The application of ℓ1‐regularized machine learning models to high‐dimensional connectomes offers a promising methodology to assess clinical‐anatomical correlations in humans. Here, we integrate the connectome‐based lesion‐symptom mapping framework with sparse partial least squares regression (sPLS‐R) to isolate elements of the connectome associated with speech repetition deficits. By mapping over 2,500 connections of the structural connectome in a cohort of 71 stroke‐induced cases of aphasia presenting with varying left‐hemisphere lesions and repetition impairment, sPLS‐R was trained on 50 subjects to algorithmically identify connectomic features on the basis of their predictive value. The highest ranking features were subsequently used to generate a parsimonious predictive model for speech repetition whose predictions were evaluated on a held‐out set of 21 subjects. A set of 10 short‐ and long‐range parieto‐temporal connections were identified, collectively delineating the broader circuitry of the dorsal white matter network of the language system. The strongest contributing feature was a short‐range connection in the supramarginal gyrus, approximating the cortical localization of area Spt, with parallel long‐range pathways interconnecting posterior nodes in supramarginal and superior temporal cortex with anterior nodes in both ventral and—notably—in dorsal premotor cortex, respectively. The collective disruption of these pathways indexed repetition performance in the held‐out set of participants, suggesting that these impairments might be characterized as a parietotemporal disconnection syndrome impacting cortical area Spt and its associated white matter circuits of the frontal lobe as opposed to being purely a disconnection of the arcuate fasciculus.
Neurosurgical resection is one of the few opportunities researchers have to image the human brain pre‐ and postfocal damage. A major challenge associated with brains undergoing surgical resection is that they often do not fit brain templates most image‐processing methodologies are based on. Manual intervention is required to reconcile the pathology, requiring time investment and introducing reproducibility concerns, and extreme cases must be excluded.
We propose an automatic longitudinal pipeline based on High Angular Resolution Diffusion Imaging acquisitions to facilitate a Pathway Lesion Symptom Mapping analysis relating focal white matter injury to functional deficits. This two‐part approach includes (i) automatic segmentation of focal white matter injury from anisotropic power differences, and (ii) modeling disconnection using tractography on the single‐subject level, which specifically identifies the disconnections associated with focal white matter damage.
The advantages of this approach stem from (1) objective and automatic lesion segmentation and tractogram generation, (2) objective and precise segmentation of affected tissue likely to be associated with damage to long‐range white matter pathways (defined by anisotropic power), (3) good performance even in the cases of anatomical distortions by use of nonlinear tensor‐based registration, which aligns images using an approach sensitive to white matter microstructure.
Mapping a system as variable and complex as the human brain requires sample sizes much larger than the current technology can support. This pipeline can be used to execute large‐scale, sufficiently powered analyses by meeting the need for an automatic approach to objectively quantify white matter disconnection.
- NSF-PAR ID:
- 10456754
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Journal of Neuroimaging
- Volume:
- 30
- Issue:
- 4
- ISSN:
- 1051-2284
- Page Range / eLocation ID:
- p. 443-457
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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