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Souza R, Wilms M, Camacho M, Pike GB, Camicioli R, Monchi O, Forkert ND. Image-encoded biological and non-biological variables may be used as shortcuts in deep learning models trained on multisite neuroimaging data. J Am Med Inform Assoc 2023; 30:1925-1933. [PMID: 37669158 PMCID: PMC10654841 DOI: 10.1093/jamia/ocad171] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 08/07/2023] [Accepted: 08/15/2023] [Indexed: 09/07/2023] Open
Abstract
OBJECTIVE This work investigates if deep learning (DL) models can classify originating site locations directly from magnetic resonance imaging (MRI) scans with and without correction for intensity differences. MATERIAL AND METHODS A large database of 1880 T1-weighted MRI scans collected across 41 sites originally for Parkinson's disease (PD) classification was used to classify sites in this study. Forty-six percent of the datasets are from PD patients, while 54% are from healthy participants. After preprocessing the T1-weighted scans, 2 additional data types were generated: intensity-harmonized T1-weighted scans and log-Jacobian deformation maps resulting from nonlinear atlas registration. Corresponding DL models were trained to classify sites for each data type. Additionally, logistic regression models were used to investigate the contribution of biological (age, sex, disease status) and non-biological (scanner type) variables to the models' decision. RESULTS A comparison of the 3 different types of data revealed that DL models trained using T1-weighted and intensity-harmonized T1-weighted scans can classify sites with an accuracy of 85%, while the model using log-Jacobian deformation maps achieved a site classification accuracy of 54%. Disease status and scanner type were found to be significant confounders. DISCUSSION Our results demonstrate that MRI scans encode relevant site-specific information that models could use as shortcuts that cannot be removed using simple intensity harmonization methods. CONCLUSION The ability of DL models to exploit site-specific biases as shortcuts raises concerns about their reliability, generalization, and deployability in clinical settings.
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Affiliation(s)
- Raissa Souza
- Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 4N1, Canada
- Biomedical Engineering Graduate Program, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Matthias Wilms
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 4N1, Canada
- Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB T2N 4N1, Canada
- Department of Pediatrics, University of Calgary, Calgary, AB T2N 4N1, Canada
- Department of Community Health Sciences, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Milton Camacho
- Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 4N1, Canada
- Biomedical Engineering Graduate Program, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - G Bruce Pike
- Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Richard Camicioli
- Department of Medicine (Neurology), Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB T6G 2E1, Canada
| | - Oury Monchi
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 4N1, Canada
- Department of Radiology, Radio-Oncology and Nuclear Medicine, Université de Montréal, Montréal, QC H3C 3J7, Canada
- Centre de Recherche, Institut Universitaire de Gériatrie de Montréal, Montréal, QC H3W 1W4, Canada
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Nils D Forkert
- Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 4N1, Canada
- Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB T2N 4N1, Canada
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada
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Sharif A, Amjad F, Arslan SA, Ahmad A. Translation and validation of urdu version short form-mcgill pain questionnaire-2. BMC Sports Sci Med Rehabil 2023; 15:102. [PMID: 37580806 PMCID: PMC10424347 DOI: 10.1186/s13102-023-00715-2] [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: 06/20/2022] [Accepted: 08/04/2023] [Indexed: 08/16/2023]
Abstract
BACKGROUND Low back pain is one of the most common complaints affecting many individuals. The McGill Pain Questionnaire is used in various clinical settings to assess different types of pain and one of the most extensively used outcomes measures for pain in the world. The purpose of this study was to translate and validate the original English version of the SF-MPQ-2 into Urdu (SF-MPQ-2-U). METHODS For this study, Mapi Research Trust protocols were followed for the forward and backward translation. Test-retest reliability was used to assess the reliability. Cronbach's alpha and Omega was used to determine internal consistency. Pearson's correlation was used to evaluate convergent validity. Confirmatory factor analysis was also conducted. RESULTS The Cronbach's alpha for SF-MPQ-2-U was 0.73 to 0.79, indicating acceptable internal consistency. Omega score for the SF-MPQ-U were 0.918. The ICC varied from 0.799 to 0.878 for domains of SF-MPQ-2-U. The CFA of the SF-MPQ-2-U met model fit indices with GFI and NFI > 0.90. The inter-scale correlation between baseline and re-test data was from 0.63 to 0.71, indicating a positive and strong correlation. The SF-MPQ-2-U and ODI-U had a baseline correlation of 0.547. The correlation of SF-MPQ-2-U & VAS at baseline data was 0.558. Pearson's correlation between subscales was r = 0.253 with p 0.01, which was statistically significant. CONCLUSION The SF-MPQ-2-U is considered to have good convergent validity at inter scale and between two scale levels. Reliability was checked by test-retest reliability, Internal consistency was checked using Cronbach's alpha and Omega that showed good internal consistency for measuring different types of pain in patients with low back pain who speak Urdu. To make the questionnaire more valid and reliable, it is recommended for the researchers to do in-depth research on larger sample size.
