1
|
Song T, Guo J, Liu B, Yang L, Dai X, Zhang F, Gong Z, Hu M, Che Q, Shi N. Trends in symptom prevalence and sequential onset of SARS-CoV-2 infection from 2020 to 2022 in East and Southeast Asia: a trajectory pattern exploration based on summary data. Arch Public Health 2024; 82:125. [PMID: 39148103 PMCID: PMC11325837 DOI: 10.1186/s13690-024-01357-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 08/10/2024] [Indexed: 08/17/2024] Open
Abstract
BACKGROUND The COVID-19 pandemic's diverse symptomatology, driven by variants, underscores the critical need for a comprehensive understanding. Employing stochastic models, our study evaluates symptom sequences across SARS-CoV-2 variants on aggregated data, yielding essential insights for targeted interventions. METHODS We conducted a meta-analysis based on research literature published before December 9, 2022, from PubMed, LitCovid, Google Scholar, and CNKI databases, to investigate the prevalence of COVID-19 symptoms during the acute phase. Registered in PROSPERO (CRD42023402568), we performed random-effects meta-analyses using the R software to estimate pooled prevalence and 95% CI. Based on our findings, we introduced the Stochastic Progression Model and Sequential Pattern Discovery using Equivalence classes (SPADE) algorithm to analyze patterns of symptom progression across different variants. RESULTS Encompassing a total of 430,100 patients from east and southeast Asia, our results reveal the highest pooled estimate for cough/dry cough across wild-type, Delta, and Omicron variants, with fever (78.18%; 95% CI: 67-89%) being the most prominent symptom for the Alpha variant. Symptoms associated with the Omicron variant primarily manifested in upper respiratory tracts, cardiovascular, and neuropsychiatric systems. Stochastic models indicate early symptoms including dry cough and fever, followed by subsequent development of sleep disorders, fatigue, and more. CONCLUSION Our study underscores the evolving symptomatology across SARS-CoV-2 variants, emphasizing similarities in fever, cough, and fatigue. The Omicron variant presents a distinct profile characterized by milder symptoms yet heightened neuropsychological challenges. Advanced analytical models validate the observed sequential progression of symptoms, reinforcing the consistency of disease trajectory.
Collapse
Affiliation(s)
- Tian Song
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, No. 16 Nanxiaojie, Dongzhimennei Ave, Dongcheng District, Beijing, 100700, P.R. China
| | - Jing Guo
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, No. 16 Nanxiaojie, Dongzhimennei Ave, Dongcheng District, Beijing, 100700, P.R. China
| | - Bin Liu
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, No. 16 Nanxiaojie, Dongzhimennei Ave, Dongcheng District, Beijing, 100700, P.R. China
| | - Lu Yang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, No. 16 Nanxiaojie, Dongzhimennei Ave, Dongcheng District, Beijing, 100700, P.R. China
| | - Xiangwei Dai
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, No. 16 Nanxiaojie, Dongzhimennei Ave, Dongcheng District, Beijing, 100700, P.R. China
| | - Fuqiang Zhang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, No. 16 Nanxiaojie, Dongzhimennei Ave, Dongcheng District, Beijing, 100700, P.R. China
| | - Zhaoyuan Gong
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, No. 16 Nanxiaojie, Dongzhimennei Ave, Dongcheng District, Beijing, 100700, P.R. China
| | - Mingzhi Hu
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, No. 16 Nanxiaojie, Dongzhimennei Ave, Dongcheng District, Beijing, 100700, P.R. China
| | - Qianzi Che
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, No. 16 Nanxiaojie, Dongzhimennei Ave, Dongcheng District, Beijing, 100700, P.R. China.
| | - Nannan Shi
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, No. 16 Nanxiaojie, Dongzhimennei Ave, Dongcheng District, Beijing, 100700, P.R. China.
| |
Collapse
|
2
|
Yoshioka H, Jin R, Hisaka A, Suzuki H. Disease progression modeling with temporal realignment: An emerging approach to deepen knowledge on chronic diseases. Pharmacol Ther 2024; 259:108655. [PMID: 38710372 DOI: 10.1016/j.pharmthera.2024.108655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 04/22/2024] [Accepted: 05/01/2024] [Indexed: 05/08/2024]
Abstract
The recent development of the first disease-modifying drug for Alzheimer's disease represents a major advancement in dementia treatment. Behind this breakthrough is a quarter century of research efforts to understand the disease not by a particular symptom at a given moment, but by long-term sequential changes in multiple biomarkers. Disease progression modeling with temporal realignment (DPM-TR) is an emerging computational approach proposed with this biomarker-based disease concept. By integrating short-term clinical observations of multiple disease biomarkers in a data-driven manner, DPM-TR provides a way to understand the progression of chronic diseases over decades and predict individual disease stages more accurately. DPM-TR has been developed primarily in the area of neurodegenerative diseases but has recently been extended to non-neurodegenerative diseases, including chronic obstructive pulmonary, autoimmune, and ophthalmologic diseases. This review focuses on opportunities for DPM-TR in clinical practice and drug development and discusses its current status and challenges.
Collapse
Affiliation(s)
- Hideki Yoshioka
- Office of Regulatory Science Research, Pharmaceuticals and Medical Devices Agency, Tokyo, Japan; Laboratory of Clinical Pharmacology and Pharmacometrics, Graduate School of Pharmaceutical Sciences, Chiba University, Chiba, Japan
| | - Ryota Jin
- Laboratory of Clinical Pharmacology and Pharmacometrics, Graduate School of Pharmaceutical Sciences, Chiba University, Chiba, Japan
| | - Akihiro Hisaka
- Laboratory of Clinical Pharmacology and Pharmacometrics, Graduate School of Pharmaceutical Sciences, Chiba University, Chiba, Japan.
| | - Hiroshi Suzuki
- Executive Director, Pharmaceuticals and Medical Devices Agency, Tokyo, Japan; Department of Pharmacy, The University of Tokyo Hospital, Faculty of Medicine, The University of Tokyo, Tokyo, Japan
| |
Collapse
|
3
|
Estarellas M, Oxtoby NP, Schott JM, Alexander DC, Young AL. Multimodal subtypes identified in Alzheimer's Disease Neuroimaging Initiative participants by missing-data-enabled subtype and stage inference. Brain Commun 2024; 6:fcae219. [PMID: 39035417 PMCID: PMC11259979 DOI: 10.1093/braincomms/fcae219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 03/14/2024] [Accepted: 06/22/2024] [Indexed: 07/23/2024] Open
Abstract
Alzheimer's disease is a highly heterogeneous disease in which different biomarkers are dynamic over different windows of the decades-long pathophysiological processes, and potentially have distinct involvement in different subgroups. Subtype and Stage Inference is an unsupervised learning algorithm that disentangles the phenotypic heterogeneity and temporal progression of disease biomarkers, providing disease insight and quantitative estimates of individual subtype and stage. However, a key limitation of Subtype and Stage Inference is that it requires a complete set of biomarkers for each subject, reducing the number of datapoints available for model fitting and limiting applications of Subtype and Stage Inference to modalities that are widely collected, e.g. volumetric biomarkers derived from structural MRI. In this study, we adapted the Subtype and Stage Inference algorithm to handle missing data, enabling the application of Subtype and Stage Inference to multimodal data (magnetic resonance imaging, positron emission tomography, cerebrospinal fluid and cognitive tests) from 789 participants in the Alzheimer's Disease Neuroimaging Initiative. Missing-data Subtype and Stage Inference identified five subtypes having distinct progression patterns, which we describe by the earliest unique abnormality as 'Typical AD with Early Tau', 'Typical AD with Late Tau', 'Cortical', 'Cognitive' and 'Subcortical'. These new multimodal subtypes were differentially associated with age, years of education, Apolipoprotein E (APOE4) status, white matter hyperintensity burden and the rate of conversion from mild cognitive impairment to Alzheimer's disease, with the 'Cognitive' subtype showing the fastest clinical progression, and the 'Subcortical' subtype the slowest. Overall, we demonstrate that missing-data Subtype and Stage Inference reveals a finer landscape of Alzheimer's disease subtypes, each of which are associated with different risk factors. Missing-data Subtype and Stage Inference has broad utility, enabling the prediction of progression in a much wider set of individuals, rather than being restricted to those with complete data.
Collapse
Affiliation(s)
- Mar Estarellas
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
- School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK
| | - Neil P Oxtoby
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Jonathan M Schott
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Alexandra L Young
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| |
Collapse
|
4
|
Petkus AJ, Donahue E, Jakowec MW, Bayram E, Van Horn JD, Litvan I, Petzinger GM, Schiehser DM. Data-driven sequence of cognitive decline in people with Parkinson's disease. J Neurol Neurosurg Psychiatry 2024:jnnp-2023-333270. [PMID: 38816189 DOI: 10.1136/jnnp-2023-333270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 05/08/2024] [Indexed: 06/01/2024]
Abstract
BACKGROUND Understanding the sequential progression of cognitive impairments in Parkinson's disease (PD) is crucial for elucidating neuropathological underpinnings, refining the assessment of PD-related cognitive decline stages and enhancing early identification for targeted interventions. The first aim of this study was to use an innovative event-based modeling (EBM) analytic approach to estimate the sequence of cognitive declines in PD. The second aim was to validate the EBM by examining associations with EBM-derived individual-specific estimates of cognitive decline severity and performance on independent cognitive screening measures. METHODS This cross-sectional observational study included 99 people with PD who completed a neuropsychological battery. Individuals were classified as meeting the criteria for mild cognitive impairment (PD-MCI) or subtle cognitive decline by consensus. An EBM was constructed to compare cognitively healthy individuals with those with PD-MCI or subtle cognitive disturbances. Multivariable linear regression estimated associations between the EBM-derived stage of cognitive decline and performance on two independent cognitive screening tests. RESULTS The EBM estimated that tests assessing executive function and visuospatial ability become abnormal early in the sequence of PD-related cognitive decline. Each higher estimated stage of cognitive decline was associated with approximately 0.24 worse performance on the Dementia Rating Scale (p<0.001) and 0.26 worse performance on the Montreal Cognitive Assessment (p<0.001) adjusting for demographic and clinical variables. CONCLUSION Findings from this study will have important clinical implications for practitioners, on specific cognitive tests to prioritise, when conducting neuropsychological evaluations with people with PD. Results also highlight the importance of frontal-subcortical system disruption impacting executive and visuospatial abilities.
Collapse
Affiliation(s)
- Andrew John Petkus
- Department of Neurology, University of Southern California, Los Angeles, California, USA
| | - Erin Donahue
- Department of Neurology, University of Southern California, Los Angeles, California, USA
| | - Michael W Jakowec
- Department of Neurology, University of Southern California, Los Angeles, California, USA
| | - Ece Bayram
- Department of Neurosciences, University of California San Diego, La Jolla, California, USA
| | - John Darrell Van Horn
- Department of Psychology, University of Virginia, Charlottesville, Virginia, USA
- School of Data Science, University of Virginia, Charlottesville, Virginia, USA
| | - Irene Litvan
- Department of Neurosciences, University of California San Diego, La Jolla, California, USA
| | - Giselle M Petzinger
- Department of Neurology, University of Southern California, Los Angeles, California, USA
| | - Dawn M Schiehser
- Veterans Administration San Diego Healthcare System (VASDHS), San Diego, California, USA
- Department of Psychiatry, University of California San Diego, La Jolla, California, USA
| |
Collapse
|
5
|
Ma H, Shi Z, Kim M, Liu B, Smith PJ, Liu Y, Wu G. Disentangling sex-dependent effects of APOE on diverse trajectories of cognitive decline in Alzheimer's disease. Neuroimage 2024; 292:120609. [PMID: 38614371 PMCID: PMC11069285 DOI: 10.1016/j.neuroimage.2024.120609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 04/10/2024] [Accepted: 04/11/2024] [Indexed: 04/15/2024] Open
Abstract
Current diagnostic systems for Alzheimer's disease (AD) rely upon clinical signs and symptoms, despite the fact that the multiplicity of clinical symptoms renders various neuropsychological assessments inadequate to reflect the underlying pathophysiological mechanisms. Since putative neuroimaging biomarkers play a crucial role in understanding the etiology of AD, we sought to stratify the diverse relationships between AD biomarkers and cognitive decline in the aging population and uncover risk factors contributing to the diversities in AD. To do so, we capitalized on a large amount of neuroimaging data from the ADNI study to examine the inflection points along the dynamic relationship between cognitive decline trajectories and whole-brain neuroimaging biomarkers, using a state-of-the-art statistical model of change point detection. Our findings indicated that the temporal relationship between AD biomarkers and cognitive decline may differ depending on the synergistic effect of genetic risk and biological sex. Specifically, tauopathy-PET biomarkers exhibit a more dynamic and age-dependent association with Mini-Mental State Examination scores (p<0.05), with inflection points at 72, 78, and 83 years old, compared with amyloid-PET and neurodegeneration (cortical thickness from MRI) biomarkers. In the landscape of health disparities in AD, our analysis indicated that biological sex moderates the rate of cognitive decline associated with APOE4 genotype. Meanwhile, we found that higher education levels may moderate the effect of APOE4, acting as a marker of cognitive reserve.
