Bayesian inference of a new Mallows model for characterising symptom sequences applied in primary progressive aphasia

Machine learning models offer the potential to understand diverse datasets in a data-driven way, powering insights into individual disease experiences and ensuring equitable healthcare. In this study, we explore Bayesian inference for characterising symptom sequences, and the associated modelling challenges. We adapted the Mallows model to account for partial rankings and right-censored data, employing custom MCMC fitting. Our evaluation, encompassing synthetic data and a primary progressive aphasia dataset, highlights the model's efficacy in revealing mean orderings and estimating ranking variance. This holds the potential to enhance clinical comprehension of symptom occurrence. However, our work encounters limitations concerning model scalability and small dataset sizes.Comment: Extended Abstract presented at Machine Learning for Health (ML4H) symposium 2023, December 10th, 2023, New Orleans, United States, 8 page

Targeted Screening for Alzheimer's Disease Clinical Trials Using Data-Driven Disease Progression Models

Heterogeneity in Alzheimer's disease progression contributes to the ongoing failure to demonstrate efficacy of putative disease-modifying therapeutics that have been trialed over the past two decades. Any treatment effect present in a subgroup of trial participants (responders) can be diluted by non-responders who ideally should have been screened out of the trial. How to identify (screen-in) the most likely potential responders is an important question that is still without an answer. Here, we pilot a computational screening tool that leverages recent advances in data-driven disease progression modeling to improve stratification. This aims to increase the sensitivity to treatment effect by screening out non-responders, which will ultimately reduce the size, duration, and cost of a clinical trial. We demonstrate the concept of such a computational screening tool by retrospectively analyzing a completed double-blind clinical trial of donepezil in people with amnestic mild cognitive impairment (clinicaltrials.gov: NCT00000173), identifying a data-driven subgroup having more severe cognitive impairment who showed clearer treatment response than observed for the full cohort

Ontogeny of cone photoreceptor mosaics in zebrafish

Cone photoreceptors in fish are typically arranged into a precise, reiterated pattern known as a “cone mosaic.” Cone mosaic patterns can vary in different fish species and in response to changes in habitat, yet their function and the mechanisms of their development remain speculative. Zebrafish ( Danio rerio ) have four cone subtypes arranged into precise rows in the adult retina. Here we describe larval zebrafish cone patterns and investigate a previously unrecognized transition between larval and adult cone mosaic patterns. Cone positions were determined in transgenic zebrafish expressing green fluorescent protein (GFP) in their UV-sensitive cones, by the use of multiplex in situ hybridization labelling of various cone opsins. We developed a “mosaic metric” statistical tool to measure local cone order. We found that ratios of the various cone subtypes in larval and adult zebrafish were statistically different. The cone photoreceptors in larvae form a regular heterotypic mosaic array; i.e., the position of any one cone spectral subtype relative to the other cone subtypes is statistically different from random. However, the cone spectral subtypes in larval zebrafish are not arranged in continuous rows as in the adult. We used cell birth dating to show that the larval cone mosaic pattern remains as a distinct region within the adult retina and does not reorganize into the adult row pattern. In addition, the abundance of cone subtypes relative to other subtypes is different in this larval remnant compared with that of larvae or canonical adult zebrafish retina. These observations provide baseline data for understanding the development of cone mosaics via comparative analysis of larval and adult cone development in a model species. J. Comp. Neurol. 518:4182–4195, 2010. © 2010 Wiley-Liss, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/77982/1/22447_ftp.pd

Spontaneous healing capacity of rabbit cranial defects of various sizes

PURPOSE: This study evaluated the spontaneous healing capacity of surgically produced cranial defects in rabbits with different healing periods in order to determine the critical size defect (CSD) of the rabbit cranium. METHODS: Thirty-two New Zealand white rabbits were used in this study. Defects of three sizes (6, 8, and 11 mm) were created in each of 16 randomly selected rabbits, and 15-mm defects were created individually in another 16 rabbits. The defects were analyzed using radiography, histologic analysis, and histometric analysis after the animal was sacrificed at 2, 4, 8, or 12 weeks postoperatively. Four samples were analyzed for each size of defect and each healing period. RESULTS: The radiographic findings indicated that defect filling gradually increased over time and that smaller defects were covered with a greater amount of radiopaque substance. Bony islands were observed at 8 weeks at the center of the defect in both histologic sections and radiographs. Histometrical values show that it was impossible to determine the precise CSD of the rabbit cranium. However, the innate healing capacity that originates from the defect margin was found to be constant regardless of the defect size. CONCLUSIONS: The results obtained for the spontaneous healing capacity of rabbit cranial defects over time and the underlying factors may provide useful guidelines for the development of a rabbit cranial model for in vivo investigations of new bone materialsope

Artificial intelligence for dementia research methods optimization

Artificial intelligence (AI) and machine learning (ML) approaches are increasingly being used in dementia research. However, several methodological challenges exist that may limit the insights we can obtain from high-dimensional data and our ability to translate these findings into improved patient outcomes. To improve reproducibility and replicability, researchers should make their well-documented code and modeling pipelines openly available. Data should also be shared where appropriate. To enhance the acceptability of models and AI-enabled systems to users, researchers should prioritize interpretable methods that provide insights into how decisions are generated. Models should be developed using multiple, diverse datasets to improve robustness, generalizability, and reduce potentially harmful bias. To improve clarity and reproducibility, researchers should adhere to reporting guidelines that are co-produced with multiple stakeholders. If these methodological challenges are overcome, AI and ML hold enormous promise for changing the landscape of dementia research and care. HIGHLIGHTS: Machine learning (ML) can improve diagnosis, prevention, and management of dementia. Inadequate reporting of ML procedures affects reproduction/replication of results. ML models built on unrepresentative datasets do not generalize to new datasets. Obligatory metrics for certain model structures and use cases have not been defined. Interpretability and trust in ML predictions are barriers to clinical translation

