Mapping causal functional contributions derived from the clinical assessment of brain damage after stroke

Multimodal radiomics based on lesion connectome predicts stroke prognosis

BACKGROUND

Stroke significantly contributes to global mortality and disability, emphasizing the critical need for effective prognostic evaluations. Connectome-based lesion-symptom mapping (CLSM) identifies structural and functional connectivity disruptions related to the lesion, while radiomics extracts high-dimensional quantitative data from multimodal medical images. Despite the potential of these methodologies, no study has yet integrated CLSM and multimodal radiomics for acute ischemic stroke (AIS).

METHODS

This retrospective study analyzed lesion, structural disconnection (SDC), and functional disconnection (FDC) maps of 148 patients with AIS and assessed their association with the National Institutes of Health Stroke Scale (NIHSS) score at admission and prognostic outcomes, measured by the modified Rankin Scale at six months. Additionally, an innovative approach was proposed by utilizing the SDC map as mask, and radiomic features were extracted and selected from T1-weighted imaging, diffusion-weighted imaging, apparent diffusion coefficient, susceptibility-weighted imaging, and fluid-attenuated inversion recovery images. Five machine learning classifiers were then used to predict the prognosis of AIS.

RESULTS

This study constructed lesion, SDC and FDC maps to correlate with NIHSS scores and prognostic outcomes, thereby revealing the neuroanatomical mechanisms underlying neural damage and prognosis. Poor prognosis was associated with distal cortical dysfunction and fiber disconnection. Fifteen radiomic features within SDC maps from multimodal imaging were selected as inputs for machine learning models. Among the five classifiers tested, Categorical Boosting achieved the highest performance (AUC = 0.930, accuracy = 0.836).

CONCLUSION

A novel model integrating CLSM and multimodal radiomics was proposed to predict long-term prognosis in AIS, which would be a promising tool for early prognostic evaluation and therapeutic planning. Further investigation is needed to assess its robustness in clinical application.

Analysis of clinical anatomical correlates of motor deficits in stroke by multivariate lesion inference based on game theory

Introduction

The exploration of causal functional inferences on the basis of deficits observed after neurological impairments is often based on the separate study of gray matter regions or white matter tracts. Here, we aimed at jointly analysing contributions of gray matter and white matter by using the domain of motor function and the approach of iterative estimated Multi-perturbation Shapley Analysis (MSA), a multivariate game-theoretical lesion inference method.

Methods

We analyzed motor scores assessed by the National Institute of Health Stroke Scale (NIHSS) together with corresponding lesion patterns of 272 stroke patients using a finely parcellated map of 150 gray matter regions and white matter tracts of the brain.

Results

MSA revealed a small set of essential causal contributions to motor function from the internal capsule, the cortico-spinal tract, and the cortico-ponto-cerebellum tract.

Discussion

These findings emphasize the connectional anatomy of motor function and, on the methodological side, confirm that the advanced multivariate method of iterative estimated MSA provides a practical strategy for the characterization of brain functions on the basis of finely resolved maps of the brain.

Hypoperfusion regions linked to National Institutes of Health Stroke Scale scores in acute stroke

BACKGROUND

The National Institutes of Health Stroke Scale (NIHSS) is widely used to assess stroke severity. While prior studies have identified subcortical regions where infarcts correlate with NIHSS scores, stroke symptoms can also arise from hypoperfusion, not just infarcts. Understanding the potential for neurological recovery post-reperfusion is essential for guiding treatment decisions. The goal of this study was to identify brain regions where hypoperfusion correlates with NIHSS scores, using computed tomography perfusion (CTP) scans in cases of acute ischemic stroke.

METHODS

In this prospective observational study, we analyzed CTP scans and NIHSS scores from 89 patients in the acute phase. We employed a unique support vector regression approach to overcome limitations of traditional mass univariate analyses. Additionally, we used stability selection to identify the most consistent features across subsets, reducing overfitting and ensuring robust predictive models. We verified the consistency of results through nested cross-validation.

RESULTS

Both cortical and subcortical areas, including white matter tracts, showed associations with NIHSS scores. These regions aligned with functions such as language, spatial attention, sensory, and motor skills, all assessed by the NIHSS.

