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
40	Advances in ERP Methods — Current Source Density (CSD)	CSD methods, using ERP data, have been successfully applied to several neuropsychiatric conditions,…
41	Advances in ERP Methods — Current Source Density (CSD)	CSD studies in alcoholism also revealed region-specific activations and altered topographic…
42	Advances in ERP Methods — LORETA	LORETA is a functional imaging method to localize source activations of the scalp-recorded EEG/ERP…
43	Advances in ERP Methods — LORETA	Several studies have used LORETA methods to investigate cognitive dysfunction in alcoholics and HR…
44	Advances in ERP Methods — LORETA	Using sLORETA in a Go/No-Go task, Pandey and colleagues (2012b) reported significantly smaller…
45	Componential Analyses of ERPs — PCA	The central idea of the principal component analysis is to reduce the dimensionality of a dataset…
46	Componential Analyses of ERPs — PCA	of their accounted variance and interpreted based on their topographic significance (Kayser and…
47	Componential Analyses of ERPs — PCA	PCA-based decomposition, along with CSD transformation, has been a useful approach to elicit…
48	Componential Analyses of ERPs — ICA	ICA decomposes ERP data into a set of components that are distinct and maximally independent time…
49	Componential Analyses of ERPs — ICA	Processing steps involved in the derivation of ICA components are illustrated in figure 3, following…
50	Componential Analyses of ERPs — ICA	Whereas some functionally meaningful components help explain the contribution of specific…
51	Componential Analyses of ERPs — Trilinear Modeling	Componential methods such as PCA and ICA estimate the individual spatial and temporal components for…
52	Componential Analyses of ERPs — Trilinear Modeling	matrix by a general loading matrix and by allowing the number of spatial and temporal components to…
53	Componential Analyses of ERPs — Trilinear Modeling	The trilinear decomposition method also has been used for resting EEG, to estimate spectral and…
54	EROs: Findings and Prospects	EROs are time-frequency measures of brain electrical activity that are temporally associated with a…
55	EROs: Findings and Prospects — ERO Findings in Alcoholism	EROs provide a useful method to investigate brain dysfunction in alcoholism and risk. Furthermore,…
56	EROs: Findings and Prospects — Delta and Theta EROs	Studies have demonstrated that P3 responses are not unitary phenomena but primarily are the outcome…
57	EROs: Findings and Prospects — Gamma Band EROs	Gamma oscillations during cognitive tasks are thought to be involved in selective attention and…
58	EROs: Findings and Prospects — Gamma Band EROs	Studies have found that early evoked gamma activity was abnormal (either higher or lower) in…
59	Advances in ERO Methods — Event-Related Desynchronization and Synchronization (ERD/ERS)	ERD/ERS is a valuable technique to unravel time–frequency–space dynamics of cortical…

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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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