Biology subjects

Sperber, C.

Publications and source records attributed to Sperber, C..

17 recordsLinked to original sources

bioRxiv2024

Benchmarking Stroke Outcome Prediction through Comprehensive Data Analysis - NeuralCup 2023

Stroke remains a leading cause of mortality and long-term disability worldwide, with variable recovery trajectories posing substantial challenges in anticipating post-event care and rehabilitation planning. To address these challenges, we established the NeuralCup consortium to benchmark predictive models of stroke outcome through a collaborative, data-driven approach. This study presents findings from 15 international teams who used a comprehensive dataset including clinical and imaging data, to identify and compare predictors of motor, cognitive, and emotional outcomes one year post-stroke. Our analyses integrated traditional statistical approaches and novel machine learning algorithms to uncover optimal recipes for predicting each domain. The differences in these optimal recipes reflect distinct brain mechanisms in response to different tasks. Key predictors across all domains included infarct characteristics, T1-weighted MRI sequences, and demographic factors. Additionally, integrating FLAIR imaging and white matter tract analysis significantly improved the prediction of cognitive and motor outcomes, respectively. These findings support a multifaceted approach to stroke outcome prediction, underscoring the potential of collaborative data science to develop personalized care strategies that enhance recovery and quality of life for stroke survivors. To encourage further model development and validation, we provide access to the training dataset at http://neuralcup.bcblab.com

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bioRxiv2024

Spatial neglect after subcortical stroke: sometimes a cortico-cortical disconnection syndrome

Background and ObjectivesSpatial neglect is commonly attributed to lesions of a predominantly right-hemispheric cortical network. Although spatial neglect was also repeatedly observed after lesions to the basal ganglia and the thalamus, many anatomical network models omit these structures. We investigated if disruption of functional or structural connectivity can explain spatial neglect in subcortical stroke. MethodsWe retrospectively investigated data of first-ever, acute stroke patients with right-sided lesions of the basal ganglia (n = 27) or the thalamus (n = 16). Based on lesion location, we estimated i) functional connectivity via lesion-network mapping with normative resting state fMRI data, ii) structural white matter disconnection and iii) tract-wise disconnection of association fibres based on normative tractography data to investigate the association of spatial neglect and disconnection measures. ResultsApart from very small clusters of functional disconnection observed in inferior/middle frontal regions in lesion-network symptom mapping for basal ganglia lesions, our analyses found no evidence of functional or structural subcortico-cortical disconnection. Instead, the multivariate consideration of lesion load to several association fibres predicted the occurrence of spatial neglect (p = 0.0048; AUC = 0.76), which were the superior longitudinal fasciculus, inferior occipitofrontal fasciculus, superior occipitofrontal fasciculus, and the uncinate fasciculus. ConclusionDisconnection of long (cortico-cortical) association fibres can explain spatial neglect in subcortical stroke. Like the competing theory of remote cortical hypoperfusion, this mechanism does not require the assumption of a genuine role for subcortical grey matter structures in spatial neglect.

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bioRxiv2024

Control without cause: How covariate control biases our insights into brain architecture and pathology

Inferential analysis of normal or pathological brain imaging data - as in brain mapping or the identification of neurological imaging markers - is often controlled for secondary variables. However, a rationale for covariate control is rarely given and formal criteria to identify appropriate covariates in such complex data are lacking. We investigated the impact and adequacy of covariate control in large-scale imaging data using the example of stroke lesion-deficit mapping. In 183 stroke patients, we evaluated control for age, sex, hypertension, or lesion volume when mapping real or simulated deficits. We found that the impact of covariate control varies and can be strong, but it does not necessarily improve the precision of results. Instead, it systematically shifts results towards the inversed associations between imaging features and the covariate. This effect of covariate control can bias results and, as shown in another experiment, can even create effects out of nothing. The widespread use of covariate control in the statistical analysis of clinical brain imaging data - and, likely, other biological high-dimensional data as well - may not generally improve statistical results, but it may just change them. Therefore, covariate control constitutes a problematic degree of freedom in the analysis of brain imaging data and may often not be justified at all.

