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Factoring the brain signatures of anesthesia concentration and level of arousal across individuals.

Barttfeld P, Bekinschtein TA, Salles A, Stamatakis EA, Adapa R, Menon DK, Sigman M - Neuroimage Clin (2015)

Bottom Line: Combining resting-state functional magnetic resonance imaging (fMRI) connectivity and behavioral analysis during sedation, we factored out general effects of the anesthetic drug propofol and a specific index of conscious report, participants' level of responsiveness.The factorial analysis shows that increasing concentration of propofol in blood specifically decreases the connectivity strength of fronto-parietal cortical loops.In contrast, loss of responsiveness is indexed by a functional disconnection between the thalamus and the frontal cortex, balanced by an increase in connectivity strength of the thalamus to the occipital and temporal regions of the cortex.

View Article: PubMed Central - PubMed

Affiliation: Universidad Torcuato di Tella, Almirante Juan Saenz Valiente 1010, Buenos Aires C1428BIJ, Argentina ; Cognitive Neuroimaging Unit, CEA DSV/I2BM, INSERM, Université Paris-Sud, Université Paris-Saclay, NeuroSpin center, 91191 Gif/Yvette, France.

ABSTRACT
Combining resting-state functional magnetic resonance imaging (fMRI) connectivity and behavioral analysis during sedation, we factored out general effects of the anesthetic drug propofol and a specific index of conscious report, participants' level of responsiveness. The factorial analysis shows that increasing concentration of propofol in blood specifically decreases the connectivity strength of fronto-parietal cortical loops. In contrast, loss of responsiveness is indexed by a functional disconnection between the thalamus and the frontal cortex, balanced by an increase in connectivity strength of the thalamus to the occipital and temporal regions of the cortex.

No MeSH data available.


Related in: MedlinePlus

a-b) Matrix of β-values showing dependences of functional brain connectivity with propofol level (a) and number of missed responses (b). c-d) Reduced connectivity matrix r for propofol (c) and missed responses (d). Blue connections represent negative Bn,m values, indicating a decrease in connectivity between systems n and m as the regressor increases, while red connections represent significantly positive Bn,m values, indicating a connectivity increase between systems n and m as regressor increases. e-f) Top ten ROIs with the highest average connectivity changes associated with propofol (e) and number of missed responses (f).
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f0005: a-b) Matrix of β-values showing dependences of functional brain connectivity with propofol level (a) and number of missed responses (b). c-d) Reduced connectivity matrix r for propofol (c) and missed responses (d). Blue connections represent negative Bn,m values, indicating a decrease in connectivity between systems n and m as the regressor increases, while red connections represent significantly positive Bn,m values, indicating a connectivity increase between systems n and m as regressor increases. e-f) Top ten ROIs with the highest average connectivity changes associated with propofol (e) and number of missed responses (f).

Mentions: For each sedation state s and participant p we measured a 141 × 141 connectivity matrix Cs,p (Fig. S2). The matrix entry Cs,p(i,j) indicates the temporal correlation of the average fMRI signal from the voxels in each ROI i and j, which henceforth is referred to as functional connectivity. To study functional connectivity changes associated with propofol concentration and responsiveness, we conducted an across-subjects multivariate linear regression, using the least squares method, between each entry ij of the connectivity matrix Cs,p(i,j), drug concentration as well as rate of missed responses as a proxy for the level of consciousness of each participant, and participant's age as a regressor of no interest. Using reaction times as a regressor instead of missed responses produced qualitatively the same results (see Fig. S3). This way we obtained two matrices Br(i,j) (Fig. 1a, b), one per regressor of interest r, in which each entry ij represents the dependence or beta (β) value for the connectivity between ROI(i) and ROI(j), and drug level (Fig. 1a) and responsiveness (Fig. 1b). To search for statistically significant effects of both regressors on functional connectivity, we first calculated the average β coefficient matrix ’r, a 5 × 5 matrix resulting from all possible pairings between resting state networks for each Br matrix. Statistical differences between the entries of r, matrices were assessed through a permutation analysis (Efron and Tibshirani, 1994) to test the hypothesis of a dependence between each regressor's variability and connectivity values. For each ROI pair we shuffled each regressor's values across participants 5000 times, each time calculating the β-values for the shuffled regressor. We set the threshold for significance at a p-value = 0.05, Bonferroni corrected for multiple comparisons (15 unique comparisons between ’r entries × 2 regressors). In Fig. 1c, d we plot as red edges (positive β value) and blue edges (negative β value) between system pairs, all z-scores that survived multiple comparisons correction.


