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Resting State Functional Networks in Gliomas: Validation With Direct Electric Stimulation of a New Tool for Planning Brain Resections.
Moretto, Manuela; Luciani, Beatrice Federica; Zigiotto, Luca; Saviola, Francesca; Tambalo, Stefano; Cabalo, Donna Gift; Annicchiarico, Luciano; Venturini, Martina; Jovicich, Jorge; Sarubbo, Silvio.
Afiliação
  • Moretto M; Center for Mind/Brain Sciences (CIMeC), University of Trento, Trento, Italy.
  • Luciani BF; Center for Mind/Brain Sciences (CIMeC), University of Trento, Trento, Italy.
  • Zigiotto L; Department of Neurosurgery, "S. Chiara" University-Hospital, Azienda Provinciale per i Servizi Sanitari, Trento, Italy.
  • Saviola F; Department of Psychology, University of Trento, Trento, Italy.
  • Tambalo S; Center for Mind/Brain Sciences (CIMeC), University of Trento, Trento, Italy.
  • Cabalo DG; Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy.
  • Annicchiarico L; Center for Mind/Brain Sciences (CIMeC), University of Trento, Trento, Italy.
  • Venturini M; Center for Mind/Brain Sciences (CIMeC), University of Trento, Trento, Italy.
  • Jovicich J; Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.
  • Sarubbo S; Department of Neurosurgery, "S. Chiara" University-Hospital, Azienda Provinciale per i Servizi Sanitari, Trento, Italy.
Neurosurgery ; 2024 Jun 05.
Article em En | MEDLINE | ID: mdl-38836617
ABSTRACT
BACKGROUND AND

OBJECTIVES:

Precise mapping of functional networks in patients with brain tumor is essential for tailoring personalized treatment strategies. Resting-state functional MRI (rs-fMRI) offers an alternative to task-based fMRI, capable of capturing multiple networks within a single acquisition, without necessitating task engagement. This study demonstrates a strong concordance between preoperative rs-fMRI maps and the gold standard intraoperative direct electric stimulation (DES) mapping during awake surgery.

METHODS:

We conducted an analysis involving 28 patients with glioma who underwent awake surgery with DES mapping. A total of 100 DES recordings were collected to map sensorimotor (SMN), language (LANG), visual (VIS), and speech articulation cognitive domains. Preoperative rs-fMRI maps were generated using an updated version of the ReStNeuMap software, specifically designed for rs-fMRI data preprocessing and automatic detection of 7 resting-state networks (SMN, LANG, VIS, speech articulation, default mode, frontoparietal, and visuospatial). To evaluate the agreement between these networks and those mapped with invasive cortical mapping, we computed patient-specific distances between them and intraoperative DES recordings.

RESULTS:

Automatically detected preoperative functional networks exhibited excellent agreement with intraoperative DES recordings. When we spatially compared DES points with their corresponding networks, we found that SMN, VIS, and speech articulatory DES points fell within the corresponding network (median distance = 0 mm), whereas for LANG a median distance of 1.6 mm was reported.

CONCLUSION:

Our findings show the remarkable consistency between key functional networks mapped noninvasively using presurgical rs-fMRI and invasive cortical mapping. This evidence highlights the utility of rs-fMRI for personalized presurgical planning, particularly in scenarios where awake surgery with DES is not feasible to protect eloquent areas during tumor resection. We have made the updated tool for automated functional network estimation publicly available, facilitating broader utilization of rs-fMRI mapping in various clinical contexts, including presurgical planning, functional reorganization over follow-up periods, and informing future treatments such as radiotherapy.

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article