Impact of region of interest definition on visual stimulation‐based cerebral vascular reactivity functional MRI with a special focus on applications in cerebral amyloid angiopathy

Cerebral vascular reactivity quantified using blood oxygen level‐dependent functional MRI in conjuncture with a visual stimulus has been proven to be a potent and early marker for cerebral amyloid angiopathy. This work investigates the influence of different postprocessing methods on the outcome of such vascular reactivity measurements. Three methods for defining the region of interest (ROI) over which the reactivity is measured are investigated: structural (transformed V1), functional (template based on the activation of a subset of subjects), and percentile (11.5 cm3 most responding voxels). Evaluation is performed both in a test–retest experiment in healthy volunteers (N = 12), as well as in 27 Dutch‐type cerebral amyloid angiopathy patients and 33 age‐ and sex‐matched control subjects. The results show that the three methods select a different subset of voxels, although all three lead to similar outcome measures in healthy subjects. However, in (severe) pathology, the percentile method leads to higher reactivity measures than the other two, due to circular analysis or “double dipping” by defining a subject‐specific ROI based on the strongest responses within each subject. Furthermore, while different voxels are included in the presence of lesions, this does not necessarily result in different outcome measures. In conclusion, to avoid bias created by the method, either a structural or a functional method is recommended. Both of these methods provide similar reactivity measures, although the functional ROI appears to be less reproducible between studies, because slightly different subsets of voxels were found to be included. On the other hand, the functional method did include fewer lesion voxels than the structural method.

[1]  S. Rombouts,et al.  Aging Effect, Reproducibility, and Test–Retest Reliability of a New Cerebral Amyloid Angiopathy MRI Severity Marker—Cerebrovascular Reactivity to Visual Stimulation , 2022, Journal of magnetic resonance imaging : JMRI.

[2]  Jason M. Johnson,et al.  Cerebrovascular Reactivity Mapping Using Resting‐State Functional MRI in Patients With Gliomas , 2022, Journal of magnetic resonance imaging : JMRI.

[3]  M. van Buchem,et al.  Longitudinal Progression of Magnetic Resonance Imaging Markers and Cognition in Dutch-Type Hereditary Cerebral Amyloid Angiopathy , 2022, Stroke.

[4]  Erin L. Mazerolle,et al.  Cerebrovascular Reactivity Across the Entire Brain in Cerebral Amyloid Angiopathy , 2022, Neurology.

[5]  Eric E. Smith,et al.  Cortical Thickness and Its Association with Clinical Cognitive and Neuroimaging Markers in Cerebral Amyloid Angiopathy. , 2021, Journal of Alzheimer's disease : JAD.

[6]  I. Marshall,et al.  Cerebrovascular Reactivity Measurement Using Magnetic Resonance Imaging: A Systematic Review , 2021, Frontiers in Physiology.

[7]  Eric E. Smith,et al.  Cerebrovascular reactivity in cerebral amyloid angiopathy, Alzheimer disease, and mild cognitive impairment , 2020, Neurology.

[8]  A. Członkowska,et al.  Cerebrovascular reactivity and disease activity in relapsing-remitting multiple sclerosis. , 2020, Advances in clinical and experimental medicine : official organ Wroclaw Medical University.

[9]  D. Attwell,et al.  Amyloid β oligomers constrict human capillaries in Alzheimer’s disease via signaling to pericytes , 2019, Science.

[10]  R. Roos,et al.  Structural and functional changes of the visual cortex in early Huntington's disease , 2018, Human brain mapping.

[11]  H. Benali,et al.  Altered dynamics of neurovascular coupling in CADASIL , 2018, Annals of clinical and translational neurology.

[12]  C. Pernet,et al.  Cerebrovascular reactivity measurement in cerebral small vessel disease: Rationale and reproducibility of a protocol for MRI acquisition and image processing , 2018, International journal of stroke : official journal of the International Stroke Society.

[13]  S. Greenberg,et al.  The growing clinical spectrum of cerebral amyloid angiopathy , 2017, Current opinion in neurology.

[14]  C. Ayata,et al.  Emerging concepts in sporadic cerebral amyloid angiopathy. , 2017, Brain : a journal of neurology.

