Biology subjects

Gell, M.

Publications and source records attributed to Gell, M..

3 recordsLinked to original sources

bioRxiv2021

Highly accurate local functional fingerprints and their stability

Precision medicine and the investigation of brain-behavior associations require biomarkers that are stable (low intraindividual variability) and unique (high interindividual variability) at the same time, hence calling them "fingerprints". The functional connectome (FC) has good "fingerprint properties", as individuals can be accurately identified in a database based on their FC. Importantly, research has shown lower intraindividual variability of more localized measures of brain function such as regional homogeneity (ReHo) and (fractional) amplitude of low-frequency fluctuations ((f)ALFF), compared to the FC. Here, with fMRI data from two publicly available datasets we demonstrate that individuals can be identified with near-perfect accuracies using local functional fingerprints, and especially the regional homogeneity (ReHo) fingerprint. Further analyses reveal that the dorsal attention network contributes most to the individual "uniqueness" of the ReHo fingerprint. Moreover, using a machine-learning setup, we show that the small intraindividual ReHo fingerprint variability across sessions is meaningful for explaining individual-level intelligence. Last, using two other publicly available datasets, clinical applicability is shown with high fingerprint accuracies and a significant correlation between fingerprint stability and intelligence in individuals with schizophrenia. Altogether, our findings suggest that the ReHo fingerprint is a good candidate for further exploration of applicability in precision medicine.

neuroscience↗

bioRxiv2021

A spatiotemporal complexity architecture of human brain activity

The human brain operates in large-scale functional networks. These networks are thought to arise from neural variability, yet the principles behind this link remain unknown. Here we report a mechanism by which the brains network architecture is tightly linked to critical episodes of neural regularity, visible as spontaneous complexity drops in functional MRI signals. These episodes support the formation of functional connections between brain regions, subserve the propagation of neural activity, and reflect inter-individual differences in age and behavior. Furthermore, complexity drops define neural states that dynamically shape the coupling strength, topological structure, and hierarchy of brain networks and comprehensively explain known structure-function relationships within the brain. These findings delineate a unifying complexity architecture of neural activity - a human complexome that underpins the brains functional network organization.

neuroscience↗

bioRxiv2021

Fingerprinting and behavioural prediction rest on distinct functional systems of the human connectome

The prediction of inter-individual behavioural differences from neuroimaging data is a rapidly evolving field of research, focusing on individualised methods to describe human brain organisation on the single-subject level. One method that harnesses such individual signatures is functional connectome fingerprinting, which can reliably identify individuals from large study populations. While connectome fingerprints have been previously associated with individual cognitive function, these associations rest on indirect evidence. Contrasting with these previous reports, here we systematically investigate the link between connectome fingerprints and the prediction of behaviour on different levels of brain network organisation (individual edges, network interactions, topographical organisation, and edge variability), using 339 resting-state fMRI datasets from the Human Connectome Project. Our analysis revealed a significant divergence between connectivity signatures that discriminate between individuals and those predictive of behaviour on all levels of network organisation. Across different parcellation schemes, thresholds and prediction algorithms, we consistently find fingerprints in higher-order multimodal association cortices, while neural correlates of behaviour display a more variable topological distribution. Furthermore, we find the standard deviation of connections between subjects to be significantly higher in fingerprinting than in prediction, making inter-individual connection variability a possible separating marker. These results demonstrate that participant identification and behavioural prediction involve highly distinct functional systems of the human connectome, suggesting that connectome fingerprints are not as functionally relevant as previously believed. The present study thus calls for a re-evaluation of the significance of functional connectivity fingerprints in personalized medicine.

neuroscience↗