Today is my Dad's birthday! Happy Birthday Papi! I LOVE you lots!!! And I hope you have a nice day. You're the best Dad ever! I want to give you a big hug and tell you that I love you. And I hope you like the breakfast that I'm making you. :) C
Sunday, January 24, 2010
Saturday, January 23, 2010
O NOSSO CARPE DIEM.
SEMPRE FALAMOS DO NOSSO PRESENTE, DO NOSSO MOMENTO E DOS PROCESSOS QUE A NOSSA VIDA SEGUE. DOS PERCURSOS QUE TEMOS QUE FAZER PARA SERMOS UMA PESSOA BEM SUCEDIDA E CHEIAS DE REALIZAÇÕES. POIS É, HOJE DE MANHÃ LI UM ARTIGO QUE FALA NESTE PROCESSO. DE SABERMOS VIVER A NOSSA VIDA, OS NOSSOS MOMENTO, NO AGORA, NEM NO LÁ NEM NO CÁ.
MUITO INTERESSANTE. NÃO VOU CITAR A REVISTA, PARA NÃO DAR A IMPRESSÃO, QUE ESTOU FAZENDO O SEU MARKINTNG. MAS ACHO QUE VALE A PENA CITAR, O QUE ELA DIZ, SOBRE O CARPE DIEM, ATRAVÉS DO SEU ESCRITOR DE ARTIGO, REINALDO RISK.
DO LATIM"CARPI DIEM, SIGNIFICA "VIVA O MOMENTO PRESENTE"
ELE AINDA DIZ QUE TEMOS: "VIVER O NOSSO MOMENTO AGORA NO AQUI, E NÃO NO LÁ".
"A VIDA NÃO PODE SER VISTA COMO LÁ, COMO LÁ ESTIVESSE UM PARAÍSO, A BONANÇA, A FELICIDADE. ESTA VISÃO PROVOCA UMA GRANDE ILUSÃO E DEIXAMOS DE VIVER O CÁ, O AQUI E O AGORA, O REAL".
SE EU VIVER SÓ PARA RECLAMAR, SÓ VAI PIORAR AS COISAS. NÃO POSSO ESQUECER DE VIVER ESTE MOMENTOS PRESENTES. ESQUECER NÃO ADIANTA. QUEM SABE ENFRENTÁ-LOS, PODERÁ NOS AJUDAR A VIVER ESTE PROCESSO DE VIDA. SOMOS SERES HUMANOS, TEMOS O DIREITO DE ERRAR E ACERTAR.. USAR DO ERRO PARA MELHOR E FAZER O MELHOR, ESTAREMOS CONTRIBUINDO PARA A NOSSA ALEGRIA DE VIVER O PRESENTE, O MOMENTO. TENHO A CERTEZA, QUE TODOS NÓS QUEREMOS ISSO PARA NOSSA VIDA. ENTÃO, VAMOS VIVER O NOSSO CARPI DIEM, COM MUITO MAIS ALEGRIA E AMOR...
VAMOS VIVER CADA MOMENTO DA MELHOR MANEIRA POSSÍVEL. ISTO SIM VALE A PENA. MAS COM RESPONSABILIDADE E AMOR..
VIVER O AMANHÃ, QUE NEM COMEÇOU É IRREAL.
VIVA O AGORA. SEJA FELIZ..
E EU AMO VOCÊ, QUE É O MEU REAL MOTIVO DE ESTAR AQUI, AGORA, MANDANDO, DEIXANDO UM GRANDE ABRAÇO A TODOS.
UM LINDO DIA. MESMO COM CHUVA.
ESTE SELO SORRISO VEM BEM DE ENCONTRO COM O TEXTO.
OBRIGADA ANNA.
Agenda da Felicidade
OBRIGADA ANNA.
Agenda da FelicidadeO Sorriso é o cartão de Visita das Pessoas Saudáveis!
Leve com Você!
REPASSO A TODOS...
MAS VALE A PENA. PORQUE ESTAMOS VIVOS. ESTE É O MELHOR PRESENTE.
UM GRANDE ABRAÇO A VOCÊ MEU AMIGO QUE ESTÁ CHEGANDO.
SOU FELIZ, PORQUE VOCÊ É O MEU MOMENTO, ALÉM DE TANTOS OUTROS.
COM MUITO CARINHO
SANDRA
VOCÊ QUE ESTÁ VINDO PELA PRIMEIRA VEZ, VENHA CONHECER...