[15]  Panagiotis Fotiadis,et al.  Relationship between white matter connectivity loss and cortical thinning in cerebral amyloid angiopathy , 2017, Human brain mapping.

[16]  Richard Frayne,et al.  Identification of neurovascular changes associated with cerebral amyloid angiopathy from subject-specific hemodynamic response functions , 2017, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[17]  Terry K Koo,et al.  A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. , 2016, Journal of chiropractic medicine.

[18]  S. Greenberg,et al.  Sporadic Cerebral Amyloid Angiopathy: Pathophysiology, Neuroimaging Features, and Clinical Implications , 2016, Seminars in Neurology.

[19]  Eric E. Smith,et al.  Longitudinal decrease in blood oxygenation level dependent response in cerebral amyloid angiopathy , 2016, NeuroImage: Clinical.

[20]  Richard Frayne,et al.  Neurovascular decoupling is associated with severity of cerebral amyloid angiopathy , 2013, Neurology.

[21]  Bruce Fischl,et al.  FreeSurfer , 2012, NeuroImage.

[22]  Bruce Fischl,et al.  Accurate and robust brain image alignment using boundary-based registration , 2009, NeuroImage.

[23]  M. van Buchem,et al.  Descriptive Analysis of the Boston Criteria Applied to a Dutch-Type Cerebral Amyloid Angiopathy Population , 2009, Stroke.

[24]  Catie Chang,et al.  Influence of heart rate on the BOLD signal: The cardiac response function , 2009, NeuroImage.

[25]  Eric E. Smith,et al.  Impaired visual evoked flow velocity response in cerebral amyloid angiopathy , 2008, Neurology.

[26]  Peter A. Bandettini,et al.  The respiration response function: The temporal dynamics of fMRI signal fluctuations related to changes in respiration , 2008, NeuroImage.

[27]  Mark W. Woolrich,et al.  Constrained linear basis sets for HRF modelling using Variational Bayes , 2004, NeuroImage.

[28]  Stephen M. Smith,et al.  Probabilistic independent component analysis for functional magnetic resonance imaging , 2004, IEEE Transactions on Medical Imaging.

[29]  Stephen M Smith,et al.  Fast robust automated brain extraction , 2002, Human brain mapping.

[30]  Michael Brady,et al.  Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.

[31]  Stephen M. Smith,et al.  Temporal Autocorrelation in Univariate Linear Modeling of FMRI Data , 2001, NeuroImage.

[32]  D. Werring,et al.  Outcome markers for clinical trials in cerebral amyloid angiopathy , 2001, The Lancet Neurology.

[33]  Stephen M. Smith,et al.  A global optimisation method for robust affine registration of brain images , 2001, Medical Image Anal..

[34]  S. Greenberg,et al.  Clinical diagnosis of cerebral amyloid angiopathy: Validation of the Boston Criteria , 2003, Current atherosclerosis reports.

[35]  K. Amunts,et al.  Brodmann's Areas 17 and 18 Brought into Stereotaxic Space—Where and How Variable? , 2000, NeuroImage.

[36]  R. Roos,et al.  Hereditary Cerebral Hemorrhage with Amyloidosis‐Dutch type (HCHWA‐D): II ‐ A Review of Histopathological Aspects , 1996, Brain pathology.

[37]  R. Roos,et al.  Hereditary Cerebral Hemorrhage with Amyloidosis‐Dutch Type (HCHWA‐D): I ‐ A Review of Clinical, Radiologic and Genetic Aspects , 1996, Brain pathology.

[38]  S. Strother,et al.  Graphical Analysis of MR Feature Space for Measurement of CSF, Gray‐Matter, and White‐Matter Volumes , 1993, Journal of computer assisted tomography.

[39]  M. Tomonaga,et al.  Cerebral Amyloid Angiopathy in the Elderly , 1981, Journal of the American Geriatrics Society.

[40]  S. Greenberg,et al.  Cerebrovascular function in pre-symptomatic and symptomatic individuals with hereditary cerebral amyloid angiopathy: a case-control study , 2017 .

[41]  M. Jenkinson Non-linear registration aka Spatial normalisation , 2007 .

[42]  Stephen M. Smith,et al.  Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm , 2001, IEEE Transactions on Medical Imaging.