Collapse
Affiliation(s)
- Haixu Ma
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Zhuoyu Shi
- Department of Psychiatry, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Minjeong Kim
- Department of Computer Science, University of North Carolina at Greensboro, NC 27412, USA
| | - Bin Liu
- Department of Statistics and Data Science, School of Management at Fudan University, Shanghai, 200433, PR China
| | - Patrick J Smith
- Department of Psychiatry, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Yufeng Liu
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, NC 27599, USA; Department of Genetics, Department of Biostatistics, University of North Carolina at Chapel Hill, NC 27599, USA.
| | - Guorong Wu
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, NC 27599, USA; Department of Psychiatry, University of North Carolina at Chapel Hill, NC 27599, USA; Department of Computer Science, University of North Carolina at Chapel Hill, NC 27599, USA; UNC Neuroscience Center, University of North Carolina at Chapel Hill, NC 27599, USA.
| |
Collapse
|
6
|
Totzek JF, Chakravarty MM, Joober R, Malla A, Shah JL, Raucher-Chéné D, Young AL, Hernaus D, Lepage M, Lavigne KM. Longitudinal inference of multiscale markers in psychosis: from hippocampal centrality to functional outcome. Mol Psychiatry 2024:10.1038/s41380-024-02549-x. [PMID: 38605172 DOI: 10.1038/s41380-024-02549-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 03/28/2024] [Accepted: 04/02/2024] [Indexed: 04/13/2024]
Abstract
Multiscale neuroscience conceptualizes mental illness as arising from aberrant interactions across and within multiple biopsychosocial scales. We leverage this framework to propose a multiscale disease progression model of psychosis, in which hippocampal-cortical dysconnectivity precedes impairments in episodic memory and social cognition, which lead to more severe negative symptoms and lower functional outcome. As psychosis represents a heterogeneous collection of biological and behavioral alterations that evolve over time, we further predict this disease progression for a subtype of the patient sample, with other patients showing normal-range performance on all variables. We sampled data from two cross-sectional datasets of first- and multi-episode psychosis, resulting in a sample of 163 patients and 119 non-clinical controls. To address our proposed disease progression model and evaluate potential heterogeneity, we applied a machine-learning algorithm, SuStaIn, to the patient data. SuStaIn uniquely integrates clustering and disease progression modeling and identified three patient subtypes. Subtype 0 showed normal-range performance on all variables. In comparison, Subtype 1 showed lower episodic memory, social cognition, functional outcome, and higher negative symptoms, while Subtype 2 showed lower hippocampal-cortical connectivity and episodic memory. Subtype 1 deteriorated from episodic memory to social cognition, negative symptoms, functional outcome to bilateral hippocampal-cortical dysconnectivity, while Subtype 2 deteriorated from bilateral hippocampal-cortical dysconnectivity to episodic memory and social cognition, functional outcome to negative symptoms. This first application of SuStaIn in a multiscale psychiatric model provides distinct disease trajectories of hippocampal-cortical connectivity, which might underlie the heterogeneous behavioral manifestations of psychosis.
Collapse
Affiliation(s)
- Jana F Totzek
- Department of Psychiatry, McGill University, Montreal, QC, Canada
- Douglas Research Centre, Montreal, QC, Canada
- Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - M Mallar Chakravarty
- Department of Psychiatry, McGill University, Montreal, QC, Canada
- Douglas Research Centre, Montreal, QC, Canada
- Department of Biological and Biomedical Engineering, McGill University, Montreal, QC, Canada
| | - Ridha Joober
- Department of Psychiatry, McGill University, Montreal, QC, Canada
- Douglas Research Centre, Montreal, QC, Canada
| | - Ashok Malla
- Department of Psychiatry, McGill University, Montreal, QC, Canada
- Douglas Research Centre, Montreal, QC, Canada
| | - Jai L Shah
- Department of Psychiatry, McGill University, Montreal, QC, Canada
- Douglas Research Centre, Montreal, QC, Canada
| | - Delphine Raucher-Chéné
- Department of Psychiatry, McGill University, Montreal, QC, Canada
- Douglas Research Centre, Montreal, QC, Canada
| | - Alexandra L Young
- Department of Computer Science, University College London, London, United Kingdom
| | - Dennis Hernaus
- Department of Psychiatry & Neuropsychology, School for Mental Health and NeuroScience MHeNS, Maastricht University, Maastricht, The Netherlands
| | - Martin Lepage
- Department of Psychiatry, McGill University, Montreal, QC, Canada
- Douglas Research Centre, Montreal, QC, Canada
| | - Katie M Lavigne
- Department of Psychiatry, McGill University, Montreal, QC, Canada.
- Douglas Research Centre, Montreal, QC, Canada.
| |
Collapse
|
7
|
Young AL, Oxtoby NP, Garbarino S, Fox NC, Barkhof F, Schott JM, Alexander DC. Data-driven modelling of neurodegenerative disease progression: thinking outside the black box. Nat Rev Neurosci 2024; 25:111-130. [PMID: 38191721 DOI: 10.1038/s41583-023-00779-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/30/2023] [Indexed: 01/10/2024]
Abstract
Data-driven disease progression models are an emerging set of computational tools that reconstruct disease timelines for long-term chronic diseases, providing unique insights into disease processes and their underlying mechanisms. Such methods combine a priori human knowledge and assumptions with large-scale data processing and parameter estimation to infer long-term disease trajectories from short-term data. In contrast to 'black box' machine learning tools, data-driven disease progression models typically require fewer data and are inherently interpretable, thereby aiding disease understanding in addition to enabling classification, prediction and stratification. In this Review, we place the current landscape of data-driven disease progression models in a general framework and discuss their enhanced utility for constructing a disease timeline compared with wider machine learning tools that construct static disease profiles. We review the insights they have enabled across multiple neurodegenerative diseases, notably Alzheimer disease, for applications such as determining temporal trajectories of disease biomarkers, testing hypotheses about disease mechanisms and uncovering disease subtypes. We outline key areas for technological development and translation to a broader range of neuroscience and non-neuroscience applications. Finally, we discuss potential pathways and barriers to integrating disease progression models into clinical practice and trial settings.
Collapse
Affiliation(s)
- Alexandra L Young
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK.
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
| | - Neil P Oxtoby
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK.
| | - Sara Garbarino
- Life Science Computational Laboratory, IRCCS Ospedale Policlinico San Martino, Genova, Italy
| | - Nick C Fox
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Frederik Barkhof
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
- Department of Radiology & Nuclear Medicine, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Jonathan M Schott
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Daniel C Alexander
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| |
Collapse
|
8
|
Bucholc M, James C, Khleifat AA, Badhwar A, Clarke N, Dehsarvi A, Madan CR, Marzi SJ, Shand C, Schilder BM, Tamburin S, Tantiangco HM, Lourida I, Llewellyn DJ, Ranson JM. Artificial intelligence for dementia research methods optimization. Alzheimers Dement 2023; 19:5934-5951. [PMID: 37639369 DOI: 10.1002/alz.13441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 07/19/2023] [Accepted: 07/23/2023] [Indexed: 08/31/2023]
Abstract
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.
Collapse
Affiliation(s)
- Magda Bucholc
- Cognitive Analytics Research Lab, School of Computing, Engineering & Intelligent Systems, Ulster University, Derry, UK
| | - Charlotte James
- NIHR Bristol Biomedical Research Centre, University Hospitals Bristol and Weston NHS Foundation Trust and University of Bristol, Bristol, UK
| | - Ahmad Al Khleifat
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - AmanPreet Badhwar
- Multiomics Investigation of Neurodegenerative Diseases (MIND) Lab, Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montréal, Quebec, Canada
- Institut de génie biomédical, Université de Montréal, Montréal, Quebec, Canada
- Département de Pharmacologie et Physiologie, Université de Montréal, Montréal, Quebec, Canada
| | - Natasha Clarke
- Multiomics Investigation of Neurodegenerative Diseases (MIND) Lab, Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montréal, Quebec, Canada
| | - Amir Dehsarvi
- Aberdeen Biomedical Imaging Centre, School of Medicine, Medical Sciences, and Nutrition, University of Aberdeen, Aberdeen, UK
| | | | - Sarah J Marzi
- UK Dementia Research Institute, Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Cameron Shand
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Brian M Schilder
- UK Dementia Research Institute, Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Stefano Tamburin
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | | | | | - David J Llewellyn
- University of Exeter Medical School, Exeter, UK
- The Alan Turing Institute, London, UK
| | | |
Collapse
|
9
|
Tandon R, Kirkpatrick A, Mitchell CS. sEBM: Scaling Event Based Models to Predict Disease Progression via Implicit Biomarker Selection and Clustering. INFORMATION PROCESSING IN MEDICAL IMAGING : PROCEEDINGS OF THE ... CONFERENCE 2023; 13939:208-221. [PMID: 38680427 PMCID: PMC11056195 DOI: 10.1007/978-3-031-34048-2_17] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/01/2024]
Abstract
The Event Based Model (EBM) is a probabilistic generative model to explore biomarker changes occurring as a disease progresses. Disease progression is hypothesized to occur through a sequence of biomarker dysregulation "events". The EBM estimates the biomarker dysregulation event sequence. It computes the data likelihood for a given dysregulation sequence, and subsequently evaluates the posterior distribution on the dysregulation sequence. Since the posterior distribution is intractable, Markov Chain Monte-Carlo is employed to generate samples under the posterior distribution. However, the set of possible sequences increases as N ! where N is the number of biomarkers (data dimension) and quickly becomes prohibitively large for effective sampling via MCMC. This work proposes the "scaled EBM" (sEBM) to enable event based modeling on large biomarker sets (e.g. high-dimensional data). First, sEBM implicitly selects a subset of biomarkers useful for modeling disease progression and infers the event sequence only for that subset. Second, sEBM clusters biomarkers with similar positions in the event sequence and only orders the "clusters", with each successive cluster corresponding to the next stage in disease progression. These two modifications used to construct the sEBM method provably reduces the possible space of event sequences by multiple orders of magnitude. The novel modifications are supported by theory and experiments on synthetic and real clinical data provides validation for sEBM to work in higher dimensional settings. Results on synthetic data with known ground truth shows that sEBM outperforms previous EBM variants as data dimensions increase. sEBM was successfully implemented with up to 300 biomarkers, which is a 6-fold increase over previous EBM applications. A real-world clinical application of sEBM is performed using 119 neuroimaging markers from publicly available Alzheimer's Disease Neuroimaging Initiative (ADNI) data to stratify subjects into 6 stages of disease progression. Subjects included cognitively normal (CN), mild cognitive impairment (MCI), and Alzheimer's Disease (AD). sEBM stage is differentiated for the 3 groups ( χ 2 p - v a l u e < 4.6 e - 32 ) . Increased sEBM stage is a strong predictor of conversion risk to AD ( p - v a l u e < 2.3 e - 14 ) for MCI subjects, as verified with a Cox proportional-hazards model adjusted for age, sex, education and APOE4 status. Like EBM, sEBM does not rely on apriori defined diagnostic labels and only uses cross-sectional data.
