The strange role of brain lesion size in cognitive neuropsychology

Metric comparison of connectome-based lesion-symptom mapping in post-stroke aphasia

Connectome-based lesion-symptom mapping relates behavioural impairments to disruption of structural brain connectivity. Connectome-based lesion-symptom mapping can be based on different approaches (diffusion MRI versus lesion mask), network scales (whole brain versus regions of interest) and measure types (tract-based, parcel-based, or network-based metrics). We evaluated the similarity of different connectome-based lesion-symptom mapping processing choices and identified factors that influence the results using multiverse analysis-the strategy of conducting and displaying the results of all reasonable processing choices. Metrics derived from lesion masks and diffusion-weighted images were tested for association with Boston Naming Test and Token Test performance in a sample of 50 participants with aphasia following left hemispheric stroke. 'Direct' measures were derived from diffusion-weighted images. 'Indirect' measures were derived by overlaying lesion masks on a white matter atlas. Parcel-based connectomes were constructed for the whole brain and regions of interest (14 language-relevant parcels). Numerous tract-based and network-based metrics were calculated. There was a high discrepancy across processing approaches (diffusion-weighted images versus lesion masks), network scales (whole brain versus regions of interest) and metric types. Results indicate weak correlations and different connectome-based lesion-symptom mapping results across the processing choices. Substantial methodological work is needed to validate the various decision points that arise when conducting connectome-based lesion-symptom mapping analyses. Multiverse analysis is a useful strategy for evaluating the similarity across different processing choices in connectome-based lesion-symptom mapping.

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.

Super-selective injection of propofol into the intracranial arteries enables Patient's self-evaluation of expected neurological deficit

INTRODUCTION

It is hard to realize the extent of the expected postoperative neurological deficit for patients themselves. The provision of appropriate information can contribute not only to examining surgical indications but also to filling the gap between patient and expert expectations. We hypothesized that propofol infusion into the intracranial arteries (ssWada) could induce focal neurological symptoms with preserved wakefulness, enabling the patients to evaluate the postsurgical risk subjectively.

METHODS

Presurgical evaluation using ssWada was performed in 28 patients with drug-resistant epilepsy. Based on anatomical knowledge, propofol was super-selectively infused into the intracranial arteries including the M1, M2, and M3 segments of the middle cerebral artery (MCA), A2 segment of the anterior cerebral artery, and P2 segment of the posterior cerebral artery to evaluate the neurological and cognitive symptoms. We retrospectively analyzed a total of 107 infusion trials, including their target vessels, and elicited symptoms of motor weakness, sensory disturbance, language, unilateral hemispatial neglect (UHN), and hemianopsia. We evaluated preserved wakefulness which enabled subjective evaluations of the symptoms and comparison of the subjective experience to the objective findings, besides adverse effects during the procedure.

RESULTS

Preserved wakefulness was found in 97.2% of all trials. Changes in neurological symptoms were positively evaluated for motor weakness in 51.4%, sensory disturbance in 5.6%, language in 48.6%, UHN in 22.4%, and hemianopsia in 32.7%. Six trials elicited seizures. Multivariate analysis showed significant correlations between symptom and infusion site of language and left side, language and MCA branches, motor weakness and A2 or M2 superior division, and hemianopsia and P2. Transient adverse effect was observed in 8 cases with 12 infusion trials (11.2 %).

CONCLUSION

The ssWada could elicit focal neurological symptoms with preserved wakefulness. The methodology enables specific evaluation of risk for cortical resection and subjective evaluation of the expected outcome by the patients.

Impaired discourse content in aphasia is associated with frontal white matter damage

Aphasia is a common consequence of stroke with severe impacts on employability, social interactions and quality of life. Producing discourse-relevant information in a real-world setting is the most important aspect of recovery because it is critical to successful communication. This study sought to identify the lesion correlates of impaired production of relevant information in spoken discourse in a large, unselected sample of participants with post-stroke aphasia. Spoken discourse (n = 80) and structural brain scans (n = 66) from participants with aphasia following left hemisphere stroke were analysed. Each participant provided 10 samples of spoken discourse elicited in three different genres, and 'correct information unit' analysis was used to quantify the informativeness of speech samples. The lesion correlates were identified using multivariate lesion-symptom mapping, voxel-wise disconnection and tract-wise analyses. Amount and speed of relevant information were highly correlated across different genres and with total lesion size. The analyses of lesion correlates converged on the same pattern: impaired production of relevant information was associated with damage to anterior dorsal white matter pathways, specifically the arcuate fasciculus, frontal aslant tract and superior longitudinal fasciculus. Damage to these pathways may be a useful biomarker for impaired informative spoken discourse and informs development of neurorehabilitation strategies.

