Indirect structural disconnection-symptom mapping

Post-stroke sensory hypersensitivity: insights from lesion-symptom and disconnection mapping

A post-injury increase in sensory sensitivity is frequently reported by acquired brain injury patients, including stroke patients. These symptoms are related to poor functional outcomes, but their underlying neural mechanisms remain unclear. Since stroke results in focal lesions that can easily be visualized on imaging, the lesions of stroke survivors can be used to study the neuroanatomy of post-injury sensory hypersensitivity. We used multivariate support vector regression lesion-symptom mapping and indirect structural disconnection mapping to uncover the lesion location and white matter tracts related to post-stroke sensory hypersensitivity. A total of 103 patients were included in the study, of which 47% reported post-stroke sensory hypersensitivity across different sensory modalities. The lesion-symptom and structural connectivity mapping identified the putamen, thalamus, amygdala and insula in the grey matter as well as fronto-insular tracts, and the fronto-striatal tract in the white matter as neural structures potentially involved in post-stroke sensory hypersensitivity. By examining the neuroanatomy of post-stroke sensory hypersensitivity in a large stroke sample, this study offers a significant advancement in our understanding of the neural basis of post-stroke sensory hypersensitivity.

Post-stroke outcome prediction based on lesion-derived features

Stroke-induced deficits result from both focal structural damage and widespread network disruption. This study investigated whether simulated measures of network disruption, derived from structural lesions, could predict functional impairments in stroke patients. We extracted four lesion-derived feature sets: lesion masks, probabilistic structural disconnection maps (pSDMs), structural and indirectly estimated functional connectivity strengths between brain regions, and topological properties of functional and structural brain networks to predict motor, executive, and processing speed deficits in 340 S patients, employing PCA-based ridge regression with leave-one-out cross validation. The findings revealed that both structural disconnection map patterns and lesion masks were strong predictors of functional deficits. Lesion masks exhibited superior predictive performance relative to unthresholded pSDMs. Furthermore, applying a probability threshold to the pSDMs - retaining only disconnections present in a sufficient proportion of healthy subjects - significantly improved predictive performance. For motor deficits, thresholded SDMs (tSDMs) with thresholds of 0.9 and 0.5 produced the highest R2 values, 0.95 for left motor deficits and 0.69 for right motor deficits, respectively. In the case of executive function and processing speed, the highest R2 values were 0.58 and 0.64, achieved with tSDM thresholds of 0.3 and 0.5, respectively. Connectome-based features exhibited lower R2 values, with structural connection strength alterations showing stronger associations with post-stroke scores compared to changes in functional connectivity. Nodal parameters (degree and clustering coefficient) had lower explanatory power than the SDM features and lesion masks. Our findings underscore the effectiveness of lesion masks and thresholded SDMs in predicting post-stroke deficits. This study contributes to the growing body of evidence supporting the reliability of simulated structural networks as a complementary approach to lesion patterns and structural disconnection in predicting post-stroke outcomes.

The challenge of long-term stroke outcome prediction and how statistical correlates do not imply predictive value

Personalized prediction of stroke outcome using lesion imaging markers is still too imprecise to make a breakthrough in clinical practice. We performed a combined prediction and brain mapping study on topographic and connectomic lesion imaging data to evaluate (i) the relationship between lesion-deficit associations and their predictive value and (ii) the influence of time since stroke. In patients with first-ever ischaemic stroke, we first applied high-dimensional machine learning models on lesion topographies or structural disconnection data to model stroke severity (National Institutes of Health Stroke Scale 24 h/3 months) and functional outcome (modified Rankin Scale 3 months) in cross-validation. Second, we mapped the topographic and connectomic lesion impact on both clinical measures. We retrospectively included 685 patients [age 67.4 ± 15.1, National Institutes of Health Stroke Scale 24 h median(IQR) = 3(1; 6), modified Rankin Scale 3 months = 1(0; 2), National Institutes of Health Stroke Scale 3 months = 0(0; 2)]. Predictions for acute stroke severity (National Institutes of Health Stroke Scale 24 h) were better with topographic lesion imaging (R² = 0.41) than with disconnection data (R² = 0.29, P = 0.0015), whereas predictions at 3 months (National Institutes of Health Stroke Scale/modified Rankin Scale) were generally close to chance level. In the analysis of lesion-deficit associations, the correlates of more severe acute stroke (National Institutes of Health Stroke Scale 24 h > 4) and poor functional outcome (modified Rankin Scale 3 months ≥ 2) were left-lateralized. The lesion location impact of both variables corresponded in right-hemisphere stroke with peaks in primary motor regions, but it markedly differed in left-hemisphere stroke. Topographic and disconnection lesion features predict acute stroke severity better than the 3-months outcome. This suggests a likely higher impact of lesion-independent factors in the longer term and highlights challenges in the prediction of global functional outcome. Prediction and brain mapping diverge, and the existence of statistically significant associations-as here for 3-months outcomes-does not imply predictive value. Routine neurological scores better capture left- than right-hemispheric lesions, further complicating the challenge of outcome prediction.

