Prediction of final infarct volume from native CT perfusion and treatment parameters using deep learning

CT Perfusion (CTP) imaging has gained importance in the diagnosis of acute stroke. Conventional perfusion analysis performs a deconvolution of the measurements and thresholds the perfusion parameters to determine the tissue status. We pursue a data-driven and deconvolution-free approach, where a deep neural network learns to predict the final infarct volume directly from the native CTP images and metadata such as the time parameters and treatment. This would allow clinicians to simulate various treatments and gain insight into predicted tissue status over time. We demonstrate on a multicenter dataset that our approach is able to predict the final infarct and effectively uses the metadata. An ablation study shows that using the native CTP measurements instead of the deconvolved measurements improves the prediction.Comment: Accepted for publication in Medical Image Analysi

White matter changes and confrontation naming in retired aging national football league athletes

Using diffusion tensor imaging (DTI), we assessed the relationship of white matter integrity and performance on the Boston Naming Test (BNT) in a group of retired professional football players and a control group. We examined correlations between fractional anisotropy (FA) and mean diffusivity (MD) with BNT T-scores in an unbiased voxelwise analysis processed with tract-based spatial statistics (TBSS). We also analyzed the DTI data by grouping voxels together as white matter tracts and testing each tract's association with BNT T-scores. Significant voxelwise correlations between FA and BNT performance were only seen in the retired football players (p < 0.02). Two tracts had mean FA values that significantly correlated with BNT performance: forceps minor and forceps major. White matter integrity is important for distributed cognitive processes, and disruption correlates with diminished performance in athletes exposed to concussive and subconcussive brain injuries, but not in controls without such exposure

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

Are visual working memory and episodic memory distinct processes? Insight from stroke patients by lesion-symptom mapping

Working memory and episodic memory are two different processes, although the nature of their interrelationship is debated. As these processes are predominantly studied in isolation, it is unclear whether they crucially rely on different neural substrates. To obtain more insight in this, 81 adults with sub-acute ischemic stroke and 29 elderly controls were assessed on a visual working memory task, followed by a surprise subsequent memory test for the same stimuli. Multivariate, atlas- and track-based lesion-symptom mapping (LSM) analyses were performed to identify anatomical correlates of visual memory. Behavioral results gave moderate evidence for independence between discriminability in working memory and subsequent memory, and strong evidence for a correlation in response bias on the two tasks in stroke patients. LSM analyses suggested there might be independent regions associated with working memory and episodic memory. Lesions in the right arcuate fasciculus were more strongly associated with discriminability in working memory than in subsequent memory, while lesions in the frontal operculum in the right hemisphere were more strongly associated with criterion setting in subsequent memory. These findings support the view that some processes involved in working memory and episodic memory rely on separate mechanisms, while acknowledging that there might also be shared processes. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00429-021-02281-0

Metabolic lesion-deficit mapping of human cognition

In theory the most powerful technique for functional localization in cognitive neuroscience, lesion-deficit mapping is in practice distorted by unmodelled network disconnections and strong ‘parasitic’ dependencies between collaterally damaged ischaemic areas. High-dimensional multivariate modelling can overcome these defects, but only at the cost of commonly impracticable data scales. Here we develop lesion-deficit mapping with metabolic lesions—discrete areas of hypometabolism typically seen on interictal 18F-fluorodeoxyglucose PET imaging in patients with focal epilepsy—that inherently capture disconnection effects, and whose structural dependence patterns are sufficiently benign to allow the derivation of robust functional anatomical maps with modest data. In this cross-sectional study of 159 patients with widely distributed focal cortical impairments, we derive lesion-deficit maps of a broad range of psychological subdomains underlying affect and cognition. We demonstrate the potential clinical utility of the approach in guiding therapeutic resection for focal epilepsy or other neurosurgical indications by applying high-dimensional modelling to predict out-of-sample verbal IQ and depression from cortical metabolism alone

Exploring aspects of memory in healthy ageing and following stroke

Memory is critical for everyday functioning. Remembering an event with rich detail requires the ability to remember the temporal order of occurrences within the event and spatial locations associated with it. But it remains unclear whether it also requires memory for the perspective from which we encoded the event, whether these three aspects of memory are affected following stroke, and which are the key brain regions upon which they rely. These questions are explored in this thesis. In the first study presented here, I examined young and elderly healthy subjects with an autobiographical memory interview and a 2D spatial memory task assessing self-perspective, and found no correlation between performance on these tasks. In the second experimental study, by assessing stroke patients on a 3D spatio-temporal memory task, I found that damage to the right intraparietal sulcus was associated with poorer memory for temporal order. However, voxelwise analyses detected no association between parietal lobe regions and accuracy in the egocentric condition of this task, or between medial temporal lobe regions and accuracy in the allocentric condition, one possible reason being that performance was near ceiling. In the third experimental study, by assessing a considerably larger group of stroke patients on a spatial memory task, I found that, as a group, patients performed worse than healthy controls, and performance was correlated with an activities of daily living scale. A spatial memory network was identified in right (but not left) hemisphere stroke patients. These findings provide evidence that spatial memory impairment is common after stroke, highlight its potential functional relevance, and increase our understanding of which regions are critical for remembering temporal order and spatial information. Furthermore, they suggest a dissociation between the mechanisms underpinning recall of 2D scenes over relatively short intervals versus remembering of real-life events across periods of many years.Open Acces

