A machine learning model identifies a functional connectome signature that predicts blood pressure levels: imaging insights from a large population of 35 882 patients

Roberta Avvisato; Imma Forzano; Fahimeh Varzideh; Pasquale Mone; Gaetano Santulli

doi:10.1093/cvr/cvad065

A machine learning model identifies a functional connectome signature that predicts blood pressure levels: imaging insights from a large population of 35 882 patients

Roberta Avvisato, Imma Forzano, Fahimeh Varzideh, Pasquale Mone, Gaetano Santulli

Medicine

Research output: Contribution to journal › Editorial › peer-review

5 Scopus citations

Original language	English (US)
Pages (from-to)	1458-1460
Number of pages	3
Journal	Cardiovascular research
Volume	119
Issue number	7
DOIs	https://doi.org/10.1093/cvr/cvad065
State	Published - Jun 1 2023

ASJC Scopus subject areas

General Medicine

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10.1093/cvr/cvad065

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A machine learning model identifies a functional connectome signature that predicts blood pressure levels: imaging insights from a large population of 35 882 patients. / Avvisato, Roberta; Forzano, Imma; Varzideh, Fahimeh et al.
In: Cardiovascular research, Vol. 119, No. 7, 01.06.2023, p. 1458-1460.

Research output: Contribution to journal › Editorial › peer-review

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title = "A machine learning model identifies a functional connectome signature that predicts blood pressure levels: imaging insights from a large population of 35 882 patients",

author = "Roberta Avvisato and Imma Forzano and Fahimeh Varzideh and Pasquale Mone and Gaetano Santulli",

note = "Funding Information: The Santulli's Lab is supported in part by the National Institutes of Health ( NIH ): National Heart, Lung, and Blood Institute ( NHLBI : R01-HL164772, R01-HL159062, R01-HL146691, T32-HL144456), National Institute of Diabetes and Digestive and Kidney Diseases ( NIDDK : R01-DK123259, R01-DK033823), National Center for Advancing Translational Sciences ( NCATS : UL1-TR002556-06, UM1-TR004400) to G.S., by the Diabetes Action Research and Education Foundation (to G.S.), and by the Monique Weill-Caulier and Irma T. Hirschl Trusts (to G.S.). F.V. is supported in part by a postdoctoral fellowship of the American Heart Association ( AHA : 22POST915561).",

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AU - Mone, Pasquale

AU - Santulli, Gaetano

N1 - Funding Information: The Santulli's Lab is supported in part by the National Institutes of Health ( NIH ): National Heart, Lung, and Blood Institute ( NHLBI : R01-HL164772, R01-HL159062, R01-HL146691, T32-HL144456), National Institute of Diabetes and Digestive and Kidney Diseases ( NIDDK : R01-DK123259, R01-DK033823), National Center for Advancing Translational Sciences ( NCATS : UL1-TR002556-06, UM1-TR004400) to G.S., by the Diabetes Action Research and Education Foundation (to G.S.), and by the Monique Weill-Caulier and Irma T. Hirschl Trusts (to G.S.). F.V. is supported in part by a postdoctoral fellowship of the American Heart Association ( AHA : 22POST915561).

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A machine learning model identifies a functional connectome signature that predicts blood pressure levels: imaging insights from a large population of 35 882 patients

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