Advances in Electrophysiological Research.

paper Primary Public

Authors: Kamarajan, Chella; Porjesz, Bernice
Year: 2015
Journal: Alcohol research : current reviews
PMID: 26259089
DOI: 10.35946/arcr.v37.1.05
PMCID: PMC4476604

Figure 1

The current source density (CSD) method provides measures of source activations, which are otherwise blurred in the scalp potentials. A1) P3 event-related potential (ERP) topography showing lower P3 amplitude (in microvolts) in alcoholics during both Go and No-Go conditions in a Go/No-Go task. A2) CSD maps (in ampere per squared radius) showing the Go condition with two bilateral sources in control subjects and only a midline source in alcoholics and illustrating the No-Go condition with a stronger, more focused source over the central region in control subjects and a weaker, more diffuse source over the central and posterior regions in alcoholics (Kamarajan et al. 2005a). B1) Topography of event-related oscillations (EROs) theta power (in microvolts squared) in alcoholics and control subjects during the loss condition in an monetary gambling tasks (MGT) task, plotted for ERO theta power during the N2-P3 complex (200 to 500 ms). B2) CSD maps of ERO theta activity showing a single and stronger midline prefrontal source during the loss condition in control subjects contrasted with bilateral and weaker prefrontal sources in alcoholics; during the gain condition, control subjects had well-defined anterior and posterior sources whereas alcoholics showed weaker and more diffuse sources (Kamarajan et al. 2012).

LLM interpretation

This figure presents EEG topography and Current Source Density (CSD) maps comparing control subjects and alcoholics across two tasks: Go/No-Go (GNG) and Monetary Gambling (MGT). Panels A1 and B1 show scalp potentials (ERP P3 amplitude in $\mu$V and ERO theta power in $\mu\text{V}^2$), while A2 and B2 show CSD maps (in $\text{A}/\text{r}^2$). Across all conditions, alcoholics exhibit lower amplitudes and more diffuse or fewer source activations compared to the more focused and stronger activations seen in control subjects.

Figure 2

Application of standardized low-resolution brain electromagnetic tomography (sLORETA) to alcoholism. Top panels: Current density in alcoholics and control subjects were compared in a Go/No-Go task using sLORETA. Alcoholics showed significantly lower current density activations in bilateral anterior prefrontal regions during No-Go–related N2 activity (yellow blobs in top panels), indicating dysfunctional inhibitory control in alcoholics (Pandey et al. 2012b). Bottom panels: A sLORETA study in an MGT task found that alcoholics showed decreased current density activation at the middle cingulate cortex region during loss-related P3 activity (red blobs in bottom panels), indicating deficient activation in the reward-related structures or networks (Kamarajan et al. 2010).

LLM interpretation

This figure presents sLORETA brain imaging maps comparing current density between alcoholics and control subjects across two tasks. The top panels (Go/No-Go task) show yellow clusters indicating significantly lower current density in the bilateral anterior prefrontal regions during No-Go-related N2 activity. The bottom panels (MGT task) show red clusters indicating decreased current density in the middle cingulate cortex during loss-related P3 activity.

Figure 3

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.

LLM interpretation

This figure is a schematic diagram illustrating the steps of Independent Component Analysis (ICA) applied to ERP data. It shows the transformation of raw ERP waveforms and a topographic headmap (A1, A2) through an unmixing matrix (B) into a set of ICA activation waveforms (C) and their corresponding projection headmaps (D). The x-axes for the waveforms represent time in milliseconds, and the headmaps visualize spatial distributions of signal strength at 850 ms.

Figure 4

Application of event-related potentials (ERPs) and event-related oscillations (EROs) in alcoholism during a visual oddball task (Jones et al. 2006b; Rangaswamy et al. 2007). The left side of the figure (panels A1–A3) compares alcoholics (ALC) and control subjects (CTL) (Jones et al. 2006b), whereas the right side of the figure (panels B1–B3) compares high-risk (HR) offspring and low-risk (LR) control subjects (Rangaswamy et al. 2007). Alcoholics showed lower P3 amplitudes than control subjects (panel A1), whereas HR offspring showed lower P3 amplitudes to targets than LR in the same visual oddball paradigm (panel B1). Panel A2 illustrates time-frequency (TF) plots for control subjects (center rectangular panel) with accompanying topographical head plots for delta (1 to 3 Hz) at the Pz electrode (right) and theta (4 to 5 Hz) at the Fz electrode (left). Panel A3 illustrates corresponding TF plots for alcoholics (center rectangular panel) with accompanying topographical head plots for delta (right) and theta (left). Alcoholics showed lower activation of both delta and theta EROs compared with control subjects (panels A2–A3) during the processing of targets. Panel B2 illustrates TF plots for LR (center rectangular panel) with accompanying topographical head plots for delta (1 to 3 Hz) at the Pz electrode (right) and theta (4 to 5 Hz) at the Fz electrode (left). Panel B3 illustrates corresponding TF plots for HR (center rectangular panel) with accompanying topographical head plots for delta (right) and theta (left). Similar to the alcoholics, HR offspring manifested lower activation in P3 (panel B1), delta and theta EROs (panels B2–B3) compared with LR control subjects.

LLM interpretation

This figure presents event-related potentials (ERPs) and time-frequency (TF) plots comparing brain activity during a visual oddball task across four groups: controls (CTL), alcoholics (ALC), low-risk (LR), and high-risk (HR) offspring. Panels A1 and B1 show ERP waveforms where ALC and HR groups exhibit lower P3 amplitudes compared to CTL and LR groups, respectively. Panels A2–A3 and B2–B3 display TF plots and corresponding topographical head maps, indicating reduced delta (1–3 Hz) and theta (4–5 Hz) event-related oscillations (EROs) in the ALC and HR groups relative to their respective controls.

