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

Multivariate and network lesion mapping reveals distinct architectures of domain-specific post-stroke cognitive impairments

BACKGROUND

The purpose of this study was to identify patterns of structural disconnection and multivariate lesion-behaviour relationships associated with post-stroke deficits across six commonly impacted cognitive domains: executive function, language, memory, numerical processing, praxis, and visuospatial attention.

METHODS

Stroke survivors (n = 593) completed a brief domain-specific cognitive assessment (the Oxford Cognitive Screen (OCS)) during acute hospitalisation. Network-level and multivariate (sparce canonical correlation) lesion mapping analyses were conducted to identify focal neural correlates and distributed patterns of structural disconnection associated with impairment on each of the 16 OCS measures.

RESULTS

Network-level and multivariate lesion mapping analyses identified significant correlates for 12/16 and 10/16 OCS measures, respectively which were largely consistent with correlates reported in past work. Language impairments were reliably localised to network- and voxel-level correlates centred in left fronto-temporal regions. Memory impairments were associated with disconnection in a large network of left hemisphere regions. Number processing deficits were associated with damage to voxels centred in the left insular/opercular cortex, as well as disconnection within the surrounding white matter tracts. Within the domain of attention, different subtypes of visuospatial neglect were linked to distinct but partially overlapping patterns of disconnection and voxel-level damage. Praxis impairment was not linked to any voxel-level regions but was significantly associated with disconnection within the left hemisphere dorsal attention network.

CONCLUSION

These results highlight the utility of routine, domain-specific cognitive assessment and imaging data for theoretically-driven lesion mapping analyses, while providing novel insight into the complex anatomical correlates of common and debilitating post-stroke cognitive impairments.

Abnormal intrinsic brain functional network dynamics in stroke and correlation with neuropsychiatric symptoms revealed based on lesion and cerebral blood flow

There has been a lack of clarity about the mechanisms of widespread network dysfunctions after stroke. This study aimed to reveal dynamic functional network alternations following stroke based on lesion and brain perfusion. We prospectively enrolled 125 acute ischaemic stroke patients (25 were transient ischemic attack (TIA) patients) and 49 healthy controls with assessed the severity of their depression, anxiety, fatigue, and apathy. We performed dynamic functional network connectivity (DFNC) analysis using the sliding window method. The common static FC biomarkers of stroke were used to define functional states and calculated stroke-specific changes in dynamic indicators. Next, ridge regression (RR) analyses were performed on the dynamic indicators using voxel-wise lesion maps, cerebral blood flow (CBF) difference maps (removal of voxels overlapping lesions) and a combination of both. Mediation analyses were used to characterize the effect of dynamic networks changes on the relationship between lesion, CBF, and neuropsychological scores. Our results showed that DFNC identified three functional states with three dynamic metrics extracted for subsequent analyses. RR analyses show that both CBF and lesions partially explain post-stroke dysfunction (CBF: dynamic indicator1: R2 = 0.110, p = 0.163; dynamic indicator2: R2 = 0.277, p = 0.006; dynamic indicator3: R2 = 0.125, p = 0.121; lesion: dynamic indicator1: R2 = 0.132, p = 0.109; dynamic indicator2: R2 = 0.238, p = 0.015; dynamic indicator3: R2 = 0.131, p = 0.110). In addition, combining the two can improve the efficacy of explanations. Finally, exploratory mediation analyses identified that dynamic functional network changes can mediate between CBF, lesion and neuropsychiatric disorders. Our results suggest that CBF and lesion can be combined to improve the interpretation of dynamic network dysfunction after stroke.

Iowa Brain-Behavior Modeling Toolkit: An Open-Source MATLAB Tool for Inferential and Predictive Modeling of Imaging-Behavior and Lesion-Deficit Relationships

The traditional analytical framework taken by neuroimaging studies in general, and lesion-behavior studies in particular, has been inferential in nature and has focused on identifying and interpreting statistically significant effects within the sample under study. While this framework is well-suited for hypothesis testing approaches, achieving the modern goal of precision medicine requires a different framework that is predictive in nature and that focuses on maximizing the predictive power of models and evaluating their ability to generalize beyond the data that were used to train them. However, few tools exist to support the development and evaluation of predictive models in the context of neuroimaging or lesion-behavior research, creating an obstacle to the widespread adoption of predictive modeling approaches in the field. Further, existing tools for lesion-behavior analysis are often unable to accommodate categorical outcome variables and often impose restrictions on the predictor data. Researchers therefore often must use different software packages and analytical approaches depending on whether they are addressing a classification vs. regression problem and on whether their predictor data correspond to binary lesion images, continuous lesion-network images, connectivity matrices, or other data modalities. To address these limitations, we have developed a MATLAB software toolkit that supports both inferential and predictive modeling frameworks, accommodates both classification and regression problems, and does not impose restrictions on the modality of the predictor data. The toolkit features both a graphical user interface and scripting interface, includes implementations of multiple mass-univariate, multivariate, and machine learning models, features built-in and customizable routines for hyper-parameter optimization, cross-validation, model stacking, and significance testing, and automatically generates text-based descriptions of key methodological details and modeling results to improve reproducibility and minimize errors in the reporting of methods and results. Here, we provide an overview and discussion of the toolkit features and demonstrate its functionality by applying it to the question of how expressive and receptive language impairments relate to lesion location, structural disconnection, and functional network disruption in a large sample of patients with left hemispheric brain lesions. We find that impairments in expressive vs. receptive language are most strongly associated with left lateral prefrontal and left posterior temporal/parietal damage, respectively. We also find that impairments in expressive vs. receptive language are associated with partially overlapping patterns of fronto-temporal structural disconnection, and that the associated functional networks are also similar. Importantly, we find that lesion location and lesion-derived network measures are highly predictive of both types of impairment, with predictions from models trained on these measures explaining ~30-40% of the variance on average when applied to data from patients not used to train the models. We have made the toolkit publicly available, and we have included a comprehensive set of tutorial notebooks to support new users in applying the toolkit in their studies.

Individual contralesional recruitment in the context of structural reserve in early motor reorganization after stroke

The concept of structural reserve in stroke reorganization assumes that the relevance of the contralesional hemisphere strongly depends on the brain tissue spared by the lesion in the affected hemisphere. Recent studies, however, have indicated that the contralesional hemisphere's impact exhibits region-specific variability with concurrently existing maladaptive and supportive influences. This challenges traditional views, necessitating a nuanced investigation of contralesional motor areas and their interaction with ipsilesional networks. Our study focused on the functional role of contralesional key motor areas and lesion-induced connectome disruption early after stroke. Online TMS data of twenty-five stroke patients was analyzed to disentangle interindividual differences in the functional roles of contralesional primary motor cortex (M1), dorsal premotor cortex (dPMC), and anterior interparietal sulcus (aIPS) for motor function. Connectome-based lesion symptom mapping and corticospinal tract lesion quantification were used to investigate how TMS effects depend on ipsilesional structural network properties. At group and individual levels, TMS interference with contralesional M1 and aIPS but not dPMC led to improved performance early after stroke. At the connectome level, a more disturbing role of contralesional M1 was related to a more severe disruption of the structural integrity of ipsilesional M1 in the affected motor network. In contrast, a detrimental influence of contralesional aIPS was linked to less disruption of the ipsilesional M1 connectivity. Our findings indicate that contralesional areas distinctively interfere with motor performance early after stroke depending on ipsilesional structural integrity, extending the concept of structural reserve to regional specificity in recovery of function.

Prediction of stroke severity: systematic evaluation of lesion representations

OBJECTIVE

To systematically evaluate which lesion-based imaging features and methods allow for the best statistical prediction of poststroke deficits across independent datasets.

METHODS

We utilized imaging and clinical data from three independent datasets of patients experiencing acute stroke (N1 = 109, N2 = 638, N3 = 794) to statistically predict acute stroke severity (NIHSS) based on lesion volume, lesion location, and structural and functional disconnection with the lesion location using normative connectomes.

