.. _codex_fmri_2022_SteinhauserEtal:


**Steinhauser et al. (2022).** *Functional dissection of neural connectivity ...*
***********************************************************************************

.. contents:: :local:

.. highlight:: Tcsh

Introduction
============

Here we present commands used in the following paper:

* | Steinhauser JL, Teed AR, Al-Zoubi O, Hurlemann R, Chen G, Khalsa
    SS (2022).  Functional dissection of neural connectivity in
    generalized anxiety disorder. 
  | `<https://www.biorxiv.org/content/10.1101/2022.01.09.475543v1>`_

**Abstract:** 
Differences in the correlated activity of networked brain regions have
been reported in individuals with generalized anxiety disorder (GAD)
but an overreliance on the null-hypothesis significance testing (NHST)
framework limits the identification and characterization of
disorder-relevant relationships. In this preregistered study, we
applied a Bayesian statistical framework as well as NHST to the
analysis of resting-state fMRI scans from females with GAD and
demographically matched healthy comparison females. Eleven a-priori
hypotheses about functional correlativity (FC) were evaluated using
Bayesian (multilevel model) and frequentist (t-test)
inference. Reduced FC between the ventromedial prefrontal cortex
(vmPFC) and the posterior-mid insula (PMI) was confirmed by both
statistical approaches. FC between the vmPFC-anterior insula, the
amygdala-PMI, and the amygdala-dorsolateral prefrontal cortex (dlPFC)
region pairs did not survive multiple comparison correction using the
frequentist approach. However, the Bayesian model provided evidence
for these region pairs having decreased FC in the GAD
group. Leveraging Bayesian modeling, we demonstrate decreased FC of
the vmPFC, insula, amygdala, and dlPFC in females with GAD. Exploiting
the Bayesian framework revealed FC abnormalities between region pairs
excluded by the frequentist analysis, as well as other previously
undescribed regions, demonstrating the benefits of applying this
statistical approach to resting state FC data.

**Study keywords:** 
resting state FMRI, EPI, MPRAGE, human, adult, RETROICOR, fast
ANATICOR, ROIs


**Main programs:** 
``afni_proc.py``, ``MBA`` (, ``@SSwarper``, ``recon-all`` (FS))


| **Github page:**
| See these authors' github page for full descriptions and downloads 
  of codes and supplementary text files:
| `<https://github.com/Jonas-Ste/GAD_MBA_FC>`_

Download scripts
================

To download, either:

* \.\.\. click the link(s) in the following table (perhaps Rightclick
  -> "Save Link As..."): 

  .. list-table:: 
     :header-rows: 0

     * - |s01|
       - run ``afni_proc.py`` for resting state analysis; note the
         inclusion of RETRIOCOR (``ricor``), fast ANATICOR
         (``-regress_anaticor_fast``), motion regression with
         principle components (PCs) from ventricle ROIs, nonlinear
         warps estimated with ``@SSwarper``, and ROI maps estimated by
         FreeSurfer's ``recon-all``
     * - |s02|
       - run ``MBA`` for matrix-based analysis; see the github page
         (link above) for the data table and ROI list

* \.\.\. or copy+paste into a terminal::

    curl -O https://afni.nimh.nih.gov/pub/dist/doc/htmldoc/codex/fmri/media/2022_SteinhauserEtal/preprocessing_command_afniproc.sh
    curl -O https://afni.nimh.nih.gov/pub/dist/doc/htmldoc/codex/fmri/media/2022_SteinhauserEtal/run_MBA_full.txt

View scripts
============


``preprocessing_command_afniproc.sh``
-------------------------------------------

.. literalinclude:: /codex/fmri/media/2022_SteinhauserEtal/preprocessing_command_afniproc.sh
   :linenos:

``run_MBA_full.txt``
-------------------------------------------

.. literalinclude:: /codex/fmri/media/2022_SteinhauserEtal/run_MBA_full.txt
   :linenos:

.. aliases for scripts, so above is easier to read
.. |s01| replace:: :download:`preprocessing_command_afniproc.sh
                   </codex/fmri/media/2022_SteinhauserEtal/preprocessing_command_afniproc.sh>`
.. |s02| replace:: :download:`run_MBA_full.txt
                   </codex/fmri/media/2022_SteinhauserEtal/run_MBA_full.txt>`