Ten problems and solutions when predicting individual outcome from lesion site after stroke

In this paper, we consider solutions to ten of the challenges faced when trying to predict an individual's functional outcome after stroke on the basis of lesion site. A primary goal is to find lesion-outcome associations that are consistently observed in large populations of stroke patients because consistent associations maximise confidence in future individualised predictions. To understand and control multiple sources of inter-patient variability, we need to systematically investigate each contributing factor and how each factor depends on other factors. This requires very large cohorts of patients, who differ from one another in typical and measurable ways, including lesion site, lesion size, functional outcome and time post stroke (weeks to decades). These multivariate investigations are complex, particularly when the contributions of different variables interact with one another. Machine learning algorithms can help to identify the most influential variables and indicate dependencies between different factors. Multivariate lesion analyses are needed to understand how the effect of damage to one brain region depends on damage or preservation in other brain regions. Such data-led investigations can reveal predictive relationships between lesion site and outcome. However, to understand and improve the predictions we need explanatory models of the neural networks and degenerate pathways that support functions of interest. This will entail integrating the results of lesion analyses with those from functional imaging (fMRI, MEG), transcranial magnetic stimulation (TMS) and diffusor tensor imaging (DTI) studies of healthy participants and patients

Neuronal bases of structural coherence in contemporary dance observation

The neuronal processes underlying dance observation have been the focus of an increasing number of brain imaging studies over the past decade. However, the existing literature mainly dealt with effects of motor and visual expertise, whereas the neural and cognitive mechanisms that underlie the interpretation of dance choreographies remained unexplored. Hence, much attention has been given to the Action Observation Network (AON) whereas the role of other potentially relevant neuro-cognitive mechanisms such as mentalizing (theory of mind) or language (narrative comprehension) in dance understanding is yet to be elucidated. We report the results of an fMRI study where the structural coherence of short contemporary dance choreographies was manipulated parametrically using the same taped movement material. Our participants were all trained dancers. The whole-brain analysis argues that the interpretation of structurally coherent dance phrases involves a subpart (Superior Parietal) of the AON as well as mentalizing regions in the dorsomedial Prefrontal Cortex. An ROI analysis based on a similar study using linguistic materials (Pallier et al. 2011) suggests that structural processing in language and dance might share certain neural mechanisms

Motor imagery as a function of disease severity in multiple sclerosis: An fMRI study

Motor imagery (MI) is defined as mental execution without any actual movement. While healthy adults usually show temporal equivalence, i.e., isochrony, between the mental simulation of an action and its actual performance, neurological disorders are associated with anisochrony. Unlike in patients with stroke and Parkinson disease, only a few studies have investigated differences of MI ability in multiple sclerosis (MS). However, the relationship among disease severity, anisochrony and brain activation patterns during MI has not been investigated yet. Here, we propose to investigate MI in MS patients using fMRI during a behavioral task executed with dominant/non-dominant hand and to evaluate whether anisochrony is associated with disease severity. Thirty-seven right-handed MS patients, 17 with clinically isolated syndrome (CIS) suggestive of MS and 20 with relapsing-remitting MS (RR-MS) and 20 right-handed healthy controls (HC) underwent fMRI during a motor task consisting in the actual or imaged movement of squeezing a foam ball with the dominant and non-dominant hand. The same tasks were performed outside the MRI room to record the number of actual and imagined ball squeezes, and calculate an Index of performance (IP) based on the ratio between actual and imagined movements. IP showed that a progressive loss of ability in simulating actions (i.e., anisochrony) more pronounced for non-dominant hand, was found as function of the disease course. Moreover, anisochrony was associated with activation of occipito-parieto-frontal areas that were more extensive at the early stages of the disease, probably in order to counteract the changes due to MS. However, the neural engagement of compensatory brain areas becomes more difficult with more challenging tasks, i.e., dominant vs. non-dominant hand, with a consequent deficit in behavioral performance. These results show a strict association between MI performance and disease severity, suggesting that, at early stages of the disease, anisochrony in MI could be considered as surrogate behavioral marker of MS severity