RESULTS

We found that prediction models trained on small single-center datasets could perform well using within-dataset cross-validation, but results did not generalize to independent datasets (median R2 N1 = 0.2%). Performance across independent datasets improved using large single-center training data (R2 N2 = 15.8%) and improved further using multicenter training data (R2 N3 = 24.4%). These results were consistent across lesion attributes and prediction models. Including either structural or functional disconnection in the models outperformed prediction based on volume or location alone (P < 0.001, FDR-corrected).

INTERPRETATION

We conclude that (1) prediction performance in independent datasets of patients with acute stroke cannot be inferred from cross-validated results within a dataset, as performance results obtained via these two methods differed consistently, (2) prediction performance can be improved by training on large and, importantly, multicenter datasets, and (3) structural and functional disconnection allow for improved prediction of acute stroke severity.

1,25-D3 ameliorates ischemic brain injury by alleviating endoplasmic reticulum stress and ferroptosis: Involvement of vitamin D receptor and p53 signaling

Endoplasmic reticulum stress (ERS) and ferroptosis are linked to cerebral ischemia reperfusion injury (CIRI). The neuroprotective properties of 1α, 25-dihydroxyvitamin D3 (VitD3 or 1,25-D3) have been well established; however, the mechanism by which VitD3 treats CIRI through ERS and ferroptosis has not been examined. Hence, we developed middle cerebral artery occlusion/reperfusion (MCAO/R) model in SD rats to ascertain if VitD3 preconditioning mediates ERS and ferroptosis involving of p53 signaling. In this study, we observed that VitD3 can reduce infarction volume and cerebral edema, which leads to the improvement of nerve function. HE, Nissl and Tunel staining showed that VitD3 treatment significantly improved the morphology of neuronal cells and reduced their death. The expression and activation of Vitamin D receptor (VDR), PKR-like ER kinase (PERK), C/EBP-homologous protein (CHOP), p53, nuclear factor erythroid 2-related factor 2 (Nrf2), glutathione peroxidase 4 (GPX4) and reactive oxygen species (ROS) in the ischemic penumbral area were detected by real-time qPCR, Western-blotting and Elisa. The results showed that after VitD3 treatment, VDR increased, ERS-related indices (PERK, CHOP) significantly decreased and ferroptosis-related indices (Nrf2, GPX4) increased. As a VDRs antagonist, pyridoxal-5-phosphate (P5P) can partially block the neuroprotective effects of VitD3. Therefore, CIRI can induce ERS and ferroptosis in the ischemic penumbra area and VitD3 may ameliorate nerve damage in CIRI rats by up-regulating VDR, alleviating p53-associated ERS and ferroptosis.

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.

Mapping stroke outcomes: A review of brain connectivity atlases

The brain connectivity-based atlas is a promising tool for understanding neural communication pathways in the brain, gaining relevance in predicting personalized outcomes for various brain pathologies. This critical review examines the robustness of the brain connectivity-based atlas for predicting post-stroke outcomes. A comprehensive literature search was conducted from 2012 to May 2023 across PubMed, Scopus, EMBASE, EBSCOhost, and Medline databases. Twenty-one studies were screened, and through analysis of these studies, we identified 18 brain connectivity atlases employed by the studies for lesion analysis in their predictions. The brain atlases were assessed for study cohorts, connectivity measures, identified brain regions, atlas applications, and limitations. Based on the analysis of these studies, most atlases were based on diffusion tensor imaging and resting-state functional magnetic resonance imaging (MRI). Studies predicting post-stroke functional outcomes relied on the atlases for multivariate lesion analysis and region of interest identification, often employing atlases derived from young, healthy populations. Current brain connectivity-based atlases for stroke applications lack standardized methods to define and map brain connectivity across atlases and cover sensorimotor functional connectivity to a limited extent. In conclusion, this review highlights the need to develop more comprehensive, robust, and adaptable brain connectivity-based atlases specifically tailored to post-stroke populations.

Using mechanistic knowledge to appraise contemporary approaches to the rehabilitation of upper limb function following stroke

It is a paradox of neurological rehabilitation that, in an era in which preclinical models have produced significant advances in our mechanistic understanding of neural plasticity, there is inadequate support for many therapies recommended for use in clinical practice. When the goal is to estimate the probability that a specific form of therapy will have a positive clinical effect, the integration of mechanistic knowledge (concerning 'the structure or way of working of the parts in a natural system') may improve the quality of inference. This is illustrated by analysis of three contemporary approaches to the rehabilitation of lateralized dysfunction affecting people living with stroke: constraint-induced movement therapy; mental practice; and mirror therapy. Damage to 'cross-road' regions of the structural (white matter) brain connectome generates deficits that span multiple domains (motor, language, attention and verbal/spatial memory). The structural integrity of these regions determines not only the initial functional status, but also the response to therapy. As structural disconnection constrains the recovery of functional capability, 'disconnectome' modelling provides a basis for personalized prognosis and precision rehabilitation. It is now feasible to refer a lesion delineated using a standard clinical scan to a (dis)connectivity atlas derived from the brains of other stroke survivors. As the individual disconnection pattern thus obtained suggests the functional domains most likely be compromised, a therapeutic regimen can be tailored accordingly. Stroke is a complex disorder that burdens individuals with distinct constellations of brain damage. Mechanistic knowledge is indispensable when seeking to ameliorate the behavioural impairments to which such damage gives rise.

Automated Measurement of Cerebral Hemorrhagic Contusions and Outcomes After Traumatic Brain Injury in the TRACK-TBI Study

Importance

Because withdrawal of life-sustaining therapy based on perceived poor prognosis is the most common cause of death after moderate or severe traumatic brain injury (TBI), the accuracy of clinical prognoses is directly associated with mortality. Although the location of brain injury is known to be important for determining recovery potential after TBI, the best available prognostic models, such as the International Mission for Prognosis and Analysis of Clinical Trials in TBI (IMPACT) score, do not currently incorporate brain injury location.

Objective

To test whether automated measurement of cerebral hemorrhagic contusion size and location is associated with improved prognostic performance of the IMPACT score.

Design, Setting, and Participants

This prognostic cohort study was performed in 18 US level 1 trauma centers between February 26, 2014, and August 8, 2018. Adult participants aged 17 years or older from the US-based Transforming Research and Clinical Knowledge in TBI (TRACK-TBI) study with moderate or severe TBI (Glasgow Coma Scale score 3-12) and contusions detected on brain computed tomography (CT) scans were included. The data analysis was performed between January 2023 and February 2024.

Exposures

Labeled contusions detected on CT scans using Brain Lesion Analysis and Segmentation Tool for Computed Tomography (BLAST-CT), a validated artificial intelligence algorithm.

Main Outcome and Measure

The primary outcome was a Glasgow Outcome Scale-Extended (GOSE) score of 4 or less at 6 months after injury. Whether frontal or temporal lobe contusion volumes improved the performance of the IMPACT score was tested using logistic regression and area under the receiver operating characteristic curve comparisons. Sparse canonical correlation analysis was used to generate a disability heat map to visualize the strongest brainwide associations with outcomes.

Results

The cohort included 291 patients with moderate or severe TBI and contusions (mean [SD] age, 42 [18] years; 221 [76%] male; median [IQR] emergency department arrival Glasgow Coma Scale score, 5 [3-10]). Only temporal contusion volumes improved the discrimination of the IMPACT score (area under the receiver operating characteristic curve, 0.86 vs 0.84; P = .03). The data-derived disability heat map of contusion locations showed that the strongest association with unfavorable outcomes was within the bilateral temporal and medial frontal lobes.

Conclusions and Relevance

These findings suggest that CT-based automated contusion measurement may be an immediately translatable strategy for improving TBI prognostic models.