CONCLUSIONS

Our findings reveal that hypoperfusion in specific brain regions, including previously underreported cortical areas, contributes to NIHSS scores in acute stroke. Moreover, this study introduces a novel brain mapping approach using CTP imaging and stability selection, offering a more comprehensive view of acute stroke impairments and the potential for recovery before structural reorganization occurs.

Specific gut microbiome signatures predict the risk of acute ischemic stroke

Introduction

Numerous studies have reported alterations in the composition of gut microbiota in patients with acute ischemic stroke (AIS), with changes becoming more pronounced as the disease progresses. However, the association between the progression of transient ischemic attack (TIA) and AIS remains unclear. This study aims to elucidate the microbial differences among TIA, AIS, and healthy controls (HC) while exploring the associations between disease progression and gut microbiota.

Methods

Fecal samples were collected from acute TIA patients (n = 28), AIS patients (n = 235), and healthy controls (n = 75) and analyzed using 16 s rRNA gene sequencing. We determined characteristic microbiota through linear discriminant analysis effect size and used the receiver operating characteristic (ROC) curve to assess their predictive value as diagnostic biomarkers.

Results

Our results showed significant gut microbial differences among the TIA, AIS, and HC groups. Patients with AIS exhibited higher abundances of Lactobacillus and Streptococcus, along with lower abundances of Butyricicoccaceae and Lachnospiraceae_UCG-004. Further analysis revealed that the abundance of characteristic bacteria, such as Lactobacillus and Streptococcus, was negatively correlated with HDL levels, while Lactobacillus was positively correlated with risk factors such as homocysteine (Hcy). In contrast, the abundance of Lachnospiraceae_UCG-004 was negatively correlated with both Hcy and D-dimer levels. ROC models based on the characteristic bacteria Streptococcus and Lactobacillus effectively distinguished TIA from AIS, yielding areas under the curve of 0.699 and 0.626, respectively.

Conclusion

We identified distinct changes in gut bacteria associated with the progression from TIA to AIS and highlighted specific characteristic bacteria as predictive biomarkers. Overall, our findings may promote the development of microbiome-oriented diagnostic methods for the early detection of AIS.

Ground-truth validation of uni- and multivariate lesion inference approaches

Lesion analysis aims to reveal the causal contributions of brain regions to brain functions. Various strategies have been used for such lesion inferences. These approaches can be broadly categorized as univariate or multivariate methods. Here we analysed data from 581 patients with acute ischaemic injury, parcellated into 41 Brodmann areas, and systematically investigated the inferences made by two univariate and two multivariate lesion analysis methods via ground-truth simulations, in which we defined a priori contributions of brain areas to assumed brain function. Particularly, we analysed single-region models, with only single areas presumed to contribute functionally, and multiple-region models, with two contributing regions that interacted in a synergistic, redundant or mutually inhibitory mode. The functional contributions could vary in proportion to the lesion damage or in a binary way. The analyses showed a considerably better performance of the tested multivariate than univariate methods in terms of accuracy and mis-inference error. Specifically, the univariate approaches of Lesion Symptom Mapping as well as Lesion Symptom Correlation mis-inferred substantial contributions from several areas even in the single-region models, and also after accounting for lesion size. By contrast, the multivariate approaches of Multi-Area Pattern Prediction, which is based on machine learning, and Multi-perturbation Shapley value Analysis, based on coalitional game theory, delivered consistently higher accuracy and specificity. Our findings suggest that the tested multivariate approaches produce largely reliable lesion inferences, without requiring lesion size consideration, while the application of the univariate methods may yield substantial mis-localizations that limit the reliability of functional attributions.

