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

Using unsupervised dimensionality reduction to identify lesion patterns predictive of post-stroke aphasia severity

Although voxel-based methods consistently identify brain regions associated with specific language functions, these techniques are limited when applied to broader behavioral measures. To better represent effects of lesions on distributed brain regions, we used a data-driven approach called non-negative matrix factorization (NMF) to identify representative stroke patterns and explore associations with aphasia severity. Lesions were segmented using structural MRIs for 107 left hemisphere stroke patients, and the Western Aphasia Battery - Revised Aphasia Quotient (AQ) was used to quantify aphasia severity. Percent spared tissue was calculated in left hemisphere white and gray matter regions. By applying NMF to spared tissue data, we identified 5 NMF 'atoms' which represent prototypical stroke patterns across this dataset. Linear regression was used to identify whether certain stroke patterns were associated with aphasia severity, adjusted for lesion volume and demographics. Two NMF atoms showed relevance in predicting AQ: strokes with low spared tissue across the whole MCA territory were associated with more severe aphasia, but strokes with high spared tissue around the insula were associated with less severe aphasia. We also identified a pattern of high spared tissue in superior fronto-parietal regions, where lesion volume was more strongly associated with severity as a result of isolating damage to more critical language areas. These representative stroke patterns offer a new way to combine information about lesion burden and location and explore anatomical associations with language dysfunction in stroke.

Visual feature processing in a large stroke cohort: evidence against modular organization

Mid-level visual processing represents a crucial stage between basic sensory input and higher-level object recognition. The conventional model posits that fundamental visual qualities, such as colour and motion, are processed in specialized, retinotopic brain regions (e.g. V4 for colour, MT/V5 for motion). Using atlas-based lesion-symptom mapping and disconnectome maps in a cohort of 307 ischaemic stroke patients, we examined the neuroanatomical correlates underlying the processing of eight mid-level visual qualities. Contrary to the predictions of the standard model, our results did not reveal consistent relationships between processing impairments and damage to traditionally associated brain regions. Although we validated our methodology by confirming the established relationship between visual field defects and damage to primary visual areas (V1, V2 and V3), we found no reliable evidence linking processing deficits to specific regions in the posterior brain. These findings challenge the traditional modular view of visual processing and suggest that mid-level visual processing might be more distributed across neural networks than previously thought. This supports alternative models where visual maps represent constellations of co-occurring information rather than specific qualities.

Localisation of function in the brain: a rethink

A modular view of brain function dominates the teaching of medical students and clinical psychologists and is implicit in day-to-day clinical practice. This view glosses over a long-standing debate. The extent of one-to-one mappings between region and function remains a controversial topic. For the cortex, localisation of function versus 'cerebral equipotentiality' was debated less than 150 years ago, and traces of this debate remain active in systems neuroscience today. The advent of functional brain imaging led to an explosion of evidence on localisation of function studied in vivo, and a gold rush to map an ever-increasing range of 'functions'. Rapid growth in knowledge was accompanied, to some extent, by a flourishing neuromythology. There are currently few clinical applications of brain mapping techniques, but new areas are emerging. An understanding of the central debate on functional localisation will bring a more nuanced view of problems encountered in clinical practice.

Symptomatology after damage to the angular gyrus through the lenses of modern lesion-symptom mapping

Brain-behavior relationships are complex. For instance, one might know a brain region's function(s) but still be unable to accurately predict deficit type or severity after damage to that region. Here, I discuss the case of damage to the angular gyrus (AG) that can cause left-right confusion, finger agnosia, attention deficit, and lexical agraphia, as well as impairment in sentence processing, episodic memory, number processing, and gesture imitation. Some of these symptoms are grouped under AG syndrome or Gerstmann's syndrome, though its exact underlying neuronal systems remain elusive. This review applies recent frameworks of brain-behavior modes and principles from modern lesion-symptom mapping to explain symptomatology after AG damage. It highlights four major issues for future studies: (1) functionally heterogeneous symptoms after AG damage need to be considered in terms of the degree of damage to (i) different subdivisions of the AG, (ii) different AG connectivity profiles that disconnect AG from distant regions, and (iii) lesion extent into neighboring regions damaged by the same infarct. (2) To explain why similar symptoms can also be observed after damage to other regions, AG damage needs to be studied in terms of the networks of regions that AG functions with, and other independent networks that might subsume the same functions. (3) To explain inter-patient variability on AG symptomatology, the degree of recovery-related brain reorganisation needs to account for time post-stroke, demographics, therapy input, and pre-stroke differences in functional anatomy. (4) A better integration of the results from lesion and functional neuroimaging investigations of AG function is required, with only the latter so far considering AG function in terms of a hub within the default mode network. Overall, this review discusses why it is so difficult to fully characterize the AG syndrome from lesion data, and how this might be addressed with modern lesion-symptom mapping.

AI-assisted assessment and treatment of aphasia: a review

Aphasia is a language disorder caused by brain injury that often results in difficulties with speech production and comprehension, significantly impacting the affected individuals' lives. Recently, artificial intelligence (AI) has been advancing in medical research. Utilizing machine learning and related technologies, AI develops sophisticated algorithms and predictive models, and can employ tools such as speech recognition and natural language processing to autonomously identify and analyze language deficits in individuals with aphasia. These advancements provide new insights and methods for assessing and treating aphasia. This article explores current AI-supported assessment and treatment approaches for aphasia and highlights key application areas. It aims to uncover how AI can enhance the process of assessment, tailor therapeutic interventions, and track the progress and outcomes of rehabilitation efforts. The article also addresses the current limitations of AI's application in aphasia and discusses prospects for future research.

Stem cell therapy: a new hope for stroke and traumatic brain injury recovery and the challenge for rural minorities in South Carolina

Stroke and traumatic brain injury (TBI) are a significant cause of death and disability nationwide. Both are considered public health concerns in rural communities in the state of South Carolina (SC), particularly affecting the African American population resulting in considerable morbidity, mortality, and economic burden. Stem cell therapy (SCT) has emerged as a potential intervention for both diseases with increasing research trials showing promising results. In this perspective article, the authors aim to discuss the current research in the field of SCT, the results of early phase trials, and the utilization of outcome measures and biomarkers of recovery. We searched PubMed from inception to December 2023 for articles on stem cell therapy in stroke and traumatic brain injury and its impact on rural communities, particularly in SC. Early phase trials of SCT in Stroke and Traumatic Brain injury yield promising safety profile and efficacy results, but the findings have not yet been consistently replicated. Early trials using mesenchymal stem cells for stroke survivors showed safety, feasibility, and improved functional outcomes using broad and domain-specific outcome measures. Neuroimaging markers of recovery such as Functional Magnetic Resonance Imaging (fMRI) and electroencephalography (EEG) combined with neuromodulation, although not widely used in SCT research, could represent a breakthrough when evaluating brain injury and its functional consequences. This article highlights the role of SCT as a promising intervention while addressing the underlying social determinants of health that affect therapeutic outcomes in relation to rural communities such as SC. It also addresses the challenges ethical concerns of stem cell sourcing, the high cost of autologous cell therapies, and the technical difficulties in ensuring transplanted cell survival and strategies to overcome barriers to clinical trial enrollment such as the ethical concerns of stem cell sourcing, the high cost of autologous cell therapies, and the technical difficulties in ensuring transplanted cell survival and equitable healthcare.

