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Lara A Boyd

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2 papers

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YNIMG Journal 2025 Journal Article

Comparison of post-stroke white matter assessment using disconnectome-symptom mapping versus quantitative diffusion MRI

Martin Domin
Sook-Lei Liew
Brenton Hordacre
Lara A Boyd
Adriana B Conforto
Justin W Andrushko
Michael R Borich
Richard C Craddock

Indirect structural disconnection-symptom mapping allows white matter impairment to be determined without the need for multi-directional diffusion (MDDW) imaging for each individual. Although widely used this method has not been validated. We analyzed a multicenter dataset obtained from 166 individuals in the chronic stage after stroke with upper limb impairment quantified with Fugl-Meyer upper extremity score (FMUE) comprising stroke lesion maps and MDDW imaging. White matter integrity was quantified (1) by diffusion-tensor-imaging-based fractional anisotropy in preselected tracts (fractional anisotropy method; FAM) and (2) by extracting a percentage of tract disconnection by masking each tract of a predefined tractography atlas using the individual map (disconnection-symptom mapping; DSM). We also calculated a lateralization index for the fractional anisotropy between both hemispheres. The following tracts were tested: corticospinal tract (CST), superior lateral fasciculus (SLF) and corpus callosum (CC) but also optic radiation (OR) as a control tract. Both methods (FAM, DSM) showed comparable results for the association of white matter integrity of the CST with FMUE. DSM showed a strong association with FMUE likely because of the number of participants who failed to show an overlap of the tracts and lesion masks (for CST: n = 57 out of 166; for CC: n = 103 out of 166) whereas with FAM these participants could be used for further analyses. On the first view, our data support the use of white matter integrity quantification based on DSM in individuals with chronic stroke. However, at least one-third-of cases (for CC even worse) showed no overlap of lesion and tract resulting in artificially high associations with clinical parameters.

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YNICL Journal 2017 Journal Article

Are we armed with the right data? Pooled individual data review of biomarkers in people with severe upper limb impairment after stroke

Kathryn S Hayward
Julia Schmidt
Keith R Lohse
Sue Peters
Julie Bernhardt
Natasha A Lannin
Lara A Boyd

To build an understanding of the neurobiology underpinning arm recovery in people with severe arm impairment due to stroke, we conducted a pooled individual data systematic review to: 1) characterize brain biomarkers; 2) determine relationship(s) between biomarkers and motor outcome; and 3) establish relationship(s) between biomarkers and motor recovery. Three electronic databases were searched up to October 2, 2015. Eligible studies included adults with severe arm impairment after stroke. Descriptive statistics were calculated to characterize brain biomarkers, and pooling of individual patient data was performed using mixed-effects linear regression to examine relationships between brain biomarkers and motor outcome and recovery. Thirty-eight articles including individual data from 372 people with severe arm impairment were analysed. The majority of individuals were in the chronic (>6months) phase post stroke (51%) and had a subcortical stroke (49%). The presence of a motor evoked potential (indexed by transcranial magnetic stimulation) was the only biomarker related to better motor outcome (p =0. 02). There was no relationship between motor outcome and stroke volume (cm3), location (cortical, subcortical, mixed) or side (left vs. right), and corticospinal tract asymmetry index (extracted from diffusion weighted imaging). Only one study had longitudinal data, thus no data pooling was possible to address change over time (preventing our third objective). Based on the available evidence, motor evoked potentials at rest were the only biomarker that predicted motor outcome in individuals with severe arm impairment following stroke. Given that few biomarkers emerged, this review highlights the need to move beyond currently known biomarkers and identify new indices with sufficient variability and sensitivity to guide recovery models in individuals with severe motor impairments following stroke. PROSPERO: CRD42015026107.

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