Search

Your search keyword '"Dadar, Mahsa"' showing total 495 results
Start Over Author "Dadar, Mahsa"
495 results on '"Dadar, Mahsa"'

5. Network structure and transcriptomic vulnerability shape atrophy in frontotemporal dementia

Author

Shafiei, Golia, Bazinet, Vincent, Dadar, Mahsa, Manera, Ana L, Collins, D Louis, Dagher, Alain, Borroni, Barbara, Sanchez-Valle, Raquel, Moreno, Fermin, Laforce, Robert, Graff, Caroline, Synofzik, Matthis, Galimberti, Daniela, Rowe, James B, Masellis, Mario, Tartaglia, Maria Carmela, Finger, Elizabeth, Vandenberghe, Rik, de Mendonça, Alexandre, Tagliavini, Fabrizio, Santana, Isabel, Butler, Chris, Gerhard, Alex, Danek, Adrian, Levin, Johannes, Otto, Markus, Sorbi, Sandro, Jiskoot, Lize C, Seelaar, Harro, van Swieten, John C, Rohrer, Jonathan D, Misic, Bratislav, Ducharme, Simon, Initiative, Frontotemporal Lobar Degeneration Neuroimaging, Rosen, Howard, Dickerson, Bradford C, Domoto-Reilly, Kimoko, Knopman, David, Boeve, Bradley F, Boxer, Adam L, Kornak, John, Miller, Bruce L, Seeley, William W, Gorno-Tempini, Maria-Luisa, McGinnis, Scott, Mandelli, Maria Luisa, Initiative, GENetic Frontotemporal dementia, Esteve, Aitana Sogorb, Nelson, Annabel, Bouzigues, Arabella, Heller, Carolin, Greaves, Caroline V, Cash, David, Thomas, David L, Todd, Emily, Benotmane, Hanya, Zetterberg, Henrik, Swift, Imogen J, Nicholas, Jennifer, Samra, Kiran, Russell, Lucy L, Bocchetta, Martina, Shafei, Rachelle, Convery, Rhian S, Timberlake, Carolyn, Cope, Thomas, Rittman, Timothy, Benussi, Alberto, Premi, Enrico, Gasparotti, Roberto, Archetti, Silvana, Gazzina, Stefano, Cantoni, Valentina, Arighi, Andrea, Fenoglio, Chiara, Scarpini, Elio, Fumagalli, Giorgio, Borracci, Vittoria, Rossi, Giacomina, Giaccone, Giorgio, Di Fede, Giuseppe, Caroppo, Paola, Tiraboschi, Pietro, Prioni, Sara, Redaelli, Veronica, Tang-Wai, David, Rogaeva, Ekaterina, Castelo-Branco, Miguel, Freedman, Morris, Keren, Ron, Black, Sandra, Mitchell, Sara, Shoesmith, Christen, Bartha, Robart, Rademakers, Rosa, van der Ende, Emma, Poos, Jackie, Papma, Janne M, Giannini, Lucia, and van Minkelen, Rick

Subjects
Biomedical and Clinical Sciences, Health Sciences, Psychology, Acquired Cognitive Impairment, Dementia, Aging, Neurodegenerative, Frontotemporal Dementia (FTD), Neurosciences, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Brain Disorders, Alzheimer's Disease, Aetiology, 2.1 Biological and endogenous factors, Neurological, Humans, Frontotemporal Dementia, Transcriptome, Brain, Pick Disease of the Brain, Atrophy, Connectome, Magnetic Resonance Imaging, Neuropsychological Tests, Frontotemporal Lobar Degeneration Neuroimaging Initiative, GENetic Frontotemporal dementia Initiative, connectome, disease epicentre, frontotemporal dementia, gene expression, network spreading, Medical and Health Sciences, Psychology and Cognitive Sciences, Neurology & Neurosurgery, Biomedical and clinical sciences, Health sciences
Abstract

