An interesting article appeared in the February 26 issue of the New York Times apparently stimulated by the movie, "The King's Speech.". The URL is:
http://www.nytimes.com/2011/02/26/us/26stutter.html?ref=todayspaper
Saturday, February 26, 2011
Tuesday, February 22, 2011
Entanglement of Dopamine, Serotonin, and GABA Systems
The dopaminergic, serotonergic and GABAergic systems in the brain tend to be interrelated in a relatively complex way. Atypical antipsychotics, designed to reduce dopamine (DA) activity, are known to affect serotonin (5-HT is the abbreviation of the chemical name for serotonin) receptors while serotonin selective reuptake inhibitors (SSRIs) affect DA and GABA levels.
We have already discussed in a previous post (see the post, "Antidepressants May Affect Stuttering") the impact of SSRIs on synaptical DA levels due to the highjacking of DA transporters (DATs). In this post, we try to unravel the role of 5-HT and its somewhat confusing relationship to both DA and GABA activity.
In particular, we are concerned with the role of the different 5-HT receptor subtypes in the control of DA activity in different areas of the brain that may affect fluency. Some of the evidence presented here is derived from in vivo (i.e., within a living organism) and in vitro (i.e., external to the living organism, for example, tissue in a laboratory vessel) animal studies. Keeping in mind that caveat, the results are generalized to the human brain.
From previous posts (see posts on "An Anxious Mind Affects Stuttering"), we recall that the substantia nigra (SN) is part of the DA pathway that involves the dorsal striatum which affects motor function. The ventral segmental area (VTA) is part of the mesolimbic system involving also the ventral striatum and the amygdala associated with the emotional aspects of the mind. Both of these DA pathways contribute to stuttering--the part of the brain governing motor function and the mesolimbic system (with inputs from the environment) generating the mind state associated with anxiety.
In what follows, the various 5-HT receptor subtypes are characterized by "5-HTxy" where x is an integer and y is an alphabet. The effect of 5-HT binding to the various 5-HT receptor subtypes as gleaned from the scientific literature is different for each of the subtypes.
In the motor neuron region of the brain, 5-HT1A receptors activate DA neurons in the SN but inhibit DA neurons in the dorsal striatum. 5HT-1B receptors slightly inhibit DA neurons in the SN, while 5-HT2C receptors play no role in the DA system of the SN.
In the mesolimbic system, 5-HT1A receptors activate DA neurons in the VTA and 5-HT2A receptors enhance DA release, while 5-HT1B receptors inhibit the release of GABA in the VTA, thus contributing to further DA activation. On the other hand, both 5-HT1C and 5-HT2C receptors inhibit the DA system originating in the VTA. The majority of receptors in the VTA are of the 5-HT1B type, while there is a moderate number of 5-HT1C and an even smaller number of 5-HT1A and (the varieties of) 5-HT2 receptors.
The effects of 5-HT on DA activity obviously would depend upon the relative densities of 5-HT receptors in the different areas of the brain and these densities might differ substantially among different individuals. But it would appear, on the average, that the net effect of 5-HT binding (as well as DAT highjacking by 5-HT) in the mesolimbic and the motor neuron regions of the brain may be to increase their DA activities, neither of which would be beneficial toward the improvement of fluency.
We have already discussed in a previous post (see the post, "Antidepressants May Affect Stuttering") the impact of SSRIs on synaptical DA levels due to the highjacking of DA transporters (DATs). In this post, we try to unravel the role of 5-HT and its somewhat confusing relationship to both DA and GABA activity.
In particular, we are concerned with the role of the different 5-HT receptor subtypes in the control of DA activity in different areas of the brain that may affect fluency. Some of the evidence presented here is derived from in vivo (i.e., within a living organism) and in vitro (i.e., external to the living organism, for example, tissue in a laboratory vessel) animal studies. Keeping in mind that caveat, the results are generalized to the human brain.