Figure 5

Application of event-related potentials (ERPs) and event-related oscillations (EROs) to alcoholism in a monetary gambling task (MGT) (Kamarajan et al. 2012). A) Alcoholics showed lower P3 amplitude of the ERP during loss and gain conditions than control subjects. B) ERO theta activity (3 to 7 Hz) was lower during the N2 and P3 time window in alcoholics compared with control subjects. C) Time-frequency plots (center panel) and topographic head plots of theta power in control subjects during loss (left) and gain (right) conditions. D) Time-frequency plots (center panel) and topographic head plots of theta power in alcoholics during loss (left) and gain (right) conditions. Theta power was lower in alcoholics during loss and gain conditions compared with control subjects

LLM interpretation

This figure consists of ERP waveforms (A), ERO theta waveforms (B), and time-frequency plots with corresponding topographic head maps (C, D) comparing control subjects and alcoholics during loss and gain conditions. Panels A and B show lower P3 amplitudes and reduced theta activity in alcoholics (red lines) compared to controls (green lines). Panels C and D utilize Z-score heatmaps and scalp maps to visualize theta power, showing higher intensity (warmer colors) in controls than in alcoholics for both conditions.

#	Section	Preview
60	Advances in ERO Methods — Event-Related Desynchronization and Synchronization (ERD/ERS)	The ERD/ERS method has been useful in understanding cognitive processing abnormalities in several…
61	Connectivity Measures During Task-Related Conditions — ERO Coherence	Coherence is an estimate of the consistency of relative amplitude and phase between two signals…
62	Connectivity Measures During Task-Related Conditions — ERO Coherence	such as wavelet analysis, to activity during a task (Torrence and Compo 1998). Using the coherence…
63	Connectivity Measures During Task-Related Conditions — ERO Phase Synchronization	Phase synchronization is a measure of phase locking between two signals (Lachaux et al. 1999) and…
64	Connectivity Measures During Task-Related Conditions — ERO Phase Synchronization	in value between 0 (no synchronization) to 1 (perfect synchronization) (Lachaux et al. 1999;…
65	Connectivity Measures During Task-Related Conditions — ERO Phase Synchronization	Although the linear coherence measure does not separate the effects of amplitude and phase in the…
66	Granger Causality Analysis	When applied to brain signals, Granger causality as a statistical method measures the degree of…
67	Granger Causality Analysis	Granger causality has been successfully used to identify coupling (connectivity) and information…
68	Potential for Translational Applications of Electrophysiological Measures of Brain Function	Electrophysiological measures and techniques have clinical applications in several important areas,…
69	Potential for Translational Applications of Electrophysiological Measures of Brain Function — Electrophysiological Measures As Endophenotypes for Alcoholism	Risk for alcoholism is complex and influenced by both genetic and environmental influences and their…
70	Potential for Translational Applications of Electrophysiological Measures of Brain Function — Electrophysiological Measures As Endophenotypes for Alcoholism	it is found in unaffected relatives of probands at a higher rate than in the general population…
71	Potential for Translational Applications of Electrophysiological Measures of Brain Function — Electrophysiological Measures As Endophenotypes for Alcoholism	One EEG measure, the beta rhythm (i.e., beta 1 [12.5 to 16 Hz], beta 2 [16.5 to 20 Hz], and beta 3…
72	Potential for Translational Applications of Electrophysiological Measures of Brain Function — Electrophysiological Measures As Endophenotypes for Alcoholism	et al. 2004; Porjesz et al. 2002a). Beta rhythm is attributed to a balance between networks of…
73	Potential for Translational Applications of Electrophysiological Measures of Brain Function — Electrophysiological Measures As Endophenotypes for Alcoholism	with conduct disorder, a precursor phenotype (Dick et al. 2006). The heritability of EEG coherence…
74	Potential for Translational Applications of Electrophysiological Measures of Brain Function — Electrophysiological Measures As Endophenotypes for Alcoholism	In addition to resting-state EEG endophenotypes, P3-related measures during cognitive tasks (e.g.,…
75	Potential for Translational Applications of Electrophysiological Measures of Brain Function — Electrophysiological Measures As Endophenotypes for Alcoholism	One neurophysiological measure that has been successfully used as an endophenotype in identifying…
76	Potential for Translational Applications of Electrophysiological Measures of Brain Function — Electrophysiological Measures As Endophenotypes for Alcoholism	for alcohol dependence, drug dependence, and affective disorder. Significant linkage and association…
77	Potential for Translational Applications of Electrophysiological Measures of Brain Function — Electrophysiological Measures As Endophenotypes for Alcoholism	and alcohol dependence during adolescence and young adulthood (Chorlian et al. 2013). Hill and…
78	Potential for Translational Applications of Electrophysiological Measures of Brain Function — Electrophysiological Measures As Endophenotypes for Alcoholism	Under the same linkage peak as the CHRM2 gene, a metabotropic glutamate receptor gene (GRM8) was…
79	Potential for Translational Applications of Electrophysiological Measures of Brain Function — Electrophysiological Measures As Endophenotypes for Alcoholism	In a family-based genome-wide association study, Kang and colleagues (2012) used the same…

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In this knowledge base

Title	Year	PMID
Random Forest Classification of Alcohol Use Disorder Using EEG Source Functional Connectivity, Neuropsychological Functioning, and Impulsivity Measures.	2020	32121585
A KCNJ6 gene polymorphism modulates theta oscillations during reward processing.	2017	27993610
Endophenotype best practices.	2017	27473600
Reward processing deficits and impulsivity in high-risk offspring of alcoholics: A study of event-related potentials during a monetary gambling task.	2015	26388585

External

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