The contribution of cutaneous thermal signals to bodily self-awareness

Thermosensory signals may contribute to the sense of body ownership, but their role remains highly debated. We test this assumption within the framework of pathological body ownership, hypothesising that skin temperature and thermoception differ between right-hemisphere stroke patients with and without Disturbed Sensation of Ownership (DSO) for the contralesional plegic upper limb. Patients with DSO exhibit lower basal hand temperatures bilaterally and impaired perception of cold and warm stimuli. Lesion mapping reveals associations in the right Rolandic Operculum and Insula, with these regions linked to lower skin temperature located posterior to those associated with thermoception deficits. Disconnections in bilateral parietal regions are associated with lower hand temperature, while disconnections in a right-lateralized thalamus-parietal hub correlate with thermoception deficits. We discuss the theoretical implications of these findings in the context of the ongoing debate on the role of homeostatic signals in shaping a coherent sense of body ownership.

Iowa Brain-Behavior Modeling Toolkit: An Open-Source MATLAB Tool for Inferential and Predictive Modeling of Imaging-Behavior and Lesion-Deficit Relationships

The traditional analytical framework taken by neuroimaging studies in general, and lesion-behavior studies in particular, has been inferential in nature and has focused on identifying and interpreting statistically significant effects within the sample under study. While this framework is well-suited for hypothesis testing approaches, achieving the modern goal of precision medicine requires a different framework that is predictive in nature and that focuses on maximizing the predictive power of models and evaluating their ability to generalize beyond the data that were used to train them. However, few tools exist to support the development and evaluation of predictive models in the context of neuroimaging or lesion-behavior research, creating an obstacle to the widespread adoption of predictive modeling approaches in the field. Further, existing tools for lesion-behavior analysis are often unable to accommodate categorical outcome variables and often impose restrictions on the predictor data. Researchers therefore often must use different software packages and analytical approaches depending on (a) whether they are addressing a classification versus regression problem and (b) whether their predictor data correspond to binary lesion images, continuous lesion-network images, connectivity matrices, or other data modalities. To address these limitations, we have developed a MATLAB software toolkit that supports both inferential and predictive modeling frameworks, accommodates both classification and regression problems, and does not impose restrictions on the modality of the predictor data. The toolkit features both a graphical user interface and scripting interface, includes implementations of multiple mass-univariate, multivariate, and machine learning models, features built-in and customizable routines for hyper-parameter optimization, cross-validation, model stacking, and significance testing, and automatically generates text-based descriptions of key methodological details and modeling results to improve reproducibility and minimize errors in the reporting of methods and results. Here, we provide an overview and discussion of the toolkit's features and demonstrate its functionality by applying it to the question of how expressive and receptive language impairments relate to lesion location, structural disconnection, and functional network disruption in a large sample of patients with left hemispheric brain lesions. We find that impairments in expressive versus receptive language are most strongly associated with left lateral prefrontal and left posterior temporal/parietal damage, respectively. We also find that impairments in expressive vs. receptive language are associated with partially overlapping patterns of fronto-temporal structural disconnection and with similar functional networks. Importantly, we find that lesion location and lesion-derived network measures are highly predictive of both types of impairment, with predictions from models trained on these measures explaining ~30%-40% of the variance on average when applied to data from patients not used to train the models. We have made the toolkit publicly available, and we have included a comprehensive set of tutorial notebooks to support new users in applying the toolkit in their studies.

