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Valsamis JJ, Luciw NJ, Haq N, Atwi S, Duchesne S, Cameron W, MacIntosh BJ. An imaging-based method of mapping multi-echo BOLD intracranial pulsatility. Magn Reson Med 2023; 90:343-352. [PMID: 36929810 DOI: 10.1002/mrm.29639] [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: 05/04/2022] [Revised: 02/16/2023] [Accepted: 02/23/2023] [Indexed: 03/18/2023]
Abstract
PURPOSE Cardiac-related intracranial pulsatility may relate to cerebrovascular health, and this information is contained in BOLD MRI data. There is broad interest in methods to isolate BOLD pulsatility, and the current study examines a deep learning approach. METHODS Multi-echo BOLD images, respiratory, and cardiac recordings were measured in 55 adults. Ground truth BOLD pulsatility maps were calculated with an established method. BOLD fast Fourier transform magnitude images were used as temporal-frequency image inputs to a U-Net deep learning model. Model performance was evaluated by mean squared error (MSE), mean absolute error (MAE), structural similarity index (SSIM), and mutual information (MI). Experiments evaluated the influence of input channel size, an age group effect during training, dependence on TE, performance without the U-Net architecture, and importance of respiratory preprocessing. RESULTS The U-Net model generated BOLD pulsatility maps with lower MSE as additional fast Fourier transform input images were used. There was no age group effect for MSE (P > 0.14). MAE and SSIM metrics did not vary across TE (P > 0.36), whereas MI showed a significant TE dependence (P < 0.05). The U-Net versus no U-Net comparison showed no significant difference for MAE (P = 0.059); however, SSIM and MI were significantly different between models (P < 0.001). Within the insula, the cross-correlation values were high (r > 0.90) when comparing the U-Net model trained with/without respiratory preprocessing. CONCLUSION Multi-echo BOLD pulsatility maps were synthesized from a U-net model that was trained to use temporal-frequency BOLD image inputs. This work adds to the deep learning methods that characterize BOLD physiological signals.
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Affiliation(s)
- Jake J Valsamis
- Hurvitz Brain Sciences Program, and Physical Sciences Platform, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | - Nicholas J Luciw
- Hurvitz Brain Sciences Program, and Physical Sciences Platform, Sunnybrook Research Institute, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Nandinee Haq
- Hurvitz Brain Sciences Program, and Physical Sciences Platform, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | - Sarah Atwi
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Simon Duchesne
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Université Laval, Quebec City, Quebec, Canada.,CERVO Brain Research Center, Quebec City, Quebec, Canada
| | | | - Bradley J MacIntosh
- Hurvitz Brain Sciences Program, and Physical Sciences Platform, Sunnybrook Research Institute, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.,Sandra E Black Centre for Brain Resilience & Recovery, Sunnybrook Research Institute, Toronto, Ontario, Canada.,Computational Radiology & Artificial Intelligence (CRAI) unit, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
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da Silva PHR, Paschoal AM, Secchinatto KF, Zotin MCZ, Dos Santos AC, Viswanathan A, Pontes-Neto OM, Leoni RF. Contrast agent-free state-of-the-art magnetic resonance imaging on cerebral small vessel disease - Part 2: Diffusion tensor imaging and functional magnetic resonance imaging. NMR IN BIOMEDICINE 2022; 35:e4743. [PMID: 35429070 DOI: 10.1002/nbm.4743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 04/04/2022] [Indexed: 06/14/2023]
Abstract
Cerebral small vessel disease (cSVD) has been widely studied using conventional magnetic resonance imaging (MRI) methods, although the association between MRI findings and clinical features of cSVD is not always concordant. We assessed the additional contribution of contrast agent-free, state-of-the-art MRI techniques, particularly diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI), to understand brain damage and structural and functional connectivity impairment related to cSVD. We performed a review following the PICOS worksheet and Search Strategy, including 152 original papers in English, published from 2000 to 2022. For each MRI method, we extracted information about their contributions regarding the origins, pathology, markers, and clinical outcomes in cSVD. In general, DTI studies have shown that changes in mean, radial, and axial diffusivity measures are related to the presence of cSVD. In addition to the classical deficit in executive functions and processing speed, fMRI studies indicate connectivity dysfunctions in other domains, such as sensorimotor, memory, and attention. Neuroimaging metrics have been correlated with the diagnosis, prognosis, and rehabilitation of patients with cSVD. In short, the application of contrast agent-free, state-of-the-art MRI techniques has provided a complete picture of cSVD markers and tools to explore questions that have not yet been clarified about this clinical condition. Longitudinal studies are desirable to look for causal relationships between image biomarkers and clinical outcomes.