Tissue Outcome Prediction in Patients with Proximal Vessel Occlusion and Mechanical Thrombectomy Using Logistic Models

Perfusion CT is established to aid selection of patients with proximal intracranial vessel occlusion for thrombectomy in the extended time window. Selection is mostly based on simple thresholding of perfusion parameter maps, which, however, does not exploit the full information hidden in the high-dimensional perfusion data. We implemented a multiparametric mass-univariate logistic model to predict tissue outcome based on data from 405 stroke patients with acute proximal vessel occlusion in the anterior circulation who underwent mechanical thrombectomy. Input parameters were acute multimodal CT imaging (perfusion, angiography, and non-contrast) as well as basic demographic and clinical parameters. The model was trained with the knowledge of recanalization status and final infarct localization. We found that perfusion parameter maps (CBF, CBV, and Tmax) were sufficient for tissue outcome prediction. Compared with single-parameter thresholding-based models, our logistic model had comparable volumetric accuracy, but was superior with respect to topographical accuracy (AUC of receiver operating characteristic). We also found higher spatial accuracy (Dice index) in an independent internal but not external cross-validation. Our results highlight the value of perfusion data compared with non-contrast CT, CT angiography and clinical information for tissue outcome-prediction. Multiparametric logistic prediction has high potential to outperform the single-parameter thresholding-based approach. In the future, the combination of tissue and functional outcome prediction might provide an individual biomarker for the benefit from mechanical thrombectomy in acute stroke care.

Multivariate mapping of low-resilient neurocognitive systems within and around low-grade gliomas

Accumulating evidence suggests that the brain exhibits a remarkable capacity for functional compensation in response to neurological damage, a resilience potential that is deeply rooted in the malleable features of its underlying anatomofunctional architecture. This propensity is particularly exemplified by diffuse low-grade glioma, a subtype of primary brain tumour. However, functional plasticity is not boundless, and surgical resections directed at structures with limited neuroplasticity can lead to incapacitating impairments. Yet, maximizing diffuse low-grade glioma resections offers substantial oncological benefits, especially when the resection extends beyond the tumour margins (i.e. supra-tumour or supratotal resection). In this context, the primary objective of this study was to identify which cerebral structures were associated with less favourable cognitive outcomes after surgery, while accounting for intra-tumour and supra-tumour features of the surgical resections. To achieve this objective, we leveraged a unique cohort of 400 patients with diffuse low-grade glioma who underwent surgery with awake cognitive mapping. Patients benefitted from a neuropsychological assessment consisting of 18 subtests administered before and 3 months after surgery. We analysed changes in performance and applied topography-focused and disconnection-focused multivariate lesion-symptom mapping using support vector regressions, in an attempt to capture resected cortico-subcortical structures less amenable to full cognitive compensation. The observed changes in performance were of a limited magnitude, suggesting an overall recovery (13 of 18 tasks recovered fully despite a mean resection extent of 92.4%). Nevertheless, lesion-symptom mapping analyses revealed that a lack of recovery in picture naming was linked to damage in the left inferior temporal gyrus and inferior longitudinal fasciculus. Likewise, for semantic fluency abilities, an association was established with damage to the left precuneus/posterior cingulate. For phonological fluency abilities, the left dorsomedial frontal cortex and the frontal aslant tract were implicated. Moreover, difficulties in spatial exploration were associated with injury to the right dorsomedial prefrontal cortex and its underlying connectivity. An exploratory analysis suggested that supra-tumour resections were associated with a less pronounced recovery following specific resection patterns, such as supra-tumour resections of the left uncinate fasciculus (picture naming), the left corticostriatal tract and the anterior corpus callosum (phonological fluency), the hippocampus and parahippocampus (episodic memory) and the right frontal-mesial areas (visuospatial exploration). Collectively, these patterns of results shed new light on both low-resilient neural systems and the prediction of cognitive recovery following glioma surgery. Furthermore, they indicate that supra-tumour resections were only occasionally less well tolerated from a cognitive viewpoint. In doing so, they have deep implications for surgical planning and rehabilitation strategies.

Data-driven biomarkers better associate with stroke motor outcomes than theory-based biomarkers

Chronic motor impairments are a leading cause of disability after stroke. Previous studies have associated motor outcomes with the degree of damage to predefined structures in the motor system, such as the corticospinal tract. However, such theory-based approaches may not take full advantage of the information contained in clinical imaging data. The present study uses data-driven approaches to model chronic motor outcomes after stroke and compares the accuracy of these associations to previously-identified theory-based biomarkers. Using a cross-validation framework, regression models were trained using lesion masks and motor outcomes data from 789 stroke patients from the Enhancing NeuroImaging Genetics through Meta Analysis (ENIGMA) Stroke Recovery Working Group. Using the explained variance metric to measure the strength of the association between chronic motor outcomes and imaging biomarkers, we compared theory-based biomarkers, like lesion load to known motor tracts, to three data-driven biomarkers: lesion load of lesion-behaviour maps, lesion load of structural networks associated with lesion-behaviour maps, and measures of regional structural disconnection. In general, data-driven biomarkers had stronger associations with chronic motor outcomes accuracy than theory-based biomarkers. Data-driven models of regional structural disconnection performed the best of all models tested (R 2 = 0.210, P < 0.001), performing significantly better than the theory-based biomarkers of lesion load of the corticospinal tract (R 2 = 0.132, P < 0.001) and of multiple descending motor tracts (R 2 = 0.180, P < 0.001). They also performed slightly, but significantly, better than other data-driven biomarkers including lesion load of lesion-behaviour maps (R 2 = 0.200, P < 0.001) and lesion load of structural networks associated with lesion-behaviour maps (R 2 = 0.167, P < 0.001). Ensemble models - combining basic demographic variables like age, sex, and time since stroke - improved the strength of associations for theory-based and data-driven biomarkers. Combining both theory-based and data-driven biomarkers with demographic variables improved predictions, and the best ensemble model achieved R 2 = 0.241, P < 0.001. Overall, these results demonstrate that out-of-sample associations between chronic motor outcomes and data-driven imaging features, particularly when lesion data is represented in terms of structural disconnection, are stronger than associations between chronic motor outcomes and theory-based biomarkers. However, combining both theory-based and data-driven models provides the most robust associations.

Vascular cognitive impairment: Advances in clinical research and management

ABSTRACT

Vascular cognitive impairment (VCI) encompasses a wide spectrum of cognitive disorders, ranging from mild cognitive impairment to vascular dementia. Its diagnosis relies on thorough clinical evaluations and neuroimaging. VCI predominately arises from vascular risk factors (VRFs) and cerebrovascular disease, either independently or in conjunction with neurodegeneration. Growing evidence underscores the prevalence of VRFs, highlighting their potential for early prediction of cognitive impairment and dementia in later life. The precise mechanisms linking vascular pathologies to cognitive deficits remain elusive. Chronic cerebrovascular pathology is the most common neuropathological feature of VCI, often interacting synergistically with neurodegenerative processes. Current research efforts are focused on developing and validating reliable biomarkers to unravel the etiology of vascular brain changes in VCI. The collaborative integration of these biomarkers into clinical practice, alongside routine incorporation into neuropathological assessments, presents a promising strategy for predicting and stratifying VCI. The cornerstone of VCI prevention remains the control of VRFs, which includes multi-domain lifestyle modifications. Identifying appropriate pharmacological approaches is also of paramount importance. In this review, we synthesize recent advancements in the field of VCI, including its definition, determinants of vascular risk, pathophysiology, neuroimaging and fluid-correlated biomarkers, predictive methodologies, and current intervention strategies. Increasingly evident is the notion that more rigorous research for VCI, which arises from a complex interplay of physiological events, is still needed to pave the way for better clinical outcomes and enhanced quality of life for affected individuals.

Indirect functional connectivity does not predict overall survival in glioblastoma

BACKGROUND

Lesion network mapping (LNM) is a popular framework to assess clinical syndromes following brain injury. The classical approach involves embedding lesions from patients into a normative functional connectome and using the corresponding functional maps as proxies for disconnections. However, previous studies indicated limited predictive power of this approach in behavioral deficits. We hypothesized similarly low predictiveness for overall survival (OS) in glioblastoma (GBM).

METHODS

A retrospective dataset of patients with GBM was included (n = 99). Lesion masks were registered in the normative space to compute disconnectivity maps. The brain functional normative connectome consisted in data from 173 healthy subjects obtained from the Human Connectome Project. A modified version of the LNM was then applied to core regions of GBM masks. Linear regression, classification, and principal component (PCA) analyses were conducted to explore the relationship between disconnectivity and OS. OS was considered both as continuous and categorical (low, intermediate, and high survival) variable.