From previous posts (see posts on "An Anxious Mind Affects Stuttering"), we recall that the substantia nigra (SN) is part of the DA pathway that involves the dorsal striatum which affects motor function. The ventral segmental area (VTA) is part of the mesolimbic system involving also the ventral striatum and the amygdala associated with the emotional aspects of the mind. Both of these DA pathways contribute to stuttering--the part of the brain governing motor function and the mesolimbic system (with inputs from the environment) generating the mind state associated with anxiety.
In what follows, the various 5-HT receptor subtypes are characterized by "5-HTxy" where x is an integer and y is an alphabet. The effect of 5-HT binding to the various 5-HT receptor subtypes as gleaned from the scientific literature is different for each of the subtypes.
In the motor neuron region of the brain, 5-HT1A receptors activate DA neurons in the SN but inhibit DA neurons in the dorsal striatum. 5HT-1B receptors slightly inhibit DA neurons in the SN, while 5-HT2C receptors play no role in the DA system of the SN.
In the mesolimbic system, 5-HT1A receptors activate DA neurons in the VTA and 5-HT2A receptors enhance DA release, while 5-HT1B receptors inhibit the release of GABA in the VTA, thus contributing to further DA activation. On the other hand, both 5-HT1C and 5-HT2C receptors inhibit the DA system originating in the VTA. The majority of receptors in the VTA are of the 5-HT1B type, while there is a moderate number of 5-HT1C and an even smaller number of 5-HT1A and (the varieties of) 5-HT2 receptors.
The effects of 5-HT on DA activity obviously would depend upon the relative densities of 5-HT receptors in the different areas of the brain and these densities might differ substantially among different individuals. But it would appear, on the average, that the net effect of 5-HT binding (as well as DAT highjacking by 5-HT) in the mesolimbic and the motor neuron regions of the brain may be to increase their DA activities, neither of which would be beneficial toward the improvement of fluency.
Thursday, February 10, 2011
An Alternative to The King's Speech
I was surprised to discover that there was a earlier portrayal of King George VI in a 2002 film entitled "Bertie and Elizabeth" starring James Wilby, Juliet Aubrey, and Alan Bates. The film begins a bit earlier prior to the marriage of Albert and Elizabeth.
Albert's speech problem is portrayed in this film as less severe than in "The King's Speech." He comes off as mostly fluent in "ordinary" conversation but blocks in stressful or conflicted situations, for example, when talking to his stern father, when giving the speech at the racetrack, and when trying to refer to Mrs. Simpson in crude terms.
As portrayed in this film, I would rank his brain involvement as at worst a 3 on a scale of 1 to 10 and his mind involvement as perhaps a 7, i.e., the physical problem (too much dopamine) is relatively mild but he is fairly reactive to his physical problem (see the post on "More on Mind/Body Problem").
Albert's speech problem is portrayed in this film as less severe than in "The King's Speech." He comes off as mostly fluent in "ordinary" conversation but blocks in stressful or conflicted situations, for example, when talking to his stern father, when giving the speech at the racetrack, and when trying to refer to Mrs. Simpson in crude terms.
As portrayed in this film, I would rank his brain involvement as at worst a 3 on a scale of 1 to 10 and his mind involvement as perhaps a 7, i.e., the physical problem (too much dopamine) is relatively mild but he is fairly reactive to his physical problem (see the post on "More on Mind/Body Problem").
Wednesday, February 9, 2011
More on Proposed Pagaclone Trial Analysis
In this post we look at enhancements to the models for pagoclone trial analyses discussed in the previous post.
For the logarithmic models in equations (2) and (3), the possibility exists that some trial participants will have become perfectly fluent, so that the after trial disfluency measure, DFA, will equal zero for these individuals. In this case the logarithm of DFA (or DFA/DFB) is undefined (going to minus infinity). One way to handle this problem is to replace DFA on the left hand side of equation (2) by (1+DFA) and then proceeding with the regression analysis.
Another approach to this problem would be to consider a logistics functional form,namely:
DFA/DFB = g * (exp(Z)/(1+exp(Z)) + e (4)
where g is another parameter to be estimated and, for example,
Z = a + b*DFB + c*T + d*SUG (5)
An alternative form for Z could instead involve the natural logarithms of DFB and SUG. The parameters a, b, c, d, and g in equation (4) can be estimated by means of a non-linear regression analysis.
