Mapping human brain lesions and their functional consequences

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 wide-spread 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 detecting the presumed network due to limited statistical power. By comparing various univariate analyses with 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. If the exact same correction factors and parameter combinations (FDR correction and dTLVC for lesion size control) were used, both univariate as well as multivariate approaches uncovered the same complex network pattern underlying spatial neglect. At the cortical level, lesion location dominantly affected the temporal cortex and its borders into inferior parietal and occipital cortices. Beyond, frontal and subcortical gray matter regions as well as white matter tracts connecting these regions were affected. Our findings underline the importance of a right network in spatial exploration and attention and specifically in the emergence of the core symptoms of spatial neglect.

Brain networks and their relevance for stroke rehabilitation

Stroke has long been regarded as focal disease with circumscribed damage leading to neurological deficits. However, advances in methods for assessing the human brain and in statistics have enabled new tools for the examination of the consequences of stroke on brain structure and function. Thereby, it has become evident that stroke has impact on the entire brain and its network properties and can therefore be considered as a network disease. The present review first gives an overview of current methodological opportunities and pitfalls for assessing stroke-induced changes and reorganization in the human brain. We then summarize principles of plasticity after stroke that have emerged from the assessment of networks. Thereby, it is shown that neurological deficits do not only arise from focal tissue damage but also from local and remote changes in white-matter tracts and in neural interactions among wide-spread networks. Similarly, plasticity and clinical improvements are associated with specific compensatory structural and functional patterns of neural network interactions. Innovative treatment approaches have started to target such network patterns to enhance recovery. Network assessments to predict treatment response and to individualize rehabilitation is a promising way to enhance specific treatment effects and overall outcome after stroke.

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 100-120 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 brain's vasculature to a similar extent as mass-univariate analyses.

Relevance of brain lesion location for cognition in vascular mild cognitive impairment

Background

Vascular mild cognitive impairment (VMCI) is a potentially transitional state between normal aging and vascular dementia. The presence of macroscopic white matter lesions (WML) of moderate or severe extension on brain MRI is the hallmark of the VMCI.

Objective

To assess the clinical relevance of the frequency of WML in patients with VMCI independently of total lesion volume (LV).

Methods

In this multicenter study, we included 110 patients with VMCI (age: 74.3 ± 6.6 years; sex: 60 women). Cognitive assessment was performed with the VMCI-Tuscany Neuropsychological Battery, which allowed to identify four VMCI groups: amnestic single (n = 9) and multi-domain (n = 76), non-amnestic single- (n = 10) and multi-domain (n = 15). Distribution and frequency of WML on MRI FLAIR images were evaluated with lesion probability map (LPM). Voxelwise statistics was performed with nonparametric permutation tests, controlling for age, sex, slice thickness, center, magnetic field strength, total LV and head size (p < .01, family-wise error-corrected for multiple comparisons across space).

Results

LPM of the WML had a fairly symmetric and widespread distribution across brain. A higher frequency of WML along association tracts of the WM such as inferior longitudinal fascicle, inferior fronto-occipital fascicle and superior longitudinal fascicle, was correlated with worst cognitive scores at the Trail Making Test Part A and Copy of the Rey–Osterrieth Complex Figure. The non-amnestic groups showed a higher frequency of WML in the anterior cingulum and superior longitudinal fascicle close to the frontal gyrus.

Conclusions

Our study showed that in patients with VMCI, independently of total LV, the higher frequency of lesions along association tracts of the WM, which mediate intrahemispheric long-range connectivity, is related with psychomotor speed and constructional praxis. Moreover, a prevalence of lesions in the frontal WM seems to characterize VMCI patients with involvement of non-amnestic domains.

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Vascular mild cognitive impairment (VMCI) has moderate-to-severe white matter lesions (WML).

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110 VMCI patients were assessed by a full neuropsychological battery and a lesion mapping approach on MRI images.

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Higher WML frequency along association tracts correlated with worst psychomotor speed and constructional praxis.

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Non-amnestic groups of VMCI had higher WML frequency in the frontal WM.

Predictors of Epilepsy Presentation in Unruptured Brain Arteriovenous Malformations: A Quantitative Evaluation of Location and Radiomics Features on T2-Weighted Imaging

OBJECTIVE

To explore predictors of epilepsy presentation in unruptured brain arteriovenous malformations (bAVMs) with quantitative evaluation of location and radiomics features on T2-weighted imaging.

