Voxelwise Bayesian lesion-deficit analysis

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.

Multiple Adjoining Word- and Face-Selective Regions in Ventral Temporal Cortex Exhibit Distinct Dynamics

The map of category-selectivity in human ventral temporal cortex (VTC) provides organizational constraints to models of object recognition. One important principle is lateral-medial response biases to stimuli that are typically viewed in the center or periphery of the visual field. However, little is known about the relative temporal dynamics and location of regions that respond preferentially to stimulus classes that are centrally viewed, such as the face- and word-processing networks. Here, word- and face-selective regions within VTC were mapped using intracranial recordings from 36 patients. Partially overlapping, but also anatomically dissociable patches of face- and word-selectivity, were found in VTC. In addition to canonical word-selective regions along the left posterior occipitotemporal sulcus, selectivity was also located medial and anterior to face-selective regions on the fusiform gyrus at the group level and within individual male and female subjects. These regions were replicated using 7 Tesla fMRI in healthy subjects. Left hemisphere word-selective regions preceded right hemisphere responses by 125 ms, potentially reflecting the left hemisphere bias for language, with no hemispheric difference in face-selective response latency. Word-selective regions along the posterior fusiform responded first, then spread medially and laterally, then anteriorally. Face-selective responses were first seen in posterior fusiform regions bilaterally, then proceeded anteriorally from there. For both words and faces, the relative delay between regions was longer than would be predicted by purely feedforward models of visual processing. The distinct time courses of responses across these regions, and between hemispheres, suggest that a complex and dynamic functional circuit supports face and word perception.SIGNIFICANCE STATEMENT Representations of visual objects in the human brain have been shown to be organized by several principles, including whether those objects tend to be viewed centrally or peripherally in the visual field. However, it remains unclear how regions that process objects that are viewed centrally, such as words and faces, are organized relative to one another. Here, invasive and noninvasive neuroimaging suggests that there is a mosaic of regions in ventral temporal cortex that respond selectively to either words or faces. These regions display differences in the strength and timing of their responses, both within and between brain hemispheres, suggesting that they play different roles in perception. These results illuminate extended, bilateral, and dynamic brain pathways that support face perception and reading.

Causal Network Inference for Neural Ensemble Activity

Interactions among cellular components forming a mesoscopic scale brain network (microcircuit) display characteristic neural dynamics. Analysis of microcircuits provides a system-level understanding of the neurobiology of health and disease. Causal discovery aims to detect causal relationships among variables based on observational data. A key barrier in causal discovery is the high dimensionality of the variable space. A method called Causal Inference for Microcircuits (CAIM) is proposed to reconstruct causal networks from calcium imaging or electrophysiology time series. CAIM combines neural recording, Bayesian network modeling, and neuron clustering. Validation experiments based on simulated data and a real-world reaching task dataset demonstrated that CAIM accurately revealed causal relationships among neural clusters.

Revisiting 'brain modes' in a new computational era: approaches for the characterization of brain-behavioural associations

The study of brain-function relationships is undergoing a conceptual and methodological transformation due to the emergence of network neuroscience and the development of multivariate methods for lesion-deficit inferences. Anticipating this process, in 1998 Godefroy and co-workers conceptualized the potential of four elementary typologies of brain-behaviour relationships named 'brain modes' (unicity, equivalence, association, summation) as building blocks able to describe the association between intact or lesioned brain regions and cognitive processes or neurological deficits. In the light of new multivariate lesion inference and network approaches, we critically revisit and update the original theoretical notion of brain modes, and provide real-life clinical examples that support their existence. To improve the characterization of elementary units of brain-behavioural relationships further, we extend such conceptualization with a fifth brain mode (mutual inhibition/masking summation). We critically assess the ability of these five brain modes to account for any type of brain-function relationship, and discuss past versus future contributions in redefining the anatomical basis of human cognition. We also address the potential of brain modes for predicting the behavioural consequences of lesions and their future role in the design of cognitive neurorehabilitation therapies.