Friday, January 22, 2010
Brain Scanning Software Showdown
You've just finished doing some research using fMRI to measure brain activity. You designed the study, recruited the volunteers, and did all the scans. Phew. Is that it? Can you publish the findings yet?
Unfortunately, no. You still need to do the analysis, and this is often the trickiest stage. The raw data produced during an fMRI experiment are meaningless - in most cases, each scan will give you a few hundred almost-identical grey pictures of the person's brain. Making sense of them requires some complex statistics.
The very first step is choosing which software to use. Just as some people swear by Firefox while others prefer Internet Explorer for browsing the web, neuroscientists have various options to choose from in terms of image analysis software. Everyone's got a favourite. In Britain, the most popular are FSL (developed at Oxford) and SPM (London), while in the USA BrainVoyager sees a lot of use.
These three all do pretty much the same thing, give or take a few minor technical differences, so which one you use ultimately makes little difference. But just as there's more than one way to skin a cat, there's more than one way to analyze a brain. A paper from Fusar-Poli et al compares the results you get with SPM to the results obtained using XBAM, a program which uses a quite different statistical approach.
Here's what happened, according to SPM, when 15 volunteers looked at pictures of faces expressing the emotion of fear, and their brain activity was compared to when they were just looking at a boring "X" on the screen (I think - either that it's compared to looking at neutral faces; the paper isn't clear, but given the size of the blobs I doubt it's that.)
Various bits of the brain were more activated by the scared face pics, as you can see by the huge, fiery blobs. The activation is mostly at the back of the brain, in occipital cortex areas which deal with vision, which is as you'd expect. The cerebellum was also strongly activated, which is a bit less expected.
Now, here's what happens if you analyze exactly the same data using XBAM, setting the statistical threshold at the same level (i.e. in theory being no more or less "strict") -
You get the same visual system blobs, but you also see activation in a number of other areas. Or as Fusar-Poli et al put it -
This begs two questions: why the difference, and which way is right?
The difference must be a product of the different methods used. By default SPM uses a technique called statistical parametric mapping (hence the name) based on the assumption of normality. FSL and BrainVoyager do too. XBAM, on the other hand, differs from more orthodox software in a number of other ways; the most basic difference is that it uses non-parametric statistics but this document lists no less than five major innovations (Edit: although see the comments below this post)
The biggest question, though, is: should we all be using XBAM? Is it "better" than SPM? This is where things get tricky. The truth is that there's no right way to statistically analyze any data, let alone fMRI data. There are lots of wrong ways, but even if you avoid making any mistakes, there are still various options as to which statistical methods to use, and which method you use depends on which assumptions you're making. XBAM rests of different assumptions from SPM.
Whether XBAM's assumptions are more appropriate than those of SPM is a difficult question. The people who wrote XBAM presumably think so, and they're very smart people. But so are the people who wrote SPM. The point is, it's a very complex issue, the mathematical details of which go far beyond the understanding of most fMRI users (myself included).
My worry about this paper is that the average Joe Neuroscientist will decide that, because XBAM produces more activation than SPM, it must be "better". The authors are careful not to say this, but for fMRI researchers working in the publish-or-perish world of modern science, and whose greatest fear is that they'll run an analysis and end up with no blobs at all, the temptation to think "the more blobs the merrier" is a powerful one.
Fusar-Poli, P., Bhattacharyya, S., Allen, P., Crippa, J., Borgwardt, S., Martin-Santos, R., Seal, M., O’Carroll, C., Atakan, Z., & Zuardi, A. (2010). Effect of image analysis software on neurofunctional activation during processing of emotional human faces Journal of Clinical Neuroscience DOI: 10.1016/j.jocn.2009.06.027
Unfortunately, no. You still need to do the analysis, and this is often the trickiest stage. The raw data produced during an fMRI experiment are meaningless - in most cases, each scan will give you a few hundred almost-identical grey pictures of the person's brain. Making sense of them requires some complex statistics.
The very first step is choosing which software to use. Just as some people swear by Firefox while others prefer Internet Explorer for browsing the web, neuroscientists have various options to choose from in terms of image analysis software. Everyone's got a favourite. In Britain, the most popular are FSL (developed at Oxford) and SPM (London), while in the USA BrainVoyager sees a lot of use.
These three all do pretty much the same thing, give or take a few minor technical differences, so which one you use ultimately makes little difference. But just as there's more than one way to skin a cat, there's more than one way to analyze a brain. A paper from Fusar-Poli et al compares the results you get with SPM to the results obtained using XBAM, a program which uses a quite different statistical approach.
