Data-driven approaches to neuroimaging biomarkers for neurological and psychiatric disorders: emerging approaches and examples

Abstract

Purpose of review: The 'holy grail' of clinical applications of neuroimaging to neurological and psychiatric disorders via personalized biomarkers has remained mostly elusive, despite considerable effort. However, there are many reasons to continue to be hopeful, as the field has made remarkable advances over the past few years, fueled by a variety of converging technical and data developments.

Recent findings: We discuss a number of advances that are accelerating the push for neuroimaging biomarkers including the advent of the 'neuroscience big data' era, biomarker data competitions, the development of more sophisticated algorithms including 'guided' data-driven approaches that facilitate automation of network-based analyses, dynamic connectivity, and deep learning. Another key advance includes multimodal data fusion approaches which can provide convergent and complementary evidence pointing to possible mechanisms as well as increase predictive accuracy.

Summary: The search for clinically relevant neuroimaging biomarkers for neurological and psychiatric disorders is rapidly accelerating. Here, we highlight some of these aspects, provide recent examples from studies in our group, and link to other ongoing work in the field. It is critical that access and use of these advanced approaches becomes mainstream, this will help propel the community forward and facilitate the production of robust and replicable neuroimaging biomarkers.

Figure 1:

left: prediction accuracy of neuroimaging studies across a range of brain disorders (adapted from), right: comparison of unimodal/multimodal and patient/control accuracies in the context of studies which predict various scores/symptoms (adapted from ).

Figure 2:

left: Spatial maps (SMs) of 53 identified intrinsic connectivity networks (ICNs), replicated across independent analysis of the HCP and GSP data, which are divided into seven different functional domains based on their anatomical and functional properties. Each color in the composite maps corresponds to a different ICN. These ICNs were used as network templates in our framework. Right: FNC matrices showing highly replicable main effects (top) and schizophrenia vs control differences (bottom). Adapted from .

Figure 3:. Prediction of medication class response to antidepressants or mood stabilizers.

An accuracy of 95% was achieved on a dataset of over 100 individuals following a rigorous assessment protocol which involved monitoring of patients over an extended period of time to obtain the medication class to which they were responsive (adapted from ).

Figure 4:. Three-way classification of schizophrenia, bipolar disorder, and controls.

A direct comparison of static and dynamic functional network connectivity approaches reveals significantly higher accuracy in the dynamic case for a three-way classification between schizophrenia, bipolar disorder, and control subjects using resting fMRI data (adapted from ).

Figure 5:. Attractor models.

(top left) by modeling the mean and the temporal derivative in estimating functional states we can identify patterns with similar magnitude but oppositely signed derivatives. These pairs can be modeled as dynamics attractors with information flowing in and out of the pairs (top right). This model can then be used to characterize stages of recovery from traumatic brain injury (bottom; adapted from ).

Figure 6:. Deep learning prediction of cognitive impairment.

(top) correct identification of controls verses Alzheimer’s disease (93% accuracy) and stable versus progressive mild cognitive impairment (81%) with visualized brain regions shown on bottom row (adapted from ).

Figure 7:. Novelty-seeking related multimodal predictive network.

(top) Using task fMRI and structural MRI data to extract multimodal features linked to novelty seeking, we identify a set of regions that were predictive of novelty seeking in the same individuals 5 years later as well as predictive of symptoms of various brain disorders including substance use, ADHD, MDD, and SZ (adapted from ).