Human brain lesion-deficit inference remapped

Same, Same but Different? A Multi-Method Review of the Processes Underlying Executive Control

Attention, working memory, and executive control are commonly considered distinct cognitive functions with important reciprocal interactions. Yet, longstanding evidence from lesion studies has demonstrated both overlap and dissociation in their behavioural expression and anatomical underpinnings, suggesting that a lower dimensional framework could be employed to further identify processes supporting goal-directed behaviour. Here, we describe the anatomical and functional correspondence between attention, working memory, and executive control by providing an overview of cognitive models, as well as recent data from lesion studies, invasive and non-invasive multimodal neuroimaging and brain stimulation. We emphasize the benefits of considering converging evidence from multiple methodologies centred on the identification of brain mechanisms supporting goal-driven behaviour. We propose that expanding on this approach should enable the construction of a comprehensive anatomo-functional framework with testable new hypotheses, and aid clinical neuroscience to intervene on impairments of executive functions.

Hayling and stroop tests tap dissociable deficits and network-level neural correlates

Although many executive function screens have been developed, it is not yet clear whether these assessments are equally effective in detecting post-stroke deficits of initiation and inhibition. This study presents a comparative analysis of the Stroop and Hayling tests aiming to evaluate whether these tests measure the same underlying cognitive functions and to identify the neural correlates of the deficits detected by both tasks. Sixty six stroke survivors and 70 healthy ageing controls completed the Hayling and Stroop tests. Stroke patients were found to exhibit qualitative performance differences across analogous Stroop and Hayling Test metrics intended to tap initiation and inhibition. The Stroop test was found to have high specificity to abnormal performance, but low sensitivity relative to the Hayling Test. Minimal overlap was present between the network-level correlates of analogous Stroop and Hayling Test metrics. Hayling Task strategy use metrics were significantly associated with distinct patterns of disconnection in stroke survivors, providing novel insight into the neural correlates of fine-grained behavioural patterns. Overall, these findings strongly suggest that the functions tapped by the Stroop and Hayling Test are both behaviourally and anatomically dissociable. The Hayling Test was found to offer improved sensitivity and detail relative to the Stroop test. This novel demonstration of the Hayling Test within the stroke population suggests that this task represents an effective measure for quantifying post-stroke initiation and inhibition deficits.

Graphs and the idiographic brain

This scientific commentary refers to ‘Integrating direct electrical brain stimulation with the human connectome’ by Coletta et al. (https://doi.org/10.1093/brain/awad402).

The neuroanatomy of visual extinction following right hemisphere brain damage: Insights from multivariate and Bayesian lesion analyses in acute stroke

Multi-target attention, that is, the ability to attend and respond to multiple visual targets presented simultaneously on the horizontal meridian across both visual fields, is essential for everyday real-world behaviour. Given the close link between the neuropsychological deficit of extinction and attentional limits in healthy subjects, investigating the anatomy that underlies extinction is uniquely capable of providing important insights concerning the anatomy critical for normal multi-target attention. Previous studies into the brain areas critical for multi-target attention and its failure in extinction patients have, however, produced heterogeneous results. In the current study, we used multivariate and Bayesian lesion analysis approaches to investigate the anatomical substrate of visual extinction in a large sample of 108 acute right hemisphere stroke patients. The use of acute stroke patient data and multivariate/Bayesian lesion analysis approaches allowed us to address limitations associated with previous studies and so obtain a more complete picture of the functional network associated with visual extinction. Our results demonstrate that the right temporo-parietal junction (TPJ) is critically associated with visual extinction. The Bayesian lesion analysis additionally implicated the right intraparietal sulcus (IPS), in line with the results of studies in neurologically healthy participants that highlighted the IPS as the area critical for multi-target attention. Our findings resolve the seemingly conflicting previous findings, and emphasise the urgent need for further research to clarify the precise cognitive role of the right TPJ in multi-target attention and its failure in extinction patients.

White matter tracts and executive functions: a review of causal and correlation evidence

Executive functions are high-level cognitive processes involving abilities such as working memory/updating, set-shifting and inhibition. These complex cognitive functions are enabled by interactions among widely distributed cognitive networks, supported by white matter tracts. Executive impairment is frequent in neurological conditions affecting white matter; however, whether specific tracts are crucial for normal executive functions is unclear. We review causal and correlation evidence from studies that used direct electrical stimulation during awake surgery for gliomas, voxel-based and tract-based lesion-symptom mapping, and diffusion tensor imaging to explore associations between the integrity of white matter tracts and executive functions in healthy and impaired adults. The corpus callosum was consistently associated with all executive processes, notably its anterior segments. Both causal and correlation evidence showed prominent support of the superior longitudinal fasciculus to executive functions, notably to working memory. More specifically, strong evidence suggested that the second branch of the superior longitudinal fasciculus is crucial for all executive functions, especially for flexibility. Global results showed left lateralization for verbal tasks and right lateralization for executive tasks with visual demands. The frontal aslant tract potentially supports executive functions, however, additional evidence is needed to clarify whether its involvement in executive tasks goes beyond the control of language. Converging evidence indicates that a right-lateralized network of tracts connecting cortical and subcortical grey matter regions supports the performance of tasks assessing response inhibition, some suggesting a role for the right anterior thalamic radiation. Finally, correlation evidence suggests a role for the cingulum bundle in executive functions, especially in tasks assessing inhibition. We discuss these findings in light of current knowledge about the functional role of these tracts, descriptions of the brain networks supporting executive functions and clinical implications for individuals with brain tumours.

Anatomical predictors of mental rotation with bodily and non-bodily stimuli: A lesion-symptom study

Mental rotation (MR) is widely regarded as a quintessential example of an embodied cognitive process. This viewpoint stems from the functional parallels between MR and the physical rotation of tangible objects, as well as participants' inclination to employ motor-based strategies when tackling MR tasks involving bodily stimuli. These commonalities imply that MR may depend on brain regions crucial for the planning and execution of motor programs. However, there is disagreement regarding the anatomy of MR between findings from functional imaging and lesion studies involving brain-injured patients. The former indicate the involvement of the right-hemispheric parietal cortex, while the latter underscore the significance of posterior areas in the left hemisphere. In this study, we aimed to discern the neural underpinnings of MR using lesion-symptom mapping (LSM) for both bodily (hands) and non-bodily (letters) stimuli. Behavioral results from the two MR tasks revealed impaired MR of bodily stimuli in patients with left hemisphere damage. LSM results pinpointed the left primary motor and somatosensory cortices, along with the superior parietal lobule, as the anatomical substrates of MR for both bodily and non-bodily stimuli. Furthermore, damage to the left angular gyrus, supramarginal gyrus, supplementary motor area, and retrosplenial cortex was associated with MR of non-bodily stimuli. These findings support the causal involvement of the left hemisphere in MR and underscore the existence of a common anatomical substrate in brain regions pertinent to motor planning and execution.

Spatial normalization for voxel-based lesion symptom mapping: impact of registration approaches

Background

Voxel-based lesion symptom mapping (VLSM) assesses the relation of lesion location at a voxel level with a specific clinical or functional outcome measure at a population level. Spatial normalization, that is, mapping the patient images into an atlas coordinate system, is an essential pre-processing step of VLSM. However, no consensus exists on the optimal registration approach to compute the transformation nor are downstream effects on VLSM statistics explored. In this work, we evaluate four registration approaches commonly used in VLSM pipelines: affine (AR), nonlinear (NLR), nonlinear with cost function masking (CFM), and enantiomorphic registration (ENR). The evaluation is based on a standard VLSM scenario: the analysis of statistical relations of brain voxels and regions in imaging data acquired early after stroke onset with follow-up modified Rankin Scale (mRS) values.

Materials and methods

Fluid-attenuated inversion recovery (FLAIR) MRI data from 122 acute ischemic stroke patients acquired between 2 and 3 days after stroke onset and corresponding lesion segmentations, and 30 days mRS values from a European multicenter stroke imaging study (I-KNOW) were available and used in this study. The relation of the voxel location with follow-up mRS was assessed by uni- as well as multi-variate statistical testing based on the lesion segmentations registered using the four different methods (AR, NLR, CFM, ENR; implementation based on the ANTs toolkit).

Results

The brain areas evaluated as important for follow-up mRS were largely consistent across the registration approaches. However, NLR, CFM, and ENR led to distortions in the patient images after the corresponding nonlinear transformations were applied. In addition, local structures (for instance the lateral ventricles) and adjacent brain areas remained insufficiently aligned with corresponding atlas structures even after nonlinear registration.