Connections among brain regions allow pathological perturbations to spread from a single source region to multiple regions. Patterns of neurodegeneration in multiple diseases, including behavioural variant of frontotemporal dementia (bvFTD), resemble the large-scale functional systems, but how bvFTD-related atrophy patterns relate to structural network organization remains unknown. Here we investigate whether neurodegeneration patterns in sporadic and genetic bvFTD are conditioned by connectome architecture. Regional atrophy patterns were estimated in both genetic bvFTD (75 patients, 247 controls) and sporadic bvFTD (70 patients, 123 controls). First, we identified distributed atrophy patterns in bvFTD, mainly targeting areas associated with the limbic intrinsic network and insular cytoarchitectonic class. Regional atrophy was significantly correlated with atrophy of structurally- and functionally-connected neighbours, demonstrating that network structure shapes atrophy patterns. The anterior insula was identified as the predominant group epicentre of brain atrophy using data-driven and simulation-based methods, with some secondary regions in frontal ventromedial and antero-medial temporal areas. We found that FTD-related genes, namely C9orf72 and TARDBP, confer local transcriptomic vulnerability to the disease, modulating the propagation of pathology through the connectome. Collectively, our results demonstrate that atrophy patterns in sporadic and genetic bvFTD are jointly shaped by global connectome architecture and local transcriptomic vulnerability, providing an explanation as to how heterogenous pathological entities can lead to the same clinical syndrome.

Published
2023

6. Impact of Weight Loss on Brain Age: Improved Brain Health Following Bariatric Surgery

Author

Zeighami, Yashar, Dadar, Mahsa, Daoust, Justine, Pelletier, Melissa, Biertho, Laurent, Bouvet-Bouchard, Leonie, Fulton, Stephanie, Tchernof, Andre, Dagher, Alain, Richard, Denis, Evans, Alan, and Michaud, Andreanne

Subjects
Quantitative Biology - Quantitative Methods
Abstract

Overweight and obese individuals tend to have increased brain age, reflecting poorer brain health likely due to grey and white matter atrophy related to obesity. However, it is unclear if older brain age associated with obesity can be reversed following weight loss and cardiometabolic health improvement. The aim of this study was to assess the impact of weight loss and cardiometabolic improvement following bariatric surgery on brain health, as measured by change in brain age estimated based on voxel-based morphometry (VBM). We used three datasets: 1) CamCAN to train the brain age prediction model, 2) Human Connectome Project to investigate whether individuals with obesity have greater brain age than individuals with normal weight, and 3) pre-surgery, as well as 4, 12, and 24 month post-surgery data from participants (n=87) who underwent a bariatric surgery to investigate whether weight loss and cardiometabolic improvement as a result of bariatric surgery lowers the brain age. Our results from the HCP dataset showed a higher brain age for individuals with obesity compared to individuals with normal weight (p<0.0001). We also found significant improvement in brain health, indicated by a decrease of 2.9 and 5.6 years in adjusted delta age at 12 and 24 months following bariatric surgery compared to baseline (p<0.0005). While the overall effect seemed to be driven by a global change across all brain regions and not from a specific region, our exploratory analysis showed lower delta age in certain brain regions at 24 months. This reduced age was also associated with post-surgery improvements in BMI, systolic/diastolic blood pressure, and HOMA-IR (p<0.05). In conclusion, these results suggest that obesity-related brain health abnormalities might be reversed by bariatric surgery-induced weight loss and widespread improvements in cardiometabolic alterations.

