Correlational Tractography and Connectometry Analysis


"Correlational tractography" tracks the precise segment of pathways that correlate with a study variable, and "connectometry" uses a permutation test to get statistical inference. The pipeline includes (1)correlational tractography: tracking the correlation of the pathway with a study variable and (2) connectometry: permuting the subject labels derive the false discovery rate of the findings.

The following documentation shows how to do connectometry in DSI Studio:

*Connectometry analysis has a substantial revision in June 2020. The following documentation applied to the new version.

Prepare a Connectometry Database

Before doing a group connectometry, you would need to Create a connectometry database to aggregate data from ALL your subjects. 

Longitudinal Scans (if applicable)
Skipping this if your study is a cross-sectional study (has no repeat scans of the same subjects). 

If your study is a longitudinal study (e.g. comparing pre- and post-treatment of the same subject), then the scans of the same individuals need to be matched to calculate their difference. The following are the steps:

a) In the main window, click [Tools][O1: Open a Connecometry Database and select the database:

b) In the top menu, select [Tools][Longitudinal scans...] to bring up the subject match window:

Click on the "Match consecutive scans" button to match scan 0 (base) and scan 1 (study), 2 and 3, 4 and 5, throughout the entire database. 

Alternatively, you can load your own matching table using a text file. The text file should be the consecutive numbers of the matching pairs. The first number in a pair will be used as the baseline, and the second number will be the study scans. For example, a text file with "0 1 2 3 4 5" will match scan 0 (base) and scan 1(study), scan 2 and scan 3, scan 4 and scan 5. Please note that the first scan in the database is labeled by 0, not 1. 

The "metric" group box allows you to define how the difference is calculated. "a-b" calculates the absolute difference (recommended) by "the study scan (a) - the baseline (b)", whereas "(a-b)/(a+b)" calculates the difference as a ratio. "a-b" is recommended here. If your data were acquired from different scanners or sites, you may need to check "Normalize QA".

Click "Ok" to calculate the difference between scans and go back to the database window. 

Use [File][Save DB as..]  to save the differences as a "modified connectometry db".

The new connectometry db will contain only the difference between the paired scans for further analysis.

Step C3: Group Connectometry

Open the db.fib.gz file in [Step C3: Group Connectometry Analysis] in the main window. 

If a dialog prompts a warning about poor image quality (low R2), you may need to remove problematic data. This can be done by (1) open the connectometry db.fib.gz file using [Tools][O1: Open a Connecometry Database] provided in the main window and manually identify and delete subjects with a registration problem or motion artifact. Possible solutions are also mentioned in the documentation for creating a connectometry database.

Step C3a: Load demographics

Otherwise, Prepare a comma-separated values (CSV) file that records any variables you would like to include in the regression model. The first row should be the name of the scalar values. The second row is the value for the first subject, and the rest follows. If there is any missing data, leave the field empty. DSI Studio will ignore subjects with missing data.

Click on the button labeled "open subjects demographics". Load the text file that records all the scalar values that will be included in the regression model. 
*If your data are from a longitudinal study and only want to look at longitudinal differences without considering any other variable, go directly to [Step C3d: Parameters].

Select Cohort (Optional)

You can include/exclude a particular group of subjects in/from the analysis. Click on the [Select Cohort] button and select criteria to [Apply]. The criteria will be listed on the [Select If] line edit. You can use [Check] button to visualize the selection results.

Step C3b: Select variables
Please choose the variables to be considered. This can include all covariates to be considered such as sex and age and also your target study variable. 
DSI Studio will use linear regression to eliminate the effect of covariates from the diffusion measures.

There are several tips in choosing the variables in the model:

TIPS: If you have multiples study variables, including too many of them in the model may lead to over-fitting unless you have enough sample size. Otherwise, include basic variables like the sex and age with the one study variable at a time.
TIPS: If your study variables are highly correlated to each other, consider using a PCA to get the principal components. (see You will need to interpret the meaning of each principal component by checking its coefficient.

Step C3c: Study variable

Choose the target variable to study, and DSI Studio will find any tracks correlated with this variable. The effect of other covariates selected in C3b will be regressed out.
[Nonparametric] will use Spearman rank-based correlation to consider the nonlinear effect between the study variable and the diffusion measure. The other covariates will still be regressed using a linear regression model.

If your data are from a longitudinal study and want to look at longitudinal differences after considering variables selected in the previous step, choose "Intercept".

