add texts

source/FC_Draw_ROI.txt

@@ -1,6 +1,8 @@
 Draw ROI
 ------------------------------
 
+*Draw ROIs* is implemented as automatically drawing spheres or cubes with ROI coordinates and a header reference 3D image. The ROI coordinages and labels are sorted in a *\*.csv* table for output indexing purpose, while the header reference image is used to define the output image properties such as bounding box, originator, orientation, inclusive mask, and voxel size.
+
 .. image:: _images/draw_roi.png
    :align: right
 

source/FC_Merge_Extract_ROI.txt

@@ -1,6 +1,8 @@
 Merge/Extract ROIs
 ------------------------------
 
+Given a number-tagged atlas, a subset of ROIs indexed by integers can be extracted and exported to one 3D image. At the opposite, given seperated ROI files, the current function can also merge them into one combined atlas-like ROI file, with ROI labels stored in a *\*.csv* table.
+
 .. image:: _images/merge_roi.png
    :align: right
 

source/FC_ROI_Calculation.txt

@@ -1,6 +1,8 @@
 ROI Calculation
 ----------------------------------------------------
 
+With a predefined atlas-like ROI file and a descriptive number-label table, the current function can extract mean time series from ROIs and voxels, and calculate Pearson's correlation as well as its Fisher-z transform. An option is provided to calculate partial correlation between each pair of ROIs, with mean signals of other ROIs as covariates.
+
 .. image:: _images/roi_calculation.png
    :align: right 
 

source/Menu_FC.txt

@@ -6,6 +6,10 @@
 FC
 ===================================
 
+Functional connectivity is calculated as the temporal correlation between pairs of time series extracted from ROIs or voxels. 
+
+In BRANT, three methods of preparing ROIs are provided, including drawing spheres/cubes from coordinates, extracting ROIs from an atlas and merging separate ROI files into one number-tagged template.
+
 .. toctree::
    :maxdepth: 2
 

source/Menu_Preprocess.txt

@@ -6,6 +6,7 @@
 Preprocess
 ===================================
 
+Raw data collected from MRI scanners are formatted as DICOM (Digital Imaging and Communications in Medicine) files, which are firstly converted to a single 4D NIfTI (Neuroimaging Informatics Technology Initiative) image for efficiently processing. For converted data, visual inspection is recommended to censor data with low quality (artifacts and distortions). Qualified data can be further processed in the preprocessing pipeline.
 
 .. toctree::
    :maxdepth: 2

source/Menu_SPON.txt

@@ -6,6 +6,9 @@
 SPON
 ===================================
 
+
+Voxel-wise metrics of time series implemented in the current module include amplitude of time series (AM), (fractional) amplitude of low frequency fluctuation (ALFF/fALFF), regional homogeneity (ReHo), functional connectivity density (FCD) and functional connectivity strength (FCS). 
+
 .. toctree::
    :maxdepth: 2
 

source/Menu_STAT.txt

@@ -6,6 +6,8 @@
 STAT
 ===================================
 
+The current module provides Student's t-tests for sample mean comparisons and several methods for image-based meta-analysis (IBMA). For multi-comparison correction, we use the Benjamini-Hochberg and the Benjamini-Yekutieli procedures to control the false discovery rate (FDR) of dependent and independent cases, and the Bonferroni procedure to control the familywise error rate (FWER).
+
 .. toctree::
    :maxdepth: 2
 

source/Menu_Utilities.txt

@@ -6,6 +6,8 @@
 Utilities
 ===================================
 
+We have added several frequently used functions in this module to facilitate DICOM image conversion, the process of quality control, ROI coordinates extraction and 2D/3D signal extraction.
+
 .. toctree::
    :maxdepth: 2
 

source/Menu_VIEW.txt

@@ -6,6 +6,8 @@
 VIEW
 ===================================
 
+To visualize voxel intensities, we have implemented the *ROI Mapping* to extract and render the surface of 3D clusters, the *Surface Mapping* to project voxel intensity to vertices on a surface. To visualize ROI-ROI connectivity, *Network Visualization* is implemented to draw spheres and rods within a rendered brain surface, to present nodes and edges of the input network.
+
 .. toctree::
    :maxdepth: 2
 

source/SPON_ALFF_FALFF.txt

@@ -1,6 +1,8 @@
 ALFF/fALFF
 ------------------------------
 
+ALFF is calculated as the amplitude of the time series in a certain frequency band, which is the averaged square root of the power spectral density of the filtered time series. To increase the stability of ALFF across subjects, fALFF was proposed as calculating the fraction of a certain frequency band against the whole available frequency band.
+
 .. image:: _images/alff_falff.png
    :align: right
 

source/SPON_AM.txt

@@ -1,6 +1,8 @@
 AM
 ----------------------------------------
 
+AM is calculated as the average amplitude and the standard deviation of the mean-subtracted time series. The AM represents the strength of time series' temporal fluctuation, which is similar to ALFF/fALFF.
+
 .. image:: _images/am.png
    :align: right
 