Conclusions

For VLSM study designs and imaging data similar to the present work, an additional benefit of nonlinear registration variants for spatial normalization seems questionable. Related distortions in the normalized images lead to uncertainties in the VLSM analyses and may offset the theoretical benefits of nonlinear registration.

Scaling behaviours of deep learning and linear algorithms for the prediction of stroke severity

Deep learning has allowed for remarkable progress in many medical scenarios. Deep learning prediction models often require 105-107 examples. It is currently unknown whether deep learning can also enhance predictions of symptoms post-stroke in real-world samples of stroke patients that are often several magnitudes smaller. Such stroke outcome predictions however could be particularly instrumental in guiding acute clinical and rehabilitation care decisions. We here compared the capacities of classically used linear and novel deep learning algorithms in their prediction of stroke severity. Our analyses relied on a total of 1430 patients assembled from the MRI-Genetics Interface Exploration collaboration and a Massachusetts General Hospital-based study. The outcome of interest was National Institutes of Health Stroke Scale-based stroke severity in the acute phase after ischaemic stroke onset, which we predict by means of MRI-derived lesion location. We automatically derived lesion segmentations from diffusion-weighted clinical MRI scans, performed spatial normalization and included a principal component analysis step, retaining 95% of the variance of the original data. We then repeatedly separated a train, validation and test set to investigate the effects of sample size; we subsampled the train set to 100, 300 and 900 and trained the algorithms to predict the stroke severity score for each sample size with regularized linear regression and an eight-layered neural network. We selected hyperparameters on the validation set. We evaluated model performance based on the explained variance (R2) in the test set. While linear regression performed significantly better for a sample size of 100 patients, deep learning started to significantly outperform linear regression when trained on 900 patients. Average prediction performance improved by ∼20% when increasing the sample size 9× [maximum for 100 patients: 0.279 ± 0.005 (R2, 95% confidence interval), 900 patients: 0.337 ± 0.006]. In summary, for sample sizes of 900 patients, deep learning showed a higher prediction performance than typically employed linear methods. These findings suggest the existence of non-linear relationships between lesion location and stroke severity that can be utilized for an improved prediction performance for larger sample sizes.

Network-based statistics distinguish anomic and Broca's aphasia

INTRODUCTION

Aphasia is a speech-language impairment commonly caused by damage to the left hemisphere. The neural mechanisms that underpin different types of aphasia and their symptoms are still not fully understood. This study aims to identify differences in resting-state functional connectivity between anomic and Broca's aphasia measured through resting-state functional magnetic resonance imaging (rs-fMRI).

METHODS

We used the network-based statistic (NBS) method, as well as voxel- and connectome-based lesion symptom mapping (V-, CLSM), to identify distinct neural correlates of the anomic and Broca's groups. To control for lesion effect, we included lesion volume as a covariate in both the NBS method and LSM.

RESULTS

NBS identified a subnetwork located in the dorsal language stream bilaterally, including supramarginal gyrus, primary sensory, motor, and auditory cortices, and insula. The connections in the subnetwork were weaker in the Broca's group than the anomic group. The properties of the subnetwork were examined through complex network measures, which indicated that regions in right inferior frontal sulcus, right paracentral lobule, and bilateral superior temporal gyrus exhibit intensive interaction. Left superior temporal gyrus, right postcentral gyrus, and left supramarginal gyrus play an important role in information flow and overall communication efficiency. Disruption of this network underlies the constellation of symptoms associated with Broca's aphasia. Whole-brain CLSM did not detect any significant connections, suggesting an advantage of NBS when thousands of connections are considered. However, CLSM identified connections that differentiated Broca's from anomic aphasia when analysis was restricted to a hypothesized network of interest.

DISCUSSION

We identified novel signatures of resting-state brain network differences between groups of individuals with anomic and Broca's aphasia. We identified a subnetwork of connections that statistically differentiated the resting-state brain networks of the two groups, in comparison with standard CLSM results that yielded isolated connections. Network-level analyses are useful tools for the investigation of the neural correlates of language deficits post-stroke.

Comprehensive voxel-wise, tract-based, and network lesion mapping reveals unique architectures of right and left visuospatial neglect

Visuospatial neglect is a common, post-stroke cognitive impairment which is widely considered to be a disconnection syndrome. However, the patterns of disconnectivity associated with visuospatial neglect remain unclear. Here, we had 480 acute stroke survivors [age = 72.8 (SD = 13.3), 44.3% female, 7.5 days post-stroke (SD = 11.3)] undertake routine clinical imaging and standardised visuospatial neglect testing. The data were used to conduct voxel-wise, tract-level, and network-level lesion-mapping analyses aimed at localising the neural correlates of left and right egocentric (body-centred) and allocentric (object-centred) visuospatial neglect. Only minimal anatomical homogeneity was present between the correlates of right and left egocentric neglect across all analysis types. This finding challenges previous work suggesting that right and left visuospatial neglect are anatomically homologous, and instead suggests that egocentric neglect may involve damage to a shared, but hemispherically asymmetric attention network. By contrast, egocentric and allocentric neglect was associated with disconnectivity in a distinct but overlapping set of network edges, with both deficits related to damage across the dorsal and ventral attention networks. Critically, this finding suggests that the distinction between egocentric and allocentric neglect is unlikely to reflect a simple dichotomy between dorsal versus ventral networks dysfunction, as is commonly asserted. Taken together, the current findings provide a fresh perspective on the neural circuitry involved in regulating visuospatial attention, and provide important clues to understanding the cognitive and perceptual processes involved in this common and debilitating neuropsychological syndrome.

Neuroanatomy of post-stroke depression: the association between symptom clusters and lesion location

Post-stroke depression affects about 30% of stroke patients and often hampers functional recovery. The diagnosis of depression encompasses heterogeneous symptoms at emotional, motivational, cognitive, behavioural or somatic levels. Evidence indicates that depression is caused by disruption of bio-aminergic fibre tracts between prefrontal and limbic or striatal brain regions comprising different functional networks. Voxel-based lesion-symptom mapping studies reported discrepant findings regarding the association between infarct locations and depression. Inconsistencies may be due to the usage of sum scores, thereby mixing different symptoms of depression. In this cross-sectional study, we used multivariate support vector regression for lesion-symptom mapping to identify regions significantly involved in distinct depressive symptom domains and global depression. MRI lesion data were included from 200 patients with acute first-ever ischaemic stroke (mean 0.9 ± 1.5 days of post-stroke). The Montgomery-Åsberg Depression Rating interview assessed depression severity in five symptom domains encompassing motivational, emotional and cognitive symptoms deficits, anxiety and somatic symptoms and was examined 8.4 days of post-stroke (±4.3). We found that global depression severity, irrespective of individual symptom domains, was primarily linked to right hemispheric lesions in the dorsolateral prefrontal cortex and inferior frontal gyrus. In contrast, when considering distinct symptom domains individually, the analyses yielded much more sensitive results in regions where the correlations with the global depression score yielded no effects. Accordingly, motivational deficits were associated with lesions in orbitofrontal cortex, dorsolateral prefrontal cortex, pre- and post-central gyri and basal ganglia, including putamen and pallidum. Lesions affecting the dorsal thalamus, anterior insula and somatosensory cortex were significantly associated with emotional symptoms such as sadness. Damage to the dorsolateral prefrontal cortex was associated with concentration deficits, cognitive symptoms of guilt and self-reproach. Furthermore, somatic symptoms, including loss of appetite and sleep disturbances, were linked to the insula, parietal operculum and amygdala lesions. Likewise, anxiety was associated with lesions impacting the central operculum, insula and inferior frontal gyrus. Interestingly, symptoms of anxiety were exclusively left hemispheric, whereas the lesion-symptom associations of the other domains were lateralized to the right hemisphere. In conclusion, this large-scale study shows that in acute stroke patients, differential post-stroke depression symptom domains are associated with specific structural correlates. Our findings extend existing concepts on the neural underpinnings of depressive symptoms, indicating that differential lesion patterns lead to distinct depressive symptoms in the first weeks of post-stroke. These findings may facilitate the development of personalized treatments to improve post-stroke rehabilitation.

