Improving and validating methods in lesion behaviour mapping

The investigation of diseased brain is one of the major methods in cognitive neuroscience. This approach allows numerous insights both into human cognition and brain architecture. Most prominent is the method of lesion behaviour mapping, where inferences about functional brain architecture are drawn from focally lesioned brains. In the last 15 years, the state-of-the-art implementation of lesion behaviour mapping has been voxel-based lesion behaviour mapping, which is based on the framework of statistical parametric mapping. Recently, the validity of this method has been criticised and multivariate methods have been proposed to complement or even replace it. In my thesis, I aim to evaluate these different methodological approaches to lesion behaviour mapping and to provide guidelines on how lesion-brain inference should be drawn. In my first empirical work, I investigate the validity of voxel-based lesion behaviour mapping. It shows that previous studies overestimated biases inherent to the method, and that validity can be improved by the use of correction factors. The second empirical work deals with a recently developed method of multivariate lesion behaviour mapping. On the one hand, I clarify how this method can be used to obtain valid lesion-brain inference. On the other hand, I show that the method is not able to overcome all limitations of voxel-based lesion behaviour mapping. In my last work, I apply multivariate lesion behaviour mapping to investigate the neural correlates of higher motor cognition. This analysis is the first to identify a brain network to underlie apraxia, a disorder of higher motor cognition, which underlines the benefits of the new multivariate approach in brain networks

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

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

Learning from communication versus observation in great apes

This research was supported by the European Research Council (ERC) under the European Union’s Seventh Framework Programme (FP7/2007-2013)/ERC Grant 609819 (SOMICS project).When human infants are intentionally addressed by others, they tend to interpret the information communicated as being relevant to them and worth acquiring. For humans, this attribution of relevance leads to a preference to learn from communication, making it possible to accumulate knowledge over generations. Great apes are sensitive to communicative cues, but do these cues also activate an expectation of relevance? In an observational learning paradigm, we demonstrated to a sample of nonhuman great apes (bonobos, chimpanzees, orangutans; N = 24) how to operate on a food dispenser device. When apes had the opportunity to choose between an effective and an ineffective method in the baseline conditions, the majority of them chose the effective method. However, when the ineffective method was demonstrated in a communicative way, they failed to prioritize efficiency, even though they were equally attentive in both conditions. This suggests that the ostensive demonstration elicited an expectation of relevance that modified apes’ interpretation of the situation, potentially leading to a preference to learn from communication, as human children do.Publisher PDFPeer reviewe

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

Physicists' Views on Scientific Realism

Do physicists believe that general relativity is true, and that electrons and phonons exist, and if so, in what sense? To what extent does the spectrum of positions among physicists correspond to philosophical positions like scientific realism, instrumentalism, or perspectivism? Does agreement with these positions correlate with demographic factors, and are realist physicists more likely to support research projects purely aimed at increasing knowledge? We conducted a questionnaire study to scrutinize the philosophical stances of physicists. We received responses from 384 physicists and 151 philosophers. Our main findings are 1) On average, physicists tend toward scientific realism, and slightly more so than philosophers of science. 2) Physicists can be clustered into five groups. Three show variants of scientific realism, one is instrumentalist, and one seems undecided or incoherent. 3) Agreement with realism weakly correlates with approval of building a bigger particle collider. 4) Agreement with realism weakly correlates with the seniority of physicists. 5) We did not find correlations with other factors, such as whether physicists focus on theoretical or experimental research and whether they engage with applied or basic research

Reducing alertness does not affect line bisection bias in neurotypical participants.

Alertness, or one's general readiness to respond to stimulation, has previously been shown to affect spatial attention. However, most of this previous research focused on speeded, laboratory-based reaction tasks, as opposed to the classical line bisection task typically used to diagnose deficits of spatial attention in clinical settings. McIntosh et al. (Cogn Brain Res 25:833-850, 2005) provide a form of line bisection task which they argue can more sensitively assess spatial attention. Ninety-eight participants were presented with this line bisection task, once with and once without spatial cues, and both before and after a 50-min vigilance task that aimed to decrease alertness. A single participant was excluded due to potentially inconsistent behaviour in the task, leaving 97 participants for the full analyses. While participants were, on a group level, less alert after the 50-min vigilance task, they showed none of the hypothesised effects of reduced alertness on spatial attention in the line bisection task, regardless of with or without spatial cues. Yet, they did show the proposed effect of decreased alertness leading to a lower level of general attention. This suggests that alertness has no effect on spatial attention, as measured by a line bisection task, in neurotypical participants. We thus conclude that, in neurotypical participants, the effect of alertness on spatial attention can be examined more sensitively with tasks requiring a speeded response compared to unspeeded tasks