Journal
NEUROIMAGE
Volume 243, Issue -, Pages -Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2021.118533
Keywords
Connectome; Resting state; Functional MRI; Functional connectivity; Individual variability
Funding
- NIH [1 R34 NS118618-01]
- McDonnell Center for Systems Neuroscience
- Otto Hahn award from the Max Planck society
- NIMH [R01MH060974]
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Research into the human connectome using resting state functional MRI has become increasingly popular in recent years, shifting focus towards individualized connectome and relationships between connectomic differences and behavioral/clinical variation. Various approaches are being developed to achieve subject-specific accuracy in parcel boundaries, along with new data representations to complement traditional parcellated connectome representation. Challenges remain in functional connectome research, but future studies are expected to improve interpretability and confidence in clinical translation of connectome insights.
Research into the human connectome (i.e., all connections in the human brain) with the use of resting state functional MRI has rapidly increased in popularity in recent years, especially with the growing availability of large-scale neuroimaging datasets. The goal of this review article is to describe innovations in functional connectome representations that have come about in the past 8 years, since the 2013 NeuroImage special issue on 'Mapping the Connectome'. In the period, research has shifted from group-level brain parcellations towards the characterization of the individualized connectome and of relationships between individual connectomic differences and behavioral/clinical variation. Achieving subject-specific accuracy in parcel boundaries while retaining cross-subject correspondence is challenging, and a variety of different approaches are being developed to meet this challenge, including improved alignment, improved noise reduction, and robust group-to-subject mapping approaches. Beyond the interest in the individualized connectome, new representations of the data are being studied to complement the traditional parcellated connectome representation (i.e., pairwise connections between distinct brain regions), such as methods that capture overlapping and smoothly varying patterns of connectivity ('gradients'). These different connectome representations offer complimentary insights into the inherent functional organization of the brain, but challenges for functional connectome research remain. Interpretability will be improved by future research towards gaining insights into the neural mechanisms underlying connectome observations obtained from functional MRI. Validation studies comparing different connectome representations are also needed to build consensus and confidence to proceed with clinical trials that may produce meaningful clinical translation of connectome insights.
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