Normative connectome-based analysis of sensorimotor deficits in acute subcortical stroke

The interrelation between acute ischemic stroke, persistent disability, and uncertain prognosis underscores the need for improved methods to predict clinical outcomes. Traditional approaches have largely focused on analysis of clinical metrics, lesion characteristics, and network connectivity, using techniques such as resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI). However, these methods are not routinely used in acute stroke diagnostics. This study introduces an innovative approach that not only considers the lesion size in relation to the National Institutes of Health Stroke Scale (NIHSS score), but also evaluates the impact of disrupted fibers and their connections to cortical regions by introducing a disconnection value. By identifying fibers traversing the lesion and estimating their number within predefined regions of interest (ROIs) using a normative connectome atlas, our method bypasses the need for individual DTI scans. In our analysis of MRI data (T1 and T2) from 51 patients with acute or subacute subcortical stroke presenting with motor or sensory deficits, we used simple linear regression to assess the explanatory power of lesion size and disconnection value on NIHSS score. Subsequent hierarchical multiple linear regression analysis determined the incremental value of disconnection metrics over lesion size alone in relation to NIHSS score. Our results showed that models incorporating the disconnection value accounted for more variance than those based solely on lesion size (lesion size explained 44% variance, disconnection value 60%). Furthermore, hierarchical regression revealed a significant improvement (p < 0.001) in model fit when adding the disconnection value, confirming its critical role in stroke assessment. Our approach, which integrates a normative connectome to quantify disconnections to cortical regions, provides a significant improvement in assessing the current state of stroke impact compared to traditional measures that focus on lesion size. This is achieved by taking into account the lesion's location and the connectivity of the affected white matter tracts, providing a more comprehensive assessment of stroke severity as reflected in the NIHSS score. Future research should extend the validation of this approach to larger and more diverse populations, with a focus on refining its applicability to clinical assessment and long-term outcome prediction.

Predicting functional impairments with lesion-derived disconnectome mapping: Validation in stroke patients with motor deficits

Focal structural damage to white matter tracts can result in functional deficits in stroke patients. Traditional voxel-based lesion-symptom mapping is commonly used to localize brain structures linked to neurological deficits. Emerging evidence suggests that the impact of structural focal damage may extend beyond immediate lesion sites. In this study, we present a disconnectome mapping approach based on support vector regression (SVR) to identify brain structures and white matter pathways associated with functional deficits in stroke patients. For clinical validation, we utilized imaging data from 340 stroke patients exhibiting motor deficits. A disconnectome map was initially derived from lesions for each patient. Bootstrap sampling was then employed to balance the sample size between a minority group of patients exhibiting right or left motor deficits and those without deficits. Subsequently, SVR analysis was used to identify voxels associated with motor deficits (p < .005). Our disconnectome-based analysis significantly outperformed alternative lesion-symptom approaches in identifying major white matter pathways within the corticospinal tracts associated with upper-lower limb motor deficits. Bootstrapping significantly increased the sensitivity (80%-87%) for identifying patients with motor deficits, with a minimum lesion size of 32 and 235 mm3 for the right and left motor deficit, respectively. Overall, the lesion-based methods achieved lower sensitivities compared with those based on disconnection maps. The primary contribution of our approach lies in introducing a bootstrapped disconnectome-based mapping approach to identify lesion-derived white matter disconnections associated with functional deficits, particularly efficient in handling imbalanced data.

Behavioral and neuroanatomical correlates of facial emotion processing in post-stroke depression

BACKGROUND

Emotion processing deficits are known to accompany depressive symptoms and are often seen in stroke patients. Little is known about the influence of post-stroke depressive (PSD) symptoms and specific brain lesions on altered emotion processing abilities and how these phenomena develop over time. This potential relationship may impact post-stroke rehabilitation of neurological and psychosocial function. To address this scientific gap, we investigated the relationship between PSD symptoms and emotion processing abilities in a longitudinal study design from the first days post-stroke into the early chronic phase.

METHODS

Twenty-six ischemic stroke patients performed an emotion processing task on videos with emotional faces ('happy,' 'sad,' 'anger,' 'fear,' and 'neutral') at different intensity levels (20%, 40%, 60%, 80%, 100%). Recognition accuracies and response times were measured, as well as scores of depressive symptoms (Montgomery-Åsberg Depression Rating Scale). Twenty-eight healthy participants matched in age and sex were included as a control group. Whole-brain support-vector regression lesion-symptom mapping (SVR-LSM) analyses were performed to investigate whether specific lesion locations were associated with the recognition accuracy of specific emotion categories.

RESULTS

Stroke patients performed worse in overall recognition accuracy compared to controls, specifically in the recognition of happy, sad, and fearful faces. Notably, more depressed stroke patients showed an increased processing towards specific negative emotions, as they responded significantly faster to angry faces and recognized sad faces of low intensities significantly more accurately. These effects obtained for the first days after stroke partly persisted to follow-up assessment several months later. SVR-LSM analyses revealed that inferior and middle frontal regions (IFG/MFG) and insula and putamen were associated with emotion-recognition deficits in stroke. Specifically, recognizing happy facial expressions was influenced by lesions affecting the anterior insula, putamen, IFG, MFG, orbitofrontal cortex, and rolandic operculum. Lesions in the posterior insula, rolandic operculum, and MFG were also related to reduced recognition accuracy of fearful facial expressions, whereas recognition deficits of sad faces were associated with frontal pole, IFG, and MFG damage.

CONCLUSION

PSD symptoms facilitate processing negative emotional stimuli, specifically angry and sad facial expressions. The recognition accuracy of different emotional categories was linked to brain lesions in emotion-related processing circuits, including insula, basal ganglia, IFG, and MFG. In summary, our study provides support for psychosocial and neural factors underlying emotional processing after stroke, contributing to the pathophysiology of PSD.

From neural noise to co-adaptability: Rethinking the multifaceted architecture of motor variability

In the last decade, the source and the functional meaning of motor variability have attracted considerable attention in behavioral and brain sciences. This construct classically combined different levels of description, variable internal robustness or coherence, and multifaceted operational meanings. We provide here a comprehensive review of the literature with the primary aim of building a precise lexicon that goes beyond the generic and monolithic use of motor variability. In the pars destruens of the work, we model three domains of motor variability related to peculiar computational elements that influence fluctuations in motor outputs. Each domain is in turn characterized by multiple sub-domains. We begin with the domains of noise and differentiation. However, the main contribution of our model concerns the domain of adaptability, which refers to variation within the same exact motor representation. In particular, we use the terms learning and (social)fitting to specify the portions of motor variability that depend on our propensity to learn and on our largely constitutive propensity to be influenced by external factors. A particular focus is on motor variability in the context of the sub-domain named co-adaptability. Further groundbreaking challenges arise in the modeling of motor variability. Therefore, in a separate pars construens, we attempt to characterize these challenges, addressing both theoretical and experimental aspects as well as potential clinical implications for neurorehabilitation. All in all, our work suggests that motor variability is neither simply detrimental nor beneficial, and that studying its fluctuations can provide meaningful insights for future research.

