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

 This is absolutely fucking useless research! 'Assessments' DO NOTHING TOWARDS RECOVERY! You don't point to EXACT PROTOCOLS that fix the deficits you found! You're all fired!

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

Karolin Weigel^1,2*†Carsten M. Klingner^1,2†Stefan Brodoehl^1,2Franziska Wagner^1,2Matthias Schwab¹Daniel Güllmar³Thomas E. Mayer⁴Felix V. Güttler⁵Ulf Teichgräber⁵Christian Gaser^1,2,6,7

• ¹Department of Neurology, Jena University Hospital, Jena, Germany
• ²Biomagnetic Center, Jena University Hospital, Jena, Germany
• ³Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital, Jena, Germany
• ⁴Section Neuroradiology, Institute of Diagnostic and Interventional Radiology, Jena University Hospital, Jena, Germany
• ⁵Institute of Diagnostic and Interventional Radiology, Jena University Hospital, Jena, Germany
• ⁶Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
• ⁷German Center for Mental Health (DZPG), Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany

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

1 Introduction

Stroke, as the second leading cause of death and the leading cause of acquired disability, remains a major burden in Western countries (Katan and Luft, 2018; Gbd 2019 Stroke Collaborators, 2021). Over the past decade, substantial advancements have been made in both diagnostic and therapeutic strategies for acute stroke. Perfusion imaging has emerged as a vital tool, offering insights into potentially salvageable at-risk tissue. Acute treatment modalities, notably thrombolysis therapy and mechanical thrombectomy, hold tremendous promise. However, they are not without risks, necessitating a careful evaluation of the potential benefits against inherent risks.

One of the challenges in acute stroke management is the absence of a reliable method to assess the future clinical implications of a specific lesion. The inability to accurately predict untreated outcomes, both in the immediate aftermath and over the long term, complicates acute treatment decisions. These studies are mostly based on either the National Institutes of Health Stroke Scale (NIHSS score) or the volume of brain tissue at risk (Goyal et al., 2016; Ma et al., 2019). However, more detailed knowledge of the potential harm of a particular lesion would be extremely useful.

The critical need to address this issue has spurred a plethora of studies aiming to predict patients’ clinical outcomes, employing various data types. These approaches can be broadly categorized into three main strategies: (i) analysis of pure clinical data, (ii) assessment of lesion size and location, and (iii) integration of network connectivity information.

Efforts to forecast clinical outcomes based solely on clinical data have yielded moderate results, indicating that patients with more severe initial symptoms generally experience poorer outcomes (Sato et al., 2008; Sablot et al., 2011; Wouters et al., 2018; Kazi et al., 2021). Another approach involves incorporating the size and location of the lesion in outcome prediction models. Initially, this approach was expected to enhance prediction accuracy significantly, as the lesion directly contributes to clinical symptoms and potential persistent disability. However, despite considerable efforts, the results have not reached a level of accuracy applicable in clinical practice (Schiemanck et al., 2005; Yoo et al., 2010). This limitation extends to other parameters in prediction models, such as cortical thickness in certain contralesional cortices (Rojas Albert et al., 2022) and metrics of brain age and resilience (Liew et al., 2023).

To obtain data on functional or structural connectivity, resting state functional magnetic resonance imaging (rs-fMRI), diffusion tensor imaging (DTI) and connectivity analyses have been employed (Horn et al., 2014; Ktena et al., 2019; Kao et al., 2020; Koch et al., 2021; Peng et al., 2023). However, these data (rs-fMRI, DTI) are not typically gathered during routine diagnostics in stroke cases. Moreover, obtaining them requires patient cooperation and extends the examination time, rendering them impractical in the hyperacute phase of stroke, where therapeutic decisions are time-sensitive. In acute stroke diagnostics, a diffusion sequence and FLAIR are necessarily acquired in MRI. Our approach utilizes a T1 and FLAIR data set, whereby even the T1 could be generated from the FLAIR (Iglesias et al., 2023). This emphasis on basic MRI sequences reduces the MRI acquisition time to just several minutes. Consequently, systemic thrombolysis and mechanical thrombectomy can be rapidly initiated during the hyperacute phase of stroke.

Our alternative approach combines individual MRI data with probabilistic information sourced from the Human Connectome Project (HCP). We hypothesized that the clinical state of a patient can be predicted using probabilistic information from the HCP, rather than individual DTI data. Specifically, we posit that this connectome information could enhance the predictability of clinical stroke severity, as measured by the NIHSS score, beyond what can be achieved by considering lesion size alone. To estimate the number of fibers traversing the lesion, we calculated the number of disconnected fibers and used the term disconnection value. In order to evaluate the hypothesis, we assessed the number of disconnected fiber tracts to specific cortical areas (quantified by the disconnection value), drawing on probabilistic data from the HCP alongside individual T1 and T2-weighted fluid-attenuated inversion recovery (FLAIR) imaging data from our cohort of 51 patients.