[NEW] MCOT | mcot set – Sathyasaith

mcot set: คุณกำลังดูกระทู้

MCOT

Multiband Censoring Optimization Tool (MCOT) for Resting-State Functional Connectivity Analyses

This is a MATLAB software package to accompany the article:

Advancing motion denoising of multiband resting-state functional connectivity fMRI data

John C. Williams, MS, Philip N. Tubiolo, BE, Jacob R. Luceno, BS, and Jared X. Van Snellenberg, PhD.

Cognitive Neuroscience and Psychosis (CNaP) Lab / Multi-Modal Translational Imaging (MMTI) Lab

Department of Psychiatry and Behavioral Health, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA.

This software allows a user to get optimal LPF-FD and GEV-DV volume censoring parameters for a specified multiband resting-state fMRI dataset.

This was created with MATLAB 2021a and tested using MATLAB R2020a and R2021a.

This software requires SPM12 to be on the user path. SPM12 is included as part of this package. It can be obtained externally at https://www.fil.ion.ucl.ac.uk/spm/software/spm12/ .

Additionally, a set of command line MATLAB tools for performing LPF-FD and GEV-DV volume censoring on fMRI data is provided in the folder, Multiband_fMRI_Volume_Censoring_Toolkit. See Multiband_fMRI_Volume_Censoring_Toolkit/readme.pdf for instructions for how to use these functions.

NOTE TO USER:

This software may be run using the provided MATLAB Application (installed from MCOT.mlappinstall) or from the MATLAB command window (by running mCotWrapper with the necessary input arguments detailed below) Use of the MATLAB application to run this software will produce identical results to running the software from the MATLAB command window. Tooltips are provided within the application to ensure that the user is guided through providing all necessary arguments.

Functions comprising the toolkit (Multiband_fMRI_Volume_Censoring_Toolkit) can be run directly from the command window in MATLAB.

Installation of this software for use in the MATLAB command window consists of downloading, copying, or moving all functions into a directory accessable to your MATLAB installation. If downloaded as a zip file, all functions should be unzipped to a single folder before use. Unzipping this software is expected to take less than one minute on a normal desktop computer.

The MCOT graphical user interface (GUI) is enabled by running the file, MCOT.mlappinstall, which will install the application into the “My Apps” tray of the MATLAB editor. The GUI can then be run by clicking on the MCOT icon in “My Apps.”

mCotWrapper

Estimate optimal volume censoring parameters for a multiband resting-state fMRI dataset. This function is called by the user if the software is being used from the MATLAB command window or called autonomously from the provided MATLAB application.

Syntax

[optimalDV, optimalFD, optimalPCT, minMSE] = mCotWrapper(workingDirectory,Name,Value)
[optimalDV, optimalFD, optimalPCT, minMSE] = mCotWrapper(workingDirectory,’continue’)

mCotWrapper(workingDirectory,Name,Value) returns the optimal volume censoring parameters LPF-FD and GEV-DV, minimum mean-squared error (MSE), and optimal percentage of volumes censored for a specified multiband resting-state fMRI dataset in a specified workingDirectory. Name-Value pairs are specific to the pipeline used for data reprocessing – either the Human Connectome Project Minimal Precprocessing Pipeline (HCP), fMRIPrep, or a custom user-implemented pipeline (denoted as ‘custom’). A summary of all Name-Value pairs is provided below.

mCotWrapper(workingDirectory,’continue’) continues the optimization pipeline based on intermediate outputs found in workingDirectory.

Input Arguments

workingDirectory

Denotes directory in which to save all intermediate and final outputs. If this directory does not yet exist, it will be created.

Name-Value Pairs

(string)
Denotes the preprocessing pipeline used.
Options: ‘HCP’, ‘fMRIPrep’, ‘custom’

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(string)
‘sourcedirectory’ does not need to be specified if ‘format’ = ‘custom’

Directory that contains all fMRI data to be included in the optimization procedure. This directory should be the direct outputs of either HCP of fMRIPrep preprocessing.

(double)
Repetition time used in the study, specified in seconds.