Quantifying the Impact of Chronic Ischemic Injury on Clinical Outcomes in Acute Stroke With Machine Learning

Acute stroke is often superimposed on chronic damage from previous cerebrovascular events. This background will inevitably modulate the impact of acute injury on clinical outcomes to an extent that will depend on the precise anatomical pattern of damage. Previous attempts to quantify such modulation have employed only reductive models that ignore anatomical detail. The combination of automated image processing, large-scale data, and machine learning now enables us to quantify the impact of this with high-dimensional multivariate models sensitive to individual variations in the detailed anatomical pattern. We introduce and validate a new automated chronic lesion segmentation routine for use with non-contrast CT brain scans, combining non-parametric outlier-detection score, Zeta, with an unsupervised 3-dimensional maximum-flow, minimum-cut algorithm. The routine was then applied to a dataset of 1,704 stroke patient scans, obtained at their presentation to a hyper-acute stroke unit (St George's Hospital, London, UK), and used to train a support vector machine (SVM) model to predict between low (0-2) and high (3-6) pre-admission and discharge modified Rankin Scale (mRS) scores, quantifying performance by the area under the receiver operating curve (AUROC). In this single center retrospective observational study, our SVM models were able to differentiate between low (0-2) and high (3-6) pre-admission and discharge mRS scores with an AUROC of 0.77 (95% confidence interval of 0.74-0.79), and 0.76 (0.74-0.78), respectively. The chronic lesion segmentation routine achieved a mean (standard deviation) sensitivity, specificity and Dice similarity coefficient of 0.746 (0.069), 0.999 (0.001), and 0.717 (0.091), respectively. We have demonstrated that machine learning models capable of capturing the high-dimensional features of chronic injuries are able to stratify patients-at the time of presentation-by pre-admission and discharge mRS scores. Our fully automated chronic stroke lesion segmentation routine simplifies this process, and utilizes routinely collected CT head scans, thereby facilitating future large-scale studies to develop supportive clinical decision tools.

A Comparison of Shallow and Deep Learning Methods for Predicting Cognitive Performance of Stroke Patients From MRI Lesion Images

Stroke causes behavioral deficits in multiple cognitive domains and there is a growing interest in predicting patient performance from neuroimaging data using machine learning techniques. Here, we investigated a deep learning approach based on convolutional neural networks (CNNs) for predicting the severity of language disorder from 3D lesion images from magnetic resonance imaging (MRI) in a heterogeneous sample of stroke patients. CNN performance was compared to that of conventional (shallow) machine learning methods, including ridge regression (RR) on the images' principal components and support vector regression. We also devised a hybrid method based on re-using CNN's high-level features as additional input to the RR model. Predictive accuracy of the four different methods was further investigated in relation to the size of the training set and the level of redundancy across lesion images in the dataset, which was evaluated in terms of location and topological properties of the lesions. The Hybrid model achieved the best performance in most cases, thereby suggesting that the high-level features extracted by CNNs are complementary to principal component analysis features and improve the model's predictive accuracy. Moreover, our analyses indicate that both the size of training data and image redundancy are critical factors in determining the accuracy of a computational model in predicting behavioral outcome from the structural brain imaging data of stroke patients.

Multivariate Approaches to Understanding Aphasia and its Neural Substrates

PURPOSE OF REVIEW

Aphasia is often characterized in terms of subtype and severity, yet these constructs have limited explanatory power, because aphasia is inherently multifactorial both in its neural substrates and in its symptomatology. The purpose of this review is to survey current and emerging multivariate approaches to understanding aphasia.

RECENT FINDINGS

Techniques such as factor analysis and principal component analysis have been used to define latent underlying factors that can account for performance on batteries of speech and language tests, and for characteristics of spontaneous speech production. Multivariate lesion-symptom mapping has been shown to outperform univariate approaches to lesion-symptom mapping for identifying brain regions where damage is associated with specific speech and language deficits. It is increasingly clear that structural damage results in functional changes in wider neural networks, which mediate speech and language outcomes. Multivariate statistical approaches are essential for understanding the complex relationships between the neural substrates of aphasia, and resultant profiles of speech and language function.

