A reusable benchmark of brain-age prediction from M/EEG resting-state signals

Population-level modeling can define quantitative measures of individual aging by applying machine learning to large volumes of brain images. These measures of brain age, obtained from the general population, helped characterize disease severity in neurological populations, improving estimates of diagnosis or prognosis. Magnetoencephalography (MEG) and Electroencephalography (EEG) have the potential to further generalize this approach towards prevention and public health by enabling assessments of brain health at large scales in socioeconomically diverse environments. However, more research is needed to define methods that can handle the complexity and diversity of M/EEG signals across diverse real-world contexts. To catalyse this effort, here we propose reusable benchmarks of competing machine learning approaches for brain age modeling. We benchmarked popular classical machine learning pipelines and deep learning architectures previously used for pathology decoding or brain age estimation in 4 international M/EEG cohorts from diverse countries and cultural contexts, including recordings from more than 2500 participants. Our benchmarks were built on top of the M/EEG adaptations of the BIDS standard, providing tools that can be applied with minimal modification on any M/EEG dataset provided in the BIDS format. Our results suggest that, regardless of whether classical machine learning or deep learning was used, the highest performance was reached by pipelines and architectures involving spatially aware representations of the M/EEG signals, leading to R^2 scores between 0.60-0.71. Hand-crafted features paired with random forest regression provided robust benchmarks even in situations in which other approaches failed. Taken together, this set of benchmarks, accompanied by open-source software and high-level Python scripts, can serve as a starting point and quantitative reference for future efforts at developing M/EEG-based measures of brain aging. The generality of the approach renders this benchmark reusable for other related objectives such as modeling specific cognitive variables or clinical endpoints

Thermodynamic and kinetic factors in the hydrothermal synthesis of hybrid frameworks: zinc 4-cyclohexene-1,2-dicarboxylates

Experimental and computational studies indicate that the formation of a series of zinc 4-cyclohexene-1,2-dicarboxylates takes place under thermodynamic rather than kinetic control

Three-dimensional lanthanide-organic frameworks based on di-, tetra-, and hexameric clusters

Three-dimensional lanthanide-organic frameworks formulated as (CH3)2NH2[Ln(pydc)2] · 1/2H2O [Ln3+ ) Eu3+ (1a) or Er3+ (1b); pydc2- corresponds to the diprotonated residue of 2,5-pyridinedicarboxylic acid (H2pydc)], [Er4(OH)4(pydc)4(H2O)3] ·H2O (2), and [PrIII 2PrIV 1.25O(OH)3(pydc)3] (3) have been isolated from typical solvothermal (1a and 1b in N,N-dimethylformamide - DMF) and hydrothermal (2 and 3) syntheses. Materials were characterized in the solid state using single-crystal X-ray diffraction, thermogravimetric analysis, vibrational spectroscopy (FT-IR and FT-Raman), electron microscopy, and CHN elemental analysis. While synthesis in DMF promotes the formation of centrosymmetric dimeric units, which act as building blocks in the construction of anionic ∞ 3{[Ln(pydc)2]-} frameworks having the channels filled by the charge-balancing (CH3)2NH2 + cations generated in situ by the solvolysis of DMF, the use of water as the solvent medium promotes clustering of the lanthanide centers: structures of 2 and 3 contain instead tetrameric [Er4(μ3-OH)4]8+ and hexameric |Pr6(μ3-O)2(μ3-OH)6| clusters which act as the building blocks of the networks, and are bridged by the H2-xpydcx- residues. It is demonstrated that this modular approach is reflected in the topological nature of the materials inducing 4-, 8-, and 14-connected uninodal networks (the nodes being the centers of gravity of the clusters) with topologies identical to those of diamond (family 1), and framework types bct (for 2) and bcu-x (for 3), respectively. The thermogravimetric studies of compound 3 further reveal a significant weight increase between ambient temperature and 450 °C with this being correlated with the uptake of oxygen from the surrounding environment by the praseodymium oxide inorganic core