If DFA were always less than or equal to DFB then we could set g=1 on a priori grounds. However, we must consider the possibility that for some participants the level of disfluency at the end of the trial might be greater than before the trial. This situation could occur if the treatment actually had a negative effect on fluency for some individuals or perhaps, in some cases, the positive effects of the treatment are overwhelmed by naturally occurring fluctuations in disfluency.
We indicated in the previous post that nonlinear terms might be introduced into the analysis. One such term that might be of interest would be an interaction between the treatment term, T, and the level of disfluency before treatment, DFB. Then equation (2) in the previous post would become:
ln(DFA) = a + b*ln(DFB) + (c + h*ln(DFB))*T + d*ln(SUG) + e (6)
where h is another parameter to be estimated, and the coefficient associated with T, namely, c+h*ln(DFB), is no longer constant but depends on the level of disfluency measured before the trial.
The addition of this nonlinear term (assuming that h is shown to be significantly different from zero in the regression analysis) implies that the percentage reduction of disfluency, namely (1 - DFA/DFB)*100, due to pagoclone depends on the initial level of disfluency. For example, an individual with a greater level of disfluency at the start of the trial may show a lower reduction of disfluency at the end of the trial, or vice versa, depending on the value of the parameter, h.
Other hypotheses regarding the introduction of nonlinear terms could also be considered as well as the addition of other explanatory variables. Regression analyses of the kind suggested in the last two posts may materially contribute to a better understanding of the efficacy of drug treatments for stuttering and the impact of these treatments as a function of the individual's characteristics.
(Copyright 2011)
For the logarithmic models in equations (2) and (3), the possibility exists that some trial participants will have become perfectly fluent, so that the after trial disfluency measure, DFA, will equal zero for these individuals. In this case the logarithm of DFA (or DFA/DFB) is undefined (going to minus infinity). One way to handle this problem is to replace DFA on the left hand side of equation (2) by (1+DFA) and then proceeding with the regression analysis.
Another approach to this problem would be to consider a logistics functional form,namely:
DFA/DFB = g * (exp(Z)/(1+exp(Z)) + e (4)
where g is another parameter to be estimated and, for example,
Z = a + b*DFB + c*T + d*SUG (5)
An alternative form for Z could instead involve the natural logarithms of DFB and SUG. The parameters a, b, c, d, and g in equation (4) can be estimated by means of a non-linear regression analysis.
If DFA were always less than or equal to DFB then we could set g=1 on a priori grounds. However, we must consider the possibility that for some participants the level of disfluency at the end of the trial might be greater than before the trial. This situation could occur if the treatment actually had a negative effect on fluency for some individuals or perhaps, in some cases, the positive effects of the treatment are overwhelmed by naturally occurring fluctuations in disfluency.
We indicated in the previous post that nonlinear terms might be introduced into the analysis. One such term that might be of interest would be an interaction between the treatment term, T, and the level of disfluency before treatment, DFB. Then equation (2) in the previous post would become:
ln(DFA) = a + b*ln(DFB) + (c + h*ln(DFB))*T + d*ln(SUG) + e (6)
where h is another parameter to be estimated, and the coefficient associated with T, namely, c+h*ln(DFB), is no longer constant but depends on the level of disfluency measured before the trial.
The addition of this nonlinear term (assuming that h is shown to be significantly different from zero in the regression analysis) implies that the percentage reduction of disfluency, namely (1 - DFA/DFB)*100, due to pagoclone depends on the initial level of disfluency. For example, an individual with a greater level of disfluency at the start of the trial may show a lower reduction of disfluency at the end of the trial, or vice versa, depending on the value of the parameter, h.
Other hypotheses regarding the introduction of nonlinear terms could also be considered as well as the addition of other explanatory variables. Regression analyses of the kind suggested in the last two posts may materially contribute to a better understanding of the efficacy of drug treatments for stuttering and the impact of these treatments as a function of the individual's characteristics.
