Individual Brain Charting dataset extension, third release for movie watching and retinotopy data.

The Individual Brain Charting (IBC) is a multi-task functional Magnetic Resonance Imaging dataset acquired at high spatial-resolution and dedicated to the cognitive mapping of the human brain. It consists in the deep phenotyping of twelve individuals, covering a broad range of psychological domains suitable for functional-atlasing applications. Here, we present the inclusion of task data from both naturalistic stimuli and trial-based designs, to uncover structures of brain activation. We rely on the Fast Shared Response Model (FastSRM) to provide a data-driven solution for modelling naturalistic stimuli, typically containing many features. We show that data from left-out runs can be reconstructed using FastSRM, enabling the extraction of networks from the visual, auditory and language systems. We also present the topographic organization of the visual system through retinotopy. In total, six new tasks were added to IBC, wherein four trial-based retinotopic tasks contributed with a mapping of the visual field to the cortex. IBC is open access: source plus derivatives imaging data and meta-data are available in public repositories.

An empirical evaluation of functional alignment using inter-subject decoding.

Inter-individual variability in the functional organization of the brain presents a major obstacle to identifying generalizable neural coding principles. Functional alignment-a class of methods that matches subjects' neural signals based on their functional similarity-is a promising strategy for addressing this variability. To date, however, a range of functional alignment methods have been proposed and their relative performance is still unclear. In this work, we benchmark five functional alignment methods for inter-subject decoding on four publicly available datasets. Specifically, we consider three existing methods: piecewise Procrustes, searchlight Procrustes, and piecewise Optimal Transport. We also introduce and benchmark two new extensions of functional alignment methods: piecewise Shared Response Modelling (SRM), and intra-subject alignment. We find that functional alignment generally improves inter-subject decoding accuracy though the best performing method depends on the research context. Specifically, SRM and Optimal Transport perform well at both the region-of-interest level of analysis as well as at the whole-brain scale when aggregated through a piecewise scheme. We also benchmark the computational efficiency of each of the surveyed methods, providing insight into their usability and scalability. Taking inter-subject decoding accuracy as a quantification of inter-subject similarity, our results support the use of functional alignment to improve inter-subject comparisons in the face of variable structure-function organization. We provide open implementations of all methods used.

Brain topography beyond parcellations: Local gradients of functional maps.

Functional neuroimaging provides the unique opportunity to characterize brain regions based on their response to tasks or ongoing activity. As such, it holds the premise to capture brain spatial organization. Yet, the conceptual framework to describe this organization has remained elusive: on the one hand, parcellations build implicitly on a piecewise constant organization, i.e. flat regions separated by sharp boundaries; on the other hand, the recently popularized concept of functional gradient hints instead at a smooth structure. Noting that both views converge to a topographic scheme that pieces together local variations of functional features, we perform a quantitative assessment of local gradient-based models. Using as a driving case the prediction of functional Magnetic Resonance Imaging (fMRI) data -concretely, the prediction of task-fMRI from rest-fMRI maps across subjects- we develop a parcel-wise linear regression model based on a dictionary of reference topographies. Our method uses multiple random parcellations -as opposed to a single fixed parcellation- and aggregates estimates across these parcellations to predict functional features in left-out subjects. Our experiments demonstrate the existence of an optimal cardinality of the parcellation to capture local gradients of functional maps.

BrainIAK: The Brain Imaging Analysis Kit.

Functional magnetic resonance imaging (fMRI) offers a rich source of data for studying the neural basis of cognition. Here, we describe the Brain Imaging Analysis Kit (BrainIAK), an open-source, free Python package that provides computationally optimized solutions to key problems in advanced fMRI analysis. A variety of techniques are presently included in BrainIAK: intersubject correlation (ISC) and intersubject functional connectivity (ISFC), functional alignment via the shared response model (SRM), full correlation matrix analysis (FCMA), a Bayesian version of representational similarity analysis (BRSA), event segmentation using hidden Markov models, topographic factor analysis (TFA), inverted encoding models (IEMs), an fMRI data simulator that uses noise characteristics from real data (fmrisim), and some emerging methods. These techniques have been optimized to leverage the efficiencies of high-performance compute (HPC) clusters, and the same code can be se amlessly transferred from a laptop to a cluster. For each of the aforementioned techniques, we describe the data analysis problem that the technique is meant to solve and how it solves that problem; we also include an example Jupyter notebook for each technique and an annotated bibliography of papers that have used and/or described that technique. In addition to the sections describing various analysis techniques in BrainIAK, we have included sections describing the future applications of BrainIAK to real-time fMRI, tutorials that we have developed and shared online to facilitate learning the techniques in BrainIAK, computational innovations in BrainIAK, and how to contribute to BrainIAK. We hope that this manuscript helps readers to understand how BrainIAK might be useful in their research.

Artificial Intelligence Algorithm Qualification: A Quality by Design Approach to Apply Artificial Intelligence in Pharma.