Distinct regional brain atrophy pattern in multiple sclerosis and neuropsychiatric systemic lupus erythematosus patients

Differentiation of systemic lupus erythematosus (SLE) from multiple sclerosis (MS) can be challenging, especially when neuropsychiatric (NP) symptoms are accompanied by white matter lesions in the brain. Given the lack of discriminative power of currently applied tools for their differentiation, there is an unmet need for other measures that can aid in distinguishing between the two autoimmune disorders. In this study we aimed at exploring whether brain atrophy measures could serve as markers differentiating MS and SLE. Thirty-seven relapsing-remitting MS and 38 SLE patients with nervous system manifestations, matched according to age and disease duration, underwent 1.5 Tesla magnetic resonance imaging (MRI), including volumetric sequences, and clinical assessment. Voxelwise analysis was performed using ANTS-SyN elastic registration protocol, FSL Randomise and Gamma methods. Cortical and subcortical segmentation was performed with Freesurfer 5.3 pipeline using T1-weighted MPRAGE sequence data. Using MRI volumetric markers of general and subcortical gray matter atrophy and clinical variables, we built a stepwise multivariable logistic diagnostic model to identify MRI parameters that best differentiate MS and SLE patients. We found that the best volumetric predictors to distinguish them were: fourth ventricle volume (sensitivity 0.86, specificity 0.57, area under the curve, AUC 0.77), posterior corpus callosum (sensitivity 0.81, specificity 0.57, AUC 0.68), and third ventricle to thalamus ratio (sensitivity 0.42, specificity 0.84, AUC 0.65). The same classifiers were identified in a subgroup analysis that included patients with a short disease duration. In MS brain atrophy and lesion load correlated with clinical disability, while in SLE age was the main determinant of brain volume. This study proposes new imaging parameters for differential diagnosis of MS and SLE with central nervous system involvement. We show there is a different pattern of atrophy in MS and SLE, and the key structural volumes that are differentially affected include fourth ventricle and posterior section of corpus callosum, followed by third ventricle to thalamus ratio. Different correlation patterns between volumetric and clinical data may suggest that while in MS atrophy is driven mainly by disease activity, in SLE it is mostly associated with age. However, these results need further replication in a larger cohort.

The dimensionalities of lesion-deficit mapping

Lesion-deficit mapping remains the most powerful method for localising function in the human brain. As the highest court of appeal where competing theories of cerebral function conflict, it ought to be held to the most stringent inferential standards. Though at first sight elegantly transferable, the mass-univariate statistical framework popularized by functional imaging is demonstrably ill-suited to the task, both theoretically and empirically. The critical difficulty lies with the handling of the data's intrinsically high dimensionality. Conceptual opacity and computational complexity lead lesion-deficit mappers to neglect two distinct sets of anatomical interactions: those between areas unified by function, and those between areas unified by the natural pattern of pathological damage. Though both are soluble through high-dimensional multivariate analysis, the consequences of ignoring them are radically different. The former will bleach and coarsen a picture of the functional anatomy that is nonetheless broadly faithful to reality; the latter may alter it beyond all recognition. That the field continues to cling to mass-univariate methods suggests the latter problem is misidentified with the former, and that their distinction is in need of elaboration. We further argue that the vicious effects of lesion-driven interactions are not limited to anatomical localisation but will inevitably degrade purely predictive models of function such as those conceived for clinical prognostic use. Finally, we suggest there is a great deal to be learnt about lesion-mapping by simulation-based modelling of lesion data, for the fundamental problems lie upstream of the experimental data themselves.

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.

Success of Anomia Treatment in Aphasia Is Associated With Preserved Architecture of Global and Left Temporal Lobe Structural Networks

BACKGROUND AND OBJECTIVE

Targeted speech therapy can lead to substantial naming improvement in some subjects with anomia following dominant-hemisphere stroke. We investigated whether treatment-induced improvement in naming is associated with poststroke preservation of structural neural network architecture.

