Computational approaches for understanding the diagnosis and treatment of Parkinson's disease

This study describes how the application of evolutionary algorithms (EAs) can be used to study motor function in humans with Parkinson's disease (PD) and in animal models of PD. Human data is obtained using commercially available sensors via a range of non-invasive procedures that follow conventional clinical practice. EAs can then be used to classify human data for a range of uses, including diagnosis and disease monitoring. New results are presented that demonstrate how EAs can also be used to classify fruit flies with and without genetic mutations that cause Parkinson's by using measurements of the proboscis extension reflex. The case is made for a computational approach that can be applied across human and animal studies of PD and lays the way for evaluation of existing and new drug therapies in a truly objective way

Loss of DPP6 in neurodegenerative dementia: a genetic player in the dysfunction of neuronal excitability

Emerging evidence suggested a converging mechanism in neurodegenerative brain diseases (NBD) involving early neuronal network dysfunctions and alterations in the homeostasis of neuronal fring as culprits of neurodegeneration. In this study, we used paired-end short-read and direct long-read whole genome sequencing to investigate an unresolved autosomal dominant dementia family signifcantly linked to 7q36. We identifed and validated a chromosomal inversion of ca. 4 Mb, segregating on the disease haplotype and disrupting the coding sequence of dipeptidyl-peptidase 6 gene (DPP6). DPP6 resequencing identifed signifcantly more rare variants—nonsense, frameshift, and missense—in early-onset Alzheimer’s disease (EOAD, p value=0.03, OR=2.21 95% CI 1.05–4.82) and frontotemporal dementia (FTD, p=0.006, OR=2.59, 95% CI 1.28–5.49) patient cohorts. DPP6 is a type II transmembrane protein with a highly structured extracellular domain and is mainly expressed in brain, where it binds to the potassium channel Kv4.2 enhancing its expression, regulating its gating properties and controlling the dendritic excitability of hippocampal neurons. Using in vitro modeling, we showed that the missense variants found in patients destabilize DPP6 and reduce its membrane expression (p<0.001 and p<0.0001) leading to a loss of protein. Reduced DPP6 and/or Kv4.2 expression was also detected in brain tissue of missense variant carriers. Loss of DPP6 is known to caus

TBK1 mutation spectrum in an extended European patient cohort with frontotemporal dementia and amyotrophic lateral sclerosis

We investigated the mutation spectrum of the TANK-Binding Kinase 1 (TBK1) gene and its associated phenotypic spectrum by exonic resequencing of TBK1 in a cohort of 2,538 patients with frontotemporal dementia (FTD), amyotrophic lateral sclerosis (ALS), or FTD plus ALS, ascertained within the European Early-Onset Dementia Consortium. We assessed pathogenicity of predicted protein-truncating mutations by measuring loss of RNA expression. Functional effect of in-frame amino acid deletions and missense mutations was further explored in vivo on protein level and in vitro by an NFκB-induced luciferase reporter assay and measuring phosphorylated TBK1. The protein-truncating mutations led to the loss of transcript through nonsense-mediated mRNA decay. For the in-frame amino acid deletions, we demonstrated loss of TBK1 or phosphorylated TBK1 protein. An important fraction of the missense mutations compromised NFκB activation indicating that at least some functions of TBK1 are lost. Although missense mutations were also present in controls, over three times more mutations affecting TBK1 functioning were found in the mutation fraction observed in patients only, suggesting high-risk alleles (P = 0.03). Total mutation frequency for confirmed TBK1 LoF mutations in the European cohort was 0.7%, with frequencies in the clinical subgroups of 0.4% in FTD, 1.3% in ALS, and 3.6% in FTD-ALS

Optimization of encoding specificity for the diagnosis of early AD: The RI-48 task

The aim of this study was to evaluate the discriminant validity of the RI-48 test, a shorter French version of the Category Cued Recall portion of the Double Memory Test developed initially by Buschke and colleagues (1997), in the diagnosis of mild and very mild Alzheimer disease (AD). The distinctive feature of the RI-48 task is that encoding specificity was increased by adding an immediate cued recall stage at the encoding phase. The results show that the RI-48 task seems to be well adapted to the clinical context and to have good psychometric properties, in particular a lack of a ceiling effect. Moreover, this task appears to be especially well suited for the diagnosis of both mild and very mild AD (sensitivity of 93% and 83.8%). From a more theoretical point of view, this study confirms the importance of optimizing the encoding specificity for the diagnosis of very mild AD, since the more encoding specificity is accentuated, the more discriminating power is increased for the diagnosis of very mild AD

Improved memory elicitation in virtual reality: New experimental results and insights

Eliciting accurate and complete knowledge from individuals is a non-trivial challenge. In this paper, we present the evaluation of a virtual-world based approach, informed by situated cognition theory, which aims to assist with knowledge elicitation. In this approach, we place users into 3D virtual worlds which represent real-world locations and ask users to describe information related to tasks completed in those locations. Through an empirical A/B evaluation of 62 users, we explore the differences in recall ability and behaviour of those viewing the virtual world via a virtual reality headset and those viewing the virtual world on a monitor. Previous results suggest that the use of a virtual reality headset was able to meaningfully improve memory recall ability within the given scenario. In this study, we adjust experiment protocol to explore the potential confounds of time taken and tool usability. After controlling for these possible confounds, we once again found that those given a virtual reality headset were able to recall more information about the given task than those viewing the virtual world on a monitor

Diagnostic performance of automated MRI volumetry by icobrain dm for Alzheimer’s disease in a clinical setting: a REMEMBER study

Magnetic resonance imaging (MRI) has become important in the diagnostic work-up of neurodegenerative diseases. icobrain dm, a CE-labeled and FDA-cleared automated brain volumetry software, has shown potential in differentiating cognitively healthy controls (HC) from Alzheimer's disease (AD) dementia (ADD) patients in selected research cohorts. This study examines the diagnostic value of icobrain dm for AD in routine clinical practice, including a comparison to the widely used FreeSurfer software, and investigates if combined brain volumes contribute to establish an AD diagnosis. The study population included HC (n = 90), subjective cognitive decline (SCD, n = 93), mild cognitive impairment (MCI, n = 357), and ADD (n = 280) patients. Through automated volumetric analyses of global, cortical, and subcortical brain structures on clinical brain MRI T1w (n = 820) images from a retrospective, multi-center study (REMEMBER), icobrain dm's (v.4.4.0) ability to differentiate disease stages via ROC analysis was compared to FreeSurfer (v.6.0). Stepwise backward regression models were constructed to investigate if combined brain volumes can differentiate between AD stages. icobrain dm outperformed FreeSurfer in processing time (15-30 min versus 9-32 h), robustness (0 versus 67 failures), and diagnostic performance for whole brain, hippocampal volumes, and lateral ventricles between HC and ADD patients. Stepwise backward regression showed improved diagnostic accuracy for pairwise group differentiations, with highest performance obtained for distinguishing HC from ADD (AUC = 0.914; Specificity 83.0%; Sensitivity 86.3%). Automated volumetry has a diagnostic value for ADD diagnosis in routine clinical practice. Our findings indicate that combined brain volumes improve diagnostic accuracy, using real-world imaging data from a clinical setting

Implicit and explicit memory for spatial information in Alzheimer's disease

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