Frequency specific alterations of the degree centrality in patients with acute basal ganglia ischemic stroke: a resting-state fMRI study

This study intended to investigate the frequency specific brain oscillation activity in patients with acute basal ganglia ischemic stroke (BGIS) by using the degree centrality (DC) method. A total of 34 acute BGIS patients and 44 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging (rs-fMRI) scanning. The DC values in three frequency bands (conventional band: 0.01-0.08 Hz, slow‑4 band: 0.027-0.073 Hz, slow‑5 band: 0.01-0.027 Hz) were calculated. A two-sample t-test was used to explore the between-group differences in the conventional frequency band. A two-way repeated-measures analysis of variance (ANOVA) was used to analyze the DC differences between groups (BGIS patients, HCs) and bands (slow‑4, slow‑5). Moreover, correlations between DC values and clinical indicators were performed. In conventional band, the DC value in the right middle temporal gyrus was decreased in BGIS patients compared with HCs. Significant differences of DC were observed between the two bands mainly in the bilateral cortical brain regions. Compared with the HCs, the BGIS patients showed increased DC in the right superior temporal gyrus and the left precuneus, but decreased mainly in the right inferior temporal gyrus, right inferior occipital gyrus, right precentral, and right supplementary motor area. Furthermore, the decreased DC in the right rolandic operculum in slow-4 band and the right superior temporal gyrus in slow-5 band were found by post hoc two-sample t-test of main effect of group. There was no significant correlation between DC values and clinical scales after Bonferroni correction. Our findings showed that the DC changes in BGIS patients were frequency specific. Functional abnormalities in local brain regions may help us to understand the underlying pathogenesis mechanism of brain functional reorganization of BGIS patients.

Use of multi-perturbation Shapley analysis in lesion studies of functional networks: The case of upper limb paresis

Understanding the impact of variation in lesion topography on the expression of functional impairments following stroke is important, as it may pave the way to modeling structure-function relations in statistical terms while pointing to constraints for adaptive remapping and functional recovery. Multi-perturbation Shapley-value analysis (MSA) is a relatively novel game-theoretical approach for multivariate lesion-symptom mapping. In this methodological paper, we provide a comprehensive explanation of MSA. We use synthetic data to assess the method's accuracy and perform parameter optimization. We then demonstrate its application using a cohort of 107 first-event subacute stroke patients, assessed for upper limb (UL) motor impairment (Fugl-Meyer Assessment scale). Under the conditions tested, MSA could correctly detect simulated ground-truth lesion-symptom relationships with a sensitivity of 75% and specificity of ~90%. For real behavioral data, MSA disclosed a strong hemispheric effect in the relative contribution of specific regions-of-interest (ROIs): poststroke UL motor function was mostly contributed by damage to ROIs associated with movement planning (supplementary motor cortex and superior frontal gyrus) following left-hemispheric damage (LHD) and by ROIs associated with movement execution (primary motor and somatosensory cortices and the ventral brainstem) following right-hemispheric damage (RHD). Residual UL motor ability following LHD was found to depend on a wider array of brain structures compared to the residual motor ability of RHD patients. The results demonstrate that MSA can provide a unique insight into the relative importance of different hubs in neural networks, which is difficult to obtain using standard univariate methods.

Lesion-symptom mapping with NIHSS sub-scores in ischemic stroke patients

Background

Lesion-symptom mapping (LSM) is a statistical technique to investigate the population-specific relationship between structural integrity and post-stroke clinical outcome. In clinical practice, patients are commonly evaluated using the National Institutes of Health Stroke Scale (NIHSS), an 11-domain clinical score to quantitate neurological deficits due to stroke. So far, LSM studies have mostly used the total NIHSS score for analysis, which might not uncover subtle structure–function relationships associated with the specific sub-domains of the NIHSS evaluation. Thus, the aim of this work was to investigate the feasibility to perform LSM analyses with sub-score information to reveal category-specific structure–function relationships that a total score may not reveal.

Methods

Employing a multivariate technique, LSM analyses were conducted using a sample of 180 patients with NIHSS assessment at 48-hour post-stroke from the ESCAPE trial. The NIHSS domains were grouped into six categories using two schemes. LSM was conducted for each category of the two groupings and the total NIHSS score.

Results

Sub-score LSMs not only identify most of the brain regions that are identified as critical by the total NIHSS score but also reveal additional brain regions critical to each function category of the NIHSS assessment without requiring extensive, specialised assessments.

Conclusion

These findings show that widely available sub-scores of clinical outcome assessments can be used to investigate more specific structure–function relationships, which may improve predictive modelling of stroke outcomes in the context of modern clinical stroke assessments and neuroimaging.

