Damage to the shortest structural paths between brain regions is associated with disruptions of resting-state functional connectivity after stroke

Focal brain lesions disrupt resting-state functional connectivity, but the underlying structural mechanisms are unclear. Here, we examined the direct and indirect effects of structural disconnections on resting-state functional connectivity in a large sample of sub-acute stroke patients with heterogeneous brain lesions. We estimated the impact of each patient\u27s lesion on the structural connectome by embedding the lesion in a diffusion MRI streamline tractography atlas constructed using data from healthy individuals. We defined direct disconnections as the loss of direct structural connections between two regions, and indirect disconnections as increases in the shortest structural path length between two regions that lack direct structural connections. We then tested the hypothesis that functional connectivity disruptions would be more severe for disconnected regions than for regions with spared connections. On average, nearly 20% of all region pairs were estimated to be either directly or indirectly disconnected by the lesions in our sample, and extensive disconnections were associated primarily with damage to deep white matter locations. Importantly, both directly and indirectly disconnected region pairs showed more severe functional connectivity disruptions than region pairs with spared direct and indirect connections, respectively, although functional connectivity disruptions tended to be most severe between region pairs that sustained direct structural disconnections. Together, these results emphasize the widespread impacts of focal brain lesions on the structural connectome and show that these impacts are reflected by disruptions of the functional connectome. Further, they indicate that in addition to direct structural disconnections, lesion-induced increases in the structural shortest path lengths between indirectly structurally connected region pairs provide information about the remote functional disruptions caused by focal brain lesions

Lesion correlates of auditory sentence comprehension deficits in post-stroke aphasia

Auditory sentence comprehension requires coordination of multiple levels of processing: auditory-phonological perception, lexical-semantic comprehension, syntactic parsing and discourse construction, as well as executive functions such as verbal working memory (WM) and cognitive control. This study examined the lesion correlates of sentence comprehension deficits in post-stroke aphasia, building on prior work on this topic by using a different and clinically-relevant measure of sentence comprehension (the Token Test) and multivariate (SCCAN) and connectome-based lesion-symptom mapping methods. The key findings were that lesions in the posterior superior temporal lobe and inferior frontal gyrus (pars triangularis) were associated with sentence comprehension deficits, which was observed in both mass univariate and multivariate lesion-symptom mapping. Graph theoretic measures of connectome disruption were not statistically significantly associated with sentence comprehension deficits after accounting for overall lesion size

Prediction of post-stroke motor recovery benefits from measures of sub-acute widespread network damages.

Following a stroke in regions of the brain responsible for motor activity, patients can lose their ability to control parts of their body. Over time, some patients recover almost completely, while others barely recover at all. It is known that lesion volume, initial motor impairment and cortico-spinal tract asymmetry significantly impact motor changes over time. Recent work suggested that disabilities arise not only from focal structural changes but also from widespread alterations in inter-regional connectivity. Models that consider damage to the entire network instead of only local structural alterations lead to a more accurate prediction of patients' recovery. However, assessing white matter connections in stroke patients is challenging and time-consuming. Here, we evaluated in a data set of 37 patients whether we could predict upper extremity motor recovery from brain connectivity measures obtained by using the patient's lesion mask to introduce virtual lesions in 60 healthy streamline tractography connectomes. This indirect estimation of the stroke impact on the whole brain connectome is more readily available than direct measures of structural connectivity obtained with magnetic resonance imaging. We added these measures to benchmark structural features, and we used a ridge regression regularization to predict motor recovery at 3 months post-injury. As hypothesized, accuracy in prediction significantly increased (R 2 = 0.68) as compared to benchmark features (R 2 = 0.38). This improved prediction of recovery could be beneficial to clinical care and might allow for a better choice of intervention

Post-stroke reorganization of transient brain activity characterizes deficits and recovery of cognitive functions