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bioRxiv2023

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.

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bioRxiv2022

Thalamocortical networks involved in Pusher Syndrome

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 - a disorder characterized by a disturbed perception of ones own upright body posture. Our results suggest a hub-like function of the posterior and lateral portions of the thalamus in the perception of ones own postural upright and identified dysfunction in a thalamo-cortical network as one likely cause of pusher syndrome. Lesion network-symptom-mapping investigating functional disconnection indicated cortical diaschisis in cerebellar, frontal, parietal, occipital, and temporal areas in patients with thalamic lesions suffering from pusher syndrome, but there was no evidence for functional diaschisis in cortical stroke and no evidence for the convergence of thalamic and cortical lesions onto a common functional network. Structural disconnectivity mapping identified several thalamo-cortical disconnections. Many of the thalamic and cortical regions disconnected by lesions that lead to pusher behavior correspond to previously reported lesion sites associated with pusher syndrome. Thus, while the presence of both, isolated thalamic and cortical lesions in context with pusher behavior has been reported previously and led to the conclusion that the correct estimation of ones own postural upright might depend on a thalamo-cortical network, our analyses 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.

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bioRxiv2021

The use of principal component analysis to measure fundamental cognitive processes in neuropsychological data

For years, dissociation studies on neurological single cases 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 and even misleading. Still, principal component analysis (PCA) - an associational method for dimensionality reduction - recently became popular for the identification of fundamental functions. The current study evaluated the ability of PCA and factor analysis (FA) to overcome the association problem in behavioural data of neurological patients and to identify the fundamental variables underlying a battery of measures. Synthetic data were simulated to resemble neuropsychological data with typical dissociation patterns and, thereby, typical patterns of dependence between variables. In most experiments, PCA and FA succeeded to measure the underlying target variables with high up to almost perfect precision. However, this success was fragile and relied on the success of factor rotation, which failed its intended purpose when no test scores existed that primarily measured each underlying target variable. Further, commonly used strategies to estimate the number of meaningful factors appear to underfactor neuropsychological data, thereby consistently underestimating the dimensionality of the data. Finally, simulations suggested a high potential of PCA to denoise data, with factor rotation providing an additional filter function. This can be invaluable in neuropsychology, where measures are often inherently noisy, and PCA can be superior to common compound measures, such as the arithmetic mean, in the measurement of variables with high reliability. In summary, PCA and FA appear to be powerful tools in neuropsychology that are well capable to infer fundamental cognitive functions with high precision, but the typical structure of neuropsychological data and limited informative value of associations in neuropsychology place clear limitations and a risk of a complete failure on the methods.

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bioRxiv2021

Left angular gyrus disconnection impairs multiplication fact retrieval

Arithmetic fact retrieval has been suggested to recruit a left-lateralized network comprising perisylvian language areas, parietal areas such as the angular gyrus (AG), and subcortical structures such as the hippocampus. However, the underlying white matter connectivity of these areas has not been evaluated systematically so far. Using simple multiplication problems, we evaluated how disconnections in parietal brain areas affected arithmetic fact retrieval following stroke. We derived disconnectivity measures by jointly considering data from n=73 patients with acute unilateral lesions in either hemisphere and a white-matter tractography atlas (HCP-842) using the Lesion Quantification Toolbox (LQT). Whole-brain voxel-based analysis indicated a left-hemispheric cluster of white matter fibers connecting the AG and superior temporal areas to be associated with a fact retrieval deficit. Subsequent analyses of direct grey-to-grey matter disconnections revealed that disconnections of additional left-hemispheric areas (e.g., between the superior temporal gyrus and parietal areas) were significantly associated with the observed fact retrieval deficit. Results imply that disconnections of parietal areas (i.e., the AG) with language-related areas (i.e., superior and middle temporal gyri) seem specifically detrimental to arithmetic fact retrieval. This suggests that arithmetic fact retrieval recruits a widespread left-hemispheric network and emphasizes the relevance of white matter connectivity for number processing.