Mechanisms and neuroanatomy of response selection in tool and non-tool action tasks: Evidence from left-hemisphere stroke

The ability to select between potential actions is central to the complex process of tool use. After left hemisphere stroke, individuals with limb apraxia make more hand action errors when gesturing the use of tools with conflicting hand actions for grasping-to-move and use (e.g., screwdriver) relative to tools that are grasped-to-move and used with the same hand action (e.g., hammer). Prior research indicates that this grasp-use interference effect is driven by abnormalities in the competitive action selection process. The goal of this project was to determine whether common mechanisms and neural substrates support the competitive selection of task-appropriate responses in both tool and non-tool domains. If so, the grasp-use interference effect in a tool use gesturing task should be correlated with response interference effects in the classic Eriksen flanker and Simon tasks, and at least partly overlapping neural regions should subserve the 3 tasks. Sixty-four left hemisphere stroke survivors (33 with apraxia) participated in the tool- and non-tool interference tasks and underwent T1 anatomical MRI. There were robust grasp-use interference effects (grasp-use conflict test) and response interference effects (Eriksen flanker and Simon tasks), but these effects were not correlated. Lesion-symptom mapping analyses showed that lesions to the left inferior parietal lobule, ventral premotor cortex, and insula were associated with grasp-use interference. Lesions to the left inferior parietal lobule, postcentral gyrus, insula, caudate, and putamen were associated with response interference in the Eriksen flanker task. Lesions to the left caudate and putamen were also associated with response interference in the Simon task. Our results suggest that the selection of hand posture for tool use is mediated by distinct cognitive mechanisms and partly distinct neuroanatomic substrates from those mapping a stimulus to an appropriate motor response in non-tool domains.

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.

A large public dataset of annotated clinical MRIs and metadata of patients with acute stroke

To extract meaningful and reproducible models of brain function from stroke images, for both clinical and research proposes, is a daunting task severely hindered by the great variability of lesion frequency and patterns. Large datasets are therefore imperative, as well as fully automated image post-processing tools to analyze them. The development of such tools, particularly with artificial intelligence, is highly dependent on the availability of large datasets to model training and testing. We present a public dataset of 2,888 multimodal clinical MRIs of patients with acute and early subacute stroke, with manual lesion segmentation, and metadata. The dataset provides high quality, large scale, human-supervised knowledge to feed artificial intelligence models and enable further development of tools to automate several tasks that currently rely on human labor, such as lesion segmentation, labeling, calculation of disease-relevant scores, and lesion-based studies relating function to frequency lesion maps.

Anatomical and functional predictors of disorientation after first-ever brain damage

BACKGROUND AND OBJECTIVES

Disorientation is a frequent consequence of acute brain injury or diffuse disorders, such as confusional states or dementia. Its anatomical correlates are debated. Impaired memory as its commonly assumed mechanism predicts that disorientation is associated with medial temporal damage. The alternative is that disorientation reflects defective orbitofrontal reality filtering (ORFi) - a specific failure to identify whether thoughts or memories refer to present reality or not. The latter is a function of the posterior orbitofrontal cortex and connected structures. This study examined the mechanisms and anatomical basis of disorientation in an unselected group of patients with first-ever subacute brain injury.

METHODS

Participants hospitalized for neurorehabilitation were asked to participate in this observational cohort study if they had first-ever organic hemispheric brain dysfunction as evident in a localizable brain lesion or verbal amnesia (often without localizable brain damage). Orientation to time, place, situation and person was tested with a 20-items questionnaire. To identify the mechanisms of disorientation, we determined its correlations with executive tasks, verbal episodic memory, and ORFi in all patients. ORFi was examined with a continuous recognition task, which measures learning and item recognition in the first run, and ORFi as reflected in the increase of false positive responses in the second run (temporal context confusion). Lesions of patients having localizable brain damage were manually delineated and normalized before entering multivariate lesion-symptom-mapping (LSM) to determine anatomical predictors of orientation.

RESULTS

Eighty-four patients (61.1 ± 14.4 years, 29 women) were included. Among measures of memory and executive functioning, a step-wise regression retained temporal context confusion (R = -0.71, p < 0.0001), item recognition (R = 0.67, p < 0.0001) and delayed free recall (R = 0.63, p < 0.0001) as significant predictors of orientation. LSM was possible in 67 participants; it revealed an association of disorientation with damage of the right OFC and the bilateral head of the caudate nucleus.

CONCLUSION

Disorientation in non-confused, non-demented patients with first-ever brain damage is associated with impaired orbitofrontal reality filtering and memory dysfunction, but not with executive dysfunction. Its main anatomical determinant is damage to the orbitofrontal cortex and its subcortical relay, the head of the caudate.

Benefit of visual speech information for word comprehension in post-stroke aphasia

Aphasia is a language disorder that often involves speech comprehension impairments affecting communication. In face-to-face settings, speech is accompanied by mouth and facial movements, but little is known about the extent to which they benefit aphasic comprehension. This study investigated the benefit of visual information accompanying speech for word comprehension in people with aphasia (PWA) and the neuroanatomic substrates of any benefit. Thirty-six PWA and 13 neurotypical matched control participants performed a picture-word verification task in which they indicated whether a picture of an animate/inanimate object matched a subsequent word produced by an actress in a video. Stimuli were either audiovisual (with visible mouth and facial movements) or auditory-only (still picture of a silhouette) with audio being clear (unedited) or degraded (6-band noise-vocoding). We found that visual speech information was more beneficial for neurotypical participants than PWA, and more beneficial for both groups when speech was degraded. A multivariate lesion-symptom mapping analysis for the degraded speech condition showed that lesions to superior temporal gyrus, underlying insula, primary and secondary somatosensory cortices, and inferior frontal gyrus were associated with reduced benefit of audiovisual compared to auditory-only speech, suggesting that the integrity of these fronto-temporo-parietal regions may facilitate cross-modal mapping. These findings provide initial insights into our understanding of the impact of audiovisual information on comprehension in aphasia and the brain regions mediating any benefit.

Mapping spoken language and cognitive deficits in post-stroke aphasia

Aphasia is an acquired disorder caused by damage, most commonly due to stroke, to brain regions involved in speech and language. While language impairment is the defining symptom of aphasia, the co-occurrence of non-language cognitive deficits and their importance in predicting rehabilitation and recovery outcomes is well documented. However, people with aphasia (PWA) are rarely tested on higher-order cognitive functions, making it difficult for studies to associate these functions with a consistent lesion correlate. Broca's area is a particular brain region of interest that has long been implicated in speech and language production. Contrary to classic models of speech and language, cumulative evidence shows that Broca's area and surrounding regions in the left inferior frontal cortex (LIFC) are involved in, but not specific to, speech production. In this study we aimed to explore the brain-behaviour relationships between tests of cognitive skill and language abilities in thirty-six adults with long-term speech production deficits caused by post-stroke aphasia. Our findings suggest that non-linguistic cognitive functions, namely executive functions and verbal working memory, explain more of the behavioural variance in PWA than classical language models imply. Additionally, lesions to the LIFC, including Broca's area, were associated with non-linguistic executive (dys)function, suggesting that lesions to this area are associated with non-language-specific higher-order cognitive deficits in aphasia. Whether executive (dys)function - and its neural correlate in Broca's area - contributes directly to PWA's language production deficits or simply co-occurs with it, adding to communication difficulties, remains unclear. These findings support contemporary models of speech production that place language processing within the context of domain-general perception, action and conceptual knowledge. An understanding of the covariance between language and non-language deficits and their underlying neural correlates will inform better targeted aphasia treatment and outcomes.

A comparison of lesion mapping analyses based on CT versus MR imaging in stroke

It is commonly asserted that MRI-derived lesion masks outperform CT-derived lesion masks in lesion-mapping analysis. However, no quantitative analysis has been conducted to support or refute this claim. This study reports an objective comparison of lesion-mapping analyses based on CT- and MRI-derived lesion masks to clarify how input imaging type may ultimately impact analysis results. Routine CT and MRI data were collected from 85 acute stroke survivors. These data were employed to create binarized lesion masks and conduct lesion-mapping analyses on simulated behavioral data. Following standard lesion-mapping analysis methodology, each voxel or region of interest (ROI) were considered as the underlying "target" within CT and MRI data independently. The resulting thresholded z-maps were compared between matched CT- and MRI-based analyses. Paired MRI- and CT-derived lesion masks were found to exhibit significant variance in location, overlap, and size. In ROI-level simulations, both CT and MRI-derived analyses yielded low Dice similarity coefficients, but CT analyses yielded a significantly higher proportion of results which overlapped with target ROIs. In single-voxel simulations, MRI-based lesion mapping was able to include more voxels than CT-based analyses, but CT-based analysis results were closer to the underlying target voxel. Simulated lesion-symptom mapping results yielded by paired CT and MRI lesion-symptom mapping analyses demonstrated moderate agreement in terms of Dice coefficient when systematic differences in cluster size and lesion overlay are considered. Overall, these results suggest that CT and MR-derived lesion-symptom mapping results do not reliably differ in accuracy. This finding is critically important as it suggests that future studies can employ CT-derived lesion masks if these scans are available within the appropriate time-window.

