1
|
Machado Reyes D, Chao H, Hahn J, Shen L, Yan P. Identifying Progression-Specific Alzheimer's Subtypes Using Multimodal Transformer. J Pers Med 2024; 14:421. [PMID: 38673048 PMCID: PMC11051083 DOI: 10.3390/jpm14040421] [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: 03/15/2024] [Revised: 04/01/2024] [Accepted: 04/08/2024] [Indexed: 04/28/2024] Open
Abstract
Alzheimer's disease (AD) is the most prevalent neurodegenerative disease, yet its current treatments are limited to stopping disease progression. Moreover, the effectiveness of these treatments remains uncertain due to the heterogeneity of the disease. Therefore, it is essential to identify disease subtypes at a very early stage. Current data-driven approaches can be used to classify subtypes during later stages of AD or related disorders, but making predictions in the asymptomatic or prodromal stage is challenging. Furthermore, the classifications of most existing models lack explainability, and these models rely solely on a single modality for assessment, limiting the scope of their analysis. Thus, we propose a multimodal framework that utilizes early-stage indicators, including imaging, genetics, and clinical assessments, to classify AD patients into progression-specific subtypes at an early stage. In our framework, we introduce a tri-modal co-attention mechanism (Tri-COAT) to explicitly capture cross-modal feature associations. Data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) (slow progressing = 177, intermediate = 302, and fast = 15) were used to train and evaluate Tri-COAT using a 10-fold stratified cross-testing approach. Our proposed model outperforms baseline models and sheds light on essential associations across multimodal features supported by known biological mechanisms. The multimodal design behind Tri-COAT allows it to achieve the highest classification area under the receiver operating characteristic curve while simultaneously providing interpretability to the model predictions through the co-attention mechanism.
Collapse
Affiliation(s)
- Diego Machado Reyes
- Department of Biomedical Engineering, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180, USA; (D.M.R.); (H.C.); (J.H.)
| | - Hanqing Chao
- Department of Biomedical Engineering, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180, USA; (D.M.R.); (H.C.); (J.H.)
| | - Juergen Hahn
- Department of Biomedical Engineering, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180, USA; (D.M.R.); (H.C.); (J.H.)
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA;
| | - Pingkun Yan
- Department of Biomedical Engineering, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180, USA; (D.M.R.); (H.C.); (J.H.)
| | | |
Collapse
|
2
|
Crane PK, Groot C, Ossenkoppele R, Mukherjee S, Choi S, Lee M, Scollard P, Gibbons LE, Sanders RE, Trittschuh E, Saykin AJ, Mez J, Nakano C, Donald CM, Sohi H, Risacher S. Cognitively defined Alzheimer's dementia subgroups have distinct atrophy patterns. Alzheimers Dement 2024; 20:1739-1752. [PMID: 38093529 PMCID: PMC10984445 DOI: 10.1002/alz.13567] [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: 07/03/2023] [Revised: 10/16/2023] [Accepted: 11/03/2023] [Indexed: 03/03/2024]
Abstract
INTRODUCTION We sought to determine structural magnetic resonance imaging (MRI) characteristics across subgroups defined based on relative cognitive domain impairments using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and to compare cognitively defined to imaging-defined subgroups. METHODS We used data from 584 people with Alzheimer's disease (AD) (461 amyloid positive, 123 unknown amyloid status) and 118 amyloid-negative controls. We used voxel-based morphometry to compare gray matter volume (GMV) for each group compared to controls and to AD-Memory. RESULTS There was pronounced bilateral lower medial temporal lobe atrophy with relative cortical sparing for AD-Memory, lower left hemisphere GMV for AD-Language, anterior lower GMV for AD-Executive, and posterior lower GMV for AD-Visuospatial. Formal asymmetry comparisons showed substantially more asymmetry in the AD-Language group than any other group (p = 1.15 × 10-10 ). For overlap between imaging-defined and cognitively defined subgroups, AD-Memory matched up with an imaging-defined limbic predominant group. DISCUSSION MRI findings differ across cognitively defined AD subgroups.
Collapse
Affiliation(s)
- Paul K. Crane
- Department of MedicineUniversity of WashingtonSeattleWashingtonUSA
| | - Colin Groot
- Clinical Memory Research UnitLund UniversityLundSweden
- Alzheimer centerAmsterdam UMC ‐ VU Medical CenterAmsterdamNetherlands
| | - Rik Ossenkoppele
- Clinical Memory Research UnitLund UniversityLundSweden
- Alzheimer centerAmsterdam UMC ‐ VU Medical CenterAmsterdamNetherlands
| | | | - Seo‐Eun Choi
- Department of MedicineUniversity of WashingtonSeattleWashingtonUSA
| | - Michael Lee
- Department of MedicineUniversity of WashingtonSeattleWashingtonUSA
| | - Phoebe Scollard
- Department of MedicineUniversity of WashingtonSeattleWashingtonUSA
| | - Laura E. Gibbons
- Department of MedicineUniversity of WashingtonSeattleWashingtonUSA
| | | | - Emily Trittschuh
- Department of Psychiatry and Behavioral SciencesUniversity of Washington, and Geriatrics ResearchEducation, and Clinical CenterVA Puget Sound Health Care SystemSeattleUSA
| | - Andrew J. Saykin
- Indiana Alzheimer's Disease Research CenterIndiana University School of MedicineIndianapolisUSA
- Department of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisUSA
| | - Jesse Mez
- Department of NeurologyBoston UniversityBostonMassachusettsUSA
| | - Connie Nakano
- Department of MedicineUniversity of WashingtonSeattleWashingtonUSA
| | | | - Harkirat Sohi
- Department of Biomedical Informatics and Medical EducationUniversity of WashingtonSeattleUSA
- Now Pacific Northwest National LaboratoryRichlandUSA
| | | | - Shannon Risacher
- Indiana Alzheimer's Disease Research CenterIndiana University School of MedicineIndianapolisUSA
- Department of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisUSA
| |
Collapse
|
3
|
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
|
4
|
Wang D, Ma X, Schulz PE, Jiang X, Kim Y. Knowledge-guided Deep Temporal Clustering for Alzheimer's Disease Subtypes in Completed Clinical Trials. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.10.13.23296985. [PMID: 37873161 PMCID: PMC10593006 DOI: 10.1101/2023.10.13.23296985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Alzheimer's disease (AD) is a multifaceted neurodegenerative disorder with varied patient progression. We aim to test the hypothesis that AD patients can be categorized into subgroups based on differences in progression. We leveraged data from three randomized clinical trials (RCTs) to develop a knowledge-guided, deep temporal clustering (KG-DTC) framework for AD subtyping. This model combined autoencoders for contextual information capture, k-means clustering for representation formation, and clinical outcome classification for clinical knowledge integration. The derived representations, encompassing demographics, APOE genotype, cognitive assessments, brain volumes, and biomarkers, were clustered using the Gaussian Mixture Model to identify AD subtypes. Our novel KG-DTC framework was developed using placebo data from 2,087 AD patients across three solanezumab clinical trials (EXPEDITION, EXPEDITION2, and EXPEDITION3), achieving high performance in outcome prediction and clustering. The KG-DTC model demonstrated superior clustering structures, especially when combined with k-means clustering loss. External validation with independent clinical trial data showed consistent clustering results, with a 0.33 silhouette score for three clusters. The model's stability was confirmed through a leave-one-out approach, with an average adjusted Rand Index around 0.945. Three distinct AD subtypes were identified, each exhibiting unique patterns of cognitive function, neurodegeneration, and amyloid beta levels. Notably, Subtype 3 (S3) showed rapid cognitive decline across multiple clinical measures (e.g., 0.64 in S1 vs. -1.06 in S2 vs. 15.09 in S3 of average ADAS total change score, p<.001). This innovative approach offers promising insights for understanding variability in treatment outcomes and personalizing AD treatment strategies.