Collapse
Affiliation(s)
- Raghav Tandon
- Laboratory for Pathology Dynamics, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA 30332, USA
- Center for Machine Learning, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Anna Kirkpatrick
- Laboratory for Pathology Dynamics, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA 30332, USA
- School of Mathematics, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Cassie S Mitchell
- Laboratory for Pathology Dynamics, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA 30332, USA
- Center for Machine Learning, Georgia Institute of Technology, Atlanta, GA 30332, USA
| |
Collapse
|
10
|
Tan S, Zhou C, Wen J, Duanmu X, Guo T, Wu H, Wu J, Cao Z, Liu X, Chen J, Wu C, Qin J, Xu J, Gu L, Yan Y, Zhang B, Zhang M, Guan X, Xu X. Presence but not the timing of onset of REM sleep behavior disorder distinguishes evolution patterns in Parkinson's disease. Neurobiol Dis 2023; 180:106084. [PMID: 36931531 DOI: 10.1016/j.nbd.2023.106084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 03/09/2023] [Accepted: 03/14/2023] [Indexed: 03/17/2023] Open
Abstract
BACKGROUND Rapid eye movement (REM) sleep behavior disorder (RBD) could develop preceding or come after motor symptoms during Parkinson's disease (PD). It remains unknown that whether PD with different timing of RBD onset relative to motor symptoms suggests different spatiotemporal sequence of neurodegeneration. This study aimed to explore the sequence of disease progression in crucially involved brain regions in PD with different timing of RBD onset. METHOD We recruited 157 PD, 16 isolated RBD (iRBD), and 78 healthy controls. PD patients were identified as (1) PD with RBD preceding motor symptoms (PD-preRBD, n = 50), (2) PD with RBD posterior to motor symptoms (PD-postRBD, n = 31), (3) PD without RBD (PD-nonRBD, n = 75). The volumes of crucial brain regions, including the basal ganglia and limbic structures in T1-weighted imaging, and the contrast-noise-ratios of locus coeruleus (LC) and substantia nigra (SN) in neuromelanin-sensitive magnetic resonance imaging, were extracted. To simulate the sequence of disease progression for cross-sectional data, an event-based model was introduced to estimate the maximum likelihood sequence of regions' involvement for each group. Then, a statistical parameter, the Bhattacharya coefficient (BC), was used to evaluate the similarity of the sequence. RESULTS The model predicted that SN occupied the highest likelihood in the maximum likelihood sequence of disease progression in the all PD subgroups, while LC was specifically positioned earlier to SN in iRBD, a prodromal phase of PD. Subsequent early involvement of LC was observed in the both PD-preRBD and PD-postRBD. In contrast, atrophy in the para-hippocampal gyrus but relatively intact LC in the early stage was demonstrated in PD-nonRBD. Then, the similarity comparisons indicated higher BC between PD-postRBD and PD-preRBD (BC = 0.76) but lower BC between PD-postRBD and PD-nonRBD group (BC = 0.41). iRBD had higher BC against PD-preRBD (BC = 0.66) and PD-postRBD (BC = 0.63) but lower BC against PD- nonRBD (BC = 0.48). CONCLUSION The spatiotemporal sequence of neurodegeneration between PD-pre and PD-post were similar but distinct from PD-nonRBD. The presence of RBD may be the essential factor for differentiating the degeneration patterns of PD, but the timing of RBD onset has currently proved to be not.
Collapse
Affiliation(s)
- Sijia Tan
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
| | - Cheng Zhou
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
| | - Jiaqi Wen
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
| | - Xiaojie Duanmu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
| | - Tao Guo
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
| | - Haoting Wu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
| | - Jingjing Wu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
| | - Zhengye Cao
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
| | - Xiaocao Liu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
| | - Jingwen Chen
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
| | - Chenqing Wu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
| | - Jianmei Qin
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
| | - Jingjing Xu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
| | - Luyan Gu
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
| | - Yaping Yan
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
| | - Baorong Zhang
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
| | - Minming Zhang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
| | - Xiaojun Guan
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
| | - Xiaojun Xu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
| |
Collapse
|
11
|
Heinzinger N, Maass A, Berron D, Yakupov R, Peters O, Fiebach J, Villringer K, Preis L, Priller J, Spruth EJ, Altenstein S, Schneider A, Fliessbach K, Wiltfang J, Bartels C, Jessen F, Maier F, Glanz W, Buerger K, Janowitz D, Perneczky R, Rauchmann BS, Teipel S, Killimann I, Göerß D, Laske C, Munk MH, Spottke A, Roy N, Heneka MT, Brosseron F, Dobisch L, Ewers M, Dechent P, Haynes JD, Scheffler K, Wolfsgruber S, Kleineidam L, Schmid M, Berger M, Düzel E, Ziegler G. Exploring the ATN classification system using brain morphology. Alzheimers Res Ther 2023; 15:50. [PMID: 36915139 PMCID: PMC10009950 DOI: 10.1186/s13195-023-01185-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 02/08/2023] [Indexed: 03/16/2023]
Abstract
BACKGROUND The NIA-AA proposed amyloid-tau-neurodegeneration (ATN) as a classification system for AD biomarkers. The amyloid cascade hypothesis (ACH) implies a sequence across ATN groups that patients might undergo during transition from healthy towards AD: A-T-N-➔A+T-N-➔A+T+N-➔A+T+N+. Here we assess the evidence for monotonic brain volume decline for this particular (amyloid-conversion first, tau-conversion second, N-conversion last) and alternative progressions using voxel-based morphometry (VBM) in a large cross-sectional MRI cohort. METHODS We used baseline data of the DELCODE cohort of 437 subjects (127 controls, 168 SCD, 87 MCI, 55 AD patients) which underwent lumbar puncture, MRI scanning, and neuropsychological assessment. ATN classification was performed using CSF-Aβ42/Aβ40 (A+/-), CSF phospho-tau (T+/-), and adjusted hippocampal volume or CSF total-tau (N+/-). We compared voxel-wise model evidence for monotonic decline of gray matter volume across various sequences over ATN groups using the Bayesian Information Criterion (including also ROIs of Braak stages). First, face validity of the ACH transition sequence A-T-N-➔A+T-N-➔A+T+N-➔A+T+N+ was compared against biologically less plausible (permuted) sequences among AD continuum ATN groups. Second, we evaluated evidence for 6 monotonic brain volume progressions from A-T-N- towards A+T+N+ including also non-AD continuum ATN groups. RESULTS The ACH-based progression A-T-N-➔A+T-N-➔A+T+N-➔A+T+N+ was consistent with cognitive decline and clinical diagnosis. Using hippocampal volume for operationalization of neurodegeneration (N), ACH was most evident in 9% of gray matter predominantly in the medial temporal lobe. Many cortical regions suggested alternative non-monotonic volume progressions over ACH progression groups, which is compatible with an early amyloid-related tissue expansion or sampling effects, e.g., due to brain reserve. Volume decline in 65% of gray matter was consistent with a progression where A status converts before T or N status (i.e., ACH/ANT) when compared to alternative sequences (TAN/TNA/NAT/NTA). Brain regions earlier affected by tau tangle deposition (Braak stage I-IV, MTL, limbic system) present stronger evidence for volume decline than late Braak stage ROIs (V/VI, cortical regions). Similar findings were observed when using CSF total-tau for N instead. CONCLUSION Using the ATN classification system, early amyloid status conversion (before tau and neurodegeneration) is associated with brain volume loss observed during AD progression. The ATN system and the ACH are compatible with monotonic progression of MTL atrophy. TRIAL REGISTRATION DRKS00007966, 04/05/2015, retrospectively registered.
Collapse
Affiliation(s)
- Nils Heinzinger
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany. .,Institute of Cognitive Neurology and Dementia Research (IKND), University Hospital Magdeburg, Otto-von-Guericke University, Leipziger Str. 44, 39120, Magdeburg, Germany.
| | - Anne Maass
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany.,Institute of Cognitive Neurology and Dementia Research (IKND), University Hospital Magdeburg, Otto-von-Guericke University, Leipziger Str. 44, 39120, Magdeburg, Germany
| | - David Berron
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany.,Institute of Cognitive Neurology and Dementia Research (IKND), University Hospital Magdeburg, Otto-von-Guericke University, Leipziger Str. 44, 39120, Magdeburg, Germany
| | - Renat Yakupov
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany.,Institute of Cognitive Neurology and Dementia Research (IKND), University Hospital Magdeburg, Otto-von-Guericke University, Leipziger Str. 44, 39120, Magdeburg, Germany
| | - Oliver Peters
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany.,Department of Psychiatry, Charité-Universitätsmedizin Berlin, Campus Benjamin Franklin, Berlin, Germany
| | - Jochen Fiebach
- Center for Stroke Research Berlin, Charité-Universitätsmedizin, Berlin, Germany
| | - Kersten Villringer
- Center for Stroke Research Berlin, Charité-Universitätsmedizin, Berlin, Germany
| | - Lukas Preis
- Department of Psychiatry, Charité-Universitätsmedizin Berlin, Campus Benjamin Franklin, Berlin, Germany
| | - Josef Priller
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany.,Department of Psychiatry and Psychotherapy, Charité, Berlin, Germany.,Department of Psychiatry and Psychotherapy, School of Medicine, Technical University of Munich, Munich, Germany.,University of Edinburgh and UK DRI, Edinburgh, UK
| | - Eike Jacob Spruth
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany.,Department of Psychiatry and Psychotherapy, Charité, Berlin, Germany
| | - Slawek Altenstein
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany.,Department of Psychiatry and Psychotherapy, Charité, Berlin, Germany
| | - Anja Schneider
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department of Neurodegenerative Diseases and Geriatric Psychiatry/Psychiatry, University of Bonn Medical Center, Bonn, Germany
| | - Klaus Fliessbach
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department of Neurodegenerative Diseases and Geriatric Psychiatry/Psychiatry, University of Bonn Medical Center, Bonn, Germany
| | - Jens Wiltfang
- German Center for Neurodegenerative Diseases (DZNE), Göttingen, Germany.,Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, University of Göttingen, Göttingen, Germany.,Department of Medical Sciences, Neurosciences and Signaling Group, Institute of Biomedicine (iBiMED), University of Aveiro, Aveiro, Portugal
| | - Claudia Bartels
- Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, University of Göttingen, Göttingen, Germany
| | - Frank Jessen
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department of Psychiatry, Medical Faculty, University of Cologne, Cologne, Germany.,Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany
| | - Franziska Maier
- Department of Psychiatry, Medical Faculty, University of Cologne, Cologne, Germany
| | - Wenzel Glanz
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Katharina Buerger
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany.,Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Daniel Janowitz
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Robert Perneczky
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany.,Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany.,Munich Cluster for Systems Neurology (SyNergy), Munich, Germany.,Ageing Epidemiology Research Unit (AGE), School of Public Health, Imperial College London, London, UK
| | - Boris-Stephan Rauchmann
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - Stefan Teipel
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany.,Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
| | - Ingo Killimann
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany.,Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
| | - Doreen Göerß
- Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
| | - Christoph Laske
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany.,Section for Dementia Research, Hertie Institute for Clinical Brain Research and Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Matthias H Munk
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany.,Section for Dementia Research, Hertie Institute for Clinical Brain Research and Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Annika Spottke
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department of Neurology, University of Bonn, Bonn, Germany
| | - Nina Roy
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Michael T Heneka
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department of Neurodegenerative Diseases and Geriatric Psychiatry/Psychiatry, University of Bonn Medical Center, Bonn, Germany
| | - Frederic Brosseron
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department of Neurodegenerative Diseases and Geriatric Psychiatry/Psychiatry, University of Bonn Medical Center, Bonn, Germany
| | - Laura Dobisch
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Michael Ewers
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Peter Dechent
- MR-Research in Neurosciences, Department of Cognitive Neurology, Georg-August-University Göttingen, Göttingen, Germany
| | - John Dylan Haynes
- Bernstein Center for Computational Neuroscience, Charité-Universitätsmedizin, Berlin, Germany
| | - Klaus Scheffler
- Department for Biomedical Magnetic Resonance, University of Tübingen, Tübingen, Germany
| | - Steffen Wolfsgruber
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department of Neurodegenerative Diseases and Geriatric Psychiatry/Psychiatry, University of Bonn Medical Center, Bonn, Germany
| | - Luca Kleineidam
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Matthias Schmid
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Institute for Medical Biometry, University Hospital Bonn, Bonn, Germany
| | - Moritz Berger
- Institute for Medical Biometry, University Hospital Bonn, Bonn, Germany
| | - Emrah Düzel
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany.,Institute of Cognitive Neurology and Dementia Research (IKND), University Hospital Magdeburg, Otto-von-Guericke University, Leipziger Str. 44, 39120, Magdeburg, Germany
| | - Gabriel Ziegler
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany.,Institute of Cognitive Neurology and Dementia Research (IKND), University Hospital Magdeburg, Otto-von-Guericke University, Leipziger Str. 44, 39120, Magdeburg, Germany
| | | |
Collapse
|
12
|
Bucholc M, James C, Al Khleifat A, Badhwar A, Clarke N, Dehsarvi A, Madan CR, Marzi SJ, Shand C, Schilder BM, Tamburin S, Tantiangco HM, Lourida I, Llewellyn DJ, Ranson JM. Artificial Intelligence for Dementia Research Methods Optimization. ARXIV 2023:arXiv:2303.01949v1. [PMID: 36911275 PMCID: PMC10002770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 03/14/2023]
Abstract
INTRODUCTION Machine learning (ML) has been extremely successful in identifying key features from high-dimensional datasets and executing complicated tasks with human expert levels of accuracy or greater. METHODS We summarize and critically evaluate current applications of ML in dementia research and highlight directions for future research. RESULTS We present an overview of ML algorithms most frequently used in dementia research and highlight future opportunities for the use of ML in clinical practice, experimental medicine, and clinical trials. We discuss issues of reproducibility, replicability and interpretability and how these impact the clinical applicability of dementia research. Finally, we give examples of how state-of-the-art methods, such as transfer learning, multi-task learning, and reinforcement learning, may be applied to overcome these issues and aid the translation of research to clinical practice in the future. DISCUSSION ML-based models hold great promise to advance our understanding of the underlying causes and pathological mechanisms of dementia.
