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Advances in Electrophysiological Research.

Kamarajan C, Porjesz B - Alcohol Res (2015)

Bottom Line: Electrophysiological measures of brain function are effective tools to understand neurocognitive phenomena and sensitive indicators of pathophysiological processes associated with various clinical conditions, including alcoholism.Researchers have recently developed sophisticated signal-processing techniques to characterize different aspects of brain dynamics, which can aid in identifying the neural mechanisms underlying alcoholism and other related complex disorders.These quantitative measures of brain function also have been successfully used as endophenotypes to identify and help understand genes associated with AUD and related disorders.Translational research also is examining how brain electrophysiological measures potentially can be applied to diagnosis, prevention, and treatment.

View Article: PubMed Central - PubMed

Affiliation: Henri Begleiter Neurodynamics Laboratory, SUNY Downstate Medical Center, Brooklyn, New York.

ABSTRACT
Electrophysiological measures of brain function are effective tools to understand neurocognitive phenomena and sensitive indicators of pathophysiological processes associated with various clinical conditions, including alcoholism. Individuals with alcohol use disorder (AUD) and their high-risk offspring have consistently shown dysfunction in several electrophysiological measures in resting state (i.e., electroencephalogram) and during cognitive tasks (i.e., event-related potentials and event-related oscillations). Researchers have recently developed sophisticated signal-processing techniques to characterize different aspects of brain dynamics, which can aid in identifying the neural mechanisms underlying alcoholism and other related complex disorders.These quantitative measures of brain function also have been successfully used as endophenotypes to identify and help understand genes associated with AUD and related disorders. Translational research also is examining how brain electrophysiological measures potentially can be applied to diagnosis, prevention, and treatment.

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Related in: MedlinePlus

Steps involved in the derivation of independent component analysis (ICA) components in event-related potential (ERP) data, as described by Jung and colleagues (2000, 2001), based on single trials from an ERP dataset from the monetary gambling tasks (MGT) task for illustrative purposes. The waveforms (panel A1) and topographic map (panel A2) of the ERP signal (S) are shown (in µV) for a trial epoch of an MGT task during the feedback of loss. The “unmixing” matrix (W) (panel B) is computed using the ICA algorithm on a “training” dataset (S) representing a larger dataset (e.g., ERP data of adult males during loss condition). “W” consists of weights in a square matrix with the size of number of input channels. The activation matrix (A) is obtained by multiplying “W” with “S” (panel C). The rows of “A” represent the time courses of the activations of ICA components. Finally, the “projections” (P) for a given “S” are the product of the inverse matrix of “W” [W-1] and the activations corresponding to the “S” for which ICA components are to be derived (panel D). “P” refers to the relative projection strengths for the respective components at each of the scalp electrodes. It is shown that the EOG activity in the signal (around 850 ms) has been well-captured by the first ICA component. The headmaps have been plotted for 850 ms post-stimulus where the EOG occurs. The 0 (zero) ms on the X-axis of the waveform plots represent the onset of a feedback signal. Downward arrows represent the continuation of the process for remaining electrodes or components.
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f3-arcr-37-1-53: Steps involved in the derivation of independent component analysis (ICA) components in event-related potential (ERP) data, as described by Jung and colleagues (2000, 2001), based on single trials from an ERP dataset from the monetary gambling tasks (MGT) task for illustrative purposes. The waveforms (panel A1) and topographic map (panel A2) of the ERP signal (S) are shown (in µV) for a trial epoch of an MGT task during the feedback of loss. The “unmixing” matrix (W) (panel B) is computed using the ICA algorithm on a “training” dataset (S) representing a larger dataset (e.g., ERP data of adult males during loss condition). “W” consists of weights in a square matrix with the size of number of input channels. The activation matrix (A) is obtained by multiplying “W” with “S” (panel C). The rows of “A” represent the time courses of the activations of ICA components. Finally, the “projections” (P) for a given “S” are the product of the inverse matrix of “W” [W-1] and the activations corresponding to the “S” for which ICA components are to be derived (panel D). “P” refers to the relative projection strengths for the respective components at each of the scalp electrodes. It is shown that the EOG activity in the signal (around 850 ms) has been well-captured by the first ICA component. The headmaps have been plotted for 850 ms post-stimulus where the EOG occurs. The 0 (zero) ms on the X-axis of the waveform plots represent the onset of a feedback signal. Downward arrows represent the continuation of the process for remaining electrodes or components.

Mentions: Processing steps involved in the derivation of ICA components are illustrated in figure 3, following the method described by Jung and colleagues (2000), visually demonstrating ICA’s ability to capture the massive electroocculogram5 (EOG) activity in the resulting component(s), although its use in decomposing meaningful components underlying ERP components have been illustrated elsewhere (Makeig and Onton 2009; Makeig et al. 1999a,b, 2004). These spatially “independent” components are thought to be suggestive of their physiological origins (e.g., eye activity projects mainly from frontal sites and progresses toward posterior sites) (Jung et al. 2001). When these resultant components are combined or “remixed,” the original “composite” signal can be obtained.


Advances in Electrophysiological Research.