RESULTS

The results revealed no significant associations between OS and network disconnection strength when analyzed at both voxel-wise and classification levels. Moreover, patients stratified into different OS groups did not exhibit significant differences in network connectivity patterns. The spatial similarity among the first PCA of network maps for each OS group suggested a lack of distinctive network patterns associated with survival duration.

CONCLUSIONS

Compared with indirect structural measures, functional indirect mapping does not provide significant predictive power for OS in patients with GBM. These findings are consistent with previous research that demonstrated the limitations of indirect functional measures in predicting clinical outcomes, underscoring the need for more comprehensive methodologies and a deeper understanding of the factors influencing clinical outcomes in this challenging disease.

Predicting Language Function Post-Stroke: A Model-Based Structural Connectivity Approach

BACKGROUND

The prediction of post-stroke language function is essential for the development of individualized treatment plans based on the personal recovery potential of aphasic stroke patients.

OBJECTIVE

To establish a framework for integrating information on connectivity disruption of the language network based on routinely collected clinical magnetic resonance (MR) images into Random Forest modeling to predict post-stroke language function.

METHODS

Language function was assessed in 76 stroke patients from the Non-Invasive Repeated Therapeutic Stimulation for Aphasia Recovery trial, using the Token Test (TT), Boston Naming Test (BNT), and Semantic Verbal Fluency (sVF) Test as primary outcome measures. Individual infarct masks were superimposed onto a diffusion tensor imaging tractogram reference set to calculate Change in Connectivity scores of language-relevant gray matter regions as estimates of structural connectivity disruption. Multivariable Random Forest models were derived to predict language function.

RESULTS

Random Forest models explained moderate to high amount of variance at baseline and follow-up for the TT (62.7% and 76.2%), BNT (47.0% and 84.3%), and sVF (52.2% and 61.1%). Initial language function and non-verbal cognitive ability were the most important variables to predict language function. Connectivity disruption explained additional variance, resulting in a prediction error increase of up to 12.8% with variable omission. Left middle temporal gyrus (12.8%) and supramarginal gyrus (9.8%) were identified as among the most important network nodes.

CONCLUSION

Connectivity disruption of the language network adds predictive value beyond lesion volume, initial language function, and non-verbal cognitive ability. Obtaining information on connectivity disruption based on routine clinical MR images constitutes a significant advancement toward practical clinical application.

Longitudinal neuropsychological performance of post-stroke adults with and without rehabilitation

This study compared the neuropsychological performance of two post-stroke groups, one undergoing rehabilitation and the other not receiving any intervention, on the acute and chronic stroke phases, and explored sociodemographic and neurological variables associated with changes in performance over time. Sixty-three adults underwent neuropsychological assessment with the Cognitive Screening Instrument (TRIACOG) less than thirty days after having a stroke and were reassessed three to six months after stroke. Thirty-eight participants did not undertake rehabilitation and twenty-five did physiotherapy and/or speech therapy between the two time points. The frequency of cognitive deficits (between groups) and the range of cognitive assessment scores over time (between and within groups) were analyzed. There was a significant decrease in the frequency of neuropsychological deficits and improvement on neuropsychological assessment scores over time only in the group undergoing rehabilitation. Severity of the neurological condition, years of education and being in rehabilitation explained the longitudinal changes in several cognitive domains measured by TRIACOG. Engaging in rehabilitation within three to six months post-stroke is crucial for enhancing the recovery of neuropsychological deficits. Cognitive screening instruments like TRIACOG can be used by health professionals to identify stroke-related neuropsychological changes and plan interventions.

Overview of systematic reviews comparing endovascular to best medical treatment for large-vessel occlusion acute ischaemic stroke: an umbrella review

Background

The literature on endovascular treatment (EVT) for large-vessel occlusion (LVO) acute ischaemic stroke (AIS) has been rapidly increasing after the publication of positive randomized-controlled clinical trials (RCTs) and a plethora of systematic reviews (SRs) showing benefit compared to best medical therapy (BMT) for LVO.

Objectives

An overview of SRs (umbrella review) and meta-analysis of primary RCTs were performed to summarize the literature and present efficacy and safety of EVT.

Design and methods

MEDLINE via Pubmed, Embase and Epistemonikos databases were searched from January 2015 until 15 October 2023. All SRs of RCTs comparing EVT to BMT were included. Quality was assessed using Risk of Bias in Systematic Reviews scores and the RoB 2 Cochrane Collaboration tool, as appropriate. GRADE approach was used to evaluate the strength of evidence. Data were presented according to the Preferred Reporting Items for Overviews of Reviews statement. The primary outcome was 3-month good functional outcome [modified Rankin scale (mRS) score 0-2].

Results

Three eligible SRs and 4 additional RCTs were included in the overview, comprising a total of 24 RCTs, corresponding to 5968 AIS patients with LVO (3044 randomized to EVT versus 2924 patients randomized to BMT). High-quality evidence shows that EVT is associated with an increased likelihood of good functional outcome [risk ratio (RR) 1.78 (95% confidence interval (CI): 1.54-2.06); 166 more per 1000 patients], independent ambulation [mRS-scores 0-3; RR 1.50 (95% CI: 1.37-1.64); 174 more per 1000 patients], excellent functional outcome [mRS-scores 0-1; RR 1.90 (95% CI: 1.62-2.22); 118 more per 1000 patients] at 3 months. EVT was associated with reduced 3-month mortality [RR 0.81 (95% CI: 0.74-0.88); 61 less per 1000 patients] despite an increase in symptomatic intracranial haemorrhage [sICH; RR 1.65 (95% CI: 1.23-2.21); 22 more per 1000 patients].

Conclusion

In patients with AIS due to LVO in the anterior or posterior circulation, within 24 h from symptom onset, EVT improves functional outcomes and increases the chance of survival despite increased sICH risk.

Registration

PROSPERO Registration Number CRD42023461138.

Network localization of pediatric lesion-induced dystonia

Objective

Dystonia is a movement disorder defined by involuntary muscle contractions leading to abnormal postures or twisting and repetitive movements. Classically dystonia has been thought of as a disorder of the basal ganglia, but newer results in idiopathic dystonia and lesion-induced dystonia in adults point to broader motor network dysfunction spanning the basal ganglia, cerebellum, premotor cortex, sensorimotor, and frontoparietal regions. It is unclear whether a similar network is shared between different etiologies of pediatric lesion-induced dystonia.

Methods

Three cohorts of pediatric patients with lesion-induced dystonia were identified. The lesion etiologies included hypoxia, kernicterus, and stroke versus comparison subjects with acquired lesions not associated with dystonia. Multivariate lesion-symptom mapping and lesion network mapping were used to evaluate the anatomy and networks associated with dystonia.

Results

Multivariate lesion-symptom mapping showed that lesions of the putamen (stroke: r = 0.50, p <0.01; hypoxia, r = 0.64, p <0.001) and globus pallidus (kernicterus, r = 0.61, p <0.01) were associated with dystonia. Lesion network mapping using normative connectome data from healthy children demonstrated that these regional findings occurred within a common brain-wide network that involves the basal ganglia, anterior and medial cerebellum, and cortical regions that overlap the cingulo-opercular and somato-cognitive-action networks.

Interpretation

We interpret these findings as novel evidence for a unified dystonia brain network that involves the somato-cognitive-action network, which is involved in higher order coordination of movement. Elucidation of this network gives insight into the functional origins of dystonia and provides novel targets to investigate for therapeutic intervention.

Neuroimaging of neuropsychological disturbances following ischaemic stroke (CONNECT): a prospective cohort study protocol

INTRODUCTION

Neuropsychiatric distubance is a common clinical manifestation in acute ischemic stroke. However, it is frequently overlooked by clinicians. This study aimed to explore the possible aetiology and pathogenesis of neuropsychiatric disturbances following ischaemic stroke (NDIS) from an anatomical and functional perspective with the help of neuroimaging methods.

