def bacs_pet_mri_date_batch(rootpath):
"""Given a path, this function finds subject folders there and finds
the date of the freesurfer processed data.
Parameters
----------
rootpath : string
Path where subject directories lie
Returns
-------
datetbl : DataFrame
Holds codea, MRI_Tp, and MRI_Scandate fields
"""
subs, _ = cf.lbls_infold(rootpath)
datetbl = pd.DataFrame(columns=['sub'], data=subs)
datetbl['MRI_Scandate'] = float('NaN')
for sub in subs:
dateout = bacs_pet_mri_date(rootpath, sub)
datetbl.loc[datetbl['sub']==sub,'MRI_Scandate'] = dateout
datetbl['codea'] = [cf.get_lbl_id(sub) for sub in datetbl['sub'].tolist()]
datetbl['MRI_Tp'] = [cf.get_tp(sub) for sub in datetbl['sub'].tolist()]
datetbl.drop('sub', axis=1, inplace=True)
datetbl = datetbl[datetbl['MRI_Tp'].notnull()]
return datetbl
def mri_run(datadir, outdir, rois):
"""Main function to collect MRI volume data
Parameters
----------
datadir : string
Full path to root directory of freesurfer processed data. Expect file tree
to be datadir/subcode/stats/aseg.stats
outdir : string
Full path to directory where data will be saved
rois : list of strings
List of freesurfer rois of interest. These volumes of these rois will be
inserted in aseg_change along with their rates of change
Returns
-------
aseg_stats : pandas DataFrame
DataFrame where each row is a scan, and columns are volumes of all
freesurfer processed regions
aseg_change : pandas DataFrame
DataFrame where each row is a scan, and columns are the volumes of interest,
their rates of change, and icv correction
"""
#get aseg_stats data from freesurfer processed data
outfile = '%sFS_aseg_stats.txt' %outdir
subs, asegout, output = extractFSasegstats(datadir, outfile)
aseg_stats = pd.read_csv(outfile, header=0, delim_whitespace=True)
#add columns for SubjID and MRI_TP
aseg_stats['codea'] = [cf.get_id(sub) for sub in subs]
aseg_stats['MRI_Tp'] = [cf.get_tp(sub) for sub in subs]
aseg_stats.drop('Measure:volume', axis=1, inplace=True)
#get dates of MRI scans that were processed with freesurfer
mridates = bacs_pet_mri_date_batch(datadir)
aseg_change = pd.merge(aseg_change, mridates, on=['codea','MRI_Tp'])
rois_icvcorr = dict([(roi, '%s_icvcorr' %roi) for roi in rois])
aseg_change = icvcorr(aseg_change, rois_icvcorr, 'IntraCranialVol')
#calculate rate of change in years
for roi in rois:
aseg_change = cf.rate_of_change(aseg_change, 'codea', 'MRI_Tp',
'MRI_Scandate', roi, '%s_sl' %roi)
cf.save_xls_and_pkl(aseg_stats, 'aseg_stats', outdir)
cf.save_xls_and_pkl(aseg_change, 'aseg_change', outdir)
return aseg_stats, aseg_change