Interpreting and validating complexity and causality in lesion-symptom prognoses

This paper considers the steps needed to generate pragmatic and interpretable lesion-symptom mappings that can be used for clinically reliable prognoses. The novel contributions are 3-fold. We first define and inter-relate five neurobiological and five methodological constraints that need to be accounted for when interpreting lesion-symptom associations and generating synthetic lesion data. The first implication is that, because of these constraints, lesion-symptom mapping needs to focus on probabilistic relationships between Lesion and Symptom, with Lesion as a multivariate spatial pattern, Symptom as a time-dependent behavioural profile and evidence that Lesion raises the probability of Symptom. The second implication is that in order to assess the strength of probabilistic causality, we need to distinguish between causal lesion sites, incidental lesion sites, spared but dysfunctional sites and intact sites, all of which might affect the accuracy of the predictions and prognoses generated. We then formulate lesion-symptom mappings in logical notations, including combinatorial rules, that are then used to evaluate and better understand complex brain-behaviour relationships. The logical and theoretical framework presented applies to any type of neurological disorder but is primarily discussed in relationship to stroke damage. Accommodating the identified constraints, we discuss how the 1965 Bradford Hill criteria for inferring probabilistic causality, post hoc, from observed correlations in epidemiology-can be applied to lesion-symptom mapping in stroke survivors. Finally, we propose that rather than rely on post hoc evaluation of how well the causality criteria have been met, the neurobiological and methodological constraints should be addressed, a priori, by changing the experimental design of lesion-symptom mappings and setting up an open platform to share and validate the discovery of reliable and accurate lesion rules that are clinically useful.

The low dimensionality of post-stroke cognitive deficits: it's the lesion anatomy!

For years, dissociation studies on neurological single-case patients 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. Still, associational computational methods for dimensionality reduction-such as principal component analysis or factor analysis-became popular for the identification of fundamental cognitive functions and to understand human cognitive brain architecture from post-stroke neuropsychological profiles. In the present in silico study with lesion imaging of 300 stroke patients, we investigated the dimensionality of artificial simulated neuropsychological profiles that exclusively contained independent fundamental cognitive functions without any underlying low-dimensional cognitive architecture. Still, the anatomy of stroke lesions alone was sufficient to create a dependence between variables that allowed a low-dimensional description of the data with principal component analysis. All criteria that we used to estimate the dimensionality of data, including the Kaiser criterion, were strongly affected by lesion anatomy, while the Joliffe criterion provided the least affected estimates. The dimensionality of profiles was reduced by 62-70% for the Kaiser criterion, up to the degree that is commonly found in neuropsychological studies on actual cognitive measures. The interpretability of such low-dimensional factors as deficits of fundamental cognitive functions and their provided insights into human cognitive architecture thus seem to be severely limited, and the heavy focus of current cognitive neuroscience on group studies and associations calls for improvements. We suggest that qualitative criteria and dissociation patterns could be used to refine estimates for the dimensionality of the cognitive architecture behind post-stroke deficits. Further, given the strong impact of lesion anatomy on the associational structure of data, we see the need for further optimization of interpretation strategies of computational factors in post-stroke lesion studies of cognitive deficits.

Associations of lesion location, structural disconnection, and functional diaschisis with depressive symptoms post stroke

Introduction

Post-stroke depressive symptoms (PSDS) are common and relevant for patient outcome, but their complex pathophysiology is ill understood. It likely involves social, psychological and biological factors. Lesion location is a readily available information in stroke patients, but it is unclear if the neurobiological substrates of PSDS are spatially localized. Building on previous analyses, we sought to determine if PSDS are associated with specific lesion locations, structural disconnection and/or localized functional diaschisis.

Methods

In a prospective observational study, we examined 270 patients with first-ever stroke with the Hospital Anxiety and Depression Scale (HADS) around 6 months post-stroke. Based on individual lesion locations and the depression subscale of the HADS we performed support vector regression lesion-symptom mapping, structural-disconnection-symptom mapping and functional lesion network-symptom-mapping, in a reanalysis of this previously published cohort to infer structure-function relationships.

Results

We found that depressive symptoms were associated with (i) lesions in the right insula, right putamen, inferior frontal gyrus and right amygdala and (ii) structural disconnection in the right temporal lobe. In contrast, we found no association with localized functional diaschisis. In addition, we were unable to confirm a previously described association between depressive symptom load and a network damage score derived from functional disconnection maps.

Discussion

Based on our results, and other recent lesion studies, we see growing evidence for a prominent role of right frontostriatal brain circuits in PSDS.

Neural Substrates of Psychomotor Speed Deficits in Cerebral Small Vessel Disease: A Brain Disconnectome Mapping Study

It remains unknown which factors influence how brain disconnectivity derived from White Matter Hyperintensity (WMH) lesions leads to psychomotor speed dysfunction, one of the earliest and most common cognitive manifestations in the cerebral Small Vessel Disease (cSVD) population. While the burden of WMH has been strongly linked to psychomotor speed performance, the effect that different locations and volumes of WMH may have on cSVD-related cognitive impairment remains unclear. Therefore, we aimed to explore (1) whether global WMH, deep WMH (DWMH), and periventricular (PVWMH) volumes display different psychomotor speed associations; (2) whether tract-specific WMH volume shows stronger cognitive associations compared with global measures of WMH volume; (3) whether specific patterns of WMH location lead to different degrees of disconnectivity. Using the BCBToolkit, we investigated which pattern of distribution and which locations of WMH lesion result in impaired psychomotor speed in a well-characterized sample (n = 195) of cSVD patients without dementia. Two key findings emerge from our study. First, global (and not tract-specific) measures of WMH volume were associated with psychomotor speed performance. Second, disconnection maps revealed the involvement of callosal tracts, association and projection fibers, and frontal and parietal cortical brain areas related to psychomotor speed, while the lesion location influenced such associations. In conclusion, psychomotor deficits are affected differently by WMH burden and topographic distribution through brain disconnection in non-demented cSVD patients.

Shared and distinct routes in speech and gesture imitation: Evidence from stroke

Dual-route models of high-level (praxis) actions distinguish between an "indirect" semantic route mediating meaningful gesture imitation, and a "direct" sensory-motor route mediates meaningless gesture imitation. Similarly, dual-route language models distinguish between an indirect route mediating production and repetition of words, and a direct route mediating non-word repetition. Although aphasia and limb apraxia frequently co-occur following left-hemisphere cerebrovascular accident (LCVA), it is unclear which aspects of these functional-neuroanatomic dual-route architectures are shared across praxis and language domains. This study focused on gesture imitation to test the hypothesis that semantic information (and portions of the indirect route) are shared across domains, whereas two distinct dorsal routes mediate sensory-motor mapping. Forty chronic LCVA and 17 neurotypical controls completed semantic memory and language tasks and imitated 3 types of gesture stimuli: (1) labeled/"named" meaningful, (2) unnamed meaningful, and (3) meaningless gestures. The comparison of accuracy between meaningless versus unnamed meaningful gestures examined the benefits of semantic information, while the comparison of unnamed meaningful versus named meaningful imitation examined additional benefits of linguistic cueing. Mixed-effects models examined group by task interaction effects on gesture ability. We found that for patients with LCVA, unnamed meaningful gestures were imitated more accurately than meaningless gestures, suggesting that semantic information was beneficial, but there was no benefit of labeling. Reduced benefit of semantic information on gesture accuracy was associated with lesions to inferior frontal and posterior temporal regions as well as semantic memory performance on a pictorial (non-gesture) task. In contrast, there was no relationship between meaningless gesture imitation and nonword repetition, indicating that measures of direct route performance are not associated across language and action. These results provide preliminary evidence that portions of the indirect semantic route are shared across the language and action domains, while two direct sensory-motor mapping routes mediate word repetition and gesture imitation.