@@ -9,7 +11,11 @@ AM
 * **filetype**: files in the filetype will be searched in input directories.
 * **4D nifti files**: if the input data is 4D, check this item. Otherwise uncheck.
 * **input dirs**: directories can be input either using a *.txt* file or spm select window.    
+<<<<<<< HEAD
 * **mean temporal ampilitude**: calculate absolute value of detrended and demeaned time series.
+=======
+* **mean temporal amplitude**: calculate absolute value of detrended and demeaned time series.
+>>>>>>> add texts
 * **standard deviation**: calculate standard deviation of time series
 * **variation**: calculate variation of time series   
 * **normalize transform**: in output file, a suffix of *_m* means the output is divided by mean intensity in the mask; a suffix of *_z* means the output is subtracted by mean intensity and divided by standard deviation.

source/SPON_FCD.txt

@@ -1,11 +1,16 @@
 FCD/FCS
 --------------------------------------------
 
+**FCD** is short for **Functional Connectivity Density** and **FCS** is short for **Functional Connectivity Strength**.
+
+A region growing algorithm was carried out to measure the local degree of each voxel under a certain threshold of Pearson's correlation. FCD in BRANT has been implemented to calculate the local FCD (lFCD), the global FCD (gFCD) and the long-range FCD (lrFCD) at one time. The lFCD of each voxel represents the number of spatially connected voxels defined by the region growing algorithm, while the gFCD, which is also referred to as the voxel-wise degree centrality, represents the number of voxels that have higher-than-threshold correlation with the seed voxel. The lrFCD is calculated as the gFCD subtracted the lFCD.
+
+Functional connectivity strength (FCS) measures the amount of information a node receives across whole graph or within a distance. Similar to FCD, the voxel-wise Pearson's correlation coefficients are firstly calculated in parallel and then Fisher-z transformed to improve normality. For each voxel, the FCS is calculated as the sum of connectivity that exceeds a given threshold divided by the number of voxels.
+
+
 .. image:: _images/FCD_new.png
    :align: right
 
-**FCD** is short for **Functional Connectivity Density** and **FCS** is short for **Functional Connectivity Strength**.
-
 * **mask**: could be whole brain mask or gray matter mask.
 * **id index**: identifier to find unique string for each subject
 * **filetype**: files in the filetype will be searched in input directories.

source/SPON_ReHo.txt

@@ -1,6 +1,8 @@
 ReHo
 -------------------------------------
 
+ReHo is calculated as the Kendall's coefficient of concordance (KCC) among a seed voxel and its neighbor voxels, which indicates the degree of spontaneous activity in the seed voxel's vicinity. Voxels of higher intensity in ReHo maps indicate greater similarity among neighboring voxels' time series.
+
 .. image:: _images/reho.png
    :align: right
 

source/STAT_IBMA.txt

@@ -1,6 +1,8 @@
 IBMA (Image-based meta-analysis)
 --------------------------------------------
 
+With statistical maps of different datasets tested using same analysis pipeline, and the demography of each sample, users can perform meta-analysis to merge the multisite statistics using image-based or matrix-based meta-analysis. We have implemented Stouffer's z-score method, Fisher's method, fixed/mixed effects model, Worsley and Friston's method and Nichols' method.
+
 .. image:: _images/ibma.png
    :align: right
 

source/Utilities_DICOM_convert.txt

@@ -1,6 +1,8 @@
 Dicom Convert
 -------------------------------
 
+Since in practice raw MRI data exported from an MR scanner consists of a large number of DICOM images storing slices and volumes of different sequences, by convention we convert the DICOM images to packed 3D or 4D NIFTI images before all processing steps. In BRANT, we use the *dcm2nii* from MRIcron/MRIcro to convert DICOM files into 4D NIfTI images by default and use wildcard characters to locate rs-fMRI image files. For the matched images, the *First N timepoints removal* is used to remove the first N frames that could be influenced by large motion or the instability of magnetic field.
+
 .. image:: _images/dicom_convert.png
    :align: right
 

source/Utilities_Head_Motion_Est.txt

@@ -1,6 +1,8 @@
 Head Motion Estimate
 ---------------------------------
 
+Head motion has been found having an impact on rs-fMRI signals. In preprocessing, six head motion parameters of (x-,y-,z-) translations and (pitch-,yaw-,roll-) rotations estimated during realignment are used as the inputs of the current function. By the default, the current function outputs the maximum absolute translation and rotation between frames as the exclusion criterion of large-motion subject. Additionally, the mean head displacement (the root-mean-square of translation parameters), the maximum head displacement, the number of micro displacement (>0.1mm), the mean absolute Euler angle of rotation, the framewise displacement (FD) and the number of frames with FD>0.5mm, are also exported to provide more subject exclusion criteria.
+
 .. image:: _images/head_motion_est.png
    :align: right
 

