Introduction and Aims: The European Academy of Neurology (EAN) joined the Horizon Europe EBRAINS 2.0 research consortium to co-develop recommendations for prospective acquisition and integration of clinical and research core multi-scale human datasets for connectome analysis in stroke, Parkinson's disease (PD), and glioma (GBM). Methods: Using an online Delphi survey method, expert consensus was sought on 28 statements for each of the following protocols in stroke, PD and GBM: clinical core/research brain MRI, minimum/extended clinical outcomes, minimum/extended cognition assessments, case report forms (CRF). The level of agreement for each statement was predefined as ≥ 80%. 536 experts from four EAN Scientific Panels (SP) (SP Neuro-oncology [n = 70], SP Movement Disorders [n = 206], SP Neuroimaging [n = 112], SP Stroke [n = 148]) were invited to participate in this Delphi process. Results: Two Delphi voting rounds were conducted to reach the predefined level of agreement (≥ 80%; range: 80.49%–100%) for all items to establish consensus on protocol recommendations for stroke, PD and GBM. Cumulative response rate from SP experts was 25% (n = 135 from 30 European countries) for the first, and 32% (n = 173 from 35 European countries) for the second Delphi voting round. Discussion: These consented protocols provide a pragmatic framework for harmonizing multi-scale data acquisition for connectome analyses in stroke, PD, and GBM. Further, these protocols allow future modular extensions for other datasets, e.g., neurophysiological data or non-imaging biomarkers, and serve as a role model for brain network analyses in other neurological disorders.

Harmonized Acquisition of Connectome MRI and Clinical Data for Stroke, Parkinson's Disease and Glioma: Delphi-Derived Protocol Recommendations by the European Academy of Neurology / Berger, T., Pini, L., Salvalaggio, A., Cauzzo, S., Klein, C., Lolich, M., Picca, A., Ramankulov, D., Rocca, M.A., Ruda, R., Sodero, A., Tessitore, A., Truelsen, T., Wiest, R., Fink, G.R., Moro, E., Ryvlin, P., Corbetta, M.. - In: EUROPEAN JOURNAL OF NEUROLOGY. - ISSN 1351-5101. - 33:9(2026). [10.1111/ene.70708]

Harmonized Acquisition of Connectome MRI and Clinical Data for Stroke, Parkinson's Disease and Glioma: Delphi-Derived Protocol Recommendations by the European Academy of Neurology

Rocca M. A.;
2026-01-01

Abstract

Introduction and Aims: The European Academy of Neurology (EAN) joined the Horizon Europe EBRAINS 2.0 research consortium to co-develop recommendations for prospective acquisition and integration of clinical and research core multi-scale human datasets for connectome analysis in stroke, Parkinson's disease (PD), and glioma (GBM). Methods: Using an online Delphi survey method, expert consensus was sought on 28 statements for each of the following protocols in stroke, PD and GBM: clinical core/research brain MRI, minimum/extended clinical outcomes, minimum/extended cognition assessments, case report forms (CRF). The level of agreement for each statement was predefined as ≥ 80%. 536 experts from four EAN Scientific Panels (SP) (SP Neuro-oncology [n = 70], SP Movement Disorders [n = 206], SP Neuroimaging [n = 112], SP Stroke [n = 148]) were invited to participate in this Delphi process. Results: Two Delphi voting rounds were conducted to reach the predefined level of agreement (≥ 80%; range: 80.49%–100%) for all items to establish consensus on protocol recommendations for stroke, PD and GBM. Cumulative response rate from SP experts was 25% (n = 135 from 30 European countries) for the first, and 32% (n = 173 from 35 European countries) for the second Delphi voting round. Discussion: These consented protocols provide a pragmatic framework for harmonizing multi-scale data acquisition for connectome analyses in stroke, PD, and GBM. Further, these protocols allow future modular extensions for other datasets, e.g., neurophysiological data or non-imaging biomarkers, and serve as a role model for brain network analyses in other neurological disorders.
2026
connectome
data harmonization
data sharing
digital health
network neuroscience
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11768/207518
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