Neuroharmony: A new tool for harmonizing volumetric MRI data from unseen scanners

•We present Neuroharmony, a harmonization tool for images from unseen scanners. •We developed Neuroharmony using a total of 15,026 sMRI images. •The tool was able to reduce scanner-related bias from unseen scans. •Neuroharmony represents a significant step towards imaging-based clinical tools. •Neuroharmony is available at https://github.com/garciadias/Neuroharmony .

Fine-grain atlases of functional modes for fMRI analysis

Population imaging markedly increased the size of functional-imaging datasets, shedding new light on the neural basis of inter-individual differences. Analyzing these large data entails new scalability challenges, computational and statistical. For this reason, brain images are typically summarized in a few signals, for instance reducing voxel-level measures with brain atlases or functional modes. A good choice of the corresponding brain networks is important, as most data analyses start from these reduced signals. We contribute finely-resolved atlases of functional modes, comprising from 64 to 1024 networks. These dictionaries of functional modes (DiFuMo) are trained on millions of fMRI functional brain volumes of total size 2.4 ​TB, spanned over 27 studies and many research groups. We demonstrate the benefits of extracting reduced signals on our fine-grain atlases for many classic functional data analysis pipelines: stimuli decoding from 12,334 brain responses, standard GLM analysis of fMRI across sessions and individuals, extraction of resting-state functional-connectomes biomarkers for 2500 individuals, data compression and meta-analysis over more than 15,000 statistical maps. In each of these analysis scenarii, we compare the performance of our functional atlases with that of other popular references, and to a simple voxel-level analysis. Results highlight the importance of using high-dimensional "soft" functional atlases, to represent and analyze brain activity while capturing its functional gradients. Analyses on high-dimensional modes achieve similar statistical performance as at the voxel level, but with much reduced computational cost and higher interpretability. In addition to making them available, we provide meaningful names for these modes, based on their anatomical location. It will facilitate reporting of results.

The NIMH Research Domain Criteria (RDoC) Initiative and Its Implications for Research on Personality Disorder

PURPOSE OF REVIEW

We discuss the implications of the Research Domain Criteria (RDoC) initiative for neuroscience research on personality disorder (PD). To organize our review, we construct a preliminary conceptual mapping of PD symptom criteria onto RDoC constructs. We then highlight recent neuroscience research, often built around concepts that correspond to RDoC elements, and discuss the findings in reference to the constructs we consider most pertinent to PD.

RECENT FINDINGS

PD symptoms were strongly conceptually tied to RDoC constructs within the Social Processes domain, implicating brain systems involved in interpersonal rejection, facial emotion perception, and self-referential processes. Negative and Positive Valence Systems were conceptually associated with many PD symptoms, with particular relevance ascribed to the latter's Reward Valuation construct, which could reflect a more widespread disruption of computational processes involved in estimating the probability and benefits of a future outcome. Within the Cognitive Systems domain, the Cognitive Control construct mainly related to PD symptoms associated with impulse control, suggesting a connection to neural circuits that underlie goal selection and behavioral control. Arousal and Regulatory Systems could only be conceptually mapped onto PD symptoms through the Arousal construct, with different symptoms reflecting either a higher or lower biological sensitivity to internal and external stimuli. The RDoC framework has promise to advance neuroscience research on PD. The Social Processes domain is especially relevant to PD, although constructs falling within the other RDoC domains could also yield important insights into the neurobiology of PD and its connections with other forms of psychopathology. Identifying RDoC constructs (e.g., habit formation) that subserve more fundamental processes relevant to personality functioning warrants further investigation.

fMRIPrep: a robust preprocessing pipeline for functional MRI

Preprocessing of functional MRI (fMRI) involves numerous steps to clean and standardize data before statistical analysis. Generally, researchers create ad-hoc preprocessing workflows for each new dataset, building upon a large inventory of tools available. The complexity of these workflows has snowballed with rapid advances in acquisition and processing. We introduce fMRIPrep, an analysis-agnostic tool that addresses the challenge of robust and reproducible preprocessing for fMRI data. FMRIPrep automatically adapts a best-in-breed workflow to the idiosyncrasies of virtually any dataset, ensuring high-quality preprocessing with no manual intervention. By introducing visual assessment checkpoints into an iterative integration framework for software-testing, we show that fMRIPrep robustly produces high-quality results on a diverse fMRI data collection. Additionally, fMRIPrep introduces less uncontrolled spatial smoothness than commonly used preprocessing tools. FMRIPrep equips neuroscientists with a high-quality, robust, easy-to-use and transparent preprocessing workflow, which can help ensure the validity of inference and the interpretability of their results.