Project: s41467-025-57867-7 | Function: DoHarmonization (python)

system: You are an expert Python programmer implementing algorithms from scientific papers.
Complete the masked function based on the algorithm description.
Follow Python best practices and maintain consistent code style.
Return **only** the complete Python function code.

user: Complete this Python function:

**DESCRIPTION:** Performs neuroimaging data harmonization using ComBat-GAM algorithm. Processes ROI volumes, applies site-specific corrections, handles out-of-sample harmonization for new sites, and calculates residuals after removing site effects while preserving biological variance.

**KEY TERMS:** {
  "harmonizationApply": "ComBat-GAM harmonization method",
  "gamma_star": "site-specific scaling parameters",
  "delta_star": "site-specific location parameters",
  "bayes_data": "harmonized neuroimaging data"
}

**CONTEXT CODE:** from PyQt5.QtGui import *
from PyQt5 import QtGui, QtCore, QtWidgets, uic
import neuroHarmonize as nh
import numpy as np
import pandas as pd

from niCHART.core import iStagingLogger

logger = iStagingLogger.get_logger(__name__)

class Harmonize(QtCore.QObject):
    """This class is an adapter to the worker class."""
    sendprogress = QtCore.pyqtSignal(str, int)
    done = QtCore.pyqtSignal()

    #constructor
    def __init__(self,datamodel, model):
        super(Harmonize,self).__init__()
        self.datamodel = datamodel
        self.datamodel.harmonization_model = model


    def DoHarmonization(self):
        """
        Performs neuroimaging data harmonization using ComBat-GAM algorithm.

        This function implements the ComBat-GAM (Combat Generalized Additive Model)
        algorithm for removing site effects from multi-site neuroimaging data. It
        processes ROI (Region of Interest) volumes from structural MRI scans across
        different acquisition sites to enable pooled analysis while preserving
        biological variance.

        The harmonization process involves:
        1. Covariate preparation (SITE, Age, Sex, DLICV_baseline)
        2. Site effect modeling with gamma and delta parameters
        3. Out-of-sample harmonization for new sites when applicable
        4. Bayesian parameter estimation and data transformation
        5. Derived volume calculation for composite ROI measures
        6. Residual computation for downstream analysis

        The ComBat-GAM approach models site effects as additive and multiplicative
        components while preserving age-related non-linear effects through smoothing
        splines, making it particularly suitable for neuroimaging harmonization.

        Returns:
            tuple: (muse, parameters) where muse contains harmonized data with
                   residuals and parameters contains gamma/delta site effects

        Raises:
            NotImplementedError: Function has been masked for testing purposes
        """
        raise NotImplementedError("This function has been masked for testing purposes")


    def AddHarmonizedMUSE(self, muse):
        print('Saving modified data to pickle file...')

        muse.set_index(self.datamodel.data.index,inplace=True)

        ROI_list = list(self.datamodel.harmonization_model['ROIs'])
        if ('MUSE_Volume_301' not in ROI_list):
            logger.info('No derived volumes in model')
            MUSEDictDataFrame= self.datamodel.GetMUSEDictDataFrame()
            Derived_numbers = list(MUSEDictDataFrame[MUSEDictDataFrame['ROI_LEVEL']=='DERIVED']['ROI_INDEX'])
            Derived_MUSE_Volumes = list('MUSE_Volume_' + str(x) for x in Derived_numbers)
            ROI_list = ROI_list + Derived_MUSE_Volumes
            ROI_list.remove('MUSE_Volume_702')
        else:
            logger.info('Model includes derived volumes')
        H_ROIs = list('H_' + str(x) for x in ROI_list)
        ROIs_ICV_Sex_Residuals = ['RES_ICV_Sex_' + x for x in self.datamodel.harmonization_model['ROIs']]
        ROIs_Residuals = ['RES_' + x for x in self.datamodel.harmonization_model['ROIs']]
        RAW_Residuals = ['RAW_RES_' + x for x in self.datamodel.harmonization_model['ROIs']]
        if ('H_MUSE_Volume_47' not in self.datamodel.data.keys()):
            self.datamodel.data.loc[:,H_ROIs] = muse[H_ROIs]
        self.datamodel.data.loc[:,ROIs_ICV_Sex_Residuals] = muse[ROIs_ICV_Sex_Residuals]
        self.datamodel.data.loc[:,ROIs_Residuals] = muse[ROIs_Residuals]
        self.datamodel.data.loc[:,RAW_Residuals] = muse[RAW_Residuals]
        

Complete the function implementation replacing "raise NotImplementedError".