Functional magnetic resonance imaging (fMRI) has been widely employed to study stroke pathophysiology. In particular, analyses of fMRI signals at rest were directed at quantifying the impact of stroke on spatial features of brain networks. However, brain networks have intrinsic time features that were, so far, disregarded in these analyses. In consequence, standard fMRI analysis failed to capture temporal imbalance resulting from stroke lesions, hence restricting their ability to reveal the interdependent pathological changes in structural and temporal network features following stroke. Here, we longitudinally analyzed hemodynamic-informed transient activity in a large cohort of stroke patients (n = 103) to assess spatial and temporal changes of brain networks after stroke. Metrics extracted from the hemodynamic-informed transient activity were replicable within- and between-individuals in healthy participants, hence supporting their robustness and their clinical applicability. While large-scale spatial patterns of brain networks were preserved after stroke, their durations were altered, with stroke subjects exhibiting a varied pattern of longer and shorter network activations compared to healthy individuals. Specifically, patients showed a longer duration in the lateral precentral gyrus and anterior cingulum, and a shorter duration in the occipital lobe and in the cerebellum. These temporal alterations were associated with white matter damage in projection and association pathways. Furthermore, they were tied to deficits in specific behavioral domains as restoration of healthy brain dynamics paralleled recovery of cognitive functions (attention, language and spatial memory), but was not significantly correlated to motor recovery. These findings underscore the critical importance of network temporal properties in dissecting the pathophysiology of brain changes after stroke, thus shedding new light on the clinical potential of time-resolved methods for fMRI analysis

Lesion Quantification Toolkit: A MATLAB software tool for estimating grey matter damage and white matter disconnections in patients with focal brain lesions

Lesion studies are an important tool for cognitive neuroscientists and neurologists. However, while brain lesion studies have traditionally aimed to localize neurological symptoms to specific anatomical loci, a growing body of evidence indicates that neurological diseases such as stroke are best conceptualized as brain network disorders. While researchers in the fields of neuroscience and neurology are therefore increasingly interested in quantifying the effects of focal brain lesions on the white matter connections that form the brain\u27s structural connectome, few dedicated tools exist to facilitate this endeavor. Here, we present the Lesion Quantification Toolkit, a publicly available MATLAB software package for quantifying the structural impacts of focal brain lesions. The Lesion Quantification Toolkit uses atlas-based approaches to estimate parcel-level grey matter lesion loads and multiple measures of white matter disconnection severity that include tract-level disconnection measures, voxel-wise disconnection maps, and parcel-wise disconnection matrices. The toolkit also estimates lesion-induced increases in the lengths of the shortest structural paths between parcel pairs, which provide information about changes in higher-order structural network topology. We describe in detail each of the different measures produced by the toolkit, discuss their applications and considerations relevant to their use, and perform example analyses using real behavioral data collected from sub-acute stroke patients. We show that analyses performed using the different measures produced by the toolkit produce results that are highly consistent with results that have been reported in the prior literature, and we demonstrate the consistency of results obtained from analyses conducted using the different disconnection measures produced by the toolkit. We anticipate that the Lesion Quantification Toolkit will empower researchers to address research questions that would be difficult or impossible to address using traditional lesion analyses alone, and ultimately, lead to advances in our understanding of how white matter disconnections contribute to the cognitive, behavioral, and physiological consequences of focal brain lesions

Stroke-related alterations in inter-areal communication

Beyond causing local ischemia and cell damage at the site of injury, stroke strongly affects long-range anatomical connections, perturbing the functional organization of brain networks. Several studies reported functional connectivity abnormalities parallelling both behavioral deficits and functional recovery across different cognitive domains. FC alterations suggest that long-range communication in the brain is altered after stroke. However, standard FC analyses cannot reveal the directionality and time scale of inter-areal information transfer. We used resting-state fMRI and covariance-based Granger causality analysis to quantify network-level information transfer and its alteration in stroke. Two main large-scale anomalies were observed in stroke patients. First, inter-hemispheric information transfer was significantly decreased with respect to healthy controls. Second, stroke caused inter-hemispheric asymmetries, as information transfer within the affected hemisphere and from the affected to the intact hemisphere was significantly reduced. Both anomalies were more prominent in resting-state networks related to attention and language, and they correlated with impaired performance in several behavioral domains. Overall, our findings support the hypothesis that stroke provokes asymmetries between the affected and spared hemisphere, with different functional consequences depending on which hemisphere is lesioned