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bioRxiv2021

Spatial neglect in the digital age - Influence of presentation format on patients' test behavior

Computerization of diagnostic neglect tests can deepen our knowledge of neglect specific abnormalities, by effortlessly providing additional behavioral markers that are hardly extractable from existing paper-and-pencil versions. However, so far it is not known whether the digitization and/or a change in size format impact neglect patients search behavior and test scores and thus require adjustments of cut-off criteria. We compared the Center of cancellation (CoC) measure of right hemisphere stroke patients with spatial neglect in two cancellation tasks across different modalities (paper-and-pencil vs. digital) and display sizes (small, medium, large). We found that the CoC measure did neither vary considerably between paper-and-pencil versus digital versions, nor between different digital size formats. The CoC derived from cancellation tasks thus seems robust to test digitization. A further aim of the present study was to evaluate three additional parameters of search behavior which became available through digitization. We observed slower search behavior, increased distance between two consecutively identified items, and signs of a more strategic search for neglect patients than control patients without neglect. Machine learning classifications indicated that - beyond the CoC measure - the latter three variables can help to differentiate stroke patients with spatial neglect from thosewithout.

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bioRxiv2021

The strange role of brain lesion size in cognitive neuropsychology

The size of brain lesions is a variable that is frequently considered in cognitive neuropsychology. In particular, lesion-deficit inference studies often control for lesion size, and the association of lesion size with post-stroke cognitive deficits and its predictive value are studied. In the present article, the role of lesion size in cognitive deficits and its computational or design-wise consideration is discussed and questioned. First, I argue that the commonly discussed role or effect of lesion size in cognitive deficits eludes us. A generally valid understanding of the causal relation of lesion size, lesion location, and cognitive deficits is unachievable. Second, founded on the theory of covariate control, I argue that lesion size control is no valid covariate control. Instead, it is identified as a procedure with only situational benefits, which is supported by empirical data. This theoretical background is used to suggest possible research practices in lesion-deficit inference, post-stroke outcome prediction, and behavioural studies. Last, control for lesion size is put into a bigger methodological and also historical context - it is identified to relate to a long-known association problem in neuropsychology, which was previously discussed from the perspectives of a mislocalisation in lesion-deficit mapping and the symptom complex approach. O_LSTHighlightsC_LST- Lesion size is a factor that is often considered or controlled in neuropsychology - No general causal relation between lesion size, lesion location and deficit exists - Lesion size in brain mapping, outcome prediction and behavioural study is discussed - Lesion size control is no valid covariate control - Practical suggestions and guidelines how to consider lesion size are provided

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bioRxiv2020

Neural correlates of imaging biomarkers for primary motor deficits after stroke - more than just the primary motor system?

Hemiparesis is a common consequence of stroke to the primary motor system. Previous studies suggested that damage to additional brain areas might play a causal role in the occurrence and severity of hemiparesis and its recovery. Knowledge of these regions might be applied in the creation of imaging biomarkers for motor outcome prediction if lesion information from such areas carries predictive value. We assessed acute and chronic paresis of the upper limb in 102 patients with unilateral stroke. In a first experiment, the neural correlates of acute and chronic upper limb paresis were mapped by lesion behaviour mapping. Following the same approach, a lesion biomarker of corticospinal tract (CST) damage was mapped. This analysis served as an artificial control condition as the biomarker, by definition, is only causally related to damage of the CST. Mapping acute or chronic upper limb paresis implicated areas outside of the primary motor system. Likewise, mapping the CST lesion biomarker implicated several areas outside of the CST with high correspondence to areas associated with upper limb paresis. Damage to areas outside of the primary motor system thus might, to some degree, not play a causal role in hemiparesis. In a second experiment, we showed that lesion information from these areas outside of the primary motor system can be used to predict motor outcome. This was even the case for the CST lesion biomarker. Although the only causal source underlying the CST lesion biomarker was damage to the CST, lesion information that mainly included non-CST regions was able to predict the biomarker (non-significantly) better than information taken from only the CST itself. These findings suggest that simple theory-based biomarkers or qualitative rules to infer post-stroke outcome from imaging data might perform sub-optimally, as they do not consider the complexity of lesion data. Instead, high-dimensional models with data-driven feature selection strategies might be required.

