{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T09:20:29Z","timestamp":1774689629747,"version":"3.50.1"},"reference-count":57,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,2,13]],"date-time":"2025-02-13T00:00:00Z","timestamp":1739404800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"the Artificial Intelligence and Computer Science Laboratory, LIACC","doi-asserted-by":"publisher","award":["UIDB\/00027\/2020"],"award-info":[{"award-number":["UIDB\/00027\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"the Artificial Intelligence and Computer Science Laboratory, LIACC","doi-asserted-by":"publisher","award":["UIDB\/04630\/2020"],"award-info":[{"award-number":["UIDB\/04630\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the FCT\/MCTES (PIDDAC)","award":["UIDB\/00027\/2020"],"award-info":[{"award-number":["UIDB\/00027\/2020"]}]},{"name":"the FCT\/MCTES (PIDDAC)","award":["UIDB\/04630\/2020"],"award-info":[{"award-number":["UIDB\/04630\/2020"]}]},{"name":"NECE-UBI, Research Centre for Business Sciences, Research Centre","award":["UIDB\/00027\/2020"],"award-info":[{"award-number":["UIDB\/00027\/2020"]}]},{"name":"NECE-UBI, Research Centre for Business Sciences, Research Centre","award":["UIDB\/04630\/2020"],"award-info":[{"award-number":["UIDB\/04630\/2020"]}]},{"DOI":"10.13039\/501100001871","name":"FCT, Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia, IP","doi-asserted-by":"publisher","award":["UIDB\/00027\/2020"],"award-info":[{"award-number":["UIDB\/00027\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"FCT, Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia, IP","doi-asserted-by":"publisher","award":["UIDB\/04630\/2020"],"award-info":[{"award-number":["UIDB\/04630\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>Artificial neural networks (ANNs) have been used for classification tasks involving functional magnetic resonance imaging (fMRI), though typically focusing only on fractions of the brain in the analysis. Recent work combined shallow neural networks (SNNs) with explainable artificial intelligence (xAI) techniques to extract insights into brain processes. While earlier studies validated this approach using motor task fMRI data, the present study applies it to Theory of Mind (ToM) cognitive tasks, using data from the Human Connectome Project\u2019s (HCP) Young Adult database. Cognitive tasks are more challenging due to the brain\u2019s non-linear functions. The HCP multimodal parcellation brain atlas segments the brain, guiding the training, pruning, and retraining of an SNN. Shapley values then explain the retrained network, with results compared to General Linear Model (GLM) analysis for validation. The initial network achieved 88.2% accuracy, dropped to 80.0% after pruning, and recovered to 84.7% post-retraining. SHAP explanations aligned with GLM findings and known ToM-related brain regions. This fMRI analysis successfully addressed a cognitively complex paradigm, demonstrating the potential of explainability techniques for understanding non-linear brain processes. The findings suggest that xAI, and knowledge extraction in particular, is valuable for advancing mental health research and brain state decoding.<\/jats:p>","DOI":"10.3390\/make7010017","type":"journal-article","created":{"date-parts":[[2025,2,13]],"date-time":"2025-02-13T08:01:19Z","timestamp":1739433679000},"page":"17","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Decoding Mental States in Social Cognition: Insights from Explainable Artificial Intelligence on HCP fMRI Data"],"prefix":"10.3390","volume":"7","author":[{"given":"Jos\u00e9 Diogo","family":"Marques dos Santos","sequence":"first","affiliation":[{"name":"Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal"},{"name":"LIACC\u2014Artificial Intelligence and Computer Science Laboratory, 4200-465 Porto, Portugal"}]},{"given":"Lu\u00eds Paulo","family":"Reis","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal"},{"name":"LIACC\u2014Artificial Intelligence and Computer Science Laboratory, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5567-944X","authenticated-orcid":false,"given":"Jos\u00e9 Paulo","family":"Marques dos Santos","sequence":"additional","affiliation":[{"name":"LIACC\u2014Artificial Intelligence and Computer Science Laboratory, 4200-465 Porto, Portugal"},{"name":"Department of Business Administration, University of Maia, 4475-690 Maia, Portugal"},{"name":"Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal"},{"name":"NECE\u2014Research Centre for Business Sciences, 6200-209 Covilh\u00e3, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,13]]},"reference":[{"key":"ref_1","unstructured":"Haykin, S. (2009). Neural Networks and Learning Machines, Prentice Hall. [3rd ed.]."