Collapse
Affiliation(s)
- Magda Bucholc
- Cognitive Analytics Research Lab, School of Computing, Engineering & Intelligent Systems, Ulster University, Derry, UK
| | - Charlotte James
- NIHR Bristol Biomedical Research Centre, University Hospitals Bristol and Weston NHS Foundation Trust and University of Bristol, Bristol, UK
| | - Ahmad Al Khleifat
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
| | - AmanPreet Badhwar
- Multiomics Investigation of Neurodegenerative Diseases (MIND) Lab, Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, Montréal, Canada
- Institut de génie biomédical, Université de Montréal, Montréal, Canada
- Département de Pharmacologie et Physiologie, Université de Montréal, Montréal, Canada
| | - Natasha Clarke
- Multiomics Investigation of Neurodegenerative Diseases (MIND) Lab, Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, Montréal, Canada
| | - Amir Dehsarvi
- Aberdeen Biomedical Imaging Centre, School of Medicine, Medical Sciences, and Nutrition, University of Aberdeen, Aberdeen, UK
| | | | - Sarah J. Marzi
- UK Dementia Research Institute, Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Cameron Shand
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Brian M. Schilder
- UK Dementia Research Institute, Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Stefano Tamburin
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | | | | | - David J. Llewellyn
- University of Exeter Medical School, Exeter, UK
- The Alan Turing Institute, London, UK
| | | |
Collapse
|
13
|
Pan D, Zeng A, Yang B, Lai G, Hu B, Song X, Jiang T. Deep Learning for Brain MRI Confirms Patterned Pathological Progression in Alzheimer's Disease. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2204717. [PMID: 36575159 PMCID: PMC9951348 DOI: 10.1002/advs.202204717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 11/29/2022] [Indexed: 06/17/2023]
Abstract
Deep learning (DL) on brain magnetic resonance imaging (MRI) data has shown excellent performance in differentiating individuals with Alzheimer's disease (AD). However, the value of DL in detecting progressive structural MRI (sMRI) abnormalities linked to AD pathology has yet to be established. In this study, an interpretable DL algorithm named the Ensemble of 3-dimensional convolutional neural network (Ensemble 3DCNN) with enhanced parsing techniques is proposed to investigate the longitudinal trajectories of whole-brain sMRI changes denoting AD onset and progression. A set of 2369 T1-weighted images from the multi-centre Alzheimer's Disease Neuroimaging Initiative and Open Access Series of Imaging Studies cohorts are applied to model derivation, validation, testing, and pattern analysis. An Ensemble-3DCNN-based P-score is generated, based on which multiple brain regions, including amygdala, insular, parahippocampal, and temporal gyrus, exhibit early and connected progressive neurodegeneration. Complex individual variability in the sMRI is also observed. This study combining non-invasive sMRI and interpretable DL in detecting patterned sMRI changes confirmed AD pathological progression, shedding new light on predicting AD progression using whole-brain sMRI.
Collapse
Affiliation(s)
- Dan Pan
- School of Electronics and InformationGuangdong Polytechnic Normal UniversityGuangzhou510665China
| | - An Zeng
- Faculty of Computers, Guangdong University of TechnologyGuangzhou510006China
| | - Baoyao Yang
- Faculty of Computers, Guangdong University of TechnologyGuangzhou510006China
| | - Gangyong Lai
- Faculty of Computers, Guangdong University of TechnologyGuangzhou510006China
| | - Bing Hu
- Department of RadiologyThe Third Affiliated Hospital of SUN Yat‐sen UniversityGuangzhou510630China
| | - Xiaowei Song
- Clinical Research CentreSurrey Memorial HospitalFraser HealthSurreyBritish ColumbiaV3V 1Z2Canada
| | - Tianzi Jiang
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing100190China
| | | |
Collapse
|
14
|
Davenport F, Gallacher J, Kourtzi Z, Koychev I, Matthews PM, Oxtoby NP, Parkes LM, Priesemann V, Rowe JB, Smye SW, Zetterberg H. Neurodegenerative disease of the brain: a survey of interdisciplinary approaches. J R Soc Interface 2023; 20:20220406. [PMID: 36651180 PMCID: PMC9846433 DOI: 10.1098/rsif.2022.0406] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Accepted: 12/16/2022] [Indexed: 01/19/2023] Open
Abstract
Neurodegenerative diseases of the brain pose a major and increasing global health challenge, with only limited progress made in developing effective therapies over the last decade. Interdisciplinary research is improving understanding of these diseases and this article reviews such approaches, with particular emphasis on tools and techniques drawn from physics, chemistry, artificial intelligence and psychology.
Collapse
Affiliation(s)
| | - John Gallacher
- Director of Dementias Platform, Department of Psychiatry, University of Oxford, Oxford, UK
| | - Zoe Kourtzi
- Professor of Cognitive Computational Neuroscience, Department of Psychology, University of Cambridge, UK
| | - Ivan Koychev
- Senior Clinical Researcher, Department of Psychiatry, University of Oxford, Oxford, UK
- Consultant Neuropsychiatrist, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Paul M. Matthews
- Department of Brain Sciences and UK Dementia Research Institute Centre, Imperial College London, Oxford, UK
| | - Neil P. Oxtoby
- UCL Centre for Medical Image Computing and Department of Computer Science, University College London, Gower Street, London, UK
| | - Laura M. Parkes
- School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Oxford Road, Manchester, M13 9PL, UK
- Geoffrey Jefferson Brain Research Centre, Manchester Academic Health Science Centre, Manchester, UK
| | - Viola Priesemann
- Max Planck Group Leader and Fellow of the Schiemann Kolleg, Max Planck Institute for Dynamics and Self-Organization and Bernstein Center for Computational Neuroscience, Göttingen, Germany
| | - James B. Rowe
- Department of Clinical Neurosciences, MRC Cognition and Brain Sciences Unit and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge, UK
| | | | - Henrik Zetterberg
- Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- UK Dementia Research Institute at UCL, London, UK
- Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong, People's Republic of China
| |
Collapse
|
15
|
Lopez SM, Aksman LM, Oxtoby NP, Vos SB, Rao J, Kaestner E, Alhusaini S, Alvim M, Bender B, Bernasconi A, Bernasconi N, Bernhardt B, Bonilha L, Caciagli L, Caldairou B, Caligiuri ME, Calvet A, Cendes F, Concha L, Conde‐Blanco E, Davoodi‐Bojd E, de Bézenac C, Delanty N, Desmond PM, Devinsky O, Domin M, Duncan JS, Focke NK, Foley S, Fortunato F, Galovic M, Gambardella A, Gleichgerrcht E, Guerrini R, Hamandi K, Ives‐Deliperi V, Jackson GD, Jahanshad N, Keller SS, Kochunov P, Kotikalapudi R, Kreilkamp BAK, Labate A, Larivière S, Lenge M, Lui E, Malpas C, Martin P, Mascalchi M, Medland SE, Meletti S, Morita‐Sherman ME, Owen TW, Richardson M, Riva A, Rüber T, Sinclair B, Soltanian‐Zadeh H, Stein DJ, Striano P, Taylor P, Thomopoulos SI, Thompson PM, Tondelli M, Vaudano AE, Vivash L, Wang Y, Weber B, Whelan CD, Wiest R, Winston GP, Yasuda CL, McDonald CR, Alexander D, Sisodiya SM, Altmann A. Event-based modeling in temporal lobe epilepsy demonstrates progressive atrophy from cross-sectional data. Epilepsia 2022; 63:2081-2095. [PMID: 35656586 PMCID: PMC9540015 DOI: 10.1111/epi.17316] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 06/01/2022] [Accepted: 06/01/2022] [Indexed: 11/29/2022]
Abstract
OBJECTIVE Recent work has shown that people with common epilepsies have characteristic patterns of cortical thinning, and that these changes may be progressive over time. Leveraging a large multicenter cross-sectional cohort, we investigated whether regional morphometric changes occur in a sequential manner, and whether these changes in people with mesial temporal lobe epilepsy and hippocampal sclerosis (MTLE-HS) correlate with clinical features. METHODS We extracted regional measures of cortical thickness, surface area, and subcortical brain volumes from T1-weighted (T1W) magnetic resonance imaging (MRI) scans collected by the ENIGMA-Epilepsy consortium, comprising 804 people with MTLE-HS and 1625 healthy controls from 25 centers. Features with a moderate case-control effect size (Cohen d ≥ .5) were used to train an event-based model (EBM), which estimates a sequence of disease-specific biomarker changes from cross-sectional data and assigns a biomarker-based fine-grained disease stage to individual patients. We tested for associations between EBM disease stage and duration of epilepsy, age at onset, and antiseizure medicine (ASM) resistance. RESULTS In MTLE-HS, decrease in ipsilateral hippocampal volume along with increased asymmetry in hippocampal volume was followed by reduced thickness in neocortical regions, reduction in ipsilateral thalamus volume, and finally, increase in ipsilateral lateral ventricle volume. EBM stage was correlated with duration of illness (Spearman ρ = .293, p = 7.03 × 10-16 ), age at onset (ρ = -.18, p = 9.82 × 10-7 ), and ASM resistance (area under the curve = .59, p = .043, Mann-Whitney U test). However, associations were driven by cases assigned to EBM Stage 0, which represents MTLE-HS with mild or nondetectable abnormality on T1W MRI. SIGNIFICANCE From cross-sectional MRI, we reconstructed a disease progression model that highlights a sequence of MRI changes that aligns with previous longitudinal studies. This model could be used to stage MTLE-HS subjects in other cohorts and help establish connections between imaging-based progression staging and clinical features.