Mapping sentence comprehension and syntactic complexity: evidence from 131 stroke survivors

Understanding and interpreting how words are organized in a sentence to convey distinct meanings is a cornerstone of human communication. The neural underpinnings of this ability, known as syntactic comprehension, are far from agreed upon in current neurocognitive models of language comprehension. Traditionally, left frontal regions (e.g. left posterior inferior frontal gyrus) were considered critical, while more recently, left temporal regions (most prominently, left posterior middle temporal gyrus) have been identified as more indispensable to syntactic comprehension. Syntactic processing has been investigated by using different types of non-canonical sentences i.e. those that do not follow prototypical word order and are considered more syntactically complex. However, non-canonical sentences can be complex for different linguistic reasons, and thus, their comprehension might rely on different neural underpinnings. In this cross-sectional study, we explored the neural correlates of syntactic comprehension by investigating the roles of left hemisphere brain regions and white matter pathways in processing sentences with different levels of syntactic complexity. Participants were assessed at a single point in time using structural MRI and behavioural tests. Employing lesion-symptom mapping and indirect structural disconnection mapping in a cohort of 131 left hemisphere stroke survivors, our analysis revealed the following left temporal regions and underlying white matter pathways as crucial for general sentence comprehension: the left mid-posterior superior temporal gyrus, middle temporal gyrus and superior temporal sulcus and the inferior longitudinal fasciculus, the inferior fronto-occipital fasciculus, the middle longitudinal fasciculus, the uncinate fasciculus and the tracts crossing the most posterior part of the corpus callosum. We further found significant involvement of different white matter tracts connecting the left temporal and frontal lobes for different sentence types. Spared connections between the left temporal and frontal regions were critical for the comprehension of non-canonical sentences requiring long-distance retrieval (spared superior longitudinal fasciculus for both subject and object extraction and spared arcuate fasciculus for object extraction) but not for comprehension of non-canonical passive sentences and canonical declarative sentences. Our results challenge traditional language models that emphasize the primary role of the left frontal regions, such as Broca's area, in basic sentence structure comprehension. Our findings suggest a gradient of syntactic complexity, rather than a clear-cut dichotomy between canonical and non-canonical sentence structures. Our findings contribute to a more nuanced understanding of the neural architecture of language comprehension and highlight potential directions for future research.

Atlas-Based Structural Disconnectomes Are Associated with Cognitive Performance in Brain Tumors

Background: Brain tumors are associated with impaired cognitive functioning, which may result from disruptions in brain structural connectivity. Estimating structural disconnections is a more advantageous representation of tumor impact and can be performed indirectly through normative brain atlases. Materials and Methods: Using a publicly available dataset of glioma and meningioma patient MRI scans and tumor masks, latent correlations were estimated between measures of structural disconnection and attention-based cognitive functioning. These measures included gray matter (GM) parcel damage, white matter tract damage, GM parcel-to-parcel disconnections, and reaction time (RTI) as part of the Cambridge Neuropsychological Test Automated Battery to assess attention. Results: Preprocessing pipelines with two different methods of minimizing the pathology impact on MRI normalization were utilized: cost-function masking and lesion filling. The results across both pipelines were nearly consistent, with significant correlations mainly found between RTI measures and the damage to the left inferior fronto-occipital and uncinate fasciculus, as well as the left prefrontal-visual disconnections. Conclusions: This alludes to the importance of left-hemispheric prefrontal-visual coupling in attention-based tasks, particularly those involving object- and feature-based attention.

