On the interplay between state-dependent reconfigurations of global signal correlation and BOLD fluctuations: An fMRI study.

BACKGROUND: The dynamics of global, state-dependent reconfigurations in brain connectivity are yet unclear. We aimed at assessing reconfigurations of the global signal correlation coefficient (GSCORR), a measure of the connectivity between each voxel timeseries and the global signal, from resting-state to a stop-signal task. The secondary aim was to assess the relationship between GSCORR and blood-oxygen-level-dependent (BOLD) activations or deactivation across three different trial-conditions (GO, STOP-correct, and STOP-incorrect). METHODS: As primary analysis we computed whole-brain, voxel-wise GSCORR during resting-state (GSCORR-rest) and stop-signal task (GSCORR-task) in 107 healthy subjects aged 21-50, deriving GSCORR-shift as GSCORR-task minus GSCORR-rest. GSCORR-tr and trGSCORR-shift were also computed on the task residual time series to quantify the impact of the task-related activity during the trials. To test the secondary aim, brain regions were firstly divided in one cluster showing significant task-related activation and one showing significant deactivation across the three trial conditions. Then, correlations between GSCORR-rest/task/shift and activation/deactivation in the two clusters were computed. As sensitivity analysis, GSCORR-shift was computed on the same sample after performing a global signal regression and GSCORR-rest/task/shift were correlated with the task performance. RESULTS: Sensory and temporo-parietal regions exhibited a negative GSCORR-shift. Conversely, associative regions (ie. left lingual gyrus, bilateral dorsal posterior cingulate gyrus, cerebellum areas, thalamus, posterolateral parietal cortex) displayed a positive GSCORR-shift (FDR-corrected p < 0.05). GSCORR-shift showed similar patterns to trGSCORR-shift (magnitude increased) and after global signal regression (magnitude decreased). Concerning BOLD changes, Brodmann area 6 and inferior parietal lobule showed activation, while posterior parietal lobule, cuneus, precuneus, middle frontal gyrus showed deactivation (FDR-corrected p < 0.05). No correlations were found between GSCORR-rest/task/shift and beta-coefficients in the activation cluster, although negative correlations were observed between GSCORR-task and GO/STOP-correct deactivation (Pearson rho=-0.299/-0.273; Bonferroni-p < 0.05). Weak associations between GSCORR and task performance were observed (uncorrected p < 0.05). CONCLUSION: GSCORR state-dependent reconfiguration indicates a reallocation of functional resources to associative areas during stop-signal task. GSCORR, activation and deactivation may represent distinct proxies of brain states with specific neurofunctional relevance.

State-dependent reductions of local brain connectivity in schizophrenia and their relation to performance and symptoms: A functional magnetic resonance imaging study.

State-dependent reallocation of cognitive resources is impaired in schizophrenia and may be underlined by alterations in brain local-connectivity. Increasing evidence suggests local connectivity reductions from rest to task in healthy individuals, while insufficient information is available for schizophrenia spectrum. Resting-state and stop-signal task fMRI scans of 107 healthy controls and 32 patients with DSM-IV-TR schizophrenia or schizoaffective disorder were analyzed. As primary aim we measured within-group shifts in local-connectivity from rest to task as voxel-wise Regional Homogeneity (ReHo-shift). Secondary aims were to test: i) Between-groups differences in ReHo-rest, ReHo-task and ReHo-shift; ii) ReHo covariations with task performance (=shorter reaction times) and severity of symptoms (SAPS/SANS scores). Age, sex, and education were accounted for as covariates. Motion, global-signal-regression, antipsychotic dosage and smoothing associations with ReHo were evaluated. Rest-to-task ReHo reductions occurred in both groups on a whole-brain level (False-Discovery-Rate p=0.05). Trends of greater ReHo reductions in patients versus controls were observed. Controls performed better than patients (p<0.001). ReHo negatively correlated with performance in both groups. ReHo-shift predicted worse performance in controls, but better performance in patients (uncorrected p=0.05). ReHo reductions correlated with severity of symptoms. State-dependent reconfigurations in local-connectivity provide new links between neurobiology and behavioral/clinical features of the schizophrenia spectrum.

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

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.