The promise of precision functional mapping for neuroimaging in psychiatry

Abstract

Precision functional mapping (PFM) is a neuroimaging approach to reliably estimate metrics of brain function from individual people via the collection of large amounts of fMRI data (hours per person). This method has revealed much about the inter-individual variation of functional brain networks. While standard group-level studies, in which we average brain measures across groups of people, are important in understanding the generalizable neural underpinnings of neuropsychiatric disorders, many disorders are heterogeneous in nature. This heterogeneity often complicates clinical care, leading to patient uncertainty when considering prognosis or treatment options. We posit that PFM methods may help streamline clinical care in the future, fast-tracking the choice of personalized treatment that is most compatible with the individual. In this review, we provide a history of PFM studies, foundational results highlighting the benefits of PFM methods in the pursuit of an advanced understanding of individual differences in functional network organization, and possible avenues where PFM can contribute to clinical translation of neuroimaging research results in the way of personalized treatment in psychiatry.

Fig. 1. Individual variation in cortical functional network organization revealed by PFM.

a Common deviations from the group average (blue and purple arrows) in 6 individuals. b Seed ROIs (white spheres A&B) delineate differences in functional connectivity and network assignments in a group average (MSCavg) and in PFM data from one individual (MSC06). (Figure adapted from Gordon et al. [24]).

Fig. 2. Novel discoveries of cortical organization and plasticity made possible by PFM.

a Seed ROIs (white spheres) show functional connectivity of inter-effector (somato-cognitive action network), foot, hand, and mouth regions. b Seed maps show functional connectivity of left primary somatomotor cortex before (Pre), during (Cast), and after casting (Post) in one participant, showing cast-induced changes. c Daily time course of functional connectivity between left and right upper extremity for each participant. A time-varying exponential decay model was used to calculate Δr values. (Nico: p = 0.002, Ashley: p < 0.001, Omar: p < 0.001) (a adapted from Gordon et al. [50]; b, c adapted from Newbold et al. [58]).

Fig. 3. Functional network organization of the subcortex and cerebellum in PFM data.

a Functional network assignment of subcortical structures in one representative male (MSC02), one representative female (MSC04), and the group average. Voxels with preferential functional connectivity to a single network are indicated with solid colors and voxels functionally connected to multiple networks are shown with cross-hatching. Three clusters of integration zones are detailed on the right. b Functional network representation of two individuals (MSC01 and MSC09) in the cerebellum. c Flatmaps of cerebellar network parcellations in two individuals (Subject1 and Subject2). (a adapted from Greene et al. [70]; b adapted from Marek et al. [83]; c adapted from Xue et al. [84]).

Fig. 4. Three promising paths towards clinical application of PFM.

The center brain image displays functional network topology from a single individual, representing one metric that can be measured with PFM (any metric of interest could be used). a Identifying individual-specific brain features that relate to clinically relevant individual differences. b Localization of individual-specific targets for intervention, here showing transcranial magnetic stimulation (TMS). c Tracking individual-specific changes in the brain over time due, here in response to treatment.