ArticleNeuroImage. Clinical2019
Finding maximally disconnected subnetworks with shortest path tractography.
Article in NeuroImage. Clinical, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Mean Motor Disconnectivity Predicts Hand Function Impairment After Stroke: Beyond Motor Cortex Output.Stroke · 2026Trial
- Hebbian plasticity induced by temporally coincident BCI enhances post-stroke motor recovery.Scientific reports · 2024Article
- Myelination and excitation-inhibition balance synergistically shape structure-function coupling across the human cortex.Nature communications · 2023Article
- The elusive metric of lesion load.Brain structure & function · 2023Review
- Preserved anatomical bypasses predict variance in language functions after stroke.Cortex; a journal devoted to the study of the nervous system and behavior · 2022Article
- The expanding horizons of network neuroscience: From description to prediction and control.NeuroImage · 2022Review
- Population-based tract-to-region connectome of the human brain and its hierarchical topology.Nature communications · 2022Article
- Single-case disconnectome lesion-symptom mapping: Identifying two subtypes of limb apraxia.Neuropsychologia · 2022Article
- Lesion Quantification Toolkit: A MATLAB software tool for estimating grey matter damage and white matter disconnections in patients with focal brain lesions.NeuroImage. Clinical · 2021Article
- Predicted disconnectome associated with progressive periventricular white matter ischemia.Cerebral circulation - cognition and behavior · 2021Article
- Dynamic representations in networked neural systems.Nature neuroscience · 2020Review
- Structural Disconnection of the Tool Use Network after Left Hemisphere Stroke Predicts Limb Apraxia Severity.Cerebral cortex communications · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Connectome-based lesion symptom mapping (CLSM) can be used to relate disruptions of brain network connectivity with clinical measures. We present a novel method that extends current CLSM approaches by introducing a fast reliable and accurate way for computing disconnectomes, i.e. identifying damaged or lesioned connections. We introduce a new algorithm that finds the maximally disconnected subgraph containing regions and region pairs with the greatest shared connectivity loss. After normalizing a stroke patient's segmented MRI lesion into template space, probability weighted structural connectivity matrices are constructed from shortest paths found in white matter voxel graphs of 210 subjects from the Human Connectome Project. Percent connectivity loss matrices are constructed by measuring the proportion of shortest-path probability weighted connections that are lost because of an intersection with the patient's lesion. Maximally disconnected subgraphs of the overall connectivity loss matrix are then derived using a computationally fast greedy algorithm that closely approximates the exact solution. We illustrate the approach in eleven stroke patients with hemiparesis by identifying expected disconnections of the corticospinal tract (CST) with cortical sensorimotor regions. Major disconnections are found in the thalamus, basal ganglia, and inferior parietal cortex. Moreover, the size of the maximally disconnected subgraph quantifies the extent of cortical disconnection and strongly correlates with multiple clinical measures. The methods provide a fast, reliable approach for both visualizing and quantifying the disconnected portion of a patient's structural connectome based on their routine clinical MRI, without reliance on concomitant diffusion weighted imaging. The method can be extended to large databases of stroke patients, multiple sclerosis or other diseases causing focal white matter injuries helping to better characterize clinically relevant white matter lesions and to identify biomarkers for the recovery potential of individual patients.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.