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Affiliation(s)
| | - André Monteiro Paschoal
- Department of Physics, FFCLRP, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
- Department of Medical Imaging, Hematology and Clinical Oncology, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
| | | | - Maria Clara Zanon Zotin
- Department of Medical Imaging, Hematology and Clinical Oncology, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
- J Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Antônio Carlos Dos Santos
- Department of Medical Imaging, Hematology and Clinical Oncology, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
| | - Anand Viswanathan
- J Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Octavio M Pontes-Neto
- Department of Neurosciences and Behavioral Science, Ribeirão Preto Medical School, University of Sao Paulo, Ribeirão Preto, São Paulo, Brazil
| | - Renata Ferranti Leoni
- Department of Physics, FFCLRP, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
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Palmer JA, Kaufman CS, Vidoni ED, Honea RA, Burns JM, Billinger SA. Cerebrovascular response to exercise interacts with individual genotype and amyloid-beta deposition to influence response inhibition with aging. Neurobiol Aging 2022; 114:15-26. [DOI: 10.1016/j.neurobiolaging.2022.02.014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 02/23/2022] [Accepted: 02/24/2022] [Indexed: 12/23/2022]
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Atwi S, Sweeny M, Cohen E, Robertson AD, Marzolini S, Swardfager W, Swartz RH, Oh PI, MacIntosh BJ. Cerebrovascular assessments to help understand brain-related changes associated with aerobic exercise after stroke. Appl Physiol Nutr Metab 2021; 46:412-415. [PMID: 33400620 DOI: 10.1139/apnm-2020-0228] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Evidence suggests exercise is "good medicine" after stroke, yet consensus is lacking on the time to initiate, type, exertion level, and duration per session. It remains a challenge to identify outcome measures for stroke-exercise trials that are sufficiently sensitive to intervention parameters. Cerebrovascular assessments, namely cerebral blood flow and intracranial pulsatility, are herein discussed as examples of quantitative brain-specific measures that may be useful to monitor exercise-related brain changes and help to guide stroke rehabilitation interventions. Novelty: Cerebral blood flow and arterial stiffness are potential vascular targets for stroke exercise trials.
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Affiliation(s)
- Sarah Atwi
- Heart and Stroke Foundation Canadian Partnership for Stroke Recovery, Sunnybrook Research Institute, University of Toronto, Toronto, ON M4N 3M5, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Michelle Sweeny
- Heart and Stroke Foundation Canadian Partnership for Stroke Recovery, Sunnybrook Research Institute, University of Toronto, Toronto, ON M4N 3M5, Canada.,Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, ON M4N 3M5, Canada
| | - Ellen Cohen
- Heart and Stroke Foundation Canadian Partnership for Stroke Recovery, Sunnybrook Research Institute, University of Toronto, Toronto, ON M4N 3M5, Canada.,Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, ON M4N 3M5, Canada
| | - Andrew D Robertson
- Heart and Stroke Foundation Canadian Partnership for Stroke Recovery, Sunnybrook Research Institute, University of Toronto, Toronto, ON M4N 3M5, Canada.,Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, ON M4N 3M5, Canada
| | - Susan Marzolini
- KITE Research Institute, Toronto Rehab-University Health Network, Toronto, ON M4G 2V6, Canada
| | - Walter Swardfager
- Heart and Stroke Foundation Canadian Partnership for Stroke Recovery, Sunnybrook Research Institute, University of Toronto, Toronto, ON M4N 3M5, Canada.,Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, ON M4N 3M5, Canada
| | - Richard H Swartz
- Heart and Stroke Foundation Canadian Partnership for Stroke Recovery, Sunnybrook Research Institute, University of Toronto, Toronto, ON M4N 3M5, Canada.,Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, ON M4N 3M5, Canada.,Department of Medicine (Neurology), Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Paul I Oh
- KITE Research Institute, Toronto Rehab-University Health Network, Toronto, ON M4G 2V6, Canada
| | - Bradley J MacIntosh
- Heart and Stroke Foundation Canadian Partnership for Stroke Recovery, Sunnybrook Research Institute, University of Toronto, Toronto, ON M4N 3M5, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.,Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, ON M4N 3M5, Canada
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