Prolonging the therapeutic window for valproic acid treatment in a swine model of traumatic brain injury and hemorrhagic shock

BACKGROUND

It has previously been shown that administration of valproic acid (VPA) can improve outcomes if given within an hour following traumatic brain injury (TBI). This short therapeutic window (TW) limits its use in real-life situations. Based upon its pharmacokinetic data, we hypothesized that TW can be extended to 3 hours if a second dose of VPA is given 8 hours after the initial dose.

METHOD

Yorkshire swine (40-45 kg; n = 10) were subjected to TBI (controlled cortical impact) and 40% blood volume hemorrhage. After 2 hours of shock, they were randomized to either (1) normal saline resuscitation (control) or (2) normal saline-VPA (150 mg/kg × two doses). First dose of VPA was started 3 hours after the TBI, with a second dose 8 hours after the first dose. Neurologic severity scores (range, 0-36) were assessed daily for 14 days, and brain lesion size was measured via magnetic resonance imaging on postinjury day 3.

RESULTS

Hemodynamic and laboratory parameters of shock were similar in both groups. Valproic acid-treated animals had significantly less neurologic impairment on days 2 (16.3 ± 2.0 vs. 7.3 ± 2.8) and 3 (10.9 ± 3.6 vs. 2.8 ± 1.1) postinjury and returned to baseline levels 54% faster. Magnetic resonance imaging showed no differences in brain lesion size on day 3. Pharmacokinetic data confirmed neuroprotective levels of VPA in the circulation.

CONCLUSION

This is the first study to demonstrate that VPA can be neuroprotective even when given 3 hours after TBI. This expanded TW has significant implications for the design of the clinical trial.

Lesion-behavior mapping indicates a strategic role for parietal substrates of associative memory

Numerous neuroimaging studies indicate that ventral parietal cortex (VPC), especially angular gyrus, plays an important role in episodic memory. However, the nature of the mnemonic processes supported by this region is far from clear. We previously found that stroke lesions in VPC and lateral temporal cortex caused deficits in cued recall of unimodal word pairs and picture pairs, and cross-modal picture-sound pairs, with larger deficits in the cross-modal task. However, those findings leave open the question whether those regions' integrity is necessary for maintenance of associative representations, or for strategic processes required for their recall. We addressed this question using associative recognition versions of those tasks. We additionally manipulated semantic relatedness of the associated memoranda, to assess VPC's involvement in semantic processing in the context of episodic memory. We analyzed performance of 62 first-event, sub-acute phase stroke patients (31 right- and 31 left-hemisphere damage) relative to 65 healthy participants, and employed voxel-based lesion-behavior mapping (VLBM) to identify task-relevant structures. Patients displayed greater false associative recognition of semantically related compared to unrelated recombined pairs. VLBM analysis implicated right lateral temporo-parietal regions in associative recognition deficits in the cross-modal pairs task, specifically for related recombined and new pairs, seemingly because of difficulty overcoming semantic relatedness bias effects on episodic discrimination. In contrast, damage to ventral parietal and lateral temporal cortex was not implicated in memory for unrelated memoranda. We interpret this pattern of lesion-behavior effects as indicating lateral temporo-parietal cortex involvement in strategic, rather than representational, roles in episodic associative memory.

Stroke lesion size - Still a useful biomarker for stroke severity and outcome in times of high-dimensional models

BACKGROUND

The volumetric size of a brain lesion is a frequently used stroke biomarker. It stands out among most imaging biomarkers for being a one-dimensional variable that is applicable in simple statistical models. In times of machine learning algorithms, the question arises of whether such a simple variable is still useful, or whether high-dimensional models on spatial lesion information are superior.

METHODS

We included 753 first-ever anterior circulation ischemic stroke patients (age 68.4±15.2 years; NIHSS at 24 h 4.4±5.1; modified Rankin Scale (mRS) at 3-months median[IQR] 1[0.75;3]) and traced lesions on diffusion-weighted MRI. In an out-of-sample model validation scheme, we predicted stroke severity as measured by NIHSS 24 h and functional stroke outcome as measured by mRS at 3 months either from spatial lesion features or lesion size.