(Copyright 2011)
Wednesday, February 2, 2011
A Proposal for Analyzing Pagoclone Trials
A typical medical trial to test the efficacy of a treatment involves two groups--the treatment group and the placebo group, the members of which are assigned randomly. The objective is to see if a response variable, i.e., treatment outcome, is different between the two groups. Analysis of variance (ANOVA) methodologies provide statistical tests as to whether or not the means of the two groups are significantly different.
This approach regards each group as a unit of observation and requires looking at the average of the responses within the two groups. The hope, when using grouped averages, is that the random assignments of individuals to the groups will have made all of the averages (as well as the standard deviations and higher order moments) of other possibly relevant explanatory variables essentially equal across the two groups. When the populations of the groups are relatively small, this assumption may not be valid.
Furthermore, grouped data analyses may obscure relationships that can be delineated when, instead, individuals within the groups are viewed as the units of observation. In addition, ANOVA methodologies are not able to handle very well the inclusion of many other variables (particularly those that are continuous) that may also explain response outcomes.
We propose an alternative approach based on regression analysis, which is utilized extensively in fields like economics, but very little in medical research. Regression analysis can be regarded as a supplement to ANOVA techniques to extract additional information from the individual data that may be hidden by grouping the data.
The regression model discussed below explicitly teases out the placebo effect as measured by a suggestibility variable. Consider a pagoclone trial consisting of N individuals in the treatment group and M in the placebo group for a total participation of N+M individuals.
Let DFB be the value of a disfluency measure for an individual before treatment (with either pagoclone or the placebo) and DFA the value of the measure after treatment. For example, DFA and DFB might be the results from the Stuttering Severity Instrument. Further, let SUG be a measure of suggestibility for an individual at the start of the trial, which can be constructed, for example, from the MISS questionnaire discussed in the last post. Define a dummy variable T such that:
T = 1 if the individual received pagoclone
= 0 if the individual received a placebo.
Then a regression model describing the treatment outcome might be specified as:
DFA = a + b*DFB + c*T + d*SUG + e (1)
where a, b, c, and d are parameters to be estimated and e is an error term. The error term includes all possible explanatory variables that may have been excluded from the model and errors in the measurement of DFA as well as of each of the explanatory variables.
The model parameters can be estimated on the basis of the observations from the N+M individuals using any of a number of statistical analysis packages supporting regression analysis. The error terms for each of the observations are used to calculate a measure of goodness of fit, namely R-squared, which ranges between 0 and 1 where 1 represents a perfect fit to the data and 0 represents no fit. The parameter estimates will each have associated standard errors that can be used to calculate significance levels for the parameters.
The model can be expanded by including nonlinear terms (and additional associated parameters) such as DFA-squared, SUG-squared, DFA*SUG, and T*SUG. Moreover, if the trial involves different dosages of pagoclone, we can take that into account by adding additional dummy variables. For example, if pagoclone is administered in two different dosages, then we utilize two dummy variables, T1 and T2 defined by:
T1 = 1 for pagoclone at level 1 dosage
= 0 otherwise
T2 = 1 for pagoclone at level 2 dosage
= 0 otherwise
So for (T1, T2), level 1 dosage is represented by (1, 0), while (0, 1) represents level 2 dosage, and (0, 0) refers to the placebo group.
An alternative model incorporating the explanatory variables in equation (1) is a log-log model expressed as:
ln(DFA) = a + b*ln(DFB) +c*T + d*ln(SUG) + e (2)
where ln is the natural logarithm. Nonlinear terms can also be introduced into this model. Once the parameters of log-log model are estimated, we can rewrite the model as:
ln(DFA/DFB) = a + (b – 1)*ln(DFB) + c*T + d*ln(SUG) + e (3)
where we used the property of logarithms that ln(DFA/DFB) = ln(DFA) – ln(DFB). The percentage reduction of disfluency is given by (1 - DFA/DFB)*100.