Collapse
|
5
|
Li Z, Chen D, Li Z, Fan H, Guo L, Sui B, Ventikos Y. A computational study of fluid transport characteristics in the brain parenchyma of dementia subtypes. J Biomech 2023; 159:111803. [PMID: 37734184 DOI: 10.1016/j.jbiomech.2023.111803] [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: 09/14/2023] [Accepted: 09/15/2023] [Indexed: 09/23/2023]
Abstract
The cerebral environment is a complex system consisting of parenchymal tissue and multiple fluids. Dementia is a common class of neurodegenerative diseases, caused by structural damages and functional deficits in the cerebral environment. In order to better understand the pathology of dementia from a cerebral fluid transport angle and provide clearer evidence that could help differentiate between dementia subtypes, such as Alzheimer's disease and vascular dementia, we conducted fluid-structure interaction modelling of the brain using a multiple-network poroelasticity model, which considers both neuropathological and cerebrovascular factors. The parenchyma was further subdivided and labelled into parcellations to obtain more localised and detailed data. The numerical results were converted to computed functional images by an in-house workflow. Different cerebral blood flow (CBF) and cerebrospinal fluid (CSF) clearance abnormalities were identified in the modelling results, when comparing Alzheimer's disease and vascular dementia. This paper presents our preliminary results as a proof of concept for a novel clinical diagnostic tool, and paves the way for a larger clinical study.
Collapse
Affiliation(s)
- Zeyan Li
- Wenzhou Safety (Emergency) Institute, Tianjin University, Wenzhou, China; School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Duanduan Chen
- Wenzhou Safety (Emergency) Institute, Tianjin University, Wenzhou, China; School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Zhiye Li
- Tiantan Neuroimaging Center for Excellence, China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Beijing, China
| | - Haojun Fan
- Wenzhou Safety (Emergency) Institute, Tianjin University, Wenzhou, China
| | - Liwei Guo
- Department of Mechanical Engineering, University College London, London, United Kingdom.
| | - Binbin Sui
- Tiantan Neuroimaging Center for Excellence, China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Beijing, China.
| | - Yiannis Ventikos
- Department of Mechanical Engineering, University College London, London, United Kingdom; School of Life Science, Beijing Institute of Technology, Beijing, China
| |
Collapse
|
6
|
Dipietro L, Gonzalez-Mego P, Ramos-Estebanez C, Zukowski LH, Mikkilineni R, Rushmore RJ, Wagner T. The evolution of Big Data in neuroscience and neurology. JOURNAL OF BIG DATA 2023; 10:116. [PMID: 37441339 PMCID: PMC10333390 DOI: 10.1186/s40537-023-00751-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Accepted: 05/08/2023] [Indexed: 07/15/2023]
Abstract
Neurological diseases are on the rise worldwide, leading to increased healthcare costs and diminished quality of life in patients. In recent years, Big Data has started to transform the fields of Neuroscience and Neurology. Scientists and clinicians are collaborating in global alliances, combining diverse datasets on a massive scale, and solving complex computational problems that demand the utilization of increasingly powerful computational resources. This Big Data revolution is opening new avenues for developing innovative treatments for neurological diseases. Our paper surveys Big Data's impact on neurological patient care, as exemplified through work done in a comprehensive selection of areas, including Connectomics, Alzheimer's Disease, Stroke, Depression, Parkinson's Disease, Pain, and Addiction (e.g., Opioid Use Disorder). We present an overview of research and the methodologies utilizing Big Data in each area, as well as their current limitations and technical challenges. Despite the potential benefits, the full potential of Big Data in these fields currently remains unrealized. We close with recommendations for future research aimed at optimizing the use of Big Data in Neuroscience and Neurology for improved patient outcomes. Supplementary Information The online version contains supplementary material available at 10.1186/s40537-023-00751-2.
Collapse
Affiliation(s)
| | - Paola Gonzalez-Mego
- Spaulding Rehabilitation/Neuromodulation Lab, Harvard Medical School, Cambridge, MA USA
| | | | | | | | | | - Timothy Wagner
- Highland Instruments, Cambridge, MA USA
- Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA USA
| |
Collapse
|
7
|
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
|
8
|
Tehsin S, Kausar S, Jameel A. Diabetic wounds and artificial intelligence: A mini-review. World J Clin Cases 2023; 11:84-91. [PMID: 36687200 PMCID: PMC9846989 DOI: 10.12998/wjcc.v11.i1.84] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 12/12/2022] [Accepted: 12/23/2022] [Indexed: 01/04/2023] Open
Abstract
Diabetic wound takes longer time to heal due to micro and macro-vascular ailment. This longer healing time can lead to infections and other health complications. Foot ulcers are one of the most common diabetic wounds. These are one of the leading cause of amputations. Medical science is continuously striving for improving quality of human life. A recent trend of amalgamation of knowledge, efforts and technological advancement of medical science experts and artificial intelligence researchers, has made tremendous success in diagnosis, prognosis and treatment of a variety of diseases. Diabetic wounds are no exception, as artificial intelligence experts are putting their research efforts to apply latest technological advancements in the field to help medical care personnel to deal with diabetic wounds in more effective manner. The presented study reviews the diagnostic and treatment research under the umbrella of Artificial Intelligence and computational science, for diabetic wound healing. Framework for diabetic wound assessment using artificial intelligence is presented. Moreover, this review is focused on existing and potential contribution of artificial intelligence to improve medical services for diabetic wound patients. The article also discusses the future directions for the betterment of the field that can lead to facilitate both, clinician and patients.
