Tractostorm: The what, why, and how of tractography dissection reproducibility.

Investigative studies of white matter (WM) brain structures using diffusion MRI (dMRI) tractography frequently require manual WM bundle segmentation, often called "virtual dissection." Human errors and personal decisions make these manual segmentations hard to reproduce, which have not yet been quantified by the dMRI community. It is our opinion that if the field of dMRI tractography wants to be taken seriously as a widespread clinical tool, it is imperative to harmonize WM bundle segmentations and develop protocols aimed to be used in clinical settings. The EADC-ADNI Harmonized Hippocampal Protocol achieved such standardization through a series of steps that must be reproduced for every WM bundle. This article is an observation of the problematic. A specific bundle segmentation protocol was used in order to provide a real-life example, but the contribution of this article is to discuss the need for reproducibility and standardized protocol, as for any measurement tool. This study required the participation of 11 experts and 13 nonexperts in neuroanatomy and "virtual dissection" across various laboratories and hospitals. Intra-rater agreement (Dice score) was approximately 0.77, while inter-rater was approximately 0.65. The protocol provided to participants was not necessarily optimal, but its design mimics, in essence, what will be required in future protocols. Reporting tractometry results such as average fractional anisotropy, volume or streamline count of a particular bundle without a sufficient reproducibility score could make the analysis and interpretations more difficult. Coordinated efforts by the diffusion MRI tractography community are needed to quantify and account for reproducibility of WM bundle extraction protocols in this era of open and collaborative science.

Investigating the relationship between resting-state EEG microstates and executive functions: A null finding.

Recent advances in cognitive neurosciences suggest that intrinsic brain networks dynamics are associated with cognitive functioning. Despite this emerging perspective, limited research exists to validate this hypothesis. This Registered Report aimed to specifically test the relationship between intrinsic brain spatio-temporal dynamics and executive functions. Resting-state EEG microstates were used to assess brain spatio-temporal dynamics, while a comprehensive battery of nine cognitive function tasks was employed to evaluate executive functions in 140 participants. We hypothesized that microstates (class C and D) metrics would correlate with an executive functions composite score. Contrary to expectations, our hypotheses were not supported by the data. We however observed a small, non-significant trend with a negative correlation between microstate D occurrences and executive functions scores (r = -.18, 95% CI [-.33, -.01]) which however did not meet the adjusted threshold for significance. In light of the inconclusive or minor effect sizes observed, the assertion that intrinsic brain networks dynamics - as measured by resting-state EEG microstate metrics - are a reliable signature of executive functioning remains unsupported.

Performance after training in a complex cognitive task is enhanced by high-definition transcranial random noise stimulation.

Interest for neuromodulation, and transcranial random noise stimulation (tRNS) in particular, is growing. It concerns patients rehabilitation, but also healthy people who want or need to improve their cognitive and learning abilities. However, there is no consensus yet regarding the efficacy of tRNS on learning and performing a complex task. In particular, the most effective electrode montage is yet to be determined. Here, we examined the effect of two different tRNS montages on learning rate, short- and long-term performance in a video game (Space Fortress) that engages multiple cognitive abilities. Sixty-one participants were randomly assigned to one of three groups (sham vs. simple-definition tRNS vs. high-definition tRNS) in a double-blind protocol. Their performance on the Space Fortress task was monitored during a 15-day experiment with baseline (day 1), stimulation (day 2 to 4), short- (day 5) and long-term (day 15) evaluations. Our results show that the high-definition tRNS group improved more on the long term than simple-definition tRNS group, tended to learn faster and had better performance retention compared to both simple-definition tRNS and sham groups. This study is the first to report that high-definition tRNS is more effective than conventional simple-definition tRNS to enhance performance in a complex task.

Functional Connectivity Within the Fronto-Parietal Network Predicts Complex Task Performance: A fNIRS Study.

Performance in complex tasks is essential for many high risk operators. The achievement of such tasks is supported by high-level cognitive functions arguably involving functional activity and connectivity in a large ensemble of brain areas that form the fronto-parietal network. Here we aimed at determining whether the functional connectivity at rest within this network could predict performance in a complex task: the Space Fortress video game. Functional Near Infrared Spectroscopy (fNIRS) data from 32 participants were recorded during a Resting-State period, the completion of a simple version of Space Fortress (monotask) and the original version (multitask). The intrinsic functional connectivity within the fronto-parietal network (i.e., during the Resting-State) was a significant predictor of performance at Space Fortress multitask but not at its monotask version. The same pattern was observed for the functional connectivity during the task. Our overall results suggest that Resting-State functional connectivity within the fronto-parietal network could be used as an intrinsic brain marker for performance prediction of a complex task achievement, but not for simple task performance.

A population-based atlas of the human pyramidal tract in 410 healthy participants.

With the advances in diffusion MRI and tractography, numerous atlases of the human pyramidal tract (PyT) have been proposed, but the inherent limitation of tractography to resolve crossing bundles within the centrum semiovale has so far prevented the complete description of the most lateral PyT projections. Here, we combined a precise manual positioning of individual subcortical regions of interest along the descending pathway of the PyT with a new bundle-specific tractography algorithm. This later is based on anatomical priors to improve streamlines tracking in crossing areas. We then extracted both left and right PyT in a large cohort of 410 healthy participants and built a population-based atlas of the whole-fanning PyT with a complete description of its most corticolateral projections. Clinical applications are envisaged, the whole-fanning PyT atlas being likely a better marker of corticospinal integrity metrics than those currently used within the frame of prediction of poststroke motor recovery. The present population-based PyT, freely available, provides an interesting tool for clinical applications to locate specific PyT damage and its impact to the short- and long-term motor recovery after stroke.