Regression analyses of the kinds discussed above may contribute to the specification and validation of suggestibility measures that would be useful in identifying trial participants who may be strongly responsive to placebos. This information can then be used to develop treatment trials that utilize limited resources more efficiently.
(Copyright 2011)
This approach regards each group as a unit of observation and requires looking at the average of the responses within the two groups. The hope, when using grouped averages, is that the random assignments of individuals to the groups will have made all of the averages (as well as the standard deviations and higher order moments) of other possibly relevant explanatory variables essentially equal across the two groups. When the populations of the groups are relatively small, this assumption may not be valid.
Furthermore, grouped data analyses may obscure relationships that can be delineated when, instead, individuals within the groups are viewed as the units of observation. In addition, ANOVA methodologies are not able to handle very well the inclusion of many other variables (particularly those that are continuous) that may also explain response outcomes.
We propose an alternative approach based on regression analysis, which is utilized extensively in fields like economics, but very little in medical research. Regression analysis can be regarded as a supplement to ANOVA techniques to extract additional information from the individual data that may be hidden by grouping the data.
The regression model discussed below explicitly teases out the placebo effect as measured by a suggestibility variable. Consider a pagoclone trial consisting of N individuals in the treatment group and M in the placebo group for a total participation of N+M individuals.
Let DFB be the value of a disfluency measure for an individual before treatment (with either pagoclone or the placebo) and DFA the value of the measure after treatment. For example, DFA and DFB might be the results from the Stuttering Severity Instrument. Further, let SUG be a measure of suggestibility for an individual at the start of the trial, which can be constructed, for example, from the MISS questionnaire discussed in the last post. Define a dummy variable T such that:
T = 1 if the individual received pagoclone
= 0 if the individual received a placebo.
Then a regression model describing the treatment outcome might be specified as:
DFA = a + b*DFB + c*T + d*SUG + e (1)
where a, b, c, and d are parameters to be estimated and e is an error term. The error term includes all possible explanatory variables that may have been excluded from the model and errors in the measurement of DFA as well as of each of the explanatory variables.
The model parameters can be estimated on the basis of the observations from the N+M individuals using any of a number of statistical analysis packages supporting regression analysis. The error terms for each of the observations are used to calculate a measure of goodness of fit, namely R-squared, which ranges between 0 and 1 where 1 represents a perfect fit to the data and 0 represents no fit. The parameter estimates will each have associated standard errors that can be used to calculate significance levels for the parameters.
The model can be expanded by including nonlinear terms (and additional associated parameters) such as DFA-squared, SUG-squared, DFA*SUG, and T*SUG. Moreover, if the trial involves different dosages of pagoclone, we can take that into account by adding additional dummy variables. For example, if pagoclone is administered in two different dosages, then we utilize two dummy variables, T1 and T2 defined by:
T1 = 1 for pagoclone at level 1 dosage
= 0 otherwise
T2 = 1 for pagoclone at level 2 dosage
= 0 otherwise
So for (T1, T2), level 1 dosage is represented by (1, 0), while (0, 1) represents level 2 dosage, and (0, 0) refers to the placebo group.
An alternative model incorporating the explanatory variables in equation (1) is a log-log model expressed as:
ln(DFA) = a + b*ln(DFB) +c*T + d*ln(SUG) + e (2)
where ln is the natural logarithm. Nonlinear terms can also be introduced into this model. Once the parameters of log-log model are estimated, we can rewrite the model as:
ln(DFA/DFB) = a + (b – 1)*ln(DFB) + c*T + d*ln(SUG) + e (3)
where we used the property of logarithms that ln(DFA/DFB) = ln(DFA) – ln(DFB). The percentage reduction of disfluency is given by (1 - DFA/DFB)*100.
Regression analyses of the kinds discussed above may contribute to the specification and validation of suggestibility measures that would be useful in identifying trial participants who may be strongly responsive to placebos. This information can then be used to develop treatment trials that utilize limited resources more efficiently.
(Copyright 2011)
Subscribe to:
Posts (Atom)