Collapse
Affiliation(s)
- Samabia Tehsin
- Computer Science, Bahria University, Karachi 75260, Sindh, Pakistan
| | - Sumaira Kausar
- Computer Science, Bahria University, Islamabad 46000, Pakistan
| | - Amina Jameel
- Department of Computer Engineering, Bahria University, Islamabad 46000, Pakistan
| |
Collapse
|
9
|
Woodward AA, Urbanowicz RJ, Naj AC, Moore JH. Genetic heterogeneity: Challenges, impacts, and methods through an associative lens. Genet Epidemiol 2022; 46:555-571. [PMID: 35924480 PMCID: PMC9669229 DOI: 10.1002/gepi.22497] [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: 05/18/2022] [Revised: 07/06/2022] [Accepted: 07/19/2022] [Indexed: 01/07/2023]
Abstract
Genetic heterogeneity describes the occurrence of the same or similar phenotypes through different genetic mechanisms in different individuals. Robustly characterizing and accounting for genetic heterogeneity is crucial to pursuing the goals of precision medicine, for discovering novel disease biomarkers, and for identifying targets for treatments. Failure to account for genetic heterogeneity may lead to missed associations and incorrect inferences. Thus, it is critical to review the impact of genetic heterogeneity on the design and analysis of population level genetic studies, aspects that are often overlooked in the literature. In this review, we first contextualize our approach to genetic heterogeneity by proposing a high-level categorization of heterogeneity into "feature," "outcome," and "associative" heterogeneity, drawing on perspectives from epidemiology and machine learning to illustrate distinctions between them. We highlight the unique nature of genetic heterogeneity as a heterogeneous pattern of association that warrants specific methodological considerations. We then focus on the challenges that preclude effective detection and characterization of genetic heterogeneity across a variety of epidemiological contexts. Finally, we discuss systems heterogeneity as an integrated approach to using genetic and other high-dimensional multi-omic data in complex disease research.
Collapse
Affiliation(s)
- Alexa A. Woodward
- Department of Biostatistics, Epidemiology and InformaticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Ryan J. Urbanowicz
- Department of Computational BiomedicineCedars‐Sinai Medical CenterLos AngelesCaliforniaUSA
| | - Adam C. Naj
- Department of Biostatistics, Epidemiology and InformaticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Jason H. Moore
- Department of Computational BiomedicineCedars‐Sinai Medical CenterLos AngelesCaliforniaUSA
| |
Collapse
|
10
|
Feng Y, Kim M, Yao X, Liu K, Long Q, Shen L. Deep multiview learning to identify imaging-driven subtypes in mild cognitive impairment. BMC Bioinformatics 2022; 23:402. [PMID: 36175853 PMCID: PMC9523890 DOI: 10.1186/s12859-022-04946-x] [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: 09/18/2022] [Accepted: 09/19/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND In Alzheimer's Diseases (AD) research, multimodal imaging analysis can unveil complementary information from multiple imaging modalities and further our understanding of the disease. One application is to discover disease subtypes using unsupervised clustering. However, existing clustering methods are often applied to input features directly, and could suffer from the curse of dimensionality with high-dimensional multimodal data. The purpose of our study is to identify multimodal imaging-driven subtypes in Mild Cognitive Impairment (MCI) participants using a multiview learning framework based on Deep Generalized Canonical Correlation Analysis (DGCCA), to learn shared latent representation with low dimensions from 3 neuroimaging modalities. RESULTS DGCCA applies non-linear transformation to input views using neural networks and is able to learn correlated embeddings with low dimensions that capture more variance than its linear counterpart, generalized CCA (GCCA). We designed experiments to compare DGCCA embeddings with single modality features and GCCA embeddings by generating 2 subtypes from each feature set using unsupervised clustering. In our validation studies, we found that amyloid PET imaging has the most discriminative features compared with structural MRI and FDG PET which DGCCA learns from but not GCCA. DGCCA subtypes show differential measures in 5 cognitive assessments, 6 brain volume measures, and conversion to AD patterns. In addition, DGCCA MCI subtypes confirmed AD genetic markers with strong signals that existing late MCI group did not identify. CONCLUSION Overall, DGCCA is able to learn effective low dimensional embeddings from multimodal data by learning non-linear projections. MCI subtypes generated from DGCCA embeddings are different from existing early and late MCI groups and show most similarity with those identified by amyloid PET features. In our validation studies, DGCCA subtypes show distinct patterns in cognitive measures, brain volumes, and are able to identify AD genetic markers. These findings indicate the promise of the imaging-driven subtypes and their power in revealing disease structures beyond early and late stage MCI.
Collapse
Affiliation(s)
- Yixue Feng
- Imaging Genetics Center, Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of South California, Los Angeles, USA
| | - Mansu Kim
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Xiaohui Yao
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Kefei Liu
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Qi Long
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Li Shen
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - for the Alzheimer’s Disease Neuroimaging Initiative
- Imaging Genetics Center, Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of South California, Los Angeles, USA
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| |
Collapse
|
11
|
Lombardi A, Diacono D, Amoroso N, Biecek P, Monaco A, Bellantuono L, Pantaleo E, Logroscino G, De Blasi R, Tangaro S, Bellotti R. A robust framework to investigate the reliability and stability of explainable artificial intelligence markers of Mild Cognitive Impairment and Alzheimer's Disease. Brain Inform 2022; 9:17. [PMID: 35882684 PMCID: PMC9325942 DOI: 10.1186/s40708-022-00165-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 07/03/2022] [Indexed: 11/11/2022] Open
Abstract
In clinical practice, several standardized neuropsychological tests have been designed to assess and monitor the neurocognitive status of patients with neurodegenerative diseases such as Alzheimer's disease. Important research efforts have been devoted so far to the development of multivariate machine learning models that combine the different test indexes to predict the diagnosis and prognosis of cognitive decline with remarkable results. However, less attention has been devoted to the explainability of these models. In this work, we present a robust framework to (i) perform a threefold classification between healthy control subjects, individuals with cognitive impairment, and subjects with dementia using different cognitive indexes and (ii) analyze the variability of the explainability SHAP values associated with the decisions taken by the predictive models. We demonstrate that the SHAP values can accurately characterize how each index affects a patient's cognitive status. Furthermore, we show that a longitudinal analysis of SHAP values can provide effective information on Alzheimer's disease progression.
Collapse
Affiliation(s)
- Angela Lombardi
- Dipartimento di Fisica, Università degli Studi di Bari Aldo Moro, Bari, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
| | - Domenico Diacono
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
| | - Nicola Amoroso
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
- Dipartimento di Farmacia - Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, Bari, Italy
| | - Przemysław Biecek
- Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warsaw, Poland
| | - Alfonso Monaco
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
| | - Loredana Bellantuono
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
- Dipartimento di Scienze mediche di base, Neuroscienze e Organi di senso, Università degli Studi di Bari Aldo Moro, Bari, Italy
| | - Ester Pantaleo
- Dipartimento di Fisica, Università degli Studi di Bari Aldo Moro, Bari, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
| | - Giancarlo Logroscino
- Dipartimento di Scienze mediche di base, Neuroscienze e Organi di senso, Università degli Studi di Bari Aldo Moro, Bari, Italy
- Pia Fondazione “Card. G. Panico”, Tricase, Italy
| | | | - Sabina Tangaro
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari Aldo Moro, Bari, Italy
| | - Roberto Bellotti
- Dipartimento di Fisica, Università degli Studi di Bari Aldo Moro, Bari, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
| |
Collapse
|
12
|
Veitch DP, Weiner MW, Aisen PS, Beckett LA, DeCarli C, Green RC, Harvey D, Jack CR, Jagust W, Landau SM, Morris JC, Okonkwo O, Perrin RJ, Petersen RC, Rivera‐Mindt M, Saykin AJ, Shaw LM, Toga AW, Tosun D, Trojanowski JQ. Using the Alzheimer's Disease Neuroimaging Initiative to improve early detection, diagnosis, and treatment of Alzheimer's disease. Alzheimers Dement 2022; 18:824-857. [PMID: 34581485 PMCID: PMC9158456 DOI: 10.1002/alz.12422] [Citation(s) in RCA: 53] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 06/08/2021] [Accepted: 06/09/2021] [Indexed: 02/06/2023]
Abstract
INTRODUCTION The Alzheimer's Disease Neuroimaging Initiative (ADNI) has accumulated 15 years of clinical, neuroimaging, cognitive, biofluid biomarker and genetic data, and biofluid samples available to researchers, resulting in more than 3500 publications. This review covers studies from 2018 to 2020. METHODS We identified 1442 publications using ADNI data by conventional search methods and selected impactful studies for inclusion. RESULTS Disease progression studies supported pivotal roles for regional amyloid beta (Aβ) and tau deposition, and identified underlying genetic contributions to Alzheimer's disease (AD). Vascular disease, immune response, inflammation, resilience, and sex modulated disease course. Biologically coherent subgroups were identified at all clinical stages. Practical algorithms and methodological changes improved determination of Aβ status. Plasma Aβ, phosphorylated tau181, and neurofilament light were promising noninvasive biomarkers. Prognostic and diagnostic models were externally validated in ADNI but studies are limited by lack of ethnocultural cohort diversity. DISCUSSION ADNI has had a profound impact in improving clinical trials for AD.
