Prediction of the information processing speed performance in multiple sclerosis using a machine learning approach in a large multicenter magnetic resonance imaging data set-Reference-Cited by-同舟云学术

Prediction of the information processing speed performance in multiple sclerosis using a machine learning approach in a large multicenter magnetic resonance imaging data set

Published:2022-10-18 Issue:1 Volume:44 Page:186-202
ISSN:1065-9471
Container-title:Human Brain Mapping
language:en
Short-container-title:Human Brain Mapping

Author:

Marzi Chiara¹²^ORCID,d'Ambrosio Alessandro¹,Diciotti Stefano²³^ORCID,Bisecco Alvino¹^ORCID,Altieri Manuela¹⁴^ORCID,Filippi Massimo⁵⁶^ORCID,Rocca Maria Assunta⁵⁶^ORCID,Storelli Loredana⁵^ORCID,Pantano Patrizia⁷⁸^ORCID,Tommasin Silvia⁷^ORCID,Cortese Rosa⁹^ORCID,De Stefano Nicola⁹^ORCID,Tedeschi Gioacchino¹^ORCID,Gallo Antonio¹^ORCID,

Affiliation:

1. MS Center and 3T‐MRI Research Unit, Department of Advanced Medical and Surgical Sciences (DAMSS) University of Campania “Luigi Vanvitelli” Napoli Italy

2. Department of Electrical, Electronic, and Information Engineering “Guglielmo Marconi” – DEI Alma Mater Studiorum – University of Bologna Bologna Italy

3. Alma Mater Research Institute for Human‐Centered Artificial Intelligence University of Bologna Bologna Italy

4. Department of Psychology University of Campania “Luigi Vanvitelli” Napoli Italy

5. Neuroimaging Research Unit, Division of Neuroscience Vita‐Salute San Raffaele University, IRCCS San Raffaele Scientific Institute Milan Italy

6. Neurology and Neurophysiology Unit Vita‐Salute San Raffaele University, IRCCS San Raffaele Scientific Institute Milan Italy

7. Department of Human Neurosciences Sapienza University of Rome Rome Italy

8. IRCCS Neuromed Pozzilli Italy

9. Department of Medicine, Surgery and Neuroscience University of Siena Siena Italy

Funder

Fondazione Italiana Sclerosi Multipla

Publisher

Wiley

Subject

Neurology (clinical),Neurology,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology,Anatomy

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/hbm.26106

Reference102 articles.

1. Neocortical volume decrease in relapsing-remitting MS patients with mild cognitive impairment

2. The Rao’s Brief Repeatable Battery and Stroop test: normative values with age, education and gender corrections in an Italian population

3. Relationships between years of education and gray matter volume, metabolism and functional connectivity in healthy elders

4. The clinico-radiological paradox in multiple sclerosis revisited

5. Evaluating and reducing the impact of white matter lesions on brain volume measurements

Cited by 18 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Predicting multiple sclerosis disease progression and outcomes with machine learning and MRI-based biomarkers: a review;Journal of Neurology;2024-09-12

2. Differentiating broadcast from viral: a causal inference approach for information diffusion analysis;Applied Intelligence;2024-08-14

3. Radiomics Nomograms Based on Multi-sequence MRI for Identifying Cognitive Impairment and Predicting Cognitive Progression in Relapsing-Remitting Multiple Sclerosis;Academic Radiology;2024-08

4. Impact of dance classes on motor and cognitive functions and gut microbiota composition in multiple sclerosis patients: Randomized controlled trial;European Journal of Sport Science;2024-07-05

5. Interpretable and Intuitive Machine Learning Approaches for Predicting Disability Progression in Relapsing-Remitting Multiple Sclerosis Based on Clinical and Gray Matter Atrophy Indicators;Academic Radiology;2024-07