The key of this network is a gradient reversal layer placed at the top of the domain classifier branch. It exploits a classic stochastic gradient descent procedure to update weights; however, during the training of this network, the forward propagation is not influenced by it, while, during back-propagation, it acts by multiplying the gradient by a negative parameter \(-\lambda\) before passing it to the preceding layer. In this way, the partial derivatives of the site loss with respect to the parameters obtain a minus sign. The network is then encouraged to learn classes-related features and discouraged to learn the domain-related ones. They called this structure the domain-adversarial neural network (DANN). [PERSON] et al. [12] employed a network with a similar structure for natural language processing problems using different datasets, such as the Quora question pairs ([[https://www.kaggle.com/c/quora-question-pairs](https://www.kaggle.com/c/quora-question-pairs)]([https://www.kaggle.com/c/quora-question-pairs](https://www.kaggle.com/c/quora-question-pairs)), accessed on 5 April 2023), the StackExchange dataset ([[https://archive.org/download/stackexchange](https://archive.org/download/stackexchange)]([https://archive.org/download/stackexchange](https://archive.org/download/stackexchange)), accessed on 5 April 2023) or the Stanford Natural Language Inference ([[https://nlp.stanford.edu/projects/snli](https://nlp.stanford.edu/projects/snli)]([https://nlp.stanford.edu/projects/snli](https://nlp.stanford.edu/projects/snli)), accessed on 5 April 2023). [PERSON] et al. [13] obtained the adversarial evolution using a loss function resulting from the combination of two terms: one for the category classification and one for the domain classification. They employed their network for the analysis of brain MRI images from the ADNI ([[https://adni.loni.usc.edu](https://adni.loni.usc.edu)]([https://adni.loni.usc.edu](https://adni.loni.usc.edu)), accessed on 5 April 2023) dataset. As the case study in our work, we used the Autism Brain Imaging Data Exchange (ABIDE), a multi-site collection of data released for the first time in 2014 for the investigation of ASD. It contains structural and functional brain scans belonging either to individuals with ASD or to typically developing control subjects (TD). Given the multi-site nature of ABIDE, a harmonization procedure is usually implemented to minimize the batch effect [14]. In this study, we compared two different approaches to the classification of functional connectivity data of subjects with ASD and TD while mitigating biases related to the acquisition instrumentation (batch effect). As described in Section 2.3, the first step involves the computation of a brain connectivity measure known as Pearson correlation. Starting from these measures, we followed two different techniques. The first approach, as detailed in Section 2.4, involves the analytical harmonization technique known as ComBat, which we implemented by means of its state-of-the-art improved version, NeuroHarmonize ([[https://github.com/rpoponio/neuroHarmonize](https://github.com/rpoponio/neuroHarmonize)]([https://github.com/rpoponio/neuroHarmonize](https://github.com/rpoponio/neuroHarmonize)), accessed on 5 April 2023). The approach that we propose utilizes statistical corrections to remove site-related biases and involves the development of a deep neural network for the classification of harmonized data. The second approach, as detailed in Section 2.5, involves the development and use of a domain-adversarial neural network. This approach aims to discriminate between ASD and TD features while minimizing the biases caused by the sites through a learning process that minimizes the information learned from each site.