Prospective and Retrospective Metacognitive Abilities and Their Association with Impaired Self-awareness in Patients with Traumatic Brain Injury

Metacognitive impairment often occurs in patients with traumatic brain injury (TBI) and is associated with clinical problems. The aim of this study was to clarify the pathology of metacognitive impairment in TBI patients using a behavioral task, clinical assessment of self-awareness, and lesion-symptom mapping. Metacognitive abilities of TBI patients and healthy controls were assessed using a modified perceptual decision-making task. Self-awareness was assessed using the Patient Competency Rating Scale and the Frontal Systems Behavior Scale. The associations between estimated metacognitive abilities, self-awareness, and neuropsychological test results were examined. The correspondence between metacognitive disabilities and brain lesions was explored by ROI-based lesion-symptom mapping using structural magnetic resonance images. Overall, 25 TBI patients and 95 healthy controls were included in the analyses. Compared with that in healthy controls, the prospective metacognitive ability of TBI patients was lower, with metacognitive evaluations revealing a bias toward overestimating their abilities. Retrospective metacognitive ability showed a negative correlation with self-awareness but not with neuropsychological test results. In the lesion-symptom mapping analysis, the left pFC was associated with lower retrospective metacognitive ability. This study contributes to a better understanding of the pathology of metacognitive and self-awareness deficits in TBI patients and may explain the cause of impaired realistic goal setting and adaptive behavior in these patients.

Reliability and validity of DTI-based indirect disconnection measures

White matter connections enable the interaction within and between brain networks. Brain lesions can cause structural disconnections that disrupt networks and thereby cognitive functions supported by them. In recent years, novel methods have been developed to quantify the extent of structural disconnection after focal lesions, using tractography data from healthy controls. These methods, however, are indirect and their reliability and validity have yet to be fully established. In this study, we present our implementation of this approach, in a tool supplemented by uncertainty metrics for the predictions overall and at voxel-level. These metrics give an indication of the reliability and are used to compare predictions with direct measures from patients' diffusion tensor imaging (DTI) data in a sample of 95 first-ever stroke patients. Results show that, except for small lesions, the tool can predict fiber loss with high reliability and compares well to direct patient DTI estimates. Clinical utility of the method was demonstrated using lesion data from a subset of patients suffering from hemianopia. Both tract-based measures outperformed lesion localization in mapping visual field defects and showed a network consistent with the known anatomy of the visual system. This study offers an important contribution to the validation of structural disconnection mapping. We show that indirect measures of structural disconnection can be a reliable and valid substitute for direct estimations of fiber loss after focal lesions. Moreover, based on these results, we argue that indirect structural disconnection measures may even be preferable to lower-quality single subject diffusion MRI when based on high-quality healthy control datasets.

The Misguided Veneration of Averageness in Clinical Neuroscience: A Call to Value Diversity over Typicality

Research and practice in clinical neurosciences often involve cognitive assessment. However, this has traditionally used a nomothetic approach, comparing the performance of patients to normative samples. This method of defining abnormality places the average test performance of neurologically healthy individuals at its center. However, evidence suggests that neurological 'abnormalities' are very common, as is the diversity of cognitive abilities. The veneration of central tendency in cognitive assessment, i.e., equating typicality with healthy or ideal, is, I argue, misguided on neurodiversity, bio-evolutionary, and cognitive neuroscientific grounds. Furthermore, the use of average performance as an anchor point for normal performance is unreliable in practice and frequently leads to the mischaracterization of cognitive impairments. Examples are explored of how individuals who are already vulnerable for socioeconomic reasons can easily be over-pathologized. At a practical level, by valuing diversity rather than typicality, cognitive assessments can become more idiographic and focused on change at the level of the individual. The use of existing methods that approach cognitive assessment ideographically is briefly discussed, including premorbid estimation methods and informant reports. Moving the focus away from averageness to valuing diversity for both clinical cognitive assessments and inclusion of diverse groups in research is, I argue, a more just and effective way forward for clinical neurosciences.

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.

Damage to temporoparietal cortex is sufficient for impaired semantic control

Semantic control allows us to focus semantic activation on currently relevant aspects of knowledge, even in the face of competition or when the required information is weakly encoded. Diverse cortical regions, including left prefrontal and posterior temporal cortex, are implicated in semantic control, however; the relative contribution of these regions is unclear. For the first time, we compared semantic aphasia (SA) patients with damage restricted to temporoparietal cortex (TPC; N = 8) to patients with infarcts encompassing prefrontal cortex (PF+; N = 22), to determine if prefrontal lesions are necessary for semantic control deficits. These SA groups were also compared with semantic dementia (SD; N = 10), characterised by degraded semantic representations. We asked whether TPC cases with semantic impairment show controlled retrieval deficits equivalent to PF+ cases or conceptual degradation similar to patients with SD. Independent of lesion location, the SA subgroups showed similarities, whereas SD patients showed a qualitatively distinct semantic impairment. Relative to SD, both TPC and PF+ SA subgroups: (1) showed few correlations in performance across tasks with differing control demands, but a strong relationship between tasks of similar difficulty; (2) exhibited attenuated effects of lexical frequency and concept familiarity, (3) showed evidence of poor semantic regulation in their verbal output - performance on picture naming was substantially improved when provided with a phonological cue, and (4) showed effects of control demands, such as retrieval difficulty, which were equivalent in severity across TPC and PF+ groups. These findings show that semantic impairment in SA is underpinned by damage to a distributed semantic control network, instantiated across anterior and posterior cortical areas.

Associations among depression, demographic variables, and language impairments in chronic post-stroke aphasia

INTRODUCTION

Depression may influence treatment participation and outcomes of people with post-stroke aphasia, yet its prevalence and associated characteristics in aphasia are poorly understood. Using retrospective data from an overarching experimental study, we examined depressive symptoms and their relationship to demographic and language characteristics in people with chronic aphasia. As a secondary objective, we compared prevalence of depressive symptoms among the overarching study's included and excluded participants.

METHODS

We examined retrospective data from 121 individuals with chronic aphasia including depression scale scores, demographic information (sex, age, time post onset of stroke, education, race/ethnicity, and Veteran status), and scores on assessments of general and modality-specific language impairments.

RESULTS

Approximately 50% of participants reported symptoms indicative of depressive disorders: 23% indicative of major depression and 27% indicative of mild depression. Sex (males) and comparatively younger age emerged as statistically significant variables associated with depressive symptoms; naming ability was minimally associated with depressive symptoms. Time post onset of stroke, education level, race/ethnicity, Veteran status, and aphasia severity were not significantly associated with depressive symptoms. Depression-scale scores were significantly higher for individuals excluded from the overarching study compared to those who were included.

CONCLUSIONS

The rate of depressive disorders in this sample was higher than rates of depression reported in the general stroke literature. Participant sex, age, and naming ability emerged as factors associated with depressive symptoms, though these links appear complex, especially given variable reports from prior research. Importantly, depressive symptoms do not appear to diminish over time for individuals with chronic aphasia. Given these results and the relatively limited documentation of depression in aphasia literature, depression remains a pressing concern for aphasia research and routine clinical care.