METHOD AND ANALYSIS

CONNECT is a prospective cohort study of neuroimaging and its functional outcome in NDIS. We aim to enrol a minimum of 300 individuals with first-ever stroke. The neuropsychological disturbances involved in this study include depression, anxiety disorder, headache, apathy, insomnia, fatigue and cognitive impairment. Using scales that have been shown to be effective in assessing the above symptoms, the NDIS evaluation battery requires at least 2 hours at baseline. Moreover, all patients will be required to complete 2 years of follow-up, during which the NDIS will be re-evaluated at 3 months, 12 months and 24 months by telephone and 6 months by outpatient interview after the index stroke. The primary outcome of our study is the incidence of NDIS at the 6-month mark. Secondary outcomes are related to the severity of NDIS as well as functional rehabilitation of patients. Functional imaging evaluation will be performed at baseline and 6-month follow-up using specific sequences including resting-state functional MRI, diffusion tensor imaging, T1-weighted imaging, T2-weighted imaging, diffusion-weighted imaging, arterial spin labelling, quantitative susceptibility mapping and fluid-attenuated inversion recovery imaging. In addition, we collect haematological information from patients to explore potential biological and genetic markers of NDIS through histological analysis.

ETHICS AND DISSEMINATION

The CONNECT Study was approved by the Ethics Review Committee of the First Hospital of the University of Science and Technology of China (2021-ky012) and written informed consent will be obtained from all participants. Results will be disseminated via a peer-reviewed journal.

TRIAL REGISTRATION NUMBER

ChiCTR2100043886.

Multiple sclerosis lesions that impair memory map to a connected memory circuit

BACKGROUND

Nearly 1 million Americans are living with multiple sclerosis (MS) and 30-50% will experience memory dysfunction. It remains unclear whether this memory dysfunction is due to overall white matter lesion burden or damage to specific neuroanatomical structures. Here we test if MS memory dysfunction is associated with white matter lesions to a specific brain circuit.

METHODS

We performed a cross-sectional analysis of standard structural images and verbal memory scores as assessed by immediate recall trials from 431 patients with MS (mean age 49.2 years, 71.9% female) enrolled at a large, academic referral center. White matter lesion locations from each patient were mapped using a validated algorithm. First, we tested for associations between memory dysfunction and total MS lesion volume. Second, we tested for associations between memory dysfunction and lesion intersection with an a priori memory circuit derived from stroke lesions. Third, we performed mediation analyses to determine which variable was most associated with memory dysfunction. Finally, we performed a data-driven analysis to derive de-novo brain circuits for MS memory dysfunction using both functional (n = 1000) and structural (n = 178) connectomes.

RESULTS

Both total lesion volume (r = 0.31, p < 0.001) and lesion damage to our a priori memory circuit (r = 0.34, p < 0.001) were associated with memory dysfunction. However, lesion damage to the memory circuit fully mediated the association of lesion volume with memory performance. Our data-driven analysis identified multiple connections associated with memory dysfunction, including peaks in the hippocampus (T = 6.05, family-wise error p = 0.000008), parahippocampus, fornix and cingulate. Finally, the overall topography of our data-driven MS memory circuit matched our a priori stroke-derived memory circuit.

CONCLUSIONS

Lesion locations associated with memory dysfunction in MS map onto a specific brain circuit centered on the hippocampus. Lesion damage to this circuit fully mediated associations between lesion volume and memory. A circuit-based approach to mapping MS symptoms based on lesions visible on standard structural imaging may prove useful for localization and prognosis of higher order deficits in MS.

Functional Lesion Network Mapping of Sensory Deficits After Ischemic Stroke

BACKGROUND

Sensory deficits are common after stroke, leading to disability and poor quality of life. Although lesion locations and patterns of structural brain network disruption have been associated with sensory disturbances, the relation with functional lesion connectivity has not yet been established.

METHODS

Retrospective analysis of a prospective cohort study of patients with acute ischemic stroke. Indirect functional lesion network mapping to identify brain regions remote from the primary lesion associated with deficits on the Rivermead Assessment of Somatosensory Performance test. Associations between Rivermead Assessment of Somatosensory Performance scores and functional connectivity of the lesion site with prespecified components of the somatosensory system.

RESULTS

One hundred one patients (mean age, 62 years; 32% women) from the TOPOS study (Topological and Clinical Prospective Study About Somatosensation in Stroke). Lesion network mapping identified a bilateral fronto-parietal network associated with sensory deficits in the acute phase after stroke. There were graded associations between deficits and functional lesion connectivity to sensory cortices, but not the thalamus.

CONCLUSIONS

Infarcts in brain regions remote from, but functionally connected, to the somatosensory network are associated with somatosensory deficits measured by the Rivermead Assessment of Somatosensory Performance test, reflecting the hierarchical functional anatomy of sensory processing. Further research is needed to translate these findings into improved prognosis and personalized rehabilitation strategies.

A low dimensional embedding of brain dynamics enhances diagnostic accuracy and behavioral prediction in stroke

Large-scale brain networks reveal structural connections as well as functional synchronization between distinct regions of the brain. The latter, referred to as functional connectivity (FC), can be derived from neuroimaging techniques such as functional magnetic resonance imaging (fMRI). FC studies have shown that brain networks are severely disrupted by stroke. However, since FC data are usually large and high-dimensional, extracting clinically useful information from this vast amount of data is still a great challenge, and our understanding of the functional consequences of stroke remains limited. Here, we propose a dimensionality reduction approach to simplify the analysis of this complex neural data. By using autoencoders, we find a low-dimensional representation encoding the fMRI data which preserves the typical FC anomalies known to be present in stroke patients. By employing the latent representations emerging from the autoencoders, we enhanced patients' diagnostics and severity classification. Furthermore, we showed how low-dimensional representation increased the accuracy of recovery prediction.

Stroke lesion size - Still a useful biomarker for stroke severity and outcome in times of high-dimensional models

BACKGROUND

The volumetric size of a brain lesion is a frequently used stroke biomarker. It stands out among most imaging biomarkers for being a one-dimensional variable that is applicable in simple statistical models. In times of machine learning algorithms, the question arises of whether such a simple variable is still useful, or whether high-dimensional models on spatial lesion information are superior.

METHODS

We included 753 first-ever anterior circulation ischemic stroke patients (age 68.4±15.2 years; NIHSS at 24 h 4.4±5.1; modified Rankin Scale (mRS) at 3-months median[IQR] 1[0.75;3]) and traced lesions on diffusion-weighted MRI. In an out-of-sample model validation scheme, we predicted stroke severity as measured by NIHSS 24 h and functional stroke outcome as measured by mRS at 3 months either from spatial lesion features or lesion size.

RESULTS

For stroke severity, the best regression model based on lesion size performed significantly above chance (p < 0.0001) with R2 = 0.322, but models with spatial lesion features performed significantly better with R2 = 0.363 (t(752) = 2.889; p = 0.004). For stroke outcome, the best classification model based on lesion size again performed significantly above chance (p < 0.0001) with an accuracy of 62.8%, which was not different from the best model with spatial lesion features (62.6%, p = 0.80). With smaller training data sets of only 150 or 50 patients, the performance of high-dimensional models with spatial lesion features decreased up to the point of being equivalent or even inferior to models trained on lesion size. The combination of lesion size and spatial lesion features in one model did not improve predictions.

CONCLUSIONS

Lesion size is a decent biomarker for stroke outcome and severity that is slightly inferior to spatial lesion features but is particularly suited in studies with small samples. When low-dimensional models are desired, lesion size provides a viable proxy biomarker for spatial lesion features, whereas high-precision prediction models in personalised prognostic medicine should operate with high-dimensional spatial imaging features in large samples.

Anterolateral temporal lobe localization of dysnomia after temporal lobe epilepsy surgery

Objectives

To evaluate what factors influence naming ability after temporal lobectomy in patients with drug-resistant epilepsy.

Methods

85 participants with drug-resistant epilepsy who underwent temporal lobe (TL) resective surgery were retrospectively identified (49 left TL and 36 right TL). Naming ability was assessed before and >3 months post-surgery using the Boston Naming Test (BNT).Multivariate lesion-symptom mapping was performed to evaluate whether lesion location related to naming deficits. Multiple regression analyses were conducted to examine if other patient characteristics were significantly associated with pre-to post-surgery changes in naming ability.