METHODS

This retrospective study identified 117 patients with unruptured bAVMs. Cases were randomly split into training dataset (n = 90) and test dataset (n = 27). On the training dataset, we applied atlas-based analysis to identify epilepsy-susceptible brain regions of bAVMs, and then applied the radiomics technique to explore shape, intensity, and textural features that were correlated with epilepsy presentation. Informative radiomics predictors were selected by least absolute shrinkage and selection operator with 3-fold cross-validation. A linear classification score was then constructed, and we tested if we could precisely identify epilepsy-susceptible bAVMs with the location and radiomics predictors.

RESULTS

Two brain regions and 4 radiomics features were screened out as predictors for epilepsy. The percent of damage of the right precentral gyrus and the right superior longitudinal fasciculus was associated with epilepsy presentation. The 4 radiomics features were Original_firstorder_Median, Wavelet-LHL_firstorder_InterquartileRange, Wavelet-HHL_firstorder_InterquartileRange, and Wavelet-HHH_glrlm_RunVariance. Epileptogenic bAVMs had larger variance of run lengths, larger median value, and interquartile range of voxel intensities. On the training dataset, these 6 predictors were able to classify epilepsy-susceptible bAVMs with accuracy at 0.822, and the area under the curve was 0.866 (95% confidence interval, 0.791-0.940). On the test dataset, sensitivity, specificity, and accuracy of classification reached 0.786, 0.769, and 0.778, respectively.

CONCLUSIONS

Epilepsy-susceptible bAVMs had distinct locations and radiomics features on T2-weighted imaging.

Mapping the contribution and strategic distribution patterns of neuroimaging features of small vessel disease in poststroke cognitive impairment

OBJECTIVES

Individual neuroimaging features of small vessel disease (SVD) have been reported to influence poststroke cognition. This study aimed to investigate the joint contribution and strategic distribution patterns of multiple types of SVD imaging features in poststroke cognitive impairment.

METHODS

We studied 145 first-ever ischaemic stroke patients with MRI and Montreal Cognitive Assessment (MoCA) examined at baseline. The local burdens of acute ischaemic lesion (AIL), white matter hyperintensity, lacune, enlarged perivascular space and cross-sectional atrophy were quantified and entered into support vector regression (SVR) models to associate with the global and domain scores of MoCA. The SVR models were optimised with feature selection through 10-fold cross-validations. The contribution of SVD features to MoCA scores was measured by the prediction accuracy in the corresponding SVR model after optimisation.

RESULTS

The combination of the neuroimaging features of SVD contributed much more to the MoCA deficits on top of AILs compared with individual SVD features, and the cognitive impact of different individual SVD features was generally similar. As identified by the optimal SVR models, the important SVD-affected regions were mainly located in the basal ganglia and white matter around it, although the specific regions varied for MoCA and its domains.

CONCLUSIONS

Multiple types of SVD neuroimaging features jointly had a significant impact on global and domain cognitive functionings after stroke on top of AILs. The map of strategic cognitive-relevant regions of SVD features may help clinicians to understand their complementary impact on poststroke cognition.

The impact of sample size on the reproducibility of voxel-based lesion-deficit mappings

This study investigated how sample size affects the reproducibility of findings from univariate voxel-based lesion-deficit analyses (e.g., voxel-based lesion-symptom mapping and voxel-based morphometry). Our effect of interest was the strength of the mapping between brain damage and speech articulation difficulties, as measured in terms of the proportion of variance explained. First, we identified a region of interest by searching on a voxel-by-voxel basis for brain areas where greater lesion load was associated with poorer speech articulation using a large sample of 360 right-handed English-speaking stroke survivors. We then randomly drew thousands of bootstrap samples from this data set that included either 30, 60, 90, 120, 180, or 360 patients. For each resample, we recorded effect size estimates and p values after conducting exactly the same lesion-deficit analysis within the previously identified region of interest and holding all procedures constant. The results show (1) how often small effect sizes in a heterogeneous population fail to be detected; (2) how effect size and its statistical significance varies with sample size; (3) how low-powered studies (due to small sample sizes) can greatly over-estimate as well as under-estimate effect sizes; and (4) how large sample sizes (N ≥ 90) can yield highly significant p values even when effect sizes are so small that they become trivial in practical terms. The implications of these findings for interpreting the results from univariate voxel-based lesion-deficit analyses are discussed.

