatedly excluding subjects from each center from the analysis. RESULTS: Group comparisons revealed atrophy in, most consistently, the frontal lobe in bvFTD beside alterations in the insula, basal ganglia and temporal lobe. Most remarkably, support vector machine classification enabled predicting diagnosis in single patients with a high accuracy of up to 84.6%, where accuracy was highest in a region-of-interest approach focusing on frontotemporal, insular regions, and basal ganglia in comparison with the whole brain approach. CONCLUSION: Our study demonstrates that MRI, a widespread imaging technology, can individually identify bvFTD with high accuracy in multi-center imaging data, paving the road to personalized diagnostic approaches in the future.

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YNICL Journal 2017 Journal Article

Predicting primary progressive aphasias with support vector machine approaches in structural MRI data

Sandrine Bisenius
Karsten Mueller
Janine Diehl-Schmid
Klaus Fassbender
Timo Grimmer
Frank Jessen
Jan Kassubek
Johannes Kornhuber

Primary progressive aphasia (PPA) encompasses the three subtypes nonfluent/agrammatic variant PPA, semantic variant PPA, and the logopenic variant PPA, which are characterized by distinct patterns of language difficulties and regional brain atrophy. To validate the potential of structural magnetic resonance imaging data for early individual diagnosis, we used support vector machine classification on grey matter density maps obtained by voxel-based morphometry analysis to discriminate PPA subtypes (44 patients: 16 nonfluent/agrammatic variant PPA, 17 semantic variant PPA, 11 logopenic variant PPA) from 20 healthy controls (matched for sample size, age, and gender) in the cohort of the multi-center study of the German consortium for frontotemporal lobar degeneration. Here, we compared a whole-brain with a meta-analysis-based disease-specific regions-of-interest approach for support vector machine classification. We also used support vector machine classification to discriminate the three PPA subtypes from each other. Whole brain support vector machine classification enabled a very high accuracy between 91 and 97% for identifying specific PPA subtypes vs. healthy controls, and 78/95% for the discrimination between semantic variant vs. nonfluent/agrammatic or logopenic PPA variants. Only for the discrimination between nonfluent/agrammatic and logopenic PPA variants accuracy was low with 55%. Interestingly, the regions that contributed the most to the support vector machine classification of patients corresponded largely to the regions that were atrophic in these patients as revealed by group comparisons. Although the whole brain approach took also into account regions that were not covered in the regions-of-interest approach, both approaches showed similar accuracies due to the disease-specificity of the selected networks. Conclusion, support vector machine classification of multi-center structural magnetic resonance imaging data enables prediction of PPA subtypes with a very high accuracy paving the road for its application in clinical settings.

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YNIMG Journal 2008 Journal Article

Imaging of amyloid plaques and cerebral glucose metabolism in semantic dementia and Alzheimer’s disease

Alexander Drzezga
Timo Grimmer
Gjermund Henriksen
Isabelle Stangier
Robert Perneczky
Janine Diehl-Schmid
Chester A. Mathis
William E. Klunk

Semantic dementia (SD) is a rare clinical syndrome, assigned to the group of frontotemporal lobar degenerations (FTLD). Histopathological analysis has not revealed the deposition of amyloid plaques in the majority of SD cases, in contrast to dementia of the Alzheimer type (AD). However, based on clinical examination alone a reliable differentiation of the underlying pathology cannot be guaranteed, i. e. AD and SD may be confused in some cases. Our aim was to determine, whether AD and SD can be differentiated in vivo by means of amyloid plaque PET imaging. In groups of AD and SD patients, matched for gender, age and overall degree of cognitive impairment, cerebral glucose metabolism was examined with [18F]Fluorodeoxyglucose (FDG)-PET and cerebral amyloid plaque density was assessed using [11C]6-OH-BTA-1 (PIB)-PET. A volume-of-interest analysis (VOI), using the cerebellum as a reference region, and voxel-based statistical group comparisons (SPM2) were carried out between the patient groups and matched groups of healthy controls. To control for a potential influence of atrophy on the PET findings, a correction of partial volume effects was performed. Characteristic patterns of hypometabolism could be demonstrated in both clinically defined AD and SD with some regional overlap and subtle differences (AD: bilateral posterior cingulate, temporoparietal and frontal cortex; SD: left>right polar temporal, frontal mesial cortex). Compared with healthy controls, significant [11C]PIB amyloid plaque tracer binding was observed only in patients with AD (in bilateral temporoparietal, frontal and posterior cingulate cortex and the precuneus) but not in SD. This difference in amyloid plaque deposition could be reproduced in direct statistical comparison of AD and SD and clearly extended the metabolic differences between the patient groups. These findings support the notion that SD can be diagnosed in vivo as a separate entity from AD using amyloid plaque imaging. In general, amyloid plaque PET may complement neuropsychological assessment regarding reliable differential diagnosis of AD and FTLD dementias based on characterization of underlying pathology and may improve the definition of individual prognosis and the selection of patients for scientific trials.

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