For example:

If you have a longitudinal connectometry database with "age", and you don't select anything for Step C3b and select "Intercept" Step C3c. This will show tracks with significance changes in the longitudinal study.
If you choose "age" for Step C3b and choose "Intercept" for Step C3c?. This will show tracks with significance changes in the longitudinal study, and the effect of age on the change will be eliminated.
If you choose "age" for Step C3b and choose "age" for Step C3c? This will show tracks with changes that are significantly correlated with age. A positive correlation means when age increases, the tracks show increased anisotropy.

Step C3d: Parameters

FDR Control

For studies confirming a hypothesis, do NOT check the FDR control(recommended)

Leave FDR control unchecked, and DSI Studio will report the FDR value as the significance level to examine the hypothesis.
My experience is that an FDR of 0.05 is highly confirmatory while 0.05~0.2 suggests a high possibility of positive findings. FDR is very different from the p-value. Any findings with FDR lower than 0.2 may be worth reporting since the results have much more "true positive" findings than "false positive" findings. FDR is not sensitive to sample size. This is its strength (unlike p-value always improves with large sample size) and also weakness (The result from small sample size may not be reliable even if the FDR is small).

If FDR control is checked, DSI Studio will output ONLY tracks that have a significant correlation (i.e., FDR lower than the threshold).

Length Threshold

Different length thresholds correspond to different null hypotheses, and a longer length is usually more specific (less sensitive) and tends to achieve a lower FDR (with fewer findings). 
The length threshold can be increased to achieve a better FDR (not always improve). 

T threshold

Higher values will map tracks with a stronger correlation effect, whereas lower values for weak correlation. 
I would recommend running separate analyses with different T-threshold (e.g., t=2, 2.5, and 3) to map different levels of correlation. Each threshold is viewed as a different hypothesis and will receive an FDR. 

Study region (Optional)

**Adding regions as constraints will also need to increase permutation count (e.g., 8000) to avoid the discrete error.

The default study region for connectometry is "whole brain", which means that connectometry will look at whole-brain region. You can choose to use an ROI in the MNI space by loading a NIFTI file of the mask. You can also choose a region from the atlas. The region type can be ROI (select tracts passing the region), ROA (discard any track passing the region), End (select track ending in the region), Seed (Only start the seeds from the region). "Whole brain" is most suitable for the exploratory purpose. If you have a specific region of interest, limiting the computation to an ROI can give a more specific result (e.g. assign cerebral peduncle as the ROI can investigate whether CST affected). Using only one ROI is good for most cases unless you have a specific need in the tracking routine. 

After assigning the regions, you may need to increase seed count because the regions setting will remove part or most of the findings and leave only sparse results. The best setting can be really case-dependent. If the count is way too low, then it can risk no findings or just get very sparse meaningless findings. On the other hand, a seed count too high can flood findings with less significant results. There is a sweet range, and in most of the case, the range is quite large, and as long as the value is not too low, the value does not make much of a difference. 

You can exclude cerebellum regions by adding cerebellum white matter and cortex as terminative regions:

Options (Optional)

Permutation count determines the total number of permutation applied to the FDR analysis. Higher values give smoother and more accurate estimation of FDR curves, but it requires more computation time. 
Pruning helps remove noisy findings. Higher values may improve FDR with the expense of sensitivity.
Normalize QA: Check this to homogenize data for potential scanner or site differences.

**If your data contains scans acquired from different protocols (e.g. b-value, resolution ...etc.), you will need to check "Normalize QA" to homogenize the data***

Step C3e: Run connectometry and report the results

Click on the [Step C3d: Run connectometry] button and wait until the computation is finished. 

After the analysis, DSI Studio will output several files to report the results. You may also click on [Step C3e: View Results in 3D] button to view the tracks with a substantial difference. 

File outputs

Results for T-statistics is stored in a FIB file:
    • bmi.t_statistics.fib.gz is a FIB file storing the t-statistics and can be opened in [Step T3:fiber tracking] to visualize T-statistics with tracks. The T statistics for positive correction is stored as "inc_t", whereas negative correlation is stored as "dec_t"
    Results for FDR include the following files:
    • bmi.t2.fdr.jpg the FDR with respect to tracking length.
    • bmi.t2.fdr_dist.values.txt records the FDR values with respect to length.
    Results for positive correlation include the following files:
    • bmi.t2.pos_corr.dist.jpg shows the histogram of track length.
    • stores tracks that are positively correlated with the study variable. It can be open with the t-statistics FIB file
    • bmi.t2.pos_corr.jpg shows the tracks in four different views. 
    Results for negative correlation include the following files:
    • bmi.t2.neg_corr.dist.jpg
    • bmi.t2.neg_corr.jpg
    General reports are stored the following HTML file:

    1. To change how DSI Studio visualizes tracks, open any FIB file in [Step T3 Fiber Tracking] and change [Step T3(c) Option]. After closing the fiber tracking window, DSI Studio will memorize the setting and apply it.
    2. There are several ways to improve the FDR, with the expense of losing sensitivity. You may increase the length threshold (e.g. 30 mm), increase the T threshold, increase pruning iterations in the advanced options.

