The Prediction of Brain Activity from Connectivity: Advances and Applications

Abstract

The human brain is composed of multiple, discrete, functionally specialized regions that are interconnected to form large-scale distributed networks. Using advanced brain-imaging methods and machine-learning analytical approaches, recent studies have demonstrated that regional brain activity during the performance of various cognitive tasks can be accurately predicted from patterns of task-independent brain connectivity. In this review article, we first present evidence for the predictability of brain activity from structural connectivity (i.e., white matter connections) and functional connectivity (i.e., temporally synchronized task-free activations). We then discuss the implications of such predictions to clinical populations, such as patients diagnosed with psychiatric disorders or neurologic diseases, and to the study of brain–behavior associations. We conclude that connectivity may serve as an infrastructure that dictates brain activity, and we pinpoint several open questions and directions for future research.

Predicting task-related brain activity from resting-state brain dynamics with fMRI Transformer

“…This is consistent with previous findings that task activation maps predicted by resting-state fMRI better predict cognitive variables such as intelligence than real task activation maps (Gal, Coldham, et al, 2022; Gal, Tik, et al, 2022). This advancement suggests that resting-state fMRI-derived task activation maps, particularly those generated by SwiFUN, hold significant potential to reflect cognitive and biological traits more accurately than traditional task activation maps, which may be under the influence of nuisance variables (head motion, scanning artifacts, attention level fluctuations) (Bernstein-Eliav & Tavor, 2022). Such capability implies broader applications for SwiFUN, including the potential for diagnosing and predicting psychiatric disorders, thereby positioning it as a valuable tool in neuroscientific research and clinical practice.…”

“…This is due to issues such as ensuring participant compliance and motivation, as well as the necessity for strict experimental control. These issues become particularly pronounced in specific groups such as children, the elderly, and individuals with neurocognitive disorders or severe psychiatric conditions (Bernstein-Eliav & Tavor, 2022; Zhang et al, 2021).…”

“…Recent research is steering towards a significant shift in predicting task reactivity, focusing on resting-state fMRI, which reveals the brain’s intrinsic activity patterns (Bernstein-Eliav & Tavor, 2022). This approach is based on the premise and findings of close relationships between resting-state and active-state functional networks (Cole et al, 2014; Elliott et al, 2019; Smith et al, 2009), suggesting that the ”connectivity fingerprint” derived from resting-state fMRI reflects salient individual differences in cognitive functioning (Finn et al, 2015; Ito et al, 2022; Schultz et al, 2022).…”

Predicting high-level visual areas in the absence of task fMRI

“…In addition to these structural connectivity predictions, previous research has demonstrated a close link between functional connectivity and task activity 25 . Note that while structural and functional connectivity are correlated, the link between these two measures remains unclear 26 .…”

Editorial: Novel perspectives and improvements in fMRI functional connectivity analysis methods used to investigate brain networks and cognitive mechanisms in humans

“…Over the last decade, there has been growing interest and important developments for characterization of the brain connectome to obtain useful and meaningful information while exploring a wide range of pathological conditions and cognitive mechanisms (Bijsterbosch et al, 2021 ; Bernstein-Eliav and Tavor, 2022 ; Srivastava et al, 2022 ). The brain connectome can be investigated using various connectivity measures such as structural (anatomical) and functional (neuronal) connectivity within and between regions in the brain (Babaeeghazvini et al, 2021 ).…”