Factoring the brain signatures of anesthesia concentration and level of arousal across individuals.

Barttfeld P, Bekinschtein TA, Salles A, Stamatakis EA, Adapa R, Menon DK, Sigman M - Neuroimage Clin (2015)

a-b) Matrix of β-values showing dependences of functional brain connectivity with propofol level (a) and number of missed responses (b). c-d) Reduced connectivity matrix r for propofol (c) and missed responses (d). Blue connections represent negative Bn,m values, indicating a decrease in connectivity between systems n and m as the regressor increases, while red connections represent significantly positive Bn,m values, indicating a connectivity increase between systems n and m as regressor increases. e-f) Top ten ROIs with the highest average connectivity changes associated with propofol (e) and number of missed responses (f).
© Copyright Policy - CC BY-NC-ND
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4588413&req=5

f0005: a-b) Matrix of β-values showing dependences of functional brain connectivity with propofol level (a) and number of missed responses (b). c-d) Reduced connectivity matrix r for propofol (c) and missed responses (d). Blue connections represent negative Bn,m values, indicating a decrease in connectivity between systems n and m as the regressor increases, while red connections represent significantly positive Bn,m values, indicating a connectivity increase between systems n and m as regressor increases. e-f) Top ten ROIs with the highest average connectivity changes associated with propofol (e) and number of missed responses (f).
Mentions: For each sedation state s and participant p we measured a 141 × 141 connectivity matrix Cs,p (Fig. S2). The matrix entry Cs,p(i,j) indicates the temporal correlation of the average fMRI signal from the voxels in each ROI i and j, which henceforth is referred to as functional connectivity. To study functional connectivity changes associated with propofol concentration and responsiveness, we conducted an across-subjects multivariate linear regression, using the least squares method, between each entry ij of the connectivity matrix Cs,p(i,j), drug concentration as well as rate of missed responses as a proxy for the level of consciousness of each participant, and participant's age as a regressor of no interest. Using reaction times as a regressor instead of missed responses produced qualitatively the same results (see Fig. S3). This way we obtained two matrices Br(i,j) (Fig. 1a, b), one per regressor of interest r, in which each entry ij represents the dependence or beta (β) value for the connectivity between ROI(i) and ROI(j), and drug level (Fig. 1a) and responsiveness (Fig. 1b). To search for statistically significant effects of both regressors on functional connectivity, we first calculated the average β coefficient matrix ’r, a 5 × 5 matrix resulting from all possible pairings between resting state networks for each Br matrix. Statistical differences between the entries of r, matrices were assessed through a permutation analysis (Efron and Tibshirani, 1994) to test the hypothesis of a dependence between each regressor's variability and connectivity values. For each ROI pair we shuffled each regressor's values across participants 5000 times, each time calculating the β-values for the shuffled regressor. We set the threshold for significance at a p-value = 0.05, Bonferroni corrected for multiple comparisons (15 unique comparisons between ’r entries × 2 regressors). In Fig. 1c, d we plot as red edges (positive β value) and blue edges (negative β value) between system pairs, all z-scores that survived multiple comparisons correction.

Bottom Line: Combining resting-state functional magnetic resonance imaging (fMRI) connectivity and behavioral analysis during sedation, we factored out general effects of the anesthetic drug propofol and a specific index of conscious report, participants' level of responsiveness.The factorial analysis shows that increasing concentration of propofol in blood specifically decreases the connectivity strength of fronto-parietal cortical loops.In contrast, loss of responsiveness is indexed by a functional disconnection between the thalamus and the frontal cortex, balanced by an increase in connectivity strength of the thalamus to the occipital and temporal regions of the cortex.

View Article: PubMed Central - PubMed

Affiliation: Universidad Torcuato di Tella, Almirante Juan Saenz Valiente 1010, Buenos Aires C1428BIJ, Argentina ; Cognitive Neuroimaging Unit, CEA DSV/I2BM, INSERM, Université Paris-Sud, Université Paris-Saclay, NeuroSpin center, 91191 Gif/Yvette, France.

ABSTRACT
Combining resting-state functional magnetic resonance imaging (fMRI) connectivity and behavioral analysis during sedation, we factored out general effects of the anesthetic drug propofol and a specific index of conscious report, participants' level of responsiveness. The factorial analysis shows that increasing concentration of propofol in blood specifically decreases the connectivity strength of fronto-parietal cortical loops. In contrast, loss of responsiveness is indexed by a functional disconnection between the thalamus and the frontal cortex, balanced by an increase in connectivity strength of the thalamus to the occipital and temporal regions of the cortex.

No MeSH data available.


Related in: MedlinePlus