Results

Lesion laterality and location were important predictors of post-surgical naming performance. Naming performance significantly improved after right temporal lobectomy ( p = 0.015) while a decrement in performance was observed following left temporal lobectomy ( p = 0.002). Lesion-symptom mapping showed the decline in naming performance was associated with surgical resection of the anterior left middle temporal gyrus (Brodmann area 21, r =0.41, p = <.001). For left hemisphere surgery, later onset of epilepsy was associated with a greater reduction in post-surgical naming performance ( p = 0.01).

Significance

There is a wide range of variability in outcomes for naming ability after temporal lobectomy, from significant improvements to decrements observed. If future studies support the association of left anterior middle temporal gyrus resection and impaired naming this may help in surgical planning and discussions of prognosis.

Data-driven biomarkers outperform theory-based biomarkers in predicting stroke motor outcomes

Chronic motor impairments are a leading cause of disability after stroke. Previous studies have predicted motor outcomes based on the degree of damage to predefined structures in the motor system, such as the corticospinal tract. However, such theory-based approaches may not take full advantage of the information contained in clinical imaging data. The present study uses data-driven approaches to predict chronic motor outcomes after stroke and compares the accuracy of these predictions to previously-identified theory-based biomarkers. Using a cross-validation framework, regression models were trained using lesion masks and motor outcomes data from 789 stroke patients (293 female/496 male) from the ENIGMA Stroke Recovery Working Group (age 64.9±18.0 years; time since stroke 12.2±0.2 months; normalised motor score 0.7±0.5 (range [0,1]). The out-of-sample prediction accuracy of two theory-based biomarkers was assessed: lesion load of the corticospinal tract, and lesion load of multiple descending motor tracts. These theory-based prediction accuracies were compared to the prediction accuracy from three data-driven biomarkers: lesion load of lesion-behaviour maps, lesion load of structural networks associated with lesion-behaviour maps, and measures of regional structural disconnection. In general, data-driven biomarkers had better prediction accuracy - as measured by higher explained variance in chronic motor outcomes - than theory-based biomarkers. Data-driven models of regional structural disconnection performed the best of all models tested (R2 = 0.210, p < 0.001), performing significantly better than predictions using the theory-based biomarkers of lesion load of the corticospinal tract (R2 = 0.132, p< 0.001) and of multiple descending motor tracts (R2 = 0.180, p < 0.001). They also performed slightly, but significantly, better than other data-driven biomarkers including lesion load of lesion-behaviour maps (R2 =0.200, p < 0.001) and lesion load of structural networks associated with lesion-behaviour maps (R2 =0.167, p < 0.001). Ensemble models - combining basic demographic variables like age, sex, and time since stroke - improved prediction accuracy for theory-based and data-driven biomarkers. Finally, combining both theory-based and data-driven biomarkers with demographic variables improved predictions, and the best ensemble model achieved R2 = 0.241, p < 0.001. Overall, these results demonstrate that models that predict chronic motor outcomes using data-driven features, particularly when lesion data is represented in terms of structural disconnection, perform better than models that predict chronic motor outcomes using theory-based features from the motor system. However, combining both theory-based and data-driven models provides the best predictions.

Localization of a Medial Temporal Lobe-Precuneus Network for Time Orientation

OBJECTIVE

Time orientation is a fundamental cognitive process in which one's personal sense of time is matched with a universal reference. Time orientation is commonly assessed through mental status examination, yet its neural correlates remain unclear. Large lesions have been associated with deficits in time orientation, but the regional anatomy implicated in time disorientation is not well established. The current study investigates the anatomy of time disorientation and its network correlates in patients with focal brain lesions.

METHODS

Time orientation was assessed 3 months or more after lesion onset using the Benton Temporal Orientation Test (BTOT) in 550 patients with acquired, focal brain lesions, 39 of whom were impaired. Multivariate lesion-symptom mapping and lesion network mapping were used to evaluate the anatomy and networks associated with time disorientation. Performance on a variety of neuropsychological tests was compared between the time oriented and time disoriented group.

RESULTS

Lesion-symptom mapping showed that lesions of the precuneus, medial temporal lobes (MTL), and occipito-temporal cortex were associated with time disorientation (r = 0.264, p < 0.001). Lesion network mapping using normative connectome data demonstrated that these regional findings occurred along a network that includes white and gray matter connecting the precuneus and MTL. There was a strong behavioral and anatomical association of time disorientation with memory impairment, such that the 2 processes could not be fully disentangled.

INTERPRETATION

We interpret these findings as novel evidence for a network involving the precuneus and the medial temporal lobe in supporting time orientation. ANN NEUROL 2023;94:421-433.

Lesion Network Mapping for Neurological Deficit in Acute Ischemic Stroke

OBJECTIVE

To create a comprehensive map of strategic lesion network localizations for neurological deficits, and identify prognostic neuroimaging biomarkers to facilitate the early detection of patients with a high risk of poor functional outcomes in acute ischemic stroke (AIS).

METHODS

In a large-scale multicenter study of 7,807 patients with AIS, we performed voxel-based lesion-symptom mapping, functional disconnection mapping (FDC), and structural disconnection mapping (SDC) to identify distinct lesion and network localizations for National Institutes of Health Stroke Scale (NIHSS) score. Impact scores were calculated based on the odds ratios or t-values of voxels from voxel-based lesion-symptom mapping, FDC, and SDC results. Ordinal regression models were used to investigate the predictive value of the impact scores on functional outcome (defined as the modified Rankin score at 3 months).

RESULTS

We constructed lesion, FDC, and SDC maps for each item of the NIHSS score, which provided insights into the neuroanatomical substrate and network localization of neurological function deficits after AIS. The lesion impact score of limb ataxia, the SDC impact score of limb deficit, and FDC impact score of sensation and dysarthria were significantly associated with modified Rankin Scale at 3 months. Adding the SDC impact score, FDC impact score, and lesion impact score to the NIHSS total score improved the performance in predicting functional outcomes, as compared with using the NIHSS score alone.

INTERPRETATION

We constructed comprehensive maps of strategic lesion network localizations for neurological deficits that were predictive of functional outcomes in AIS. These results may provide specifically localized targets for future neuromodulation therapies. ANN NEUROL 2023;94:572-584.

Atlasing white matter and grey matter joint contributions to resting-state networks in the human brain

Over the past two decades, the study of resting-state functional magnetic resonance imaging has revealed that functional connectivity within and between networks is linked to cognitive states and pathologies. However, the white matter connections supporting this connectivity remain only partially described. We developed a method to jointly map the white and grey matter contributing to each resting-state network (RSN). Using the Human Connectome Project, we generated an atlas of 30 RSNs. The method also highlighted the overlap between networks, which revealed that most of the brain's white matter (89%) is shared between multiple RSNs, with 16% shared by at least 7 RSNs. These overlaps, especially the existence of regions shared by numerous networks, suggest that white matter lesions in these areas might strongly impact the communication within networks. We provide an atlas and an open-source software to explore the joint contribution of white and grey matter to RSNs and facilitate the study of the impact of white matter damage to these networks. In a first application of the software with clinical data, we were able to link stroke patients and impacted RSNs, showing that their symptoms aligned well with the estimated functions of the networks.

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.

Functional and structural lesion network mapping in neurological and psychiatric disorders: a systematic review

Background

The traditional approach to studying the neurobiological mechanisms of brain disorders and localizing brain function involves identifying brain abnormalities and comparing them to matched controls. This method has been instrumental in clinical neurology, providing insight into the functional roles of different brain regions. However, it becomes challenging when lesions in diverse regions produce similar symptoms. To address this, researchers have begun mapping brain lesions to functional or structural networks, a process known as lesion network mapping (LNM). This approach seeks to identify common brain circuits associated with lesions in various areas. In this review, we focus on recent studies that have utilized LNM to map neurological and psychiatric symptoms, shedding light on how this method enhances our understanding of brain network functions.

Methods

We conducted a systematic search of four databases: PubMed, Scopus, and Web of Science, using the term "Lesion network mapping." Our focus was on observational studies that applied lesion network mapping in the context of neurological and psychiatric disorders.