RESULTS

For stroke severity, the best regression model based on lesion size performed significantly above chance (p < 0.0001) with R2 = 0.322, but models with spatial lesion features performed significantly better with R2 = 0.363 (t(752) = 2.889; p = 0.004). For stroke outcome, the best classification model based on lesion size again performed significantly above chance (p < 0.0001) with an accuracy of 62.8%, which was not different from the best model with spatial lesion features (62.6%, p = 0.80). With smaller training data sets of only 150 or 50 patients, the performance of high-dimensional models with spatial lesion features decreased up to the point of being equivalent or even inferior to models trained on lesion size. The combination of lesion size and spatial lesion features in one model did not improve predictions.

CONCLUSIONS

Lesion size is a decent biomarker for stroke outcome and severity that is slightly inferior to spatial lesion features but is particularly suited in studies with small samples. When low-dimensional models are desired, lesion size provides a viable proxy biomarker for spatial lesion features, whereas high-precision prediction models in personalised prognostic medicine should operate with high-dimensional spatial imaging features in large samples.

Using predictive validity to compare associations between brain damage and behavior

Lesion-behavior mapping (LBM) provides a statistical map of the association between voxel-wise brain damage and individual differences in behavior. To understand whether two behaviors are mediated by damage to distinct regions, researchers often compare LBM weight outputs by either the Overlap method or the Correlation method. However, these methods lack statistical criteria to determine whether two LBM are distinct versus the same and are disconnected from a major goal of LBMs: predicting behavior from brain damage. Without such criteria, researchers may draw conclusions from numeric differences between LBMs that are irrelevant to predicting behavior. We developed and validated a predictive validity comparison method (PVC) that establishes a statistical criterion for comparing two LBMs using predictive accuracy: two LBMs are distinct if and only if they provide unique predictive power for the behaviors being assessed. We applied PVC to two lesion-behavior stroke data sets, demonstrating its utility for determining when behaviors arise from the same versus different lesion patterns. Using region-of-interest-based simulations derived from proportion damage from a large data set (n = 131), PVC accurately detected when behaviors were mediated by different regions (high sensitivity) versus the same region (high specificity). Both the Overlap method and Correlation method performed poorly on the simulated data. By objectively determining whether two behavioral deficits can be explained by single versus distinct patterns of brain damage, PVC provides a critical advance in establishing the brain bases of behavior. We have developed and released a GUI-driven web app to encourage widespread adoption.

Neuroanatomy of reduced distortion of body-centred spatial coding during body tilt in stroke patients

Awareness of the direction of the body's (longitudinal) axis is fundamental for action and perception. The perceived body axis orientation is strongly biased during body tilt; however, the neural substrates underlying this phenomenon remain largely unknown. Here, we tackled this issue using a neuropsychological approach in patients with hemispheric stroke. Thirty-seven stroke patients and 20 age-matched healthy controls adjusted a visual line with the perceived body longitudinal axis when the body was upright or laterally tilted by 10 degrees. The bias of the perceived body axis caused by body tilt, termed tilt-dependent error (TDE), was compared between the groups. The TDE was significantly smaller (i.e., less affected performance by body tilt) in the stroke group (15.9 ± 15.9°) than in the control group (25.7 ± 17.1°). Lesion subtraction analysis and Bayesian lesion-symptom inference revealed that the abnormally reduced TDEs were associated with lesions in the right occipitotemporal cortex, such as the superior and middle temporal gyri. Our findings contribute to a better understanding of the neuroanatomy of body-centred spatial coding during whole-body tilt.

The low dimensionality of post-stroke cognitive deficits: it's the lesion anatomy!

For years, dissociation studies on neurological single-case patients with brain lesions were the dominant method to infer fundamental cognitive functions in neuropsychology. In contrast, the association between deficits was considered to be of less epistemological value. Still, associational computational methods for dimensionality reduction-such as principal component analysis or factor analysis-became popular for the identification of fundamental cognitive functions and to understand human cognitive brain architecture from post-stroke neuropsychological profiles. In the present in silico study with lesion imaging of 300 stroke patients, we investigated the dimensionality of artificial simulated neuropsychological profiles that exclusively contained independent fundamental cognitive functions without any underlying low-dimensional cognitive architecture. Still, the anatomy of stroke lesions alone was sufficient to create a dependence between variables that allowed a low-dimensional description of the data with principal component analysis. All criteria that we used to estimate the dimensionality of data, including the Kaiser criterion, were strongly affected by lesion anatomy, while the Joliffe criterion provided the least affected estimates. The dimensionality of profiles was reduced by 62-70% for the Kaiser criterion, up to the degree that is commonly found in neuropsychological studies on actual cognitive measures. The interpretability of such low-dimensional factors as deficits of fundamental cognitive functions and their provided insights into human cognitive architecture thus seem to be severely limited, and the heavy focus of current cognitive neuroscience on group studies and associations calls for improvements. We suggest that qualitative criteria and dissociation patterns could be used to refine estimates for the dimensionality of the cognitive architecture behind post-stroke deficits. Further, given the strong impact of lesion anatomy on the associational structure of data, we see the need for further optimization of interpretation strategies of computational factors in post-stroke lesion studies of cognitive deficits.