Kamarajan C, Porjesz B - Alcohol Res (2015)

Steps involved in the derivation of independent component analysis (ICA) components in event-related potential (ERP) data, as described by Jung and colleagues (2000, 2001), based on single trials from an ERP dataset from the monetary gambling tasks (MGT) task for illustrative purposes. The waveforms (panel A1) and topographic map (panel A2) of the ERP signal (S) are shown (in µV) for a trial epoch of an MGT task during the feedback of loss. The “unmixing” matrix (W) (panel B) is computed using the ICA algorithm on a “training” dataset (S) representing a larger dataset (e.g., ERP data of adult males during loss condition). “W” consists of weights in a square matrix with the size of number of input channels. The activation matrix (A) is obtained by multiplying “W” with “S” (panel C). The rows of “A” represent the time courses of the activations of ICA components. Finally, the “projections” (P) for a given “S” are the product of the inverse matrix of “W” [W-1] and the activations corresponding to the “S” for which ICA components are to be derived (panel D). “P” refers to the relative projection strengths for the respective components at each of the scalp electrodes. It is shown that the EOG activity in the signal (around 850 ms) has been well-captured by the first ICA component. The headmaps have been plotted for 850 ms post-stimulus where the EOG occurs. The 0 (zero) ms on the X-axis of the waveform plots represent the onset of a feedback signal. Downward arrows represent the continuation of the process for remaining electrodes or components.
© Copyright Policy - public-domain
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4476604&req=5

f3-arcr-37-1-53: Steps involved in the derivation of independent component analysis (ICA) components in event-related potential (ERP) data, as described by Jung and colleagues (2000, 2001), based on single trials from an ERP dataset from the monetary gambling tasks (MGT) task for illustrative purposes. The waveforms (panel A1) and topographic map (panel A2) of the ERP signal (S) are shown (in µV) for a trial epoch of an MGT task during the feedback of loss. The “unmixing” matrix (W) (panel B) is computed using the ICA algorithm on a “training” dataset (S) representing a larger dataset (e.g., ERP data of adult males during loss condition). “W” consists of weights in a square matrix with the size of number of input channels. The activation matrix (A) is obtained by multiplying “W” with “S” (panel C). The rows of “A” represent the time courses of the activations of ICA components. Finally, the “projections” (P) for a given “S” are the product of the inverse matrix of “W” [W-1] and the activations corresponding to the “S” for which ICA components are to be derived (panel D). “P” refers to the relative projection strengths for the respective components at each of the scalp electrodes. It is shown that the EOG activity in the signal (around 850 ms) has been well-captured by the first ICA component. The headmaps have been plotted for 850 ms post-stimulus where the EOG occurs. The 0 (zero) ms on the X-axis of the waveform plots represent the onset of a feedback signal. Downward arrows represent the continuation of the process for remaining electrodes or components.
Mentions: Processing steps involved in the derivation of ICA components are illustrated in figure 3, following the method described by Jung and colleagues (2000), visually demonstrating ICA’s ability to capture the massive electroocculogram5 (EOG) activity in the resulting component(s), although its use in decomposing meaningful components underlying ERP components have been illustrated elsewhere (Makeig and Onton 2009; Makeig et al. 1999a,b, 2004). These spatially “independent” components are thought to be suggestive of their physiological origins (e.g., eye activity projects mainly from frontal sites and progresses toward posterior sites) (Jung et al. 2001). When these resultant components are combined or “remixed,” the original “composite” signal can be obtained.

Bottom Line: Electrophysiological measures of brain function are effective tools to understand neurocognitive phenomena and sensitive indicators of pathophysiological processes associated with various clinical conditions, including alcoholism.Researchers have recently developed sophisticated signal-processing techniques to characterize different aspects of brain dynamics, which can aid in identifying the neural mechanisms underlying alcoholism and other related complex disorders.These quantitative measures of brain function also have been successfully used as endophenotypes to identify and help understand genes associated with AUD and related disorders.Translational research also is examining how brain electrophysiological measures potentially can be applied to diagnosis, prevention, and treatment.

View Article: PubMed Central - PubMed

Affiliation: Henri Begleiter Neurodynamics Laboratory, SUNY Downstate Medical Center, Brooklyn, New York.

ABSTRACT
Electrophysiological measures of brain function are effective tools to understand neurocognitive phenomena and sensitive indicators of pathophysiological processes associated with various clinical conditions, including alcoholism. Individuals with alcohol use disorder (AUD) and their high-risk offspring have consistently shown dysfunction in several electrophysiological measures in resting state (i.e., electroencephalogram) and during cognitive tasks (i.e., event-related potentials and event-related oscillations). Researchers have recently developed sophisticated signal-processing techniques to characterize different aspects of brain dynamics, which can aid in identifying the neural mechanisms underlying alcoholism and other related complex disorders.These quantitative measures of brain function also have been successfully used as endophenotypes to identify and help understand genes associated with AUD and related disorders. Translational research also is examining how brain electrophysiological measures potentially can be applied to diagnosis, prevention, and treatment.

Show MeSH
Related in: MedlinePlus