An Individualized Tractography Pipeline for the Nucleus Basalis of Meynert Lateral Tract.


Journal

medRxiv : the preprint server for health sciences
Titre abrégé: medRxiv
Pays: United States
ID NLM: 101767986

Informations de publication

Date de publication:
01 Sep 2023
Historique:
pubmed: 11 9 2023
medline: 11 9 2023
entrez: 11 9 2023
Statut: epublish

Résumé

At the center of the cortical cholinergic network, the nucleus basalis of Meynert (NBM) is crucial for the cognitive domains most vulnerable in PD. Preclinical evidence has demonstrated the positive impact of NBM deep brain stimulation (DBS) on cognition but early human trials have had mixed results. It is possible that DBS of the lateral NBM efferent white matter fiber bundle may be more effective at improving cognitive-motor function. However, precise tractography modelling is required to identify the optimal target for neurosurgical planning. Individualized tractography approaches have been shown to be highly effective for accurately identifying DBS targets but have yet to be developed for the NBM. Using structural and diffusion weighted imaging, we developed a tractography pipeline for precise individualized identification of the lateral NBM target tract. Using dice similarity coefficients, the reliability of the tractography outputs was assessed across three cohorts to investigate: 1) whether this manual pipeline is more reliable than an existing automated pipeline currently used in the literature; 2) the inter- and intra-rater reliability of our pipeline in research scans of patients with PD; and 3) the reliability and practicality of this pipeline in clinical scans of DBS patients. The individualized manual pipeline was found to be significantly more reliable than the existing automated pipeline for both the segmentation of the NBM region itself (p<0.001) and the reconstruction of the target lateral tract (p=0.002). There was also no significant difference between the reliability of two different raters in the PD cohort (p=0.25), which showed high inter- (mean Dice coefficient >0.6) and intra-rater (mean Dice coefficient >0.7) reliability across runs. Finally, the pipeline was shown to be highly reliable within the clinical scans (mean Dice coefficient = 0.77). However, accurate reconstruction was only evident in 7/10 tracts. We have developed a reliable tractography pipeline for the identification and analysis of the NBM lateral tract in research and clinical grade imaging of healthy young adult and PD patient scans.

Sections du résumé

Background UNASSIGNED
At the center of the cortical cholinergic network, the nucleus basalis of Meynert (NBM) is crucial for the cognitive domains most vulnerable in PD. Preclinical evidence has demonstrated the positive impact of NBM deep brain stimulation (DBS) on cognition but early human trials have had mixed results. It is possible that DBS of the lateral NBM efferent white matter fiber bundle may be more effective at improving cognitive-motor function. However, precise tractography modelling is required to identify the optimal target for neurosurgical planning. Individualized tractography approaches have been shown to be highly effective for accurately identifying DBS targets but have yet to be developed for the NBM.
Methods UNASSIGNED
Using structural and diffusion weighted imaging, we developed a tractography pipeline for precise individualized identification of the lateral NBM target tract. Using dice similarity coefficients, the reliability of the tractography outputs was assessed across three cohorts to investigate: 1) whether this manual pipeline is more reliable than an existing automated pipeline currently used in the literature; 2) the inter- and intra-rater reliability of our pipeline in research scans of patients with PD; and 3) the reliability and practicality of this pipeline in clinical scans of DBS patients.
Results UNASSIGNED
The individualized manual pipeline was found to be significantly more reliable than the existing automated pipeline for both the segmentation of the NBM region itself (p<0.001) and the reconstruction of the target lateral tract (p=0.002). There was also no significant difference between the reliability of two different raters in the PD cohort (p=0.25), which showed high inter- (mean Dice coefficient >0.6) and intra-rater (mean Dice coefficient >0.7) reliability across runs. Finally, the pipeline was shown to be highly reliable within the clinical scans (mean Dice coefficient = 0.77). However, accurate reconstruction was only evident in 7/10 tracts.
Conclusion UNASSIGNED
We have developed a reliable tractography pipeline for the identification and analysis of the NBM lateral tract in research and clinical grade imaging of healthy young adult and PD patient scans.

Identifiants

pubmed: 37693520
doi: 10.1101/2023.08.31.23294922
pmc: PMC10491381
pii:
doi:

Types de publication

Preprint

Langues

eng

Subventions

Organisme : NINDS NIH HHS
ID : R21 NS096398
Pays : United States
Organisme : NIMH NIH HHS
ID : U54 MH091657
Pays : United States
Organisme : NINDS NIH HHS
ID : UG3 NS128150
Pays : United States
Organisme : NINDS NIH HHS
ID : UH3 NS107709
Pays : United States

Déclaration de conflit d'intérêts

Conflict of Interest JMH is a consultant for Neuralink, serves on the Medical Advisory Board of Enspire DBS, and is a shareholder in Maplight Therapeutics.

Auteurs

Rachel A Crockett (RA)

Department of Neurology and Neurological Sciences, Stanford University School of Medicine, California, USA.

Kevin B Wilkins (KB)

Department of Neurology and Neurological Sciences, Stanford University School of Medicine, California, USA.

Michael M Zeineh (MM)

Department of Radiology, Stanford University School of Medicine, California, USA.
Wu Tsai Neurosciences Institute, Stanford University, California, USA.
Bio-X, Stanford University, California, USA.

Jennifer A McNab (JA)

Department of Radiology, Stanford University School of Medicine, California, USA.
Wu Tsai Neurosciences Institute, Stanford University, California, USA.
Bio-X, Stanford University, California, USA.

Jaimie M Henderson (JM)

Wu Tsai Neurosciences Institute, Stanford University, California, USA.
Bio-X, Stanford University, California, USA.
Department of Neurosurgery, Stanford University School of Medicine, California, USA.

Vivek P Buch (VP)

Wu Tsai Neurosciences Institute, Stanford University, California, USA.
Bio-X, Stanford University, California, USA.
Department of Neurosurgery, Stanford University School of Medicine, California, USA.

Helen M Brontë-Stewart (HM)

Department of Neurology and Neurological Sciences, Stanford University School of Medicine, California, USA.
Wu Tsai Neurosciences Institute, Stanford University, California, USA.
Bio-X, Stanford University, California, USA.
Department of Neurosurgery, Stanford University School of Medicine, California, USA.

Classifications MeSH