Large-scale lesion symptom mapping of depression identifies brain regions for risk and resilience

Understanding neural circuits that support mood is a central goal of affective neuroscience, and improved understanding of the anatomy could inform more targeted interventions in mood disorders. Lesion studies provide a method of inferring the anatomical sites causally related to specific functions, including mood. Here, we performed a large-scale study evaluating the location of acquired, focal brain lesions in relation to symptoms of depression. Five hundred and twenty-six individuals participated in the study across two sites (356 male, average age 52.4 ± 14.5 years). Each subject had a focal brain lesion identified on structural imaging and an assessment of depression using the Beck Depression Inventory-II, both obtained in the chronic period post-lesion (>3 months). Multivariate lesion-symptom mapping was performed to identify lesion sites associated with higher or lower depression symptom burden, which we refer to as 'risk' versus 'resilience' regions. The brain networks and white matter tracts associated with peak regional findings were identified using functional and structural lesion network mapping, respectively. Lesion-symptom mapping identified brain regions significantly associated with both higher and lower depression severity (r = 0.11; P = 0.01). Peak 'risk' regions include the bilateral anterior insula, bilateral dorsolateral prefrontal cortex and left dorsomedial prefrontal cortex. Functional lesion network mapping demonstrated that these 'risk' regions localized to nodes of the salience network. Peak 'resilience' regions include the right orbitofrontal cortex, right medial prefrontal cortex and right inferolateral temporal cortex, nodes of the default mode network. Structural lesion network mapping implicated dorsal prefrontal white matter tracts as 'risk' tracts and ventral prefrontal white matter tracts as 'resilience' tracts, although the structural lesion network mapping findings did not survive correction for multiple comparisons. Taken together, these results demonstrate that lesions to specific nodes of the salience network and default mode network are associated with greater risk versus resiliency for depression symptoms in the setting of focal brain lesions.

A framework for focal and connectomic mapping of transiently disrupted brain function

The distributed nature of the neural substrate, and the difficulty of establishing necessity from correlative data, combine to render the mapping of brain function a far harder task than it seems. Methods capable of combining connective anatomical information with focal disruption of function are needed to disambiguate local from global neural dependence, and critical from merely coincidental activity. Here we present a comprehensive framework for focal and connective spatial inference based on sparse disruptive data, and demonstrate its application in the context of transient direct electrical stimulation of the human medial frontal wall during the pre-surgical evaluation of patients with focal epilepsy. Our framework formalizes voxel-wise mass-univariate inference on sparsely sampled data within the statistical parametric mapping framework, encompassing the analysis of distributed maps defined by any criterion of connectivity. Applied to the medial frontal wall, this transient dysconnectome approach reveals marked discrepancies between local and distributed associations of major categories of motor and sensory behaviour, revealing differentiation by remote connectivity to which purely local analysis is blind. Our framework enables disruptive mapping of the human brain based on sparsely sampled data with minimal spatial assumptions, good statistical efficiency, flexible model formulation, and explicit comparison of local and distributed effects.

Disentangling poststroke cognitive deficits and their neuroanatomical correlates through combined multivariable and multioutcome lesion-symptom mapping

Studies in patients with brain lesions play a fundamental role in unraveling the brain's functional anatomy. Lesion-symptom mapping (LSM) techniques can relate lesion location to cognitive performance. However, a limitation of current LSM approaches is that they can only evaluate one cognitive outcome at a time, without considering interdependencies between different cognitive tests. To overcome this challenge, we implemented canonical correlation analysis (CCA) as combined multivariable and multioutcome LSM approach. We performed a proof-of-concept study on 1075 patients with acute ischemic stroke to explore whether addition of CCA to a multivariable single-outcome LSM approach (support vector regression) could identify infarct locations associated with deficits in three well-defined verbal memory functions (encoding, consolidation, retrieval) based on four verbal memory subscores derived from the Seoul Verbal Learning Test (immediate recall, delayed recall, recognition, learning ability). We evaluated whether CCA could extract cognitive score patterns that matched prior knowledge of these verbal memory functions, and if these patterns could be linked to more specific infarct locations than through single-outcome LSM alone. Two of the canonical modes identified with CCA showed distinct cognitive patterns that matched prior knowledge on encoding and consolidation. In addition, CCA revealed that each canonical mode was linked to a distinct infarct pattern, while with multivariable single-outcome LSM individual verbal memory subscores were associated with largely overlapping patterns. In conclusion, our findings demonstrate that CCA can complement single-outcome LSM techniques to help disentangle cognitive functions and their neuroanatomical correlates.

The elusive metric of lesion load

One of the widely used metrics in lesion-symptom mapping is lesion load that codes the amount of damage to a given brain region of interest. Lesion load aims to reduce the complex 3D lesion information into a feature that can reflect both site of damage, defined by the location of the region of interest, and size of damage within that region of interest. Basically, the process of estimation of lesion load converts a voxel-based lesion map into a region-based lesion map, with regions defined as atlas-based or data-driven spatial patterns. Here, after examining current definitions of lesion load, four methodological issues are discussed: (1) lesion load is agnostic to the location of damage within the region of interest, and it disregards damage outside the region of interest, (2) lesion load estimates are prone to errors introduced by the uncertainty in lesion delineation, spatial warping of the lesion/region, and binarization of the lesion/region, (3) lesion load calculation depends on brain parcellation selection, and (4) lesion load does not necessarily reflect a white matter disconnection. Overall, lesion load, when calculated in a robust way, can serve as a clinically-useful feature for explaining and predicting post-stroke outcome and recovery.

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.

Disconnection in a left-hemispheric temporo-parietal network 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 non-neocortical 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 gray-to-gray 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.

Use of multi-perturbation Shapley analysis in lesion studies of functional networks: The case of upper limb paresis

Understanding the impact of variation in lesion topography on the expression of functional impairments following stroke is important, as it may pave the way to modeling structure-function relations in statistical terms while pointing to constraints for adaptive remapping and functional recovery. Multi-perturbation Shapley-value analysis (MSA) is a relatively novel game-theoretical approach for multivariate lesion-symptom mapping. In this methodological paper, we provide a comprehensive explanation of MSA. We use synthetic data to assess the method's accuracy and perform parameter optimization. We then demonstrate its application using a cohort of 107 first-event subacute stroke patients, assessed for upper limb (UL) motor impairment (Fugl-Meyer Assessment scale). Under the conditions tested, MSA could correctly detect simulated ground-truth lesion-symptom relationships with a sensitivity of 75% and specificity of ~90%. For real behavioral data, MSA disclosed a strong hemispheric effect in the relative contribution of specific regions-of-interest (ROIs): poststroke UL motor function was mostly contributed by damage to ROIs associated with movement planning (supplementary motor cortex and superior frontal gyrus) following left-hemispheric damage (LHD) and by ROIs associated with movement execution (primary motor and somatosensory cortices and the ventral brainstem) following right-hemispheric damage (RHD). Residual UL motor ability following LHD was found to depend on a wider array of brain structures compared to the residual motor ability of RHD patients. The results demonstrate that MSA can provide a unique insight into the relative importance of different hubs in neural networks, which is difficult to obtain using standard univariate methods.

The cortical regions and white matter tracts underlying auditory comprehension in patients with primary brain tumor

The comprehension of spoken language is one of the most essential language functions in humans. However, the neurological underpinnings of auditory comprehension remain under debate. Here we used multi-modal neuroimaging analyses on a group of patients with low-grade gliomas to localize cortical regions and white matter tracts responsible for auditory language comprehension. Region-of-interests and voxel-level whole-brain analyses showed that cortical areas in the posterior temporal lobe are crucial for language comprehension. The fiber integrity assessed with diffusion tensor imaging of the arcuate fasciculus and the inferior longitudinal fasciculus was strongly correlated with both auditory comprehension and the grey matter volume of the inferior temporal and middle temporal gyri. Together, our findings provide direct evidence for an integrated network of auditory comprehension whereby the superior temporal gyrus and sulcus, the posterior parts of the middle and inferior temporal gyri serve as auditory comprehension cortex, and the arcuate fasciculus and the inferior longitudinal fasciculus subserve as crucial structural connectivity. These findings provide critical evidence on the neural underpinnings of language comprehension.

The neuroanatomy of visuospatial neglect: A systematic review and analysis of lesion-mapping methodology

While visuospatial neglect is commonly associated with damage to the right posterior parietal cortex, neglect is an anatomically heterogenous syndrome. This project presents a systematic review of 34 lesion-mapping studies reporting on the anatomical correlates of neglect. Specifically, the reported correlates of egocentric versus allocentric, acute versus chronic, personal versus extra-personal, and left versus right hemisphere neglect are summarised. The quality of each included lesion-mapping analysis was then evaluated to identify methodological factors which may help account for the reported variance in correlates of neglect. Overall, the existing literature strongly suggests that egocentric and allocentric neglect represent anatomically dissociable conditions and that the anatomy of these conditions may not be entirely homologous across hemispheres. Studies which have compared the anatomy of acute versus chronic neglect have found that these conditions are associated with distinct lesion loci, while studies comparing the correlates of peripersonal/extrapersonal neglect are split as to whether these neglect subtypes are anatomically dissociable. The included studies employed a wide range of lesion-mapping analysis techniques, each producing results of varying quality and generalisability. This review concludes that the reported underlying anatomical correlates of heterogeneous visuospatial neglect vary considerably. Future, high quality studies are needed to investigate patterns of disconnection associated with clearly defined forms of visuospatial neglect in large and representative samples.

