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Enregistrement W4407798759 · doi:10.4103/neuroindia.ni_697_20

Lesion Network Mapping – A Novel Tool for Neurologists in Localizing Single Cases with Unusual Clinical Presentations

2025· article· en· W4407798759 sur OpenAlexaboutno aff
Appaswamy Thirumal Prabhakar, Aditya Nair, Atif Sheikh, Ajith Sivadasan, Vivek Mathew

Notice bibliographique

RevueNeurology India · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueNeurological disorders and treatments
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineLesionClinical neurologyRadiologyPathologyNeuroscience

Résumé

récupéré en direct d'OpenAlex

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 &#8805; 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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,015
Score d'incertitude au seuil0,577

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,102
Tête enseignante GPT0,378
Écart entre enseignants0,276 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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