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.
Homotopic connectivity during resting state has been proposed as a risk marker for neurologic and psychiatric conditions, but a precise characterization of its trajectory through development is currently lacking. Voxel-Mirrored Homotopic Connectivity (VMHC) was evaluated in a sample of 85 neurotypical individuals aged 7-18 years.VMHC associations with age, handedness, sex, and motion were explored at the voxel-wise level. VMHC correlates were also explored within 14 functional networks.Primary and secondary outcomes were repeated in a sample of 107 adults aged 21-50 years. In adults, VMHC was negatively correlated with age only in the posterior insula (false discovery rate p < .05, >30-voxel clusters), while a distributed effect among the medial axis was observed in minors. Four out of 14 considered networks showed significant negative correlations between VMHC and age in minors (basal ganglia r = -.280, p = .010; anterior salience r = -.245, p = .024; language r = -.222, p = .041; primary visual r = -.257, p = .017), but not adults. In minors, a positive effect of motion on VMHC was observed only in the putamen. Sex did not significantly influence age effects on VMHC. The current study showed a specific decrease in VMHC for minors as a function of age, but not adults, supporting the notion that interhemispheric interactions can shape late neurodevelopment.
Introduction The high technical barrier to entry in the field of neuroimaging can hinder early insight from promising results and the development of evidence-based clinical practice. Objectives The working group focused on published literature in order to develop a new methodology in the analysis, visualization, and representation of fMRI data in the psychiatric setting. Methods Three valid and established measures were chosen, in order to achieve dimensionality reduction, stability and explainability of results, namely Regional-Homogeneity; fractional Amplitude of Low-Frequency Fluctuations; Eigenvector-Centrality. Each measure was color coded and individual images per subject compiled, averaging results by functional networks as described the FIND lab of the University of Stanford. 272 individual scans were processed (130 neurotypicals, 50 patients with Schizophrenia, 49 with Bipolar Disorder, 43 with ADHD). Results The discriminative power between clinical groups of the novel method was significant both by human eye, and later confirmation by statistical tests, and by computer vision algorithms (Convolutional Neural Networks). The precision-recall Area Under the Curve, dividing by 80/20 proportion between train and test sets, was >84.5% for each group. The group of patients with Bipolar Disorder showed a partial overlap with the group of patients suffering from Schizophrenia – by a dominance of Eigenvector-Centrality and Regional-Homogeneity, as well as a lower prevalence of fractional Amplitude of Low-Frequency Fluctuations, for both in comparison to controls. Conclusions The present study offers preliminary evidence for the adoption of i-ECO (integrated-Explainability through Color Coding) in fMRI analyses during rest in the Psychiatric field. Disclosure No significant relationships.
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