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

Abstract

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.

Keywords: NIHSS score; acute ischemic stroke; brain connectivity; normative connectome; sensorimotor deficits.

FIGURE 1

The figure shows a step-by-step overview for processing MRI scans and mapping fiber tracts to investigate the effects of stroke lesions on brain connectivity. We start with co-registration of FLAIR to T1-weighted images using SPM12 and utilize the derived parameters to align lesion masks. Subsequently, the Lesion Segmentation Toolbox (LST) is employed for lesion identification and filling on T1 images, a process designed to mitigate the bias of spatial normalization to the MNI152NLin2009cAsym space with CAT12. The HCP1065 Population-Averaged Tractography Atlas then facilitates the identification of fiber tracts intersecting the lesions. These fibers are projected onto a cortical mesh in MNI space. To ensure consistent analysis across subjects, lesion projections from the right hemisphere are mirrored to the left hemisphere. Affected cortical regions, specifically the precentral, postcentral, and superiorparietal areas deliniated by the Desikan-Killiany (DK40) atlas, are quantified by the logarithmically (log) scaled number of terminating fibers, elucidating the extent of disconnection following stroke. To obtain the disconnection value, we multiplied the total number from the three specified regions. The color spectrum in the fiber detection and projection images serves as an indicator of disconnection: warmer tones denote a greater number of severed fibers, whereas cooler tones indicate fewer disconnections.

FIGURE 2

This figure illustrates a more detailed description of the use of the normative connectome atlas. The used HCP1065 atlas provides coordinates for 0.5 million fiber tracts, resampled to match our 1.5 mm voxel size. Each voxel now contains a count of fibers, log-transformed for standardization. This helps to identify fibers that cross lesions. Fibers traversing lesions are identified, creating a map (A) showing their full length and endpoints. The CAT12 surface mesh of the MNI152NLin2009cAsym template now maps voxel values using inward surface normals to find maximum values within a given depth (B). The resulting surface map (C) finally encodes the number of fibers traversing lesions. Again, values are stored as log-transformed values to account for the wide range of values.

FIGURE 3

This figure illustrates the mapping of the average number of fibers intersecting stroke lesions in 51 stroke patients. The data are projected onto a standardized brain template in MNI152NLin2009cAsym space, allowing precise localization of affected brain regions. Logarithmic scaling is used to manage the wide range of fiber counts, allowing more interpretable visualization. A value on this scale means that, on average, 10 fibers overlap a given point on the brain surface. The projection highlights the cortical areas most affected by the stroke, providing insight into the impact of the lesion on brain structure. This technique makes it possible to quantify the extent of disconnection in specific regions.

FIGURE 4

Correlation analysis: The left panel illustrates a linear regression analysis depicting the relationship between the NIHSS scores and the logarithmically transformed lesion sizes. The right panel shows the correlation between NIHSS scores and disconnection values.