Age estimation using tooth cementum annulations: bias and sources of inaccuracy

Background: Counting the tooth cementum annulations (TCA) is a method for estimating the age at death of adults by sections of their tooth root. The objective of this study was to assess the precision of counting the cementum incremental lines and the congruence between known age and age estimates. Possible factors affecting the accuracy of the estimate were also analyzed. Methods: A sample of 67 permanent teeth extracted from individuals with known age (18 84 years) and sex was analyzed to calculate the dental age. Results: Results demonstrate an excellent inter- and intra-observer reliability of annuli counting, with dissimilarities within the limits of agreement. A moderate positive correlation was found between chronological age and TCA. Our results showed that age congruence rates differed across age groups (85% congruence in individuals <= 30 years; 75% in individuals aged 31-60 years; 60% in the over 60s). Considering the bias, this method showed a clear tendency to underestimate age in specimens from old people. After age 43, the TCA estimate is highly inaccurate exceeding the underestimation of 10 years, on average, in comparison to the chronological age. Both chronological age and dental arch seem to influence the accuracy of estimates, unlike sex and the tooth root number. Conclusions: TCA analysis is characterized by high precision and low accuracy, decreasing with age. Therefore, its applicability is limited in elderly subjects. The choice of methods for age estimation in adult skeletal remains should take into account the particular age range of individuals. We recommend using different age estimation methods to verify the reliability of the performed assessments

Detailed volumetric analysis of the hypothalamus in behavioral variant frontotemporal dementia

Abnormal eating behaviors are frequently reported in behavioral variant frontotemporal dementia (bvFTD). The hypothalamus is the regulatory center for feeding and satiety but its involvement in bvFTD has not been fully clarified, partly due to its difficult identification on MR images. We measured hypothalamic volume in 18 patients with bvFTD (including 9 MAPT and 6 C9orf72 mutation carriers) and 18 cognitively normal controls using a novel optimized multimodal segmentation protocol, combining 3D T1 and T2-weighted 3T MRIs (intrarater intraclass correlation coefficients ≥0.93). The whole hypothalamus was subsequently segmented into five subunits: the anterior (superior and inferior), tuberal (superior and inferior), and posterior regions. The presence of abnormal eating behavior was assessed with the revised version of the Cambridge Behavioural Inventory (CBI-R). The bvFTD group showed a 17 % lower hypothalamic volume compared with controls (p < 0.001): mean 783 (standard deviation 113) versus 944 (73) mm(3) (corrected for total intracranial volume). In the hypothalamic subunit analysis, the superior parts of the anterior and tuberal regions and the posterior region were significantly smaller in the bvFTD group compared with controls. There was a trend for a smaller hypothalamic volume, particularly in the superior tuberal region, in those with severe eating disturbance scores on the CBI-R. Differences were seen between the two genetic subgroups with significantly smaller volumes in the MAPT but not the C9orf72 group compared with controls. In summary, bvFTD patients had lower hypothalamic volumes compared with controls. Different genetic mutations may have a differential impact on the hypothalamus

Output order and variability in free recall are linked to cognitive ability and hippocampal volume in elderly individuals.

Adapted from the work of Kahana and colleagues (e.g., Kahana, 1996), we present two measures of order of recall in neuropsychological free recall tests. These are the position on the study list of the first recalled item, and the degree of variability in the order in which items are reported at test (i.e., the temporal distance across the first four recalled items). We tested two hypotheses in separate experiments: (1) whether these measures predicted generalized cognitive ability, and (2) whether they predicted gray matter hippocampal volume. To test hypothesis 1, we conducted ordinal regression analyses on data from a group of 452 participants, aged 60 or above. Memory performance was measured with Rey's AVLT and generalized cognitive ability was measured with the MMSE test. To test hypothesis 2, we conducted a linear regression analysis on data from a sample of 79 cognitively intact individuals aged 60 or over. Memory was measured with the BSRT and hippocampal volume was extracted from MRI images. Results of Experiment 1 showed that the position of the first item recalled and the degree of output order variability correlated with MMSE scores only in the delayed test, but not in the immediate test. In Experiment 2, the degree of variability in the recall sequence of the delayed trial correlated (negatively) with hippocampal size. These findings confirm the importance of delayed primacy as a marker of cognitive ability, and are consistent with the idea that the hippocampus is involved in coding the temporal context of learned episodes

Genetic clustering on the hippocampal surface for genome-wide association studies

Imaging genetics aims to discover how variants in the human genome influence brain measures derived from images. Genome-wide association scans (GWAS) can screen the genome for common differences in our DNA that relate to brain measures. In small samples, GWAS has low power as individual gene effects are weak and one must also correct for multiple comparisons across the genome and the image. Here we extend recent work on genetic clustering of images, to analyze surface-based models of anatomy using GWAS. We performed spherical harmonic analysis of hippocampal surfaces, automatically extracted from brain MRI scans of 1254 subjects. We clustered hippocampal surface regions with common genetic influences by examining genetic correlations (rg) between the normalized deformation values at all pairs of surface points. Using genetic correlations to cluster surface measures, we were able to boost effect sizes for genetic associations, compared to clustering with traditional phenotypic correlations using Pearson's r

A European Academy of Neurology guideline on medical management issues in dementia