Brain dysconnectome: a potential biomarker for functional outcome after mechanical thrombectomy

Mechanical thrombectomy (MT) is a safe and effective procedure that has improved the prognosis of patients with large vessel obstruction (LVO) stroke. Even though we select patients based on various clinical and imaging criteria, more than half of those undergoing this procedure remain with severe disability. Several factors, both pre-treatment (pre-stroke mRS, NIHSS, event-recanalization time, ASPECTS, core/ penumbra volumes) and post-treatment (TICI, NIHSS, final infarct volume, complications) have been identified as predictors of outcome. Among these factors, the volume of the lesion and the size of the hypo-perfused region of the brain are used to make individual decisions about treatment. In retrospective studies, the volume of the lesion weakly correlates with clinical outcomes, while lesion location is more predictive. In addition measures of large-scale (brain networks) disruption have been developed based on the concept of structural and functional disconnection, i.e., the ensemble of structural and functional connections that are directly or indirectly damaged by the focal injury. These disconnection measures have been shown to be strongly predictive of acute impairment and recovery of function. Here we plan to measure in a group of patients with LVO who underwent MT the relationship between outcome (3 month-mRS) and the location of the lesion or its effect on structural and functional networks. To examine whether the outcome is more related to the vascular distribution stroke or their effects on structural-functional brain networks, we mapped the lesions onto a vascular atlas, a gray matter functional regions’ atlas, or a white matter structural connections’ atlas. Then using multi-variate statistical models, we tested which atlas was more predictive of clinical outcome. The same analysis was then applied to structural and functional disconnection patterns. A total of n=66 patients underwent MT at the Neurology Unit of Padua hospital from January 2019 to June 2022. They were examined with the mRS and the NIHSS at admission and discharge. The mRS was also administered at three months post-stroke for measuring clinical outcomes. The location and volume of the lesions were measured from CT, and FLAIR MRI scans performed after MT and manually segmented using the software ITK-SNAP. We computed voxel-wise maps of structural and functional disconnections that were significantly related to functional outcomes. We also investigated the relationship between lesion location computed on three different atlases (vascular, functional grey matter, and structural white matter atlas) and 3-month mRS. The mean pre-event mRS was 0.5±0.9, post-MT mRS was 3.1±1.9, at three-month mRS was 2.5±2.1. A voxel-wise analysis of the functional disconnection showed a significant involvement of the sensory-motor network (SMN) (R2= 0.340), the visual network (VIS)(R2= 0.379), and the dorsal attention network (DAN)(R2=0.318). The voxel-wise structural disconnection analysis localized sensorimotor pathways and long-range association pathways. The prediction of lesion topography on clinical outcome was more robust for the functional atlas (R2=0.382), followed by the structural atlas (R2=0.338), while the vascular atlas provided the lowest prediction (R2=0.146). Structural disconnection performed better than functional disconnection in predicting outcomes (respectively R2=0.339 and R2=0.205). Stroke lesion topography is a strong prognostic factor of outcome at three months when computed on an atlas of functional and structural networks, as compared to a vascular-based atlas. These findings indicate that pre-treatment evaluations for MT shall take into consideration the network structure of the brain and less its vascular supply. Structural disconnection measures are of high prognostic value. Future studies will define the prognostic value of a network-based atlas in a pre-treatment setting.Mechanical thrombectomy (MT) is a safe and effective procedure that has improved the prognosis of patients with large vessel obstruction (LVO) stroke. Even though we select patients based on various clinical and imaging criteria, more than half of those undergoing this procedure remain with severe disability. Several factors, both pre-treatment (pre-stroke mRS, NIHSS, event-recanalization time, ASPECTS, core/ penumbra volumes) and post-treatment (TICI, NIHSS, final infarct volume, complications) have been identified as predictors of outcome. Among these factors, the volume of the lesion and the size of the hypo-perfused region of the brain are used to make individual decisions about treatment. In retrospective studies, the volume of the lesion weakly correlates with clinical outcomes, while lesion location is more predictive. In addition measures of large-scale (brain networks) disruption have been developed based on the concept of structural and functional disconnection, i.e., the ensemble of structural and functional connections that are directly or indirectly damaged by the focal injury. These disconnection measures have been shown to be strongly predictive of acute impairment and recovery of function. Here we plan to measure in a group of patients with LVO who underwent MT the relationship between outcome (3 month-mRS) and the location of the lesion or its effect on structural and functional networks. To examine whether the outcome is more related to the vascular distribution stroke or their effects on structural-functional brain networks, we mapped the lesions onto a vascular atlas, a gray matter functional regions’ atlas, or a white matter structural connections’ atlas. Then using multi-variate statistical models, we tested which atlas was more predictive of clinical outcome. The same analysis was then applied to structural and functional disconnection patterns. A total of n=66 patients underwent MT at the Neurology Unit of Padua hospital from January 2019 to June 2022. They were examined with the mRS and the NIHSS at admission and discharge. The mRS was also administered at three months post-stroke for measuring clinical outcomes. The location and volume of the lesions were measured from CT, and FLAIR MRI scans performed after MT and manually segmented using the software ITK-SNAP. We computed voxel-wise maps of structural and functional disconnections that were significantly related to functional outcomes. We also investigated the relationship between lesion location computed on three different atlases (vascular, functional grey matter, and structural white matter atlas) and 3-month mRS. The mean pre-event mRS was 0.5±0.9, post-MT mRS was 3.1±1.9, at three-month mRS was 2.5±2.1. A voxel-wise analysis of the functional disconnection showed a significant involvement of the sensory-motor network (SMN) (R2= 0.340), the visual network (VIS)(R2= 0.379), and the dorsal attention network (DAN)(R2=0.318). The voxel-wise structural disconnection analysis localized sensorimotor pathways and long-range association pathways. The prediction of lesion topography on clinical outcome was more robust for the functional atlas (R2=0.382), followed by the structural atlas (R2=0.338), while the vascular atlas provided the lowest prediction (R2=0.146). Structural disconnection performed better than functional disconnection in predicting outcomes (respectively R2=0.339 and R2=0.205). Stroke lesion topography is a strong prognostic factor of outcome at three months when computed on an atlas of functional and structural networks, as compared to a vascular-based atlas. These findings indicate that pre-treatment evaluations for MT shall take into consideration the network structure of the brain and less its vascular supply. Structural disconnection measures are of high prognostic value. Future studies will define the prognostic value of a network-based atlas in a pre-treatment setting