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Affiliation(s)
- Amna Sharif
- University Institute of Physical Therapy, University of Lahore, Lahore, Pakistan
| | | | | | - Ashfaq Ahmad
- University Institute of Physical Therapy, University of Lahore, Lahore, Pakistan
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Sim JE, Song HN, Choi JU, Lee JE, Baek IY, Cho HH, Kim JH, Chung JW, Kim GM, Park HJ, Bang OY, Seo WK. The effect of intensive statin therapy in non-symptomatic intracranial arteries: The STAMINA-MRI sub-study. Front Neurol 2023; 14:1069502. [PMID: 37056360 PMCID: PMC10088516 DOI: 10.3389/fneur.2023.1069502] [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: 10/14/2022] [Accepted: 03/02/2023] [Indexed: 03/30/2023] Open
Abstract
Background and aims Pleiotropic effects of statins result in the stabilization of symptomatic intracranial arterial plaque. However, little is known about the effect of statins in non-symptomatic cerebral arteries. We hypothesized that intensive statin therapy could produce a change in the non-symptomatic cerebral arteries. Methods This is a sub-study of a prospective observational study under the title of "Intensive Statin Treatment in Acute Ischemic Stroke Patients with Intracranial Atherosclerosis: a High-Resolution Magnetic Resonance Imaging (HR-MRI) study." Patients with statin-naive acute ischemic stroke who had symptomatic intracranial artery stenosis (above 50%) were recruited for this study. HR-MRI was performed to assess the patients' cerebral arterial status before and 6 months after the statin therapy. To demonstrate the effect of statins in the non-symptomatic segment of intracranial cerebral arteries, we excluded symptomatic segments from the data to be analyzed. We compared the morphological changes using cerebrovascular morphometry. Results A total of 54 patients (mean age: 62.9 ± 14.4 years, 59.3% women) were included in this study. Intensive statin therapy produced significant morphological changes of overall cerebral arteries. Among the morphological features, the arterial luminal area showed the highest number of significant changes with a range from 5.7 and 6.7%. Systolic blood pressure (SBP) was an independent factor associated with relative changes in posterior circulation bed maximal diameter percentage change (beta -0.21, 95% confidence interval -0.36 to -0.07, p = 0.005). Conclusion Intensive statin therapy produced a favorable morphological change in cerebral arteries of not only the target arterial segment but also non-symptomatic arterial segments. The change in cerebral arterial luminal diameter was influenced by the baseline SBP and was dependent on the topographic distribution of the cerebral arteries.Clinical Trial Registration: ClinicalTrials.gov, identifier NCT02458755.