Here's what happened, according to SPM, when 15 volunteers looked at pictures of faces expressing the emotion of fear, and their brain activity was compared to when they were just looking at a boring "X" on the screen (I think - either that it's compared to looking at neutral faces; the paper isn't clear, but given the size of the blobs I doubt it's that.)
Various bits of the brain were more activated by the scared face pics, as you can see by the huge, fiery blobs. The activation is mostly at the back of the brain, in occipital cortex areas which deal with vision, which is as you'd expect. The cerebellum was also strongly activated, which is a bit less expected.Now, here's what happens if you analyze exactly the same data using XBAM, setting the statistical threshold at the same level (i.e. in theory being no more or less "strict") -
You get the same visual system blobs, but you also see activation in a number of other areas. Or as Fusar-Poli et al put it -Analysis using both programs revealed that during the processing of emotional faces, as compared to the baseline stimulus, there was an increased activation in the visual areas (occipital, fusiform and lingual gyri), in the cerebellum, in the parietal cortex [etc] ... Conversely, the temporal regions, insula and putamen were found to be activated using the XBAM analysis software only.
*
This begs two questions: why the difference, and which way is right?
The difference must be a product of the different methods used. By default SPM uses a technique called statistical parametric mapping (hence the name) based on the assumption of normality. FSL and BrainVoyager do too. XBAM, on the other hand, differs from more orthodox software in a number of other ways; the most basic difference is that it uses non-parametric statistics but this document lists no less than five major innovations (Edit: although see the comments below this post)
- "not to assume normality but to use permutation testing to construct the null distribution used to make inference about the probability of an "activation" under the null hypothesis."
- "recognizing the existence of correlation in the residuals after fitting a statistical model to the data."
- using "a mixed effects analysis of group level fMRI data by taking into account both intra and inter subject variances."
- using "3D cluster level statistics based on cluster mass (the sum of all the statistical values in the cluster) rather than cluster area (number of voxels)."
- using "a wavelet-based time series permutation approach that permitted the handling of complex noise processes in fMRI data rather than simple stationary autocorrelation."
The biggest question, though, is: should we all be using XBAM? Is it "better" than SPM? This is where things get tricky. The truth is that there's no right way to statistically analyze any data, let alone fMRI data. There are lots of wrong ways, but even if you avoid making any mistakes, there are still various options as to which statistical methods to use, and which method you use depends on which assumptions you're making. XBAM rests of different assumptions from SPM.
Whether XBAM's assumptions are more appropriate than those of SPM is a difficult question. The people who wrote XBAM presumably think so, and they're very smart people. But so are the people who wrote SPM. The point is, it's a very complex issue, the mathematical details of which go far beyond the understanding of most fMRI users (myself included).
My worry about this paper is that the average Joe Neuroscientist will decide that, because XBAM produces more activation than SPM, it must be "better". The authors are careful not to say this, but for fMRI researchers working in the publish-or-perish world of modern science, and whose greatest fear is that they'll run an analysis and end up with no blobs at all, the temptation to think "the more blobs the merrier" is a powerful one.
Fusar-Poli, P., Bhattacharyya, S., Allen, P., Crippa, J., Borgwardt, S., Martin-Santos, R., Seal, M., O’Carroll, C., Atakan, Z., & Zuardi, A. (2010). Effect of image analysis software on neurofunctional activation during processing of emotional human faces Journal of Clinical Neuroscience DOI: 10.1016/j.jocn.2009.06.027
Brain Scanning Software Showdown
You've just finished doing some research using fMRI to measure brain activity. You designed the study, recruited the volunteers, and did all the scans. Phew. Is that it? Can you publish the findings yet?
Unfortunately, no. You still need to do the analysis, and this is often the trickiest stage. The raw data produced during an fMRI experiment are meaningless - in most cases, each scan will give you a few hundred almost-identical grey pictures of the person's brain. Making sense of them requires some complex statistics.
The very first step is choosing which software to use. Just as some people swear by Firefox while others prefer Internet Explorer for browsing the web, neuroscientists have various options to choose from in terms of image analysis software. Everyone's got a favourite. In Britain, the most popular are FSL (developed at Oxford) and SPM (London), while in the USA BrainVoyager sees a lot of use.
These three all do pretty much the same thing, give or take a few minor technical differences, so which one you use ultimately makes little difference. But just as there's more than one way to skin a cat, there's more than one way to analyze a brain. A paper from Fusar-Poli et al compares the results you get with SPM to the results obtained using XBAM, a program which uses a quite different statistical approach.