The Role of Anatomic Connectivity in Inhibitory Control Revealed by Combining Connectome-based Lesion-symptom Mapping with Event-related Potentials

Inhibitory control refers to the ability to suppress cognitive or motor processes. Current neurocognitive models indicate that this function mainly involves the anterior cingulate cortex and the inferior frontal cortex. However, how the communication between these areas influence inhibitory control performance and their functional response remains unknown. We addressed this question by injecting behavioral and electrophysiological markers of inhibitory control recorded during a Go/NoGo task as the 'symptoms' in a connectome-based lesion-symptom mapping approach in a sample of 96 first unilateral stroke patients. This approach enables us to identify the white matter tracts whose disruption by the lesions causally influences brain functional activity during inhibitory control. We found a central role of left frontotemporal and frontobasal intrahemispheric connections, as well as of the connections between the left temporoparietal and right temporal areas in inhibitory control performance. We also found that connections between the left temporal and right superior parietal areas modulate the conflict-related N2 event-related potential component and between the left temporal parietal area and right temporal and occipital areas for the inhibition P3 component. Our study supports the role of a distributed bilateral network in inhibitory control and reveals that combining lesion-symptom mapping approaches with functional indices of cognitive processes could shed new light on post-stroke functional reorganization. It may further help to refine the interpretation of classical electrophysiological markers of executive control in stroke patients.

Data-driven biomarkers better associate with stroke motor outcomes than theory-based biomarkers

Chronic motor impairments are a leading cause of disability after stroke. Previous studies have associated motor outcomes with the degree of damage to predefined structures in the motor system, such as the corticospinal tract. However, such theory-based approaches may not take full advantage of the information contained in clinical imaging data. The present study uses data-driven approaches to model chronic motor outcomes after stroke and compares the accuracy of these associations to previously-identified theory-based biomarkers. Using a cross-validation framework, regression models were trained using lesion masks and motor outcomes data from 789 stroke patients from the Enhancing NeuroImaging Genetics through Meta Analysis (ENIGMA) Stroke Recovery Working Group. Using the explained variance metric to measure the strength of the association between chronic motor outcomes and imaging biomarkers, we compared theory-based biomarkers, like lesion load to known motor tracts, to three data-driven biomarkers: lesion load of lesion-behaviour maps, lesion load of structural networks associated with lesion-behaviour maps, and measures of regional structural disconnection. In general, data-driven biomarkers had stronger associations with chronic motor outcomes accuracy than theory-based biomarkers. Data-driven models of regional structural disconnection performed the best of all models tested (R 2 = 0.210, P < 0.001), performing significantly better than the theory-based biomarkers of lesion load of the corticospinal tract (R 2 = 0.132, P < 0.001) and of multiple descending motor tracts (R 2 = 0.180, P < 0.001). They also performed slightly, but significantly, better than other data-driven biomarkers including lesion load of lesion-behaviour maps (R 2 = 0.200, P < 0.001) and lesion load of structural networks associated with lesion-behaviour maps (R 2 = 0.167, P < 0.001). Ensemble models - combining basic demographic variables like age, sex, and time since stroke - improved the strength of associations for theory-based and data-driven biomarkers. Combining both theory-based and data-driven biomarkers with demographic variables improved predictions, and the best ensemble model achieved R 2 = 0.241, P < 0.001. Overall, these results demonstrate that out-of-sample associations between chronic motor outcomes and data-driven imaging features, particularly when lesion data is represented in terms of structural disconnection, are stronger than associations between chronic motor outcomes and theory-based biomarkers. However, combining both theory-based and data-driven models provides the most robust associations.

Stable multivariate lesion symptom mapping

Multivariate lesion-symptom mapping (MLSM) considers lesion information across the entire brain to predict impairments. The strength of this approach is also its weakness-considering many brain features together synergistically can uncover complex brain-behavior relationships but exposes a high-dimensional feature space that a model is expected to learn. Successfully distinguishing between features in this landscape can be difficult for models, particularly in the presence of irrelevant or redundant features. Here, we propose stable multivariate lesion-symptom mapping (sMLSM), which integrates the identification of reliable features with stability selection into conventional MLSM and describe our open-source MATLAB implementation. Usage is showcased with our publicly available dataset of chronic stroke survivors (N=167) and further validated in our independent public acute stroke dataset (N = 1106). We demonstrate that sMLSM eliminates inconsistent features highlighted by MLSM, reduces variation in feature weights, enables the model to learn more complex patterns of brain damage, and improves model accuracy for predicting aphasia severity in a way that tends to be robust regarding the choice of parameters for identifying reliable features. Critically, sMLSM more consistently outperforms predictions based on lesion size alone. This advantage is evident starting at modest sample sizes (N>75). Spatial distribution of feature importance is different in sMLSM, which highlights the features identified by univariate lesion symptom mapping while also implicating select regions emphasized by MLSM. Beyond improved prediction accuracy, sMLSM can offer deeper insight into reliable biomarkers of impairment, informing our understanding of neurobiology.