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Santos, J.P., and Moutinho, L. (2011, January 16\u201318). Tackling the cognitive processes that underlie brands\u2019 assessments using artificial neural networks and whole brain fMRI acquisitions. Proceedings of the 2011 IEEE International Workshop on Pattern Recognition in NeuroImaging (PRNI), Seoul, Republic of Korea.","DOI":"10.1109\/PRNI.2011.22"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Marques dos Santos, J.P., Moutinho, L., and Castelo-Branco, M. (2014, January 2). \u2019Mind reading\u2019: Hitting cognition by using ANNs to analyze fMRI data in a paradigm exempted from motor responses. Proceedings of the International Workshop on Artificial Neural Networks and Intelligent Information Processing (ANNIIP 2014), Vienna, Austria.","DOI":"10.5220\/0005126400450052"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"S199","DOI":"10.1016\/j.neuroimage.2008.11.007","article-title":"Machine learning classifiers and fMRI: A tutorial overview","volume":"45","author":"Pereira","year":"2009","journal-title":"Neuroimage"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1007\/978-3-031-07802-6_22","article-title":"Towards XAI: Interpretable Shallow Neural Network Used to Model HCP\u2019s fMRI Motor Paradigm Data","volume":"Volume 13347","author":"Rojas","year":"2022","journal-title":"Bioinformatics and Biomedical Engineering"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1007\/978-3-031-25891-6_31","article-title":"Path weights analyses in a shallow neural network to reach Explainable Artificial Intelligence (XAI) of fMRI data","volume":"Volume 13811","author":"Nicosia","year":"2023","journal-title":"Machine Learning, Optimization, and Data Science"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"169","DOI":"10.3390\/make5010010","article-title":"Explainable machine learning","volume":"5","author":"Garcke","year":"2023","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"52138","DOI":"10.1109\/ACCESS.2018.2870052","article-title":"Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)","volume":"6","author":"Adadi","year":"2018","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"031002","DOI":"10.1088\/1741-2552\/abc902","article-title":"A survey on deep learning-based non-invasive brain signals: Recent advances and new frontiers","volume":"18","author":"Zhang","year":"2021","journal-title":"J. Neural Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1007\/s44163-024-00161-0","article-title":"Unleashing the power of advanced technologies for revolutionary medical imaging: Pioneering the healthcare frontier with artificial intelligence","volume":"4","author":"Chauhan","year":"2024","journal-title":"Discov. Artif. Intell."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.neuroimage.2004.05.020","article-title":"Combinatorial codes in ventral temporal lobe for object recognition: Haxby (2001) revisited: Is there a \u201cface\u201d area?","volume":"23","author":"Hanson","year":"2004","journal-title":"NeuroImage"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Du, Y., Fu, Z., and Calhoun, V.D. (2018). Classification and prediction of brain disorders using functional connectivity: Promising but challenging. Front. Neurosci., 12.","DOI":"10.3389\/fnins.2018.00525"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Wen, D., Wei, Z., Zhou, Y., Li, G., Zhang, X., and Han, W. (2018). Deep learning methods to process fMRI data and their application in the diagnosis of cognitive impairment: A brief overview and our opinion. Front. Neuroinform., 12.","DOI":"10.3389\/fninf.2018.00023"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"nsae014","DOI":"10.1093\/scan\/nsae014","article-title":"Deep social neuroscience: The promise and peril of using artificial neural networks to study the social brain","volume":"19","author":"Sievers","year":"2024","journal-title":"Soc. Cogn. Affect. Neurosci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.tics.2010.12.004","article-title":"Value, pleasure and choice in the ventral prefrontal cortex","volume":"15","author":"Grabenhorst","year":"2011","journal-title":"Trends Cogn. Sci."},{"key":"ref_16","unstructured":"Glimcher, P.W., and Fehr, E. (2014). Valuation, Intertemporal Choice, and Self-Control. Neuroeconomics: Decision Making and the Brain, Academic Press. [2nd ed.]."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.dsp.2017.10.011","article-title":"Methods for interpreting and understanding deep neural networks","volume":"73","author":"Montavon","year":"2018","journal-title":"Digit. Signal Process."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Holzinger, A., Goebel, R., Fong, R., Moon, T., M\u00fcller, K.-R., and Samek, W. (2022). Explainable AI Methods\u2014A Brief Overview. xxAI\u2014Beyond Explainable AI, Springer International Publishing. International Workshop, Held in Conjunction with ICML 2020, July 18, 2020, Vienna, Austria, Revised and Extended Papers.","DOI":"10.1007\/978-3-031-04083-2"},{"key":"ref_19","first-page":"5","article-title":"Towards explainable artificial intelligence","volume":"Volume 11700","author":"Samek","year":"2019","journal-title":"Explainable AI: Interpreting, Explaining and Visualizing Deep Learning"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"4793","DOI":"10.1109\/TNNLS.2020.3027314","article-title":"A survey on Explainable Artificial Intelligence (XAI): Toward medical XAI","volume":"32","author":"Tjoa","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_21","unstructured":"Besold, T.R., and Kutz, O. (2017, January 14\u201317). What Does Explainable AI Really Mean? A New Conceptualization of Perspectives. Proceedings of the 1st International Workshop on Comprehensibility and Explanation in AI and ML, CEX 2017, Bari, Italy."