METHODS

Twenty-four patients with poststroke chronic aphasia underwent 30 hours of speech therapy over a 2-week period and were assessed at baseline and after therapy. Whole brain maps of neural architecture were constructed from pretreatment diffusion tensor magnetic resonance imaging to derive measures of global brain network architecture (network small-worldness) and regional network influence (nodal betweenness centrality). Their relationship with naming recovery was evaluated with multiple linear regressions.

RESULTS

Treatment-induced improvement in correct naming was associated with poststroke preservation of global network small worldness and of betweenness centrality in temporal lobe cortical regions. Together with baseline aphasia severity, these measures explained 78% of the variability in treatment response.

CONCLUSIONS

Preservation of global and left temporal structural connectivity broadly explains the variability in treatment-related naming improvement in aphasia. These findings corroborate and expand on previous classical lesion-symptom mapping studies by elucidating some of the mechanisms by which brain damage may relate to treated aphasia recovery. Favorable naming outcomes may result from the intact connections between spared cortical areas that are functionally responsive to treatment.

Combining diffusion tensor imaging and gray matter volumetry to investigate motor functioning in chronic stroke

Motor impairment after stroke is related to the integrity of the corticospinal tract (CST). However, considerable variability in motor impairment remains unexplained. To increase the accuracy in evaluating long-term motor function after ischemic stroke, we tested the hypothesis that combining diffusion tensor imaging (DTI) and gray matter (GM) volumetry can better characterize long-term motor deficit than either method alone in patients with chronic stroke. We recruited 31 patients whose Medical Research Council strength grade was ≤ 3/5 in the extensor muscles of the affected upper extremity in the acute phase. We used the Upper Extremity Fugl-Meyer (UE-FM) assessment to evaluate motor impairment, and as the primary outcome variable. We computed the fractional anisotropy ratio of the entire CST (CST_ratio) and the volume of interest ratio (VOI_ratio), between ipsilesional and contralesional hemispheres, to explain long-term motor impairment. The results showed that CST_ratio, VOI_ratio of motor-related brain regions, and VOI_ratio in the temporal lobe were correlated with UE-FM. A multiple regression model including CST_ratio and VOI_ratio of the caudate nucleus explained 40.7% of the variability in UE-FM. The adjusted R2 of the regression model with CST_ratio as an independent variable was 29.4%, and that of using VOI_ratio of the caudate nucleus as an independent variable was 23.1%. These results suggest that combining DTI and GM volumetry may achieve better explanation of long-term motor deficit in stroke patients, than using either measure individually. This finding may provide guidance in determining optimal neurorehabilitative interventions.

Anosognosia for hemiplegia: The contributory role of right inferior frontal gyrus

OBJECTIVE

Awareness of motor functioning is most likely a complex process that requires integration of sensory-motor feedback to constantly update the system on the functioning of the limb during motor behavior. Using lesion mapping procedures and behavioral measures, the current study aimed to evaluate neural correlates of anosognosia for hemiplegia (AHP) in the acute stage (first 48 hr) of right hemisphere stroke.

METHOD

Thirty-five individuals with right hemisphere stroke who presented to an urban medical center within 24 hr of symptom onset were included in the study. All 35 individuals had hemiplegia, and 8 of these individuals exhibited AHP.

RESULTS

Fisher's exact test statistical map of lesion-deficit association (range is between-log(p) 4 to 11) found maximal value of 10.9 located in pars orbitalis (Brodmann's Area 47; BA). In this selected location, 6 out of 8 patients with AHP had tissue abnormality, whereas none of the unaffected subjects had tissue abnormality in BA 47. Right BA 44/45 was also found to be lesioned more frequently in individuals with AHP (75%) than without AHP (11%).

CONCLUSIONS

The current study findings provide preliminary support for unique involvement of the right inferior frontal gyrus (IFG), pars orbitalis (BA 47) in AHP. The current data suggest that frontal operculum may play a key role in awareness of limb functioning.