Published
2021

8. The neurophysiological brain-fingerprint of Parkinson’s disease

Author

Breitner, John, Poirier, Judes, Baillet, Sylvain, Bellec, Pierre, Bohbot, Véronique, Chakravarty, Mallar, Collins, Louis, Etienne, Pierre, Evans, Alan, Gauthier, Serge, Hoge, Rick, Ituria-Medina, Yasser, Multhaup, Gerhard, Münter, Lisa-Marie, Rajah, Natasha, Rosa-Neto, Pedro, Soucy, Jean-Paul, Vachon-Presseau, Etienne, Villeneuve, Sylvia, Amouyel, Philippe, Appleby, Melissa, Ashton, Nicholas, Auld, Daniel, Ayranci, Gülebru, Bedetti, Christophe, Beland, Marie-Lise, Blennow, Kaj, Westman, Ann Brinkmalm, Cuello, Claudio, Dadar, Mahsa, Daoust, Leslie-Ann, Das, Samir, Dauar-Tedeschi, Marina, De Beaumont, Louis, Dea, Doris, Descoteaux, Maxime, Dufour, Marianne, Farzin, Sarah, Ferdinand, Fabiola, Fonov, Vladimir, Gonneaud, Julie, Kat, Justin, Kazazian, Christina, Labonté, Anne, Lafaille-Magnan, Marie-Elyse, Lalancette, Marc, Lambert, Jean-Charles, Leoutsakos, Jeannie-Marie, Mahar, Laura, Mathieu, Axel, McSweeney, Melissa, Meyer, Pierre-François, Miron, Justin, Near, Jamie, NewboldFox, Holly, Nilsson, Nathalie, Orban, Pierre, Picard, Cynthia, Binette, Alexa Pichet, Poline, Jean-Baptiste, Rabipour, Sheida, Salaciak, Alyssa, Settimi, Matthew, Subramaniapillai, Sivaniya, Tam, Angela, Tardif, Christine, Théroux, Louise, Tremblay-Mercier, Jennifer, Tullo, Stephanie, Ulku, Irem, Vallée, Isabelle, Zetterberg, Henrik, Nair, Vasavan, Pruessner, Jens, Aisen, Paul, Anthal, Elena, Barkun, Alan, Beaudry, Thomas, Benbouhoud, Fatiha, Brandt, Jason, Carmo, Leopoldina, Carrier, Charles Edouard, Cheewakriengkrai, Laksanun, Courcot, Blandine, Couture, Doris, Craft, Suzanne, Dansereau, Christian, Debacker, Clément, Desautels, René, Dubuc, Sylvie, Duclair, Guerda, Eisenberg, Mark, El-Khoury, Rana, Faubert, Anne-Marie, Fontaine, David, Frappier, Josée, Frenette, Joanne, Gagné, Guylaine, Gervais, Valérie, Giles, Renuka, Gordon, Renee, Jack, Clifford, Jutras, Benoit, Khachaturian, Zaven, Knopman, David, Kostopoulos, Penelope, Lapalme, Félix, Lee, Tanya, Lepage, Claude, Leppert, Illana, Madjar, Cécile, Maillet, David, Maltais, Jean-Robert, Mathotaarachchi, Sulantha, Mayrand, Ginette, Michaud, Diane, Montine, Thomas, Morris, John, Pagé, Véronique, Pascoal, Tharick, Peillieux, Sandra, Petkova, Mirela, Pogossova, Galina, Rioux, Pierre, Sager, Mark, Saint-Fort, Eunice Farah, Savard, Mélissa, Sperling, Reisa, Tabrizi, Shirin, Tariot, Pierre, Teigner, Eduard, Thomas, Ronald, Toussaint, Paule-Joanne, Tuwaig, Miranda, Venugopalan, Vinod, Verfaillie, Sander, Vogel, Jacob, Wan, Karen, Wang, Seqian, Yu, Elsa, Beaulieu-Boire, Isabelle, Blanchet, Pierre, Bogard, Sarah, Bouchard, Manon, Chouinard, Sylvain, Cicchetti, Francesca, Cloutier, Martin, Dagher, Alain, Degroot, Clotilde, Desautels, Alex, Dion, Marie Hélène, Drouin-Ouellet, Janelle, Dufresne, Anne-Marie, Dupré, Nicolas, Duquette, Antoine, Durcan, Thomas, Fellows, Lesley K., Fon, Edward, Gagnon, Jean-François, Gan-Or, Ziv, Genge, Angela, Jodoin, Nicolas, Karamchandani, Jason, Lafontaine, Anne-Louise, Langlois, Mélanie, Leveille, Etienne, Lévesque, Martin, Melmed, Calvin, Monchi, Oury, Montplaisir, Jacques, Panisset, Michel, Parent, Martin, Pham-An, Minh-Thy, Postuma, Ronald, Pourcher, Emmanuelle, Rao, Trisha, Rivest, Jean, Rouleau, Guy, Sharp, Madeleine, Soland, Valérie, Sidel, Michael, Wing Sun, Sonia Lai, Thiel, Alexander, Vitali, Paolo, da Silva Castanheira, Jason, Wiesman, Alex I., Hansen, Justine Y., and Misic, Bratislav

