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Behavioral variant frontotemporal dementia (bvFTD) is characterized by profound and early deficits in social cognition (SC) and executive functions (EF). To date it remains unclear whether deficits of the respective cognitive domains are based on the degeneration of distinct brain regions. In 103 patients with a diagnosis of bvFTD (possible/probable/definite: N = 40/58/5) from the frontotemporal lobar degeneration (FTLD) consortium Germany cohort (age 62.5±9.4 years, gender 38 female/65 male) we applied multimodal structural imaging, i.e. voxel-based morphometry, cortical thickness (CTH) and networks of structural covariance via source based morphometry. We cross-sectionally investigated associations with performance in a modified Reading the Mind in the Eyes Test (RMET; reflective of theory of mind - ToM) and five different tests reflective of EF (i.e. Hamasch-Five-Point Test, semantic and phonemic Fluency, Trail Making Test, Stroop interference). Finally, we investigated the conjunction of RMET correlates with functional networks commonly associated with SC respectively ToM and EF as extracted meta-analytically within the Neurosynth database. RMET performance was mainly associated with gray matter volume (GMV) and CTH within temporal and insular cortical regions and less within the prefrontal cortex (PFC), whereas EF performance was mainly associated with prefrontal regions (GMV and CTH). Overlap of RMET and EF associations was primarily located within the insula, adjacent subcortical structures (i.e. putamen) and the dorsolateral PFC (dlPFC). These patterns were more pronounced after adjustment for the respective other cognitive domain. Corroborative results were obtained in analyses of structural covariance networks. Overlap of RMET with meta-analytically extracted functional networks commonly associated with SC, ToM and EF was again primarily located within the temporal and insular region and the dlPFC. In addition, on a meta-analytical level, strong associations were found for temporal cortical RMET correlates with SC and ToM in particular. These data indicate a temporo-frontal dissociation of bvFTD related disturbances of ToM and EF, with atrophy of the anterior temporal lobe being critically involved in ToM deficits. The consistent overlap within the insular cortex may be attributable to the multimodal and integrative role of this region in socioemotional and cognitive processing., (© 2024. The Author(s).)

Background: Visinin-like protein 1 (VILIP-1) belongs to the group of emerging biomarkers with the potential to support the early diagnosis of Alzheimer's disease (AD). However, studies investigating the differential diagnostic potential in cerebrospinal fluid (CSF) are rare and are not available for blood., Methods: We set up a novel, sensitive single molecule array (Simoa) assay for the detection of VILIP-1 in CSF and serum. In total, paired CSF and serum samples from 234 patients were investigated: 73 AD, 18 behavioral variant frontotemporal dementia (bvFTD), 26 parkinsonian syndromes, 20 amyotrophic lateral sclerosis (ALS), 22 Creutzfeldt-Jakob disease (CJD), and 75 non-neurodegenerative control (Con) patients. The differential diagnostic potential of CSF and serum VILIP-1 was assessed using the receiver operating characteristic curve analysis and findings were compared to core AD biomarkers., Results: CSF and serum VILIP-1 levels correlated weakly (r=0.32 (CI: 0.20-0.43), p<0.0001). VILIP-1 concentrations in CSF and serum were elevated in AD compared to Con (p<0.0001 and p<0.01) and CJD (p<0.0001 for CSF and serum), and an increase in CSF was observed already in early AD stages (p<0.0001). In the discrimination of AD versus Con, we could demonstrate a strong diagnostic potential for CSF VILIP-1 alone (area under the curve (AUC): 0.87), CSF VILIP-1/CSF Abeta 1-42 (AUC: 0.98), and serum VILIP-1/CSF Abeta 1-42 ratio (AUC: 0.89)., Conclusions: We here report on the successful establishment of a novel Simoa assay for VILIP-1 and illustrate the potential of CSF and serum VILIP-1 in the differential diagnosis of AD with highest levels in CJD., (© 2022. The Author(s).)

Importance: The entry of artificial intelligence into medicine is pending. Several methods have been used for the predictions of structured neuroimaging data, yet nobody compared them in this context., Objective: Multi-class prediction is key for building computational aid systems for differential diagnosis. We compared support vector machine, random forest, gradient boosting, and deep feed-forward neural networks for the classification of different neurodegenerative syndromes based on structural magnetic resonance imaging., Design, Setting, and Participants: Atlas-based volumetry was performed on multi-centric T1-weighted MRI data from 940 subjects, i.e., 124 healthy controls and 816 patients with ten different neurodegenerative diseases, leading to a multi-diagnostic multi-class classification task with eleven different classes., Interventions: N.A., Main Outcomes and Measures: Cohen's kappa, accuracy, and F1-score to assess model performance., Results: Overall, the neural network produced both the best performance measures and the most robust results. The smaller classes however were better classified by either the ensemble learning methods or the support vector machine, while performance measures for small classes were comparatively low, as expected. Diseases with regionally specific and pronounced atrophy patterns were generally better classified than diseases with widespread and rather weak atrophy., Conclusions and Relevance: Our study furthermore underlines the necessity of larger data sets but also calls for a careful consideration of different machine learning methods that can handle the type of data and the classification task best., (© 2022. The Author(s).)

Background: With upcoming therapeutic interventions for patients with primary progressive aphasia (PPA), instruments for the follow-up of patients are needed to describe disease progression and to evaluate potential therapeutic effects. So far, volumetric brain changes have been proposed as clinical endpoints in the literature, but cognitive scores are still lacking. This study followed disease progression predominantly in language-based performance within 1 year and defined a PPA sum score which can be used in therapeutic interventions., Methods: We assessed 28 patients with nonfluent variant PPA, 17 with semantic variant PPA, 13 with logopenic variant PPA, and 28 healthy controls in detail for 1 year. The most informative neuropsychological assessments were combined to a sum score, and associations between brain atrophy were investigated followed by a sample size calculation for clinical trials., Results: Significant absolute changes up to 20% in cognitive tests were found after 1 year. Semantic and phonemic word fluency, Boston Naming Test, Digit Span, Token Test, AAT Written language, and Cookie Test were identified as the best markers for disease progression. These tasks provide the basis of a new PPA sum score. Assuming a therapeutic effect of 50% reduction in cognitive decline for sample size calculations, a number of 56 cases is needed to find a significant treatment effect. Correlations between cognitive decline and atrophy showed a correlation up to r = 0.7 between the sum score and frontal structures, namely the superior and inferior frontal gyrus, as well as with left-sided subcortical structures., Conclusion: Our findings support the high performance of the proposed sum score in the follow-up of PPA and recommend it as an outcome measure in intervention studies.