Search

Background: Phelan-McDermid syndrome (PMS) is a rare multisystem disease with global developmental delay and autistic features. Genetically, the disease is based on a heterozygous deletion of chromosome 22q13.3 with involvement of at least part of the SHANK3 gene or heterozygous pathogenic variants in SHANK3. Pathophysiologically, this syndrome has been regarded as a synaptopathy, but current data suggest an additional concept, since axonal functions of neurons are also impaired, thus, the specific pathophysiological processes in this disease are not yet fully understood. Since symptoms of the autism spectrum, regression, and stagnation in development occur, we investigated whether neuroinflammatory and neurodegenerative processes may also play a role. To this end, we analysed biomarkers in cerebrospinal fluid (CSF) and parameters from magnetic resonance imaging with high-resolution structural T1w volumetry and diffusion tensor imaging analysis in 19 Phelan-McDermid syndrome patients. Results: CSF showed no inflammation but abnormalities in tau protein and amyloid-ß concentrations, however, with no typical biomarker pattern as in Alzheimer's disease. It could be demonstrated that these CSF changes were correlated with integrity losses of the fibres in the corticospinal tract as well as in the splenium and dorsal part of the cingulum. High CSF levels of tau protein were associated with loss of integrity of fibres in the corticospinal tract; lower levels of amyloid-ß were associated with decreasing integrity of fibre tracts of the splenium and posterior cingulate gyrus. Volumetric investigations showed global atrophy of the white matter, but not the grey matter, and particularly not in temporal or mesiotemporal regions, as is typical in later stages of Alzheimer's disease. Conclusions: In summary, alterations of neurodegenerative CSF markers in PMS individuals could be demonstrated which were correlated with structural connectivity losses of the corticospinal tract, the splenium, and the dorsal part of the cingulum, which can also be associated with typical clinical symptoms in these patients. These findings might represent a state of dysfunctional processes with ongoing degenerative and regenerative processes or a kind of accelerated aging. This study should foster further clinical diagnostics like tau- and amyloid-PET imaging as well as novel scientific approaches especially in basic research for further mechanistic proof. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]