Uncovering spatiotemporal patterns of atrophy in progressive supranuclear palsy using unsupervised machine learning

To better understand the pathological and phenotypic heterogeneity of progressive supranuclear palsy and the links between the two, we applied a novel unsupervised machine learning algorithm (Subtype and Stage Inference) to the largest MRI data set to date of people with clinically diagnosed progressive supranuclear palsy (including progressive supranuclear palsy-Richardson and variant progressive supranuclear palsy syndromes). Our cohort is comprised of 426 progressive supranuclear palsy cases, of which 367 had at least one follow-up scan, and 290 controls. Of the progressive supranuclear palsy cases, 357 were clinically diagnosed with progressive supranuclear palsy-Richardson, 52 with a progressive supranuclear palsy-cortical variant (progressive supranuclear palsy-frontal, progressive supranuclear palsy-speech/language, or progressive supranuclear palsy-corticobasal), and 17 with a progressive supranuclear palsy-subcortical variant (progressive supranuclear palsy-parkinsonism or progressive supranuclear palsy-progressive gait freezing). Subtype and Stage Inference was applied to volumetric MRI features extracted from baseline structural (T1-weighted) MRI scans and then used to subtype and stage follow-up scans. The subtypes and stages at follow-up were used to validate the longitudinal consistency of subtype and stage assignments. We further compared the clinical phenotypes of each subtype to gain insight into the relationship between progressive supranuclear palsy pathology, atrophy patterns, and clinical presentation. The data supported two subtypes, each with a distinct progression of atrophy: a 'subcortical' subtype, in which early atrophy was most prominent in the brainstem, ventral diencephalon, superior cerebellar peduncles, and the dentate nucleus, and a 'cortical' subtype, in which there was early atrophy in the frontal lobes and the insula alongside brainstem atrophy. There was a strong association between clinical diagnosis and the Subtype and Stage Inference subtype with 82% of progressive supranuclear palsy-subcortical cases and 81% of progressive supranuclear palsy-Richardson cases assigned to the subcortical subtype and 82% of progressive supranuclear palsy-cortical cases assigned to the cortical subtype. The increasing stage was associated with worsening clinical scores, whilst the 'subcortical' subtype was associated with worse clinical severity scores compared to the 'cortical subtype' (progressive supranuclear palsy rating scale and Unified Parkinson's Disease Rating Scale). Validation experiments showed that subtype assignment was longitudinally stable (95% of scans were assigned to the same subtype at follow-up) and individual staging was longitudinally consistent with 90% remaining at the same stage or progressing to a later stage at follow-up. In summary, we applied Subtype and Stage Inference to structural MRI data and empirically identified two distinct subtypes of spatiotemporal atrophy in progressive supranuclear palsy. These image-based subtypes were differentially enriched for progressive supranuclear palsy clinical syndromes and showed different clinical characteristics. Being able to accurately subtype and stage progressive supranuclear palsy patients at baseline has important implications for screening patients on entry to clinical trials, as well as tracking disease progression

Standard set of health outcome measures for older persons

Background: The International Consortium for Health Outcomes Measurement (ICHOM) was founded in 2012 to propose consensus-based measurement tools and documentation for different conditions and populations.This article describes how the ICHOM Older Person Working Group followed a consensus-driven modified Delphi technique to develop multiple global outcome measures in older persons. The standard set of outcome measures developed by this group will support the ability of healthcare systems to improve their care pathways and quality of care. An additional benefit will be the opportunity to compare variations in outcomes which encourages and supports learning between different health care systems that drives quality improvement. These outcome measures were not developed for use in research. They are aimed at non researchers in healthcare provision and those who pay for these services. Methods: A modified Delphi technique utilising a value based healthcare framework was applied by an international panel to arrive at consensus decisions.To inform the panel meetings, information was sought from literature reviews, longitudinal ageing surveys and a focus group. Results: The outcome measures developed and recommended were participation in decision making, autonomy and control, mood and emotional health, loneliness and isolation, pain, activities of daily living, frailty, time spent in hospital, overall survival, carer burden, polypharmacy, falls and place of death mapped to a three tier value based healthcare framework. Conclusions: The first global health standard set of outcome measures in older persons has been developed to enable health care systems improve the quality of care provided to older persons

Unpublished Canadian Music for Jazz Ensemble: Selection and Analysis for Schools

The article describes the research of Cameron Walter into unpublished Canadian jazz ensemble music suitable for student performers. Walter drew upon the earlier work of creators of Canadian repertoire guidelists for the John Adaskin Project, which has involved developing an understanding of teaching situations, consultation with experts in the repertoire and experienced teachers, classroom testing of selected repertoire, and finally the creation of a guide to the selected repertoire. Walter sought to develop standards by which the level of difficulty and appropriateness of big band jazz performance pieces might be judged and placed in curricular contexts. He identified and located unpublished Canadian works for jazz ensemble, established guidelines for levels of difficulty (technical, improvisational, and musical) and for pedagogical value, and then oversaw the testing of these pieces in classrooms. This has resulted in his Guide to Unpublished Canadian Jazz Ensemble Music Suitable for Student Performers, published as one of the John Adaskin Project guidelists, and also in his guide to Canadian jazz ensemble music which he rates as “ Very Difficult.” The latter guide is available through the Canadian Music Education Research Centre (CMERC). Assessment charts are included in the article as an appendix