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"42200","DOI":"10.1109\/ACCESS.2020.2976199","article-title":"Explainable machine learning for scientific insights and discoveries","volume":"8","author":"Roscher","year":"2020","journal-title":"IEEE Access"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1036","DOI":"10.3390\/make5030054","article-title":"Defining a digital twin: A data science-based unification","volume":"5","year":"2023","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"615","DOI":"10.3390\/make3030032","article-title":"Classification of explainable artificial intelligence methods through their output formats","volume":"3","author":"Vilone","year":"2021","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1997","DOI":"10.3390\/make6030098","article-title":"Tertiary review on explainable artificial intelligence: Where do we stand?","volume":"6","author":"Jutte","year":"2024","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"ref_26","unstructured":"Marques dos Santos, J.D., and Marques dos Santos, J.P. (2023, January 26\u201328). Explaining ANN-modeled fMRI Data with Path-Weights and Layer-Wise Relevance Propagation. Proceedings of the 1st World Conference on eXplainable Artificial Intelligence (xAI 2023), Lisbon, Portugal."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Cacioppo, J.T., Visser, P.S., and Pickett, C.L. (2006). Four Brain Regions for One Theory of Mind?. Social Neuroscience: People Thinking About Thinking People, MIT Press.","DOI":"10.7551\/mitpress\/6304.001.0001"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1002\/wcs.29","article-title":"Social neuroscience","volume":"1","author":"Norman","year":"2010","journal-title":"Wiley Interdiscip. Rev. Cogn. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/17470910600683549","article-title":"Social Neuroscience: A new journal","volume":"1","author":"Decety","year":"2006","journal-title":"Soc. Neurosci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/S0959-4388(00)00202-6","article-title":"The neurobiology of social cognition","volume":"11","author":"Adolphs","year":"2001","journal-title":"Curr. Opin. Neurobiol."},{"key":"ref_31","unstructured":"Glimcher, P.W., and Fehr, E. (2014). Understanding Others: Brain Mechanisms of Theory of Mind and Empathy. Neuroeconomics: Decision Making and the Brain, Academic Press. [2nd ed.]."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"243","DOI":"10.2307\/1416950","article-title":"An experimental study of apparent behavior","volume":"57","author":"Heider","year":"1944","journal-title":"Am. J. Psychol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.neuroimage.2013.05.041","article-title":"The WU-Minn Human Connectome Project: An overview","volume":"80","author":"Smith","year":"2013","journal-title":"NeuroImage"},{"key":"ref_34","first-page":"cer-10-16","article-title":"The Human Connectome Project: Progress and Prospects","volume":"2016","author":"Glasser","year":"2016","journal-title":"Cerebrum"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"118543","DOI":"10.1016\/j.neuroimage.2021.118543","article-title":"The Human Connectome Project: A retrospective","volume":"244","author":"Elam","year":"2021","journal-title":"NeuroImage"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"782","DOI":"10.1016\/j.neuroimage.2011.09.015","article-title":"FSL","volume":"62","author":"Jenkinson","year":"2012","journal-title":"NeuroImage"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/S1361-8415(01)00036-6","article-title":"A global optimisation method for robust affine registration of brain images","volume":"5","author":"Jenkinson","year":"2001","journal-title":"Med. Image Anal."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"825","DOI":"10.1006\/nimg.2002.1132","article-title":"Improved optimization for the robust and accurate linear registration and motion correction of brain images","volume":"17","author":"Jenkinson","year":"2002","journal-title":"Neuroimage"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1002\/hbm.10062","article-title":"Fast robust automated brain extraction","volume":"17","author":"Smith","year":"2002","journal-title":"Hum. Brain Mapp."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1370","DOI":"10.1006\/nimg.2001.0931","article-title":"Temporal autocorrelation in univariate linear modeling of FMRI data","volume":"14","author":"Woolrich","year":"2001","journal-title":"Neuroimage"},{"key":"ref_41","unstructured":"Jezzard, P., Matthews, P.M., and Smith, S.M. (2001). Statistical analysis of activation images. Functional MRI: An Introduction to Methods, Oxford University Press."