(logical)
Denotes whether or not to use global signal regression when performing optimization.
Default: false (no GSR)

(double)
Number of volumes to remove from the beginning of each resting-state fMRI run to allow for MR signal equilibration.
Default: 0

(char)
‘runnames’ does not need to be specified if ‘format’ = ‘custom’

Specifies the run/task identifiers in each study. Inputs vary by preprocessing pipeline.

For ‘format’ = ‘HCP’:
‘runnames’ will be entered as a cell array of char vectors, as the HCP pipeline allows for custom naming of runs. For example, if a HCP-preprocessed study contains 4 runs per subject, denoted by the filename suffixes ‘_rs1’, ‘_rs2’, ‘_rs3’, and ‘_rs4’, they should be specified as the cell array {‘_rs1′,’_rs2′,’_rs3′,’_rs4’}.

For ‘format’ = ‘fMRIPrep’:
‘runnames will be entered as a single char vector denoting the task name used in the fMRIPrep preprocessing pipeline. For example, if the task name ‘RSFC’ was used in fMRIPrep, ‘runnames’ will be specified as ‘RSFC’ .

*** This argument is ONLY needed for fMRIPrep ***
Specifies the image space used for data normalization during fMRIPrep preprocessing.
Examples include: MNI152NLin2009cAsym, MNI152NLin6Asym
Full list of supported spaces can be found through the fMRIPrep documentation.

(double)
Specifies the minimum number of seconds of data that each resting-state run must contain after volume censoring before it is removed from the study.
default = 120

([lowHz highHz])
Specifies the -3 dB cutoff frequencies of a second-order zero-phase Butterworth band-pass Filter (‘lowHz’ refers to the lower frequency cutoff, ‘highHz’ refers to the higher frequency cutoff).
Defaults:
lowHz = 0.009
highHz = 0.08

(double)
Specifies the number of seconds of data to remove from the beginning and end of each resting-state run after band-pass filtering is performed to mitigate filter edge effects.
Default: 22

————The following arguments only need to be specified is ‘format’ = ‘custom’————

(cell array)

An N x M array where N is the number of subjects and M is the maximum number of runs per subject. Each entry is a full path to a resting-state fMRI run. Cells can be left empty if a subject is missing runs.

(cell)

An N x 3 cell array, where N is the number of subject. The first column is a full file path to the whole brain mask of each subject. The second column is a full file path to the white matter mask of each subject. The third column is a full file path to the cerebrospinal fluid mask of each subject.

(cell array)

An N x M cell array, where N is the number of subjects and M is the maximum number of runs per subejct. Each entry is a Y x 6 array, where Y is the number of volumes in each resting-state run and each column is a translational or rotational motion parameter. The order of these columns is: x-translation, y-translation, z-translation, roll, pitch, yaw.

Output Arguments

The minimum value of MSE over the range of evaluate volume censoring paramters.

The optimal GEV-DV volume censoring parameter corresponding to minMSE.

The optimal LPF-FD volume censoring paramter corresponding to minMSE.

The percentage of volumes censored from the dataset at minMSE.

Example Function Calls

For HCP Preprocessed Data with Default Band-Pass Filtering Parameters:
[optimalDV, optimalFD, optimalPCT, minMSE] = mCotWrapper(‘C:\Users\Owner\Desktop\workingDir’,’sourcedirectory’,’C:\Users\Owner\Desktop\sourceDir’,’format’,’HCP’,’tr’,0.72,’useGSR’,false,’ntrim’,10,’runnames’,{‘_rs1′,’_rs2′,’_rs3′,’_rs4′},’minimumSecondsDataPerRun’,60,’filterCutoffs’,[0.009 0.08],’secTrimPostBpf’,22)

For fMRIPrep Preprocessed Data with Default Band-Pass Filtering Parameters:
[optimalDV, optimalFD, optimalPCT, minMSE] = mCotWrapper(‘C:\Users\Owner\Desktop\workingDir’,’sourcedirectory’,’C:\Users\Owner\Desktop\sourceDir’,’format’,’fMRIPrep’,’tr’,0.72,’useGSR’,false,’ntrim’,10,’runnames’,’RSFC’,’minimumSecondsDataPerRun’,60,’filterCutoffs’,[0.009 0.08],’secTrimPostBpf’,22)

