Universität zu Köln

Yakushev, Igor, Drzezga, Alexander and Habeck, Christian (2017). Metabolic connectivity: methods and applications. Curr. Opin. Neurol., 30 (6). S. 677 - 686. PHILADELPHIA: LIPPINCOTT WILLIAMS & WILKINS. ISSN 1473-6551

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Abstract

Purpose of review Metabolic connectivity modelling aims to detect functionally interacting brain regions based on PET recordings with the glucose analogue [F-18]fluorodeoxyglucose (FDG). Here, we outline the most popular metabolic connectivity methods and summarize recent applications in clinical and basic neuroscience. Recent findings Metabolic connectivity is modelled by various methods including a seed correlation, sparse inverse covariance estimation, independent component analysis and graph theory. Given its multivariate nature, metabolic connectivity possess added value relative to conventional univariate analyses of FDG-PET data. As such, metabolic connectivity provides valuable insights into pathophysiology and diagnosis of dementing, movement disorders, and epilepsy. Metabolic connectivity can also identify resting state networks resembling patterns of functional connectivity as derived from functional MRI data. Summary Metabolic connectivity is a valuable concept in the fast-developing field of brain connectivity, at least as reasonable as functional connectivity of functional MRI. So far, the value of metabolic connectivity is best established in neurodegenerative disorders, but studies in other brain diseases as well as in the healthy state are emerging. Growing evidence indicates that metabolic connectivity may serve a marker of normal and pathological cognitive function. A relationship of metabolic connectivity with structural and functional connectivity is yet to be established.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Yakushev, Igor UNSPECIFIED UNSPECIFIED UNSPECIFIED Drzezga, Alexander UNSPECIFIED UNSPECIFIED UNSPECIFIED Habeck, Christian UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-210071
DOI:	10.1097/WCO.0000000000000494
Journal or Publication Title:	Curr. Opin. Neurol.
Volume:	30
Number:	6
Page Range:	S. 677 - 686
Date:	2017
Publisher:	LIPPINCOTT WILLIAMS & WILKINS
Place of Publication:	PHILADELPHIA
ISSN:	1473-6551
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language INDEPENDENT COMPONENT ANALYSIS; INVERSE COVARIANCE ESTIMATION; MILD COGNITIVE IMPAIRMENT; ONSET ALZHEIMERS-DISEASE; BRAIN NETWORKS; FUNCTIONAL CONNECTIVITY; WORKING-MEMORY; HEALTHY-ADULTS; FDG-PET; ORGANIZATION Multiple languages Clinical Neurology; Neurosciences Multiple languages
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/21007