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"en": "Neuronal oscillations, their inter-areal synchronization and scale-free dynamics constitute fundamental mechanisms for cognition by regulating neuronal communication. These oscillatory dynamics have large inter-individual variability that is partly heritable. However, the genetic underpinnings of oscillatory dynamics have remained poorly understood. We investigated whether local and global oscillation dynamics were influenced by polymorphisms in Catechol-O-methyltransferase (COMT) Val158Met and brain-derived neurotrophic factor (BDNF) Val66Met genes that regulate brain catecholaminergic and serotonergic levels. Resting-state magnetoencephalography (MEG) was recorded from 82 participants and local oscillation amplitudes, their scale-free long-range temporal correlations (LRTCs), and global synchronization were estimated from source-reconstructed MEG data. Both COMT and BDNF polymorphisms influenced local oscillation dynamics, while only BDNF polymorphism influenced global synchronization. Computational modelling of near-critical synchronization dynamics suggested that COMT and BDNF polymorphisms influenced local oscillations via variances in brain net excitation levels. We demonstrate that genetic polymorphisms COMT and BDNF contribute to inter-individual variability in local and global oscillation dynamics.,Resting-state brain activity was recorded from healthy volunteers (N=82, 18–55 years of age; mean age: 29 years; 6 left-handed; 44 female) with 306-channel MEG (Vectorview, Elekta-Neuromag, LtD) at a sampling rate of 600 Hz. The subjects fixated on a central fixation cross throughout the ~8 min resting-state MEG-recording (duration 7.8 ± 2.9 min, mean ± standard deviation). MEG data was preprocessed using temporal signal-space separation with Maxfilter software and artifact removal was carried out using independent component analysis. MNE inverse operators were then computed for all wavelet frequencies and used to project the sensor-space data into source-space. Source-vertex time series were collapsed into cortical parcel time series with individual collapse operators that maximize source-reconstruction accuracy. A cortical parcellation in individual anatomy but with labels shared among the subject was obtained by iteratively splitting the 148-parcel Destrieux atlas into 400 parcels. The parcels were also assigned functional labels based on Yeo’s 7-functional-brain-systems atlas. 26 Morlet wavelets (log-spaced center frequencies 3-60 Hz) were used for obtaining the amplitude and phase time series of cortical parcels. LRTCs in amplitude time series were quantified with detrended fluctuation analysis (DFA) where the power-law-scaling exponent β is the slope of the DFA function and obtained with linear regression. Phase synchronization between parcels was computed using the weighted phase-lag index (wPLI). Amplitude, DFA, and phase synchonization values were then collapsed back to the 148-parcel Destrieux atlas, and phase synchonization also to the 7 systems (per hemisphere) of the Yeo atlas.,This data includes mean amplitudes, connectivity matrices, and detrended fluctuation analysis scaling exponents derived from 82 subjects' magnetoencephalographic resting-state recordings. The results of polymorphism analysis can be found, along with parcellation information, in the \"settings\" subfolder. This data can be used for replicating the main results from \"Genetic polymorphisms in COMT and BDNF influence synchronization dynamics of human neuronal oscillations\" with the python code in the associated code repository https://github.com/palvalab/RS-Gen."
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"en": "The collection and preprocessing of this dataset has been reported in detail in Juola J., Hovi A., Rautiainen M., 2022. A spectral analysis of stem bark for boreal and temperate tree species. Ecology and Evolution. DOI:10.1002/ece3.8718\r\n\r\nThe dataset includes stem bark reflectance spectra of ten boreal and temperate tree species. We measured hemispherical-directional reflectance factors (HDRF) of stem bark samples. The data were measured using a Specim IQ imaging spectrometer attached to a tree mounting setup. The imaging spectrometer operates in the visible and near-infrared wavelength region (400–1000 nm). The measurements were made in the Greater Helsinki area in Finland, and in Järvselja in Estonia. We measured 20 trees per species, accounting to a total of 200 unique stem bark samples. One sample refers to a hyperspectral reflectance image taken of a single tree stem. The 200 mean spectral signatures (presented in this dataset) were calculated for each sample by averaging the HDRF values over all pixels per wavelength. The measurements were conducted from May to August 2020. All measurements were made under natural illumination conditions and the images were taken in perpendicular view-angle to the tree stem. The dataset includes two tabular files that contain: 1) the mean reflectance spectrum for each of the ten common boreal and temperate tree species (calculated from the 20 samples per species), and 2) all 200 reflectance spectra (20 per tree species). The sampled species were: Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) Karst), silver birch (Betula pendula Roth), grey alder (Alnus incana (L.) Moench), black alder (Alnus glutinosa (L.) Gaertn.), English oak (Quercus robur L.), European aspen (Populus tremula L.), European ash (Fraxinus excelsior L.), Norway maple (Acer platanoides L.), and littleleaf linden (Tilia cordata Mill.).\r\n\r\nThe two spreadsheets are organized as follows: first three rows that start with hashtag (#) contain comments for citing, first column \"tree_species\" (Pinus_sylvestris, Picea_abies, Betula_pendula...) indicates the tree species, and the subsequent columns \"wl397.32\"-\"wl1003.58\" indicate the measured HDRF value at that wavelength. The encoding of the csv file is UTF-8. \r\n\r\nThe data in file \"mean_tree_species_stem_bark_reflectance_spectra_Juola_et_al_2021.csv\" is the same as what can be found in Version 1. We only changed the first row's naming to make it clearer.\r\n\r\nIf you use this dataset, please cite Juola et al. [1,2]:\r\n[1] Juola J., Hovi A., Rautiainen M., 2022. A spectral analysis of stem bark for boreal and temperate tree species. Ecology and Evolution. DOI:10.1002/ece3.8718\r\n[2] Juola J., Hovi A., Rautiainen M., 2022. A dataset of stem bark reflectance spectra for boreal and temperate tree species. Mendeley Data, V2, doi: 10.17632/pwfxgzz5fj.2"
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