Here's what happened, according to SPM, when 15 volunteers looked at pictures of faces expressing the emotion of fear, and their brain activity was compared to when they were just looking at a boring "X" on the screen (I think - either that it's compared to looking at neutral faces; the paper isn't clear, but given the size of the blobs I doubt it's that.)
Various bits of the brain were more activated by the scared face pics, as you can see by the huge, fiery blobs. The activation is mostly at the back of the brain, in occipital cortex areas which deal with vision, which is as you'd expect. The cerebellum was also strongly activated, which is a bit less expected.
Now, here's what happens if you analyze exactly the same data using XBAM, setting the statistical threshold at the same level (i.e. in theory being no more or less "strict") -
You get the same visual system blobs, but you also see activation in a number of other areas. Or as Fusar-Poli et al put it -
This begs two questions: why the difference, and which way is right?
The difference must be a product of the different methods used. By default SPM uses a technique called statistical parametric mapping (hence the name) based on the assumption of normality. FSL and BrainVoyager do too. XBAM, on the other hand, differs from more orthodox software in a number of other ways; the most basic difference is that it uses non-parametric statistics but this document lists no less than five major innovations (Edit: although see the comments below this post)
The biggest question, though, is: should we all be using XBAM? Is it "better" than SPM? This is where things get tricky. The truth is that there's no right way to statistically analyze any data, let alone fMRI data. There are lots of wrong ways, but even if you avoid making any mistakes, there are still various options as to which statistical methods to use, and which method you use depends on which assumptions you're making. XBAM rests of different assumptions from SPM.
Whether XBAM's assumptions are more appropriate than those of SPM is a difficult question. The people who wrote XBAM presumably think so, and they're very smart people. But so are the people who wrote SPM. The point is, it's a very complex issue, the mathematical details of which go far beyond the understanding of most fMRI users (myself included).
My worry about this paper is that the average Joe Neuroscientist will decide that, because XBAM produces more activation than SPM, it must be "better". The authors are careful not to say this, but for fMRI researchers working in the publish-or-perish world of modern science, and whose greatest fear is that they'll run an analysis and end up with no blobs at all, the temptation to think "the more blobs the merrier" is a powerful one.
Fusar-Poli, P., Bhattacharyya, S., Allen, P., Crippa, J., Borgwardt, S., Martin-Santos, R., Seal, M., O’Carroll, C., Atakan, Z., & Zuardi, A. (2010). Effect of image analysis software on neurofunctional activation during processing of emotional human faces Journal of Clinical Neuroscience DOI: 10.1016/j.jocn.2009.06.027
Unfortunately, no. You still need to do the analysis, and this is often the trickiest stage. The raw data produced during an fMRI experiment are meaningless - in most cases, each scan will give you a few hundred almost-identical grey pictures of the person's brain. Making sense of them requires some complex statistics.
The very first step is choosing which software to use. Just as some people swear by Firefox while others prefer Internet Explorer for browsing the web, neuroscientists have various options to choose from in terms of image analysis software. Everyone's got a favourite. In Britain, the most popular are FSL (developed at Oxford) and SPM (London), while in the USA BrainVoyager sees a lot of use.
These three all do pretty much the same thing, give or take a few minor technical differences, so which one you use ultimately makes little difference. But just as there's more than one way to skin a cat, there's more than one way to analyze a brain. A paper from Fusar-Poli et al compares the results you get with SPM to the results obtained using XBAM, a program which uses a quite different statistical approach.
Here's what happened, according to SPM, when 15 volunteers looked at pictures of faces expressing the emotion of fear, and their brain activity was compared to when they were just looking at a boring "X" on the screen (I think - either that it's compared to looking at neutral faces; the paper isn't clear, but given the size of the blobs I doubt it's that.)
Various bits of the brain were more activated by the scared face pics, as you can see by the huge, fiery blobs. The activation is mostly at the back of the brain, in occipital cortex areas which deal with vision, which is as you'd expect. The cerebellum was also strongly activated, which is a bit less expected.Now, here's what happens if you analyze exactly the same data using XBAM, setting the statistical threshold at the same level (i.e. in theory being no more or less "strict") -
You get the same visual system blobs, but you also see activation in a number of other areas. Or as Fusar-Poli et al put it -Analysis using both programs revealed that during the processing of emotional faces, as compared to the baseline stimulus, there was an increased activation in the visual areas (occipital, fusiform and lingual gyri), in the cerebellum, in the parietal cortex [etc] ... Conversely, the temporal regions, insula and putamen were found to be activated using the XBAM analysis software only.