Published
2024
Full Text
View/download PDF

10. Ventricle features as reliable differentiators between the bvFTD and other dementias

Author

Manera, Ana L., Dadar, Mahsa, Collins, D. Louis, and Ducharme, Simon

Subjects
Quantitative Biology - Neurons and Cognition
Abstract

INTRODUCTION: Lateral ventricles are reliable and sensitive indicators of brain atrophy and disease progression in behavioral variant frontotemporal dementia (bvFTD). We aimed to investigate whether an automated tool using ventricular features could improve diagnostic accuracy in bvFTD across neurodegenerative diseases. METHODS: Using 678 subjects (2750 timepoints), differences in ventricular features were assessed between bvFTD, normal controls and other dementia cohorts. RESULTS: Ventricular antero-posterior ratio (APR) was the only feature that was significantly different and increased faster in bvFTD compared to all other cohorts. We achieved a 10-fold cross-validation accuracy of 80% (77% sensitivity, 82% specificity) in differentiating bvFTD from all other cohorts with ventricular features, and 76% accuracy using only the single APR feature. DISCUSSION: APR could be a useful and easy-to-implement feature to aid bvFTD diagnosis. We have made our ventricle feature estimation and bvFTD diagnostic tool publicly available, allowing application of our model in other studies.

Published
2021

11. Automatic prediction of cognitive and functional decline can significantly decrease the number of subjects required for clinical trials in early Alzheimer's disease

Author

Shafiee, Neda, Dadar, Mahsa, Ducharme, Simon, and Collins, D. Louis

Subjects
Quantitative Biology - Quantitative Methods
Abstract

INTRODUCTION: Heterogeneity in the progression of Alzheimer's disease makes it challenging to predict the rate of cognitive and functional decline for individual patients. Tools for short-term prediction could help enrich clinical trial designs and focus prevention strategies on the most at-risk patients. METHOD: We built a prognostic model using baseline cognitive scores and MRI-based features to determine which subjects with mild cognitive impairment remained stable and which functionally declined (measured by a two-point increase in CDR-SB) over 2 and 3-year follow-up periods, periods typical of the length of clinical trials. RESULTS: Combining both sets of features yields 77% accuracy (81% sensitivity and 75% specificity) to predict cognitive decline at 2 years (74% accuracy at 3 years with 75% sensitivity and 73% specificity). Using this tool to select trial participants yields a 3.8-fold decrease in the required sample size for a 2-year study (2.8-fold decrease for a 3-year study) for a hypothesized 25% treatment effect to reduce cognitive decline. DISCUSSION: This cohort enrichment tool could accelerate treatment development by increasing power in clinical trials.