The elusive metric of lesion load

One of the widely used metrics in lesion-symptom mapping is lesion load that codes the amount of damage to a given brain region of interest. Lesion load aims to reduce the complex 3D lesion information into a feature that can reflect both site of damage, defined by the location of the region of interest, and size of damage within that region of interest. Basically, the process of estimation of lesion load converts a voxel-based lesion map into a region-based lesion map, with regions defined as atlas-based or data-driven spatial patterns. Here, after examining current definitions of lesion load, four methodological issues are discussed: (1) lesion load is agnostic to the location of damage within the region of interest, and it disregards damage outside the region of interest, (2) lesion load estimates are prone to errors introduced by the uncertainty in lesion delineation, spatial warping of the lesion/region, and binarization of the lesion/region, (3) lesion load calculation depends on brain parcellation selection, and (4) lesion load does not necessarily reflect a white matter disconnection. Overall, lesion load, when calculated in a robust way, can serve as a clinically-useful feature for explaining and predicting post-stroke outcome and recovery.

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.

Right hemispheric white matter hyperintensities improve the prediction of spatial neglect severity in acute stroke

White matter hyperintensities (WMH) are frequently observed in brain scans of elderly people. They are associated with an increased risk of stroke, cognitive decline, and dementia. However, it is unknown yet if measures of WMH provide information that improve the understanding of poststroke outcome compared to only state-of-the-art stereotaxic structural lesion data. We implemented high-dimensional machine learning models, based on support vector regression, to predict the severity of spatial neglect in 103 acute right hemispheric stroke patients. We found that (1) the additional information of right hemispheric or bilateral voxel-based topographic WMH extent indeed yielded a significant improvement in predicting acute neglect severity (compared to the voxel-based stroke lesion map alone). (2) Periventricular WMH appeared more relevant for prediction than deep subcortical WMH. (3) Among different measures of WMH, voxel-based maps as measures of topographic extent allowed more accurate predictions compared to the use of traditional ordinally assessed visual rating scales (Fazekas-scale, Cardiovascular Health Study-scale). In summary, topographic WMH appear to be a valuable clinical imaging biomarker for predicting the severity of cognitive deficits and bears great potential for rehabilitation guidance of acute stroke patients.

Mapping the human praxis network: an investigation of white matter disconnection in limb apraxia of gesture production

Left hemispheric cerebral stroke can cause apraxia, a motor cognitive disorder characterized by deficits of higher-order motor skills such as the failure to accurately produce meaningful gestures. This disorder provides unique insights into the anatomical and cognitive architecture of the human praxis system. The present study aimed to map the structural brain network that is damaged in apraxia. We assessed the ability to perform meaningful gestures with the hand in 101 patients with chronic left hemisphere stroke. Structural white matter fibre damage was directly assessed by diffusion tensor imaging and fractional anisotropy mapping. We used multivariate topographical inference on tract-based fractional anisotropy topographies to identify white matter disconnection associated with apraxia. We found relevant pathological white matter alterations in a densely connected fronto-temporo-parietal network of short and long association fibres. Hence, the findings suggest that heterogeneous topographical results in previous lesion mapping studies might not only result from differences in study design, but also from the general methodological limitations of univariate topographical mapping in uncovering the structural praxis network. A striking role of middle and superior temporal lobe disconnection, including temporo-temporal short association fibres, was found, suggesting strong involvement of the temporal lobe in the praxis network. Further, the results stressed the importance of subcortical disconnections for the emergence of apractic symptoms. Our study provides a fine-grain view into the structural connectivity of the human praxis network and suggests a potential value of disconnection measures in the clinical prediction of behavioural post-stroke outcome.