Trial registration number

NCT01778335.

Game-theoretical mapping of fundamental brain functions based on lesion deficits in acute stroke

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.

CT imaging character of different brain regions in different ages of Diannan small-ear pigs

Objective

To collect and establish normal data of the brain regions of Diannan small-ear (DSE pigs, basically throughout measuring and comparing computed tomography values of the barin.

Methods

12 ordinary DSE pigs were divided into juvenile, adult, old groups based on the physiological ages (n=4) A SOMATOM Definition AS 64-row 128-slice 4D spiral CT scanner (SOMATOM Definition AS, Germany) was used to collect CT images of DSE pigs, record and analyze the scanning data.

Results

Compared with the juvenile group, the CT values of the right frontal lobe, right parietal lobe, right temporal lobe, left temporal lobe, and right occipital lobe were significantly higher in the elderly group. Compared with the adult group, the CT values of the right frontal lobe, left frontal lobe, right parietal lobe, right temporal lobe, left temporal lobe, and right occipital lobe of the elderly group were significantly higher.

Conclusion

The results of the study can be used to evaluate the changes in CT values of various brain regions in piglets of different ages and future pig head injury models or other studies that require CT-based analysis.

Interaction between cognitive reserve and age moderates effect of lesion load on stroke outcome

The concepts of brain reserve and cognitive reserve were recently suggested as valuable predictors of stroke outcome. To test this hypothesis, we used age, years of education and lesion size as clinically feasible coarse proxies of brain reserve, cognitive reserve, and the extent of stroke pathology correspondingly. Linear and logistic regression models were used to predict cognitive outcome (Montreal Cognitive Assessment) and stroke-induced impairment and disability (NIH Stroke Scale; modified Rankin Score) in a sample of 104 chronic stroke patients carefully controlled for potential confounds. Results revealed 46% of explained variance for cognitive outcome (p < 0.001) and yielded a significant three-way interaction: Larger lesions did not lead to cognitive impairment in younger patients with higher education, but did so in younger patients with lower education. Conversely, even small lesions led to poor cognitive outcome in older patients with lower education, but didn’t in older patients with higher education. We observed comparable three-way interactions for clinical scores of stroke-induced impairment and disability both in the acute and chronic stroke phase. In line with the hypothesis, years of education conjointly with age moderated effects of lesion on stroke outcome. This non-additive effect of cognitive reserve suggests its post-stroke protective impact on stroke outcome.

Revisiting 'brain modes' in a new computational era: approaches for the characterization of brain-behavioural associations

The study of brain-function relationships is undergoing a conceptual and methodological transformation due to the emergence of network neuroscience and the development of multivariate methods for lesion-deficit inferences. Anticipating this process, in 1998 Godefroy and co-workers conceptualized the potential of four elementary typologies of brain-behaviour relationships named 'brain modes' (unicity, equivalence, association, summation) as building blocks able to describe the association between intact or lesioned brain regions and cognitive processes or neurological deficits. In the light of new multivariate lesion inference and network approaches, we critically revisit and update the original theoretical notion of brain modes, and provide real-life clinical examples that support their existence. To improve the characterization of elementary units of brain-behavioural relationships further, we extend such conceptualization with a fifth brain mode (mutual inhibition/masking summation). We critically assess the ability of these five brain modes to account for any type of brain-function relationship, and discuss past versus future contributions in redefining the anatomical basis of human cognition. We also address the potential of brain modes for predicting the behavioural consequences of lesions and their future role in the design of cognitive neurorehabilitation therapies.

Neural architectures of music - Insights from acquired amusia

The ability to perceive and produce music is a quintessential element of human life, present in all known cultures. Modern functional neuroimaging has revealed that music listening activates a large-scale bilateral network of cortical and subcortical regions in the healthy brain. Even the most accurate structural studies do not reveal which brain areas are critical and causally linked to music processing. Such questions may be answered by analysing the effects of focal brain lesions in patients´ ability to perceive music. In this sense, acquired amusia after stroke provides a unique opportunity to investigate the neural architectures crucial for normal music processing. Based on the first large-scale longitudinal studies on stroke-induced amusia using modern multi-modal magnetic resonance imaging (MRI) techniques, such as advanced lesion-symptom mapping, grey and white matter morphometry, tractography and functional connectivity, we discuss neural structures critical for music processing, consider music processing in light of the dual-stream model in the right hemisphere, and propose a neural model for acquired amusia.