source/Utilities_ROI_coordinates.txt

@@ -1,6 +1,8 @@
 ROI coordinates
 -------------------------
-
+
+To visualize the topological strcture of network connections, ROI coordinates are expected as the centers of spheres. In the current function, coordinate of each number-tagged ROI is calculated as the center of mass with weights and then exported to a *\*.csv* table.
+
 .. image:: _images/roi_coordinates.png
    :align: right
 

source/Utilities_TSNR.txt

@@ -1,5 +1,7 @@
 TSNR (Temporal Signal to Noise Ratio)
 ---------------------------------------------------
+
+Influenced by the magnetic field inhomogeneity at air-tissue interfaces, rs-fMRI signals at orbitofrontal and temporal medial and polar areas suffer from a certain degree of distortions and signal loss. To exclude spurious voxels, we use the thresholded voxel-wise TSNR, which is calculated as the average intensity of time series divided ty the standart deviation, to generate subject-level or group-leval whole-brain mask.
 
 .. image:: _images/tsnr_new.png
    :align: right

source/Utilities_Visual_Check.txt

@@ -1,6 +1,8 @@
 Visual Check
 ---------------------------
 
+The current function provides batch operations to visually inspect artifacts and normalization quality, by calling *Display* from SPM. We've added keyboard operations to the *Display* figure that users can press up/down to switch fMRI volumes of one subject and press left/right to switch subjects. Before running the frame-by-frame inspection, the current function exports screenshots of selected slices overlaid by a semi-transparent brain mask for a glimpse of the overall image quality.
+
 .. image:: _images/visual_check.png
    :align: right
 

source/VIEW_Network_Visualization.txt

@@ -1,6 +1,8 @@
 Network Visualization
 ----------------------------
-
+
+Using a *\*.txt* file storing symmetric connectivity matrix and a *\*.csv* table with nodal information (such as coordinate, label, module and color) as input, we can draw spheres and rods to visualize nodes and edges.
+
 .. image:: _images/network_visualization.png
    :align: right
 

source/VIEW_ROI_Mapping.txt

@@ -1,6 +1,10 @@
 ROI Mapping
 ----------------------
+
+When visualizing ROIs from an atlas or clusters from a user-defined 3D volume (e.g., clusters with significant difference between sample means), we can use the current function to extract and shade the surface of each number-tagged ROI/cluster in random or user defined colors. The ROIs/clusters of the input 3D image should be tagged with positive-integers. With an additional input of a reference *\*.csv* table containing number-label pairs (as described in `Utilities -> DICOM Convert`_), we can further parse the labels of each shaded ROI/cluster and present them in a legend.
 
+.. _`Utilities -> DICOM Convert`: Utilities_DICOM_convert.html
+
 .. image:: _images/roi_mapping.png
    :align: right
 

source/VIEW_Surface_Mapping.txt

@@ -1,6 +1,8 @@
 Surface Mapping
 ---------------------
 
+Besides shading each ROI/cluster, we can also project the voxel intensities to the surface. By the default, we use a rendered human brain surface constructed from vertices and triangular faces loaded from a pregenerated file. To draw another surface, users can input a binarized 3D mask, with which BRANT can extract the generate vertices and faces and render a new surface. When projecting a 3D volume to surface, the vertices on the surface are shaded as the intensity of the nearest voxel, while the material of the surface, the color maps of positive and negative intensities, the lighting and shading algorithm can be adjusted.
+
 .. image:: _images/surface_mapping.png
    :align: right
 

source/index.txt

@@ -7,6 +7,8 @@ Welcome to Brant!
 ===================================
 To facilitate data processing and deal with above listed issues, we’ve written an extendable MATLAB based toolbox BRANT (BRAinNetome Toolkit), which integrates fMRI data preprocessing, voxel-wise spontaneous activity analysis, functional connectivity analysis, complex network analysis, statistical analysis, data visualization as well as several useful utilities. We designed the toolbox using dynamically generated GUIs, with which other developers can generate their own GUIs by adding a few lines of MATLAB code. Also, to simplify the input process during using BRANT, most functions are initialized with default settings, users will only need to specify several necessary parameters, with free access to all.
 
+Functions of BRANT are arranged into 7 modules, which are preprocessing, functional connectivity (FC), spontaneous activity (SPON), complex network analysis (NET), statistics (STAT), visualization (View) and utilities. More details on proper module can be found in its own part.
+
 .. image:: _images/main_gui.png
    :align: center