Lesion Network Mapping – A Novel Tool for Neurologists in Localizing Single Cases with Unusual Clinical Presentations
Bibliographic record
Abstract
Sir, Lesion-network mapping is a recently validated technique that identifies regions functionally connected to a lesion location, allowing one to localize symptoms even when lesions occur in different brain locations.[1] This has been used widely to study various neurological conditions that have well-defined brain lesions (e.g. Peduncular hallucinosis, post-stroke pain, mania due to structural lesions, central hypoventilation following stroke) by identifying the brain networks connected to it.[1] This approach involves three steps: (i) transferring the brain lesion onto a reference brain (ii) assessing the functional connectivity of the lesion volume with the rest of the brain using normative connectome data; and (iii) statistical testing or overlapping the networks to identify regions common to a clinical syndrome. Here, we use this network localization approach to determine the neuroanatomical substrate for single case of a patient with isolated unilateral lower limb sensory loss following a lateral medullary infarction. 74-year-old diabetic and hypertensive, presented with sudden onset numbness over the left lower limb. There was no sensory loss involving the upper limbs. There was no history of dysphagia, dysarthria, or vertigo. There was no history of bowel or bladder dysfunction. On examination, he was conscious and oriented, had no cranial nerve palsies or limb weakness He had decreased sensations to pain and proprioception localized to the left lower limb with preserved touch and vibration sense. Though he had no cerebellar signs on testing he swayed towards the left while walking. In view of isolated sensory loss over the lower limb- the following sites were considered: partial thoracic spinal cord, sensory cortex- leg area, and thalamus. His MRI brain showed a small laterally placed infarct in the caudal medulla. The lesion was mapped onto a template brain in Montreal Neurological Institute (MNI) space manually using MRIcron (https://www.nitrc.org/projects/mricron. Lesion network mapping is a recently validated technique that identifies regions functionally connected to a lesion location, allowing one to localize symptoms even when lesions occur in different brain locations using normal connectome data.[1] An open-source normative rs-fMRI dataset assembled from the 1000 healthy Brain Genomics Superstruct Project (https://dataverse.harvard.edu/dataverse/GSP) was used for lesion network mapping.[2] Pre-processing of resting-state fMRI data was performed with SPM-12 (Wellcome Department of Imaging Neuroscience, London, UK) and the CONN functional connectivity toolbox[3] both implemented in Matlab R2015a (The MathWorks. Natick, MA, USA). The lesion map was used as seed in a resting-state functional connectivity MRI analysis using the CONN toolbox. A connectivity r-map thus obtained for the individual lesion was converted to t- maps and thresholded at t > ±9 to create a binarized map of significantly functionally connected regions to the lesion site (whole-brain voxel-wise FWE-corrected P < 0.05; uncorrected P < 10−<).[1,4,5] The results of the lesion network mapping showed significant connectivity of the lesion location to the ipsilateral sensory cortex and lobe 2 of the cerebellum [Figure 1].Figure 1: Method of network localization and the results. The patient’s MRI brain is viewed and the lesion is mapped to the template brain. The brain network associated with the lesion was identified using resting-state functional connectivity from a large cohort of normal subjects. The lesion network map was thresholded at T ≥ 9 and the regions of significant connectivity was identified over the medial aspect of the sensory cortex (MNI coordinates x = –18, y = –42, z = +78) and lobe 2 of the cerebellum (x = –4, y = –86, z = –34)Our patient presented with symptoms of sudden onset isolated lower limb numbness. As there were no associated symptoms, his findings were difficult to localize clinically. Though the MRI revealed a lateral medullary infarction, the mechanism by which the medullary lesion produced the current clinical findings was not clear. Based on literature it is known that lateral medullary infarction cause rarely produces an isolated lower sensory loss and can also have a combination of ipsilateral pain and proprioceptive involvement.[6] Animal studies have shown that in addition to pain and temperature the spinolthalamic tracts also carry proprioceptive information.[7] Since location of the lesion is in the region of the spinothalamic tract we postulated that the patient’s symptoms were due to disruption of the spinothalmic tract. The symptoms could thus be explained by possible damage to proprioceptive fibers in the spinothalmic tract or by involvement of the ascending or decussating dorsal column sensory tracts traversing to the opposite medial lemniscus.[6] As a proof to our hypothesis, the results of the network localization were able to demonstrate the connectivity of the lesion location in the lateral medulla to the sensory cortex. Thus demonstrating, the patient’s unilateral lower limb sensory loss was due to disruption of the sensory afferents at the level of the spinothalamic tract in the medulla. Neuroimaging technology has grown exponentially in the last few years and very often this know-how is restricted to the neuroscience community and does not percolate to clinical neurology. Our study highlights that in patients with unusual clinical presentations and lesion locations, the networks connected to the lesion, using the methods of lesion mapping technology can help the neurologist in localization. Lesion network mapping is a useful tool in the setting of uncommon clinical presentations. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".