Ischemic Stroke in Pontine and Corona Radiata: Location Specific Impairment of Neural Network Investigated With Resting State fMRI

Objective: This study aims to investigate location-specific functional remodeling following ischemic stroke in pons and corona radiata.

Methods: This study was approved by the local Institutional Review Board. Written consent was obtained from each of the participants prior to the MRI examination. Thirty six subjects with first ever acute ischemic stroke in pons (PS, n = 15, aged 62.8 ± 11.01 years) or corona radiata (CRS, n = 21, aged 59.33 ± 13.84 years) as well as 30 age and sex matched healthy controls (HC, n = 30, aged 60 ± 6.43 years) were examined with resting state functional magnetic resonance imaging (rs-fMRI). Regional homogeneity (ReHo) and degree centrality (DC) were calculated using a voxel-based approach. Intergroup differences in ReHo and DC were explored using a permutation test with a threshold-free cluster enhancement (PT TFCE, number of permutations = 1,000, family-wise error rate (FWER) < 0.05).

Results: ReHo and DC alterations were identified in distributed anatomies for both PS and CRS groups. DC mainly increased in the bilateral anterior and posterior cingulate cortex, the inferior frontal-orbital gyrus, and decreased in the bilateral cuneus, calcarine, and the precuneus, while ReHo mainly decreased in the precentral and the postcentral gyri, inferior parietal lobules, precuneus, posterior cingulate cortex, and the superior occipital gyrus. PS and CRS groups were not significantly different in ReHo or DC (FWER > 0.05).

Conclusions: Focal ischemic stroke in pons or corona radiata leads to extensive alterations in the functional network centrality. IS-induced network remodeling is more anatomy-specific than pathway-specific, which may underpin the clinicotopographical profiles during the disease dynamic. Approaches targeting neural pathway and functional connectivity may shed light on a better characterization and management innovation of ischemic stroke.

Perturbation-driven paradoxical facilitation of visuo-spatial function: Revisiting the 'Sprague effect'

The 'Sprague Effect' described in the seminal paper of James Sprague (Science 153:1544-1547, 1966a) is an unexpected paradoxical effect in which a second brain lesion reversed functional deficits induced by an earlier lesion. It was observed initially in the cat where severe and permanent contralateral visually guided attentional deficits generated by the ablation of large areas of the visual cortex were reversed by the subsequent removal of the superior colliculus (SC) opposite to the cortical lesion or by the splitting of the collicular commissure. Physiologically, this effect has been explained in several ways-most notably by the reduction of the functional inhibition of the ipsilateral SC by the contralateral SC, and the restoration of normal interactions between cortical and midbrain structures after ablation. In the present review, we aim at reappraising the 'Sprague Effect' by critically analyzing studies that have been conducted in the feline and human brain. Moreover, we assess applications of the 'Sprague Effect' in the rehabilitation of visually guided attentional impairments by using non-invasive therapeutic approaches such as transcranial magnetic stimulation (TMS) and transcranial direct-current stimulation (tDCS). We also review theoretical models of the effect that emphasize the inhibition and balancing between the two hemispheres and show implications for lesion inference approaches. Last, we critically review whether the resulting inter-hemispheric rivalry theories lead toward an efficient rehabilitation of stroke in humans. We conclude by emphasizing key challenges in the field of 'Sprague Effect' applications in order to design better therapies for brain-damaged patients.

Reprint of: Mapping human brain lesions and their functional consequences

Neuroscience has a long history of inferring brain function by examining the relationship between brain injury and subsequent behavioral impairments. The primary advantage of this method over correlative methods is that it can tell us if a certain brain region is necessary for a given cognitive function. In addition, lesion-based analyses provide unique insights into clinical deficits. In the last decade, statistical voxel-based lesion behavior mapping (VLBM) emerged as a powerful method for understanding the architecture of the human brain. This review illustrates how VLBM improves our knowledge of functional brain architecture, as well as how it is inherently limited by its mass-univariate approach. A wide array of recently developed methods appear to supplement traditional VLBM. This paper provides an overview of these new methods, including the use of specialized imaging modalities, the combination of structural imaging with normative connectome data, as well as multivariate analyses of structural imaging data. We see these new methods as complementing rather than replacing traditional VLBM, providing synergistic tools to answer related questions. Finally, we discuss the potential for these methods to become established in cognitive neuroscience and in clinical applications.