Location-weighted versus Volume-weighted Mismatch at MRI for Response to Mechanical Thrombectomy in Acute Stroke

Background A target mismatch profile can identify good clinical response to recanalization after acute ischemic stroke, but does not consider region specificities. Purpose To test whether location-weighted infarction core and mismatch, determined from diffusion and perfusion MRI performed in patients with acute stroke, could improve prediction of good clinical response to mechanical thrombectomy compared with a target mismatch profile. Materials and Methods In this secondary analysis, two prospectively collected independent stroke data sets (2012-2015 and 2017-2019) were analyzed. From the brain before stroke (BBS) study data (data set 1), an eloquent map was computed through voxel-wise associations between the infarction core (based on diffusion MRI on days 1-3 following stroke) and National Institutes of Health Stroke Scale (NIHSS) score. The French acute multimodal imaging to select patients for mechanical thrombectomy (FRAME) data (data set 2) consisted of large vessel occlusion-related acute ischemic stroke successfully recanalized. From acute MRI studies (performed on arrival, prior to thrombectomy) in data set 2, target mismatch and eloquent (vs noneloquent) infarction core and mismatch were computed from the intersection of diffusion- and perfusion-detected lesions with the coregistered eloquent map. Associations of these imaging metrics with early neurologic improvement were tested in multivariable regression models, and areas under the receiver operating characteristic curve (AUCs) were compared. Results Data sets 1 and 2 included 321 (median age, 69 years [IQR, 58-80 years]; 207 men) and 173 (median age, 74 years [IQR, 65-82 years]; 90 women) patients, respectively. Eloquent mismatch was positively and independently associated with good clinical response (odds ratio [OR], 1.14; 95% CI: 1.02, 1.27; P = .02) and eloquent infarction core was negatively associated with good response (OR, 0.85; 95% CI: 0.77, 0.95; P = .004), while noneloquent mismatch was not associated with good response (OR, 1.03; 95% CI: 0.98, 1.07; P = .20). Moreover, adding eloquent metrics improved the prediction accuracy (AUC, 0.73; 95% CI: 0.65, 0.81) compared with clinical variables alone (AUC, 0.65; 95% CI: 0.56, 0.73; P = .01) or a target mismatch profile (AUC, 0.67; 95% CI: 0.59, 0.76; P = .03). Conclusion Location-weighted infarction core and mismatch on diffusion and perfusion MRI scans improved the identification of patients with acute stroke who would benefit from mechanical thrombectomy compared with the volume-based target mismatch profile. Clinical trial registration no. NCT03045146 © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Nael in this issue.

Presurgical Executive Functioning in Low-Grade Glioma Patients Cannot Be Topographically Mapped

Executive dysfunctions have a high prevalence in low-grade glioma patients and may be the result of structural disconnections of particular subcortical tracts and/or networks. However, little research has focused on preoperative low-grade glioma patients. The frontotemporoparietal network has been closely linked to executive functions and is substantiated by the superior longitudinal fasciculus. The aim of this study was to investigate their role in executive functions in low-grade glioma patients. Patients from two neurological centers were included with IDH-mutated low-grade gliomas. The sets of preoperative predictors were (i) distance between the tumor and superior longitudinal fasciculus, (ii) structural integrity of the superior longitudinal fasciculus, (iii) overlap between tumor and cortical networks, and (iv) white matter disconnection of the same networks. Linear regression and random forest analyses were performed. The group of 156 patients demonstrated significantly lower performance than normative samples and had a higher prevalence of executive impairments. However, both regression and random forest analyses did not demonstrate significant results, meaning that neither structural, cortical network overlap, nor network disconnection predictors explained executive performance. Overall, our null results indicate that there is no straightforward topographical explanation of executive performance in low-grade glioma patients. We extensively discuss possible explanations, including plasticity-induced network-level equipotentiality. Finally, we stress the need for the development of novel methods to unveil the complex and interacting mechanisms that cause executive deficits in low-grade glioma patients.

Graph lesion-deficit mapping of fluid intelligence

Fluid intelligence is arguably the defining feature of human cognition. Yet the nature of its relationship with the brain remains a contentious topic. Influential proposals drawing primarily on functional imaging data have implicated 'multiple demand' frontoparietal and more widely distributed cortical networks, but extant lesion-deficit studies with greater causal power are almost all small, methodologically constrained, and inconclusive. The task demands large samples of patients, comprehensive investigation of performance, fine-grained anatomical mapping, and robust lesion-deficit inference, yet to be brought to bear on it. We assessed 165 healthy controls and 227 frontal or non-frontal patients with unilateral brain lesions on the best-established test of fluid intelligence, Raven's Advanced Progressive Matrices, employing an array of lesion-deficit inferential models responsive to the potentially distributed nature of fluid intelligence. Non-parametric Bayesian stochastic block models were used to reveal the community structure of lesion deficit networks, disentangling functional from confounding pathological distributed effects. Impaired performance was confined to patients with frontal lesions [F(2,387) = 18.491; P < 0.001; frontal worse than non-frontal and healthy participants P < 0.01, P <0.001], more marked on the right than left [F(4,385) = 12.237; P < 0.001; right worse than left and healthy participants P < 0.01, P < 0.001]. Patients with non-frontal lesions were indistinguishable from controls and showed no modulation by laterality. Neither the presence nor the extent of multiple demand network involvement affected performance. Both conventional network-based statistics and non-parametric Bayesian stochastic block modelling heavily implicated the right frontal lobe. Crucially, this localization was confirmed on explicitly disentangling functional from pathology-driven effects within a layered stochastic block model, prominently highlighting a right frontal network involving middle and inferior frontal gyrus, pre- and post-central gyri, with a weak contribution from right superior parietal lobule. Similar results were obtained with standard lesion-deficit analyses. Our study represents the first large-scale investigation of the distributed neural substrates of fluid intelligence in the focally injured brain. Combining novel graph-based lesion-deficit mapping with detailed investigation of cognitive performance in a large sample of patients provides crucial information about the neural basis of intelligence. Our findings indicate that a set of predominantly right frontal regions, rather than a more widely distributed network, is critical to the high-level functions involved in fluid intelligence. Further they suggest that Raven's Advanced Progressive Matrices is a useful clinical index of fluid intelligence and a sensitive marker of right frontal lobe dysfunction.

The impact of etiology in lesion-symptom mapping - A direct comparison between tumor and stroke

INTRODUCTION

Lesion-symptom mapping is a key tool in understanding the relationship between brain structures and behavior. However, the behavioral consequences of lesions from different etiologies may vary because of how they affect brain tissue and how they are distributed. The inclusion of different etiologies would increase the statistical power but has been critically debated. Meanwhile, findings from lesion studies are a valuable resource for clinicians and used across different etiologies. Therefore, the main objective of the present study was to directly compare lesion-symptom maps for memory and language functions from two populations, a tumor versus a stroke population.

METHODS

Data from two different studies were combined. Both the brain tumor (N = 196) and stroke (N = 147) patient populations underwent neuropsychological testing and an MRI, pre-operatively for the tumor population and within three months after stroke. For this study, we selected two internationally widely used standardized cognitive tasks, the Rey Auditory Verbal Learning Test and the Verbal Fluency Test. We used a state-of-the-art machine learning-based, multivariate voxel-wise approach to produce lesion-symptom maps for these cognitive tasks for both populations separately and combined.

RESULTS

Our lesion-symptom mapping results for the separate patient populations largely followed the expected neuroanatomical pattern based on previous literature. Substantial differences in lesion distribution hindered direct comparison. Still, in brain areas with adequate coverage in both groups, considerable LSM differences between the two populations were present for both memory and fluency tasks. Post-hoc analyses of these locations confirmed that the cognitive consequences of focal brain damage varied between etiologies.

CONCLUSION

The differences in the lesion-symptom maps between the stroke and tumor population could partly be explained by differences in lesion volume and topography. Despite these methodological limitations, both the lesion-symptom mapping results and the post-hoc analyses confirmed that etiology matters when investigating the cognitive consequences of lesions with lesion-symptom mapping. Therefore, caution is advised with generalizing lesion-symptom results across etiologies.