Collapse
Affiliation(s)
- Seymour M. Lopez
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical EngineeringUniversity College LondonLondonUK
| | - Leon M. Aksman
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical EngineeringUniversity College LondonLondonUK
- Stevens Neuroimaging and Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Neil P. Oxtoby
- Centre for Medical Image Computing, Department of Computer ScienceUniversity College LondonLondonUK
| | - Sjoerd B. Vos
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical EngineeringUniversity College LondonLondonUK
- Neuroradiological Academic Unit, UCL Queen Square Institute of NeurologyUniversity College LondonLondonUK
| | - Jun Rao
- Department of PsychiatryUniversity of California, San DiegoLa JollaCaliforniaUSA
| | - Erik Kaestner
- Department of PsychiatryUniversity of California, San DiegoLa JollaCaliforniaUSA
| | - Saud Alhusaini
- Department of NeurologyAlpert Medical School of Brown UniversityProvidenceRhode IslandUSA
- Department of Molecular and Cellular TherapeuticsRoyal College of Surgeons in IrelandDublinIreland
| | - Marina Alvim
- Department of Neurology and Neuroimaging LaboratoryUniversity of CampinasCampinasBrazil
| | - Benjamin Bender
- Department of Radiology, Diagnostic and Interventional NeuroradiologyUniversity Hospital TübingenTübingenGermany
| | - Andrea Bernasconi
- Neuroimaging of Epilepsy LaboratoryMontreal Neurological Institute, McGill UniversityMontrealQuebecCanada
| | - Neda Bernasconi
- Neuroimaging of Epilepsy LaboratoryMontreal Neurological Institute, McGill UniversityMontrealQuebecCanada
| | - Boris Bernhardt
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and HospitalMcGill UniversityMontrealQuebecCanada
| | | | - Lorenzo Caciagli
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and HospitalMcGill UniversityMontrealQuebecCanada
- Department of Clinical and Experimental EpilepsyUCL Queen Square Institute of Neurology, University College LondonLondonUK
| | - Benoit Caldairou
- Neuroimaging of Epilepsy LaboratoryMontreal Neurological Institute, McGill UniversityMontrealQuebecCanada
| | - Maria Eugenia Caligiuri
- Neuroscience Research Center, Department of Medical and Surgical SciencesMagna Græcia University of CatanzaroCatanzaroItaly
| | - Angels Calvet
- Magnetic Resonance Image Core FacilityAugust Pi i Sunyer Biomedical Research Institute, University of BarcelonaBarcelonaSpain
| | - Fernando Cendes
- Department of Neurology and Neuroimaging LaboratoryUniversity of CampinasCampinasBrazil
| | - Luis Concha
- Institute of NeurobiologyNational Autonomous University of MexicoQuerétaroMexico
| | - Estefania Conde‐Blanco
- Epilepsy Program, Neurology DepartmentHospital Clinic of BarcelonaBarcelonaSpain
- August Pi i Sunyer Biomedical Research InstituteBarcelonaSpain
| | | | - Christophe de Bézenac
- Department of Pharmacology and TherapeuticsInstitute of Systems, Molecular and Integrative Biology, University of LiverpoolLiverpoolUK
| | - Norman Delanty
- Department of Molecular and Cellular TherapeuticsRoyal College of Surgeons in IrelandDublinIreland
- FutureNeuro SFI Research Centre for Rare and Chronic Neurological DiseasesDublinIreland
| | - Patricia M. Desmond
- Department of Radiology, Royal Melbourne HospitalUniversity of MelbourneMelbourneVictoriaAustralia
| | - Orrin Devinsky
- New York University Grossman School of MedicineNew YorkNew YorkUSA
| | - Martin Domin
- Functional Imaging Unit, Department of Diagnostic Radiology and NeuroradiologyGreifswald University MedicineGreifswaldGermany
| | - John S. Duncan
- Department of NeurologyEmory UniversityAtlantaUSA
- Chalfont Centre for EpilepsyChalfont St PeterUK
| | - Niels K. Focke
- Department of NeurologyUniversity Medical CenterGöttingenGermany
| | - Sonya Foley
- Cardiff University Brain Research Imaging Centre, School of PsychologyCardiff UniversityCardiffUK
| | - Francesco Fortunato
- Institute of Neurology, Department of Medical and Surgical SciencesMagna Græcia University of CatanzaroCatanzaroItaly
| | - Marian Galovic
- Department of Clinical and Experimental EpilepsyUCL Queen Square Institute of Neurology, University College LondonLondonUK
- Department of NeurologyUniversity Hospital ZurichZurichSwitzerland
| | - Antonio Gambardella
- Neuroscience Research Center, Department of Medical and Surgical SciencesMagna Græcia University of CatanzaroCatanzaroItaly
- Institute of Neurology, Department of Medical and Surgical SciencesMagna Græcia University of CatanzaroCatanzaroItaly
| | | | - Renzo Guerrini
- Neuroscience DepartmentUniversity of FlorenceFlorenceItaly
| | - Khalid Hamandi
- Cardiff University Brain Research Imaging Centre, School of PsychologyCardiff UniversityCardiffUK
- Wales Epilepsy Unit, Department of NeurologyUniversity Hospital of WalesCardiffUK
| | | | - Graeme D. Jackson
- Florey Institute of Neuroscience and Mental Health, Austin CampusHeidelbergVictoriaAustralia
- University of MelbourneParkvilleVictoriaAustralia
- Department of NeurologyAustin HealthHeidelbergVictoriaAustralia
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaMarina del ReyCaliforniaUSA
| | - Simon S. Keller
- Institute of Systems, Molecular and Integrative BiologyUniversity of LiverpoolLiverpoolUK
| | - Peter Kochunov
- Department of PsychiatryUniversity of Maryland School of MedicineBaltimoreMarylandUSA
| | - Raviteja Kotikalapudi
- Department of Radiology, Diagnostic and Interventional NeuroradiologyUniversity Hospital TübingenTübingenGermany
- Department of Clinical NeurophysiologyUniversity Hospital GöttingenGöttingenGermany
- Department of Neurology and EpileptologyHertie Institute for Clinical Brain Research, University of TübingenTübingenGermany
| | - Barbara A. K. Kreilkamp
- Institute of Systems, Molecular and Integrative BiologyUniversity of LiverpoolLiverpoolUK
- Clinical NeurophysiologyUniversity Medical Center GöttingenGöttingenGermany
| | - Angelo Labate
- Neuroscience Research Center, Department of Medical and Surgical SciencesMagna Græcia University of CatanzaroCatanzaroItaly
- Institute of Neurology, Department of Medical and Surgical SciencesMagna Græcia University of CatanzaroCatanzaroItaly
| | - Sara Larivière
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and HospitalMcGill UniversityMontrealQuebecCanada
| | - Matteo Lenge
- Pediatric Neurology, Neurogenetics and Neurobiology Unit and LaboratoriesA. Meyer Children's Hospital, University of FlorenceFlorenceItaly
- Functional and Epilepsy Neurosurgery Unit, Neurosurgery DepartmentA. Meyer Children's Hospital, University of FlorenceFlorenceItaly
| | - Elaine Lui
- Department of Radiology, Royal Melbourne HospitalUniversity of MelbourneMelbourneVictoriaAustralia
| | - Charles Malpas
- Department of NeurologyRoyal Melbourne HospitalMelbourneVictoriaAustralia
- Department of Medicine, Royal Melbourne HospitalUniversity of MelbourneParkvilleVictoriaAustralia
| | - Pascal Martin
- Department of PsychiatryUniversity of Maryland School of MedicineBaltimoreMarylandUSA
| | - Mario Mascalchi
- Mario Serio Department of Clinical and Experimental Medical SciencesUniversity of FlorenceFlorenceItaly
| | - Sarah E. Medland
- Psychiatric GeneticsQIMR Berghofer Medical Research InstituteBrisbaneQueenslandAustralia
| | - Stefano Meletti
- Department of Biomedical, Metabolic, and Neural SciencesUniversity of Modena and Reggio EmiliaModenaItaly
- Neurology Unit, OCB HospitalModena University HospitalModenaItaly
| | - Marcia E. Morita‐Sherman
- Department of NeurologyUniversity of CampinasCampinasBrazil
- Cleveland Clinic Neurological InstituteClevelandOhioUSA
| | - Thomas W. Owen
- School of ComputingNewcastle UniversityNewcastle Upon TyneUK
| | | | - Antonella Riva
- Giannina Gaslini Institute, Scientific Institute for Research and Health CareGenoaItaly
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child HealthUniversity of GenoaGenoaItaly
| | - Theodor Rüber
- Department of EpileptologyUniversity Hospital BonnBonnGermany
| | - Ben Sinclair
- Department of Neuroscience, Central Clinical School, Alfred HospitalMonash UniversityMelbourneVictoriaAustralia
- Departments of Medicine and Radiology, Royal Melbourne HospitalUniversity of MelbourneParkvilleVictoriaAustralia
| | - Hamid Soltanian‐Zadeh
- Radiology and Research AdministrationHenry Ford Health SystemDetroitMichiganUSA
- School of Electrical and Computer EngineeringCollege of Engineering, University of TehranTehranIran
| | - Dan J. Stein
- SA MRC Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry and Neuroscience InstituteUniversity of Cape TownCape TownSouth Africa
| | - Pasquale Striano
- Giannina Gaslini Institute, Scientific Institute for Research and Health CareGenoaItaly
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child HealthUniversity of GenoaGenoaItaly
| | - Peter N. Taylor
- Department of Clinical and Experimental EpilepsyUCL Queen Square Institute of Neurology, University College LondonLondonUK
- School of ComputingNewcastle UniversityNewcastle Upon TyneUK
| | - Sophia I. Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaMarina del ReyCaliforniaUSA
| | - Paul M. Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaMarina del ReyCaliforniaUSA
| | - Manuela Tondelli
- Department of Biomedical, Metabolic, and Neural SciencesUniversity of Modena and Reggio EmiliaModenaItaly
- Primary Care DepartmentLocal Health Authority of ModenaModenaItaly
| | - Anna Elisabetta Vaudano
- Department of Biomedical, Metabolic, and Neural SciencesUniversity of Modena and Reggio EmiliaModenaItaly
- Neurology Unit, OCB HospitalModena University HospitalModenaItaly
| | - Lucy Vivash
- Department of Neuroscience, Central Clinical School, Alfred HospitalMonash UniversityMelbourneVictoriaAustralia
- Departments of Medicine and Radiology, Royal Melbourne HospitalUniversity of MelbourneParkvilleVictoriaAustralia
| | - Yujiang Wang
- Department of Clinical and Experimental EpilepsyUCL Queen Square Institute of Neurology, University College LondonLondonUK
- School of ComputingNewcastle UniversityNewcastle Upon TyneUK
| | - Bernd Weber
- Institute of Experimental Epileptology and Cognition ResearchUniversity of BonnBonnGermany
| | - Christopher D. Whelan
- Department of Molecular and Cellular TherapeuticsRoyal College of Surgeons in IrelandDublinIreland
| | - Roland Wiest
- Support Center for Advanced NeuroimagingUniversity Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of BernBernSwitzerland
| | - Gavin P. Winston
- Department of Clinical and Experimental EpilepsyUCL Queen Square Institute of Neurology, University College LondonLondonUK
- Chalfont Centre for EpilepsyChalfont St PeterUK
- Department of Medicine, Division of NeurologyQueen's UniversityKingstonOntarioCanada
| | - Clarissa Lin Yasuda
- Department of Neurology and Neuroimaging LaboratoryUniversity of CampinasCampinasBrazil
| | - Carrie R. McDonald
- Department of PsychiatryUniversity of California, San DiegoLa JollaCaliforniaUSA
| | - Daniel C. Alexander
- Centre for Medical Image Computing, Department of Computer ScienceUniversity College LondonLondonUK
| | - Sanjay M. Sisodiya
- Department of Clinical and Experimental EpilepsyUCL Queen Square Institute of Neurology, University College LondonLondonUK
- Chalfont Centre for EpilepsyChalfont St PeterUK
| | - Andre Altmann
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical EngineeringUniversity College LondonLondonUK
| |
Collapse
|
16
|
Whitwell JL, Martin PR, Graff-Radford J, Machulda MM, Sintini I, Buciuc M, Senjem ML, Schwarz CG, Botha H, Carrasquillo MM, Ertekin-Taner N, Lowe VJ, Jack CR, Josephs KA. Investigating Heterogeneity and Neuroanatomic Correlates of Longitudinal Clinical Decline in Atypical Alzheimer Disease. Neurology 2022; 98:e2436-e2445. [PMID: 35483899 PMCID: PMC9231842 DOI: 10.1212/wnl.0000000000200336] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 02/21/2022] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND AND OBJECTIVES The aims of this work were to compare rates of longitudinal change in neurologic and neuropsychological test performance between the logopenic progressive aphasia (LPA) and posterior cortical atrophy (PCA) variants of atypical Alzheimer disease (AD) and to use unbiased principal component analysis to assess heterogeneity in patterns of change and relationships to demographics and concurrent brain atrophy. METHODS Patients with PCA or LPA who were positive for amyloid and tau AD biomarkers and had undergone serial neurologic and neuropsychological assessments and structural MRI were identified. Rates of change in 13 clinical measures were compared between groups in a case-control design, and principal component analysis was used to assess patterns of clinical change unbiased by clinical phenotype. Components were correlated with rates of regional brain atrophy with tensor-based morphometry. RESULTS Twenty-eight patients with PCA and 27 patients with LPA were identified. Those with LPA showed worse baseline performance and faster rates of decline in naming, repetition, and working memory, as well as faster rates of decline in verbal episodic memory, compared to those with PCA. Conversely, patients with PCA showed worse baseline performance in tests of visuospatial and perceptual function and on the Clinical Dementia Rating Scale and faster rates of decline in visuoperceptual function compared to those with LPA. Principal component analysis showed that patterns of clinical decline were highly heterogeneous across the cohort, with 10 principal components required to explain >90% of the variance. The first principal component reflected overall severity, with higher scores in LPA than PCA reflecting faster decline in LPA, and was related to left temporoparietal atrophy. The second and third principal components were not related to clinical phenotype but showed some relationship to regional atrophy. No relationships were identified between the principal components and age, sex, disease duration, amyloid PET findings, or apolipoprotein genotype. DISCUSSION Longitudinal patterns of clinical decline differ between LPA and PCA but are heterogeneous and related to different patterns of topographic spread. PCA is associated with a more slowly progressive course than LPA.