Collapse
Affiliation(s)
- Dallas P. Veitch
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA,Department of Veterans Affairs Medical CenterNorthern California Institute for Research and Education (NCIRE)San FranciscoCaliforniaUSA
| | - Michael W. Weiner
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA,Department of RadiologyUniversity of California, San FranciscoSan FranciscoCaliforniaUSA,Department of MedicineUniversity of California, San FranciscoSan FranciscoCaliforniaUSA,Department of PsychiatryUniversity of California, San FranciscoSan FranciscoCaliforniaUSA,Department of NeurologyUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - Paul S. Aisen
- Alzheimer's Therapeutic Research InstituteUniversity of Southern CaliforniaSan DiegoCaliforniaUSA
| | - Laurel A. Beckett
- Division of Biostatistics, Department of Public Health SciencesUniversity of California DavisDavisCaliforniaUSA
| | - Charles DeCarli
- Department of Neurology and Center for NeuroscienceUniversity of California DavisDavisCaliforniaUSA
| | - Robert C. Green
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Broad Institute, Ariadne Labsand Harvard Medical SchoolBostonMassachusettsUSA
| | - Danielle Harvey
- Division of Biostatistics, Department of Public Health SciencesUniversity of California DavisDavisCaliforniaUSA
| | | | - William Jagust
- Helen Wills Neuroscience InstituteUniversity of California BerkeleyBerkeleyCaliforniaUSA
| | - Susan M. Landau
- Helen Wills Neuroscience InstituteUniversity of California BerkeleyBerkeleyCaliforniaUSA
| | - John C. Morris
- Knight Alzheimer's Disease Research CenterWashington University School of MedicineSaint LouisMissouriUSA
| | - Ozioma Okonkwo
- Wisconsin Alzheimer's Disease Research Center and Department of MedicineUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
| | - Richard J. Perrin
- Knight Alzheimer's Disease Research CenterWashington University School of MedicineSaint LouisMissouriUSA,Department of NeurologyWashington University School of MedicineSaint LouisMissouriUSA,Department of Pathology and ImmunologyWashington University School of MedicineSaint LouisMissouriUSA
| | | | | | - Andrew J. Saykin
- Department of Radiology and Imaging Sciences and Indiana Alzheimer's Disease Research CenterIndiana University School of MedicineIndianapolisIndianaUSA,Department of Medical and Molecular GeneticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Leslie M. Shaw
- Department of Pathology and Laboratory Medicine, Center for Neurodegenerative Research, School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Arthur W. Toga
- Laboratory of Neuroimaging, USC Stevens Institute of Neuroimaging and Informatics, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Duygu Tosun
- Department of RadiologyUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - John Q. Trojanowski
- Department of Pathology and Laboratory Medicine, Center for Neurodegenerative Research, School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | | |
Collapse
|
13
|
Valera-Bermejo JM, De Marco M, Venneri A. Altered Interplay Among Large-Scale Brain Functional Networks Modulates Multi-Domain Anosognosia in Early Alzheimer’s Disease. Front Aging Neurosci 2022; 13:781465. [PMID: 35185517 PMCID: PMC8851037 DOI: 10.3389/fnagi.2021.781465] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 12/29/2021] [Indexed: 12/11/2022] Open
Abstract
Decline in self-awareness is a prevalent symptom in Alzheimer’s disease (AD). Current data suggest that an early breakdown in the brain’s default mode network (DMN) is closely associated with the main symptomatic features in AD patients. In parallel, the integrity of the DMN has been shown to be heavily implicated in retained self-awareness abilities in healthy individuals and AD patients. However, the global contribution to awareness skills of other large-scale networks is still poorly understood. Resting-state functional magnetic resonance imaging (rs-fMRI) scans were acquired and pre-processed from 53 early-stage AD individuals. A group-level independent component analysis was run to isolate and reconstruct four intrinsic connectivity large-scale brain functional networks, namely left and right central executive fronto-parietal networks (FPN), salience network, and anterior and posterior DMN. Hypothesis-driven seed-based connectivity analyses were run to clarify the region-specific underpinnings of multi-domain anosognosia. Multiple regression models were run on large-scale network- and seed-based connectivity maps, including scores of memory, non-memory and total anosognosia obtained via the Measurement of Anosognosia Questionnaire. Memory anosognosia scores were associated with selective lower fronto-temporal connectivity and higher parieto-temporal connectivity. Non-memory anosognosia scores were associated with higher connectivity between the anterior DMN and the cerebellum, between the left medial prefrontal seeds and the contralateral prefrontal cortex, and between the left hippocampal seed and the left insula; lower connectivity was observed between the right prefrontal cortex and the right lingual seed. Lastly, total anosognosia scores were associated with large-scale network alterations, namely reduced left-FPN expression in the left posterior cingulate, reduced right-FPN expression in the left inferior lingual gyrus and adjacent inferior occipital cortex, and increased right-FPN expression in the right anterior cingulate. Seed-based analyses yielded significant connectivity differences only in the connectivity pattern associated with the left hippocampal seed by displaying lower intercommunication with the right prefrontal cortex, but higher connectivity with the left caudate nucleus. These findings support the hypothesis that alterations in functional connectivity of frontal lobe regions involved in executive-related mechanisms represent the neural correlates of domain-specific anosognosia in early AD. Up-regulated connectivity with subcortical structures appears to contribute to changes in the network dynamics interplay and fosters the appearance of anosognosia.