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.

Context-sensitive computational mechanistic explanation in cognitive neuroscience

Mainstream cognitive neuroscience aims to build mechanistic explanations of behavior by mapping abilities described at the organismal level via the subpersonal level of computation onto specific brain networks. We provide an integrative review of these commitments and their mismatch with empirical research findings. Context-dependent neural tuning, neural reuse, degeneracy, plasticity, functional recovery, and the neural correlates of enculturated skills each show that there is a lack of stable mappings between organismal, computational, and neural levels of analysis. We furthermore highlight recent research suggesting that task context at the organismal level determines the dynamic parcellation of functional components at the neural level. Such instability prevents the establishment of specific computational descriptions of neural function, which remains a central goal of many brain mappers - including those who are sympathetic to the notion of many-to-many mappings between organismal and neural levels. This between-level instability presents a deep epistemological challenge and requires a reorientation of methodological and theoretical commitments within cognitive neuroscience. We demonstrate the need for change to brain mapping efforts in the face of instability if cognitive neuroscience is to maintain its central goal of constructing computational mechanistic explanations of behavior; we show that such explanations must be contextual at all levels.

Understanding, facilitating and predicting aphasia recovery after rehabilitation

Purpose: This paper reviews several studies whose aim was to understand the nature of language recovery in chronic aphasia and identify predictors of how people may recover their language functions after a brain injury.Method: Several studies that mostly draw from data collected within the Centre for Neurobiology of Language Recovery were reviewed and categorised in four aspects of language impairment and recovery in aphasia: (a) neural markers for language impairment and recovery, (b) language and cognitive markers for language impairment and recovery, (c) effective treatments and (d) predictive modelling of treatment-induced rehabilitation.Result: Language impairment and recovery in stroke-induced aphasia is multi-factorial, including patient-specific and treatment-specific factors. A combination of these factors may help us predict treatment responsiveness even before treatment begins.Conclusion: Continued work on this topic will lead to a better understanding of the mechanisms that underly language impairment and treatment-induced recovery in aphasia, and, consequently, use this information to predict each person's recovery profile trajectory and provide optimal prescriptions regarding the type and dosage of treatment.

Precision medicine in stroke: towards personalized outcome predictions using artificial intelligence

Stroke ranks among the leading causes for morbidity and mortality worldwide. New and continuously improving treatment options such as thrombolysis and thrombectomy have revolutionized acute stroke treatment in recent years. Following modern rhythms, the next revolution might well be the strategic use of the steadily increasing amounts of patient-related data for generating models enabling individualized outcome predictions. Milestones have already been achieved in several health care domains, as big data and artificial intelligence have entered everyday life. The aim of this review is to synoptically illustrate and discuss how artificial intelligence approaches may help to compute single-patient predictions in stroke outcome research in the acute, subacute and chronic stage. We will present approaches considering demographic, clinical and electrophysiological data, as well as data originating from various imaging modalities and combinations thereof. We will outline their advantages, disadvantages, their potential pitfalls and the promises they hold with a special focus on a clinical audience. Throughout the review we will highlight methodological aspects of novel machine-learning approaches as they are particularly crucial to realize precision medicine. We will finally provide an outlook on how artificial intelligence approaches might contribute to enhancing favourable outcomes after stroke.

Sex-specific cognitive effects of mild traumatic brain injury to the frontal and temporal lobes

BACKGROUND

Cognitive deficits are the most enduring and debilitating sequelae of mild traumatic brain injury (mTBI). However, relatively little is known about whether the cognitive effects of mTBI vary with respect to time post-injury, biological sex, and injury location.

OBJECTIVES

The aim of this study was to assess the effect of the side and site of mTBI and to determine whether these effects are sexually dimorphic.

METHODS

Male and female ICR mice were subjected to either a sham procedure or mTBI to the temporal lobes (right-sided or left-sided) or to the frontal lobes (bilateral) using a weight-drop model. After recovery, mice underwent a battery of behavioral tests at two post-injury time points.

RESULTS

Different mTBI impact locations produced dissociable patterns of memory deficits; the extent of these deficits varied across sexes, time points, and memory domains. In both sexes, frontal mTBI mice exhibited a delayed onset of spatial memory deficits. Additionally, the performance of the frontal and left temporal injured males and females was more variable than that of controls. Interestingly, only in females does the effect of mTBI on visual recognition memory depend on the time post-injury. Moreover, only in females does spatial recognition memory remain relatively intact after mTBI to the left temporal lobe.

CONCLUSION

This study showed that different mTBI impact sites produce dissociable and sex-specific patterns of cognitive deficits in mice. The results emphasize the importance of considering the injury site/side and biological sex when evaluating the cognitive sequelae of mTBI.

Understanding Language Reorganization With Neuroimaging: How Language Adapts to Different Focal Lesions and Insights Into Clinical Applications

When the language-dominant hemisphere is damaged by a focal lesion, the brain may reorganize the language network through functional and structural changes known as adaptive plasticity. Adaptive plasticity is documented for triggers including ischemic, tumoral, and epileptic focal lesions, with effects in clinical practice. Many questions remain regarding language plasticity. Different lesions may induce different patterns of reorganization depending on pathologic features, location in the brain, and timing of onset. Neuroimaging provides insights into language plasticity due to its non-invasiveness, ability to image the whole brain, and large-scale implementation. This review provides an overview of language plasticity on MRI with insights for patient care. First, we describe the structural and functional language network as depicted by neuroimaging. Second, we explore language reorganization triggered by stroke, brain tumors, and epileptic lesions and analyze applications in clinical diagnosis and treatment planning. By comparing different focal lesions, we investigate determinants of language plasticity including lesion location and timing of onset, longitudinal evolution of reorganization, and the relationship between structural and functional changes.

Tackling the Complexity of Lesion-Symptoms Mapping: How to Bridge the Gap Between Data Scientists and Clinicians?

Accurate and predictive lesion-symptoms mapping is a major goal in the field of clinical neurosciences. Recent studies have called for a reappraisal of the results given by the standard univariate voxel-based lesion-symptom mapping technique, emphasizing the need of developing multivariate methods. While the organization of large datasets and their analysis with machine learning (ML) approaches represents an opportunity to increase prediction accuracy, the complexity and dimensionality of the problem remain a major obstacle. Acknowledging the difficulty of inferring individual outcomes from the observation of spatial patterns of lesions, we propose here to base prediction on new individuals on models of brain connectivity, whereby the disruption of a given network predicts the occurrence of selective deficits. Well-suited ML tools are necessary to capture the relevant information from limited datasets and perform reliable inference.