Collapse
Affiliation(s)
- Jennifer L Whitwell
- From the Departments of Radiology (J.W., I.S., M.L.S., C.G.S., V.J.L., C.R.J.), Quantitative Health Sciences (P.R.M.), Neurology (J.G.-R., M.B., H.B., K.A.J.), Psychiatry and Psychology (M.M.), and Information Technology (M.L.S.), Mayo Clinic, Rochester, MN; and Department of Neuroscience (M.M.C., N.E.-T.), Mayo Clinic, Jacksonville, FL.
| | - Peter R Martin
- From the Departments of Radiology (J.W., I.S., M.L.S., C.G.S., V.J.L., C.R.J.), Quantitative Health Sciences (P.R.M.), Neurology (J.G.-R., M.B., H.B., K.A.J.), Psychiatry and Psychology (M.M.), and Information Technology (M.L.S.), Mayo Clinic, Rochester, MN; and Department of Neuroscience (M.M.C., N.E.-T.), Mayo Clinic, Jacksonville, FL
| | - Jonathan Graff-Radford
- From the Departments of Radiology (J.W., I.S., M.L.S., C.G.S., V.J.L., C.R.J.), Quantitative Health Sciences (P.R.M.), Neurology (J.G.-R., M.B., H.B., K.A.J.), Psychiatry and Psychology (M.M.), and Information Technology (M.L.S.), Mayo Clinic, Rochester, MN; and Department of Neuroscience (M.M.C., N.E.-T.), Mayo Clinic, Jacksonville, FL
| | - Mary M Machulda
- From the Departments of Radiology (J.W., I.S., M.L.S., C.G.S., V.J.L., C.R.J.), Quantitative Health Sciences (P.R.M.), Neurology (J.G.-R., M.B., H.B., K.A.J.), Psychiatry and Psychology (M.M.), and Information Technology (M.L.S.), Mayo Clinic, Rochester, MN; and Department of Neuroscience (M.M.C., N.E.-T.), Mayo Clinic, Jacksonville, FL
| | - Irene Sintini
- From the Departments of Radiology (J.W., I.S., M.L.S., C.G.S., V.J.L., C.R.J.), Quantitative Health Sciences (P.R.M.), Neurology (J.G.-R., M.B., H.B., K.A.J.), Psychiatry and Psychology (M.M.), and Information Technology (M.L.S.), Mayo Clinic, Rochester, MN; and Department of Neuroscience (M.M.C., N.E.-T.), Mayo Clinic, Jacksonville, FL
| | - Marina Buciuc
- From the Departments of Radiology (J.W., I.S., M.L.S., C.G.S., V.J.L., C.R.J.), Quantitative Health Sciences (P.R.M.), Neurology (J.G.-R., M.B., H.B., K.A.J.), Psychiatry and Psychology (M.M.), and Information Technology (M.L.S.), Mayo Clinic, Rochester, MN; and Department of Neuroscience (M.M.C., N.E.-T.), Mayo Clinic, Jacksonville, FL
| | - Matthew L Senjem
- From the Departments of Radiology (J.W., I.S., M.L.S., C.G.S., V.J.L., C.R.J.), Quantitative Health Sciences (P.R.M.), Neurology (J.G.-R., M.B., H.B., K.A.J.), Psychiatry and Psychology (M.M.), and Information Technology (M.L.S.), Mayo Clinic, Rochester, MN; and Department of Neuroscience (M.M.C., N.E.-T.), Mayo Clinic, Jacksonville, FL
| | - Christopher G Schwarz
- From the Departments of Radiology (J.W., I.S., M.L.S., C.G.S., V.J.L., C.R.J.), Quantitative Health Sciences (P.R.M.), Neurology (J.G.-R., M.B., H.B., K.A.J.), Psychiatry and Psychology (M.M.), and Information Technology (M.L.S.), Mayo Clinic, Rochester, MN; and Department of Neuroscience (M.M.C., N.E.-T.), Mayo Clinic, Jacksonville, FL
| | - Hugo Botha
- From the Departments of Radiology (J.W., I.S., M.L.S., C.G.S., V.J.L., C.R.J.), Quantitative Health Sciences (P.R.M.), Neurology (J.G.-R., M.B., H.B., K.A.J.), Psychiatry and Psychology (M.M.), and Information Technology (M.L.S.), Mayo Clinic, Rochester, MN; and Department of Neuroscience (M.M.C., N.E.-T.), Mayo Clinic, Jacksonville, FL
| | - Minerva M Carrasquillo
- From the Departments of Radiology (J.W., I.S., M.L.S., C.G.S., V.J.L., C.R.J.), Quantitative Health Sciences (P.R.M.), Neurology (J.G.-R., M.B., H.B., K.A.J.), Psychiatry and Psychology (M.M.), and Information Technology (M.L.S.), Mayo Clinic, Rochester, MN; and Department of Neuroscience (M.M.C., N.E.-T.), Mayo Clinic, Jacksonville, FL
| | - Nilufer Ertekin-Taner
- From the Departments of Radiology (J.W., I.S., M.L.S., C.G.S., V.J.L., C.R.J.), Quantitative Health Sciences (P.R.M.), Neurology (J.G.-R., M.B., H.B., K.A.J.), Psychiatry and Psychology (M.M.), and Information Technology (M.L.S.), Mayo Clinic, Rochester, MN; and Department of Neuroscience (M.M.C., N.E.-T.), Mayo Clinic, Jacksonville, FL
| | - Val J Lowe
- From the Departments of Radiology (J.W., I.S., M.L.S., C.G.S., V.J.L., C.R.J.), Quantitative Health Sciences (P.R.M.), Neurology (J.G.-R., M.B., H.B., K.A.J.), Psychiatry and Psychology (M.M.), and Information Technology (M.L.S.), Mayo Clinic, Rochester, MN; and Department of Neuroscience (M.M.C., N.E.-T.), Mayo Clinic, Jacksonville, FL
| | - Clifford R Jack
- From the Departments of Radiology (J.W., I.S., M.L.S., C.G.S., V.J.L., C.R.J.), Quantitative Health Sciences (P.R.M.), Neurology (J.G.-R., M.B., H.B., K.A.J.), Psychiatry and Psychology (M.M.), and Information Technology (M.L.S.), Mayo Clinic, Rochester, MN; and Department of Neuroscience (M.M.C., N.E.-T.), Mayo Clinic, Jacksonville, FL
| | - Keith A Josephs
- From the Departments of Radiology (J.W., I.S., M.L.S., C.G.S., V.J.L., C.R.J.), Quantitative Health Sciences (P.R.M.), Neurology (J.G.-R., M.B., H.B., K.A.J.), Psychiatry and Psychology (M.M.), and Information Technology (M.L.S.), Mayo Clinic, Rochester, MN; and Department of Neuroscience (M.M.C., N.E.-T.), Mayo Clinic, Jacksonville, FL
| |
Collapse
|
17
|
Oxtoby NP, Shand C, Cash DM, Alexander DC, Barkhof F. Targeted Screening for Alzheimer's Disease Clinical Trials Using Data-Driven Disease Progression Models. Front Artif Intell 2022; 5:660581. [PMID: 35719690 PMCID: PMC9204250 DOI: 10.3389/frai.2022.660581] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 04/25/2022] [Indexed: 11/23/2022] Open
Abstract
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.
Collapse
Affiliation(s)
- Neil P. Oxtoby
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom,*Correspondence: Neil P. Oxtoby
| | - Cameron Shand
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - David M. Cash
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Daniel C. Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Frederik Barkhof
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom,Amsterdam University Medical Center, Amsterdam, Netherlands
| |
Collapse
|
18
|
Golriz Khatami S, Salimi Y, Hofmann-Apitius M, Oxtoby NP, Birkenbihl C. Comparison and aggregation of event sequences across ten cohorts to describe the consensus biomarker evolution in Alzheimer's disease. Alzheimers Res Ther 2022; 14:55. [PMID: 35443691 PMCID: PMC9020023 DOI: 10.1186/s13195-022-01001-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 04/06/2022] [Indexed: 11/10/2022]
Abstract
BACKGROUND Previous models of Alzheimer's disease (AD) progression were primarily hypothetical or based on data originating from single cohort studies. However, cohort datasets are subject to specific inclusion and exclusion criteria that influence the signals observed in their collected data. Furthermore, each study measures only a subset of AD-relevant variables. To gain a comprehensive understanding of AD progression, the heterogeneity and robustness of estimated progression patterns must be understood, and complementary information contained in cohort datasets be leveraged. METHODS We compared ten event-based models that we fit to ten independent AD cohort datasets. Additionally, we designed and applied a novel rank aggregation algorithm that combines partially overlapping, individual event sequences into a meta-sequence containing the complementary information from each cohort. RESULTS We observed overall consistency across the ten event-based model sequences (average pairwise Kendall's tau correlation coefficient of 0.69 ± 0.28), despite variance in the positioning of mainly imaging variables. The changes described in the aggregated meta-sequence are broadly consistent with the current understanding of AD progression, starting with cerebrospinal fluid amyloid beta, followed by tauopathy, memory impairment, FDG-PET, and ultimately brain deterioration and impairment of visual memory. CONCLUSION Overall, the event-based models demonstrated similar and robust disease cascades across independent AD cohorts. Aggregation of data-driven results can combine complementary strengths and information of patient-level datasets. Accordingly, the derived meta-sequence draws a more complete picture of AD pathology compared to models relying on single cohorts.
Collapse
Affiliation(s)
- Sepehr Golriz Khatami
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757, Sankt Augustin, Germany.
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53115, Bonn, Germany.
| | - Yasamin Salimi
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757, Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53115, Bonn, Germany
| | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757, Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53115, Bonn, Germany
| | - Neil P Oxtoby
- Centre for Medical Image Computing and Department of Computer Science, University College London, Gower St, London, WC1E 6BT, UK
| | - Colin Birkenbihl
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757, Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53115, Bonn, Germany
| |
Collapse
|
19
|
Scotton WJ, Bocchetta M, Todd E, Cash DM, Oxtoby N, VandeVrede L, Heuer H, Alexander DC, Rowe JB, Morris HR, Boxer A, Rohrer JD, Wijeratne PA. A data-driven model of brain volume changes in progressive supranuclear palsy. Brain Commun 2022; 4:fcac098. [PMID: 35602649 PMCID: PMC9118104 DOI: 10.1093/braincomms/fcac098] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 12/08/2021] [Accepted: 04/11/2022] [Indexed: 11/13/2022] Open
Abstract
The most common clinical phenotype of progressive supranuclear palsy is Richardson syndrome, characterized by levodopa unresponsive symmetric parkinsonism, with a vertical supranuclear gaze palsy, early falls and cognitive impairment. There is currently no detailed understanding of the full sequence of disease pathophysiology in progressive supranuclear palsy. Determining the sequence of brain atrophy in progressive supranuclear palsy could provide important insights into the mechanisms of disease progression, as well as guide patient stratification and monitoring for clinical trials. We used a probabilistic event-based model applied to cross-sectional structural MRI scans in a large international cohort, to determine the sequence of brain atrophy in clinically diagnosed progressive supranuclear palsy Richardson syndrome. A total of 341 people with Richardson syndrome (of whom 255 had 12-month follow-up imaging) and 260 controls were included in the study. We used a combination of 12-month follow-up MRI scans, and a validated clinical rating score (progressive supranuclear palsy rating scale) to demonstrate the longitudinal consistency and utility of the event-based model's staging system. The event-based model estimated that the earliest atrophy occurs in the brainstem and subcortical regions followed by progression caudally into the superior cerebellar peduncle and deep cerebellar nuclei, and rostrally to the cortex. The sequence of cortical atrophy progresses in an anterior to posterior direction, beginning in the insula and then the frontal lobe before spreading to the temporal, parietal and finally the occipital lobe. This in vivo ordering accords with the post-mortem neuropathological staging of progressive supranuclear palsy and was robust under cross-validation. Using longitudinal information from 12-month follow-up scans, we demonstrate that subjects consistently move to later stages over this time interval, supporting the validity of the model. In addition, both clinical severity (progressive supranuclear palsy rating scale) and disease duration were significantly correlated with the predicted subject event-based model stage (P < 0.01). Our results provide new insights into the sequence of atrophy progression in progressive supranuclear palsy and offer potential utility to stratify people with this disease on entry into clinical trials based on disease stage, as well as track disease progression.
Collapse
Affiliation(s)
- W. J. Scotton
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen
Square Institute of Neurology, University College London, London, UK
- Correspondence to: William J. Scotton UCL Institute of Neurology
Department of Neurodegeneration Dementia Research Centre First Floor, 8-11 Queen Square,
WC1N 3AR London, UK E-mail:
| | - M. Bocchetta
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen
Square Institute of Neurology, University College London, London, UK
| | - E. Todd
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen
Square Institute of Neurology, University College London, London, UK
| | - D. M. Cash
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen
Square Institute of Neurology, University College London, London, UK
| | - N. Oxtoby
- Centre for Medical Image Computing, Department of Computer Science, University
College London, London, UK
| | - L. VandeVrede
- Department of Neurology, Memory and Aging Center, University of
California, San Francisco, CA, USA
| | - H. Heuer
- Department of Neurology, Memory and Aging Center, University of
California, San Francisco, CA, USA
| | | | - D. C. Alexander
- Centre for Medical Image Computing, Department of Computer Science, University
College London, London, UK
| | - J. B. Rowe
- Department of Clinical Neurosciences, Cambridge University, Cambridge
University Hospitals NHS Trust, Cambridge, UK
- Medical Research Council Cognition and Brain Sciences Unit, Cambridge
University, Cambridge, UK
| | - H. R. Morris
- Department of Clinical and Movement Neurosciences, University College London
Queen Square Institute of Neurology, London, UK
- Movement Disorders Centre, University College London Queen Square Institute of
Neurology, London, UK
| | - A. Boxer
- Department of Neurology, Memory and Aging Center, University of
California, San Francisco, CA, USA
| | - J. D. Rohrer
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen
Square Institute of Neurology, University College London, London, UK
| | - P. A. Wijeratne
- Centre for Medical Image Computing, Department of Computer Science, University
College London, London, UK
| |
Collapse
|
20
|
Aksman LM, Wijeratne PA, Oxtoby NP, Eshaghi A, Shand C, Altmann A, Alexander DC, Young AL. pySuStaIn: a Python implementation of the Subtype and Stage Inference algorithm. SOFTWAREX 2021; 16:100811. [PMID: 34926780 PMCID: PMC8682799 DOI: 10.1016/j.softx.2021.100811] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Progressive disorders are highly heterogeneous. Symptom-based clinical classification of these disorders may not reflect the underlying pathobiology. Data-driven subtyping and staging of patients has the potential to disentangle the complex spatiotemporal patterns of disease progression. Tools that enable this are in high demand from clinical and treatment-development communities. Here we describe the pySuStaIn software package, a Python-based implementation of the Subtype and Stage Inference (SuStaIn) algorithm. SuStaIn unravels the complexity of heterogeneous diseases by inferring multiple disease progression patterns (subtypes) and individual severity (stages) from cross-sectional data. The primary aims of pySuStaIn are to enable widespread application and translation of SuStaIn via an accessible Python package that supports simple extension and generalization to novel modelling situations within a single, consistent architecture.
