White Matter Hyperintensity Quantification in Large-Scale Clinical Acute Ischemic Stroke Cohorts – The MRI-GENIE Study

Schirmer, Markus D.; Dalca, Adrian V.; Sridharan, Ramesh; Donahue, Kathleen; Nardin, Marco; Mocking, Steven J. T.; McIntosh, Elissa C.; Frid, Petrea; Wassélius, Johan; Cole, John W.; Holmegaard, Lukas; Jern, Christina; Jiménez‐Conde, Jordi; Lemmens, Robin; Lindgren, Arne; Meschia, James F.; Roquer, Jaume; Rundek, Tatjana; Sacco, Ralph L.; Schmidt, Reinhold; Sharma, Pankaj; Słowik, Agnieszka; Thijs, Vincent; Woo, Daniel; Vagal, Achala; Xu, Huichun; Kittner, Steven J.; McArdle, Patrick F.; Mitchell, Braxton D.; Rosand, Jonathan; Worrall, Bradford B.; Wu, Ona; Golland, Polina; Rost, Natalia S.

doi:10.1101/552844

Cited by 18 publications

(30 citation statements)

References 24 publications

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“…Both structural and functional images were preprocessed using the Configurable Pipeline for the Analysis of Connectomes (CPAC) 45 . First, bias field correction of the anatomical images was performed 46 , followed by brain extraction employing a convolutional neural network 47 . Subsequently, the images were registered to the MNI (Montreal Neurological Institute) anatomical template using non-linear registration (ANTs) 48 .…”

Section: Image Processingmentioning

confidence: 99%

Brain connectivity measures improve modeling of functional outcome after acute ischemic stroke

Ktena

Schirmer

Etherton

et al. 2019

Preprint

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Background : The ability to model long-term functional outcomes after acute ischemic stroke (AIS) represents a major clinical challenge. One approach to potentially improve prediction modeling involves the analysis of connectomics. The field of connectomics represents the brain's connectivity as a graph, whose topological properties have helped uncover underlying mechanisms of brain function in health and disease. Specifically, we assessed the impact of stroke lesions on rich club (RC) organization, a high capacity backbone system of brain function. Methods : In a hospital-based cohort of 41 AIS patients, we investigated the effect of acute infarcts on the brain's pre-stroke RC backbone and post-stroke functional connectomes with respect to post-stroke outcome. Functional connectomes were created utilizing three anatomical atlases and characteristic path-length ( L ) was calculated for each connectome. The number of RC regions (N RC ) affected were manually determined using each patient's diffusion weighted image (DWI). We investigated differences in L with respect to outcome (modified Rankin Scale score (mRS); 90-days; poor: mRS>2) and the National Institutes of Health Stroke Scale (NIHSS; early: 2-5 days; late: 90-day follow-up). Furthermore, we assessed the effect of including N RC and L in 'outcome' models, using linear regression and assessing the explained variance (R 2 ). Results : Of 41 patients (mean age (range): 70 (45-89) years), 61% were male. There were differences in L between patients with good and poor outcome (mRS). Including NRC in the backward selection models of outcome, R 2 increased between 1.3-and 2.6-fold beyond that of traditional markers (age and acute lesion volume) for NIHSS and mRS. Conclusion : In this proof-of-concept study, we showed that information on network topology can be leveraged to improve modeling of post-stroke functional outcome. Future studies are warranted to validate this approach in larger prospective studies of outcome prediction in stroke.

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Section: Image Processingmentioning

confidence: 99%

Brain connectivity measures improve modeling of functional outcome after acute ischemic stroke

Ktena

Schirmer

Etherton

et al. 2019

Preprint

Self Cite

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“…Structural and functional images were preprocessed using the Configurable Pipeline for the Analysis of Connectomes 25 , including bias field correction 26 , brain extraction 27 , non-linear registration 28 to the MNI (Montreal Neurological Institute) anatomical template. Cerebrospinal fluid (CSF), grey and white matter masks were generated using FSL FAST 29 .…”

Section: Image Processingmentioning

confidence: 99%

Brain Connectivity Measures Improve Modeling of Functional Outcome After Acute Ischemic Stroke