The strange role of brain lesion size in cognitive neuropsychology

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

Strategies for feature extraction from structural brain imaging in lesion-deficit modelling

High‐dimensional modelling of post‐stroke deficits from structural brain imaging is highly relevant to basic cognitive neuroscience and bears the potential to be translationally used to guide individual rehabilitation measures. One strategy to optimise model performance is well‐informed feature selection and representation. However, different feature representation strategies were so far used, and it is not known what strategy is best for modelling purposes. The present study compared the three common main strategies: voxel‐wise representation, lesion‐anatomical componential feature reduction and region‐wise atlas‐based feature representation. We used multivariate, machine‐learning‐based lesion‐deficit models to predict post‐stroke deficits based on structural lesion data. Support vector regression was tuned by nested cross‐validation techniques and tested on held‐out validation data to estimate model performance. While we consistently found the numerically best models for lower‐dimensional, featurised data and almost always for principal components extracted from lesion maps, our results indicate only minor, non‐significant differences between different feature representation styles. Hence, our findings demonstrate the general suitability of all three commonly applied feature representations in lesion‐deficit modelling. Likewise, model performance between qualitatively different popular brain atlases was not significantly different. Our findings also highlight potential minor benefits in individual fine‐tuning of feature representations and the challenge posed by the high, multifaceted complexity of lesion data, where lesion‐anatomical and functional criteria might suggest opposing solutions to feature reduction.

A data-driven approach to post-stroke aphasia classification and lesion-based prediction

Aphasia is an acquired impairment in the production or comprehension of language, typically caused by left hemisphere stroke. The subtyping framework used in clinical aphasiology today is based on the Wernicke-Lichtheim model of aphasia formulated in the late 19th century, which emphasizes the distinction between language production and comprehension. The current study used a data-driven approach that combined modern statistical, machine learning, and neuroimaging tools to examine behavioural deficit profiles and their lesion correlates and predictors in a large cohort of individuals with post-stroke aphasia. First, individuals with aphasia were clustered based on their behavioural deficit profiles using community detection analysis (CDA) and these clusters were compared with the traditional aphasia subtypes. Random forest classifiers were built to evaluate how well individual lesion profiles predict cluster membership. The results of the CDA analyses did not align with the traditional model of aphasia in either behavioural or neuroanatomical patterns. Instead, the results suggested that the primary distinction in aphasia (after severity) is between phonological and semantic processing rather than between production and comprehension. Further, lesion-based classification reached 75% accuracy for the CDA-based categories and only 60% for categories based on the traditional fluent/non-fluent aphasia distinction. The results of this study provide a data-driven basis for a new approach to classification of post-stroke aphasia subtypes in both research and clinical settings.

Prediction of Clinical Outcomes in Acute Ischaemic Stroke Patients: A Comparative Study

Background: Clinical stroke rehabilitation decision making relies on multi-modal data, including imaging and other clinical assessments. However, most previously described methods for predicting long-term stroke outcomes do not make use of the full multi-modal data available. The aim of this work was to develop and evaluate the benefit of nested regression models that utilise clinical assessments as well as image-based biomarkers to model 30-day NIHSS.

Method: 221 subjects were pooled from two prospective trials with follow-up MRI or CT scans, and NIHSS assessed at baseline, as well as 48-hours and 30 days after symptom onset. Three prediction models for 30-day NIHSS were developed using a support vector regression model: one clinical model based on modifiable and non-modifiable risk factors (M_CLINICAL) and two nested regression models that aggregate clinical and image-based features that differed with respect to the method used for selection of important brain regions for the modelling task. The first model used the widely accepted RreliefF (M_RELIEF) machine learning method for this purpose, while the second model employed a lesion-symptom mapping technique (M_LSM) often used in neuroscience to investigate structure-function relationships and identify eloquent regions in the brain.

Results: The two nested models achieved a similar performance while considerably outperforming the clinical model. However, M_RELIEF required fewer brain regions and achieved a lower mean absolute error than M_LSM while being less computationally expensive.

Conclusion: Aggregating clinical and imaging information leads to considerably better outcome prediction models. While lesion-symptom mapping is a useful tool to investigate structure-function relationships of the brain, it does not lead to better outcome predictions compared to a simple data-driven feature selection approach, which is less computationally expensive and easier to implement.

Machine learning-based multimodal prediction of language outcomes in chronic aphasia

Recent studies have combined multiple neuroimaging modalities to gain further understanding of the neurobiological substrates of aphasia. Following this line of work, the current study uses machine learning approaches to predict aphasia severity and specific language measures based on a multimodal neuroimaging dataset. A total of 116 individuals with chronic left-hemisphere stroke were included in the study. Neuroimaging data included task-based functional magnetic resonance imaging (fMRI), diffusion-based fractional anisotropy (FA)-values, cerebral blood flow (CBF), and lesion-load data. The Western Aphasia Battery was used to measure aphasia severity and specific language functions. As a primary analysis, we constructed support vector regression (SVR) models predicting language measures based on (i) each neuroimaging modality separately, (ii) lesion volume alone, and (iii) a combination of all modalities. Prediction accuracy across models was subsequently statistically compared. Prediction accuracy across modalities and language measures varied substantially (predicted vs. empirical correlation range: r = .00-.67). The multimodal prediction model yielded the most accurate prediction in all cases (r = .53-.67). Statistical superiority in favor of the multimodal model was achieved in 28/30 model comparisons (p-value range: <.001-.046). Our results indicate that different neuroimaging modalities carry complementary information that can be integrated to more accurately depict how brain damage and remaining functionality of intact brain tissue translate into language function in aphasia.

Thalamic Nuclei and Thalamocortical Pathways After Left Hemispheric Stroke and Their Association with Picture Naming

Background: Previous studies utilized lesion-centric approaches to study the role of the thalamus in language. In this study, we tested the hypotheses that non-lesioned dorsomedial and ventral anterior nuclei (DMVAC) and pulvinar lateral posterior nuclei complexes (PLC) of the thalamus and their projections to the left hemisphere show secondary effects of the strokes, and that their microstructural integrity is closely related to language-related functions. Methods: Subjects with language impairments after a left-hemispheric cortical and/or subcortical, early stroke (n = 31, ≤6 months) or late stroke (n = 30, ≥12 months) sparing thalamus underwent the Boston Naming Test (BNT) and diffusion tensor imaging (DTI). The tissue integrity of DMVAC, PLC, and their cortical projections was quantified with DTI. The right-left asymmetry profiles of these structures were evaluated in relation to the time since stroke. The association between microstructural integrity and BNT score was investigated in relation to stroke chronicity with partial correlation analyses adjusted for confounds. Results: In both early stroke and late stroke groups, left-sided tracts showed significantly higher mean diffusivities (MDs), which were likely due to Wallerian degeneration. Higher MD values of the cortical projections from the left PLC (r = -0.5, p = 0.005) and DMVAC (r = -0.53, p = 0.002) were correlated with lower BNT score in the late stroke but not early stroke group. Conclusion: Nonlesioned thalamic nuclei and thalamocortical pathways show rightward lateralization of the microstructural integrity after a left hemispheric stroke, and this pattern is associated with poorer naming.

Structural brain disconnectivity mapping of post-stroke fatigue

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We tested for associations between post stroke fatigue (PSF) and both lesion characteristics and brain structural disconnectome in 84 S patients.

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Results provided no evidence supporting a simple association between PSF severity and lesion characteristics or disconnectivity.

