Dataset Information

Individual-Level Lesion-Network Mapping to Visualize the Effects of a Stroke Lesion on the Brain Network: Connectograms in Stroke Syndromes.

ABSTRACT: BACKGROUND AND PURPOSE:Similar-sized stroke lesions at similar locations can have different prognoses in clinical practice. Lesion-network mapping elucidates network-level effects of lesions that cause specific neurologic symptoms and signs, and also provides a group-level understanding. This study visualized the effects of stroke lesions on the functional brain networks of individual patients. METHODS:We enrolled patients with ischemic stroke who were hospitalized within 1 week of the stroke occurrence. Resting-state functional magnetic resonance imaging was performed 3 months after the index stroke. For image preprocessing, acute stroke lesions were visually delineated based on diffusion-weighted images obtained at admission, and the lesion mask was drawn using MRIcron software. Correlation matrices were calculated from 280 brain regions using the Brainnetome Atlas, and connectograms were visualized using in-house MATLAB code. RESULTS:We found characteristic differences in connectograms between pairs of patients who had comparable splenial, frontal cortical, cerebellar, and thalamocapsular lesions. Two representative patients with bilateral thalamic infarctions showed significant differences in their reconstructed connectograms. The cognitive function had recovered well at 3 months after stroke occurrence in patients with well-maintained interhemispheric and intrahemispheric connectivities. CONCLUSIONS:This pilot study has visualized the effects of stroke lesions on the functional brain networks of individual patients. Consideration of the neurobiologic mechanisms underlying the differences between their connectograms has yielded new hypotheses about differences in the effects of stroke lesions.

SUBMITTER: Lim JS

PROVIDER: S-EPMC6974837 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Publications

Individual-Level Lesion-Network Mapping to Visualize the Effects of a Stroke Lesion on the Brain Network: Connectograms in Stroke Syndromes.

Lim Jae Sung JS Lee Jae Joong JJ Woo Choong Wan CW Song Jooyeon J Oh Mi Sun MS Yu Kyung Ho KH Lee Byung Chul BC

Journal of clinical neurology (Seoul, Korea) 20200101 1

<h4>Background and purpose</h4>Similar-sized stroke lesions at similar locations can have different prognoses in clinical practice. Lesion-network mapping elucidates network-level effects of lesions that cause specific neurologic symptoms and signs, and also provides a group-level understanding. This study visualized the effects of stroke lesions on the functional brain networks of individual patients.<h4>Methods</h4>We enrolled patients with ischemic stroke who were hospitalized within 1 week o ...[more]

PMID: 31942767

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Project description:Background and purpose: Early recognition and management of post-stroke dysphagia (PSD) based on MRI may reduce the incidence of complications. Combining clinical symptoms with applications of MRI, we aimed to identify the risk factors of PSD, develop a prediction scale with high accuracy and map key dysphagia brain areas. Methods: A total of 275 acute ischemic stroke patients were enrolled in this study, and 113 (41.1%) patients were diagnosed with PSD. All patients underwent the water-swallowing test (WST) and volume-viscosity swallow test (V-VST) within first 24 h following admission to assess swallowing. Vascular factors were evaluated and MRI brain scans were obtained within 3 days after symptom onset for each participant admitted to the hospital. T-test, chi-squared test and Fisher's exact test were used to investigate the associations of various patient characteristics with dysphagia, and multivariable logistic regression models were used to construct a prediction scale. Scale accuracy was assessed using receiver operating characteristic (ROC) analysis. We extracted white matter hyperintensities for each patient as potential brain lesions. Voxel-based lesion-symptom mapping (VLSM) was used to identify key brain areas for dysphagia. Results: Risk factors related with PSD were older age, history of atrial fibrillation, higher fasting blood glucose, NIH stroke scale, TOAST classification, progressive stroke, middle cerebral artery lesion and anterior cerebral artery lesion. Three variables with most significant associations, including NIH stroke scale, TOAST classification and progressive stroke, combined with age and gender, were used to construct a dysphagia prediction scale with high accuracy (AUC = 0.86). VLSM identified left inferior parietal gyrus as a key brain region for PSD. Conclusion: Risk factors of PSD were identified and a predictive model of dysphagia was constructed intelligently and automatically. The left inferior parietal gyrus was identified as a key brain area for dysphagia, which provides a new symptom-based treatment target for early rehabilitation in the future.