Collapse
Affiliation(s)
- Leon M Aksman
- Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California
- Centre for Medical Image Computing, Departments of Computer Science and Medical Physics, University College London
| | - Peter A Wijeratne
- Centre for Medical Image Computing, Departments of Computer Science and Medical Physics, University College London
| | - Neil P Oxtoby
- Centre for Medical Image Computing, Departments of Computer Science and Medical Physics, University College London
| | - Arman Eshaghi
- Centre for Medical Image Computing, Departments of Computer Science and Medical Physics, University College London
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London
| | - Cameron Shand
- Centre for Medical Image Computing, Departments of Computer Science and Medical Physics, University College London
| | - Andre Altmann
- Centre for Medical Image Computing, Departments of Computer Science and Medical Physics, University College London
| | - Daniel C Alexander
- Centre for Medical Image Computing, Departments of Computer Science and Medical Physics, University College London
| | - Alexandra L Young
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London
| |
Collapse
|
21
|
Wijeratne PA, Johnson EB, Gregory S, Georgiou-Karistianis N, Paulsen JS, Scahill RI, Tabrizi SJ, Alexander DC. A Multi-Study Model-Based Evaluation of the Sequence of Imaging and Clinical Biomarker Changes in Huntington's Disease. Front Big Data 2021; 4:662200. [PMID: 34423286 PMCID: PMC8374237 DOI: 10.3389/fdata.2021.662200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Accepted: 07/07/2021] [Indexed: 11/25/2022] Open
Abstract
Understanding the order and progression of change in biomarkers of neurodegeneration is essential to detect the effects of pharmacological interventions on these biomarkers. In Huntington’s disease (HD), motor, cognitive and MRI biomarkers are currently used in clinical trials of drug efficacy. Here for the first time we use directly compare data from three large observational studies of HD (total N = 532) using a probabilistic event-based model (EBM) to characterise the order in which motor, cognitive and MRI biomarkers become abnormal. We also investigate the impact of the genetic cause of HD, cytosine-adenine-guanine (CAG) repeat length, on progression through these stages. We find that EBM uncovers a broadly consistent order of events across all three studies; that EBM stage reflects clinical stage; and that EBM stage is related to age and genetic burden. Our findings indicate that measures of subcortical and white matter volume become abnormal prior to clinical and cognitive biomarkers. Importantly, CAG repeat length has a large impact on the timing of onset of each stage and progression through the stages, with a longer repeat length resulting in earlier onset and faster progression. Our results can be used to help design clinical trials of treatments for Huntington’s disease, influencing the choice of biomarkers and the recruitment of participants.
Collapse
Affiliation(s)
- Peter A Wijeratne
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom.,Huntington's Disease Research Centre, Department of Neurodegenerative Disease, University College London, Queen Square Institute of Neurology, London, United Kingdom
| | - Eileanoir B Johnson
- Huntington's Disease Research Centre, Department of Neurodegenerative Disease, University College London, Queen Square Institute of Neurology, London, United Kingdom
| | - Sarah Gregory
- Huntington's Disease Research Centre, Department of Neurodegenerative Disease, University College London, Queen Square Institute of Neurology, London, United Kingdom
| | - Nellie Georgiou-Karistianis
- Monash Institute of Cognitive and Clinical Neurosciences, School of Psychological Sciences, Faculty of Nursing, Medicine, and Health Sciences, Monash University Clayton Campus, Clayton, VIC, Australia
| | - Jane S Paulsen
- Departments of Neurology and Psychiatry, Carver College of Medicine, University of Iowa, Iowa City, IA, United States
| | - Rachael I Scahill
- Huntington's Disease Research Centre, Department of Neurodegenerative Disease, University College London, Queen Square Institute of Neurology, London, United Kingdom
| | - Sarah J Tabrizi
- Huntington's Disease Research Centre, Department of Neurodegenerative Disease, University College London, Queen Square Institute of Neurology, London, United Kingdom
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| |
Collapse
|
22
|
Quattrini G, Marizzoni M, Pizzini FB, Galazzo IB, Aiello M, Didic M, Soricelli A, Albani D, Romano M, Blin O, Forloni G, Golay X, Jovicich J, Nathan PJ, Richardson JC, Salvatore M, Frisoni GB, Pievani M. Convergent and Discriminant Validity of Default Mode Network and Limbic Network Perfusion in Amnestic Mild Cognitive Impairment Patients. J Alzheimers Dis 2021; 82:1797-1808. [PMID: 34219733 DOI: 10.3233/jad-210531] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Previous studies reported default mode network (DMN) and limbic network (LIN) brain perfusion deficits in patients with amnestic mild cognitive impairment (aMCI), frequently a prodromal stage of Alzheimer's disease (AD). However, the validity of these measures as AD markers has not yet been tested using MRI arterial spin labeling (ASL). OBJECTIVE To investigate the convergent and discriminant validity of DMN and LIN perfusion in aMCI. METHODS We collected core AD markers (amyloid-β 42 [Aβ42], phosphorylated tau 181 levels in cerebrospinal fluid [CSF]), neurodegenerative (hippocampal volumes and CSF total tau), vascular (white matter hyperintensities), genetic (apolipoprotein E [APOE] status), and cognitive features (memory functioning on Paired Associate Learning test [PAL]) in 14 aMCI patients. Cerebral blood flow (CBF) was extracted from DMN and LIN using ASL and correlated with AD features to assess convergent validity. Discriminant validity was assessed carrying out the same analysis with AD-unrelated features, i.e., somatomotor and visual networks' perfusion, cerebellar volume, and processing speed. RESULTS Perfusion was reduced in the DMN (F = 5.486, p = 0.039) and LIN (F = 12.678, p = 0.004) in APOE ɛ4 carriers compared to non-carriers. LIN perfusion correlated with CSF Aβ42 levels (r = 0.678, p = 0.022) and memory impairment (PAL, number of errors, r = -0.779, p = 0.002). No significant correlation was detected with tau, neurodegeneration, and vascular features, nor with AD-unrelated features. CONCLUSION Our results support the validity of DMN and LIN ASL perfusion as AD markers in aMCI, indicating a significant correlation between CBF and amyloidosis, APOE ɛ4, and memory impairment.
Collapse
Affiliation(s)
- Giulia Quattrini
- Laboratory of Alzheimer's Neuroimaging and Epidemiology (LANE), IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.,Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Moira Marizzoni
- Laboratory of Alzheimer's Neuroimaging and Epidemiology (LANE), IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.,Laboratory of Biological Psychiatry, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Francesca B Pizzini
- Radiology, Department of Diagnostic and Public Health, University of Verona, Verona, Italy
| | | | | | - Mira Didic
- Aix-Marseille Univ, INSERM, INS, Instit Neurosci des Syst, Marseille, France.,APHM, Timone, Service de Neurologie et Neuropsychologie, Hôpital Timone Adultes, Marseille, France
| | - Andrea Soricelli
- IRCCS SDN, Napoli, Italy.,Department of Sport Sciences, University of Naples Parthenope, Naples, Italy
| | - Diego Albani
- Neuroscience Department, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - Melissa Romano
- Laboratory of Alzheimer's Neuroimaging and Epidemiology (LANE), IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Olivier Blin
- Aix-Marseille Univ, INSERM, INS, Instit Neurosci des Syst, DHUNE, Ap-Hm, Marseille, France
| | - Gianluigi Forloni
- Neuroscience Department, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - Xavier Golay
- Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Jorge Jovicich
- Center for Mind/Brain Sciences - CIMeC, University of Trento, Rovereto, Italy
| | - Pradeep J Nathan
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Jill C Richardson
- Neurosciences Therapeutic Area, GlaxoSmithKline R&D, Gunnels Wood Road, Stevenage, United Kingdom
| | | | - Giovanni B Frisoni
- Memory Clinic and LANVIE-Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Geneva, Switzerland
| | - Michela Pievani
- Laboratory of Alzheimer's Neuroimaging and Epidemiology (LANE), IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | | |
Collapse
|
23
|
Young AL, Vogel JW, Aksman LM, Wijeratne PA, Eshaghi A, Oxtoby NP, Williams SCR, Alexander DC. Ordinal SuStaIn: Subtype and Stage Inference for Clinical Scores, Visual Ratings, and Other Ordinal Data. Front Artif Intell 2021; 4:613261. [PMID: 34458723 PMCID: PMC8387598 DOI: 10.3389/frai.2021.613261] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Accepted: 07/20/2021] [Indexed: 12/28/2022] Open
Abstract
Subtype and Stage Inference (SuStaIn) is an unsupervised learning algorithm that uniquely enables the identification of subgroups of individuals with distinct pseudo-temporal disease progression patterns from cross-sectional datasets. SuStaIn has been used to identify data-driven subgroups and perform patient stratification in neurodegenerative diseases and in lung diseases from continuous biomarker measurements predominantly obtained from imaging. However, the SuStaIn algorithm is not currently applicable to discrete ordinal data, such as visual ratings of images, neuropathological ratings, and clinical and neuropsychological test scores, restricting the applicability of SuStaIn to a narrower range of settings. Here we propose 'Ordinal SuStaIn', an ordinal version of the SuStaIn algorithm that uses a scored events model of disease progression to enable the application of SuStaIn to ordinal data. We demonstrate the validity of Ordinal SuStaIn by benchmarking the performance of the algorithm on simulated data. We further demonstrate that Ordinal SuStaIn out-performs the existing continuous version of SuStaIn (Z-score SuStaIn) on discrete scored data, providing much more accurate subtype progression patterns, better subtyping and staging of individuals, and accurate uncertainty estimates. We then apply Ordinal SuStaIn to six different sub-scales of the Clinical Dementia Rating scale (CDR) using data from the Alzheimer's disease Neuroimaging Initiative (ADNI) study to identify individuals with distinct patterns of functional decline. Using data from 819 ADNI1 participants we identified three distinct CDR subtype progression patterns, which were independently verified using data from 790 ADNI2 participants. Our results provide insight into patterns of decline in daily activities in Alzheimer's disease and a mechanism for stratifying individuals into groups with difficulties in different domains. Ordinal SuStaIn is broadly applicable across different types of ratings data, including visual ratings from imaging, neuropathological ratings and clinical or behavioural ratings data.
Collapse
Affiliation(s)
- Alexandra L. Young
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
- Centre for Medical Image Computing, University College London, London, United Kingdom
- Department of Computer Science, University College London, London, United Kingdom
| | - Jacob W. Vogel
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, Unites States
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, Unites States
| | - Leon M. Aksman
- Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, Unites States
| | - Peter A. Wijeratne
- Centre for Medical Image Computing, University College London, London, United Kingdom
- Department of Computer Science, University College London, London, United Kingdom
| | - Arman Eshaghi
- Department of Computer Science, University College London, London, United Kingdom
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, Faculty of Brain Sciences, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Neil P. Oxtoby
- Centre for Medical Image Computing, University College London, London, United Kingdom
- Department of Computer Science, University College London, London, United Kingdom
| | - Steven C. R. Williams
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Daniel C. Alexander
- Centre for Medical Image Computing, University College London, London, United Kingdom
- Department of Computer Science, University College London, London, United Kingdom
| | | |
Collapse
|
24
|
Archetti D, Young AL, Oxtoby NP, Ferreira D, Mårtensson G, Westman E, Alexander DC, Frisoni GB, Redolfi A. Inter-Cohort Validation of SuStaIn Model for Alzheimer's Disease. Front Big Data 2021; 4:661110. [PMID: 34095821 PMCID: PMC8173213 DOI: 10.3389/fdata.2021.661110] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Accepted: 05/04/2021] [Indexed: 01/15/2023] Open
Abstract
Alzheimer’s disease (AD) is a neurodegenerative disorder which spans several years from preclinical manifestations to dementia. In recent years, interest in the application of machine learning (ML) algorithms to personalized medicine has grown considerably, and a major challenge that such models face is the transferability from the research settings to clinical practice. The objective of this work was to demonstrate the transferability of the Subtype and Stage Inference (SuStaIn) model from well-characterized research data set, employed as training set, to independent less-structured and heterogeneous test sets representative of the clinical setting. The training set was composed of MRI data of 1043 subjects from the Alzheimer’s disease Neuroimaging Initiative (ADNI), and the test set was composed of data from 767 subjects from OASIS, Pharma-Cog, and ViTA clinical datasets. Both sets included subjects covering the entire spectrum of AD, and for both sets volumes of relevant brain regions were derived from T1-3D MRI scans processed with Freesurfer v5.3 cross-sectional stream. In order to assess the predictive value of the model, subpopulations of subjects with stable mild cognitive impairment (MCI) and MCIs that progressed to AD dementia (pMCI) were identified in both sets. SuStaIn identified three disease subtypes, of which the most prevalent corresponded to the typical atrophy pattern of AD. The other SuStaIn subtypes exhibited similarities with the previously defined hippocampal sparing and limbic predominant atrophy patterns of AD. Subject subtyping proved to be consistent in time for all cohorts and the staging provided by the model was correlated with cognitive performance. Classification of subjects on the basis of a combination of SuStaIn subtype and stage, mini mental state examination and amyloid-β1-42 cerebrospinal fluid concentration was proven to predict conversion from MCI to AD dementia on par with other novel statistical algorithms, with ROC curves that were not statistically different for the training and test sets and with area under curve respectively equal to 0.77 and 0.76. This study proves the transferability of a SuStaIn model for AD from research data to less-structured clinical cohorts, and indicates transferability to the clinical setting.