Published
2021

14. Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge

Author

Kuijf, Hugo J., Biesbroek, J. Matthijs, de Bresser, Jeroen, Heinen, Rutger, Andermatt, Simon, Bento, Mariana, Berseth, Matt, Belyaev, Mikhail, Cardoso, M. Jorge, Casamitjana, Adrià, Collins, D. Louis, Dadar, Mahsa, Georgiou, Achilleas, Ghafoorian, Mohsen, Jin, Dakai, Khademi, April, Knight, Jesse, Li, Hongwei, Lladó, Xavier, Luna, Miguel, Mahmood, Qaiser, McKinley, Richard, Mehrtash, Alireza, Ourselin, Sébastien, Park, Bo-yong, Park, Hyunjin, Park, Sang Hyun, Pezold, Simon, Puybareau, Elodie, Rittner, Leticia, Sudre, Carole H., Valverde, Sergi, Vilaplana, Verónica, Wiest, Roland, Xu, Yongchao, Xu, Ziyue, Zeng, Guodong, Zhang, Jianguo, Zheng, Guoyan, Chen, Christopher, van der Flier, Wiesje, Barkhof, Frederik, Viergever, Max A., and Biessels, Geert Jan

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Quantification of cerebral white matter hyperintensities (WMH) of presumed vascular origin is of key importance in many neurological research studies. Currently, measurements are often still obtained from manual segmentations on brain MR images, which is a laborious procedure. Automatic WMH segmentation methods exist, but a standardized comparison of the performance of such methods is lacking. We organized a scientific challenge, in which developers could evaluate their method on a standardized multi-center/-scanner image dataset, giving an objective comparison: the WMH Segmentation Challenge (https://wmh.isi.uu.nl/). Sixty T1+FLAIR images from three MR scanners were released with manual WMH segmentations for training. A test set of 110 images from five MR scanners was used for evaluation. Segmentation methods had to be containerized and submitted to the challenge organizers. Five evaluation metrics were used to rank the methods: (1) Dice similarity coefficient, (2) modified Hausdorff distance (95th percentile), (3) absolute log-transformed volume difference, (4) sensitivity for detecting individual lesions, and (5) F1-score for individual lesions. Additionally, methods were ranked on their inter-scanner robustness. Twenty participants submitted their method for evaluation. This paper provides a detailed analysis of the results. In brief, there is a cluster of four methods that rank significantly better than the other methods, with one clear winner. The inter-scanner robustness ranking shows that not all methods generalize to unseen scanners. The challenge remains open for future submissions and provides a public platform for method evaluation., Comment: Accepted for publication in IEEE Transactions on Medical Imaging

Published
2019
Full Text
View/download PDF

17. Structural neuroimaging as clinical predictor: a review of machine learning applications

Author

Mateos-Pérez, José María, Dadar, Mahsa, Lacalle-Aurioles, María, Iturria-Medina, Yasser, Zeighami, Yashar, and Evans, Alan C.

Subjects
Quantitative Biology - Quantitative Methods
Abstract

In this paper, we provide an extensive overview of machine learning techniques applied to structural magnetic resonance imaging (MRI) data to obtain clinical classifiers. We specifically address practical problems commonly encountered in the literature, with the aim of helping researchers improve the application of these techniques in future works. Additionally, we survey how these algorithms are applied to a wide range of diseases and disorders (e.g. Alzheimer's disease (AD), Parkinson's disease (PD), autism, multiple sclerosis, traumatic brain injury, etc.) in order to provide a comprehensive view of the state of the art in different fields., Comment: 70 pages, 3 figures. First two authors share first authorship

Published
2018
Full Text
View/download PDF

24. Beyond Hypertension: Examining Variable Blood Pressure's Role in Cognition and Brain Structure.

Author

Morrison, Cassandra, Oliver, Michael D, Kamal, Farooq, and Dadar, Mahsa

Subjects
DEMENTIA risk factors, BRAIN anatomy, STATISTICAL models, PSYCHOLOGICAL resilience, COGNITIVE testing, RESEARCH funding, FUNERAL industry, GRAY matter (Nerve tissue), WHITE matter (Nerve tissue), NEUROLOGY, BLOOD pressure, PATIENT aftercare
Abstract