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1732","DOI":"10.1016\/j.neuroimage.2003.12.023","article-title":"Multilevel linear modelling for FMRI group analysis using Bayesian inference","volume":"21","author":"Woolrich","year":"2004","journal-title":"Neuroimage"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1052","DOI":"10.1016\/S1053-8119(03)00435-X","article-title":"General multilevel linear modeling for group analysis in FMRI","volume":"20","author":"Beckmann","year":"2003","journal-title":"Neuroimage"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1038\/nature18933","article-title":"A multi-modal parcellation of human cerebral cortex","volume":"536","author":"Glasser","year":"2016","journal-title":"Nature"},{"key":"ref_45","unstructured":"Limas, M.C., Mer\u00e9, J.B.O., Marcos, A.G., Ascacibar, F.J.M.d.P., Espinoza, A.V.P., El\u00edas, F.A., and Ramos, J.M.P. (2014). AMORE: A MORE Flexible Neural Network Package (0.2-15), 0.2-15."},{"key":"ref_46","unstructured":"Baliga, V.B., Armstrong, M.S., Press, E.R., Bonnet-Lebrun, A.-S., and Sciaini, M. (2023). Pathviewr: Wrangle, Analyze, and Visualize Animal Movement Data (v. 1.1.7)."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Marques dos Santos, J.P., and Marques dos Santos, J.D. (2024). XAI (Explainable Artificial Intelligence) in Neuromarketing\/Consumer Neuroscience: An fMRI study on brand perception. Front. Hum. Neurosci., 18.","DOI":"10.3389\/fnhum.2024.1305164"},{"key":"ref_48","unstructured":"Shrikumar, A., Greenside, P., and Kundaje, A. (2017, January 6\u201311). Learning important features through propagating activation differences. Proceedings of the 34th International Conference on Machine Learning, PMLR, Sydney, Australia."},{"key":"ref_49","unstructured":"Lundberg, S.M., and Lee, S.-I. (2017, January 4\u20139). A unified approach to interpreting model predictions. Proceedings of the Advances in Neural Information Processing Systems 30 (NIPS 2017), Long Beach, CA, USA."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1016\/j.neuron.2006.05.001","article-title":"The neural basis of mentalizing","volume":"50","author":"Frith","year":"2006","journal-title":"Neuron"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.neubiorev.2014.01.009","article-title":"Fractionating theory of mind: A meta-analysis of functional brain imaging studies","volume":"42","author":"Schurz","year":"2014","journal-title":"Neurosci. Biobehav. Rev."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"967","DOI":"10.1016\/j.neuroimage.2004.07.054","article-title":"Brain activation associated with evaluative processes of guilt and embarrassment: An fMRI study","volume":"23","author":"Takahashi","year":"2004","journal-title":"NeuroImage"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2658","DOI":"10.1016\/j.neuropsychologia.2010.05.012","article-title":"What gets the attention of the temporo-parietal junction? An fMRI investigation of attention and theory of mind","volume":"48","author":"Young","year":"2010","journal-title":"Neuropsychologia"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"137267","DOI":"10.1016\/j.neulet.2023.137267","article-title":"Heterogeneity of social cognition between visual perspective-taking and theory of mind in the temporo-parietal junction","volume":"807","author":"Ogawa","year":"2023","journal-title":"Neurosci. Lett."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.cortex.2016.04.017","article-title":"Social representations and contextual adjustments as two distinct components of the Theory of Mind brain network: Evidence from the REMICS task","volume":"81","author":"Lavoie","year":"2016","journal-title":"Cortex"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.neulet.2009.07.064","article-title":"Individual differences in the theory of mind and superior temporal sulcus","volume":"463","author":"Otsuka","year":"2009","journal-title":"Neurosci. Lett."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Qin, X., Huang, H., Liu, Y., Zheng, F., Zhou, Y., and Wang, H. (2023). Increased functional connectivity involving the parahippocampal gyrus in patients with schizophrenia during Theory of Mind processing: A psychophysiological interaction study. Brain Sci., 13.","DOI":"10.3390\/brainsci13040692"}],"container-title":["Machine Learning and Knowledge Extraction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-4990\/7\/1\/17\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:32:56Z","timestamp":1760027576000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-4990\/7\/1\/17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,13]]},"references-count":57,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["make7010017"],"URL":"https:\/\/doi.org\/10.3390\/make7010017","relation":{},"ISSN":["2504-4990"],"issn-type":[{"value":"2504-4990","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,13]]}}}