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PSF was strongly correlated with depression.

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Further studies including patients with more severe symptoms are needed to generalize the findings across a wider clinical spectrum.

Stroke patients commonly suffer from post stroke fatigue (PSF). Despite a general consensus that brain perturbations constitute a precipitating event in the multifactorial etiology of PSF, the specific predictive value of conventional lesion characteristics such as size and localization remains unclear. The current study represents a novel approach to assess the neural correlates of PSF in chronic stroke patients. While previous research has focused primarily on lesion location or size, with mixed or inconclusive results, we targeted the extended structural network implicated by the lesion, and evaluated the added explanatory value of a structural disconnectivity approach with regards to the brain correlates of PSF. To this end, we estimated individual structural brain disconnectome maps in 84 S survivors in the chronic phase (≥3 months post stroke) using information about lesion location and normative white matter pathways obtained from 170 healthy individuals. PSF was measured by the Fatigue Severity Scale (FSS). Voxel wise analyses using non-parametric permutation-based inference were conducted on disconnectome maps to estimate regional effects of disconnectivity. Associations between PSF and global disconnectivity and clinical lesion characteristics were tested by linear models, and we estimated Bayes factor to quantify the evidence for the null and alternative hypotheses, respectively. The results revealed no significant associations between PSF and disconnectome measures or lesion characteristics, with moderate evidence in favor of the null hypothesis. These results suggest that symptoms of post-stroke fatigue among chronic stroke patients are not simply explained by lesion characteristics or the extent and distribution of structural brain disconnectome, and are discussed in light of methodological considerations.

The neuroanatomical-functional paradox in spinal cord injury

Although lesion size is widely considered to be the most reliable predictor of outcome after CNS injury, lesions of comparable size can produce vastly different magnitudes of functional impairment and subsequent recovery. This neuroanatomical-functional paradox is likely to contribute to the many failed attempts to independently replicate findings from animal models of neurotrauma. In humans, the analogous clinical-radiological paradox could explain why individuals with similar injuries can respond differently to rehabilitation. We describe the neuroanatomical-functional paradox in the context of traumatic spinal cord injury (SCI) and discuss the underlying mechanisms of the paradox, including the concepts of lesion-affected and recovery-related networks. We also consider the various secondary complications that further limit the accuracy of outcome prediction in SCI and provide suggestions for how to increase the predictive, translational value of preclinical SCI models.

An empirical comparison of univariate versus multivariate methods for the analysis of brain-behavior mapping

Lesion symptom mapping (LSM) tools are used on brain injury data to identify the neural structures critical for a given behavior or symptom. Univariate lesion symptom mapping (ULSM) methods provide statistical comparisons of behavioral test scores in patients with and without a lesion on a voxel by voxel basis. More recently, multivariate lesion symptom mapping (MLSM) methods have been developed that consider the effects of all lesioned voxels in one model simultaneously. In the current study, we provide a much-needed systematic comparison of several ULSM and MLSM methods, using both synthetic and real data to identify the potential strengths and weaknesses of both approaches. We tested the spatial precision of each LSM method for both single and dual (network type) anatomical target simulations across anatomical target location, sample size, noise level, and lesion smoothing. Additionally, we performed false positive simulations to identify the characteristics associated with each method's spurious findings. Simulations showed no clear superiority of either ULSM or MLSM methods overall, but rather highlighted specific advantages of different methods. No single method produced a thresholded LSM map that exclusively delineated brain regions associated with the target behavior. Thus, different LSM methods are indicated, depending on the particular study design, specific hypotheses, and sample size. Overall, we recommend the use of both ULSM and MLSM methods in tandem to enhance confidence in the results: Brain foci identified as significant across both types of methods are unlikely to be spurious and can be confidently reported as robust results.

The role of microstructural integrity of major language pathways in narrative speech in the first year after stroke

BACKGROUND AND PURPOSE

Left hemisphere stroke often results in a variety of language deficits due to varying patterns of damage to language networks. The Cookie Theft picture description task, a classic, quick bedside assessment, has been shown to quantify narrative speech reliably. In this study, we utilized diffusion tensor imaging (DTI) to assess language network white matter tract correlates of lexical-semantic and syntactic impairments longitudinally.

METHODS

Twenty-eight patients with mild to severe language impairments after left hemispheric lobar and/or subcortical ischemic stroke underwent the Cookie Theft picture description test and DTI up to three different time points: within the first three months, six months and twelve months after stroke. Dorsal and ventral stream language pathways were segmented to obtain DTI integrity metrics of both hemispheres. Multivariable regression models and partial correlation analyses adjusted for age, education, and lesion load were conducted to evaluate the temporal DTI profile of the white matter microstructural integrity of the language tracts as neural correlates of narrative speech within the first year after stroke.

RESULTS

Among all the major language white matter pathways, the integrity of the left arcuate (AF), inferior fronto-occipital, and inferior longitudinal fasciculi (ILF) were related to picture description performance. After FDR correction, left ILF fractional anisotropy correlated with syntactic cohesiveness (r=0.85,p=0.00087) within the first three months after stroke, whereas at one year post-stroke, the strongest correlations were found between lexical-semantic performance and left AF radial diffusivity (r = -0.71, p = 0.00065).

CONCLUSION

Our study provides a temporal profile of associations between the integrity of the main language pathways and lexical semantics and syntactic impairments in left hemispheric strokes.

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

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

Clinical and neuroimaging factors associated with aphasia severity in stroke patients: diffusion tensor imaging study

This study investigated factors associated with aphasia severity at both 2 weeks and 3 months after stroke using demographic and clinical variables, brain diffusion tensor imaging (DTI) parameters, and lesion volume measurements. Patients with left hemisphere stroke were assessed at 2 weeks (n = 68) and at 3 months (n = 20) after stroke. Demographic, clinical, and neuroimaging data were collected; language functions were assessed using the Western Aphasia Battery. For neuroimaging, DTI parameters, including the laterality index (LI) of fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity, mean diffusivity and fibre density (FD) of the arcuate fasciculus (AF), and lesion volume, were measured. Lesion volume, cortical involvement, and the National Institutes of Health Stroke Scale score significantly predicted aphasia severity at 2 weeks after stroke, whereas the aphasia quotient and presence of depression during the early subacute stage were significant predictors at 3 months after stroke. According to Pearson correlation, LI-AD and LI-FD were significantly correlated with the aphasia quotient 2 weeks after ischaemic stroke, and the LI-FA was significantly correlated with the aphasia quotient 2 weeks after haemorrhagic stroke, suggesting that the extent and mechanism of AF injuries differ between ischaemic and haemorrhagic strokes. These differences may contribute to aphasia severity.

