Thanks to our method, the moments in time when functional brain interplays are synchronized across subjects can be determined and visualized. The main advantage of our approach is its ability to dynamically track changes in inter-subject functional brain responses in a statistically sound manner. Our pipeline enables the assessment of synchronicity between functional signals of different subjects.
And this can then be compared between different populations such as healthy controls and those affected by disease and disorder such as autism and schizophrenia. Our method can benefit any research seeking to dynamically track synchronicity between different sets of time courses. And this can be applied also to other imaging modalities such as electroencephalography.
Visual demonstration of the method will allow new users to comprehend the different processing steps of the method as well as the functionality of the graphical user interface. Demonstrating the imaging procedure will be Roberto Martuzzi, the leading MRI operator of our research center. For each volunteer to be considered in the analysis, perform at least one functional Magnetic Resonance Imaging or fMRI session in which the scanned volunteer is subjected to the time logged paradigm of interest and one functional imaging session in which the scanned volunteer lies at rest in the scanner with their eyes closed and is instructed not to fall asleep.
Then acquire a structural MRI volume. To open the first preprocessing graphical user interface window, enter JOVE_GUI1 in the MATLAB terminal. Then click Enter fMRI data, select the appropriate realigned functional volumes and enter the repetition time of the data in seconds in the dedicated editable text window.
Click Enter T1 data and select the three appropriate probabilistic tissue type volumes. Click Enter motion file and select the text file containing the motion parameters from the session of interest. Then select whether or not the data should be detrended and which covariates should be regressed out.
To preprocess the data, click Preprocess and wait for the display to appear in the window. The data can be re-preprocessed differently by modifying the options and re-clicking on the Preprocess button. Next, enter JOVE_GUI2 in the MATLAB terminal to open the second preprocessing graphical user interface window and click select data to select the preprocessed data file.
Click Select atlas and select the Neuroimaging Informatics Technology Initiative or NIFTI file representing the atlas to be used for the parcellation. Click Select inverse warp and select the NIFTI file representing the deformation field from the Montreal Neurological Institute to the native space. Then click select fMRI volume and select any of the fMRI data volumes.
To enter the scrubbing-related information, in the scrubbing type list, select the number of frames to be scrubbed out before and after the tagged frames. In the scrubbing threshold text window, enter the frame wise displacement threshold value above which an fMRI volume should be scrubbed in millimeters. Then enter the size of the sliding window W to be used for the Inter-Subjection Functional Correlation or ISFC computations in repetition times and click Plot to display the indicative atlas time courses before and after the scrubbing and filtering steps.
For sliding window ISFC computations, enter JOVE_GUI3 in the MATLAB terminal to open the first ISCF-related graphical user interface window and click Load data to select all of the appropriate data files. Select whether the selected session segments should undergo phase randomization and enter the window size in repetition times over which connectivity measurements should be computed as well as the step size in repetition times by which successive windows should be shifted. Modify the session types table to specify which of the loaded session segments were acquired upon the same experimental condition using increasing integer numbers from one and up to tag the different types of segments.
In the appropriate editable text windows, enter the number of bootstrapping folds over which to perform the ISFC computations and the number of subjects that should constitute the reference group for each fold of ISFC computations. In the timing parameters section, enter the specifications about which subportion of the time courses should be analyzed and click Plot to perform the ISFC computations. The displays will be gradually updated over time along with the amount of elapsed bootstrapping folds.
To open the second ISFC-related graphical user interface window, enter JOVE_GUI4 in the MATLAB terminal. To select the stimulus-related ISFC output files, click Load ISFC data and select the stimulus-related ISFC output files. To select the null ISFC output files, click Load null data and select either the resting state ISFC or the phase randomized stimulus-related ISFC output files depending on the used null data generation scheme.
Then click Load codebook and select the codebook file. In the appropriate editable text window, enter the alpha value in percent at which the ISFC time courses should be thresholded for highlighting significant changes. To construct a null distribution, click Plot to initiate the ISFC thresholding process for which all of the available null ISFC measurements are aggregated for a given connection and after which the stimulus-related ISFC measurements will be thresholded according to the selected alpha value.
To visualize the ISFC spatial patterns at different time points, drag the slider below the ISFC excursion plot. In these representative sessions, the assessed movie was displayed from five to 353 seconds with a resting state segment following the movie from 386 to 678 seconds. In addition, one 310 second solely resting state session was acquired for each subject.
ISFC time courses were generated at window lengths of W 10 and W 5 repetition times for three different representative connections involving a region mediating response inhibition and another involved in respectively the expectation of moving objects, sensory coordination, or the processing of word meanings. The noise is larger at W 5 repetition times and time-locked ISFC changes are observed in the connection one and two movie-watching time courses. Here, the fraction of subjects showing statistically significant time-locked ISFC transients is shown for the same three connections.
The transients are more numerous at larger window lengths as more robust ISFC measurements ease the extraction and at larger alpha values as more false positives are detected. In these representative data, one movie extract in particular was involved in the response inhibition linked to moving objects and drove widespread ISFC changes. The window length should be neither too short for robust estimates nor too long to capture ISFC dynamics.
And a sufficiently stringent alpha value should be used to limit false positives. ISFC measurements can be clustered into whole brain ISFC states for special temporal characteristics examination. Significant ISFC transients can also be represented as a graph reflective of information flow.