Collapse
Affiliation(s)
- Damiano Archetti
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Alexandra L Young
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,Department of Computer Science, UCL Centre for Medical Image Computing, London, United Kingdom
| | - Neil P Oxtoby
- Department of Computer Science, UCL Centre for Medical Image Computing, London, United Kingdom
| | - Daniel Ferreira
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.,Department of Radiology, Mayo Clinic, Rochester, MN, United States
| | - Gustav Mårtensson
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Eric Westman
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Daniel C Alexander
- Department of Computer Science, UCL Centre for Medical Image Computing, London, United Kingdom
| | - Giovanni B Frisoni
- Memory Clinic and LANVIE - Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Geneva, Switzerland.,Laboratory of Alzheimer's Neuroimaging and Epidemiology - LANE, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Alberto Redolfi
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | | |
Collapse
|
25
|
Oxtoby NP, Leyland LA, Aksman LM, Thomas GEC, Bunting EL, Wijeratne PA, Young AL, Zarkali A, Tan MMX, Bremner FD, Keane PA, Morris HR, Schrag AE, Alexander DC, Weil RS. Sequence of clinical and neurodegeneration events in Parkinson's disease progression. Brain 2021; 144:975-988. [PMID: 33543247 PMCID: PMC8041043 DOI: 10.1093/brain/awaa461] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 10/05/2020] [Accepted: 10/24/2020] [Indexed: 02/07/2023] Open
Abstract
Dementia is one of the most debilitating aspects of Parkinson's disease. There are no validated biomarkers that can track Parkinson's disease progression, nor accurately identify patients who will develop dementia and when. Understanding the sequence of observable changes in Parkinson's disease in people at elevated risk for developing dementia could provide an integrated biomarker for identifying and managing individuals who will develop Parkinson's dementia. We aimed to estimate the sequence of clinical and neurodegeneration events, and variability in this sequence, using data-driven statistical modelling in two separate Parkinson's cohorts, focusing on patients at elevated risk for dementia due to their age at symptom onset. We updated a novel version of an event-based model that has only recently been extended to cope naturally with clinical data, enabling its application in Parkinson's disease for the first time. The observational cohorts included healthy control subjects and patients with Parkinson's disease, of whom those diagnosed at age 65 or older were classified as having high risk of dementia. The model estimates that Parkinson's progression in patients at elevated risk for dementia starts with classic prodromal features of Parkinson's disease (olfaction, sleep), followed by early deficits in visual cognition and increased brain iron content, followed later by a less certain ordering of neurodegeneration in the substantia nigra and cortex, neuropsychological cognitive deficits, retinal thinning in dopamine layers, and further deficits in visual cognition. Importantly, we also characterize variation in the sequence. We found consistent, cross-validated results within cohorts, and agreement between cohorts on the subset of features available in both cohorts. Our sequencing results add powerful support to the increasing body of evidence suggesting that visual processing specifically is affected early in patients with Parkinson's disease at elevated risk of dementia. This opens a route to earlier and more precise detection, as well as a more detailed understanding of the pathological mechanisms underpinning Parkinson's dementia.
Collapse
Affiliation(s)
- Neil P Oxtoby
- Centre for Medical Image Computing, Department of Computer Science and Department of Medical Physics and Biomedical Engineering, UCL, London, UK
| | | | - Leon M Aksman
- Centre for Medical Image Computing, Department of Computer Science and Department of Medical Physics and Biomedical Engineering, UCL, London, UK
| | - George E C Thomas
- Dementia Research Centre, UCL Institute of Neurology, UCL, London, UK
| | - Emma L Bunting
- Dementia Research Centre, UCL Institute of Neurology, UCL, London, UK
| | - Peter A Wijeratne
- Centre for Medical Image Computing, Department of Computer Science and Department of Medical Physics and Biomedical Engineering, UCL, London, UK
| | - Alexandra L Young
- Centre for Medical Image Computing, Department of Computer Science and Department of Medical Physics and Biomedical Engineering, UCL, London, UK
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Angelika Zarkali
- Dementia Research Centre, UCL Institute of Neurology, UCL, London, UK
| | - Manuela M X Tan
- Department of Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, UCL, London, UK
- Movement Disorders Consortium, UCL, London, UK
| | - Fion D Bremner
- Neuro-ophthalmology, National Hospital for Neurology and Neurosurgery, University College London Hospitals, London, UK
| | - Pearse A Keane
- Institute of Ophthalmology, UCL, London, UK
- Moorfields Eye Hospital, London, UK
| | - Huw R Morris
- Department of Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, UCL, London, UK
- Movement Disorders Consortium, UCL, London, UK
| | - Anette E Schrag
- Department of Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, UCL, London, UK
- Movement Disorders Consortium, UCL, London, UK
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science and Department of Medical Physics and Biomedical Engineering, UCL, London, UK
| | - Rimona S Weil
- Dementia Research Centre, UCL Institute of Neurology, UCL, London, UK
- Movement Disorders Consortium, UCL, London, UK
- The Wellcome Centre for Human Neuroimaging, UCL Institute of Neurology, UCL, London, UK
| |
Collapse
|
26
|
Bellio M, Oxtoby NP, Walker Z, Henley S, Ribbens A, Blandford A, Alexander DC, Yong KXX. Analyzing large Alzheimer's disease cognitive datasets: Considerations and challenges. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2020; 12:e12135. [PMID: 33313379 PMCID: PMC7720865 DOI: 10.1002/dad2.12135] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 10/13/2020] [Accepted: 10/13/2020] [Indexed: 11/17/2022]
Abstract
Recent data-sharing initiatives of clinical and preclinical Alzheimer's disease (AD) have led to a growing number of non-clinical researchers analyzing these datasets using modern data-driven computational methods. Cognitive tests are key components of such datasets, representing the principal clinical tool to establish phenotypes and monitor symptomatic progression. Despite the potential of computational analyses in complementing the clinical understanding of AD, the characteristics and multifactorial nature of cognitive tests are often unfamiliar to computational researchers and other non-specialist audiences. This perspective paper outlines core features, idiosyncrasies, and applications of cognitive test data. We report tests commonly featured in data-sharing initiatives, highlight key considerations in their selection and analysis, and provide suggestions to avoid risks of misinterpretation. Ultimately, the greater transparency of cognitive measures will maximize insights offered in AD, particularly regarding understanding the extent and basis of AD phenotypic heterogeneity.
Collapse
Affiliation(s)
- Maura Bellio
- UCL Centre for Medical Image Computing (CMIC)Department of Computer ScienceUniversity College LondonLondonUK
- UCL Interaction Centre (UCLIC)Department of Computer ScienceUniversity College LondonLondonUK
| | - Neil P. Oxtoby
- UCL Centre for Medical Image Computing (CMIC)Department of Computer ScienceUniversity College LondonLondonUK
| | - Zuzana Walker
- Division of PsychiatryUniversity College LondonLondonUK
| | - Susie Henley
- Dementia Research CentreDepartment of Neurodegeneration, National Hospital for Neurology and NeurosurgeryUCL Queen Square Institute of NeurologyUniversity College LondonLondonUK
| | | | - Ann Blandford
- UCL Interaction Centre (UCLIC)Department of Computer ScienceUniversity College LondonLondonUK
| | - Daniel C. Alexander
- UCL Centre for Medical Image Computing (CMIC)Department of Computer ScienceUniversity College LondonLondonUK
| | - Keir X. X. Yong
- Dementia Research CentreDepartment of Neurodegeneration, National Hospital for Neurology and NeurosurgeryUCL Queen Square Institute of NeurologyUniversity College LondonLondonUK
| |
Collapse
|
27
|
O'Connor A, Weston PSJ, Pavisic IM, Ryan NS, Collins JD, Lu K, Crutch SJ, Alexander DC, Fox NC, Oxtoby NP. Quantitative detection and staging of presymptomatic cognitive decline in familial Alzheimer's disease: a retrospective cohort analysis. Alzheimers Res Ther 2020; 12:126. [PMID: 33023653 PMCID: PMC7539456 DOI: 10.1186/s13195-020-00695-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Accepted: 09/17/2020] [Indexed: 12/30/2022]
Abstract
BACKGROUND Understanding the earliest manifestations of Alzheimer's disease (AD) is key to realising disease-modifying treatments. Advances in neuroimaging and fluid biomarkers have improved our ability to identify AD pathology in vivo. The critical next step is improved detection and staging of early cognitive change. We studied an asymptomatic familial Alzheimer's disease (FAD) cohort to characterise preclinical cognitive change. METHODS Data included 35 asymptomatic participants at 50% risk of carrying a pathogenic FAD mutation. Participants completed a multi-domain neuropsychology battery. After accounting for sex, age and education, we used event-based modelling to estimate the sequence of cognitive decline in presymptomatic FAD, and uncertainty in the sequence. We assigned individuals to their most likely model stage of cumulative cognitive decline, given their data. Linear regression of estimated years to symptom onset against model stage was used to estimate the timing of preclinical cognitive decline. RESULTS Cognitive change in mutation carriers was first detected in measures of accelerated long-term forgetting, up to 10 years before estimated symptom onset. Measures of subjective cognitive decline also revealed early abnormalities. Our data-driven model demonstrated subtle cognitive impairment across multiple cognitive domains in clinically normal individuals on the AD continuum. CONCLUSIONS Data-driven modelling of neuropsychological test scores has potential to differentiate cognitive decline from cognitive stability and to estimate a fine-grained sequence of decline across cognitive domains and functions, in the preclinical phase of Alzheimer's disease. This can improve the design of future presymptomatic trials by informing enrichment strategies and guiding the selection of outcome measures.
Collapse
Affiliation(s)
- Antoinette O'Connor
- Dementia Research Centre, UCL Queen Square Institute Of Neurology, 8-11 Queen Square, London, WC1N 3AR, UK. antoinette.o'
- UK Dementia Research Institute at UCL, UCL, London, UK. antoinette.o'
| | - Philip S J Weston
- Dementia Research Centre, UCL Queen Square Institute Of Neurology, 8-11 Queen Square, London, WC1N 3AR, UK
| | - Ivanna M Pavisic
- Dementia Research Centre, UCL Queen Square Institute Of Neurology, 8-11 Queen Square, London, WC1N 3AR, UK
- UK Dementia Research Institute at UCL, UCL, London, UK
| | - Natalie S Ryan
- Dementia Research Centre, UCL Queen Square Institute Of Neurology, 8-11 Queen Square, London, WC1N 3AR, UK
- UK Dementia Research Institute at UCL, UCL, London, UK
| | - Jessica D Collins
- Dementia Research Centre, UCL Queen Square Institute Of Neurology, 8-11 Queen Square, London, WC1N 3AR, UK
| | - Kirsty Lu
- Dementia Research Centre, UCL Queen Square Institute Of Neurology, 8-11 Queen Square, London, WC1N 3AR, UK
| | - Sebastian J Crutch
- Dementia Research Centre, UCL Queen Square Institute Of Neurology, 8-11 Queen Square, London, WC1N 3AR, UK
| | - Daniel C Alexander
- Department of Computer Science, UCL Centre for Medical Image Computing, 1st Floor, 90 High Holborn, London, WC1V 6LJ, UK
| | - Nick C Fox
- Dementia Research Centre, UCL Queen Square Institute Of Neurology, 8-11 Queen Square, London, WC1N 3AR, UK
- UK Dementia Research Institute at UCL, UCL, London, UK
| | - Neil P Oxtoby
- Department of Computer Science, UCL Centre for Medical Image Computing, 1st Floor, 90 High Holborn, London, WC1V 6LJ, UK.
| |
Collapse
|