Alcohol increases spontaneous BOLD signal fluctuations in the visual network

Esposito, Fabrizio; Pignataro, Giuseppe; Renzo, Gianfranco Di; Spinali, Alessandra; Paccone, Antonella; Tedeschi, Gioacchino; Annunziato, Lucio

doi:10.1016/j.neuroimage.2010.06.061

Cited by 61 publications

(52 citation statements)

References 82 publications

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“…The signal intensity change in the selected ROI was determined to be statistically significant in both cases. These induced BOLD signal measurement results are in good correlation with studies performed at higher field strengths, where alcohol and caffeine had been used for BOLD signal influencing purposes [7,8,11,18]. The reason for the signal intensity increase after alcohol consumption could be the decrease in the deoxygenated hemoglobin concentration that is caused by blood flow and blood volume increases.…”

Section: Discussionsupporting

confidence: 77%

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The Early Effect of Alcohol and Caffeine on a BOLD Signal Measured in Human Hand at Low-Field MRI

et al. 2012

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Even though low-field magnetic resonance imaging (MRI) remains widely used, it has some limitations preventing common use of particular MR methods, such as functional MPI (fMRI). This does not apply to the effects of alcohol or caffeine, as these substances influence the blood-oxygen-level-dependent (BOLD) signal. This paper focused on testing early effects of caffeine and alcohol on induced ''BOLD'' imaging on a modern low-field system. A study based on alcohol and caffeine consumption was performed on ten volunteers. The signal intensity variations caused by blood flow changes were observed in 5-min intervals in the skin surrounding dermal veins of the hand. The signal intensity variations were significant in the region of interest, with a subtracted venal signal 15 min after consumption of alcohol, and in caffeine case, the results were significant even sooner. A low-field system is feasible for imaging the acute hemodynamic response, thus caffeine and alcohol can be used to enhance low-field fMRI experiments without the pre-measurement delay.

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Section: Discussionsupporting

confidence: 77%

“…Thus, after alcohol consumption, a positive BOLD effect can be measured. This can be useful to increase the measured resting-state BOLD signal [8]. The mechanism by which alcohol ingestion causes cerebral and dermal vasodilation is unclear, but it may result from a direct action on central vascular control mechanisms [9].…”

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confidence: 99%

The Early Effect of Alcohol and Caffeine on a BOLD Signal Measured in Human Hand at Low-Field MRI

et al. 2012

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“…From each data set, 40 independent components (ICs), corresponding to one sixth of the number of time points (Greicius et al 2007) and accounting for more than 99.9% of the total variance, were extracted using the plug-in of BrainVoyager QX implementing the fastICA algorithm (Hyvarinen 1999). To select the IC component associated with the DMN, we used a DMN spatial template from a previous study on the same MRI scanner with the same protocol and pre-processing (Esposito et al 2010). The DMN template consisted of an inclusive binary mask obtained from the mean DMN map of a separate population of control subjects and was here applied to each single-subject IC, in such a way to select the best-fitting whole-brain component map as the one with the highest goodness of fit values (GOF = mean IC value inside mask – mean IC value outside mask) (Greicius et al 2004, 2007).…”

Section: Methodsmentioning

confidence: 99%

Multi-View Ensemble Classification of Brain Connectivity Images for Neurodegeneration Type Discrimination

et al. 2017

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Brain connectivity analyses using voxels as features are not robust enough for single-patient classification because of the inter-subject anatomical and functional variability. To construct more robust features, voxels can be aggregated into clusters that are maximally coherent across subjects. Moreover, combining multi-modal neuroimaging and multi-view data integration techniques allows generating multiple independent connectivity features for the same patient. Structural and functional connectivity features were extracted from multi-modal MRI images with a clustering technique, and used for the multi-view classification of different phenotypes of neurodegeneration by an ensemble learning method (random forest). Two different multi-view models (intermediate and late data integration) were trained on, and tested for the classification of, individual whole-brain default-mode network (DMN) and fractional anisotropy (FA) maps, from 41 amyotrophic lateral sclerosis (ALS) patients, 37 Parkinson’s disease (PD) patients and 43 healthy control (HC) subjects. Both multi-view data models exhibited ensemble classification accuracies significantly above chance. In ALS patients, multi-view models exhibited the best performances (intermediate: 82.9%, late: 80.5% correct classification) and were more discriminative than each single-view model. In PD patients and controls, multi-view models’ performances were lower (PD: 59.5%, 62.2%; HC: 56.8%, 59.1%) but higher than at least one single-view model. Training the models only on patients, produced more than 85% patients correctly discriminated as ALS or PD type and maximal performances for multi-view models. These results highlight the potentials of mining complementary information from the integration of multiple data views in the classification of connectivity patterns from multi-modal brain images in the study of neurodegenerative diseases.

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“…So far, these studies provided little or no evidence of changes in DMN connectivity after administration of alcohol [35], [36] or morphine [36], compared to placebo. However, one study reported that the dopamine agonist L-Dopa decreased functional connectivity of medial DMN regions [37].…”

Section: Introductionmentioning

confidence: 99%

Escitalopram Decreases Cross-Regional Functional Connectivity within the Default-Mode Network

et al. 2013

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The default-mode network (DMN), which comprises medial frontal, temporal and parietal regions, is part of the brain’s intrinsic organization. The serotonergic (5-HT) neurotransmitter system projects to DMN regions from midbrain efferents, and manipulation of this system could thus reveal insights into the neurobiological mechanisms of DMN functioning. Here, we investigate intrinsic functional connectivity of the DMN as a function of activity of the serotonergic system, through the administration of the selective serotonin reuptake inhibitor (SSRI) escitalopram. We quantified DMN functional connectivity using an approach based on dual-regression. Specifically, we decomposed group data of a subset of the functional time series using spatial independent component analysis, and projected the group spatial modes to the same and an independent resting state time series of individual participants. We found no effects of escitalopram on global functional connectivity of the DMN at the map-level; that is, escitalopram did not alter the global functional architecture of the DMN. However, we found that escitalopram decreased DMN regional pairwise connectivity, which included anterior and posterior cingulate cortex, hippocampal complex and lateral parietal regions. Further, regional DMN connectivity covaried with alertness ratings across participants. Our findings show that escitalopram altered intrinsic regional DMN connectivity, which suggests that the serotonergic system plays an important role in DMN connectivity and its contribution to cognition. Pharmacological challenge designs may be a useful addition to resting-state functional MRI to investigate intrinsic brain functional organization.

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Alcohol increases spontaneous BOLD signal fluctuations in the visual network

Cited by 61 publications

References 82 publications

The Early Effect of Alcohol and Caffeine on a BOLD Signal Measured in Human Hand at Low-Field MRI

The Early Effect of Alcohol and Caffeine on a BOLD Signal Measured in Human Hand at Low-Field MRI

Multi-View Ensemble Classification of Brain Connectivity Images for Neurodegeneration Type Discrimination

Escitalopram Decreases Cross-Regional Functional Connectivity within the Default-Mode Network

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