The utility of lesion classification in predicting language and treatment outcomes in chronic stroke-induced aphasia

Stroke recovery models can improve prognostication of therapy response in patients with chronic aphasia, yet quantifying the effect of lesion on recovery is challenging. This study aimed to evaluate the utility of lesion classification via gray matter (GM)-only versus combined GM plus white matter (WM) metrics and to determine structural measures associated with aphasia severity, naming skills, and treatment outcomes. Thirty-four patients with chronic aphasia due to left hemisphere infarct completed T1-weighted and DTI scans and language assessments prior to receiving a 12-week naming treatment. GM metrics included the amount of spared tissue within five cortical masks. WM integrity was indexed by spared tissue and fractional anisotropy (FA) from four homologous left and right association tracts. Clustering of GM-only and GM + WM metrics via k-medoids yielded four patient clusters that captured two lesion characteristics, size and location. Linear regression models revealed that both GM-only and GM + WM clustering predicted baseline aphasia severity and naming skills, but only GM + WM clustering predicted treatment outcomes. Spearman correlations revealed that without controlling for lesion volume, the majority of left hemisphere metrics were related to language measures. However, adjusting for lesion volume, no relationships with aphasia severity remained significant. FA from two ventral left WM tracts was related to naming and treatment success, independent of lesion size. In sum, lesion volume and GM metrics are sufficient predictors of overall aphasia severity in patients with chronic stroke, whereas diffusion metrics reflecting WM tract integrity may add predictive power to language recovery outcomes after rehabilitation.

The Effects of a TMS Double Perturbation to a Cortical Network

Transcranial magnetic stimulation (TMS) is often used to understand the function of individual brain regions, but this ignores the fact that TMS may affect network-level rather than nodal-level processes. We examine the effects of a double perturbation to two frontoparietal network nodes, compared with the effects of single lesions to either node. We hypothesized that Bayesian evidence for the absence of effects that build upon one another indicates that a single perturbation is consequential to network-level processes. Twenty-three humans performed pro-saccades (look toward) and anti-saccades (look away) after receiving continuous theta-burst stimulation (cTBS) to right frontal eye fields (FEFs), dorsolateral prefrontal cortex (DLPFC), or somatosensory cortex (S1; the control region). On a subset of trials, a TMS pulse was applied to right posterior parietal cortex (PPC). FEF, DLPFC, and PPC are important frontoparietal network nodes for generating anti-saccades. Bayesian t tests were used to test hypotheses for enhanced double perturbation effects (cTBS plus TMS pulse) on saccade behaviors, against the alternative hypothesis that double perturbation effects to a network are not greater than single perturbation effects. In one case, we observed strong evidence [Bayes factor (BF10) = 325] that PPC TMS following DLPFC cTBS enhanced impairments in ipsilateral anti-saccade amplitudes over DLPFC cTBS alone, and not over the effect of the PPC pulse alone (BF10 = 0.75), suggesting that double perturbation effects do not augment one another. Rather, this suggests that computations are distributed across the network, and in some cases there can be compensation for cTBS perturbations.

Practical Application of Networks in Neurosurgery: Combined 3-Dimensional Printing, Neuronavigation, and Preoperative Surgical Planning

BACKGROUND

A plethora of cutting-edge neuroimaging analyses have been developed and published, yet they have not hitherto been realized as improvements in neurosurgical outcomes. In this paper we propose a novel interface between neuroimaging and neurosurgery for aiding translational research. Our objective is to create a method for applying advanced neuroimaging and network analysis findings to neurosurgery and illustrate its application through the presentation of 2 detailed case vignettes.

METHODS

This interface comprises a combination of network visualization, 3-dimensional printing, and ex-vivo neuronavigation to enable preoperative planning according to functional neuroanatomy. Clinical cases were selected from a prospective cohort study.

RESULTS

The first case vignette describes a low-grade glioma with potential language and executive function network involvement that underwent a successful complete resection of the lesion with preservation of network features. The second case describes a low-grade glioma in an apparently noneloquent location that underwent a subtotal resection but demonstrated unexpected and significant impairment in executive function postoperatively that subsequently abated during follow-up. In both examples the neuroimaging and network data highlight the complexity of the surrounding functional neuroanatomy at the individual level, beyond that which can be perceived on standard structural sequences.

CONCLUSIONS

The described interface has widespread applications for translational research including preoperative planning, neurosurgical training, and detailed patient counseling. A protocol for assessing its effectiveness and safety is proposed. Finally, recommendations for effective translation of findings from neuroimaging to neurosurgery are discussed, with the aim of making clinically meaningful improvements to neurosurgical practice.

Educational fMRI: From the Lab to the Classroom

Functional MRI (fMRI) findings hold many potential applications for education, and yet, the translation of fMRI findings to education has not flowed. Here, we address the types of fMRI that could better support applications of neuroscience to the classroom. This 'educational fMRI' comprises eight main challenges: (1) collecting artifact-free fMRI data in school-aged participants and in vulnerable young populations, (2) investigating heterogenous cohorts with wide variability in learning abilities and disabilities, (3) studying the brain under natural and ecological conditions, given that many practical topics of interest for education can be addressed only in ecological contexts, (4) depicting complex age-dependent associations of brain and behaviour with multi-modal imaging, (5) assessing changes in brain function related to developmental trajectories and instructional intervention with longitudinal designs, (6) providing system-level mechanistic explanations of brain function, so that useful individualized predictions about learning can be generated, (7) reporting negative findings, so that resources are not wasted on developing ineffective interventions, and (8) sharing data and creating large-scale longitudinal data repositories to ensure transparency and reproducibility of fMRI findings for education. These issues are of paramount importance to the development of optimal fMRI practices for educational applications.

A Taxonomy of Brain-Behavior Relationships After Stroke

Purpose Understanding the brain basis of language and cognitive outcomes is a major goal of aphasia research. Prior studies have not often considered the many ways that brain features can relate to behavioral outcomes or the mechanisms underlying these relationships. The purpose of this review article is to provide a new framework for understanding the ways that brain features may relate to language and cognitive outcomes from stroke. Method Brain-behavior relationships that may be important for aphasia outcomes are organized into a taxonomy, including features of the lesion and features of brain tissue spared by the lesion. Features of spared brain tissue are categorized into those that change after stroke and those that do not. Features that change are further subdivided, and multiple mechanisms of brain change after stroke are discussed. Results Features of the stroke, including size, location, and white matter damage, relate to many behavioral outcomes and likely account for most of the variance in outcomes. Features of the spared brain tissue that are unchanged by stroke, such as prior ischemic disease in the white matter, contribute to outcomes. Many different neurobiological and behavioral mechanisms may drive changes in the brain after stroke in association with behavioral recovery. Changes primarily driven by neurobiology are likely to occur in brain regions with a systematic relationship to the stroke distribution. Changes primarily driven by behavior are likely to occur in brain networks related to the behavior driving the change. Conclusions Organizing the various hypothesized brain-behavior relationships according to this framework and considering the mechanisms that drive these relationships may help investigators develop specific experimental designs and more complete statistical models to explain language and cognitive abilities after stroke. Eight main recommendations for future research are provided. Presentation Video https://doi.org/10.23641/asha.10257578.

