diff --git a/neuroscout_cli/commands/get.py b/neuroscout_cli/commands/get.py
index 5987308..f427754 100644
--- a/neuroscout_cli/commands/get.py
+++ b/neuroscout_cli/commands/get.py
@@ -2,16 +2,16 @@
 import tarfile
 import logging
 import sys
+import datalad
 
 from pathlib import Path
 from shutil import copy
 from packaging import version
 from neuroscout_cli.commands.base import Command
 from neuroscout_cli import __version__ as VERSION
-from datalad.api import install, get
-import datalad
 from bids.utils import convert_JSON
-from ..tools.convert import check_convert_model
+from ..tools.convert import check_convert_mode
+from pyns.fetch_utils import fetch_images
 
 datalad.ui.ui.set_backend('console')
                           
@@ -32,7 +32,7 @@ def __init__(self, options):
         self.main_dir.mkdir(parents=True, exist_ok=True)
         self.bundle_dir.mkdir(parents=True, exist_ok=True)        
 
-    def download_bundle(self, no_get=False):
+    def download(self, no_get=False):
         """ Download analysis bundle and setup preproc dir """
         # If tarball doesn't exist, download it
         bundle_tarball = self.bundle_dir / f'{self.bundle_id}.tar.gz'
@@ -58,81 +58,26 @@ def download_bundle(self, no_get=False):
             (self.bundle_dir / 'model.json').absolute()   
             ) # Convert if necessary
 
-        self.dataset_dir = self.download_dir / self.resources['dataset_name']
-
-        # Install DataLad dataset if dataset_dir does not exist
-        if not self.dataset_dir.exists() and not no_get:
-            # Use datalad to install the preproc dataset
-            install(source=self.resources['preproc_address'],
-                    path=str(self.dataset_dir))
-
-        for option in ['preproc', 'fmriprep']:
-            if (self.dataset_dir / option).exists():
-                self.preproc_dir = (self.dataset_dir / option).absolute()
-                break
-        else:
-            self.preproc_dir = self.dataset_dir
-
-        return 0
-    
-    def download_data(self, no_get=False):
-        """ Use DataLad to download necessary data to disk """
+        # Load model
         with self.model_path.open() as f:
             model = convert_JSON(json.load(f))
 
-        try:
-            # Custom logic to fetch and avoid indexing dataset
-            paths = []
-            
-            # Custom logic to fetch relevant files
-            # Avoiding PyBIDS for peformance gains in indexing
-            tasks = model['input'].get('task', '')
-            if not isinstance(tasks, list):
-                subjects = [tasks]
-            tasks = [ f'task-{t}*' for t in tasks]
+        self.preproc_dir, paths = fetch_images(
+            self.resources['dataset_name'], self.dataset_dir, no_get=no_get, 
+            preproc_address=self.resources['preproc_address'],
+            datalad_jobs=self.options.get('datalad_jobs', -1),
+            fetch_json=True, fetch_brain_mask=True, **model['input'])
 
-            subjects = model['input'].get('subject', ['*'])
-            if not isinstance(subjects, list):
-                subjects = [subjects]
-                
-            run_ids = model['input'].get('run', [''])
-            if not isinstance(run_ids, list):
-                runs = [run_ids]
-            runs = [f'run-{r}*'  if r else r for r in run_ids]
-            runs += [f'run-{str(r).zfill(2)}*'  if r else r for r in run_ids]
-            runs = list(set(runs))
-
-            for sub in subjects:
-                for run in runs:
-                    for task in tasks:
-                        pre =  f'sub-{sub}/**/func/*{task}{run}space-MNI152NLin2009cAsym*'
-                        paths += list(self.preproc_dir.glob(pre + 'preproc*.nii.gz'))
-                        paths += list(self.preproc_dir.glob(pre + 'brain_mask.nii.gz'))
-
-            if not paths:
-                raise Exception("No images suitable for download.")
-            
-            # Get all JSON files
-            paths += list(self.preproc_dir.rglob('*.json'))
+        self.dataset_dir = self.preproc_dir.parent
 
-            # Get with DataLad
-            if not no_get:
-                get([str(p) for p in paths], dataset=self.dataset_dir, jobs=self.options['datalad_jobs'])
-
-        except Exception as exp:
-            if hasattr(exp, 'failed'):
-                message = exp.failed[0]['message']
-                raise ValueError("Datalad failed. Reason: {}".format(message))
-            else:
-                raise exp
-
-        # Copy meta-data to root of preproc_dir
-        meta = list(self.bundle_dir.glob('task-*json'))[0]
-        if not (self.preproc_dir/ meta.parts[-1]).exists():
-            copy(meta, self.preproc_dir)
+        if not no_get:
+            # Copy meta-data to root of preproc_dir
+            meta = list(self.bundle_dir.glob('task-*json'))[0]
+            if not (self.preproc_dir/ meta.parts[-1]).exists():
+                copy(meta, self.preproc_dir)
 
         return 0
-
+    
     def _check_version(self):
         # Check version
         req = self.resources.get('version_required', 0.3)
@@ -149,10 +94,10 @@ def _check_version(self):
             sys.exit(1)
 
     def run(self, no_get=False):
-        retcode = self.download_bundle(no_get=no_get)
-        
-        if not self.options.get('bundle_only', False):
-            retcode = self.download_data(no_get=no_get)
+        bundle_only = self.options.get('bundle_only', False)
+        if bundle_only:
+            no_get = True
+        retcode = self.download(no_get=no_get)
             
         return retcode