Biophysical Linkage between MRI and EEG Amplitude in Closed Head Injury

Thatcher, Robert W.; Biver, Carl J.; McAlaster, R.; Camacho, Marc A.; Salazar, Andrés M.

doi:10.1006/nimg.1998.0330

Cited by 75 publications

(35 citation statements)

References 47 publications

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“…30 Quantitative features of brain electrical activity (QEEG) used in the BrainScope technology have been reported in the literature to be sensitive to changes in brain activity associated with TBI. [31][32][33] Further, changes in connectivity reported in TBI using diffusion tensor imaging (DTI) are consistent with the phase synchrony abnormalities reported using QEEG. 34 The features contributing most to classification algorithms used in this study included those representative of measures that reflect changes in power and frequency distributions, as well as those features that measure disturbances in connectivity between regions (including coherence, phase synchrony and asymmetry), and ratios of these quantities such that the numerator and denominator may come from different bands and channels, in order to capture temporal and spatial relationships in brain activity among different regions and frequency bands.…”

Section: Prichep Et Alsupporting

confidence: 65%

Identification of Hematomas in Mild Traumatic Brain Injury Using an Index of Quantitative Brain Electrical Activity

Prichep

Naunheim

Mould

et al. 2015

Journal of Neurotrauma

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Rapid identification of traumatic intracranial hematomas following closed head injury represents a significant health care need because of the potentially life-threatening risk they present. This study demonstrates the clinical utility of an index of brain electrical activity used to identify intracranial hematomas in traumatic brain injury (TBI) presenting to the emergency department (ED). Brain electrical activity was recorded from a limited montage located on the forehead of 394 closed head injured patients who were referred for CT scans as part of their standard ED assessment. A total of 116 of these patients were found to be CT positive (CT + ), of which 46 patients with traumatic intracranial hematomas (CT + ) were identified for study. A total of 278 patients were found to be CT negative (CT -) and were used as controls. CT scans were subjected to quanitative measurements of volume of blood and distance of bleed from recording electrodes by blinded independent experts, implementing a validated method for hematoma measurement. Using an algorithm based on brain electrical activity developed on a large independent cohort of TBI patients and controls (TBI-Index), patients were classified as either positive or negative for structural brain injury. Sensitivity to hematomas was found to be 95.7% (95% CI = 85.2, 99.5), specificity was 43.9% (95% CI = 38.0, 49.9). There was no significant relationship between the TBI-Index and distance of the bleed from recording sites (F = 0.044, p = 0.833), or volume of blood measured F = 0.179, p = 0.674). Results of this study are a validation and extension of previously published retrospective findings in an independent population, and provide evidence that a TBI-Index for structural brain injury is a highly sensitive measure for the detection of potentially life-threatening traumatic intracranial hematomas, and could contribute to the rapid, quantitative evaluation and treatment of such patients.

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Section: Prichep Et Alsupporting

confidence: 65%

Identification of Hematomas in Mild Traumatic Brain Injury Using an Index of Quantitative Brain Electrical Activity

Prichep

Naunheim

Mould

et al. 2015

Journal of Neurotrauma

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“…Unlike delta and alpha activities, theta activity is not significantly correlated with advancing age after the first two decades (Babiloni et al, 2006a) suggesting that it is not influenced by lifetime cumulative cerebral pathology. T2 relaxation time, a sensitive indicator of brain injury, correlates positively with delta amplitude, negatively with alpha and beta amplitudes, but is not significantly correlated with theta amplitude (Thatcher et al, 1998). …”

Section: Introductionmentioning

confidence: 99%

Theta EEG source localization using LORETA in partial epilepsy patients with and without medication

Clemens

Bessenyei

Fekete

et al. 2010

Clinical Neurophysiology

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Objective. To investigate and localize the sources of spontaneous, scalp-recorded theta activity in patients with partial epilepsy (PE).Methods. 9 patients with beginning, untreated PE (Group1), 31 patients with already treated PE (Group2), and 14 healthy persons were investigated by means of spectral analysis and LORETA, low resolution electromagnetic tomography (1 Hz very narrow band analysis, age-adjusted, Z-scored values). The frequency of main interest was 4 to 8 Hz Results. Group analysis. Group1 displayed bilateral theta maxima in the temporal theta area (TTA), parietal theta area (PTA), and frontal theta area (FTA). In Group2, theta activity increased all over the scalp as compared to the normative mean (Z=0) and also to Group1. Maximum activity was found in the TTA, PTA, and FTA. However, in the PTA and FTA the centers of the abnormality shifted towards the medial cortex. Individual analysis: all the patients showed preferential activation (maximum Zvalues) within one of the three theta areas. 2Conclusion. EEG activity in the theta band is increased in anatomically meaningful patterns in PE patients, which differs from the anatomical distribution of theta in healthy persons.Significance. The findings contribute to our understanding of the sources of theta rhythms and the pathophysiology of PE.

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“…• Monitor alertness, coma and brain death [54,55] • Locate areas of damage following head injuries [56,57], stroke [58,59] or brain haemorrhage [60,61] • Detect Alzheimer's disease [62][63][64][65] and brain tumours [66,67] • Investigate sleep disorders [68,69] and epilepsy [70,71] • Monitor human brain development [72,73] • Measure the depth of anaesthesia [74] • Test the effect of drugs [75,76].…”

Section: Electroencephalogram (Eeg)mentioning

confidence: 99%

From Auditory and Visual to Immersive Neurofeedback: Application to Diagnosis of Alzheimer’s Disease

Elgendi

Dauwels

Rebsamen

et al. 2014

Neural Computation, Neural Devices, and Neural Prosthesis

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Abstract. In neurofeedback, brain waves are transformed into sounds or music, graphics, and other representations, to provide real-time information on ongoing waves and patterns in the brain. Here we present various forms of neurofeedback, including sonification, sonification in combination with visualization, and at last, immersive neurofeedback, where auditory and visual feedback is provided in a multi-sided immersive environment in which participants are completely surrounded by virtual imagery and 3D sound. Neural feedback may potentially improve the user's (or patient's) ability to control brain activity, the diagnosis of medical conditions, and the rehabilitation of neurological or psychiatric disorders. Several psychological and medical studies have confirmed that virtual immersive activity is enjoyable, stimulating, and can have a healing effect. As an illustration, neurofeedback is generated from electroencephalograms (EEG) of Alzheimer's disease (AD) patients and healthy subjects. The auditory, visual, and immersive representations of Alzheimer's EEG differ substantially from healthy EEG, potentially yielding novel diagnostic tools. Moreover, such alternative representations of AD EEG are natural and intuitive, and hence easily accessible to laymen (AD patients and family members), and can provide insight into the abnormal brainwaves associated with AD.

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Biophysical Linkage between MRI and EEG Amplitude in Closed Head Injury

Cited by 75 publications

References 47 publications

Identification of Hematomas in Mild Traumatic Brain Injury Using an Index of Quantitative Brain Electrical Activity

Identification of Hematomas in Mild Traumatic Brain Injury Using an Index of Quantitative Brain Electrical Activity

Theta EEG source localization using LORETA in partial epilepsy patients with and without medication

From Auditory and Visual to Immersive Neurofeedback: Application to Diagnosis of Alzheimer’s Disease

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