Objectives Hypertension or high blood pressure (BP) is one of the 12 modifiable risk factors that contribute to 40% of dementia cases that could be delayed or prevented. Although hypertension is associated with cognitive decline and structural brain changes, less is known about the long-term association between variable BP and cognitive/brain changes. This study examined the relationship between variable BP and longitudinal cognitive, white matter hyperintensity (WMH), gray matter (GM), and white matter (WM) volume change over time and postmortem neuropathology. Methods A total of 4,606 participants (32,776 follow-ups) from RADC Research Resource Sharing Hub (RUSH) and 2,114 participants (9,827 follow-ups) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were included. Participants were divided into 1 of 3 groups: normal, high, or variable BP. Linear-mixed models investigated the relationship between BP and cognition, brain structure, and neuropathology. Results Older adults with variable BP exhibited the highest rate of cognitive decline followed by high and then normal BP. Increased GM volume loss and WMH burden were also observed in variable compared to high and normal BP. In postmortem neuropathology, both variable and high BP had increased rates compared to normal BP. Results were consistent across the RUSH and ADNI participants, supporting the generalizability of the findings. Discussion Damages potentially associated with variable BP may reduce resilience to future dementia-related pathology and increased the risk of dementia more than that caused by high BP. Improved treatment and management of variable BP may help reduce cognitive decline in the older adult population. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

28. Validation of T1w‐based segmentations of white matter hyperintensity volumes in large‐scale datasets of aging

Author

Dadar, Mahsa, Maranzano, Josefina, Ducharme, Simon, Carmichael, Owen T, Decarli, Charles, Collins, D Louis, and Initiative, Alzheimer's Disease Neuroimaging

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Aging, Brain Disorders, Dementia, Neurodegenerative, Acquired Cognitive Impairment, Neurological, Aged, Alzheimer Disease, Brain, Datasets as Topic, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Organ Size, Pattern Recognition, Automated, White Matter, Azheimer's disease, cerebrovascular disease, cognitive impairment, small vessel disease, T1w hypointensities, white matter integrity, Alzheimer's Disease Neuroimaging Initiative, Neurosciences, Cognitive Sciences, Experimental Psychology, Biological psychology, Cognitive and computational psychology
Abstract

IntroductionFluid-attenuated Inversion Recovery (FLAIR) and dual T2w and proton density (PD) magnetic resonance images (MRIs) are considered to be the optimum sequences for detecting white matter hyperintensities (WMHs) in aging and Alzheimer's disease populations. However, many existing large multisite studies forgo their acquisition in favor of other MRI sequences due to economic and time constraints.MethodsIn this article, we have investigated whether FLAIR and T2w/PD sequences are necessary to detect WMHs in Alzheimer's and aging studies, compared to using only T1w images. Using a previously validated automated tool based on a Random Forests classifier, WMHs were segmented for the baseline visits of subjects from ADC, ADNI1, and ADNI2/GO studies with and without T2w/PD and FLAIR information. The obtained WMH loads (WMHLs) in different lobes were then correlated with manually segmented WMHLs, each other, age, cognitive, and clinical measures to assess the strength of the correlations with and without using T2w/PD and FLAIR information.ResultsThe WMHLs obtained from T1w-Only segmentations correlated with the manual WMHLs (ADNI1: r = .743, p

Published
2018

34. Performance comparison of 10 different classification techniques in segmenting white matter hyperintensities in aging

Author

Dadar, Mahsa, Maranzano, Josefina, Misquitta, Karen, Anor, Cassandra J, Fonov, Vladimir S, Tartaglia, M Carmela, Carmichael, Owen T, Decarli, Charles, Collins, D Louis, and Initiative, Alzheimer's Disease Neuroimaging