Disconnection-Based Prediction of Poststroke Dysphagia

BACKGROUND AND PURPOSE

Dysphagia is a common deficit after a stroke and is associated with serious complications. It is not yet fully clear which brain regions are directly related to swallowing. Previous lesion symptom mapping studies may have overlooked structural disconnections that could be responsible for poststroke dysphagia. Here, we aimed to predict and explain the relationship between poststroke dysphagia and the topologic distribution of structural disconnection via a multivariate predictive framework.

MATERIALS AND METHODS

We enrolled first-ever ischemic stroke patients classified as full per-oral nutrition (71 patients) and nonoral nutrition necessary (43 patients). After propensity score matching, 43 patients for each group were enrolled (full per-oral nutrition group with 17 women, 68 ± 15 years; nonoral nutrition necessary group with 13 women, 75 ± 11 years). The structural disconnectome was estimated by using the lesion segmented from acute phase diffusion-weighted images. The prediction of poststroke dysphagia by using the structural disconnectome and demographics was performed in a leave-one-out manner.

RESULTS

Using both direct and indirect disconnection matrices of the motor network, the disconnectome-based prediction model could predict poststroke dysphagia above the level of chance (accuracy = 68.6%, permutation P = .001). When combined with demographic data, the classification accuracy reached 72.1%. The edges connecting the right insula and left motor strip were the most informative in prediction.

CONCLUSIONS

Poststroke dysphagia could be predicted by using the structural disconnectome derived from acute phase diffusion-weighted images. Specifically, the direct and indirect disconnection within the motor network was the most informative in predicting poststroke dysphagia.

The need for hemispheric separation in pairwise structural disconnection studies

The development of new approaches indirectly measuring the structural disconnectome has recently led to an increase in studies investigating pairwise structural disconnections following brain damage. Previous studies jointly analyzed patients with left hemispheric and patients with right hemispheric lesions when investigating a behavior of interest. An alternative approach would be to perform analyses separated by hemisphere, which has been applied in only a minority of studies to date. The present simulation study investigated whether joint or separate analyses (or both equally) are appropriate to reveal the ground truth disconnections. In fact, both approaches resulted in very different patterns of disconnection. In contrast to analyses separated by hemisphere, joint analyses introduced a bias to the disadvantage of intra-hemispheric disconnections. Intra-hemispheric disconnections were statistically underpowered in the joint analysis and thus surpassed the significance threshold with more difficulty compared to inter-hemispheric disconnections. This statistical imbalance was also shown by a greater number of significant inter-hemispheric than significant intra-hemispheric disconnections. This bias from joint analyses is based on mechanisms similar to those underlying the "partial injury problem." We therefore conclude that pairwise structural disconnections in patients with unilateral left hemispheric and with unilateral right hemispheric lesions exhibiting a specific behavior (or disorder) of interest should be studied separately by hemisphere rather than in a joint analysis.

Thalamocortical disconnection involved in pusher syndrome

The presence of both isolated thalamic and isolated cortical lesions have been reported in the context of pusher syndrome-a disorder characterized by a disturbed perception of one's own upright body posture, following unilateral left- or right-sided stroke. In recent times, indirect quantification of functional and structural disconnection increases the knowledge derived from focal brain lesions by inferring subsequent brain network damage from the respective lesion. We applied both measures to a sample of 124 stroke patients to investigate brain disconnection in pusher syndrome. Our results suggest a hub-like function of the posterior and lateral portions of the thalamus in the perception of one's own postural upright. Lesion network symptom mapping investigating functional disconnection indicated cortical diaschisis in cerebellar, frontal, parietal and temporal areas in patients with thalamic lesions suffering from pusher syndrome, but there was no evidence for functional diaschisis in pusher patients with cortical stroke and no evidence for the convergence of thalamic and cortical lesions onto a common functional network. Structural disconnection mapping identified posterior thalamic disconnection to temporal, pre-, post- and paracentral regions. Fibre tracking between the thalamic and cortical pusher lesion hotspots indicated that in cortical lesions of patients with pusher syndrome, it is disconnectivity to the posterior thalamus caused by accompanying white matter damage, rather than the direct cortical lesions themselves, that lead to the emergence of pusher syndrome. Our analyses thus offer the first evidence for a direct thalamo-cortical (or cortico-thalamic) interconnection and, more importantly, shed light on the location of the respective thalamo-cortical disconnections. Pusher syndrome seems to be a consequence of direct damage or of disconnection of the posterior thalamus.

