Michele De Filippo De Grazia

About Michele De Filippo De Grazia

Michele De Filippo De Grazia, With an exceptional h-index of 14 and a recent h-index of 12 (since 2020), a distinguished researcher at Università degli Studi di Padova, specializes in the field of Machine Learning, Computational Models, Sensorimotor Transformations.

His recent articles reflect a diverse array of research interests and contributions to the field:

Susceptibility to multitasking in chronic stroke is associated to damage of the multiple demand system and leads to lateralized visuospatial deficits

A developmental approach for training deep belief networks

Prediction of Neuropsychological Scores from Functional Connectivity Matrices Using Deep Autoencoders

Recovery of neural dynamics criticality in personalized whole-brain models of stroke

A comparison of feature extraction methods for prediction of neuropsychological scores from functional connectivity data of stroke patients

A novel stroke lesion network mapping approach: improved accuracy yet still low deficit prediction

Assessment of machine learning pipelines for prediction of behavioral deficits from brain disconnectomes

Sensorimotor, attentional, and neuroanatomical predictors of upper limb motor deficits and rehabilitation outcome after stroke

Michele De Filippo De Grazia Information

University

Position

Computational Cognitive Neuroscience Lab Department of General Psychology

Citations(all)

788

Citations(since 2020)

602

Cited By

366

hIndex(all)

14

hIndex(since 2020)

12

i10Index(all)

17

i10Index(since 2020)

15

Email

University Profile Page

Google Scholar

Michele De Filippo De Grazia Skills & Research Interests

Machine Learning

Computational Models

Sensorimotor Transformations

Top articles of Michele De Filippo De Grazia

Susceptibility to multitasking in chronic stroke is associated to damage of the multiple demand system and leads to lateralized visuospatial deficits

bioRxiv

2023

A developmental approach for training deep belief networks

Cognitive Computation

2023/1

Prediction of Neuropsychological Scores from Functional Connectivity Matrices Using Deep Autoencoders

2022/7/15

Alberto Testolin
Alberto Testolin

H-Index: 12

Michele De Filippo De Grazia
Michele De Filippo De Grazia

H-Index: 9

Marco Zorzi
Marco Zorzi

H-Index: 37

Recovery of neural dynamics criticality in personalized whole-brain models of stroke

Nature Communications

2022/6/27

A comparison of feature extraction methods for prediction of neuropsychological scores from functional connectivity data of stroke patients

Brain Informatics

2021/12

Alberto Testolin
Alberto Testolin

H-Index: 12

Michele De Filippo De Grazia
Michele De Filippo De Grazia

H-Index: 9

Marco Zorzi
Marco Zorzi

H-Index: 37

A novel stroke lesion network mapping approach: improved accuracy yet still low deficit prediction

Brain communications

2021/10/1

Assessment of machine learning pipelines for prediction of behavioral deficits from brain disconnectomes

2021/9/15

Sensorimotor, attentional, and neuroanatomical predictors of upper limb motor deficits and rehabilitation outcome after stroke

Neural Plasticity

2021/4/2

Reply: Lesion network mapping predicts post-stroke behavioural deficits and improves localization

Brain

2021/4/1

Reply: Lesion network mapping: where do we go from here?

Brain

2021/1/1

A systematic assessment of feature extraction methods for robust prediction of neuropsychological scores from functional connectivity data

2020/9/15

Alberto Testolin
Alberto Testolin

H-Index: 12

Michele De Filippo De Grazia
Michele De Filippo De Grazia

H-Index: 9

Marco Zorzi
Marco Zorzi

H-Index: 37

Post-stroke deficit prediction from lesion and indirect structural and functional disconnection

Brain

2020/7/1

See List of Professors in Michele De Filippo De Grazia University(Università degli Studi di Padova)

Co-Authors

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