Collapse
Affiliation(s)
| | - Matteo De Marco
- Department of Life Sciences, Brunel University London, Uxbridge, United Kingdom
| | - Annalena Venneri
- Department of Neuroscience, University of Sheffield, Sheffield, United Kingdom
- Department of Life Sciences, Brunel University London, Uxbridge, United Kingdom
- *Correspondence: Annalena Venneri,
| |
Collapse
|
14
|
Jellinger KA. Recent update on the heterogeneity of the Alzheimer’s disease spectrum. J Neural Transm (Vienna) 2021; 129:1-24. [DOI: 10.1007/s00702-021-02449-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 11/25/2021] [Indexed: 02/03/2023]
|
15
|
The Role of Big Data in Aging and Older People’s Health Research: A Systematic Review and Ecological Framework. SUSTAINABILITY 2021. [DOI: 10.3390/su132111587] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Big data has been prominent in studying aging and older people’s health. It has promoted modeling and analyses in biological and geriatric research (like cellular senescence), developed health management platforms, and supported decision-making in public healthcare and social security. However, current studies are still limited within a single subject, rather than flourished as interdisciplinary research in the context of big data. The research perspectives have not changed, nor has big data brought itself out of the role as a modeling tool. When embedding big data as a data product, analysis tool, and resolution service into different spatial, temporal, and organizational scales of aging processes, it would present as a connection, integration, and interaction simultaneously in conducting interdisciplinary research. Therefore, this paper attempts to propose an ecological framework for big data based on aging and older people’s health research. Following the scoping process of PRISMA, 35 studies were reviewed to validate our ecological framework. Although restricted by issues like digital divides and privacy security, we encourage researchers to capture various elements and their interactions in the human-environment system from a macro and dynamic perspective rather than simply pursuing accuracy.
Collapse
|
16
|
Fabrizio C, Termine A, Caltagirone C, Sancesario G. Artificial Intelligence for Alzheimer's Disease: Promise or Challenge? Diagnostics (Basel) 2021; 11:1473. [PMID: 34441407 PMCID: PMC8391160 DOI: 10.3390/diagnostics11081473] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 08/12/2021] [Accepted: 08/12/2021] [Indexed: 01/23/2023] Open
Abstract
Decades of experimental and clinical research have contributed to unraveling many mechanisms in the pathogenesis of Alzheimer's disease (AD), but the puzzle is still incomplete. Although we can suppose that there is no complete set of puzzle pieces, the recent growth of open data-sharing initiatives collecting lifestyle, clinical, and biological data from AD patients has provided a potentially unlimited amount of information about the disease, far exceeding the human ability to make sense of it. Moreover, integrating Big Data from multi-omics studies provides the potential to explore the pathophysiological mechanisms of the entire biological continuum of AD. In this context, Artificial Intelligence (AI) offers a wide variety of methods to analyze large and complex data in order to improve knowledge in the AD field. In this review, we focus on recent findings and future challenges for AI in AD research. In particular, we discuss the use of Computer-Aided Diagnosis tools for AD diagnosis and the use of AI to potentially support clinical practices for the prediction of individual risk of AD conversion as well as patient stratification in order to finally develop effective and personalized therapies.
Collapse
Affiliation(s)
- Carlo Fabrizio
- Laboratory of Experimental and Behavioral Neurophysiology, IRCCS Santa Lucia Foundation, 00143 Rome, Italy; (C.F.); (A.T.)
| | - Andrea Termine
- Laboratory of Experimental and Behavioral Neurophysiology, IRCCS Santa Lucia Foundation, 00143 Rome, Italy; (C.F.); (A.T.)
| | - Carlo Caltagirone
- Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, 00179 Rome, Italy;
| | - Giulia Sancesario
- Biobank, IRCCS Santa Lucia Foundation, 00179 Rome, Italy
- European Center for Brain Research, Experimental Neuroscience, 00143 Rome, Italy
| |
Collapse
|
17
|
Prakash J, Wang V, Quinn RE, Mitchell CS. Unsupervised Machine Learning to Identify Separable Clinical Alzheimer's Disease Sub-Populations. Brain Sci 2021; 11:977. [PMID: 34439596 PMCID: PMC8392842 DOI: 10.3390/brainsci11080977] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 07/10/2021] [Accepted: 07/20/2021] [Indexed: 11/20/2022] Open
Abstract
Heterogeneity among Alzheimer's disease (AD) patients confounds clinical trial patient selection and therapeutic efficacy evaluation. This work defines separable AD clinical sub-populations using unsupervised machine learning. Clustering (t-SNE followed by k-means) of patient features and association rule mining (ARM) was performed on the ADNIMERGE dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Patient sociodemographics, brain imaging, biomarkers, cognitive tests, and medication usage were included for analysis. Four AD clinical sub-populations were identified using between-cluster mean fold changes [cognitive performance, brain volume]: cluster-1 represented least severe disease [+17.3, +13.3]; cluster-0 [-4.6, +3.8] and cluster-3 [+10.8, -4.9] represented mid-severity sub-populations; cluster-2 represented most severe disease [-18.4, -8.4]. ARM assessed frequently occurring pharmacologic substances within the 4 sub-populations. No drug class was associated with the least severe AD (cluster-1), likely due to lesser antecedent disease. Anti-hyperlipidemia drugs associated with cluster-0 (mid-severity, higher volume). Interestingly, antioxidants vitamin C and E associated with cluster-3 (mid-severity, higher cognition). Anti-depressants like Zoloft associated with most severe disease (cluster-2). Vitamin D is protective for AD, but ARM identified significant underutilization across all AD sub-populations. Identification and feature characterization of four distinct AD sub-population "clusters" using standard clinical features enhances future clinical trial selection criteria and cross-study comparative analysis.
Collapse
Affiliation(s)
- Jayant Prakash
- Laboratory for Pathology Dynamics, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA 30332, USA; (J.P.); (V.W.); (R.E.Q.III)
- Department of Computer Science, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Velda Wang
- Laboratory for Pathology Dynamics, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA 30332, USA; (J.P.); (V.W.); (R.E.Q.III)
| | - Robert E. Quinn
- Laboratory for Pathology Dynamics, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA 30332, USA; (J.P.); (V.W.); (R.E.Q.III)
- Department of Computer Science, 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; (J.P.); (V.W.); (R.E.Q.III)
- Center for Machine Learning, Georgia Institute of Technology, Atlanta, GA 30332, USA
| |
Collapse
|
18
|
Simoes JP, Daoud E, Shabbir M, Amanat S, Assouly K, Biswas R, Casolani C, Dode A, Enzler F, Jacquemin L, Joergensen M, Kok T, Liyanage N, Lourenco M, Makani P, Mehdi M, Ramadhani AL, Riha C, Santacruz JL, Schiller A, Schoisswohl S, Trpchevska N, Genitsaridi E. Multidisciplinary Tinnitus Research: Challenges and Future Directions From the Perspective of Early Stage Researchers. Front Aging Neurosci 2021; 13:647285. [PMID: 34177549 PMCID: PMC8225955 DOI: 10.3389/fnagi.2021.647285] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Accepted: 03/19/2021] [Indexed: 12/15/2022] Open
Abstract
Tinnitus can be a burdensome condition on both individual and societal levels. Many aspects of this condition remain elusive, including its underlying mechanisms, ultimately hindering the development of a cure. Interdisciplinary approaches are required to overcome long-established research challenges. This review summarizes current knowledge in various tinnitus-relevant research fields including tinnitus generating mechanisms, heterogeneity, epidemiology, assessment, and treatment development, in an effort to highlight the main challenges and provide suggestions for future research to overcome them. Four common themes across different areas were identified as future research direction: (1) Further establishment of multicenter and multidisciplinary collaborations; (2) Systematic reviews and syntheses of existing knowledge; (3) Standardization of research methods including tinnitus assessment, data acquisition, and data analysis protocols; (4) The design of studies with large sample sizes and the creation of large tinnitus-specific databases that would allow in-depth exploration of tinnitus heterogeneity.
