Synthetic data have emerged as an attractive option for developing machine-learning methods in human neuroimaging, particularly in magnetic resonance imaging (MRI)—a modality where image contrast depends enormously on acquisition hardware and parameters. This retrospective paper reviews a family of recently proposed methods, based on synthetic data, for generalizable machine learning in brain MRI analysis. Central to this framework is the concept of domain randomization, which involves training neural networks on a vastly diverse array of synthetically generated images with random contrast properties. This technique has enabled robust, adaptable models that are capable of handling diverse MRI contrasts, resolutions, and pathologies, while working out-of-the-box, without retraining. We have successfully applied this method to tasks such as whole-brain segmentation (SynthSeg), skull-stripping (SynthStrip), registration (SynthMorph, EasyReg), super-resolution, and MR contrast transfer (SynthSR). Beyond these applications, the paper discusses other possible use cases and future work in our methodology. Neural networks trained with synthetic data enable the analysis of clinical MRI, including large retrospective datasets, while greatly alleviating (and sometimes eliminating) the need for substantial labeled datasets, and offer enormous potential as robust tools to address various research goals.

Diffusion-weighted magnetic resonance imaging (dMRI) of the brain offers unique capabilities including noninvasive probing of tissue microstructure and structural connectivity. It is widely used for clinical assessment of disease and injury, and for neuroscience research. Analyzing the dMRI data to extract useful information for medical and scientific purposes can be challenging. The dMRI measurements may suffer from strong noise and artifacts, and may exhibit high intersession and interscanner variability in the data, as well as intersubject heterogeneity in brain structure. Moreover, the relationship between measurements and the phenomena of interest can be highly complex. Recent years have witnessed increasing use of machine learning methods for dMRI analysis. This manuscript aims to assess these efforts, with a focus on methods that have addressed data preprocessing and harmonization, microstructure mapping, tractography, and white matter tract analysis. We study the main findings, strengths, and weaknesses of the existing methods and suggest topics for future research. We find that machine learning may be exceptionally suited to tackle some of the difficult tasks in dMRI analysis. However, for this to happen, several shortcomings of existing methods and critical unresolved issues need to be addressed. There is a pressing need to improve evaluation practices, to increase the availability of rich training datasets and validation benchmarks, as well as model generalizability, reliability, and explainability concerns.

The present meta-analysis investigated the impact of non-invasive stimulation, using transcranial direct current stimulation (tDCS) and transcranial magnetic stimulation (TMS) targeting the posterior cerebellum, on social and emotional mentalizing about others. Prior research has convincingly shown that the posterior cerebellum supports social and emotional cognition. We identified 14 studies targeting the cerebellum with appropriate control conditions (i.e., sham, control site), which exclude general learning effects of the task or placebo effects. The studies included 29 task conditions where stimulation before or during a social or emotional task was applied on healthy samples. The results showed significant evidence that sustained anodal tDCS and TMS generally improved social and emotional performance after stimulation, in comparison with sham or control conditions, with a small effect size. In contrast, cathodal stimulation showed mixed facilitatory and inhibitory results. In addition, short TMS pulses, administered with the aim of interfering with ongoing social or emotional processes, induced a small but consistent inhibitory effect. Control tasks without social or emotional components also showed significant improvement after sustained anodal tDCS and TMS, suggesting that transcranial stimulation of the cerebellum may also improve other functions. This was not the case for short TMS pulses, which did not modulate non-social and non-emotional control tasks. Taken together, this meta-analysis shows that cerebellar neurostimulation confirms a causal role of the cerebellum in socio-emotional cognition, has a small but significant effect on improving socio-emotional skills, and may, therefore, have important clinical applications in pathologies where social and emotional cognition is impaired.