Multivariate lesion-symptom mapping using support vector regression

Lesion analysis is a classic approach to study brain functions. Because brain function is a result of coherent activations of a collection of functionally related voxels, lesion-symptom relations are generally contributed by multiple voxels simultaneously. Although voxel-based lesion-symptom mapping (VLSM) has made substantial contributions to the understanding of brain-behavior relationships, a better understanding of the brain-behavior relationship contributed by multiple brain regions needs a multivariate lesion-symptom mapping (MLSM). The purpose of this artilce was to develop an MLSM using a machine learning-based multivariate regression algorithm: support vector regression (SVR). In the proposed SVR-LSM, the symptom relation to the entire lesion map as opposed to each isolated voxel is modeled using a nonlinear function, so the intervoxel correlations are intrinsically considered, resulting in a potentially more sensitive way to examine lesion-symptom relationships. To explore the relative merits of VLSM and SVR-LSM we used both approaches in the analysis of a synthetic dataset. SVR-LSM showed much higher sensitivity and specificity for detecting the synthetic lesion-behavior relations than VLSM. When applied to lesion data and language measures from patients with brain damages, SVR-LSM reproduced the essential pattern of previous findings identified by VLSM and showed higher sensitivity than VLSM for identifying the lesion-behavior relations. Our data also showed the possibility of using lesion data to predict continuous behavior scores.

Examining the multifactorial nature of a cognitive process using Bayesian brain-behavior modeling

Establishing relationships among brain structures and cognitive functions is a central task in cognitive neuroscience. Existing methods to establish associations among a set of function variables and a set of brain regions, such as dissociation logic and conjunction analysis, are hypothesis-driven. We propose a new data-driven approach to structure-function association analysis. We validated it by analyzing a simulated atrophy study. We applied the proposed method to a study of aging and dementia. We found that the most significant age-related and dementia-related volume reductions were in the hippocampal formation and the supramarginal gyrus, respectively. These findings suggest a multi-component brain-aging model.

Tract-based Bayesian multivariate analysis of mild traumatic brain injury

Purpose. Detecting brain regions characterizing mild traumatic brain injury (mTBI) by combining Tract-Based Spatial Statistics (TBSS) and Graphical-model-based Multivariate Analysis (GAMMA). Materials and Methods. This study included 39 mTBI patients and 28 normal controls. Local research ethics committee approved this prospective study. Diffusion-tensor imaging was performed in mTBI patients within 7 days of injury. Skeletonized fractional anisotropy (FA) maps were generated by using TBSS. Brain regions characterizing mTBI were detected by GAMMA. Results. Two clusters of lower frontal white matter FA were present in mTBI patients. We constructed classifiers based on FA values in these two clusters to differentiate mTBI and controls. The mean accuracy, sensitivity, and specificity, across five different classifiers, were 0.80, 0.94, and 0.61, respectively. Conclusions. Combining TBSS and GAMMA can detect neuroimaging biomarkers characterizing mTBI.

Graphical model based multivariate analysis (GAMMA): an open-source, cross-platform neuroimaging data analysis software package

The GAMMA suite is an open-source, cross-platform data-mining software package designed to analyze neuroimaging data. Analyzing brain image volumes is a very challenging problem, due to undersampling and the potential for multivariate nonlinear interactions among variables. The GAMMA suite provides a set of tools to facilitate the analysis of neuroimaging data.

Neuroanatomical correlates of oral reading in acute left hemispheric stroke

Oral reading is a complex skill involving the interaction of orthographic, phonological, and semantic processes. Functional imaging studies with nonimpaired adult readers have identified a widely distributed network of frontal, inferior parietal, posterior temporal, and occipital brain regions involved in the task. However, while functional imaging can identify cortical regions engaged in the process under examination, it cannot identify those brain regions essential for the task. The current study aimed to identify those neuroanatomical regions critical for successful oral reading by examining the relationship between word and nonword oral reading deficits and areas of tissue dysfunction in acute stroke. We evaluated 91 patients with left hemisphere ischemic stroke with a test of oral word and nonword reading, and magnetic resonance diffusion-weighted and perfusion-weighted imaging, within 24-48h of stroke onset. A voxel-wise statistical map showed that impairments in word and nonword reading were associated with a distributed network of brain regions, including the inferior and middle frontal gyri, the middle temporal gyrus, the supramarginal and angular gyri, and the middle occipital gyrus. In addition, lesions associated with word deficits were found to be distributed more frontally, while nonword deficits were associated with lesions distributed more posteriorly.

