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The power of using functional fMRI on small rodents to study brain pharmacology and disease.

Jonckers E, Shah D, Hamaide J, Verhoye M, Van der Linden A - Front Pharmacol (2015)

Bottom Line: Functional magnetic resonance imaging (fMRI) is an excellent tool to study the effect of pharmacological modulations on brain function in a non-invasive and longitudinal manner.The second part of this review describes applications of the aforementioned techniques in pharmacologically induced, as well as in traumatic and transgenic disease models and illustrates how multiple fMRI methods can be applied successfully to evaluate different aspects of a specific disorder.In conclusion, by describing several exemplary studies, we aim to highlight the advantages of functional MRI in exploring the acute and long-term effects of pharmacological substances and/or pathology on brain functioning along with several methodological considerations.

View Article: PubMed Central - PubMed

Affiliation: Bio-Imaging Lab, Department of Biomedical Sciences, University of Antwerp Antwerp, Belgium.

ABSTRACT
Functional magnetic resonance imaging (fMRI) is an excellent tool to study the effect of pharmacological modulations on brain function in a non-invasive and longitudinal manner. We introduce several blood oxygenation level dependent (BOLD) fMRI techniques, including resting state (rsfMRI), stimulus-evoked (st-fMRI), and pharmacological MRI (phMRI). Respectively, these techniques permit the assessment of functional connectivity during rest as well as brain activation triggered by sensory stimulation and/or a pharmacological challenge. The first part of this review describes the physiological basis of BOLD fMRI and the hemodynamic response on which the MRI contrast is based. Specific emphasis goes to possible effects of anesthesia and the animal's physiological conditions on neural activity and the hemodynamic response. The second part of this review describes applications of the aforementioned techniques in pharmacologically induced, as well as in traumatic and transgenic disease models and illustrates how multiple fMRI methods can be applied successfully to evaluate different aspects of a specific disorder. For example, fMRI techniques can be used to pinpoint the neural substrate of a disease beyond previously defined hypothesis-driven regions-of-interest. In addition, fMRI techniques allow one to dissect how specific modifications (e.g., treatment, lesion etc.) modulate the functioning of specific brain areas (st-fMRI, phMRI) and how functional connectivity (rsfMRI) between several brain regions is affected, both in acute and extended time frames. Furthermore, fMRI techniques can be used to assess/explore the efficacy of novel treatments in depth, both in fundamental research as well as in preclinical settings. In conclusion, by describing several exemplary studies, we aim to highlight the advantages of functional MRI in exploring the acute and long-term effects of pharmacological substances and/or pathology on brain functioning along with several methodological considerations.

No MeSH data available.


Related in: MedlinePlus

Basic principles of rsfMRI Analysis. (A) Independent Component Analysis divides the BOLD signal of all brain-voxels in different spatially confined independent sources, or components. Each ICA component consists of brain regions with correlated BOLD time courses. In other words, voxels of one component represent regions that are functionally connected. (B) In voxel-based analyses, the mean BOLD signal time course of a specific seed region is extracted from a series of EPI images. This time course is compared to the time course of all other voxels in the brain, resulting in a functional connectivity map (voxel based processing). (C) In an ROI based approach, the mean BOLD signal time courses of multiple brain regions are compared, resulting in FC matrices showing the strength of connectivity between each pair of brain regions (warmer colors indicate stronger functional connectivity, colder colors represent anti-correlation). These matrices can then be used to visualize brain networks as nodes (brain regions) and edges (connections). Moreover, the brain network can be divided into modules that represent brain circuits where similar time courses are displayed by different colors (graph approach). (Adopted from Jonckers et al., 2013b).
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Figure 3: Basic principles of rsfMRI Analysis. (A) Independent Component Analysis divides the BOLD signal of all brain-voxels in different spatially confined independent sources, or components. Each ICA component consists of brain regions with correlated BOLD time courses. In other words, voxels of one component represent regions that are functionally connected. (B) In voxel-based analyses, the mean BOLD signal time course of a specific seed region is extracted from a series of EPI images. This time course is compared to the time course of all other voxels in the brain, resulting in a functional connectivity map (voxel based processing). (C) In an ROI based approach, the mean BOLD signal time courses of multiple brain regions are compared, resulting in FC matrices showing the strength of connectivity between each pair of brain regions (warmer colors indicate stronger functional connectivity, colder colors represent anti-correlation). These matrices can then be used to visualize brain networks as nodes (brain regions) and edges (connections). Moreover, the brain network can be divided into modules that represent brain circuits where similar time courses are displayed by different colors (graph approach). (Adopted from Jonckers et al., 2013b).

Mentions: For processing of rsfMRI data, various software packages exist, supporting different processing strategies (Margulies et al., 2010). The most widely used methods in FC analysis of resting state data are ROI-based (Biswal et al., 1995), seed-based and model-free, data-driven approaches such as ICA (Calhoun et al., 2001; Beckmann and Smith, 2004). Other data-driven techniques are clustering approaches and graph analysis [for methodological review: (Margulies et al., 2010)] (see Figure 3). Finally alternative processing techniques are available to map the directionality of the connectivity, such as dynamic causal modeling (Friston et al., 2003) and Granger causality analysis (Roebroeck et al., 2005) which can also be applied on activity-induced fMRI data to define regions that drive the activation. Apart from FC analysis, also the low frequency fluctuation themselves can be affected during disease conditions and studied using ALFF analysis. ALFF is defined as the total power within the frequency range between 0.01 and 0.1 Hz, and thus indexes the strength or intensity of Low Frequency Fluctuations. Measurements of ALFF are more often applied in humans but some rodent students already report changes in ALFF in animal models (Li et al., 2012; Yao et al., 2012).


