Abnormal perilesional BOLD signal is not correlated with stroke patients' behavior

Cerebrovascular Reactivity Has Negligible Contribution to Hemodynamic Lag After Stroke: Implications for Functional Magnetic Resonance Imaging Studies

BACKGROUND

Functional magnetic resonance imaging (fMRI) is ubiquitously used to study poststroke recovery. However, the fMRI-derived hemodynamic responses are vulnerable to vascular insult which can result in reduced magnitude and temporal delays (lag) in the hemodynamic response function (HRF). The cause of HRF lag remains controversial, and a better understanding of it is required to ensure accurate interpretation of poststroke fMRI studies. In this longitudinal study, we investigate the relationship between hemodynamic lag and cerebrovascular reactivity (CVR) following stroke.

METHODS

Voxel-wise lag maps were calculated relative to a mean gray matter reference signal for 27 healthy controls and 59 patients with stroke across 2 time points (≈2 weeks and ≈4 months poststroke) and 2 conditions: resting-state and breath-holding. The breath-holding condition was additionally used to calculate CVR in response to hypercapnia. HRF lag was computed for both conditions across tissue compartments: lesion, perilesional tissue, unaffected tissue of the lesioned hemisphere, and their homolog regions in the unaffected hemisphere. CVR and lag maps were correlated. Group, condition, and time effects were assessed using ANOVA analyses.

RESULTS

Compared with the average gray matter signal, a relative hemodynamic lead was observed in the primary sensorimotor cortices in resting-state and bilateral inferior parietal cortices in the breath-holding condition. Whole-brain hemodynamic lag was significantly correlated across conditions irrespective of group, with regional differences across conditions suggestive of a neural network pattern. Patients showed relative lag in the lesioned hemisphere which significantly reduced over time. Breath-hold derived lag and CVR had no significant voxel-wise correlation in controls, or patients within the lesioned hemisphere or the homologous regions of the lesion and perilesional tissue in the right hemisphere (mean r<0.1).

CONCLUSIONS

The contribution of altered CVR to HRF lag was negligible. We suggest that HRF lag is largely independent of CVR, and could partly reflect intrinsic neural network dynamics among other factors.

Combining brain perturbation and neuroimaging in non-human primates

Brain perturbation studies allow detailed causal inferences of behavioral and neural processes. Because the combination of brain perturbation methods and neural measurement techniques is inherently challenging, research in humans has predominantly focused on non-invasive, indirect brain perturbations, or neurological lesion studies. Non-human primates have been indispensable as a neurobiological system that is highly similar to humans while simultaneously being more experimentally tractable, allowing visualization of the functional and structural impact of systematic brain perturbation. This review considers the state of the art in non-human primate brain perturbation with a focus on approaches that can be combined with neuroimaging. We consider both non-reversible (lesions) and reversible or temporary perturbations such as electrical, pharmacological, optical, optogenetic, chemogenetic, pathway-selective, and ultrasound based interference methods. Method-specific considerations from the research and development community are offered to facilitate research in this field and support further innovations. We conclude by identifying novel avenues for further research and innovation and by highlighting the clinical translational potential of the methods.

Dynamics of language reorganization after left temporo-parietal and frontal stroke

