Garrido Salmon, C. E. | Search bioRxiv

bioRxiv2021

Long-term effects of network-based fMRI neurofeedback training for sustained attention

Neurofeedback allows for learning voluntary control over ones own brain activity, aiming to enhance cognition and clinical symptoms. A recent study improved sustained attention temporarily by training healthy participants to up-regulate the differential activity of the sustained attention network (SAN) minus the default mode network (DMN). However, long-term learning effects of functional magnetic resonance imaging (fMRI) neurofeedback training remain under-explored. Here, we evaluate the effects of network-based fMRI neurofeedback training for sustained attention by assessing behavioral and brain measures before, one day after, and two months after training. The behavioral measures include task as well as questionnaire scores, and the brain measures include activity and connectivity during self-regulation runs without feedback (i.e., transfer runs) and during resting-state runs. Neurally, we found that participants maintained their ability to control the differential activity during follow-up sessions. Further, exploratory analyses showed that the training-induced increase in FC between the DMN and occipital gyrus was maintained during follow-up transfer runs, but not during follow-up resting-state runs. Behaviorally, we found that enhanced sustained attention right after training returned to baseline level during follow-up. The discrepancy between lasting regulation-related brain changes but transient behavioral and resting-state effects raises the question of how neural changes induced by neurofeedback training translate to potential behavioral improvements. Since neurofeedback directly targets brain measures to indirectly improve behavior long-term, a better understanding of the brain-behavior associations during and after neurofeedback training is needed to develop its full potential as a promising scientific and clinical tool. Key pointsO_LIParticipants were still able to self-regulate the differential activity between large-scale networks two months after the end of neurofeedback training and this during transfer runs without feedback. C_LIO_LILasting brain changes were also observed in the functional connectivity of trained regions in runs during which participants engaged in active self-regulation as well as during resting-state runs without concomitant self-regulation. C_LIO_LIThe increased sustained attention we observed right after the end of neurofeedback training did not persist two months later. C_LI

neuroscience↗

bioRxiv2021

On the prediction of human intelligence from neuroimaging: A systematic review of methods and reporting

Human intelligence is one of the main objects of study in cognitive neuroscience. Reviews and meta-analyses have proved to be fundamental to establish and cement neuroscientific theories on intelligence. The prediction of intelligence using in vivo neuroimaging data and machine learning has become a widely accepted and replicated result. Here, we present a systematic review of this growing area of research, based on studies that employ structural, functional, and/or diffusion MRI to predict human intelligence in cognitively normal subjects using machine-learning. We performed a systematic assessment of methodological and reporting quality, using the PROBAST and TRIPOD assessment forms and 30 studies identified through a systematic search. We observed that fMRI is the most employed modality, resting-state functional connectivity (RSFC) is the most studied predictor, and the Human Connectome Project is the most employed dataset. A meta-analysis revealed a significant difference between the performance obtained in the prediction of general and fluid intelligence from fMRI data, confirming that the quality of measurement moderates this association. The expected performance of studies predicting general intelligence from fMRI was estimated to be r = 0.42 (CI95% = [0.35, 0.50]) while for studies predicting fluid intelligence obtained from a single test, expected performance was estimated as r = 0.15 (CI95% = [0.13, 0.17]). We further enumerate some virtues and pitfalls we identified in the methods for the assessment of intelligence and machine learning. The lack of treatment of confounder variables, including kinship, and small sample sizes were two common occurrences in the literature which increased risk of bias. Reporting quality was fair across studies, although reporting of results and discussion could be vastly improved. We conclude that the current literature on the prediction of intelligence from neuroimaging data is reaching maturity. Performance has been reliably demonstrated, although extending findings to new populations is imperative. Current results could be used by future works to foment new theories on the biological basis of intelligence differences.

neuroscience↗