Macroscale imaging: a potential biomarker for post stroke functional outcome?

To determine whether long-term functional outcomes in stroke patients can be predicted by the amount of acutely damaged white matter tracts. We collected acute behavioral and neuroimaging data from a group of first-time stroke patients and add those from the other(s) databases. (n=114 + n) within one week (check with the other DBs) post-stroke. Functional outcome was telephonically evaluated using the Stroke Impact Scale 3.0 at 12 months post-stroke. For each patient, we calculated the absolute number of white matter tracts affected by the ischemic lesion from our anatomical scans. We measured a numerical index that considers white matter tract density (WMTD index). We compared the ability of the WMTD index, considered individually, or within a series of prediction models including demographics and behavioral data), to predict chronic outcomes. Multiple linear regression was used to assess the quality of prediction of the most informative model.To determine whether long-term functional outcomes in stroke patients can be predicted by the amount of acutely damaged white matter tracts. We collected acute behavioral and neuroimaging data from a group of first-time stroke patients and add those from the other(s) databases. (n=114 + n) within one week (check with the other DBs) post-stroke. Functional outcome was telephonically evaluated using the Stroke Impact Scale 3.0 at 12 months post-stroke. For each patient, we calculated the absolute number of white matter tracts affected by the ischemic lesion from our anatomical scans. We measured a numerical index that considers white matter tract density (WMTD index). We compared the ability of the WMTD index, considered individually, or within a series of prediction models including demographics and behavioral data), to predict chronic outcome. Multiple linear regression was used to assess the quality of prediction of the most informative model