The loss and recovery of language functions are still incompletely understood. This longitudinal functional MRI study investigated the neural mechanisms underlying language recovery in patients with post-stroke aphasia putting particular emphasis on the impact of lesion site. To identify patterns of language-related activation, an auditory functional MRI sentence comprehension paradigm was administered to patients with circumscribed lesions of either left frontal (n = 17) or temporo-parietal (n = 17) cortex. Patients were examined repeatedly during the acute (≤1 week, t1), subacute (1-2 weeks, t2) and chronic phase (>6 months, t3) post-stroke; healthy age-matched control subjects (n = 17) were tested once. The separation into two patient groups with circumscribed lesions allowed for a direct comparison of the contributions of distinct lesion-dependent network components to language reorganization between both groups. We hypothesized that activation of left hemisphere spared and perilesional cortex as well as lesion-homologue cortex in the right hemisphere varies between patient groups and across time. In addition, we expected that domain-general networks serving cognitive control independently contribute to language recovery. First, we found a global network disturbance in the acute phase that is characterized by reduced functional MRI language activation including areas distant to the lesion (i.e. diaschisis) and subsequent subacute network reactivation (i.e. resolution of diaschisis). These phenomena were driven by temporo-parietal lesions. Second, we identified a lesion-independent sequential activation pattern with increased activity of perilesional cortex and bilateral domain-general networks in the subacute phase followed by reorganization of left temporal language areas in the chronic phase. Third, we observed involvement of lesion-homologue cortex only in patients with frontal but not temporo-parietal lesions. Fourth, irrespective of lesion location, language reorganization predominantly occurred in pre-existing networks showing comparable activation in healthy controls. Finally, we detected different relationships of performance and activation in language and domain-general networks demonstrating the functional relevance for language recovery. Our findings highlight that the dynamics of language reorganization clearly depend on lesion location and hence open new perspectives for neurobiologically motivated strategies of language rehabilitation, such as individually-tailored targeted application of neuro-stimulation.

Is the Blood Oxygenation Level-Dependent fMRI Response to Motor Tasks Altered in Children After Neonatal Stroke?

Functional MRI is increasingly being used in the assessment of brain activation and connectivity following stroke. Many of these studies rely on the Blood Oxygenation Level Dependent (BOLD) contrast. However, the stability, as well as the accuracy of the BOLD response to motor task in the ipsilesional hemisphere, remains ambiguous. In this work, the BOLD signal acquired from both healthy and affected hemispheres was analyzed in 7-year-old children who sustained a Neonatal Arterial Ischemic Stroke (NAIS). Accordingly, a repetitive motor task of the contralesional and the ipsilesional hands was performed by 33 patients with unilateral lesions. These patients were divided into two groups: those without cerebral palsy (NAIS), and those with cerebral palsy (CP). The BOLD signal time course was obtained from distinctly defined regions of interest (ROIs) extracted from the functional activation maps of 30 healthy controls with similar age and demographic characteristics as the patients. An ROI covering both the primary motor cortex (M1) and the primary somatosensory cortex (S1) was also tested. Compared with controls, NAIS patients without CP had similar BOLD amplitude variation for both the contralesional and the ipsilesional hand movements. However, in the case of NAIS patients with CP, a significant difference in the averaged BOLD amplitude was found between the healthy and affected hemisphere. In both cases, no progressive attenuation of the BOLD signal amplitude was observed throughout the task epochs. Besides, results also showed a correlation between the BOLD signal percentage variation of the lesioned hemisphere and the dexterity level. These findings suggest that for patients who sustained a NAIS with no extensive permanent motor impairment, BOLD signal-based data analysis can be a valuable tool for the evaluation of functional brain networks.

Treatment-related changes in neural activation vary according to treatment response and extent of spared tissue in patients with chronic aphasia

Neuroimaging studies of aphasia recovery have linked treatment-related improvements in language processing to changes in functional brain activation in left hemisphere language regions and their right hemisphere homologues. Although there is some consensus that better behavioral outcomes are achieved when activation is restored to the left hemisphere, the circumstances that dictate how and why regions in both hemispheres respond to naming therapy are still unclear. In this study, an fMRI picture-naming task was used to examine 16 regions of interest in 26 patients with chronic aphasia before and after 12 weeks of semantic naming treatment. Ten control patients who did not receive treatment and 17 healthy controls were also scanned. Naming therapy resulted in a significant increase in cortical activation, an effect that was largely driven by patients who responded most favorably to treatment, as patients who responded less favorably (as well as those who did not receive treatment) had little change in activation over time. Relative to healthy controls, patients had higher pre-treatment activation in the bilateral inferior frontal gyri (IFG) and lower activation in the bilateral angular gyri; after treatment, they had higher activation in bilateral IFG, as well as in the right middle frontal gyrus. These results suggest that the predominant effect of beneficial naming treatment was an upregulation of traditional language areas and their right hemisphere homologues and, in particular, regions associated with phonological and semantic/executive semantic processing, as well as broader domain general functions. Additionally, in some left hemisphere regions, post-treatment changes in activation were greater when there was more damage than when there was less damage, indicating that spared tissue in otherwise highly damaged regions can be modulated by treatment.