Project description:Lesion analysis is a fundamental and classical approach for inferring the causal contributions of brain regions to brain function. However, many studies have been limited by the shortcomings of methodology or clinical data. Aiming to overcome these limitations, we here use an objective multivariate approach based on game theory, Multi-perturbation Shapley value Analysis, in conjunction with data from a large cohort of 394 acute stroke patients, to derive causal contributions of brain regions to four principal functional components of the widely used National Institutes of Health Stroke Score measure. The analysis was based on a high-resolution parcellation of the brain into 294 grey and white matter regions. Through initial lesion symptom mapping for identifying all potential candidate regions and repeated iterations of the game-theoretical approach to remove non-significant contributions, the analysis derived the smallest sets of regions contributing to each of the four principal functional components as well as functional interactions among the regions. Specifically, the factor 'language and consciousness' was related to contributions of cortical regions in the left hemisphere, including the prefrontal gyrus, the middle frontal gyrus, the ventromedial putamen and the inferior frontal gyrus. Right and left motor functions were associated with contributions of the left and right dorsolateral putamen and the posterior limb of the internal capsule, correspondingly. Moreover, the superior corona radiata and the paracentral lobe of the right hemisphere as well as the right caudal area 23 of the cingulate gyrus were mainly related to left motor function, while the prefrontal gyrus, the external capsule and the sagittal stratum fasciculi of the left hemisphere contributed to right motor function. Our approach demonstrates a practically feasible strategy for applying an objective lesion inference method to a high-resolution map of the human brain and distilling a small, characteristic set of grey and white matter structures contributing to fundamental brain functions. In addition, we present novel findings of synergistic interactions between brain regions that provide insight into the functional organization of brain networks.

Project description:Damage to the right fusiform face area can disrupt the ability to recognize faces, a classic example of how damage to a specialized brain region can disrupt a specialized brain function. However, similar symptoms can arise from damage to other brain regions, and face recognition is now thought to depend on a distributed brain network. The extent of this network and which regions are critical for facial recognition remains unclear. Here, we derive this network empirically based on lesion locations causing clinically significant impairments in facial recognition. Cases of acquired prosopagnosia were identified through a systematic literature search and lesion locations were mapped to a common brain atlas. The network of brain regions connected to each lesion location was identified using resting state functional connectivity from healthy participants (n = 1000), a technique termed lesion network mapping. Lesion networks were overlapped to identify connections common to lesions causing prosopagnosia. Reproducibility was assessed using split-half replication. Specificity was assessed through comparison with non-specific control lesions (n = 135) and with control lesions associated with symptoms other than prosopagnosia (n = 155). Finally, we tested whether our facial recognition network derived from clinically evident cases of prosopagnosia could predict subclinical facial agnosia in an independent lesion cohort (n = 31). Our systematic literature search identified 44 lesions causing prosopagnosia, only 29 of which intersected the right fusiform face area. However, all 44 lesion locations fell within a single brain network defined by connectivity to the right fusiform face area. Less consistent connectivity was found to other face-selective regions. Surprisingly, all 44 lesion locations were also functionally connected, through negative correlation, with regions in the left frontal cortex. This connectivity pattern was highly reproducible and specific to lesions causing prosopagnosia. Positive connectivity to the right fusiform face area and negative connectivity to left frontal regions were independent predictors of prosopagnosia and predicted subclinical facial agnosia in an independent lesion cohort. We conclude that lesions causing prosopagnosia localize to a single functionally connected brain network defined by connectivity to the right fusiform face area and to left frontal regions. Implications of these findings for models of facial recognition deficits are discussed.

Project description:The aim of this work was to quantitatively model cross-sectional relationships between structural connectome disruptions caused by cerebral infarction and measures of clinical performance. Imaging biomarkers of 41 ischemic stroke patients (72.0 ± 12.0 years, 20 female) were related to their baseline performance in 18 cognitive, physical and daily life activity assessments. Individual estimates of structural connectivity disruption in gray matter regions were computed using the Change in Connectivity (ChaCo) score. ChaCo scores were utilized because they can be calculated using routinely collected clinical magnetic resonance imagings. Partial Least Squares Regression (PLSR) was used to predict various acute impairment and activity measures from ChaCo scores and patient demographics. Statistical methods of cross-validation, bootstrapping and multiple comparisons correction were implemented to minimize over-fitting and Type I errors. Multiple linear regression models based on lesion volume and lateralization information were constructed for comparison. All models based on connectivity disruption had lower Akaike Information Criterion and almost all had better goodness-of-fit values (R(2) : 0.26-0.92) than models based on lesion characteristics (R(2) : 0.06-0.50). Confidence intervals of PLSR coefficients identified brain regions important in predicting each clinical assessment. Appropriate mapping of eloquent functions, that is, language and motor, and replication of results across pathologies provided validation of this method. Models of complex functions provided new insights into brain-behavior relationships. In addition to the potential applications in prognostication and rehabilitation development, this quantitative approach provides insight into the structural networks underlying complex functions like activities of daily living and cognition. Quantitative analysis of big data will be invaluable in understanding complex brain-behavior relationships.

Dataset Information

Individual-Level Lesion-Network Mapping to Visualize the Effects of a Stroke Lesion on the Brain Network: Connectograms in Stroke Syndromes.

Publications

Individual-Level Lesion-Network Mapping to Visualize the Effects of a Stroke Lesion on the Brain Network: Connectograms in Stroke Syndromes.

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