Frank Jessen

YNICL Journal 2025 Journal Article

Amyloid PET predicts atrophy in older adults without dementia: Results from the AMYPAD Prognostic & Natural History study

• Leonard Pieperhoff
• Luigi Lorenzini
• Sophie Mastenbroek
• Mario Tranfa
• Mahnaz Shekari
• Alle Meije Wink
• Robin Wolz
• Sylke Grootoonk

Highlights • Aβ-PET predicts atrophy in key brain regions in older adults without dementia.• Fusiform volumetric loss is linked to Aβ independent of CSF tau levels.• Temporal atrophy is stronger in women with higher Aβ burden.• APOE-ε4 carriers show larger Aβ-driven frontal & hippocampal atrophy.

YNIMG Journal 2023 Journal Article

An automated, geometry-based method for hippocampal shape and thickness analysis

• Kersten Diers
• Hannah Baumeister
• Frank Jessen
• Emrah Düzel
• David Berron
• Martin Reuter

The hippocampus is one of the most studied neuroanatomical structures due to its involvement in attention, learning, and memory as well as its atrophy in ageing, neurological, and psychiatric diseases. Hippocampal shape changes, however, are complex and cannot be fully characterized by a single summary metric such as hippocampal volume as determined from MR images. In this work, we propose an automated, geometry-based approach for the unfolding, point-wise correspondence, and local analysis of hippocampal shape features such as thickness and curvature. Starting from an automated segmentation of hippocampal subfields, we create a 3D tetrahedral mesh model as well as a 3D intrinsic coordinate system of the hippocampal body. From this coordinate system, we derive local curvature and thickness estimates as well as a 2D sheet for hippocampal unfolding. We evaluate the performance of our algorithm with a series of experiments to quantify neurodegenerative changes in Mild Cognitive Impairment and Alzheimer's disease dementia. We find that hippocampal thickness estimates detect known differences between clinical groups and can determine the location of these effects on the hippocampal sheet. Further, thickness estimates improve classification of clinical groups and cognitively unimpaired controls when added as an additional predictor. Comparable results are obtained with different datasets and segmentation algorithms. Taken together, we replicate canonical findings on hippocampal volume/shape changes in dementia, extend them by gaining insight into their spatial localization on the hippocampal sheet, and provide additional, complementary information beyond traditional measures. We provide a new set of sensitive processing and analysis tools for the analysis of hippocampal geometry that allows comparisons across studies without relying on image registration or requiring manual intervention.

YNICL Journal 2021 Journal Article

Association between composite scores of domain-specific cognitive functions and regional patterns of atrophy and functional connectivity in the Alzheimer’s disease spectrum

• Chimezie O. Amaefule
• Martin Dyrba
• Steffen Wolfsgruber
• Alexandra Polcher
• Anja Schneider
• Klaus Fliessbach
• Annika Spottke
• Dix Meiberth

BACKGROUND: Cognitive decline has been found to be associated with gray matter atrophy and disruption of functional neural networks in Alzheimer's disease (AD) in structural and functional imaging (fMRI) studies. Most previous studies have used single test scores of cognitive performance among monocentric cohorts. However, cognitive domain composite scores could be more reliable than single test scores due to the reduction of measurement error. Adopting a multicentric resting state fMRI (rs-fMRI) and cognitive domain approach, we provide a comprehensive description of the structural and functional correlates of the key cognitive domains of AD. METHOD: We analyzed MRI, rs-fMRI and cognitive domain score data of 490 participants from an interim baseline release of the multicenter DELCODE study cohort, including 54 people with AD, 86 with Mild Cognitive Impairment (MCI), 175 with Subjective Cognitive Decline (SCD), and 175 Healthy Controls (HC) in the AD-spectrum. Resulting cognitive domain composite scores (executive, visuo-spatial, memory, working memory and language) from the DELCODE neuropsychological battery (DELCODE-NP), were previously derived using confirmatory factor analysis. Statistical analyses examined the differences between diagnostic groups, and the association of composite scores with regional atrophy and network-specific functional connectivity among the patient subgroup of SCD, MCI and AD. RESULT: Cognitive performance, atrophy patterns and functional connectivity significantly differed between diagnostic groups in the AD-spectrum. Regional gray matter atrophy was positively associated with visuospatial and other cognitive impairments among the patient subgroup in the AD-spectrum. Except for the visual network, patterns of network-specific resting-state functional connectivity were positively associated with distinct cognitive impairments among the patient subgroup in the AD-spectrum. CONCLUSION: Consistent associations between cognitive domain scores and both regional atrophy and network-specific functional connectivity (except for the visual network), support the utility of a multicentric and cognitive domain approach towards explicating the relationship between imaging markers and cognition in the AD-spectrum.