Disconnectomics to unravel the network underlying deficits of spatial exploration and attention

Spatial attention and exploration are related to a predominantly right hemispheric network structure. However, the areas of the brain involved and their exact role is still debated. Spatial neglect following right hemispheric stroke lesions has been frequently viewed as a model to study these processes in humans. Previous investigations on the anatomical basis on spatial neglect predominantly focused on focal brain damage and lesion-behaviour mapping analyses. This approach might not be suited to detect remote areas structurally spared but which might contribute to the behavioural deficit. In the present study of a sample of 203 right hemispheric stroke patients, we combined connectome lesion-symptom mapping with multivariate support vector regression to unravel the complex and disconnected network structure in spatial neglect. We delineated three central nodes that were extensively disconnected from other intrahemispheric areas, namely the right superior parietal lobule, the insula, and the temporal pole. Additionally, the analysis allocated central roles within this network to the inferior frontal gyrus (pars triangularis and opercularis), right middle temporal gyrus, right temporal pole and left and right orbitofrontal cortices, including interhemispheric disconnection. Our results suggest that these structures-although not necessarily directly damaged-might play a role within the network underlying spatial neglect in humans.

GeoSPM: Geostatistical parametric mapping for medicine

The characteristics and determinants of health and disease are often organized in space, reflecting our spatially extended nature. Understanding the influence of such factors requires models capable of capturing spatial relations. Drawing on statistical parametric mapping, a framework for topological inference well established in the realm of neuroimaging, we propose and validate an approach to the spatial analysis of diverse clinical data-GeoSPM-based on differential geometry and random field theory. We evaluate GeoSPM across an extensive array of synthetic simulations encompassing diverse spatial relationships, sampling, and corruption by noise, and demonstrate its application on large-scale data from UK Biobank. GeoSPM is readily interpretable, can be implemented with ease by non-specialists, enables flexible modeling of complex spatial relations, exhibits robustness to noise and under-sampling, offers principled criteria of statistical significance, and is through computational efficiency readily scalable to large datasets. We provide a complete, open-source software implementation.

Structural disconnection-based prediction of poststroke depression

Poststroke depression (PSD) is a common complication of stroke. Brain network disruptions caused by stroke are potential biological determinants of PSD but their conclusive roles are unavailable. Our study aimed to identify the strategic structural disconnection (SDC) pattern for PSD at three months poststroke and assess the predictive value of SDC information. Our prospective cohort of 697 first-ever acute ischemic stroke patients were recruited from three hospitals in central China. Sociodemographic, clinical, psychological and neuroimaging data were collected at baseline and depression status was assessed at three months poststroke. Voxel-based disconnection-symptom mapping found that SDCs involving bilateral temporal white matter and posterior corpus callosum, as well as white matter next to bilateral prefrontal cortex and posterior parietal cortex, were associated with PSD. This PSD-specific SDC pattern was used to derive SDC scores for all participants. SDC score was an independent predictor of PSD after adjusting for all imaging and clinical-sociodemographic-psychological covariates (odds ratio, 1.25; 95% confidence interval, 1.07, 1.48; P = 0.006). Split-half replication showed the stability and generalizability of above results. When added to the clinical-sociodemographic-psychological prediction model, SDC score significantly improved the model performance and ranked the highest in terms of predictor importance. In conclusion, a strategic SDC pattern involving multiple lobes bilaterally is identified for PSD at 3 months poststroke. The SDC score is an independent predictor of PSD and may improve the predictive performance of the clinical-sociodemographic-psychological prediction model, providing new evidence for the brain-behavior mechanism and biopsychosocial theory of PSD.

Network-level prediction of set-shifting deterioration after lower-grade glioma resection

OBJECTIVE

The aim of this study was to predict set-shifting deterioration after resection of low-grade glioma.

METHODS

The authors retrospectively analyzed a bicentric series of 102 patients who underwent surgery for low-grade glioma. The difference between the completion times of the Trail Making Test parts B and A (TMT B-A) was evaluated preoperatively and 3-4 months after surgery. High dimensionality of the information related to the surgical cavity topography was reduced to a small set of predictors in four different ways: 1) overlap between surgical cavity and each of the 122 cortical parcels composing Yeo's 17-network parcellation of the brain; 2) Tractotron: disconnection by the cavity of the major white matter bundles; 3) overlap between the surgical cavity and each of Yeo's networks; and 4) disconets: signature of structural disconnection by the cavity of each of Yeo's networks. A random forest algorithm was implemented to predict the postoperative change in the TMT B-A z-score.

RESULTS

The last two network-based approaches yielded significant accuracies in left-out subjects (area under the receiver operating characteristic curve [AUC] approximately equal to 0.8, p approximately equal to 0.001) and outperformed the two alternatives. In single tree hierarchical models, the degree of damage to Yeo corticocortical network 12 (CC 12) was a critical node: patients with damage to CC 12 higher than 7.5% (cortical overlap) or 7.2% (disconets) had much higher risk to deteriorate, establishing for the first time a causal link between damage to this network and impaired set-shifting.

CONCLUSIONS

The authors' results give strong support to the idea that network-level approaches are a powerful way to address the lesion-symptom mapping problem, enabling machine learning-powered individual outcome predictions.

Structural disconnections associated with language impairments in chronic post-stroke aphasia using disconnectome maps

Inconsistent findings have been reported about the impact of structural disconnections on language function in post-stroke aphasia. This study investigated patterns of structural disconnections associated with chronic language impairments using disconnectome maps. Seventy-six individuals with post-stroke aphasia underwent a battery of language assessments and a structural MRI scan. Support-vector regression disconnectome-symptom mapping analyses were performed to examine the correlations between disconnectome maps, representing the probability of disconnection at each white matter voxel and different language scores. To further understand whether significant disconnections were primarily representing focal damage or a more extended network of seemingly preserved but disconnected areas beyond the lesion site, results were qualitatively compared to support-vector regression lesion-symptom mapping analyses. Part of the left white matter perisylvian network was similarly disconnected in 90% of the individuals with aphasia. Surrounding this common left perisylvian disconnectome, specific structural disconnections in the left fronto-temporo-parietal network were significantly associated with aphasia severity and with lower performance in auditory comprehension, syntactic comprehension, syntactic production, repetition and naming tasks. Auditory comprehension, repetition and syntactic processing deficits were related to disconnections in areas that overlapped with and extended beyond lesion sites significant in SVR-LSM analyses. In contrast, overall language abilities as measured by aphasia severity and naming seemed to be mostly explained by focal damage at the level of the insular and central opercular cortices, given the high overlap between SVR-DSM and SVR-LSM results for these scores. While focal damage seems to be sufficient to explain broad measures of language performance, the structural disconnections between language areas provide additional information on the neural basis of specific and persistent language impairments at the chronic stage beyond lesion volume. Leveraging routinely available clinical data, disconnectome mapping furthers our understanding of anatomical connectivity constraints that may limit the recovery of some language abilities in chronic post-stroke aphasia.

Efficient and effective assessment of deficits and their neural bases in stroke aphasia

OBJECTIVE

Multi-assessment batteries are necessary for diagnosing and quantifying the multifaceted deficits observed post-stroke. Extensive batteries are thorough but impractically long for clinical settings or large-scale research studies. Clinically-targeted "shallow" batteries superficially cover a wide range of language skills relatively quickly but can struggle to identify mild deficits or quantify the impairment level. Our aim was to compare these batteries across a large group of chronic stroke aphasia and to test a novel data-driven reduced version of an extensive battery that maintained sensitivity to mild impairment, ability to grade deficits and the underlying component structure.

METHODS

We tested 75 chronic left-sided stroke participants, spanning global to mild aphasia. The underlying structure of these three batteries was analysed using cross-validation and principal component analysis, in addition to univariate and multivariate lesion-symptom mapping.

RESULTS

This revealed a four-factor solution for the extensive and data-reduced batteries, identifying phonology, semantic skills, fluency and executive function in contrast to a two-factor solution using the shallow battery (language severity and cognitive severity). Lesion symptom mapping using participants' factor scores identified convergent neural structures for phonology (superior temporal gyrus), semantics (inferior temporal gyrus), speech fluency (precentral gyrus) and executive function (lateral occipitotemporal cortex). The two shallow battery components converged with the phonology and executive function clusters. In addition, we show that multivariate models could predict the component scores using neural data, however not for every component.

CONCLUSIONS

Overall, the data-driven battery appears to be an effective way to save time yet retain maintained sensitivity to mild impairment, ability to grade deficits and the underlying component structure observed in post-stroke aphasia.

