Abstract
Post-stroke cognitive and linguistic impairments are debilitating conditions, with limited therapeutic options. Domain-general brain networks play an important role in stroke recovery and characterizing their residual function with functional MRI has the potential to yield biomarkers capable of guiding patient-specific rehabilitation. However, this is challenging as such detailed characterization requires testing patients on multitudes of cognitive tasks in the scanner, rendering experimental sessions unfeasibly lengthy. Thus, the current status quo in clinical neuroimaging research involves testing patients on a very limited number of tasks, in the hope that it will reveal a useful neuroimaging biomarker for the whole cohort. Given the great heterogeneity among stroke patients and the volume of possible tasks this approach is unsustainable. Advancing task-based functional MRI biomarker discovery requires a paradigm shift in order to be able to swiftly characterize residual network activity in individual patients using a diverse range of cognitive tasks. Here, we overcome this problem by leveraging neuroadaptive Bayesian optimization, an approach combining real-time functional MRI with machine-learning, by intelligently searching across many tasks, this approach rapidly maps out patient-specific profiles of residual domain-general network function. We used this technique in a cross-sectional study with 11 left-hemispheric stroke patients with chronic aphasia (four female, age ± standard deviation: 59 ± 10.9 years) and 14 healthy, age-matched control subjects (eight female, age ± standard deviation: 55.6 ± 6.8 years). To assess intra-subject reliability of the functional profiles obtained, we conducted two independent runs per subject, for which the algorithm was entirely reinitialized. Our results demonstrate that this technique is both feasible and robust, yielding reliable patient-specific functional profiles. Moreover, we show that group-level results are not representative of patient-specific results. Whereas controls have highly similar profiles, patients show idiosyncratic profiles of network abnormalities that are associated with behavioural performance. In summary, our study highlights the importance of moving beyond traditional ‘one-size-fits-all’ approaches where patients are treated as one group and single tasks are used. Our approach can be extended to diverse brain networks and combined with brain stimulation or other therapeutics, thereby opening new avenues for precision medicine targeting a diverse range of neurological and psychiatric conditions.
Highlights
Cognitive and linguistic impairments following brain injury such as stroke are a leading cause of disability, affecting over a quarter of a million people in the UK (Stroke Association UK) and over a million in the US (American Speech-Language-Hearing Association), with numbers expected to increase dramatically given the ageing population (Béjot et al, 2016)
At the group level, it appears that patients do not show a qualitatively different frontoparietal networks (FPNs)-default-mode network (DMN) dissociation pattern across the task space compared to controls (Fig. 3a), but only seem to have a slightly diminished dissociation between these two networks for the Semantic Judgement, Calculation and Encoding tasks
Patients did not show an altered FPN>DMN dissociation pattern across the task space compared to controls
Summary
Cognitive and linguistic impairments following brain injury such as stroke are a leading cause of disability, affecting over a quarter of a million people in the UK (Stroke Association UK) and over a million in the US (American Speech-Language-Hearing Association), with numbers expected to increase dramatically given the ageing population (Béjot et al, 2016). Given the great heterogeneity in stroke patients with respect to their pathophysiology and resulting functional deficits, functional magnetic resonance imaging (fMRI) is a promising method for discovering candidate biomarkers capable of distinguishing patient subgroups as it allows non-invasive mapping of brain (dys-)function with spatial precision. To date, no fMRI-derived biomarker is considered as ready to be used in clinical trials for predicting recovery of cognitive or language function (Boyd et al, 2017). FMRI measures of brain network activation and connectivity during task execution (“task-based fMRI”) show promising potential as clinically relevant biomarkers and thereby represent a developmental priority (Boyd et al, 2017). A major challenge for any progress in this direction is selecting the optimal task (or battery of tasks) to be administered to patients in the MR scanner
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