*
This begs two questions: why the difference, and which way is right?
The difference must be a product of the different methods used. By default SPM uses a technique called statistical parametric mapping (hence the name) based on the assumption of normality. FSL and BrainVoyager do too. XBAM, on the other hand, differs from more orthodox software in a number of other ways; the most basic difference is that it uses non-parametric statistics but this document lists no less than five major innovations (Edit: although see the comments below this post)
- "not to assume normality but to use permutation testing to construct the null distribution used to make inference about the probability of an "activation" under the null hypothesis."
- "recognizing the existence of correlation in the residuals after fitting a statistical model to the data."
- using "a mixed effects analysis of group level fMRI data by taking into account both intra and inter subject variances."
- using "3D cluster level statistics based on cluster mass (the sum of all the statistical values in the cluster) rather than cluster area (number of voxels)."
- using "a wavelet-based time series permutation approach that permitted the handling of complex noise processes in fMRI data rather than simple stationary autocorrelation."
The biggest question, though, is: should we all be using XBAM? Is it "better" than SPM? This is where things get tricky. The truth is that there's no right way to statistically analyze any data, let alone fMRI data. There are lots of wrong ways, but even if you avoid making any mistakes, there are still various options as to which statistical methods to use, and which method you use depends on which assumptions you're making. XBAM rests of different assumptions from SPM.
Whether XBAM's assumptions are more appropriate than those of SPM is a difficult question. The people who wrote XBAM presumably think so, and they're very smart people. But so are the people who wrote SPM. The point is, it's a very complex issue, the mathematical details of which go far beyond the understanding of most fMRI users (myself included).
My worry about this paper is that the average Joe Neuroscientist will decide that, because XBAM produces more activation than SPM, it must be "better". The authors are careful not to say this, but for fMRI researchers working in the publish-or-perish world of modern science, and whose greatest fear is that they'll run an analysis and end up with no blobs at all, the temptation to think "the more blobs the merrier" is a powerful one.
Fusar-Poli, P., Bhattacharyya, S., Allen, P., Crippa, J., Borgwardt, S., Martin-Santos, R., Seal, M., O’Carroll, C., Atakan, Z., & Zuardi, A. (2010). Effect of image analysis software on neurofunctional activation during processing of emotional human faces Journal of Clinical Neuroscience DOI: 10.1016/j.jocn.2009.06.027
BOM DIA MEUS QUERIDOS(AS)!!!
QUERO LHE DIZER QUE ESTOU MUITO FELIZ COM A SUA COMPANHIA. ONTEM PASSEI VISITANDO ALGUNS AMIGOS QUE ATEMPO EU NÃO VIA. SÃO MEUS SEGUIDORES E SEGUIDORAS.
A CURIOSA FOI ATRÁS DELES, PARA VER COMO ANDAM.. ALGUNS ELA ENCONTROU OUTROS NÃO... MAS O QUE IMPORTA, É QUE TODOS E TODAS MORAM DENTRO DO MEU CORAÇÃO.
AS VEZES PRECISAMOS DAR UM TEMPO NAS POSTAGENS E VERMOS COMO ESTÃO ESTES AMIGOS(AS), QUE NOS SEGUEM E AS VEZES NÃO VOLTAM MAIS. FICO FELIZ QUE A MAIORIA EU ENCONTREI E ESTAVAM BENS.. OUTROS NÃO CONSEGUI ACESSAR.
MAIS FICA AQUI O MEU CARINHO E PRÉSTIMOS DE BOAS VINDAS. SUA COMPANHIA É SEMPRE MUITO AGRADÁVEL...
PARA AS MULHERES QUE AINDA NÃO LEVARAM O SEU SELO, AINDA DÁ TEMPO. É SÓ DESCER NA POSTAGEM ABAIXO E LEVAR O SEU CARINHO. AO SAIR DEIXE SEU RECADINHO E SABEREI QUE LEVOU. PARA QUEM VEIO E LEVOU MEU MUITO OBRIGADO.
AOS HOMENS QUE CONTRIBUÍRAM COM O SEU CARINHO, O MEU GRANDE ABRAÇO.
E DIGO MAIS...VOCÊS, SÃO A NOSSA RAZÃO DE LOUCURA!!!! AMAMOS CADA UM DE VOCÊS.. CADA MULHERES TEM O SEU GRANDE HOMEM..
E CADA HOMEM TEM AO SEU LADO UMA GRANDE MULHER... ENTÃO CADA UM É IMPORTANTE PARA O OUTRO..CADA UM TEM O SEU VALOR!!!