Data-driven biomarkers outperform theory-based biomarkers in predicting stroke motor outcomes

Chronic motor impairments are a leading cause of disability after stroke. Previous studies have predicted motor outcomes based on the degree of damage to predefined structures in the motor system, such as the corticospinal tract. However, such theory-based approaches may not take full advantage of the information contained in clinical imaging data. The present study uses data-driven approaches to predict chronic motor outcomes after stroke and compares the accuracy of these predictions to previously-identified theory-based biomarkers. Using a cross-validation framework, regression models were trained using lesion masks and motor outcomes data from 789 stroke patients (293 female/496 male) from the ENIGMA Stroke Recovery Working Group (age 64.9±18.0 years; time since stroke 12.2±0.2 months; normalised motor score 0.7±0.5 (range [0,1]). The out-of-sample prediction accuracy of two theory-based biomarkers was assessed: lesion load of the corticospinal tract, and lesion load of multiple descending motor tracts. These theory-based prediction accuracies were compared to the prediction accuracy from three data-driven biomarkers: lesion load of lesion-behaviour maps, lesion load of structural networks associated with lesion-behaviour maps, and measures of regional structural disconnection. In general, data-driven biomarkers had better prediction accuracy - as measured by higher explained variance in chronic motor outcomes - than theory-based biomarkers. Data-driven models of regional structural disconnection performed the best of all models tested (R2 = 0.210, p < 0.001), performing significantly better than predictions using the theory-based biomarkers of lesion load of the corticospinal tract (R2 = 0.132, p< 0.001) and of multiple descending motor tracts (R2 = 0.180, p < 0.001). They also performed slightly, but significantly, better than other data-driven biomarkers including lesion load of lesion-behaviour maps (R2 =0.200, p < 0.001) and lesion load of structural networks associated with lesion-behaviour maps (R2 =0.167, p < 0.001). Ensemble models - combining basic demographic variables like age, sex, and time since stroke - improved prediction accuracy for theory-based and data-driven biomarkers. Finally, combining both theory-based and data-driven biomarkers with demographic variables improved predictions, and the best ensemble model achieved R2 = 0.241, p < 0.001. Overall, these results demonstrate that models that predict chronic motor outcomes using data-driven features, particularly when lesion data is represented in terms of structural disconnection, perform better than models that predict chronic motor outcomes using theory-based features from the motor system. However, combining both theory-based and data-driven models provides the best predictions.

Regional, circuit and network heterogeneity of brain abnormalities in psychiatric disorders

The substantial individual heterogeneity that characterizes people with mental illness is often ignored by classical case-control research, which relies on group mean comparisons. Here we present a comprehensive, multiscale characterization of the heterogeneity of gray matter volume (GMV) differences in 1,294 cases diagnosed with one of six conditions (attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, depression, obsessive-compulsive disorder and schizophrenia) and 1,465 matched controls. Normative models indicated that person-specific deviations from population expectations for regional GMV were highly heterogeneous, affecting the same area in <7% of people with the same diagnosis. However, these deviations were embedded within common functional circuits and networks in up to 56% of cases. The salience-ventral attention system was implicated transdiagnostically, with other systems selectively involved in depression, bipolar disorder, schizophrenia and attention-deficit/hyperactivity disorder. Phenotypic differences between cases assigned the same diagnosis may thus arise from the heterogeneous localization of specific regional deviations, whereas phenotypic similarities may be attributable to the dysfunction of common functional circuits and networks.