Non-invasive Brain Stimulation: A Paradigm Shift in Understanding Brain Oscillations

Cognitive neuroscience set out to understand the neural mechanisms underlying cognition. One central question is how oscillatory brain activity relates to cognitive processes. Up to now, most of the evidence supporting this relationship was correlative in nature. This situation changed dramatically with the recent development of non-invasive brain stimulation (NIBS) techniques, which open up new vistas for neuroscience by allowing researchers for the first time to validate their correlational theories by manipulating brain functioning directly. In this review, we focus on transcranial alternating current stimulation (tACS), an electrical brain stimulation method that applies sinusoidal currents to the intact scalp of human individuals to directly interfere with ongoing brain oscillations. We outline how tACS can impact human brain oscillations by employing different levels of observation from non-invasive tACS application in healthy volunteers and intracranial recordings in patients to animal studies demonstrating the effectiveness of alternating electric fields on neurons in vitro and in vivo. These findings likely translate to humans as comparable effects can be observed in human and animal studies. Neural entrainment and plasticity are suggested to mediate the behavioral effects of tACS. Furthermore, we focus on mechanistic theories about the relationship between certain cognitive functions and specific parameters of brain oscillaitons such as its amplitude, frequency, phase and phase coherence. For each of these parameters we present the current state of testing its functional relevance by means of tACS. Recent developments in the field of tACS are outlined which include the stimulation with physiologically inspired non-sinusoidal waveforms, stimulation protocols which allow for the observation of online-effects, and closed loop applications of tACS.

The Original Social Network: White Matter and Social Cognition

Social neuroscience has traditionally focused on the functionality of gray matter regions, ignoring the critical role played by axonal fiber pathways in supporting complex social processes. In this paper, we argue that research on white matter is essential for understanding a range of topics in social neuroscience, such as face processing, theory of mind, empathy, and imitation, as well as clinical disorders defined by aberrant social behavior, such as prosopagnosia, autism, and schizophrenia. We provide practical advice on how best to carry out these studies, which ultimately will substantially deepen our understanding of the neurobiological basis of social behavior.

Neural mechanisms of two different verbal working memory tasks: A VLSM study

Currently, a distributed bilateral network of frontal-parietal areas is regarded as the neural substrate of working memory (WM), with the verbal WM network being more left-lateralized. This conclusion is based primarily on functional magnetic resonance imaging (fMRI) data that provides correlational evidence for brain regions involved in a task. However, fMRI cannot differentiate the areas that are fundamentally required for performing a task. These data can only come from brain-injured individuals who fail the task after the loss of specific brain areas. In addition to the lack of complimentary data, is the issue of the variety in the WM tasks used to assess verbal WM. When different tasks are assumed to measure the same behavior, this may mask the contributions of different brain regions. Here, we investigated the neural substrate of WM by using voxel-based lesion symptom mapping (VLSM) in 49 individuals with stroke-induced left hemisphere brain injuries. These participants completed two different verbal WM tasks: complex listening span and a word 2-back task. Behavioral results indicated that the two tasks were only slightly related, while the VLSM analysis revealed different critical regions associated with each task. Specifically, significant detriments in performance on the complex span task were found with lesions in the inferior frontal gyrus, while for the 2-back task, significant deficits were seen after injury to the superior and middle temporal gyri. Thus, the two tasks depend on the structural integrity of different, non-overlapping frontal and temporal brain regions, suggesting distinct neural and cognitive mechanisms triggered by the two tasks: rehearsal and cue-dependent selection in the complex span task, versus updating/auditory recognition in the 2-back task. These findings call into question the common practice of using these two tasks interchangeably in verbal WM research and undermine the legitimacy of aggregating data from studies with different WM tasks. Thus, the present study points out the importance of lesion studies in complementing functional neuroimaging findings and highlights the need to consider task demands in neuroimaging and neuropsychological investigations of WM.

A hitchhiker's guide to lesion-behaviour mapping

Lesion-behaviour mapping is an influential and popular approach to anatomically localise cognitive brain functions in the human brain. Multiple considerations, ranging from patient selection, assessment of lesion location and patient behaviour, spatial normalisation, statistical testing, to the anatomical interpretation of obtained results, are necessary to optimize a lesion-behaviour mapping study and arrive at meaningful conclusions. Here, we provide a hitchhiker's guide, giving practical guidelines and references for each step of the typical lesion-behaviour mapping study pipeline.