Collapse
Affiliation(s)
- Jorge Piano Simoes
- Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany
| | - Elza Daoud
- Centre National de la Recherche Scientifique, Aix-Marseille University, Marseille, France
| | - Maryam Shabbir
- Hearing Sciences, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Sana Amanat
- Otology & Neurotology Group CTS 495, Department of Genomic Medicine, GENYO - Centre for Genomics and Oncological Research Pfizer/University of Granada/Junta de Andalucía, PTS, Granada, Spain
| | - Kelly Assouly
- Department of Otorhinolaryngology and Head & Neck Surgery, University Medical Center Utrecht, Utrecht, Netherlands
- Department of Clinical and Experimental Neuroscience, University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, Netherlands
- Cochlear Technology Centre, Mechelen, Belgium
| | - Roshni Biswas
- Hearing Sciences, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom
- Laboratory of Lifestyle Epidemiology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Chiara Casolani
- Hearing Systems, Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
- Oticon A/S, Smoerum, Denmark
- Interacoustics Research Unit, Lyngby, Denmark
| | - Albi Dode
- Institute of Databases and Information Systems, Ulm University, Ulm, Germany
| | - Falco Enzler
- Centre National de la Recherche Scientifique, Aix-Marseille University, Marseille, France
| | - Laure Jacquemin
- Department of Otorhinolaryngology Head and Neck Surgery, Antwerp University Hospital, Edegem, Belgium
- Department of Translational Neurosciences, Faculty of Medicine and Health Sciences, Antwerp University, Wilrijk, Belgium
| | - Mie Joergensen
- Hearing Systems, Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
- WS Audiology, Lynge, Denmark
| | - Tori Kok
- Ear Institute, University College London, London, United Kingdom
| | - Nuwan Liyanage
- University of Zurich, Zurich, Switzerland
- Department of Otorhinolaryngology, Head and Neck Surgery, University Hospital Zurich, Zurich, Switzerland
| | - Matheus Lourenco
- Experimental Health Psychology, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
- Health Psychology Research Group, Faculty of Psychology and Educational Sciences, University of Leuven, Leuven, Belgium
| | - Punitkumar Makani
- Department of Otorhinolaryngology, Head and Neck Surgery, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
- Graduate School of Medical Sciences (Research School of Behavioral and Cognitive Neurosciences), University of Groningen, Groningen, Netherlands
| | - Muntazir Mehdi
- Institute of Distributed Systems, Ulm University, Ulm, Germany
| | - Anissa L. Ramadhani
- Radiological Sciences, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Constanze Riha
- Chair of Neuropsychology, Department of Psychology, University of Zurich, Zurich, Switzerland
| | - Jose Lopez Santacruz
- Department of Otorhinolaryngology, Head and Neck Surgery, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
- Graduate School of Medical Sciences (Research School of Behavioral and Cognitive Neurosciences), University of Groningen, Groningen, Netherlands
| | - Axel Schiller
- Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany
| | - Stefan Schoisswohl
- Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany
| | - Natalia Trpchevska
- Department of Physiology and Pharmacology, Experimental Audiology Laboratory, Karolinska Institutet, Stockholm, Sweden
| | - Eleni Genitsaridi
- Hearing Sciences, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom
- Nottingham Biomedical Research Centre, National Institute for Health Research, Nottingham, United Kingdom
| |
Collapse
|
19
|
Rauchmann BS, Ersoezlue E, Stoecklein S, Keeser D, Brosseron F, Buerger K, Dechent P, Dobisch L, Ertl-Wagner B, Fliessbach K, Haynes JD, Heneka MT, Incesoy EI, Janowitz D, Kilimann I, Laske C, Metzger CD, Munk MH, Peters O, Priller J, Ramirez A, Roeske S, Roy N, Scheffler K, Schneider A, Spottke A, Spruth EJ, Teipel S, Tscheuschler M, Vukovich R, Wagner M, Wiltfang J, Yakupov R, Duezel E, Jessen F, Perneczky R. Resting-State Network Alterations Differ between Alzheimer's Disease Atrophy Subtypes. Cereb Cortex 2021; 31:4901-4915. [PMID: 34080613 DOI: 10.1093/cercor/bhab130] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 04/17/2021] [Accepted: 04/20/2021] [Indexed: 11/14/2022] Open
Abstract
Several Alzheimer's disease (AD) atrophy subtypes were identified, but their brain network properties are unclear. We analyzed data from two independent datasets, including 166 participants (103 AD/63 controls) from the DZNE-longitudinal cognitive impairment and dementia study and 151 participants (121 AD/30 controls) from the AD neuroimaging initiative cohorts, aiming to identify differences between AD atrophy subtypes in resting-state functional magnetic resonance imaging intra-network connectivity (INC) and global and nodal network properties. Using a data-driven clustering approach, we identified four AD atrophy subtypes with differences in functional connectivity, accompanied by clinical and biomarker alterations, including a medio-temporal-predominant (S-MT), a limbic-predominant (S-L), a diffuse (S-D), and a mild-atrophy (S-MA) subtype. S-MT and S-D showed INC reduction in the default mode, dorsal attention, visual and limbic network, and a pronounced reduction of "global efficiency" and decrease of the "clustering coefficient" in parietal and temporal lobes. Despite severe atrophy in limbic areas, the S-L exhibited only marginal global network but substantial nodal network failure. S-MA, in contrast, showed limited impairment in clinical and cognitive scores but pronounced global network failure. Our results contribute toward a better understanding of heterogeneity in AD with the detection of distinct differences in functional connectivity networks accompanied by CSF biomarker and cognitive differences in AD subtypes.