Considerations of power and sample size in rehabilitation research

With the current emphasis on power and reproducibility, pressures are rising to increase sample sizes in rehabilitation research in order to reflect more accurate effect estimation and generalizable results. The conventional way of increasing power by enrolling more participants is less feasible in some fields of research. In particular, rehabilitation research faces considerable challenges in achieving this goal. We describe the specific challenges to increasing power by recruiting large sample sizes and obtaining large effects in rehabilitation research. Specifically, we discuss how variability within clinical populations, lack of common standards for selecting appropriate control groups; potentially reduced reliability of measurements of brain function in individuals recovering from a brain injury; biases involved in a priori effect size estimation, and higher budgetary and staffing requirements can influence considerations of sample and effect size in rehabilitation. We also describe solutions to these challenges, such as increased sampling per participant, improving experimental control, appropriate analyses, transparent result reporting and using innovative ways of harnessing the inherent variability of clinical populations. These solutions can improve statistical power and produce reliable and valid results even in the face of limited availability of large samples.

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

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

A lesion and connectivity-based hierarchical model of chronic aphasia recovery dissociates patients and healthy controls

Traditional models of left hemisphere stroke recovery propose that reactivation of remaining ipsilesional tissue is optimal for language processing whereas reliance on contralesional right hemisphere homologues is less beneficial or possibly maladaptive in the chronic recovery stage. However, neuroimaging evidence for this proposal is mixed. This study aimed to elucidate patterns of effective connectivity in patients with chronic aphasia in light of healthy control connectivity patterns and in relation to damaged tissue within left hemisphere regions of interest and according to performance on a semantic decision task. Using fMRI and dynamic causal modeling, biologically-plausible models within four model families were created to correspond to potential neural recovery patterns, including Family A: Left-lateralized connectivity (i.e., no/minimal damage), Family B: Bilateral anterior-weighted connectivity (i.e., posterior damage), Family C: Bilateral posterior-weighted connectivity (i.e., anterior damage) and Family D: Right-lateralized connectivity (i.e., extensive damage). Controls exhibited a strong preference for left-lateralized network models (Family A) whereas patients demonstrated a split preference for Families A and C. At the level of connections, controls exhibited stronger left intrahemispheric task-modulated connections than did patients. Within the patient group, damage to left superior frontal structures resulted in greater right intrahemispheric connectivity whereas damage to left ventral structures resulted in heightened modulation of left frontal regions. Lesion metrics best predicted accuracy on the fMRI task and aphasia severity whereas left intrahemispheric connectivity predicted fMRI task reaction times. These results are discussed within the context of the hierarchical recovery model of chronic aphasia.

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The semantic network in neurologically-intact, healthy controls was characterized by left-lateralized connectivity.

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Patient connectivity was split between left-lateralized and bilateral, posterior-weighted (i.e., anterior damage) models.

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Controls solely recruited LITG-driven connections whereas patients recruited a distributed network of connections.

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Within the patient group, intra- and inter-hemispheric connections were related to lesion site and/or size. Lesion size predicted aphasia severity and fMRI task accuracy, and effective connectivity predicted task reaction times.

Brain activity patterns of phonemic representations are atypical in beginning readers with family risk for dyslexia

There is an ongoing debate whether phonological deficits in dyslexics should be attributed to (a) less specified representations of speech sounds, like suggested by studies in young children with a familial risk for dyslexia, or (b) to an impaired access to these phonemic representations, as suggested by studies in adults with dyslexia. These conflicting findings are rooted in between study differences in sample characteristics and/or testing techniques. The current study uses the same multivariate functional MRI (fMRI) approach as previously used in adults with dyslexia to investigate phonemic representations in 30 beginning readers with a familial risk and 24 beginning readers without a familial risk of dyslexia, of whom 20 were later retrospectively classified as dyslexic. Based on fMRI response patterns evoked by listening to different utterances of /bA/ and /dA/ sounds, multivoxel analyses indicate that the underlying activation patterns of the two phonemes were distinct in children with a low family risk but not in children with high family risk. However, no group differences were observed between children that were later classified as typical versus dyslexic readers, regardless of their family risk status, indicating that poor phonemic representations constitute a risk for dyslexia but are not sufficient to result in reading problems. We hypothesize that poor phonemic representations are trait (family risk) and not state (dyslexia) dependent, and that representational deficits only lead to reading difficulties when they are present in conjunction with other neuroanatomical or-functional deficits.

Diffusion Tensor Imaging Biomarkers to Predict Motor Outcomes in Stroke: A Narrative Review

Stroke is a leading cause of disability worldwide. Motor impairments occur in most of the patients with stroke in the acute phase and contribute substantially to disability. Diffusion tensor imaging (DTI) biomarkers such as fractional anisotropy (FA) measured at an early phase after stroke have emerged as potential predictors of motor recovery. In this narrative review, we: (1) review key concepts of diffusion MRI (dMRI); (2) present an overview of state-of-art methodological aspects of data collection, analysis and reporting; and (3) critically review challenges of DTI in stroke as well as results of studies that investigated the correlation between DTI metrics within the corticospinal tract and motor outcomes at different stages after stroke. We reviewed studies published between January, 2008 and December, 2018, that reported correlations between DTI metrics collected within the first 24 h (hyperacute), 2-7 days (acute), and >7-90 days (early subacute) after stroke. Nineteen studies were included. Our review shows that there is no consensus about gold standards for DTI data collection or processing. We found great methodological differences across studies that evaluated DTI metrics within the corticospinal tract. Despite heterogeneity in stroke lesions and analysis approaches, the majority of studies reported significant correlations between DTI biomarkers and motor impairments. It remains to be determined whether DTI results could enhance the predictive value of motor disability models based on clinical and neurophysiological variables.

Multivariate prediction of functional outcome using lesion topography characterized by acute diffusion tensor imaging

The relationship between stroke topography and functional outcome has largely been studied with binary manual lesion segmentations. However, stroke topography may be better characterized by continuous variables capable of reflecting the severity of ischemia, which may be more pertinent for long-term outcome. Diffusion Tensor Imaging (DTI) constitutes a powerful means of quantifying the degree of acute ischemia and its potential relation to functional outcome. Our aim was to investigate whether using more clinically pertinent imaging parameters with powerful machine learning techniques could improve prediction models and thus provide valuable insight on critical brain areas important for long-term outcome. Eighty-seven thrombolyzed patients underwent a DTI sequence at 24 h post-stroke. Functional outcome was evaluated at 3 months post-stroke with the modified Rankin Score and was dichotomized into good (mRS ≤ 2) and poor (mRS > 2) outcome. We used support vector machines (SVM) to classify patients into good vs. poor outcome and evaluate the accuracy of different models built with fractional anisotropy, mean diffusivity, axial diffusivity, radial diffusivity asymmetry maps, and lesion segmentations in combination with lesion volume, age, recanalization status, and thrombectomy treatment. SVM classifiers built with axial diffusivity maps yielded the best accuracy of all imaging parameters (median [IQR] accuracy = 82.8 [79.3-86.2]%), compared to that of lesion segmentations (76.7 [73.3-82.8]%) when predicting 3-month functional outcome. The analysis revealed a strong contribution of clinical variables, notably - in descending order - lesion volume, thrombectomy treatment, and recanalization status, in addition to the deep white matter at the crossroads of major white matter tracts, represented by brain regions where model weights were highest. Axial diffusivity is a more appropriate imaging marker to characterize stroke topography for predicting long-term outcome than binary lesion segmentations.

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

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