Predicting language outcome and recovery after stroke: the PLORAS system

The ability to comprehend and produce speech after stroke depends on whether the areas of the brain that support language have been damaged. Here, we review two different ways to predict language outcome after stroke. The first depends on understanding the neural circuits that support language. This model-based approach is a challenging endeavor because language is a complex cognitive function that involves the interaction of many different brain areas. The second approach, by contrast, does not require an understanding of why a lesion impairs language; instead, predictions are made on the basis of the recovery of previous patients with the same lesion. This approach requires a database that records the speech and language capabilities of a large population of patients who have, collectively, incurred a comprehensive range of focal brain lesions. In addition, a system is required that converts an MRI scan from a new patient into a three-dimensional description of the lesion and compares this lesion against all others on the database. The outputs of this system are the longitudinal language outcomes of corresponding patients in the database. This approach will provide the patient with a range of probable recovery patterns over a variety of language measures.

Voxel-based Bayesian lesion-symptom mapping

Most existing voxel-based lesion-symptom mapping methods are based on the same statistical foundation: null hypothesis significance testing (NHST). The two major limitations of these methods are the inability to infer that there is no difference in lesion proportions, and a requirement for multiple-comparison correction. We propose a Bayesian approach that directly models the posterior distribution of lesion-proportion difference, and makes decisions based on inference on this posterior distribution. Compared to NHST-based approaches, our Bayesian approach yields inference results with clearer semantics, and does not require multiple-comparison correction. We evaluated our Bayesian method using simulated data, and data from a study of acute ischemic left-hemispheric stroke. Results of both experiments indicate that the Bayesian approach is sensitive in detecting regions that characterize group differences.

An evaluation of traditional and novel tools for lesion behavior mapping

Kinkingnéhun et al. (Kinkingnéhun, S., Volle, E., Pélégrini-Issac, M., Golmard, J.L., Lehéricy, S., du Boisguéheneuc, F., Zhang-Nunes, S., Sosson, D., Duffau, H., Samson, Y., Levy, R., Dubois, B., 2007. A novel approach to clinical-radiological correlations: Anatomo-Clinical Overlapping Maps (AnaCOM): method and validation. NeuroImage 37: 1237-1249.) have recently described a novel approach for lesion-behavior mapping (LBM), referred to as Anatomo-Clinical Overlapping Maps (AnaCOM). Conventional voxelwise LBM tools apply statistics to contrast behavioral performance of patients with lesions that encompass given voxels to control patients where these voxels are spared. In contrast, AnaCOM contrasts performance of patients with injury involving given voxels to the performance of neurologically healthy participants. The authors correctly note that their procedure can offer substantially more statistical power than conventional LBM methods. We compared AnaCOM to conventional LBM techniques by examining hemiparesis (a common consequence of stroke) as the behavior of interest. We found that AnaCOM detected many regions of the middle cerebral artery territory not associated with the motor system. We suggest that conventional LBM techniques detect regions that are damaged in patients with a deficit while spared in those without a deficit, while AnaCOM detects regions that are associated with a deficit. Therefore, this new measure may offer poor specificity. Furthermore, on theoretical grounds we suggest that permutation-based thresholding will be a more sensitive method for controlling familywise error than the method of counting lesion-overlap clusters used by AnaCOM. Finally, we note that the within group variability tends to be smaller for neurologically healthy controls than in neurological patients, due to ceiling effects. Therefore, we suggest that nonparametric measures or the Welch's t-test are more appropriate than the conventional pooled variance t-test used by AnaCOM.