BACKGROUND AND PURPOSE: Dementia is one of the most common disorders and is associated with increased morbidity, mortality and decreased quality of life. The present guideline addresses important medical management issues including systematic medical follow‐up, vascular risk factors in dementia, pain in dementia, use of antipsychotics in dementia and epilepsy in dementia. METHODS: A systematic review of the literature was carried out. Based on the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) framework, we developed a guideline. Where recommendations based on GRADE were not possible, a good practice statement was formulated. RESULTS: Systematic management of vascular risk factors should be performed in patients with mild to moderate dementia as prevention of cerebrovascular pathology may impact on the progression of dementia (Good Practice statement). Individuals with dementia (without previous stroke) and atrial fibrillation should be treated with anticoagulants (weak recommendation). Discontinuation of opioids should be considered in certain individuals with dementia (e.g. for whom there are no signs or symptoms of pain or no clear indication, or suspicion of side effects; Good Practice statement). Behavioral symptoms in persons with dementia should not be treated with mild analgesics (weak recommendation). In all patients with dementia treated with opioids, assessment of the individual risk–benefit ratio should be performed at regular intervals. Regular, preplanned medical follow‐up should be offered to all patients with dementia. The setting will depend on the organization of local health services and should, as a minimum, include general practitioners with easy access to dementia specialists (Good Practice statement). Individuals with dementia and agitation and/or aggression should be treated with atypical antipsychotics only after all non‐pharmacological measures have been proven to be without benefit or in the case of severe self‐harm or harm to others (weak recommendation). Antipsychotics should be discontinued after cessation of behavioral disturbances and in patients in whom there are side effects (Good Practice statement). For treatment of epilepsy in individuals with dementia, newer anticonvulsants should be considered as first‐line therapy (Good Practice statement). CONCLUSION: This GRADE‐based guideline offers recommendations on several important medical issues in patients with dementia, and thus adds important guidance for clinicians. For some issues, very little or no evidence was identified, highlighting the importance of further studies within these areas

Predicting AT(N) pathologies in Alzheimer’s disease from blood-based proteomic data using neural networks

Background and objective: Blood-based biomarkers represent a promising approach to help identify early Alzheimer's disease (AD). Previous research has applied traditional machine learning (ML) to analyze plasma omics data and search for potential biomarkers, but the most modern ML methods based on deep learning has however been scarcely explored. In the current study, we aim to harness the power of state-of-the-art deep learning neural networks (NNs) to identify plasma proteins that predict amyloid, tau, and neurodegeneration (AT[N]) pathologies in AD. Methods: We measured 3,635 proteins using SOMAscan in 881 participants from the European Medical Information Framework for AD Multimodal Biomarker Discovery study (EMIF-AD MBD). Participants underwent measurements of brain amyloid β (Aβ) burden, phosphorylated tau (p-tau) burden, and total tau (t-tau) burden to determine their AT(N) statuses. We ranked proteins by their association with Aβ, p-tau, t-tau, and AT(N), and fed the top 100 proteins along with age and apolipoprotein E (APOE) status into NN classifiers as input features to predict these four outcomes relevant to AD. We compared NN performance of using proteins, age, and APOE genotype with performance of using age and APOE status alone to identify protein panels that optimally improved the prediction over these main risk factors. Proteins that improved the prediction for each outcome were aggregated and nominated for pathway enrichment and protein-protein interaction enrichment analysis. Results: Age and APOE alone predicted Aβ, p-tau, t-tau, and AT(N) burden with area under the curve (AUC) scores of 0.748, 0.662, 0.710, and 0.795. The addition of proteins significantly improved AUCs to 0.782, 0.674, 0.734, and 0.831, respectively. The identified proteins were enriched in five clusters of AD-associated pathways including human immunodeficiency virus 1 infection, p53 signaling pathway, and phosphoinositide-3-kinase-protein kinase B/Akt signaling pathway. Conclusion: Combined with age and APOE genotype, the proteins identified have the potential to serve as blood-based biomarkers for AD and await validation in future studies. While the NNs did not achieve better scores than the support vector machine model used in our previous study, their performances were likely limited by small sample size. Keywords: Alzheimer’s disease; amyloid β; artificial neural networks; machine learning; neurodegeneration; plasma proteomics; ta

Automated hippocampal segmentation in 3D MRI using random undersampling with boosting algorithm

The automated identification of brain structure in Magnetic Resonance Imaging is very important both in neuroscience research and as a possible clinical diagnostic tool. In this study, a novel strategy for fully automated hippocampal segmentation in MRI is presented. It is based on a supervised algorithm, called RUSBoost, which combines data random undersampling with a boosting algorithm. RUSBoost is an algorithm specifically designed for imbalanced classification, suitable for large data sets because it uses random undersampling of the majority class. The RUSBoost performances were compared with those of ADABoost, Random Forest and the publicly available brain segmentation package, FreeSurfer. This study was conducted on a data set of 50 T1-weighted structural brain images. The RUSBoost-based segmentation tool achieved the best results with a Dice’s index of (Formula presented.) (Formula presented.) for the left (right) brain hemisphere. An independent data set of 50 T1-weighted structural brain scans was used for an independent validation of the fully trained strategies. Again the RUSBoost segmentations compared favorably with manual segmentations with the highest performances among the four tools. Moreover, the Pearson correlation coefficient between hippocampal volumes computed by manual and RUSBoost segmentations was 0.83 (0.82) for left (right) side, statistically significant, and higher than those computed by Adaboost, Random Forest and FreeSurfer. The proposed method may be suitable for accurate, robust and statistically significant segmentations of hippocampi