Literature DB >> 11236877

Electrical impedance tomography of human brain activity with a two-dimensional ring of scalp electrodes.

A T Tidswell1, A Gibson, R H Bayford, D S Holder.   

Abstract

Previously, electrical impedance tomography (EIT) has been used to image impedance decreases in the exposed cortex of rabbits during brain activity. These are due to increased blood volume at the site of the stimulated cortex; as blood has a lower impedance than brain, the impedance decreases. During human brain activity similar blood flow changes have been detected using positron emission tomography (PET) and functional magnetic resonance imaging (fMRI). If blood volume also changes then the impedance of human cortex will change during brain activity; this could theoretically be imaged with EIT. EIT data were recorded from a ring of 16 scalp electrodes in 34 recordings in 19 adult volunteers before, during and after stimulation with (1) a visual stimulus produced by an 8 Hz oscillating checkerboard pattern or (2) sensory stimulation of the median nerve at the wrist by a 3 Hz electrical square wave stimulus. Reproducible impedance changes, with a similar timecourse to the stimulus, were seen in all experiments. Significant impedance changes were seen in 21 +/- 5% (n = 16, mean +/- SEM) and 19 +/- 3% (n = 18) of the electrode measurements for visual and somatosensory paradigms respectively. The reconstructed 2D EIT images showed reproducible impedance changes in the approximate region of the stimulated cortex in 7/16 visual and 5/18 somatosensory experiments. This demonstrates that reproducible impedance changes can be measured during human brain activity. The final images contained spatial noise; the reasons for this and strategies to reduce this in future are discussed.

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Mesh:

Year:  2001        PMID: 11236877     DOI: 10.1088/0967-3334/22/1/320

Source DB:  PubMed          Journal:  Physiol Meas        ISSN: 0967-3334            Impact factor:   2.833


  10 in total

1.  A 3D reconstruction algorithm for EIT using a handheld probe for breast cancer detection.

Authors:  Tzu-Jen Kao; D Isaacson; J C Newell; G J Saulnier
Journal:  Physiol Meas       Date:  2006-04-18       Impact factor: 2.833

2.  Reducing boundary effects in static EIT imaging.

Authors:  Tzu-Jen Kao; Bong Seok Kim; D Isaacson; J C Newell; G J Saulnier
Journal:  Physiol Meas       Date:  2006-04-18       Impact factor: 2.833

3.  EIT image reconstruction with four dimensional regularization.

Authors:  Tao Dai; Manuchehr Soleimani; Andy Adler
Journal:  Med Biol Eng Comput       Date:  2008-07-17       Impact factor: 2.602

4.  A robust current pattern for the detection of intraventricular hemorrhage in neonates using electrical impedance tomography.

Authors:  T Tang; Sungho Oh; R J Sadleir
Journal:  Ann Biomed Eng       Date:  2010-03-18       Impact factor: 3.934

5.  Electrode configurations for detection of intraventricular haemorrhage in the premature neonate.

Authors:  R J Sadleir; Te Tang
Journal:  Physiol Meas       Date:  2008-12-15       Impact factor: 2.833

6.  Detection of intraventricular blood using EIT in a neonatal piglet model.

Authors:  R J Sadleir; Te Tang; Aaron S Tucker; Peggy Borum; Michael Weiss
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2009

7.  Intracranial electrical impedance tomography: a method of continuous monitoring in an animal model of head trauma.

Authors:  Preston K Manwaring; Karen L Moodie; Alexander Hartov; Kim H Manwaring; Ryan J Halter
Journal:  Anesth Analg       Date:  2013-07-10       Impact factor: 5.108

8.  Wavelet-based artifact identification and separation technique for EEG signals during galvanic vestibular stimulation.

Authors:  Mani Adib; Edmond Cretu
Journal:  Comput Math Methods Med       Date:  2013-06-24       Impact factor: 2.238

Review 9.  Advances in electrical impedance tomography-based brain imaging.

Authors:  Xi-Yang Ke; Wei Hou; Qi Huang; Xue Hou; Xue-Ying Bao; Wei-Xuan Kong; Cheng-Xiang Li; Yu-Qi Qiu; Si-Yi Hu; Li-Hua Dong
Journal:  Mil Med Res       Date:  2022-02-28

10.  Stroke damage detection using classification trees on electrical bioimpedance cerebral spectroscopy measurements.

Authors:  Seyed Reza Atefi; Fernando Seoane; Thorleif Thorlin; Kaj Lindecrantz
Journal:  Sensors (Basel)       Date:  2013-08-07       Impact factor: 3.576

  10 in total

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