Functional coupling of simultaneous electrical and metabolic activity in the human brain-Reference-Cited by-同舟云学术

Functional coupling of simultaneous electrical and metabolic activity in the human brain

Published:2004 Issue:4 Volume:21 Page:257-270
ISSN:1065-9471
Container-title:Human Brain Mapping
language:en
Short-container-title:Hum. Brain Mapp.

Author:

Oakes Terrence R.,Pizzagalli Diego A.,Hendrick Andrew M.,Horras Katherine A.,Larson Christine L.,Abercrombie Heather C.,Schaefer Stacey M.,Koger John V.,Davidson Richard J.

Publisher

Wiley

Subject

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

Link

http://onlinelibrary.wiley.com/wol1/doi/10.1002/hbm.20004/fullpdf

Reference74 articles.

1. Metabolic rate in the right amygdala predicts negative affect in depressed patients

2. Modeling the Link between Functional Imaging and Neuronal Activity: Synaptic Metabolic Demand and Spike Rates

3. Location of Sources of Evoked Scalp Potentials: Corrections for Skull and Scalp Thicknesses

4. Evaluation of inverse methods and head models for EEG source localization using a human skull phantom

5. Alpha oscillations in brain functioning: an integrative theory

Cited by 187 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. Emotion-Induced Frontal Alpha Asymmetry as a Candidate Predictor of Relapse After Discontinuation of Antidepressant Medication;Biological Psychiatry: Cognitive Neuroscience and Neuroimaging;2024-08

2. Neural rhythms of narcissism: Facets of narcissism are associated with different neural sources in resting‐state EEG;European Journal of Neuroscience;2024-07-28

3. Metabolic mode estimated by breathing reflects long-term motor memory;2024-07-01

4. Differential roles of brain oscillations in numerical processing: evidence from resting-state EEG and mental number line;Frontiers in Human Neuroscience;2024-06-21

5. Die Rolle des EEG als Neuro-Marker für Patienten mit Depression: Ein systematischer Überblick;Fortschritte in der nicht-invasiven biomedizinischen Signalverarbeitung mit ML;2024