Ktena

Schirmer

Etherton

et al. 2019

Stroke

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Background and Purpose: The ability to model long-term functional outcomes after acute ischemic stroke (AIS) represents a major clinical challenge. One approach to potentially improve prediction modeling involves the analysis of connectomics. The field of connectomics represents the brain's connectivity as a graph, whose topological properties have helped uncover underlying mechanisms of brain function in health and disease. Specifically, we assessed the impact of stroke lesions on rich club (RC) organization, a high capacity backbone system of brain function. Methods: In a hospital-based cohort of 41 AIS patients, we investigated the effect of acute infarcts on the brain's pre-stroke RC backbone and post-stroke functional connectomes with respect to post-stroke outcome. Functional connectomes were created utilizing three anatomical atlases and characteristic path-length (L) was calculated for each connectome. The number of RC regions (NRC) affected were manually determined using each patient's diffusion weighted image (DWI). We investigated differences in L with respect to outcome (modified Rankin Scale score (mRS); 90-days) and the National Institutes of Health Stroke Scale (NIHSS; early: 2-5 days; late: 90-day follow-up). Furthermore, we assessed the effect of including NRC and L in 'outcome' models, using linear regression and assessing the explained variance (R 2). Results: Of 41 patients (mean age (range): 70 (45-89) years), 61% were male. Lower L was generally associated with better outcome. Including NRC in the backward selection models of outcome, R 2 increased between 1.3-and 2.6-fold beyond that of traditional markers (age and acute lesion volume) for NIHSS and mRS. Conclusions: In this proof-of-concept study, we showed that information on network topology can be leveraged to improve modeling of post-stroke functional outcome. Future studies are warranted to validate this approach in larger prospective studies of outcome prediction in stroke.

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“…The volume of white matter hyperintensity (WMH) ( Figure 1A) was automatically segmented on FLAIR images using a previously described automated algorithm (Schirmer, Dalca, et al, 2019). In brief, images first undergo brain extraction, intensity normalization, and affine registration to a template space, which enables the use of spatial priors .…”

Section: White Matter Hyperintensitymentioning

confidence: 99%

The INECO Frontal Screening for the Evaluation of Executive Dysfunction in Cerebral Small Vessel Disease: Evidence from Quantitative MRI in a CADASIL Cohort from Colombia

Schoemaker

Zuluaga

Viswanathan

et al. 2020

J Int Neuropsychol Soc

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Objectives: Executive dysfunction is a predominant cognitive symptom in cerebral small vessel disease (SVD). The Institute of Cognitive Neurology Frontal Screening (IFS) is a well-validated screening tool allowing the rapid assessment of multiple components of executive function in Spanish-speaking individuals. In this study, we examined performance on the IFS in subjects with cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL), an inherited condition leading to the early onset of SVD. We further explored associations between performance on the IFS and magnetic resonance imaging (MRI) markers of SVD. Methods: We recruited 24 asymptomatic CADASIL subjects and 23 noncarriers from Colombia. All subjects underwent a research MRI and a neuropsychological evaluation, including the IFS. Structural MRI markers of SVD were quantified in each subject, together with an SVD Sum Score representing the overall burden of cerebrovascular alterations. General linear model, correlation, and receiver operating characteristic curve analyses were used to explore group differences on the IFS and relationships with MRI markers of SVD. Results: CADASIL subjects had a significantly reduced performance on the IFS Total Score. Performance on the IFS correlated with all quantified markers of SVD, except for brain atrophy and perivascular spaces enlargement. Finally, while the IFS Total Score was not able to accurately discriminate between carriers and noncarriers, it showed adequate sensitivity and specificity in detecting the presence of multiple MRI markers of SVD. Conclusions: These results suggest that the IFS may be a useful screening tool to assess executive function and disease severity in the context of SVD.

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White Matter Hyperintensity Quantification in Large-Scale Clinical Acute Ischemic Stroke Cohorts – The MRI-GENIE Study

Cited by 18 publications

References 24 publications

Brain connectivity measures improve modeling of functional outcome after acute ischemic stroke

Brain connectivity measures improve modeling of functional outcome after acute ischemic stroke

Brain Connectivity Measures Improve Modeling of Functional Outcome After Acute Ischemic Stroke

The INECO Frontal Screening for the Evaluation of Executive Dysfunction in Cerebral Small Vessel Disease: Evidence from Quantitative MRI in a CADASIL Cohort from Colombia

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