Collapse
Affiliation(s)
- Boris-Stephan Rauchmann
- Department of Radiology, University Hospital, LMU, Munich 81377, Germany.,Department of Psychiatry and Psychotherapy, University Hospital, LMU, Munich 80336, Germany
| | - Ersin Ersoezlue
- Department of Psychiatry and Psychotherapy, University Hospital, LMU, Munich 80336, Germany
| | - Sophia Stoecklein
- Department of Radiology, University Hospital, LMU, Munich 81377, Germany
| | - Daniel Keeser
- Department of Radiology, University Hospital, LMU, Munich 81377, Germany.,Department of Psychiatry and Psychotherapy, University Hospital, LMU, Munich 80336, Germany
| | - Frederic Brosseron
- German Center for Neurodegenerative Diseases (DZNE), Bonn 53127, Germany.,Department for Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn 53127, Germany
| | - Katharina Buerger
- German Center for Neurodegenerative Diseases (DZNE), Munich 81377, Germany.,Institute for Stroke and Dementia Research (ISD), University Hospital, LMU, Munich 81377, Germany
| | - Peter Dechent
- MR-Research in Neurology and Psychiatry, Georg-August-University Goettingen, Göttingen 37077, Germany
| | - Laura Dobisch
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg 39120, Germany
| | - Birgit Ertl-Wagner
- Department of Radiology, University Hospital, LMU, Munich 81377, Germany.,Department of Medical Imaging, The Hospital for Sick Children, University of Toronto, Toronto, Ontario M5T 1W7, Canada
| | - Klaus Fliessbach
- German Center for Neurodegenerative Diseases (DZNE), Bonn 53127, Germany.,Department for Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn 53127, Germany
| | - John Dylan Haynes
- Bernstein Center for Computational Neuroscience, Charité, Berlin 10115, Germany
| | - Michael T Heneka
- German Center for Neurodegenerative Diseases (DZNE), Bonn 53127, Germany.,Department for Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn 53127, Germany
| | - Enise I Incesoy
- German Center for Neurodegenerative Diseases (DZNE), Berlin 10117, Germany.,Charité - Universitaetsmedizin Berlin, Institute of Psychiatry and Psychotherapy, Berlin 10117, Germany
| | - Daniel Janowitz
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU, Munich 81377, Germany
| | - Ingo Kilimann
- German Center for Neurodegenerative Diseases (DZNE), Rostock 18147, Germany.,Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock 18147
| | - Christoph Laske
- German Center for Neurodegenerative Diseases (DZNE), Tuebingen 72076, Germany.,Section for Dementia Research, Hertie Institute for Clinical Brain Research and Department of Psychiatry and Psychotherapy, University of Tuebingen, Tuebingen 72076, Germany
| | - Coraline D Metzger
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg 39120, Germany.,Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, Magdeburg 39120, Germany.,Department of Psychiatry and Psychotherapy, Otto-von-Guericke University, Magdeburg 39120, Germany
| | - Matthias H Munk
- German Center for Neurodegenerative Diseases (DZNE), Tuebingen 72076, Germany.,Section for Dementia Research, Hertie Institute for Clinical Brain Research and Department of Psychiatry and Psychotherapy, University of Tuebingen, Tuebingen 72076, Germany
| | - Oliver Peters
- German Center for Neurodegenerative Diseases (DZNE), Berlin 10117, Germany.,Charité - Universitaetsmedizin Berlin, Institute of Psychiatry and Psychotherapy, Berlin 10117, Germany
| | - Josef Priller
- German Center for Neurodegenerative Diseases (DZNE), Berlin 10117, Germany.,Department of Psychiatry and Psychotherapy, Charité, Berlin 10117, Germany
| | - Alfredo Ramirez
- German Center for Neurodegenerative Diseases (DZNE), Bonn 53127, Germany.,Department for Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn 53127, Germany.,Division of Neurogenetics and Molecular Psychiatry, Department of Psychiatry, University of Cologne, Medical Faculty, Cologne 50937, Germany
| | - Sandra Roeske
- German Center for Neurodegenerative Diseases (DZNE), Bonn 53127, Germany
| | - Nina Roy
- German Center for Neurodegenerative Diseases (DZNE), Bonn 53127, Germany
| | - Klaus Scheffler
- Department for Biomedical Magnetic Resonance, University of Tuebingen, Tuebingen 72076, Germany
| | - Anja Schneider
- German Center for Neurodegenerative Diseases (DZNE), Bonn 53127, Germany.,Department for Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn 53127, Germany
| | - Annika Spottke
- German Center for Neurodegenerative Diseases (DZNE), Bonn 53127, Germany.,Department of Neurology, University of Bonn, Bonn 53127, Germany
| | - Eike Jakob Spruth
- German Center for Neurodegenerative Diseases (DZNE), Berlin 10117, Germany.,Department of Psychiatry and Psychotherapy, Charité, Berlin 10117, Germany
| | - Stefan Teipel
- German Center for Neurodegenerative Diseases (DZNE), Rostock 18147, Germany.,Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock 18147
| | - Maike Tscheuschler
- Department of Psychiatry, University of Cologne, Medical Faculty, Cologne 50924, Germany
| | - Ruth Vukovich
- Department of Psychiatry and Psychotherapy, University Medical Center Goettingen, University of Goettingen, Goettingen 37075, Germany
| | - Michael Wagner
- German Center for Neurodegenerative Diseases (DZNE), Bonn 53127, Germany.,Department for Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn 53127, Germany
| | - Jens Wiltfang
- Department of Psychiatry and Psychotherapy, University Medical Center Goettingen, University of Goettingen, Goettingen 37075, Germany.,German Center for Neurodegenerative Diseases (DZNE), Goettingen 37075, Germany.,Neurosciences and Signaling Group, Institute of Biomedicine (iBiMED), Department of Medical Sciences, University of Aveiro, Aveiro 3810-193, Portugal
| | - Renat Yakupov
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg 39120, Germany
| | - Emrah Duezel
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg 39120, Germany.,Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, Magdeburg 39120, Germany
| | - Frank Jessen
- German Center for Neurodegenerative Diseases (DZNE), Bonn 53127, Germany.,Department of Psychiatry, University of Cologne, Medical Faculty, Cologne 50924, Germany.,Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne 50931, Germany
| | - Robert Perneczky
- Department of Psychiatry and Psychotherapy, University Hospital, LMU, Munich 80336, Germany.,German Center for Neurodegenerative Diseases (DZNE), Munich 81377, Germany.,Munich Cluster for Systems Neurology (SyNergy), Munich 81377, Germany.,Ageing Epidemiology (AGE) Research Unit, School of Public Health, Imperial College, London W6 8RP, UK
| | | |
Collapse
|
20
|
Jellinger KA. Pathobiological Subtypes of Alzheimer Disease. Dement Geriatr Cogn Disord 2021; 49:321-333. [PMID: 33429401 DOI: 10.1159/000508625] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Accepted: 05/11/2020] [Indexed: 11/19/2022] Open
Abstract
Alzheimer disease (AD), the most common form of dementia, is a heterogenous disorder with various pathobiological subtypes. In addition to the 4 major subtypes based on the distribution of tau pathology and brain atrophy (typical, limbic predominant, hippocampal sparing, and minimal atrophy [MA]), several other clinical variants showing distinct regional patterns of tau burden have been identified: nonamnestic, corticobasal syndromal, primary progressive aphasia, posterior cortical atrophy, behavioral/dysexecutive, and mild dementia variants. Among the subtypes, differences were found in age at onset, sex distribution, cognitive status, disease duration, APOE genotype, and biomarker levels. The patterns of key network destructions parallel the tau and atrophy patterns of the AD subgroups essentially. Interruption of key networks, in particular the default-mode network that is responsible for cognitive decline, is consistent in hetero-genous AD groups. AD pathology is often associated with co-pathologies: cerebrovascular lesions, Lewy pathology, and TDP-43 proteinopathies. These mixed pathologies essentially influence the clinical picture of AD and may accel-erate disease progression. Unraveling the heterogeneity among the AD spectrum entities is important for opening a window to pathogenic mechanisms affecting the brain and enabling precision medicine approaches as a basis for developing preventive and ultimately successful disease-modifying therapies for AD.