YNICL Journal 2020 Journal Article

Multimodal MRI analysis of basal forebrain structure and function across the Alzheimer’s disease spectrum

• Meret Herdick
• Martin Dyrba
• Hans-Christian J. Fritz
• Slawek Altenstein
• Tommaso Ballarini
• Frederic Brosseron
• Katharina Buerger
• Arda Can Cetindag

BACKGROUND: Dysfunction of the cholinergic basal forebrain (cBF) is associated with cognitive decline in Alzheimer's disease (AD). Multimodal MRI allows for the investigation of cBF changes in-vivo. In this study we assessed alterations in cBF functional connectivity (FC), mean diffusivity (MD), and volume across the spectrum of AD. We further assessed effects of amyloid pathology on these changes. METHODS: Participants included healthy controls, and subjects with subjective cognitive decline (SCD), mild cognitive impairment (MCI), or AD dementia (ADD) from the multicenter DELCODE study. Resting-state functional MRI (rs-fMRI) and structural MRI data was available for 477 subjects, and a subset of 243 subjects also had DTI data available. Differences between diagnostic groups were investigated using seed-based FC, volumetric, and MD analyses of functionally defined anterior (a-cBF) and posterior (p-cBF) subdivisions of a cytoarchitectonic cBF region-of-interest. In complementary analyses groups were stratified according to amyloid status based on CSF Aβ42/40 biomarker data, which was available in a subset of participants. RESULTS: a-cBF and p-cBF subdivisions showed regional FC profiles that were highly consistent with previously reported patterns, but there were only minimal differences between diagnostic groups. Compared to controls, cBF volumes and MD were significantly different in MCI and ADD but not in SCD. The Aβ42/40 stratified analyses largely matched these results. CONCLUSIONS: We reproduced subregion-specific FC profiles of the cBF in a clinical sample spanning the AD spectrum. At least in this multicentric cohort study, cBF-FC did not show marked changes along the AD spectrum, and multimodal MRI did not provide more sensitive measures of AD-related cBF changes compared to volumetry.

YNICL Journal 2019 Journal Article

Subregional volume reduction of the cholinergic forebrain in subjective cognitive decline (SCD)

• Lukas Scheef
• Michel J. Grothe
• Alexander Koppara
• Marcel Daamen
• Henning Boecker
• Hans Biersack
• Hans H. Schild
• Michael Wagner

Subjective cognitive decline (SCD) patients are considered as a risk population for preclinical Alzheimer's Disease (AD). Supporting this idea, previous studies in SCD populations report subtle alterations in various cognitive and neuroimaging biomarkers that are typically affected during AD progression. To extend these observations, the present study examined whether SCD patients show atrophy of cholinergic basal forebrain nuclei (chBFN), analogous with recent findings in prodromal and clinical AD patients. We assessed volume reductions of the chBFN in 24 SCD subjects compared to 49 matched controls on 3D-T1-weighted MR images based on a postmortem derived atlas. Furthermore, we assessed whether chBFN atrophy was linked with cognitive, structural and metabolic biomarker alterations we previously reported in this SCD cohort: Using correlation analyses we tested for associations between the volumes of the chBFN with the hippocampal gray matter volume, and posterior medial glucose consumption, and the trajectory of verbal memory performance. The SCD cases showed a significant total volume reduction of the chBFN, with largest effect sizes in the Ch1/2 and Ch4p subdivisions of the chBFN. The latter was associated with a reduced glucose metabolism in the precuneus for the SCD group only. These data show an early involvement of the cholinergic basal forebrain nuclei in SCD predominantly in Ch1/2 and Ch4p which supports the conceptual link between SCD and preclinical AD.

YNIMG Journal 2017 Journal Article

In vivo MRI assessment of the human locus coeruleus along its rostrocaudal extent in young and older adults

• Matthew J. Betts
• Arturo Cardenas-Blanco
• Martin Kanowski
• Frank Jessen
• Emrah Düzel

The locus coeruleus (LC), a major origin of noradrenergic projections in the central nervous system (CNS), may serve a critical role in the pathogenesis of neurodegenerative disorders such as Alzheimer's disease (AD) and Parkinson's disease (PD). As such, there is considerable interest to develop magnetic resonance imaging (MRI) techniques to assess the integrity of the LC in vivo. The high neuromelanin content of the LC serves as an endogenous contrast for MRI but existing protocols suffer from low spatial resolution along the rostrocaudal axis of the LC rendering it difficult to differentiate its integrity in caudal and rostral portions. This study presents a novel approach to investigate the human LC in vivo using T1-weighted Fast Low Angle Shot (FLASH) MRI at 3 T (T). Using high-resolution isotropic imaging to minimise the effect of low spatial resolution in the slice direction, this study aimed to characterise the rostrocaudal distribution of LC signal intensity attributed to neuromelanin from 25 young (22–30) and 57 older (61–80) adults. We found a significant age-related increase in maximum but not median signal intensity, indicating age-related differences were not homogenous. Instead, they were confined to the rostral third of the LC with relative sparing of the caudal portion. The findings presented demonstrate in vivo T1-weighted FLASH imaging may be used to characterise signal intensity changes across the entire rostrocaudal length of the LC (a corresponding standardised LC map is available for download), which may help to identify how the human LC is differentially affected in aging and neurodegenerative disease.

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.

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.

YNIMG Journal 2001 Journal Article

The role of the right inferior parietal lobule in the calculation of saccade amplitude: An fMRI study

• Klaus Hoenig
• Frank Jessen
• Dirk Granath
• Nikolaus Freymann
• Jürgen Reul
• Reinhard Heun
• Michael Wagner

YNIMG Journal 2000 Journal Article

Interindividual variability of cerebral activation in a verbal memory paradigm

• Reinhard Heun
• Frank Jessen
• Uwe Klose
• Michael Erb
• Dirk-Oliver Granath
• Wolfgang Grodd