Results

Following our screening process, we included 52 studies, comprising a total of 6,814 subjects, in our systematic review. These studies, which utilized functional connectivity, revealed several regions and network overlaps across various movement and psychiatric disorders. For instance, the cerebellum was found to be part of a common network for conditions such as essential tremor relief, parkinsonism, Holmes tremor, freezing of gait, cervical dystonia, infantile spasms, and tics. Additionally, the thalamus was identified as part of a common network for essential tremor relief, Holmes tremor, and executive function deficits. The dorsal attention network was significantly associated with fall risk in elderly individuals and parkinsonism.

Conclusion

LNM has proven to be a powerful tool in localizing a broad range of neuropsychiatric, behavioral, and movement disorders. It holds promise in identifying new treatment targets through symptom mapping. Nonetheless, the validity of these approaches should be confirmed by more comprehensive prospective studies.

Predicting Motor Outcomes Using Atlas-Based Voxel Features of Post-Stroke Neuroimaging: A Scoping Review

BACKGROUND

Atlas-based voxel features have the potential to aid motor outcome prognostication after stroke, but are seldom used in clinically feasible prediction models. This could be because neuroimaging feature development is a non-standardized, complex, multistep process. This is a barrier to entry for researchers and poses issues for reproducibility and validation in a field of research where sample sizes are typically small.

OBJECTIVES

The primary aim of this review is to describe the methodologies currently used in motor outcome prediction studies using atlas-based voxel neuroimaging features. Another aim is to identify neuroanatomical regions commonly used for motor outcome prediction.

METHODS

A Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocol was constructed and OVID Medline and Scopus databases were searched for relevant studies. The studies were then screened and details about imaging modality, image acquisition, image normalization, lesion segmentation, region of interest determination, and imaging measures were extracted.

RESULTS

Seventeen studies were included and examined. Common limitations were a lack of detailed reporting on image acquisition and the specific brain templates used for normalization and a lack of clear reasoning behind the atlas or imaging measure selection. A wide variety of sensorimotor regions relate to motor outcomes and there is no consensus use of one single sensorimotor atlas for motor outcome prediction.

CONCLUSION

There is an ongoing need to validate imaging predictors and further improve methodological techniques and reporting standards in neuroimaging feature development for motor outcome prediction post-stroke.

Using in vivo functional and structural connectivity to predict chronic stroke aphasia deficits

Focal brain damage caused by stroke can result in aphasia and advances in cognitive neuroscience suggest that impairment may be associated with network-level disorder rather than just circumscribed cortical damage. Several studies have shown meaningful relationships between brain-behaviour using lesions; however, only a handful of studies have incorporated in vivo structural and functional connectivity. Patients with chronic post-stroke aphasia were assessed with structural (n = 68) and functional (n = 39) MRI to assess whether predicting performance can be improved with multiple modalities and if additional variance can be explained compared to lesion models alone. These neural measurements were used to construct models to predict four key language-cognitive factors: (i) phonology; (ii) semantics; (iii) executive function; and (iv) fluency. Our results showed that each factor (except executive ability) could be significantly related to each neural measurement alone; however, structural and functional connectivity models did not explain additional variance above the lesion models. We did find evidence that the structural and functional predictors may be linked to the core lesion sites. First, the predictive functional connectivity features were found to be located within functional resting-state networks identified in healthy controls, suggesting that the result might reflect functionally specific reorganization (damage to a node within a network can result in disruption to the entire network). Second, predictive structural connectivity features were located within core lesion sites, suggesting that multimodal information may be redundant in prediction modelling. In addition, we observed that the optimum sparsity within the regularized regression models differed for each behavioural component and across different imaging features, suggesting that future studies should consider optimizing hyperparameters related to sparsity per target. Together, the results indicate that the observed network-level disruption was predicted by the lesion alone and does not significantly improve model performance in predicting the profile of language impairment.

The functional anatomy of dystonia: Recent developments

While dystonia has traditionally been viewed as a disorder of the basal ganglia, the involvement of other key brain structures is now accepted. However, just what these structures are remains to be defined. Neuroimaging has been an especially valuable tool in dystonia, yet traditional cross-sectional designs have not been able to separate causal from compensatory brain activity. Therefore, this chapter discusses recent studies using causal brain lesions, and animal models, to converge upon the brain regions responsible for dystonia with increasing precision. This evidence strongly implicates the basal ganglia, thalamus, brainstem, cerebellum, and somatosensory cortex, yet shows that different types of dystonia involve different nodes of this brain network. Nearly all of these nodes fall within the recently identified two-way networks connecting the basal ganglia and cerebellum, suggesting dysfunction of these specific pathways. Localisation of the functional anatomy of dystonia has strong implications for targeted treatment options, such as deep brain stimulation, and non-invasive brain stimulation.

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

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

The elusive metric of lesion load

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

Lead-DBS v3.0: Mapping deep brain stimulation effects to local anatomy and global networks

Following its introduction in 2014 and with support of a broad international community, the open-source toolbox Lead-DBS has evolved into a comprehensive neuroimaging platform dedicated to localizing, reconstructing, and visualizing electrodes implanted in the human brain, in the context of deep brain stimulation (DBS) and epilepsy monitoring. Expanding clinical indications for DBS, increasing availability of related research tools, and a growing community of clinician-scientist researchers, however, have led to an ongoing need to maintain, update, and standardize the codebase of Lead-DBS. Major development efforts of the platform in recent years have now yielded an end-to-end solution for DBS-based neuroimaging analysis allowing comprehensive image preprocessing, lead localization, stimulation volume modeling, and statistical analysis within a single tool. The aim of the present manuscript is to introduce fundamental additions to the Lead-DBS pipeline including a deformation warpfield editor and novel algorithms for electrode localization. Furthermore, we introduce a total of three comprehensive tools to map DBS effects to local, tract- and brain network-levels. These updates are demonstrated using a single patient example (for subject-level analysis), as well as a retrospective cohort of 51 Parkinson's disease patients who underwent DBS of the subthalamic nucleus (for group-level analysis). Their applicability is further demonstrated by comparing the various methodological choices and the amount of explained variance in clinical outcomes across analysis streams. Finally, based on an increasing need to standardize folder and file naming specifications across research groups in neuroscience, we introduce the brain imaging data structure (BIDS) derivative standard for Lead-DBS. Thus, this multi-institutional collaborative effort represents an important stage in the evolution of a comprehensive, open-source pipeline for DBS imaging and connectomics.

Icariside II preconditioning evokes robust neuroprotection against ischaemic stroke, by targeting Nrf2 and the OXPHOS/NF-κB/ferroptosis pathway

BACKGROUND AND PURPOSE

Astrocytic nuclear factor erythroid-derived 2-related factor 2 (Nrf2) is a potential therapeutic target of ischaemic preconditioning (IPC). Icariside II (ICS II) is a naturally occurring flavonoid derived from Herba Epimedii with Nrf2 induction potency. This study was designed to clarify if exposure to ICS II mimicks IPC neuroprotection and if Nrf2 from astrocytes contributes to ICS II preconditioning against ischaemic stroke.

EXPERIMENTAL APPROACH

Mice with transient middle cerebral artery occlusion (MCAO)-induced focal cerebral ischaemia and primary astrocytes challenged with oxygen-glucose deprivation (OGD) were used to explore the neuroprotective effect of ICS II preconditioning. Additionally, Nrf2-deficient mice were pretreated with ICS II to determine whether ICS II exerts its neuroprotection by activating Nrf2.

KEY RESULTS

ICS II pretreatment mitigated cerebral injury in the mouse model of ischaemic stroke along with improving long-term recovery. Furthermore, proteomics screening identified Nrf2 as a crucial gene evoked by ICS II treatment and required for the anti-oxidative effect and anti-inflammatory effect of ICS II. Also, ICS II directly bound to Nrf2 and reinforced the transcriptional activity of Nrf2 after MCAO. Moreover, ICS II pretreatment exerted cytoprotective effects on astrocyte cultures following lethal OGD exposure, by promoting Nrf2 nuclear translocation and activating the OXPHOS/NF-κB/ferroptosis axis, while neuroprotection was decreased in Nrf2-deficient mice and Nrf2 siRNA blocked effects of ICS II.

CONCLUSION AND IMPLICATIONS

ICS II preconditioning provides robust neuroprotection against ischaemic stroke via the astrocytic Nrf2-mediated OXPHOS/NF-κB/ferroptosis axis. Thus, ICS II could be a promising Nrf2 activator to treat ischaemic stroke.