Quality is defined by the American Society for Quality (ASQ) as "the totality of features and characteristics of a product or service that bears on its ability to satisfy given needs." Therefore, quality is applicable to processes that supply outcomes with values that can be measured. Statistical control is an effective methodology that provides the outcome of quality of goods, bringing an added value that other methods, like quality by inspection, do not offer. The statistical methods applied to process control have been thoroughly developed, and the mathematics that supports them have been broadly demonstrated. Artificial Intelligence (AI) is a field in which mathematics, statistics, and programming play a joint role, and its results can also be applied to disciplines like quality control. Nevertheless, its utilization is subordinate to the qualification of the implemented algorithms. This research presents a standard procedure to AI algorithms, allowing their usage in regulated environments to ensure the quality of the delivered products or services (e.g., in drugs and medicines manufacturing). The regulated principles are defined by the concept of quality by design (QbD), which is a notion introduced in the pharmaceutical industry as a good practice for process management under multivariate analysis. This study intended to provide guidance for qualifying AI algorithms using QbD guidelines as the foundation for this purpose.

Erratum for Toni Manzano, Cristina Fernandez, Toni Ruiz, and Hugo Richard, "AI Algorithm Qualification".

Erratum for Toni Manzano, Cristina Fernandez, Toni Ruiz, and Hugo Richard, "AI Algorithm Qualification," Accepted Article, August 2020.

[Neurological complications of COVID-19]. / Atteintes neurologiques dans l'infection au SARS-CoV-2 (COVID-19).

Neurological manifestations are likely to be more frequent and complex during COVID-19 than originally anticipated.

Cyanotoxin release from the benthic, mat-forming cyanobacterium Microseira (Lyngbya) wollei in the St. Lawrence River, Canada.

Benthic cyanobacterial mats occurring in the St. Lawrence River fluvial lakes Saint-Louis and Saint-Pierre are dominated by Microseira (Lyngbya) wollei which produce several cyanotoxins including LWTX-1 that is characteristic of Microseira wollei. This cyanotoxin is not only present in the filaments forming benthic mats, but was also measured in the water overlying the mats. LWTX-1 was found in all cyanobacterial filament samples (75.29-103.26 ng mg-1) and all overlying water samples (3.01-11.03 ng L-1). Toxin concentrations measured in overlying water and dry biomass were strongly correlated (r = 0.94). Furthermore, LWTX-1 concentration in water was positively correlated with the dissolved organic carbon in water (r = 0.74) and % nitrogen content in cyanobacterial filaments (r = 0.52). A preliminary study was conducted to determine the release and degradation rates of LWTX-1 from a M. wollei mat kept under laboratory conditions over a 3-month period. Toxin measurements revealed an early, massive toxin release followed by a typical decaying function, with a half-life in the order of 17 days. Our results raise concerns about the occurrence and downstream advection of dissolved cyanotoxins from Microseira mats in the aquatic environment. Graphical abstract.

Plasmonic nanopipette biosensor.

Integrating a SERS immunoassay on a plasmonic "patch clamp" nanopipette enabled nanobiosensing for the detection of IgG. A SERS response was obtained using a sandwich assay benefiting from plasmon coupling between a capture Au nanoparticle (AuNP) on a nanotip and a second AuNP modified with a Raman active reporter and an antibody selective for IgG. The impact of nanoparticle shape and surface coverage was investigated alongside the choice of Raman active reporter, deposition pH, and plasmonic coupling, in an attempt to fully understand the plasmonic properties of nanopipettes and to optimize the nanobiosensor for the detection of IgG. These probes will find applications in various fields due to their nanoscale size leading to the possibility of spatially and temporally addressing their location near cells to monitor secretion of biomolecules.

Tuning the 3D plasmon field of nanohole arrays.

Modern photonics is being revolutionized through the use of nanostructured plasmonic materials, which confine light to sub-diffraction limit resolution providing universal, sensitive, and simple transducers for molecular sensors. Understanding the mechanisms by which light interacts with plasmonic crystals is essential for developing application-focussed devices. The strong influence of grating coupling on electromagnetic field distribution, frequency and degeneracy of plasmon bands has now been characterized using hexagonal nanohole arrays. An equation for nanohole arrays was derived to demonstrate the strong influence of incidence and rotation angle on optical properties of 2D plasmonic crystals such as nanohole arrays. Consequently, we report experimental data that are in strong agreement with finite difference time-domain (FDTD) simulations that clearly demonstrate the influence of the grating coupling conditions on the optical properties (such as plasmon degeneracy and bandwidth), and on the distribution of the plasmon field around nanohole arrays (including tuneable penetration depths and highly localized fields). The tuneable 3D plasmon field allowed for controlled sensing properties and by increasing the angle of incidence to 30 degrees, the resonance wavelength was tuned from 1000 to 600 nm, and the sensitivity was enhanced by nearly 300% for a protein assay using surface plasmon resonance (SPR) and by 40% with surface-enhanced Raman scattering (SERS) sensors.

Angle-dependent resonance of localized and propagating surface plasmons in microhole arrays for enhanced biosensing.

The presence of microhole arrays in thin Au films is suited for the excitation of localized and propagating surface plasmon (SP) modes. Conditions can be established to excite a resonance between the localized and propagating SP modes, which further enhanced the local electromagnetic (EM) field. The co-excitation of localized and propagating SP modes depends on the angle of incidence (θ(exc)) and refractive index of the solution interrogated. As a consequence of the enhanced EM field, enhanced sensitivity and an improved response for binding events by about a factor of 3 to 5 was observed with SPR sensors in the Kretschmann configuration for a set of experimental conditions (λ(SPR), θ(exc), and η). Thus, microhole arrays can improve sensing applications of SPR based on classical prism-based instrumentation and are suited for SP-coupled spectroscopic techniques.