Exploring the brain's structural connectome: A quantitative stroke lesion‐dysfunction mapping study

Kuceyeski, Amy; Navi, Babak B.; Kamel, Hooman; Relkin, Norman; Villanueva, Mark; Raj, Ashish; Toglia, Joan; O’Dell, Michael W.; Iadecola, Costantino

doi:10.1002/hbm.22761

Cited by 47 publications

(81 citation statements)

References 59 publications

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“…Our results also complement prior studies that have implicated WM damage and SDC as important factors in determining the severity of cognitive and behavioral deficits after stroke (Catani et al, 2012;Chechlacz et al, 2013;Corbetta et al, 2015;Forkel et al, 2014;Griffis et al, 2017b, Kuceyeski et al, 2015Marebwa et al, 2017;Thiebaut De Schotten et al, 2014;Yourganov et al, 2016). Speculatively, these relationships may be partially mediated by the large-scale functional disruptions precipitated by SDCs.…”

Section: Structural Correlates Of Functional Connectivity Disruptionssupporting

confidence: 90%

Structural disconnections explain brain network dysfunction after stroke

Griffis

Metcalf

Corbetta

et al. 2019

Preprint

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Functional connectivity (FC) studies have identified physiological signatures of stroke that correlate with behavior. Using structural and functional MRI data from 114 stroke patients, 24 matched controls, and the Human Connectome Project, we tested the hypothesis that structural disconnection, not damage to critical regions, underlies FC disruptions. Disconnection severity outperformed damage to putative FC connector nodes for explaining reductions in system modularity, and multivariate models based on disconnection outperformed damage models for explaining FC disruptions within and between systems. Across patients, disconnection and FC patterns exhibited a lowdimensional covariance dominated by a single axis linking interhemispheric disconnections to reductions in FC measures of interhemispheric system integration, ipsilesional system segregation, and system modularity, and that correlated with multiple behavioral deficits. These findings clarify the structural basis of FC disruptions in stroke patients and demonstrate a low-dimensional link between perturbations of the structural connectome, disruptions of the functional connectome, and behavioral deficits. Stroke disrupts system-scale functional connectivityResting-state fMRI data were used to measure FC between 324 cortical parcels associated with different brain systems ( Fig. 2A-C). We defined twelve system-scale summary measures to capture reductions of interhemispheric system integration, ipsilesional system segregation, and system modularity. For each patient, we extracted the mean interhemispheric FC values for nine bilateral cortical systems (Fig. 2D, left) and averaged across systems to summarize interhemispheric system integration across the cortex (Fig. 2D, left inset). We also extracted the mean FC between the ipsilesional DAN and DMN to quantify ipsilesional system segregation (Fig. 2D, middle), and we averaged modularity estimates for a priori system partitions across multiple edge density thresholds to summarize overall network structure ( Fig. 2D, right; mean shown in inset).The mean FC matrices for patients and controls had similar topographies ( Fig. 2B; r=0.96, p<0.001), but subtracting the patient matrix from the control matrix revealed magnitude differences that were often in opposite directions for connections with positive vs. negative values in the mean control matrix (Fig. 2C; r=-0.42, p<0.001), consistent with reduced integration within systems and reduced segregation between systems in patients. As expected, patients showed marked abnormalities in FC summary measures of interhemispheric integration, ipsilesional segregation, and system modularity (Fig. 2D). These measures were used as dependent variables in subsequent analyses.

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Section: Structural Correlates Of Functional Connectivity Disruptionssupporting

confidence: 90%

Structural disconnections explain brain network dysfunction after stroke

Griffis

Metcalf

Corbetta

et al. 2019

Preprint

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“…Region-pair connections have the same justification-there we count the damaged fibers out of the total amount of fibers connecting two regions, which should be proportional between younger and older healthy controls. In addition, our previous lesion-dysfunction mapping study in a similar population (some patients were included in both studies) used the NeMo Tool to map disruptions of connectivity in eloquent regions to the proper associated patient dysfunction [Kuceyeski et al, 2015b]. Our previous work validating the NeMo Tool's application in this type of population increases confidence in the current results.…”

Section: Limitationsmentioning

confidence: 74%

“…Lesion masks in this cohort were created and processed as in our previous studies [Kuceyeski et al, ; Kuceyeski et al, ]. Specifically, masks indicating acute stroke injury as identified by apparent DWI hyperintensities were hand drawn by two raters (BN and AK) using FSLview [Smith et al, ].…”

