AReineberg

usage() { echo "Usage: $0 [-i <mris_anatomical_stats output>]" 1>&2; exit 1; }

while getopts ":i:" o; do
	case "${o}" in
		i)
			i=${OPTARG}
			;;
		*)
			usage
			;;

AReineberg

#!/bin/bash

usage() { echo "Usage: $0 [-s <subject>]" 1>&2; exit 1; }

while getopts ":s:" o; do
	case "${o}" in
		s)
			s=${OPTARG}
			;;
		*)

AReineberg

#!/bin/bash
# execute one directory above labeldir and volumedir
# usage: ./volumes_from_labels.sh

volumedir='volumedir'
if [ ! -d $volumedir ]; then
    mkdir volumedir
fi
    
for lab in `ls labeldir/l*.label`; do

AReineberg

#!/bin/bash
# Usage: make_design.sh -s subjects.txt -b behavior.txt -f 5 -n workingmemory
# behavior file should be space delimited with subject in first field

usage() { echo "Usage: $0 [-s <subject list>] [-b <behavior file>] [-f <behavior field>] [-n <behavior name>]" 1>&2; exit 1; }

while getopts ":s:b:f:n:" o; do
	case "${o}" in
		s)
			s=${OPTARG}

AReineberg

overlay = MRIread('lh.thickness.fsaverage.mgh');
asc = load('lh_thickness_data.asc');
asc_reshaped = reshape(asc(:,5), overlay.volsize)
overlay.vol = fast_mat2vol(asc_reshaped, overlay.volsize);
MRIwrite(overlay, 'lh_thickness_data.mgh');

AReineberg

#!/bin/bash
# 
# Use fslmaths with -thr and -uthr options to extract
# each individual network value and save in net/value.
# Concatenate networks temporally.
#
# 2016-02-20 
# Banich Lab
# University of Colorado Boulder
#

AReineberg

#!/bin/bash
#
# Convert Yeo's 7 network liberal parcellation from FreeSurfer space to FSL MNI 2mm space.
# Reference network source:
# https://surfer.nmr.mgh.harvard.edu/fswiki/CorticalParcellation_Yeo2011
#
# 2016-02-20 
# Banich Lab
# University of Colorado Boulder
#

AReineberg

import numpy as np
import pandas as pd
from bokeh.plotting import *
from bokeh.objects import HoverTool, ColumnDataSource

#
########## Load data ##########
#
data_path = '/path/to/data/'
node_file = data_path + '/nodes.csv' # 1 column of node names - header = "name"

AReineberg

% requires brain connectivity toolbox in path
files = dir('*.csv');
n = size(files, 1);

%
modules = csvread('modules.txt');
mods_of_interest = [1, 4, 6, 7, 8, 10, 11, 14];
keep_list = find(ismember(modules, mods_of_interest));
mods_short = modules(keep_list);