Multimodal and multidomain lesion network mapping enhances prediction of sensorimotor behavior in stroke patients

Beyond the characteristics of a brain lesion, such as its etiology, size or location, lesion network mapping (LNM) has shown that similar symptoms after a lesion reflects similar dis-connectivity patterns, thereby linking symptoms to brain networks. Here, we extend LNM by using a multimodal strategy, combining functional and structural networks from 1000 healthy participants in the Human Connectome Project. We apply multimodal LNM to a cohort of 54 stroke patients with the aim of predicting sensorimotor behavior, as assessed through a combination of motor and sensory tests. Results are two-fold. First, multimodal LNM reveals that the functional modality contributes more than the structural one in the prediction of sensorimotor behavior. Second, when looking at each modality individually, the performance of the structural networks strongly depended on whether sensorimotor performance was corrected for lesion size, thereby eliminating the effect that larger lesions generally produce more severe sensorimotor impairment. In contrast, functional networks provided similar performance regardless of whether or not the effect of lesion size was removed. Overall, these results support the extension of LNM to its multimodal form, highlighting the synergistic and additive nature of different types of network modalities, and their corresponding influence on behavioral performance after brain injury.

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

INTRODUCTION

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

METHODS

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

RESULTS

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

CONCLUSION

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

Deep Brain Stimulation in the Subthalamic Nucleus Can Improve Skilled Forelimb Movements and Retune Dynamics of Striatal Networks in a Rat Stroke Model

Recovery of upper limb (UL) impairment after stroke is limited in stroke survivors. Since stroke can be considered as a network disorder, neuromodulation may be an approach to improve UL motor dysfunction. Here, we evaluated the effect of high-frequency stimulation (HFS) of the subthalamic nucleus (STN) in rats on forelimb grasping using the single-pellet reaching (SPR) test after stroke and determined costimulated brain regions during STN-HFS using 2-[18F]Fluoro-2-deoxyglucose-([18F]FDG)-positron emission tomography (PET). After a 4-week training of SPR, photothrombotic stroke was induced in the sensorimotor cortex of the dominant hemisphere. Thereafter, an electrode was implanted in the STN ipsilateral to the infarction, followed by a continuous STN-HFS or sham stimulation for 7 days. On postinterventional day 2 and 7, an SPR test was performed during STN-HFS. Success rate of grasping was compared between these two time points. [18F]FDG-PET was conducted on day 2 and 3 after stroke, without and with STN-HFS, respectively. STN-HFS resulted in a significant improvement of SPR compared to sham stimulation. During STN-HFS, a significantly higher [18F]FDG-uptake was observed in the corticosubthalamic/pallidosubthalamic circuit, particularly ipsilateral to the stimulated side. Additionally, STN-HFS led to an increased glucose metabolism within the brainstem. These data demonstrate that STN-HFS supports rehabilitation of skilled forelimb movements, probably by retuning dysfunctional motor centers within the cerebral network.

Central precuneus lesions are associated with impaired executive function

The functional roles of the precuneus are unclear. Focal precuneus lesions are rare, making it difficult to identify robust brain-behavior relationships. Distinct functional subdivisions of the precuneus have been proposed based on unique connectivity profiles. This includes an association of the anterior division with bodily awareness, the central region with complex cognition, and the posterior division with visual processing. Our goal was to test the hypothesis that the central precuneus is preferentially involved (compared to the other sectors of the precuneus) in executive function, as estimated from performance on the trail-making test (TMT). 35 patients with focal brain lesions involving the precuneus were included from the University of Iowa and Montpellier University. Multivariate lesion symptom mapping of TMT performance was performed to evaluate whether lesion location was associated with impaired task performance. Lesion symptom mapping revealed a statistically significant association of central precuneus lesions with impaired TMT performance (r = 0.43, p < 0.01). Further, a functional network derived from this precuneus region showed connectivity to other cortical areas implicated in executive function, including the dorsolateral prefrontal cortex and inferior parietal lobe. This analysis provides support for the role of the central precuneus in executive function, consistent with the unique connectivity pattern of the central precuneus with a broader network implicated in cognitive control and executive function.

Mapping lesion, structural disconnection, and functional disconnection to symptoms in semantic aphasia

Patients with semantic aphasia have impaired control of semantic retrieval, often accompanied by executive dysfunction following left hemisphere stroke. Many but not all of these patients have damage to the left inferior frontal gyrus, important for semantic and cognitive control. Yet semantic and cognitive control networks are highly distributed, including posterior as well as anterior components. Accordingly, semantic aphasia might not only reflect local damage but also white matter structural and functional disconnection. Here, we characterise the lesions and predicted patterns of structural and functional disconnection in individuals with semantic aphasia and relate these effects to semantic and executive impairment. Impaired semantic cognition was associated with infarction in distributed left-hemisphere regions, including in the left anterior inferior frontal and posterior temporal cortex. Lesions were associated with executive dysfunction within a set of adjacent but distinct left frontoparietal clusters. Performance on executive tasks was also associated with interhemispheric structural disconnection across the corpus callosum. In contrast, poor semantic cognition was associated with small left-lateralized structurally disconnected clusters, including in the left posterior temporal cortex. Little insight was gained from functional disconnection symptom mapping. These results demonstrate that while left-lateralized semantic and executive control regions are often damaged together in stroke aphasia, these deficits are associated with distinct patterns of structural disconnection, consistent with the bilateral nature of executive control and the left-lateralized yet distributed semantic control network.

Longitudinal prediction of motor dysfunction after stroke: a disconnectome study

Motricity is the most commonly affected ability after a stroke. While many clinical studies attempt to predict motor symptoms at different chronic time points after a stroke, longitudinal acute-to-chronic studies remain scarce. Taking advantage of recent advances in mapping brain disconnections, we predict motor outcomes in 62 patients assessed longitudinally two weeks, three months, and one year after their stroke. Results indicate that brain disconnection patterns accurately predict motor impairments. However, disconnection patterns leading to impairment differ between the three-time points and between left and right motor impairments. These results were cross-validated using resampling techniques. In sum, we demonstrated that while some neuroplasticity mechanisms exist changing the structure-function relationship, disconnection patterns prevail when predicting motor impairment at different time points after stroke.

Advanced Diffusion MRI of Stroke Recovery

There is an urgent need for ways to improve our understanding of poststroke recovery to inform the development of novel rehabilitative interventions, and improve the clinical management of stroke patients. Supported by the notion that predictive information on poststroke recovery is embedded not only in the individual brain regions, but also the connections throughout the brain, majority of previous investigations have focused on the relationship between brain functional connections and post-stroke deficit and recovery. However, considering the fact that it is the static anatomical brain connections that constrain and facilitate the dynamic functional brain connections, the microstructures and structural connections of the brain may potentially be better alternatives to the functional MRI-based biomarkers of stroke recovery. This review, therefore, seeks to provide an overview of the basic concept and applications of two recently proposed advanced diffusion MRI techniques, namely lesion network mapping and fixel-based morphometry, that may be useful for the investigation of stroke recovery at the local and global levels of the brain. This review will also highlight the application of some of other emerging advanced diffusion MRI techniques that warrant further investigation in the context of stroke recovery research.

Inferring the dynamical effects of stroke lesions through whole-brain modeling

Understanding the effect of focal lesions (stroke) on brain structure-function traditionally relies on behavioral analyses and correlation with neuroimaging data. Here we use structural disconnection maps from individual lesions to derive a causal mechanistic generative whole-brain model able to explain both functional connectivity alterations and behavioral deficits induced by stroke. As compared to other models that use only the local lesion information, the similarity to the empirical fMRI connectivity increases when the widespread structural disconnection information is considered. The presented model classifies behavioral impairment severity with higher accuracy than other types of information (e.g.: functional connectivity). We assessed topological measures that characterize the functional effects of damage. With the obtained results, we were able to understand how network dynamics change emerge, in a nontrivial way, after a stroke injury of the underlying complex brain system. This type of modeling, including structural disconnection information, helps to deepen our understanding of the underlying mechanisms of stroke lesions.