Precision neuroradiology: mapping the nodes and networks that link genes to behaviour

Abstract

What is the future of neuroradiology in the era of precision medicine? As with any big change, this transformation in medicine presents both challenges and opportunities, and to flourish in this new environment we will have to adapt. It is difficult to predict exactly how neuroradiology will evolve in this shifting landscape, but there will be changes in both what we image and what we do. In terms of imaging, we will need to move beyond simply imaging brain anatomy and toward imaging function, both at the molecular and circuit level. In terms of what we do, we will need to move from the periphery of the clinical enterprise toward its center, with a new emphasis on integrating imaging with genetic and clinical data to form a comprehensive picture of the patient that can be used to direct further testing and care.The payoff is that these changes will align neuroradiology with the emerging field of precision psychiatry, which promises to replace symptom-based diagnosis and trial-and-error treatment of psychiatric disorders with diagnoses based on quantifiable genetic, imaging, physiologic, and behavioural criteria and therapies targeted to the particular pathophysiology of individual patients. Here we review some of the recent developments in behavioural genetics and neuroscience that are laying the foundation for precision psychiatry. By no means comprehensive, our goal is to introduce some of the perspectives and techniques that are likely to be relevant to the precision neuroradiologist of the future.

Figure 1.

Precision Medicine in Psychiatry. The goal is to use empirical methods, including genetics and imaging, to stratify patients with psychiatric disease into subgroups that have greater biological validity than those derived from traditional, purely symptom based, diagnostic criteria.

Figure 2.

Alzheimer disease (AD) Polygenic hazard score (PHS) quantifies an individual’s relative age-specific risk for developing AD. Survival analysis using both Kaplan–Meier estimates (dashed lines) and Cox proportional hazard model fits (solid lines) from the ADGC (Alzheimer’s Disease Genetics Consortium) Phase one case–control dataset shows how PHS quartiles capture differences in age-specific genetic risk for AD. Figure reproduced from Reference 41 under creative commons license.

Figure 3.

Neuromodulation via in-vivo ultrasonic drug uncaging in a rodent model. A. Schematic shows recording electrode in primary visual cortex (V1) and LED light stimulus for visual evoked potential (VEP) experiments. B. Running average of the VEP following sonication (‘FUS’, horizontal black line) applied to V1, normalized by the response 60 s prior to FUS administration shows transient inhibition of VEP when sonication is applied in conjunction with administration of propofol-loaded nanoparticles. C. Left: Cortical sonication target (red) for PET experiments. Right: axial and coronal PET images acquired following sonication in conjunction with administration of blank or propofol-loaded nanoparticles (black dashed ellipse = expected sonication target; Color bar = normalized FDG uptake). D. Average normalized spatial effect on FDG uptake at the sonication site for 1.2 MPa sonication with propofol-loaded nanoparticles. E. Group level averages of FDG uptake across a sagittal slice centered at the sonication site for each condition. Figure courtesy of Dr. Raag Airan, Stanford University.