The colors of our brain: an integrated approach for dimensionality reduction and explainability in fMRI through color coding (i-ECO)

• Published in: Brain imaging and behavior 2022-06, Vol.16 (3), p.977-990
• Main Authors: Tarchi, Livio, Damiani, Stefano, La Torraca Vittori, Paolo, Marini, Simone, Nazzicari, Nelson, Castellini, Giovanni, Pisano, Tiziana, Politi, Pierluigi, Ricca, Valdo
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Attention deficit hyperactivity disorder; Biomedical and Life Sciences; Biomedicine; Bipolar disorder; Brain - diagnostic imaging; Brain mapping; Brain Mapping - methods; Brain research; Clinical medicine; Color; Color coding; Consortia; Diagnostic systems; Eigenvectors; Functional magnetic resonance imaging; Homogeneity; Humans; Integrated approach; Literature reviews; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medical imaging; Mental disorders; Network analysis; Neural coding; Neural networks; Neuropsychology; Neuroradiology; Neurosciences; Original Research; Psychiatry; Schizophrenia; Schizophrenia - diagnostic imaging; Spectrum analysis; Time series; Work study
• ISSN: 1931-7557; 1931-7565
• DOI: 10.1007/s11682-021-00584-8

Several systematic reviews have highlighted the role of multiple sources in the investigation of psychiatric illness. For what concerns fMRI, the focus of recent literature preferentially lies on three lines of research, namely: functional connectivity, network analysis and spectral analysis. Data was gathered from the UCLA Consortium for Neuropsychiatric Phenomics. The sample was composed by 130 neurotypicals, 50 participants diagnosed with Schizophrenia, 49 with Bipolar disorder and 43 with ADHD. Single fMRI scans were reduced in their dimensionality by a novel method (i-ECO) averaging results per Region of Interest and through an additive color method (RGB): local connectivity values (Regional Homogeneity), network centrality measures (Eigenvector Centrality), spectral dimensions (fractional Amplitude of Low-Frequency Fluctuations). Average images per diagnostic group were plotted and described. The discriminative power of this novel method for visualizing and analyzing fMRI results in an integrative manner was explored through the usage of convolutional neural networks. The new methodology of i-ECO showed between-groups differences that could be easily appreciated by the human eye. The precision-recall Area Under the Curve (PR-AUC) of our models was > 84.5% for each diagnostic group as evaluated on the test-set – 80/20 split. In conclusion, this study provides evidence for an integrative and easy-to-understand approach in the analysis and visualization of fMRI results. A high discriminative power for psychiatric conditions was reached. This proof-of-work study may serve to investigate further developments over more extensive datasets covering a wider range of psychiatric diagnoses.