Brain networks and their relevance for stroke rehabilitation

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

Reprint of: Mapping human brain lesions and their functional consequences

Neuroscience has a long history of inferring brain function by examining the relationship between brain injury and subsequent behavioral impairments. The primary advantage of this method over correlative methods is that it can tell us if a certain brain region is necessary for a given cognitive function. In addition, lesion-based analyses provide unique insights into clinical deficits. In the last decade, statistical voxel-based lesion behavior mapping (VLBM) emerged as a powerful method for understanding the architecture of the human brain. This review illustrates how VLBM improves our knowledge of functional brain architecture, as well as how it is inherently limited by its mass-univariate approach. A wide array of recently developed methods appear to supplement traditional VLBM. This paper provides an overview of these new methods, including the use of specialized imaging modalities, the combination of structural imaging with normative connectome data, as well as multivariate analyses of structural imaging data. We see these new methods as complementing rather than replacing traditional VLBM, providing synergistic tools to answer related questions. Finally, we discuss the potential for these methods to become established in cognitive neuroscience and in clinical applications.

Contributions of Neuroimaging to Understanding Language Deficits in Acute Stroke

Advances in structural and functional imaging techniques have provided new insights into our understanding of brain and language relationships. In this article, we review the various structural and functional imaging methods currently used to study language deficits in acute stroke. We also discuss the advantages and the limitations of each imaging modality and the applications of each modality in the clinical and research settings in the study of language deficits.

Mapping human brain lesions and their functional consequences

Neuroscience has a long history of inferring brain function by examining the relationship between brain injury and subsequent behavioral impairments. The primary advantage of this method over correlative methods is that it can tell us if a certain brain region is necessary for a given cognitive function. In addition, lesion-based analyses provide unique insights into clinical deficits. In the last decade, statistical voxel-based lesion behavior mapping (VLBM) emerged as a powerful method for understanding the architecture of the human brain. This review illustrates how VLBM improves our knowledge of functional brain architecture, as well as how it is inherently limited by its mass-univariate approach. A wide array of recently developed methods appear to supplement traditional VLBM. This paper provides an overview of these new methods, including the use of specialized imaging modalities, the combination of structural imaging with normative connectome data, as well as multivariate analyses of structural imaging data. We see these new methods as complementing rather than replacing traditional VLBM, providing synergistic tools to answer related questions. Finally, we discuss the potential for these methods to become established in cognitive neuroscience and in clinical applications.

The effects of hemodynamic lag on functional connectivity and behavior after stroke

Stroke disrupts the brain's vascular supply, not only within but also outside areas of infarction. We investigated temporal delays (lag) in resting state functional magnetic resonance imaging signals in 130 stroke patients scanned two weeks, three months and 12 months post stroke onset. Thirty controls were scanned twice at an interval of three months. Hemodynamic lag was determined using cross-correlation with the global gray matter signal. Behavioral performance in multiple domains was assessed in all patients. Regional cerebral blood flow and carotid patency were assessed in subsets of the cohort using arterial spin labeling and carotid Doppler ultrasonography. Significant hemodynamic lag was observed in 30% of stroke patients sub-acutely. Approximately 10% of patients showed lag at one-year post-stroke. Hemodynamic lag corresponded to gross aberrancy in functional connectivity measures, performance deficits in multiple domains and local and global perfusion deficits. Correcting for lag partially normalized abnormalities in measured functional connectivity. Yet post-stroke FC-behavior relationships in the motor and attention systems persisted even after hemodynamic delays were corrected. Resting state fMRI can reliably identify areas of hemodynamic delay following stroke. Our data reveal that hemodynamic delay is common sub-acutely, alters functional connectivity, and may be of clinical importance.