Mental flexibility depends on a largely distributed white matter network: Causal evidence from connectome-based lesion-symptom mapping

Mental flexibility (MF) refers to the capacity to dynamically switch from one task to another. Current neurocognitive models suggest that since this function requires interactions between multiple remote brain areas, the integrity of the anatomic tracts connecting these brain areas is necessary to maintain performance. We tested this hypothesis by assessing with a connectome-based lesion-symptom mapping approach the effects of white matter lesions on the brain's structural connectome and their association with performance on the trail making test, a neuropsychological test of MF, in a sample of 167 first unilateral stroke patients. We found associations between MF deficits and damage of i) left lateralized fronto-temporo-parietal connections and interhemispheric connections between left temporo-parietal and right parietal areas; ii) left cortico-basal connections; and iii) left cortico-pontine connections. We further identified a relationship between MF and white matter disconnections within cortical areas composing the cognitive control, default mode and attention functional networks. These results for a central role of white matter integrity in MF extend current literature by providing causal evidence for a functional interdependence among the regional cortical and subcortical structures composing the MF network. Our results further emphasize the necessity to consider connectomics in lesion-symptom mapping analyses to establish comprehensive neurocognitive models of high-order cognitive functions.

Bayesian lesion-deficit inference with Bayes factor mapping: Key advantages, limitations, and a toolbox

Statistical lesion-symptom mapping is largely dominated by frequentist approaches with null hypothesis significance testing. They are popular for mapping functional brain anatomy but are accompanied by some challenges and limitations. The typical analysis design and the structure of clinical lesion data are linked to the multiple comparison problem, an association problem, limitations to statistical power, and a lack of insights into evidence for the null hypothesis. Bayesian lesion deficit inference (BLDI) could be an improvement as it collects evidence for the null hypothesis, i.e. the absence of effects, and does not accumulate α-errors with repeated testing. We implemented BLDI by Bayes factor mapping with Bayesian t-tests and general linear models and evaluated its performance in comparison to frequentist lesion-symptom mapping with a permutation-based family-wise error correction. We mapped the voxel-wise neural correlates of simulated deficits in an in-silico-study with 300 stroke patients, and the voxel-wise and disconnection-wise neural correlates of phonemic verbal fluency and constructive ability in 137 stroke patients. Both the performance of frequentist and Bayesian lesion-deficit inference varied largely across analyses. In general, BLDI could find areas with evidence for the null hypothesis and was statistically more liberal in providing evidence for the alternative hypothesis, i.e. the identification of lesion-deficit associations. BLDI performed better in situations in which the frequentist method is typically strongly limited, for example with on average small lesions and in situations with low power, where BLDI also provided unprecedented transparency in terms of the informative value of the data. On the other hand, BLDI suffered more from the association problem, which led to a pronounced overshoot of lesion-deficit associations in analyses with high statistical power. We further implemented a new approach to lesion size control, adaptive lesion size control, that, in many situations, was able to counter the limitations imposed by the association problem, and increased true evidence both for the null and the alternative hypothesis. In summary, our results suggest that BLDI is a valuable addition to the method portfolio of lesion-deficit inference with some specific and exclusive advantages: it deals better with smaller lesions and low statistical power (i.e. small samples and effect sizes) and identifies regions with absent lesion-deficit associations. However, it is not superior to established frequentist approaches in all respects and therefore not to be seen as a general replacement. To make Bayesian lesion-deficit inference widely accessible, we published an R toolkit for the analysis of voxel-wise and disconnection-wise data.

Longitudinal prediction of motor dysfunction after stroke: a disconnectome study

Motricity is the most commonly affected ability after a stroke. While many clinical studies attempt to predict motor symptoms at different chronic time points after a stroke, longitudinal acute-to-chronic studies remain scarce. Taking advantage of recent advances in mapping brain disconnections, we predict motor outcomes in 62 patients assessed longitudinally two weeks, three months, and one year after their stroke. Results indicate that brain disconnection patterns accurately predict motor impairments. However, disconnection patterns leading to impairment differ between the three-time points and between left and right motor impairments. These results were cross-validated using resampling techniques. In sum, we demonstrated that while some neuroplasticity mechanisms exist changing the structure-function relationship, disconnection patterns prevail when predicting motor impairment at different time points after stroke.