diff --git a/R/otherfunc.R b/R/otherfunc.R
index ccfc1e6..efaf0a1 100644
--- a/R/otherfunc.R
+++ b/R/otherfunc.R
@@ -683,12 +683,13 @@ surf_to_vol=function(surf_data, filename)
 #'
 #' @description Correlates the significant clusters of an earlier vertex-wise analysis with a database of task-based fMRI and voxel-based morphometric statistical maps and associate them with relevant key words
 #'
-#' @details The \href{https://nimare.readthedocs.io/en/stable/index.html}{NiMARE} python module is used for the imaging decoding and is imported via the reticulate package. The function also downloads the \href{https://github.com/neurosynth/neurosynth-data}{neurosynth} database in the package's inst/extdata direcotry (~8 Mb) for the analysis.
+#' @details The \href{https://nimare.readthedocs.io/en/stable/index.html}{NiMARE} python module is used for the imaging decoding and is imported via the reticulate package. The function needs the \href{https://github.com/neurosynth/neurosynth-data}{neurosynth} database in the package's inst/extdata directory to be downloaded (~8 Mb) for the analysis. To maximise the speed of processing, keywords were excluded from the original database if they were: the name of an anatomical brain region, protocol-specific descriptive words that are not meaningful out of context (e.g., “studied”, “clips”, “engaged”), and broad categories that do not specifically point to a content (“disorder”, “psychological”, “cognitive”). A full list can be obtained with the keyword_list argument.
 #'
 #' @param surf_data a numeric vector with a length of 20484
 #' @param contrast A string object indicating whether to decode the positive or negative mask ('positive' or 'negative')
+#' @param keywords_list A boolean stating whether to return the full list of keywords in the imported neurosynth database
 #'
-#' @returns A data.frame object listing the keywords and their Pearson's R values
+#' @returns A data.frame object listing the keywords and their Pearson's R values. If keywords_list is TRUE, a list containing the same data.frame object and the list of all the database keywords.
 #' @examples
 #' CTv = rbinom(20484, 1, 0.001) 
 #' decode_surf_data(CTv, 'positive')
@@ -696,7 +697,7 @@ surf_to_vol=function(surf_data, filename)
 #' @export
 
 ##CT image decoding
-decode_surf_data=function(surf_data,contrast="positive")
+decode_surf_data=function(surf_data,contrast="positive", keywords_list=F)
 {
   #Check if required python dependencies and neurosynth are present, nothing will happen if absent
   VWRfirstrun("neurosynth")
@@ -751,7 +752,18 @@ decode_surf_data=function(surf_data,contrast="positive")
   colnames(result)=c("keyword","r")
   result=result[order(-result$r),]
   
-  return(result)
+  if (keywords_list==T) {
+    neurosynth_dset = nimare.dataset$Dataset$load(system.file("extdata/neurosynth_dataset.pkl.gz", package='VertexWiseR'))
+    keywords=colnames(neurosynth_dset$annotations)
+    keywords=sapply(strsplit(keywords, '__'),"[", 2)[-c(1:3)]
+    result=list(result,keywords)
+  }
+  else
+  {
+    return(result)
+  }
+  
+  
   } 
 }  
 
diff --git a/man/decode_surf_data.Rd b/man/decode_surf_data.Rd
index 5112228..a6d7f14 100644
--- a/man/decode_surf_data.Rd
+++ b/man/decode_surf_data.Rd
@@ -4,12 +4,14 @@
 \alias{decode_surf_data}
 \title{Decode surface data}
 \usage{
-decode_surf_data(surf_data, contrast = "positive")
+decode_surf_data(surf_data, contrast = "positive", keyword_list = F)
 }
 \arguments{
 \item{surf_data}{a numeric vector with a length of 20484}
 
 \item{contrast}{A string object indicating whether to decode the positive or negative mask ('positive' or 'negative')}
+
+\item{keyword_list}{A boolean stating whether to return the full list of keywords in the imported neurosynth database}
 }
 \value{
 A data.frame object listing the keywords and their Pearson's R values
@@ -18,7 +20,7 @@ A data.frame object listing the keywords and their Pearson's R values
 Correlates the significant clusters of an earlier vertex-wise analysis with a database of task-based fMRI and voxel-based morphometric statistical maps and associate them with relevant key words
 }
 \details{
-The \href{https://nimare.readthedocs.io/en/stable/index.html}{NiMARE} python module is used for the imaging decoding and is imported via the reticulate package. The function also downloads the \href{https://github.com/neurosynth/neurosynth-data}{neurosynth} database in the package's inst/extdata direcotry (~8 Mb) for the analysis.
+The \href{https://nimare.readthedocs.io/en/stable/index.html}{NiMARE} python module is used for the imaging decoding and is imported via the reticulate package. The function needs the \href{https://github.com/neurosynth/neurosynth-data}{neurosynth} database in the package's inst/extdata directory to be downloaded (~8 Mb) for the analysis. To maximise the speed of processing, keywords were excluded from the original database if they were: the name of an anatomical brain region, protocol-specific descriptive words that are not meaningful out of context (e.g., “studied”, “clips”, “engaged”), and broad categories that do not specifically point to a content (“disorder”, “psychological”, “cognitive”). A full list can be obtained with the keyword_list argument.
 }
 \examples{
 CTv = rbinom(20484, 1, 0.001)