Collapse
|
21
|
La Cognata V, Morello G, Cavallaro S. Omics Data and Their Integrative Analysis to Support Stratified Medicine in Neurodegenerative Diseases. Int J Mol Sci 2021; 22:ijms22094820. [PMID: 34062930 PMCID: PMC8125201 DOI: 10.3390/ijms22094820] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 04/23/2021] [Accepted: 04/29/2021] [Indexed: 12/17/2022] Open
Abstract
Molecular and clinical heterogeneity is increasingly recognized as a common characteristic of neurodegenerative diseases (NDs), such as Alzheimer's disease, Parkinson's disease and amyotrophic lateral sclerosis. This heterogeneity makes difficult the development of early diagnosis and effective treatment approaches, as well as the design and testing of new drugs. As such, the stratification of patients into meaningful disease subgroups, with clinical and biological relevance, may improve disease management and the development of effective treatments. To this end, omics technologies-such as genomics, transcriptomics, proteomics and metabolomics-are contributing to offer a more comprehensive view of molecular pathways underlying the development of NDs, helping to differentiate subtypes of patients based on their specific molecular signatures. In this article, we discuss how omics technologies and their integration have provided new insights into the molecular heterogeneity underlying the most prevalent NDs, aiding to define early diagnosis and progression markers as well as therapeutic targets that can translate into stratified treatment approaches, bringing us closer to the goal of personalized medicine in neurology.
Collapse
|
22
|
Liskova A, Samec M, Koklesova L, Kudela E, Kubatka P, Golubnitschaja O. Mitochondriopathies as a Clue to Systemic Disorders-Analytical Tools and Mitigating Measures in Context of Predictive, Preventive, and Personalized (3P) Medicine. Int J Mol Sci 2021; 22:ijms22042007. [PMID: 33670490 PMCID: PMC7922866 DOI: 10.3390/ijms22042007] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 02/11/2021] [Accepted: 02/14/2021] [Indexed: 02/06/2023] Open
Abstract
The mitochondrial respiratory chain is the main site of reactive oxygen species (ROS) production in the cell. Although mitochondria possess a powerful antioxidant system, an excess of ROS cannot be completely neutralized and cumulative oxidative damage may lead to decreasing mitochondrial efficiency in energy production, as well as an increasing ROS excess, which is known to cause a critical imbalance in antioxidant/oxidant mechanisms and a "vicious circle" in mitochondrial injury. Due to insufficient energy production, chronic exposure to ROS overproduction consequently leads to the oxidative damage of life-important biomolecules, including nucleic acids, proteins, lipids, and amino acids, among others. Different forms of mitochondrial dysfunction (mitochondriopathies) may affect the brain, heart, peripheral nervous and endocrine systems, eyes, ears, gut, and kidney, among other organs. Consequently, mitochondriopathies have been proposed as an attractive diagnostic target to be investigated in any patient with unexplained progressive multisystem disorder. This review article highlights the pathomechanisms of mitochondriopathies, details advanced analytical tools, and suggests predictive approaches, targeted prevention and personalization of medical services as instrumental for the overall management of mitochondriopathy-related cascading pathologies.
Collapse
Affiliation(s)
- Alena Liskova
- Clinic of Obstetrics and Gynecology, Jessenius Faculty of Medicine, Comenius University in Bratislava, 036 01 Martin, Slovakia; (A.L.); (M.S.); (L.K.); (E.K.)
| | - Marek Samec
- Clinic of Obstetrics and Gynecology, Jessenius Faculty of Medicine, Comenius University in Bratislava, 036 01 Martin, Slovakia; (A.L.); (M.S.); (L.K.); (E.K.)
| | - Lenka Koklesova
- Clinic of Obstetrics and Gynecology, Jessenius Faculty of Medicine, Comenius University in Bratislava, 036 01 Martin, Slovakia; (A.L.); (M.S.); (L.K.); (E.K.)
| | - Erik Kudela
- Clinic of Obstetrics and Gynecology, Jessenius Faculty of Medicine, Comenius University in Bratislava, 036 01 Martin, Slovakia; (A.L.); (M.S.); (L.K.); (E.K.)
| | - Peter Kubatka
- Department of Medical Biology, Jessenius Faculty of Medicine, Comenius University in Bratislava, 036 01 Martin, Slovakia
- European Association for Predictive, Preventive and Personalised Medicine, EPMA, 1160 Brussels, Belgium
- Correspondence: (P.K.); (O.G.)
| | - Olga Golubnitschaja
- European Association for Predictive, Preventive and Personalised Medicine, EPMA, 1160 Brussels, Belgium
- Predictive, Preventive and Personalised (3P) Medicine, Department of Radiation Oncology, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, 53127 Bonn, Germany
- Correspondence: (P.K.); (O.G.)
| |
Collapse
|
23
|
Multi-domain potential biomarkers for post-traumatic stress disorder (PTSD) severity in recent trauma survivors. Transl Psychiatry 2020; 10:208. [PMID: 32594097 PMCID: PMC7320966 DOI: 10.1038/s41398-020-00898-z] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/26/2019] [Revised: 05/28/2020] [Accepted: 06/02/2020] [Indexed: 12/02/2022] Open
Abstract
Contemporary symptom-based diagnosis of post-traumatic stress disorder (PTSD) largely overlooks related neurobehavioral mechanisms and relies entirely on subjective interpersonal reporting. Previous studies associating biomarkers with PTSD have mostly used symptom-based diagnosis as the main outcome measure, disregarding the wide variability and richness of PTSD phenotypical features. Here, we aimed to computationally derive potential biomarkers that could efficiently differentiate PTSD subtypes among recent trauma survivors. A three-staged semi-unsupervised method ("3C") was used to firstly categorize individuals by current PTSD symptom severity, then derive clusters based on clinical features related to PTSD (e.g. anxiety and depression), and finally to classify participants' cluster membership using objective multi-domain features. A total of 256 features were extracted from psychometrics, cognitive functioning, and both structural and functional MRI data, obtained from 101 adult civilians (age = 34.80 ± 11.95; 51 females) evaluated within 1 month of trauma exposure. The features that best differentiated cluster membership were assessed by importance analysis, classification tree, and ANOVA. Results revealed that entorhinal and rostral anterior cingulate cortices volumes (structural MRI domain), in-task amygdala's functional connectivity with the insula and thalamus (functional MRI domain), executive function and cognitive flexibility (cognitive testing domain) best differentiated between two clusters associated with PTSD severity. Cross-validation established the results' robustness and consistency within this sample. The neural and cognitive potential biomarkers revealed by the 3C analytics offer objective classifiers of post-traumatic morbidity shortly following trauma. They also map onto previously documented neurobehavioral mechanisms associated with PTSD and demonstrate the usefulness of standardized and objective measurements as differentiating clinical sub-classes shortly after trauma.
Collapse
|