Z-scoring

This section describes how to run the z-scoring module to normalize single patient data against a cohort of controls.

Setting up your control dataset

You will need to set up a csv file with two mandatory columns: One column containing the subject IDs for all controls to be included in the analysis (column name: ID), as well as one column with corresponding session name to be analyzed (column name: SES). Note: if your dataset does not contain session information, then leave the SES column blank.

An example control table can be found in our GitHub repository.

Regional analysis

After defining your directories as in the previous section (-proc), you can run the regional z-scoring analysis as follows:

Basic z-brains run: hippocampal processing
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# Define path to control dataset csv
hc_info=/PATH/TO/CSV/control.csv

# Information on patient to be analyzed
px_id="PX001"
px_ses="01"

z-brains -sub "$id" -ses "$ses" \
    -rawdir "${rawdir}" \
    -micapipedir "${micapipedir}" \
    -hippdir "${hippdir}" \
    -outdir "${outdir}" \
    -run regional \
    -approach "zscore" \
    -demo_cn "${hc_info}" \
    -verbose 2

Asymmetry analysis

Similar to the regional analysis, you can generated z-scored asymmetry maps with the following approach:

Basic z-brains run: hippocampal processing
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# Define path to control dataset csv
hc_info=/PATH/TO/CSV/control.csv

# Information on patient to be analyzed
px_id="PX001"
px_ses="01"

z-brains -sub "$id" -ses "$ses" \
    -rawdir "${rawdir}" \
    -micapipedir "${micapipedir}" \
    -hippdir "${hippdir}" \
    -outdir "${outdir}" \
    -run asymmetry \
    -approach "zscore" \
    -demo_cn "${hc_info}" \
    -verbose 2