    Troubleshooting (Important):
    1. If the direction of the correlation is wrong, try checking/unchecking "normalize QA" to fix the problem.
    2. If you have very different FDR results at different permutation counts, increase the permutation count until the FDR converges. 
    3. If the finding only shows very short tracks, try lowing the T threshold 
    4. If the finding only contains very few tracks, please increase the permutation count in the advance option (may need more computation time).
    5. If the finding has many short fragments, consider increasing the length threshold or pruning count.

    Interpret and report the result

    Please note that correlation does not necessarily imply causality. The results can be the "cause" or the "response" of the study variable.

    The following are the strategies to report results.

    Strategy 1: report FDR at different T-scores gives an overview of finding from high sensitivity (low T) to high specificity (high T) 
    You can visualize the results at different T threshold (see the next section about visualization). The FDR value will be different for different track lengths. An FDR < 0.05 indicates a highly confirmative finding. An FDR > 0.2 suggests that there are substantial amounts of false findings. The result is thus inconclusive and not reliable. An FDR between 0.05 and 0.2 covers a wide spectrum of reliability. 
    Strategy 2: report FDR at different subject subgroup (e.g., male group versus female group)
    Strategy 3: Convert the finding to ROI using [Tracts][Tract to ROI] to track the entire pathways. This allows you to confirm the anatomical structure of the findings.

    Report FDR for each bundle

    The FDR in report.html is for the overall results, but findings with a longer distance will have a lower FDR than the others. You can report the FDR of each bundle based on its length. 

    To do this, first, open the 3D interface at Step C4f or open *.t_statistics.fib.gz in Step T3 and load * files. 

    Then isolate the bundles you are interested in using Tract Editing and get the length using [Tracts][Statistics]. If the length is 60mm, then the voxel distance is 30. 

    Last, check the FDR values in *.fdr_dist.values.txt and look up the row with voxel distance=30 to get the FDR values. There is one FDR for positive correlation and one for negative. Make sure you get the right one.

    Visualize T-statistics along the tracks

    1) Click on the [Step C3e: View Results in 3D] button to view the tracks picked up by connectometry analysis. Two sets of tracks will be presented. One is for positive correlation, and another is for negative correlation in multiple comparisons.   

    Another way to show the results is to open the t-statistic FIB file in [Step T3:fiber tracking] from the main window and load the trk files generated from connectometry analysis under the [Tracts] menu. You may find the trk files placed under same directory of the demographic files.

    2) Switch slice to [icbm_wm] in the left lower [Step T3b: Regions] window, and add isosurface by [Slices][Add isosurface]. You can adjust the opacity of the surface in the [Step T3c: Options] at the right upper corner under the [Surface Rendering] option.

    (3) In the [Step T3c: Options] at the right upper corner, under [Tract Rendering], change [Color] from "Directional" to "Local Index". This will invoke a color dialog. In the [Index] field, select "inc_t" to visualize T statistics related to positive correlation or "dec_t" to visualize those with negative correlation.

    Scatter plots

    We can further plot a scatter plot between the study variable and the diffusion variable (e.g. QA or nQA) averaged from the tracks

    First, open the connectometry database (*.db.fib.gz) in [Step T3: Fiber Tracking] and load the tracks from *.pos_corr.trk.gz (track positively correlated with the study variable) or *.neg_corr.trk.gz (tracks negatively correlated) using [Tracts][Open Tracts]. Then use [Tracts][Statistics] to get along tracks metrics for each subject in the connectometry database. The metric (here use QA for example) has raw QA or normalized QA (used if you check normalize QA in the advance option). This step will take a while.

    The QA value can be copied to the clipboard and paste in an Excel sheet. We can place them with the demographics values in and plot a scatter plot.

