How to copy text from a Word Document with Track Changes

If you ever wanted to copy text from a word document, while also copying the tracked changes, you are in luck (thanks to this blog entry).

The command for Windows/Mac as follows:
(1) Mac:

  • Select the text (with track changes) and press command + fn + F3 to CUT  the text and then select the window you want to paste the text into and press: command + fn + shift + F3.

(2) Windows:

  • Select the text (with track changes) and press Ctrl + F3 to CUT  the text and then select the window you want to paste the text into and press: Ctrl + shift + F3 to paste.


How to rotate the built-in screen of iMac or other newer macs

I wanted to rotate the screen of my work iMac by 90 degrees, so that I can enjoy my vertical monitor setup from home and VNC (remote screen) into work iMac. After a lot of search, I found that the built-in display settings, to change the rotation, is not available in iMac. Even the “option” + “cmd” key press, while clicking on Display (under system preferences) didn’t work on my iMac [Note: it did work on my MacBook Pro, though]. So I installed the SwitchResX software and voila the screen can be rotated [Note: you might need to install the helper functions and change the rotation using the monitor icon in the task bar]

Converter to find PMCID and NIHMSID from PMID

Given a set of PubMed IDs (PMIDs) you can use this converter to obtain the corresponding PMCIDs and/or NIHMS IDs if they exist. A PMCID will be available if the article is in PubMed Central (PMC). An NIHMS ID will be available if the manuscript has been deposited via the NIH Manuscript Submission (NIHMS) system. The tool is able to process maximum 2000 IDs in a single retrieval. If you have a larger number of IDs, split your list into smaller subsets for consecutive retrievals.

Issue: Spell check not working in Microsoft Word

For some reason the spell check was not working in my word document and I didn’t know how to fix it. A quick Google search helped and here is the solution that worked for me,

(1) Select all the text in your document (ctrl+A or command+A)
(2) Click on Tools->Language
(3) Make sure the “Do not check spelling or grammar” is unchecked (it could be showing a minus sign, click on it twice to make it unchecked.
(4) Voila!!! fixed 

For more information on this issue or if the above solution doesn’t work, see here

NEUROIMAGING: how to extract brain from T1w images using AFNI

I am a big fan of FSL. However, the brain extraction tool (BET) is not very user friendly at times – especially in terms of finding the perfect parameters for “fractional intensity threshold” and “vertical gradient in fractional intensity threshold”. Luckily I recently learned how to use AFNI for the same purpose and found it very robust among different kind of scans (or data from different scanners). Best of all, no parameter to set.

3dSkullStrip -input ${anat}.nii.gz -o_ply ${anat}_surf.nii.gz
3dcalc -a ${anat}.nii.gz -b ${anat}_surf.nii.gz -expr ‘a*step(b)’ -prefix ${anat}_brain.nii.gz

Semi-autoMatic Artifact Removal Tool (SMART)

SMART was developed during my dissertation work at the University of Texas at Austin. The idea was to preprocess electroencephalogram (EEG) data that was collected while participants meditated with their eyes closed. EEG is infamous for getting easily corrupted by muscular and other artifactual sources of noise. Thus, we used a two step process of pre-processing EEG data. First, we used second-order blind source separation to find sources that were uncorrelated in time (hence second-order). Second, we developed a novel semi-automatic web-based tool (or SMART) to identify non-neural sources of artifact (e.g., muscular artifact, ocular artifact, and  interference by power lines).

The identification of artifacts was done in SMART by using three key pieces of information – topological, temporal (mainly auto-correlation function), and spectral structure of each component. Based on simple rules, first SMART identified sources as different kinds of artifacts. Followed by generating a web-based tool, so that the researcher can quickly and efficiently go over all the components and their corresponding properties to judge whether SMART correctly classified the source under consideration as noise or not. If not, then the researcher can override source’s classification by clicking a radio button. The screenshot below better portrays the point.

The source code is provided here. For more information on the SMART tool, please see the following references. Also, if you use this tool in your research, please cite these as well.

  1. Saggar M, et al. (2012) Intensive training induces longitudinal changes in meditation state-related EEG oscillatory activity.   Front. Hum. Neurosci. 6:256. doi: 10.3389/fnhum.2012.00256
  2. Saggar, M. (2011) Computational analysis of meditation. Retrieved from University of Texas Digital Repository: Electronic Theses and Dissertations, (Austin, TX), Pages: 24-44.

NOTE: SMART for other imaging modalities (fMRI and fNIRS) is under testing and will be released very soon.