Indirect structural disconnection-symptom mapping

In vivo tracking of white matter fibres catalysed a modern perspective on the pivotal role of brain connectome disruption in neuropsychological deficits. However, the examination of white matter integrity in neurological patients by diffusion-weighted magnetic resonance imaging bears conceptual limitations and is not widely applicable, as it requires imaging-compatible patients and resources beyond the capabilities of many researchers. The indirect estimation of structural disconnection offers an elegant and economical alternative. For this approach, a patient's structural lesion information and normative connectome data are combined to estimate different measures of lesion-induced structural disconnection. Using one of several toolboxes, this method is relatively easy to implement and is even available to scientists without expertise in fibre tracking analyses. Nevertheless, the anatomo-behavioural statistical mapping of structural brain disconnection requires analysis steps that are not covered by these toolboxes. In this paper, we first review the current state of indirect lesion disconnection estimation, the different existing measures, and the available software. Second, we aim to fill the remaining methodological gap in statistical disconnection-symptom mapping by providing an overview and guide to disconnection data and the statistical mapping of their relationship to behavioural measurements using either univariate or multivariate statistical modelling. To assist in the practical implementation of statistical analyses, we have included software tutorials and analysis scripts.

fMRI informed voxel-based lesion analysis to identify lesions associated with right-hemispheric activation in aphasia recovery

Several mechanisms have been attributed to post-stroke loss and recovery of language functions. However, the significance and timing of domain-general and homotopic right-hemispheric activation is controversial. We aimed to examine the effect of left-hemispheric lesion location and time post-stroke on right-hemispheric activation. Voxel-based lesion analyses were informed by auditory language-related fMRI activation of 71 patients with left middle cerebral artery stroke examined longitudinally in the acute, subacute and early chronic phase. Language activation was determined in several right-hemispheric regions of interest and served as regressor of interest for voxel-based lesion analyses. We found that an acute to chronic increase of language activation in the right supplementary motor area was associated with lesions to the left extreme capsule as part of the ventral language pathway. Importantly, this activation increase correlated significantly with improvement of out-of-scanner comprehension abilities. We interpret our findings in terms of successful domain-general compensation in patients with critical left frontotemporal disconnection due to damage to the ventral language pathway but relatively spared cortical language areas.

Mapping correlated neurological deficits after stroke to distributed brain networks

Understanding the relationships between brain organization and behavior is a central goal of neuroscience. Traditional teaching emphasizes that the human cerebrum includes many distinct areas for which damage or dysfunction would lead to a unique and specific behavioral syndrome. This teaching implies that brain areas correspond to encapsulated modules that are specialized for specific cognitive operations. However, empirically, local damage from stroke more often produces one of a small number of clusters of deficits and disrupts brain-wide connectivity in a small number of predictable ways (relative to the vast complexity of behavior and brain connectivity). Behaviors that involve shared operations show correlated deficits following a stroke, consistent with a low-dimensional behavioral space. Because of the networked organization of the brain, local damage from a stroke can result in widespread functional abnormalities, matching the low dimensionality of behavioral deficit. In alignment with this, neurological disease, psychiatric disease, and altered brain states produce behavioral changes that are highly correlated across a range of behaviors. We discuss how known structural and functional network priors in addition to graph theoretical concepts such as modularity and entropy have provided inroads to understanding this more complex relationship between brain and behavior. This model for brain disease has important implications for normal brain-behavior relationships and the treatment of neurological and psychiatric diseases.

Academic skills after brain injury: A lifespan perspective

OBJECTIVES

This study investigated academic skills outcomes after brain injury and identified the influence of age and injury factors across the lifespan.

METHOD

Our sample included 651 participants with focal brain lesions. Math, reading, and spelling data from the Wide Range Achievement Test (WRAT) were used as the academic skills outcomes. Age of lesion onset ranged from 0 to 85 years old. Linear regressions were conducted to identify the relation between age and injury factors and academic skills outcomes. Lesion-symptom mapping was conducted to identify the brain areas that, when lesioned, were associated with deficits in academic skills.

RESULTS

A quadratic model of age of lesion onset significantly predicted math (R² = .28, p < .001), reading (R² = .29, p < .001), and spelling outcomes (R² = .32, p < .001), while accounting for various covariates. Education, sex, lesion size and laterality, etiology, and seizure history were additional reliable predictors of academic skills outcomes across the lifespan. Academic skill deficits were associated with damage to various brain areas across the left-hemisphere frontal, temporal, and parietal lobes, the insular area, and left- and right-hemisphere white matter.

CONCLUSIONS

This study supports age of lesion onset as a relevant predictor of academic skills after brain injury in a lifespan sample. Several other variables (e.g., education, sex, lesion characteristics, and seizure history) are notable in the prediction of outcomes across the lifespan. Future work could investigate more diverse samples and emphasize recruitment of early onset injuries to examine generalizability and potential critical periods for academic skills. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Systematic perturbation of an artificial neural network: A step towards quantifying causal contributions in the brain

Lesion inference analysis is a fundamental approach for characterizing the causal contributions of neural elements to brain function. This approach has gained new prominence through the arrival of modern perturbation techniques with unprecedented levels of spatiotemporal precision. While inferences drawn from brain perturbations are conceptually powerful, they face methodological difficulties. Particularly, they are challenged to disentangle the true causal contributions of the involved elements, since often functions arise from coalitions of distributed, interacting elements, and localized perturbations have unknown global consequences. To elucidate these limitations, we systematically and exhaustively lesioned a small artificial neural network (ANN) playing a classic arcade game. We determined the functional contributions of all nodes and links, contrasting results from sequential single-element perturbations with simultaneous perturbations of multiple elements. We found that lesioning individual elements, one at a time, produced biased results. By contrast, multi-site lesion analysis captured crucial details that were missed by single-site lesions. We conclude that even small and seemingly simple ANNs show surprising complexity that needs to be addressed by multi-lesioning for a coherent causal characterization.

The motto “No causation without manipulation” is canonical to scientific endeavors. In particular, neuroscience seeks to identify which brain elements are causally involved in cognition and behavior, by perturbing them. However, due to multi-dimensional interactions among the elements, this goal has remained challenging. Here, we used an Artificial Neural Network as a ground-truth model to compare the inferential capacities of two principal approaches, lesioning a system one element at a time versus sampling from the set of all possible combinations of lesions. We show that lesioning one element at a time provides misleading results. Hence, we argue for employing exhaustive perturbation regimes. We further advocate using simulation experiments and ground-truth models to verify the assumptions and limitations of current approaches for brain mapping by perturbation.

Post-stroke outcomes predicted from multivariate lesion-behaviour and lesion network mapping

Clinicians and scientists alike have long sought to predict the course and severity of chronic post-stroke cognitive and motor outcomes, as the ability to do so would inform treatment and rehabilitation strategies. However, it remains difficult to make accurate predictions about chronic post-stroke outcomes due, in large part, to high inter-individual variability in recovery and a reliance on clinical heuristics rather than empirical methods. The neuroanatomical location of a stroke is a key variable associated with long-term outcomes, and because lesion location can be derived from routinely collected clinical neuroimaging data there is an opportunity to use this information to make empirically based predictions about post-stroke deficits. For example, lesion location can be compared to statistically weighted multivariate lesion-behaviour maps of neuroanatomical regions that, when damaged, are associated with specific deficits based on aggregated outcome data from large cohorts. Here, our goal was to evaluate whether we can leverage lesion-behaviour maps based on data from two large cohorts of individuals with focal brain lesions to make predictions of 12-month cognitive and motor outcomes in an independent sample of stroke patients. Further, we evaluated whether we could augment these predictions by estimating the structural and functional networks disrupted in association with each lesion-behaviour map through the use of structural and functional lesion network mapping, which use normative structural and functional connectivity data from neurologically healthy individuals to elucidate lesion-associated networks. We derived these brain network maps using the anatomical regions with the strongest association with impairment for each cognitive and motor outcome based on lesion-behaviour map results. These peak regional findings became the 'seeds' to generate networks, an approach that offers potentially greater precision compared to previously used single-lesion approaches. Next, in an independent sample, we quantified the overlap of each lesion location with the lesion-behaviour maps and structural and functional lesion network mapping and evaluated how much variance each could explain in 12-month behavioural outcomes using a latent growth curve statistical model. We found that each lesion-deficit mapping modality was able to predict a statistically significant amount of variance in cognitive and motor outcomes. Both structural and functional lesion network maps were able to predict variance in 12-month outcomes beyond lesion-behaviour mapping. Functional lesion network mapping performed best for the prediction of language deficits, and structural lesion network mapping performed best for the prediction of motor deficits. Altogether, these results support the notion that lesion location and lesion network mapping can be combined to improve the prediction of post-stroke deficits at 12-months.