AGRADEÇO O CARINHO DE CADA UM QUE AQUI PASSOU... TENHAM UM LINDO DIA!!!!
NÃO SAIA SEM LEVAR O SELO ABAIXO, MINHA QUERIDA AMIGA!!
MULHERES PODEROSAS....
PARA VOCÊ QUE ESTÁ CHEGANDO AGORA, VENHA COMIGO, VOU LHE MOSTRAR MINHAS OUTRAS CASA.
É COM MUITO PRAZER TE RECEBO LÁ..
Meus Mimos!
VENHA TOMAR UM CAFEZINHO MUITO ESPECIAL. FEITO COM MUITO CARINHO PARA VOCÊ!
Poetas-Um Vôo Livre
Sinal de Liberdade-uma expressão de sentimento
Blog Coletivo-Uma Interação de Amigos
VENHA TOMAR UM CAFEZINHO MUITO ESPECIAL. FEITO COM MUITO CARINHO PARA VOCÊ!
Poetas-Um Vôo Livre
Sinal de Liberdade-uma expressão de sentimento
Blog Coletivo-Uma Interação de Amigos
Wednesday, January 20, 2010
Heart Shirt
I really like this shirt. Disney makes some really fun things! I wonder if my Nana and I can make one of these. It's a little too hard for me. But I think my Nana could help me. She's good at sewing stuff. And I think it would be fun to wear for Valentine's day! :) C
The Sweet Taste of Cannabinoids
Every stoner knows about the munchies, the fondness for junk food that comes with smoking marijuana. Movies have been made about it.It's not just that being on drugs makes you like eating: stimulants, like cocaine and amphetamine, decrease appetite. The munchies are something specific to marijuana. But why?
New research from a Japanese team reveals that marijuana directly affects the cells in the taste buds which detect sweet flavours - Endocannabinoids selectively enhance sweet taste.
Yoshida et al studied mice, and recorded the electrical signals from the chorda tympani (CT), which carries taste information from the tongue to the brain.
They found that injecting the mice with two chemicals, 2AG and AEA, markedly increased the strength of the signals produced in response to sweet tastes - such as sugar, or the sweetener saccharine. However, neither had any effect on the strength of the response to other flavours, like salty, bitter, or sour. Mice given endocannabinoids were also more eager to eat and drink sweet things, which confirms previous findings.
2-AG and AEA are both endocannabinoids, an important class of neurotransmitters. Marijuana's main active ingredient, Δ9-THC, works by mimicking the action of endocannabinoids. Although Δ9-THC wasn't tested in this study, it's extremely likely that it has the same effects as 2-AG and AEA.
In follow-up experiments, Yoshida et al found that endocannabinoids enhance sweet taste responses by acting on cannabinoid type 1 (CB1) receptors on the tongue's sweet taste cells themselves. In fact, over half of the sweet receptor cells expressed CB1 receptors!This is an important finding, because CB1 receptors are already known to regulate the pleasurable response to sweet foods (amongst other things) in the brain. These new data don't challenge this, but suggest that CB1 also modulates the most basic aspects of sweet taste perception. The munchies are probably caused by Δ9-THC acting at multiple levels of nervous system.
This paper also sheds light on CB1 antagonists. Given that drugs which activate CB1 make people eat more, it would make sense if CB1 blockers made people eat less, and therefore lose weight, a kind of anti-munchies effect. And indeed they do. Which is why rimonabant, a CB1 antagonist, was released onto the market in 2006 as a weight loss drug. It worked pretty well, although unfortunately it also it caused clinical depression in some people, so it was banned in Europe in 2008 and was never approved in the USA for the same reason.
The depression was almost certainly caused by antagonism at CB1 receptors in the brain, but Yoshida et al's findings suggest that a CB1 antagonist which didn't enter the brain, and only affected peripheral sites such as the taste buds, might be able to make people less fond of sweet foods without causing the same side-effects. Who knows - in a few years you might even be able to buy CB1 antagonist chewing gum to help you stick to your diet...
Yoshida, R., Ohkuri, T., Jyotaki, M., Yasuo, T., Horio, N., Yasumatsu, K., Sanematsu, K., Shigemura, N., Yamamoto, T., Margolskee, R., & Ninomiya, Y. (2009). Endocannabinoids selectively enhance sweet taste Proceedings of the National Academy of Sciences, 107 (2), 935-939 DOI: 10.1073/pnas.0912048107
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