The power of using functional fMRI on small rodents to study brain pharmacology and disease.

Jonckers E, Shah D, Hamaide J, Verhoye M, Van der Linden A - Front Pharmacol (2015)

Basic principles of rsfMRI Analysis. (A) Independent Component Analysis divides the BOLD signal of all brain-voxels in different spatially confined independent sources, or components. Each ICA component consists of brain regions with correlated BOLD time courses. In other words, voxels of one component represent regions that are functionally connected. (B) In voxel-based analyses, the mean BOLD signal time course of a specific seed region is extracted from a series of EPI images. This time course is compared to the time course of all other voxels in the brain, resulting in a functional connectivity map (voxel based processing). (C) In an ROI based approach, the mean BOLD signal time courses of multiple brain regions are compared, resulting in FC matrices showing the strength of connectivity between each pair of brain regions (warmer colors indicate stronger functional connectivity, colder colors represent anti-correlation). These matrices can then be used to visualize brain networks as nodes (brain regions) and edges (connections). Moreover, the brain network can be divided into modules that represent brain circuits where similar time courses are displayed by different colors (graph approach). (Adopted from Jonckers et al., 2013b).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4612660&req=5

Figure 3: Basic principles of rsfMRI Analysis. (A) Independent Component Analysis divides the BOLD signal of all brain-voxels in different spatially confined independent sources, or components. Each ICA component consists of brain regions with correlated BOLD time courses. In other words, voxels of one component represent regions that are functionally connected. (B) In voxel-based analyses, the mean BOLD signal time course of a specific seed region is extracted from a series of EPI images. This time course is compared to the time course of all other voxels in the brain, resulting in a functional connectivity map (voxel based processing). (C) In an ROI based approach, the mean BOLD signal time courses of multiple brain regions are compared, resulting in FC matrices showing the strength of connectivity between each pair of brain regions (warmer colors indicate stronger functional connectivity, colder colors represent anti-correlation). These matrices can then be used to visualize brain networks as nodes (brain regions) and edges (connections). Moreover, the brain network can be divided into modules that represent brain circuits where similar time courses are displayed by different colors (graph approach). (Adopted from Jonckers et al., 2013b).
Mentions: For processing of rsfMRI data, various software packages exist, supporting different processing strategies (Margulies et al., 2010). The most widely used methods in FC analysis of resting state data are ROI-based (Biswal et al., 1995), seed-based and model-free, data-driven approaches such as ICA (Calhoun et al., 2001; Beckmann and Smith, 2004). Other data-driven techniques are clustering approaches and graph analysis [for methodological review: (Margulies et al., 2010)] (see Figure 3). Finally alternative processing techniques are available to map the directionality of the connectivity, such as dynamic causal modeling (Friston et al., 2003) and Granger causality analysis (Roebroeck et al., 2005) which can also be applied on activity-induced fMRI data to define regions that drive the activation. Apart from FC analysis, also the low frequency fluctuation themselves can be affected during disease conditions and studied using ALFF analysis. ALFF is defined as the total power within the frequency range between 0.01 and 0.1 Hz, and thus indexes the strength or intensity of Low Frequency Fluctuations. Measurements of ALFF are more often applied in humans but some rodent students already report changes in ALFF in animal models (Li et al., 2012; Yao et al., 2012).

Bottom Line: Functional magnetic resonance imaging (fMRI) is an excellent tool to study the effect of pharmacological modulations on brain function in a non-invasive and longitudinal manner.The second part of this review describes applications of the aforementioned techniques in pharmacologically induced, as well as in traumatic and transgenic disease models and illustrates how multiple fMRI methods can be applied successfully to evaluate different aspects of a specific disorder.In conclusion, by describing several exemplary studies, we aim to highlight the advantages of functional MRI in exploring the acute and long-term effects of pharmacological substances and/or pathology on brain functioning along with several methodological considerations.

View Article: PubMed Central - PubMed

Affiliation: Bio-Imaging Lab, Department of Biomedical Sciences, University of Antwerp Antwerp, Belgium.

ABSTRACT
Functional magnetic resonance imaging (fMRI) is an excellent tool to study the effect of pharmacological modulations on brain function in a non-invasive and longitudinal manner. We introduce several blood oxygenation level dependent (BOLD) fMRI techniques, including resting state (rsfMRI), stimulus-evoked (st-fMRI), and pharmacological MRI (phMRI). Respectively, these techniques permit the assessment of functional connectivity during rest as well as brain activation triggered by sensory stimulation and/or a pharmacological challenge. The first part of this review describes the physiological basis of BOLD fMRI and the hemodynamic response on which the MRI contrast is based. Specific emphasis goes to possible effects of anesthesia and the animal's physiological conditions on neural activity and the hemodynamic response. The second part of this review describes applications of the aforementioned techniques in pharmacologically induced, as well as in traumatic and transgenic disease models and illustrates how multiple fMRI methods can be applied successfully to evaluate different aspects of a specific disorder. For example, fMRI techniques can be used to pinpoint the neural substrate of a disease beyond previously defined hypothesis-driven regions-of-interest. In addition, fMRI techniques allow one to dissect how specific modifications (e.g., treatment, lesion etc.) modulate the functioning of specific brain areas (st-fMRI, phMRI) and how functional connectivity (rsfMRI) between several brain regions is affected, both in acute and extended time frames. Furthermore, fMRI techniques can be used to assess/explore the efficacy of novel treatments in depth, both in fundamental research as well as in preclinical settings. In conclusion, by describing several exemplary studies, we aim to highlight the advantages of functional MRI in exploring the acute and long-term effects of pharmacological substances and/or pathology on brain functioning along with several methodological considerations.

No MeSH data available.


Related in: MedlinePlus