For Custom Preprocessed Data with Default Band-Pass Filtering Parameters (and with variables filenamematrix, maskmatrix, and motionparameters loaded into the workspace):
[optimalDV, optimalFD, optimalPCT, minMSE] = mCotWrapper(‘C:\Users\Owner\Desktop\workingDir’,’format’,’custom’,’filenamematrix’,filenamematrix,’maskmatrix’,maskmatrix,’motionparameters’,motionparameters,’tr’,0.72,’useGSR’,false,’ntrim’,10,’minimumSecondsDataPerRun’,60,’filterCutoffs’,[0.009 0.08],’secTrimPostBpf’,22)

This software is released under the GNU General Public License Version 3.

[Update] Share price, News & Analysis | mcot set – Sathyasaith

MCOT Public Company Limited, together with its subsidiaries, engages in the multi-media business in Thailand. It operates through Television and News Services, Radio Services, Engineering Services, New Business Services segments. The company provides digital terrestrial television broadcasting network services comprising high definition variety and standard definition family channels with programs comprising news and situation report, entertainment, edutainment, general knowledge, and sports; and engages in the production, marketing, and management of radio business comprising 62 central and regional radio stations broadcasting in FM and AM frequencies that broadcasts various informative and entertaining radio programs in the areas of politics, economy, society, foreign affairs, technology, music, sports, tourism, health and recreation activities, and news reports and analyses, as well as knowledge and entertainment programs of local interest.

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The company is down 3.0% from its price of ฿3.70 on 26 November 2020. The Thai market is up 6.0% over the last 90 days, indicating the company underperformed over that time. It also underperformed the Media industry, which is up 1.0% over the same period.

Up from ฿3.90, the current price target is provided by 1 analyst. The new target price is 16% above the current share price of ฿3.62. As of last close, the stock is down 46% over the past year.

The company reported a decent first quarter result with improved earnings and profit margins, although revenues were weaker. First quarter 2021 results: Revenue: ฿361.2m (down 1.2% from 1Q 2020). Net income: ฿35.5m (up ฿986.9m from 1Q 2020). Profit margin: 9.8% (up from net loss in 1Q 2020). Over the last 3 years on average, earnings per share has increased by 13% per year but the company’s share price has fallen by 19% per year, which means it is significantly lagging earnings.

The analyst covering MCOT expects the company to break even for the first time. New forecast suggests the company will make a profit of ฿113.0m in 2021. Earnings growth of 105% is required to achieve expected profit on schedule.

The company reported a strong second quarter result with improved earnings, revenues and profit margins. Second quarter 2021 results: Revenue: ฿388.5m (up 22% from 2Q 2020). Net income: ฿43.6m (up ฿225.6m from 2Q 2020). Profit margin: 11% (up from net loss in 2Q 2020). Over the last 3 years on average, earnings per share has increased by 1% per year but the company’s share price has fallen by 16% per year, which means it is significantly lagging earnings.

The company reported a decent third quarter result with improved earnings and profit margins, although revenues were weaker. Third quarter 2021 results: Revenue: ฿391.7m (down 2.5% from 3Q 2020). Net income: ฿21.1m (up ฿125.0m from 3Q 2020). Profit margin: 5.4% (up from net loss in 3Q 2020). Over the last 3 years on average, earnings per share has fallen by 12% per year whereas the company’s share price has fallen by 10% per year.

Unless specified all financial data is based on a yearly period but updated quarterly. This is known as Trailing Twelve Month (TTM) or Last Twelve Month (LTM) Data. Learn more here .

Simply Wall Street Pty Ltd (ACN 600 056 611), is a Corporate Authorised Representative (Authorised Representative Number: 467183) of Sanlam Private Wealth Pty Ltd (AFSL No. 337927). Any advice contained in this website is general advice only and has been prepared without considering your objectives, financial situation or needs. You should not rely on any advice and/or information contained in this website and before making any investment decision we recommend that you consider whether it is appropriate for your situation and seek appropriate financial, taxation and legal advice. Please read our Financial Services Guide before deciding whether to obtain financial services from us.


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