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Affiliation(s)
- Jae Eun Sim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Ha-Na Song
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Jong-Un Choi
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Ji-Eun Lee
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - In Young Baek
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Hwan-Ho Cho
- Department of Electronic Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Republic of Korea
| | - Jong-Hoon Kim
- Department of Electronic Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Republic of Korea
| | - Jong-Won Chung
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Gyeong-Moon Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Hyun-Jin Park
- School of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon, Republic of Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea
| | - Oh Young Bang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Woo-Keun Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
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Mouches P, Wilms M, Aulakh A, Langner S, Forkert ND. Multimodal brain age prediction fusing morphometric and imaging data and association with cardiovascular risk factors. Front Neurol 2022; 13:979774. [PMID: 36588902 PMCID: PMC9794870 DOI: 10.3389/fneur.2022.979774] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 11/16/2022] [Indexed: 12/15/2022] Open
Abstract
Introduction The difference between the chronological and biological brain age, called the brain age gap (BAG), has been identified as a promising biomarker to detect deviation from normal brain aging and to indicate the presence of neurodegenerative diseases. Moreover, the BAG has been shown to encode biological information about general health, which can be measured through cardiovascular risk factors. Current approaches for biological brain age estimation, and therefore BAG estimation, either depend on hand-crafted, morphological measurements extracted from brain magnetic resonance imaging (MRI) or on direct analysis of brain MRI images. The former can be processed with traditional machine learning models while the latter is commonly processed with convolutional neural networks (CNNs). Using a multimodal setting, this study aims to compare both approaches in terms of biological brain age prediction accuracy and biological information captured in the BAG. Methods T1-weighted MRI, containing brain tissue information, and magnetic resonance angiography (MRA), providing information about brain arteries, from 1,658 predominantly healthy adults were used. The volumes, surface areas, and cortical thickness of brain structures were extracted from the T1-weighted MRI data, while artery density and thickness within the major blood flow territories and thickness of the major arteries were extracted from MRA data. Independent multilayer perceptron and CNN models were trained to estimate the brain age from the hand-crafted features and image data, respectively. Next, both approaches were fused to assess the benefits of combining image data and hand-crafted features for brain age prediction. Results The combined model achieved a mean absolute error of 4 years between the chronological and predicted biological brain age. Among the independent models, the lowest mean absolute error was observed for the CNN using T1-weighted MRI data (4.2 years). When evaluating the BAGs obtained using the different approaches and imaging modalities, diverging associations between cardiovascular risk factors were found. For example, BAGs obtained from the CNN models showed an association with systolic blood pressure, while BAGs obtained from hand-crafted measurements showed greater associations with obesity markers. Discussion In conclusion, the use of more diverse sources of data can improve brain age estimation modeling and capture more diverse biological deviations from normal aging.
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Affiliation(s)
- Pauline Mouches
- Biomedical Engineering Program, University of Calgary, Calgary, AB, Canada,Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada,Department of Radiology, University of Calgary, Calgary, AB, Canada,*Correspondence: Pauline Mouches
| | - Matthias Wilms
- Department of Paediatrics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada,Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada,Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada
| | - Agampreet Aulakh
- Schulich School of Engineering, University of Calgary, Calgary, AB, Canada
| | - Sönke Langner
- Institute for Diagnostic Radiology and Neuroradiology, Rostock University Medical Center, Rostock, Germany
| | - Nils D. Forkert
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada,Department of Radiology, University of Calgary, Calgary, AB, Canada,Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada
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Zhang B, Yang Z, Li J, Wang B, Shi H, Wang H, Li Y. Modification of cerebrovascular morphologies during different stages of life. J Cereb Blood Flow Metab 2022; 42:2151-2160. [PMID: 35775187 PMCID: PMC9580171 DOI: 10.1177/0271678x221111609] [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] [Indexed: 11/16/2022]
Abstract
To expand previous understanding of age-related vascular changes, we examined the association between aging and characteristics of cerebral arteries among 1133 participants aged 35 to 75 years recruited from Shanghai, China. Characteristics of the cerebral vessels including arterial branch density, mean radius, and mean tortuosity were quantified using MR angiography. The radius, tortuosity, and length of the basilar artery (BA) and the M1 segment of middle cerebral artery (MCA) were also accessed. Linear regression model was used to examine the association between age and vasculature features. The sample was divided into four subgroups by age and the association was analyzed in each subgroup. Age was found to be a significant predictor for cerebrovascular modifications after adjusting for vascular risk factors. Further analysis in subgroup revealed that the associations were due to the predominate effect of the vascular modifications happened during the younger years (35-54 years). The radius of either BA or MCA was associated with aging only in subjects aged 45-54 years. In conclusion, rapid alterations in all three morphological features assessed have been noticed to be associated with aging in the 45-54 subgroup, suggesting the potential importance of the 5th decade for early preservation method for vascular aging.