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bioRxiv2020

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

Left hemispheric cerebral stroke can cause apraxia, a motor-cognitive disorder characterised 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.

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bioRxiv2020

Disconnection somewhere down the line: Multivariate lesion-symptom mapping of the line bisection error

Line Bisection is a simple task frequently used in stroke patients to diagnose disorders of spatial perception characterized by a directional bisection bias to the ipsilesional side. However, previous anatomical and behavioural findings are contradictory, and the diagnostic validity of the line bisection task has been challenged. We hereby aimed to re-analyse the anatomical basis of pathological line bisection by using multivariate lesion-symptom mapping and disconnection-symptom mapping based on support vector regression in a sample of 163 right hemispheric acute stroke patients. In line with some previous studies, we observed that pathological line bisection was related to more than a single focal lesion location. Cortical damage primarily to right parietal areas, particularly the inferior parietal lobe, including the angular gyrus, as well as damage to the right basal ganglia contributed to the pathology. In contrast to some previous studies, an involvement of frontal cortical brain areas in the line bisection task was not observed. Subcortically, damage to the right superior longitudinal fasciculus (I, II and III) and arcuate fasciculus as well as the internal capsule was associated with line bisection errors. Moreover, white matter damage of interhemispheric fibre bundles, such as the anterior commissure and posterior parts of the corpus callosum projecting into the left hemisphere, was predictive of pathological deviation in the line bisection task.

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bioRxiv2019

Rethinking causality and data complexity in brain lesion-behaviour inference and its implications for lesion-behaviour modelling

Modelling behavioural deficits based on structural lesion imaging is a popular approach to map functions in the human brain, and efforts to translationally apply lesion-behaviour modelling to predict post-stroke outcomes are on the rise. The high-dimensional complexity of lesion data, however, evokes challenges in both lesion behaviour mapping and post stroke outcome prediction. This paper aims to deepen the understanding of this complexity by reframing it from the perspective of causal and non-causal dependencies in the data, and by discussing what this complexity implies for different data modelling approaches. By means of theoretical discussion and empirical examination, several common strategies and views are challenged, and future research perspectives are outlined. A main conclusion is that lesion-behaviour inference is subject to a lesion-anatomical bias that cannot be overcome by using multivariate models or any other algorithm that is blind to causality behind relations in the data. This affects the validity of lesion behaviour mapping and might even wrongfully identify paradoxical effects of lesion-induced functional facilitation - but, as this paper argues, only to a minor degree. Thus, multivariate lesion-brain inference appears to be a valuable tool to deepen our understanding of the human brain, but only because it takes into account the functional relation between brain areas. The perspective of causality and inter-variable dependence is further used to point out challenges in improving lesion behaviour models. Firstly, the dependencies in the data open up different possible strategies of data reduction, and considering those might improve post-stroke outcome prediction. Secondly, the role of non-topographical causal predictors of post stroke behaviour is discussed. The present article argues that, given these predictors, different strategies are required in the evaluation of model quality in lesion behaviour mapping and post stroke outcome prediction.

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bioRxiv2019

Using machine learning-based lesion behavior mapping to identify anatomical networks of cognitive dysfunction: spatial neglect and attention

Previous lesion behavior studies primarily used univariate lesion behavior mapping techniques to map the anatomical basis of spatial neglect after right brain damage. These studies led to inconsistent results and lively controversies. Given these inconsistencies, the idea of a widespread network that might underlie spatial orientation and neglect has been pushed forward. In such case, univariate lesion behavior mapping methods might have been inherently limited in uncover the presumed network in a single study due to limited statistical power. By using multivariate lesion-mapping based on support vector regression, we aimed to validate the network hypothesis directly in a large sample of 203 newly recruited right brain damaged patients. In a single analysis, this method identified a network of parietal, temporal, frontal, and subcortical regions, which also included white matter tracts connecting these regions. The results were compared to univariate analyses of the same patient sample using different combinations of lesion volume correction and statistical thresholding. The comparison revealed clear benefits of multivariate lesion behavior mapping in identifying brain networks.

