Open Access

Introduction: It has been shown that Alzheimer’s disease (AD) is accompanied by marked structural brain changes that can be detected several years before clinical diagnosis via structural magnetic resonance (MR) imaging. In this study, we developed a structural MR-based biomarker for in vivo detection of AD using a supervised machine learning approach. Based on an individual’s pattern of brain atrophy a continuous AD score is assigned which measures the similarity with brain atrophy patterns seen in clinical cases of AD. Methods: The underlying statistical model was trained with MR scans of patients and healthy controls from the Alzheimer’s Disease Neuroimaging Initiative (ADNI-1 screening). Validation was performed within ADNI-1 and in an independent patient sample from the Open Access Series of Imaging Studies (OASIS-1). In addition, our analyses included data from a large general population sample of the Study of Health in Pomerania (SHIP-Trend). Results: Based on the proposed AD score we were able to differentiate patients from healthy controls in ADNI-1 and OASIS-1 with an accuracy of 89% (AUC = 95%) and 87% (AUC = 93%), respectively. Moreover, we found the AD score to be significantly associated with cognitive functioning as assessed by the Mini-Mental State Examination in the OASIS-1 sample after correcting for diagnosis, age, sex, age·sex, and total intracranial volume (Cohen’s f2 = 0.13). Additional analyses showed that the prediction accuracy of AD status based on both the AD score and the MMSE score is significantly higher than when using just one of them. In SHIP-Trend we found the AD score to be weakly but significantly associated with a test of verbal memory consisting of an immediate and a delayed word list recall (again after correcting for age, sex, age·sex, and total intracranial volume, Cohen’s f2 = 0.009). This association was mainly driven by the immediate recall performance. Discussion: In summary, our proposed biomarker well differentiated between patients and healthy controls in an independent test sample. It was associated with measures of cognitive functioning both in a patient sample and a general population sample. Our approach might be useful for defining robust MR-based biomarkers for other neurodegenerative diseases, too.

Abstract Objectives To examine the association between third molars and orofacial pain. We hypothesized that impacted third molars are a cause of orofacial pain. Methods Magnetic resonance images of 1808 participants from two population‐based cohorts from Northeastern Germany were analysed to define the status of third molars according to the Pell and Gregory classification. A self‐reported questionnaire and a clinical dental examination were used to detect chronic and acute complaints of orofacial pain, masticatory muscle pain, migraine and other types of headache. Logistic regression models were used to analyse the associations between third molar status and orofacial pain. Results Individuals with impacted third molars in the maxilla had a higher chance of chronic orofacial pain than those with erupted third molars (odds ratio 2.19; 95% CI 1.19‐4.02). No such association was detected for third molars in the lower jaw. Third molars were not associated with masticatory muscle pain, migraine or other types of headache. Conclusions Impacted maxillary third molars might be a cause of chronic orofacial pain. Thus, physicians should consider the eruption/impaction status of third molars in their decision‐making process when treating patients who complain of orofacial pain.