Brain anomaly networks uncover heterogeneous functional reorganization patterns after stroke

Stroke has a large physical, psychological, and financial burden on patients, their families, and society. Based on functional networks (FNs) constructed from resting state fMRI data, network connectivity after stroke is commonly conjectured to be more randomly reconfigured. We find that this hypothesis depends on the severity of stroke. Head movement-corrected, resting-state fMRI data were acquired from 32 patients after stroke, and 37 healthy volunteers. We constructed anomaly FNs, which combine time series information of a patient with the healthy control group. We propose data-driven techniques to automatically identify regions of interest that are stroke relevant. Graph analysis based on anomaly FNs suggests consistently that strong connections in healthy controls are broken down specifically and characteristically for brain areas that are related to sensorimotor functions and frontoparietal control systems, but new links in stroke patients are rebuilt randomly from all possible areas. Entropic measures of complexity are proposed for characterizing the functional connectivity reorganization patterns, which are correlated with hand and wrist function assessments of stroke patients and show high potential for clinical use.

Considerations for the Use of Neuroimaging Technologies for Predicting Recovery of Speech and Language in Aphasia

PURPOSE

The number of research articles aimed at identifying neuroimaging biomarkers for predicting recovery from aphasia continues to grow. Although the clinical use of these biomarkers to determine prognosis has been proposed, there has been little discussion of how this would be accomplished. This is an important issue because the best translational science occurs when translation is considered early in the research process. The purpose of this clinical focus article is to present a framework to guide the discussion of how neuroimaging biomarkers for recovery from aphasia could be implemented clinically.

METHOD

The genomics literature reveals that implementing genetic testing in the real-world poses both opportunities and challenges. There is much similarity between these opportunities and challenges and those related to implementing neuroimaging testing to predict recovery in aphasia. Therefore, the Center for Disease Control's model list of questions aimed at guiding the review of genetic testing has been adapted to guide the discussion of using neuroimaging biomarkers as predictors of recovery in aphasia.

CONCLUSION

The adapted model list presented here is a first and useful step toward initiating a discussion of how neuroimaging biomarkers of recovery could be employed clinically to provide improved quality of care for individuals with aphasia.

Mapping human brain lesions and their functional consequences

Neuroscience has a long history of inferring brain function by examining the relationship between brain injury and subsequent behavioral impairments. The primary advantage of this method over correlative methods is that it can tell us if a certain brain region is necessary for a given cognitive function. In addition, lesion-based analyses provide unique insights into clinical deficits. In the last decade, statistical voxel-based lesion behavior mapping (VLBM) emerged as a powerful method for understanding the architecture of the human brain. This review illustrates how VLBM improves our knowledge of functional brain architecture, as well as how it is inherently limited by its mass-univariate approach. A wide array of recently developed methods appear to supplement traditional VLBM. This paper provides an overview of these new methods, including the use of specialized imaging modalities, the combination of structural imaging with normative connectome data, as well as multivariate analyses of structural imaging data. We see these new methods as complementing rather than replacing traditional VLBM, providing synergistic tools to answer related questions. Finally, we discuss the potential for these methods to become established in cognitive neuroscience and in clinical applications.