    Tracks in mosaic T1w

    The finding can be visualized in slices using the following steps:

    1) Switch slice image to "T1w".
    2) In the main menu, add tracks to ROI by [Tracks][Tracks to ROI]
    3) In the Options window (right upper corner), under the "Region Window" node. Change the slice layout to Mosaic 2
    4) Change the contrast of the slices using the slider on the top of the region window to enhance the tracks regions.

    Network Property Analysis for Connectometry Findings

    The connectometry findings usually present a group of fiber bundles, and how these bundles affect the network topology can be further analyzed to better present the results.

    The following are steps to carry out this analysis:

    1. After group connectometry analysis finishes, click on the "View Results in 3D" button or open the trk file in STEP3 Fiber Tracking. Alternatively you can open the FIB file generated within the demographic folder in Step T3 Fiber Tracking, and load the trk files generated by group connectometry.

    2. Remove the all tracks using [Tracts][Delete Tracts][Delect all].

    4. Run whole brain fiber tracking (No ROI or seed) to get a total of 100,000 tracks using default tracking settings ([Option][Restore Tracking Settings]).

    5. Click [Tracts][Connectivity matrix] to bring up the connectivity dialog. 

    6. Select "FreesurferDKT" as the parcellation atlas. 

    7. Switch to the "Network measures" tab and copy the content (e.g. density, ...etc.) to the Excel sheet.

    8. Close the connectivity dialog. 

    9. Load the group connectometry tracks (trk files). There should be one file for positive correlation and one for the negative correlation after group connectometry finishes (unless there is no finding). Load the one you want to study using [Tracts][Open Tracts]

    10. Select the loaded tracks, and transform it into a region using [Tracts][Tract to ROI]. The region will appeat on the left. Change region's type from "ROI" to "ROA". Remove the connectometry tracks after the conversion.

    11. Filter whole-brain tracks (generated in 4.) using the [Tracts][Filter Tracts by ROI/ROA/END. You should find some tracks being deleted using the connectometry regions.

    12. Now repeat 5. 6. 7. to get network measures, and you can then compare the difference between these two set of measures 

    13. Calculate the value differences as a percentage change in 

        a. clustering_coeff_average(binary)
        b. network_characteristic_path_length(binary)
        c. small-worldness(binary)
        d. global_efficiency(binary)
        e. local_efficiency(binary).

    14. If the change of clustering_coeff_average(binary) is +5% and the finding is from a positive/negative correlation of a study factor X, then you may report: "The network topology analysis on connectometry results show that X increases/decreases 5% of clustering coefficient in the network topology." Repeat the same reporting format for other network measures.

    15. Provide a table summarizing the results using the following:

    Table: Effect of X, Y, Z on network topology measures
    Study  VariableClustering Coefficient (%)Network Characteristic Path Length (%)Global Efficiency (%)Local Efficiency (%)Small
    Worldness (%)

    Get SDF values along the track

    Once you get tracks that show significant difference or correlation. You can open the connectometry db at [STEP 3 Fiber tracking] and load the resulting tracks. Click on track statistics and you will get SDF values along the tracks for "each subject". This allows you to plot the difference between the groups.

    Recommended Studies using Connectometry Analysis

    da Silva, Nadia Moreira, et al. "Network reorganisation following anterior temporal lobe resection and relation with post-surgery seizure relapse: a longitudinal study." NeuroImage: Clinical (2020): 102320. (link)


    [1] F.-C. Yeh, D. Badre, and T. Verstynen, “Connectometry: a statistical approach harnessing the analytical potential of the local connectome”, Neuroimage, accepted , 2015. (pdf)(link)
    [2] F-C. Yeh, P-F. Tang, and W-Y. I. Tseng, “Diffusion MRI Connectometry Automatically Reveals Affected Fiber Pathways in Individuals with Chronic Stroke”, Neuroimage: Clinical 2 (2013) 912-921, (pdf)
    [3] F. C. Yeh and W. Y. Tseng, "NTU-90: a high angular resolution brain atlas constructed by q-space diffeomorphic reconstruction," Neuroimage 58, 91-99 (2011). (pdf)
    [4] Yeh F-C, Vettel JM, Singh A, Poczos B, Grafton ST, Erickson KI, et al. (2016) Quantifying Differences and Similarities in Whole-Brain White Matter Architecture Using Local Connectome Fingerprints. PLoS Comput Biol 12(11): e1005203. doi:10.1371/journal.pcbi.1005203

    Frank Yeh,
    Oct 22, 2015, 6:38 PM