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Acquired Cognitive Impairment, Dementia, Biomedical Imaging, Aging, Brain Disorders, Aged, Aged, 80 and over, Alzheimer Disease, Datasets as Topic, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, White Matter, White matter hyperintensities, Segmentation, Classification, Alzheimer's disease, Alzheimer's Disease Neuroimaging Initiative, Medical and Health Sciences, Psychology and Cognitive Sciences, Neurology & Neurosurgery, Biomedical and clinical sciences, Health sciences
Abstract

IntroductionWhite matter hyperintensities (WMHs) are areas of abnormal signal on magnetic resonance images (MRIs) that characterize various types of histopathological lesions. The load and location of WMHs are important clinical measures that may indicate the presence of small vessel disease in aging and Alzheimer's disease (AD) patients. Manually segmenting WMHs is time consuming and prone to inter-rater and intra-rater variabilities. Automated tools that can accurately and robustly detect these lesions can be used to measure the vascular burden in individuals with AD or the elderly population in general. Many WMH segmentation techniques use a classifier in combination with a set of intensity and location features to segment WMHs, however, the optimal choice of classifier is unknown.MethodsWe compare 10 different linear and nonlinear classification techniques to identify WMHs from MRI data. Each classifier is trained and optimized based on a set of features obtained from co-registered MR images containing spatial location and intensity information. We further assess the performance of the classifiers using different combinations of MRI contrast information. The performances of the different classifiers were compared on three heterogeneous multi-site datasets, including images acquired with different scanners and different scan-parameters. These included data from the ADC study from University of California Davis, the NACC database and the ADNI study. The classifiers (naïve Bayes, logistic regression, decision trees, random forests, support vector machines, k-nearest neighbors, bagging, and boosting) were evaluated using a variety of voxel-wise and volumetric similarity measures such as Dice Kappa similarity index (SI), Intra-Class Correlation (ICC), and sensitivity as well as computational burden and processing times. These investigations enable meaningful comparisons between the performances of different classifiers to determine the most suitable classifiers for segmentation of WMHs. In the spirit of open-source science, we also make available a fully automated tool for segmentation of WMHs with pre-trained classifiers for all these techniques.ResultsRandom Forests yielded the best performance among all classifiers with mean Dice Kappa (SI) of 0.66±0.17 and ICC=0.99 for the ADC dataset (using T1w, T2w, PD, and FLAIR scans), SI=0.72±0.10, ICC=0.93 for the NACC dataset (using T1w and FLAIR scans), SI=0.66±0.23, ICC=0.94 for ADNI1 dataset (using T1w, T2w, and PD scans) and SI=0.72±0.19, ICC=0.96 for ADNI2/GO dataset (using T1w and FLAIR scans). Not using the T2w/PD information did not change the performance of the Random Forest classifier (SI=0.66±0.17, ICC=0.99). However, not using FLAIR information in the ADC dataset significantly decreased the Dice Kappa, but the volumetric correlation did not drastically change (SI=0.47±0.21, ICC=0.95).ConclusionOur investigations showed that with appropriate features, most off-the-shelf classifiers are able to accurately detect WMHs in presence of FLAIR scan information, while Random Forests had the best performance across all datasets. However, we observed that the performances of most linear classifiers and some nonlinear classifiers drastically decline in absence of FLAIR information, with Random Forest still retaining the best performance.

Published
2017

46. Associations of white matter hyperintensities with cerebrovascular architecture in Alzheimer’s disease and related dementias

Author

Housni, Ikrame, primary, Singh, Manpreet, additional, Dadar, Mahsa, additional, Detcheverry, Flavie, additional, Anastassiadis, Chloe, additional, Filali‐Mouhim, Ali, additional, Masellis, Mario, additional, Ismail, Zahinoor, additional, Duchesne, Simon, additional, Tartaglia, Carmela, additional, Smith, Eric E., additional, Narayanan, Sridar, additional, and Badhwar, AmanPreet, additional

Published
2023
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results