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bioRxiv2019

The network underlying human higher-order motor control: Insights from machine learning-based lesion-behaviour mapping in apraxia

Neurological patients with apraxia of pantomime provide us with a unique opportunity to study the neural correlates of higher-order motor function. Previous studies using lesion-behaviour mapping methods led to inconsistent anatomical results, reporting various lesion locations to induce this symptom. We hypothesised that the inconsistencies might arise from limitations of mass-univariate lesion-behaviour mapping approaches if our ability to pantomime the use of objects is organised in a brain network. Thus, we investigated apraxia of pantomime by using multivariate lesion behaviour mapping based both on support vector regression and sparse canonical correlations in a sample of 130 left-hemisphere stroke patients. Both multivariate methods identified multiple areas to underlie high-order motor control, including inferior parietal lobule, precentral gyrus, posterior parts of middle temporal cortex, and insula. Further, long association fibres were affected, such as the superior longitudinal fascicle, inferior occipito-frontal fascicle, uncinated fascicle, and superior occipito-frontal fascicle. The findings thus not only underline the benefits of multivariate lesion-behaviour mapping in brain networks, but they also uncovered that higher-order motor control indeed is based on a common anatomical network.

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bioRxiv2018

An empirical evaluation of multivariate lesion behaviour mapping using support vector regression

Multivariate lesion behaviour mapping based on machine learning algorithms has recently been suggested to complement the methods of anatomo-behavioural approaches in cognitive neuroscience. Several studies applied and validated support vector regression-based lesion symptom mapping (SVR-LSM) to map anatomo-behavioural relations. However, this promising method, as well as the multivariate approach per se, still bears many open questions. By using large lesion samples in three simulation experiments, the present study empirically tested the validity of several methodological aspects. We found that i) correction for multiple comparisons is required in the current implementation of SVR-LSM, ii) that sample sizes of at least 100 to 120 subjects are required to optimally model voxel-wise lesion location in SVR-LSM, and iii) that SVR-LSM is susceptible to misplacement of statistical topographies along the brains vasculature to a similar extent as mass-univariate analyses.

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bioRxiv2018

Right-sided brain lesions predominate among patients with lesional mania: evidence from a systematic review and pooled lesion analysis

BackgroundDespite claims that lesional mania is associated with right-hemisphere lesions, supporting evidence is scarce, and association with specific brain areas has not been demonstrated.\n\nAimsTo test whether focal brain lesions in lesional mania are more often right-than left-sided, and if lesions converge on areas relevant to mood regulation.\n\nMethodsWe performed a systematic literature search (PROSPERO registration CRD42016053675) on PubMed and Web-Of-Science, using terms that reflected diagnoses and structures of interest, and lesional mechanisms. Two researchers reviewed the articles separately according to PRISMA Guidelines, to select reports of adult-onset hypomania, mania or mixed state following a focal brain lesion. When available, eligible lesion images were manually traced onto the corresponding slices of MNI space, and lesion topography analyzed using standard brain atlases. Pooled-analyses of individual patient data were performed.\n\nResultsData from 207 lesional mania patients was extracted from 110 reports. Among patients with focal lesions (N=197) more patients had lesions involving the right (84.3%) than the left (34.5%) hemisphere. Among 54 lesion images that were available, right-sided predominance of lesions was confirmed, and found to be was conserved across multiple brain regions, including the temporal lobe, fusiform gyrus and thalamus. These, in addition to several frontal lobe areas, were also identified as preferential lesion sites in comparisons with control lesions.\n\nConclusionsPooled-analyses, based on the most comprehensive dataset of lesional mania available to date, confirm a preferential association with right-hemisphere lesions, while suggesting that several brain areas/circuits, relevant to mood regulation, are most frequently affected.

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