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Affiliation(s)
- Boyu Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Ministry of Education, Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Shanghai, China
| | - Zidong Yang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Ministry of Education, Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Shanghai, China
| | - Jing Li
- Department of Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Bei Wang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Ministry of Education, Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Shanghai, China
| | - Huazheng Shi
- Shanghai Universal Medical Imaging Diagnostic Center, Shanghai, China
| | - He Wang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Ministry of Education, Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Shanghai, China.,Human Phenome Institute, Fudan University, Shanghai, China
| | - Yuehua Li
- Department of Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Mouches P, Wilms M, Rajashekar D, Langner S, Forkert ND. Multimodal biological brain age prediction using magnetic resonance imaging and angiography with the identification of predictive regions. Hum Brain Mapp 2022; 43:2554-2566. [PMID: 35138012 PMCID: PMC9057090 DOI: 10.1002/hbm.25805] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 01/24/2022] [Accepted: 01/25/2022] [Indexed: 02/06/2023] Open
Abstract
Biological brain age predicted using machine learning models based on high-resolution imaging data has been suggested as a potential biomarker for neurological and cerebrovascular diseases. In this work, we aimed to develop deep learning models to predict the biological brain age using structural magnetic resonance imaging and angiography datasets from a large database of 2074 adults (21-81 years). Since different imaging modalities can provide complementary information, combining them might allow to identify more complex aging patterns, with angiography data, for instance, showing vascular aging effects complementary to the atrophic brain tissue changes seen in T1-weighted MRI sequences. We used saliency maps to investigate the contribution of cortical, subcortical, and arterial structures to the prediction. Our results show that combining T1-weighted and angiography MR data led to a significantly improved brain age prediction accuracy, with a mean absolute error of 3.85 years comparing the predicted and chronological age. The most predictive brain regions included the lateral sulcus, the fourth ventricle, and the amygdala, while the brain arteries contributing the most to the prediction included the basilar artery, the middle cerebral artery M2 segments, and the left posterior cerebral artery. Our study proposes a framework for brain age prediction using multimodal imaging, which gives accurate predictions and allows identifying the most predictive regions for this task, which can serve as a surrogate for the brain regions that are most affected by aging.
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Affiliation(s)
- Pauline Mouches
- Department of Radiology, University of Calgary, Calgary, Alberta, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada.,Biomedical Engineering Program, University of Calgary, Calgary, Alberta, Canada
| | - Matthias Wilms
- Department of Radiology, University of Calgary, Calgary, Alberta, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada.,Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada
| | - Deepthi Rajashekar
- Department of Radiology, University of Calgary, Calgary, Alberta, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada.,Biomedical Engineering Program, University of Calgary, Calgary, Alberta, Canada
| | - Sönke Langner
- Institute for Diagnostic Radiology and Neuroradiology, Rostock University Medical Center, Rostock, Germany
| | - Nils D Forkert
- Department of Radiology, University of Calgary, Calgary, Alberta, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada.,Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada
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Abstract
In India, increasing lifespan and decreasing fertility rates have resulted in a growing number of older persons. By 2050, people over 60 years of age are predicted to constitute 19.1% of the total population. This ageing of the population is expected to be accompanied by a dramatic increase in the prevalence of dementia. The aetiopathogenesis of dementia has been the subject of a number of prospective longitudinal studies in North America and Europe; however, the findings from these studies cannot simply be translated to the Indian population. The population of India is extremely diverse in terms of socio-economic, cultural, linguistic, geographical, lifestyle-related and genetic factors. Indeed, preliminary data from recently initiated longitudinal studies in India indicate that the prevalence of vascular and metabolic risk factors, as well as white matter hyperintensities, differs between urban and rural cohorts. More information on the complex role of vascular risk factors, gender and genetic influences on dementia prevalence and progression in Indian populations is urgently needed. Low-cost, culturally appropriate and scalable interventions need to be developed expeditiously and implemented through public health measures to reduce the growing burden of dementia. Here, we review the literature concerning dementia epidemiology and risk factors in the Indian population and discuss the future work that needs to be performed to put in place public health interventions to mitigate the burden of dementia.
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