Section: Methodsmentioning

confidence: 99%

“…It is one of a limited, albeit increasing, number of studies that have utilized healthy connectome information to predict network effects in various pathological states [Boes et al, ; Honey and Sporns, ; Raj et al, ; Seeley et al, ]. The NeMo Tool has recently been used to investigate lesion‐dysfunction relationships [Kuceyeski et al, ] and longitudinal remote degeneration [Kuceyeski et al, ] in stroke. It has also been used to relate white matter and gray matter pathologies in normal aging [Glodzik et al, ] and reveal lesion‐symptom relationships in subtle cognitive impairment associated with early multiple sclerosis [Kuceyeski et al, ].…”

Section: Introductionmentioning

confidence: 99%

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Structural connectome disruption at baseline predicts 6-months post-stroke outcome

et al. 2016

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In this study, models based on quantitative imaging biomarkers of post-stroke structural connectome disruption were used to predict six-month outcomes in various domains. Demographic information and clinical MRIs were collected from 40 ischemic stroke subjects (age: 68.1±13.2 years, 17 female, NIHSS: 6.8±5.6). Diffusion-weighted images were used to create lesion masks, which were uploaded to the Network Modification (NeMo) Tool. The NeMo Tool, using only clinical MRIs, allows estimation of connectome disruption at three levels: whole brain, individual gray matter regions and between pairs of gray matter regions. Partial Least Squares Regression models were constructed for each level of connectome disruption and for each of the three six-month outcomes: applied cognitive, basic mobility and daily activity. Models based on lesion volume were created for comparison. Cross-validation, bootstrapping and multiple comparisons corrections were implemented to minimize over-fitting and Type I errors. The regional disconnection model best predicted applied cognitive (R2 = 0.56) and basic mobility outcomes (R2 = 0.70), while the pairwise disconnection model best predicted the daily activity measure (R2 = 0.72). These results demonstrate that models based on connectome disruption metrics were more accurate than ones based on lesion volume and that increasing anatomical specificity of disconnection metrics does not always increase model accuracy, likely due to statistical adjustments for concomitant increases in data dimensionality. This work establishes that the NeMo Tool's measures of baseline connectome disruption, acquired using only routinely collected MRI scans, can predict 6-month post-stroke outcomes in various functional domains including cognition, motor function and daily activities.

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“…However, disconnections can occur at any segment along WM tracts, and, consequently, lesions apparently unrelated to each other can cause disruption of the same tract, producing similar symptoms. Supporting this rationale, recent studies have shown that structural disconnections can predict cognitive performance more accurately than lesion location [Hope et al, ; Kuceyeski et al, ]. Nonoverlapping lesions can also affect the same functional network, leading to similar clinical syndromes [Boes et al, ].…”

Section: Discussionmentioning

confidence: 99%

Enhanced estimations of post‐stroke aphasia severity using stacked multimodal predictions

Pustina

Coslett

Ungar

et al. 2017

Human Brain Mapping

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The severity of post-stroke aphasia and the potential for recovery are highly variable and difficult to predict. Evidence suggests that optimal estimation of aphasia severity requires the integration of multiple neuroimaging modalities and the adoption of new methods that can detect multivariate brain-behavior relationships. We created and tested a multimodal framework that relies on three information sources (lesion maps, structural connectivity, and functional connectivity) to create an array of unimodal predictions which are then fed into a final model that creates “stacked multimodal predictions” (STAMP). Cross-validated predictions of four aphasia scores (picture naming, sentence repetition, sentence comprehension, and overall aphasia severity) were obtained from 53 left hemispheric chronic stroke patients (age: 57.1±12.3yrs, post-stroke interval: 20 months, 25 female). Results showed accurate predictions for all four aphasia scores (correlation true vs. predicted: r=0.79–0.88). The accuracy was slightly smaller but yet significant (r=0.66) in a full split cross-validation with each patient considered as new. Critically, multimodal predictions produced more accurate results that any single modality alone. Topological maps of the brain regions involved in the prediction were recovered and compared with traditional voxel-based lesion-to-symptom maps, revealing high spatial congruency. These results suggest that neuroimaging modalities carry complementary information potentially useful for the prediction of aphasia scores. More broadly, this study shows that the translation of neuroimaging findings into clinically useful tools calls for a shift in perspective from unimodal to multimodal neuroimaging, from univariate to multivariate methods, from linear to non-linear models, and, conceptually, from inferential to predictive brain mapping.

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Exploring the brain's structural connectome: A quantitative stroke lesion‐dysfunction mapping study

Cited by 47 publications

References 59 publications

Structural disconnections explain brain network dysfunction after stroke

Structural disconnections explain brain network dysfunction after stroke

Structural connectome disruption at baseline predicts 6-months post-stroke outcome

Enhanced estimations of post‐stroke aphasia severity using stacked multimodal predictions

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