Automated segmentation of chronic stroke lesions using LINDA: Lesion identification with neighborhood data analysis

The gold standard for identifying stroke lesions is manual tracing, a method that is known to be observer dependent and time consuming, thus impractical for big data studies. We propose LINDA (Lesion Identification with Neighborhood Data Analysis), an automated segmentation algorithm capable of learning the relationship between existing manual segmentations and a single T1-weighted MRI. A dataset of 60 left hemispheric chronic stroke patients is used to build the method and test it with k-fold and leave-one-out procedures. With respect to manual tracings, predicted lesion maps showed a mean dice overlap of 0.696 ± 0.16, Hausdorff distance of 17.9 ± 9.8 mm, and average displacement of 2.54 ± 1.38 mm. The manual and predicted lesion volumes correlated at r = 0.961. An additional dataset of 45 patients was utilized to test LINDA with independent data, achieving high accuracy rates and confirming its cross-institutional applicability. To investigate the cost of moving from manual tracings to automated segmentation, we performed comparative lesion-to-symptom mapping (LSM) on five behavioral scores. Predicted and manual lesions produced similar neuro-cognitive maps, albeit with some discussed discrepancies. Of note, region-wise LSM was more robust to the prediction error than voxel-wise LSM. Our results show that, while several limitations exist, our current results compete with or exceed the state-of-the-art, producing consistent predictions, very low failure rates, and transferable knowledge between labs. This work also establishes a new viewpoint on evaluating automated methods not only with segmentation accuracy but also with brain-behavior relationships. LINDA is made available online with trained models from over 100 patients.

Improvement in Touch Sensation after Stroke is Associated with Resting Functional Connectivity Changes

Background

Distributed brain networks are known to be involved in facilitating behavioral improvement after stroke, yet few, if any, studies have investigated the relationship between improved touch sensation after stroke and changes in functional brain connectivity.

Objective

We aimed to identify how recovery of somatosensory function in the first 6 months after stroke was associated with functional network changes as measured using resting-state connectivity analysis of functional magnetic resonance imaging (fMRI) data.

Methods

Ten stroke survivors underwent clinical testing and resting-state fMRI scans at 1 and 6 months post-stroke. Ten age-matched healthy participants were included as controls.

Results

Patients demonstrated a wide range of severity of touch impairment 1 month post-stroke, followed by variable improvement over time. In the stroke group, significantly stronger interhemispheric functional correlations between regions of the somatosensory system, and with visual and frontal areas, were found at 6 months than at 1 month post-stroke. Clinical improvement in touch discrimination was associated with stronger correlations at 6 months between contralesional secondary somatosensory cortex (SII) and inferior parietal cortex and middle temporal gyrus, and between contralesional thalamus and cerebellum.

Conclusion

The strength of connectivity between somatosensory regions and distributed brain networks, including vision and attention networks, may change over time in stroke survivors with impaired touch discrimination. Connectivity changes from contralesional SII and contralesional thalamus are associated with improved touch sensation at 6 months post-stroke. These functional connectivity changes could represent future targets for therapy.

Beyond BOLD: optimizing functional imaging in stroke populations

Blood oxygenation level-dependent (BOLD) signal changes are often assumed to directly reflect neural activity changes. Yet the real relationship is indirect, reliant on numerous assumptions, and subject to several sources of noise. Deviations from the core assumptions of BOLD contrast functional magnetic resonance imaging (fMRI), and their implications, have been well characterized in healthy populations, but are frequently neglected in stroke populations. In addition to conspicuous local structural and vascular changes after stroke, there are many less obvious challenges in the imaging of stroke populations. Perilesional ischemic changes, remodeling in regions distant to lesion sites, and diffuse perfusion changes all complicate interpretation of BOLD signal changes in standard fMRI protocols. Most stroke patients are also older than the young populations on which assumptions of neurovascular coupling and the typical analysis pipelines are based. We present a review of the evidence to show that the basic assumption of neurovascular coupling on which BOLD-fMRI relies does not capture the complex changes arising from stroke, both pathological and recovery related. As a result, estimating neural activity using the canonical hemodynamic response function is inappropriate in a number of contexts. We review methods designed to better estimate neural activity in stroke populations. One promising alternative to event-related fMRI is a resting-state-derived functional connectivity approach. Resting-state fMRI is well suited to stroke populations because it makes no performance demands on patients and is capable of revealing network-based pathology beyond the lesion site.