Game theoretical mapping of causal interactions underlying visuo-spatial attention in the human brain based on stroke lesions

Anatomical studies conducted in neurological conditions have developed our understanding of the causal relationships between brain lesions and their clinical consequences. The analysis of lesion patterns extended across brain networks has been particularly useful in offering new insights on brain-behavior relationships. Here we applied multiperturbation Shapley value Analysis (MSA), a multivariate method based on coalitional game theory inferring causal regional contributions to specific behavioral outcomes from the characteristic functional deficits after stroke lesions. We established the causal patterns of contributions and interactions of nodes of the attentional orienting network on the basis of lesion and behavioral data from 25 right hemisphere stroke patients tested in visuo-spatial attention tasks. We calculated the percentage of damaged voxels for five right hemisphere cortical regions contributing to attentional orienting, involving seven specific Brodmann Areas (BA): Frontal Eye Fields, (FEF-BA6), Intraparietal Sulcus (IPS-BA7), Inferior Frontal Gyrus (IFG-BA44/BA45), Temporo-Parietal Junction (TPJ-BA39/BA40), and Inferior Occipital Gyrus (IOG-BA19). We computed the MSA contributions of these seven BAs to three behavioral clinical tests (line bisection, bells cancellation, and letter cancelation). Our analyses indicated IPS as the main contributor to the attentional orienting and also revealed synergistic influences among IPS, TPJ, and IOG (for bells cancellation and line bisection) and between TPJ and IFG (for bells and letter cancellation tasks). The findings demonstrate the ability of the MSA approach to infer plausible causal contributions of relevant right hemisphere sites in poststroke visuo-spatial attention and awareness disorders. Hum Brain Mapp 38:3454-3471, 2017. © 2017 Wiley Periodicals, Inc.

Technical considerations of a game-theoretical approach for lesion symptom mapping

Background

Various strategies have been used for inferring brain functions from stroke lesions. We explored a new mathematical approach based on game theory, the so-called multi-perturbation Shapley value analysis (MSA), to assess causal function localizations and interactions from multiple perturbation data. We applied MSA to a dataset composed of lesion patterns of 148 acute stroke patients and their National Institutes of Health Stroke Scale (NIHSS) scores, to systematically investigate the influence of different parameter settings on the outcomes of the approach. Specifically, we investigated aspects of MSA methodology including the choice of the predictor algorithm (typology and kernel functions), training dataset (original versus binary), as well as the influence of lesion thresholds. We assessed the suitability of MSA for processing real clinical lesion data and established the central parameters for this analysis.

Results

We derived general recommendations for the analysis of clinical datasets by MSA and showed that, for the studied dataset, the best approach was to use a linear-kernel support vector machine predictor, trained with a binary training dataset, where the binarization was implemented through a median threshold of lesion size for each region. We demonstrated that the results obtained with different MSA variants lead to almost identical results as the basic MSA.

Conclusions

MSA is a feasible approach for the multivariate lesion analysis of clinical stroke data. Informed choices need to be made to set parameters that may affect the analysis outcome.

Picturing the Size and Site of Stroke With an Expanded National Institutes of Health Stroke Scale

Background and Purpose—

The National Institutes of Health Stroke Scale (NIHSS) includes minimal assessment of cognitive function, particularly in right hemisphere (RH) stroke. Descriptions of the Cookie Theft picture from the NIHSS allow analyses that (1) correlate with aphasia severity and (2) identify communication deficits in RH stroke. We hypothesized that analysis of the picture description contributes valuable information about volume and location of acute stroke.

Methods—

We evaluated 67 patients with acute ischemic stroke (34 left hemisphere [LH]; 33 RH) with the NIHSS, analysis of the Cookie Theft picture, and magnetic resonance imaging, compared with 35 sex- and age-matched controls. We evaluated descriptions for total content units (CU), syllables, ratio of left:right CU, CU/minute, and percent interpretive CU, based on previous studies. Lesion volume and percent damage to regions of interest were measured on diffusion-weighted imaging. Multivariable linear regression identified variables associated with infarct volume, independently of NIHSS score, age and sex.

Results—

Patients with RH and LH stroke differed from controls, but not from each other, on CU, syllables/CU, and CU/minute. Left:right CU was lower in RH compared with LH stroke. CU, syllables/CU, and NIHSS each correlated with lesion volume in LH and RH stroke. Lesion volume was best accounted by a model that included CU, syllables/CU, NIHSS, left:right CU, percent interpretive CU, and age, in LH and RH stroke. Each discourse variable and NIHSS score were associated with percent damage to different regions of interest, independently of lesion volume and age.

Conclusions—

Brief picture description analysis complements NIHSS scores in predicting stroke volume and location.