dataesr/scientific-paragraphs-categorization huggingface dataset:
See  Bonaiuto et al. (2018a)  for a complete description of the task paradigm and structure. Each block contained a total of 180 trials. Participants completed three blocks per session, and 1-5 sessions on different days, resulting in 540-2700 trials per participant ( M = 1822. 5, SD = 813. 2). The task was implemented in MATLAB (The MathWorks, Inc. , Natick, MA) using the Cogent 2000 toolbox ( http://www. vislab. ucl. ac. uk/cogent. php ).
MEG acquisition and preprocessing MEG data were acquired using a 275-channel Canadian Thin Films (CTF) MEG system with superconducting quantum interference device (SQUID)-based axial gradiometers (VSM MedTech, Vancouver, Canada) in a magnetically shielded room. A projector was used to display visual stimuli on a screen ( ∼50 cm from the participant), and a button box was used for participant responses. The data collected were digitized continuously at a sampling rate of 1200 Hz. MEG data preprocessing and analyses were performed using SPM12 ( http://www. fil. ion. ucl. ac. uk/spm/ ) using MATLAB R2014a. The data were filtered (5th order Butterworth bandpass filter: 2-100 Hz, Notch filter: 50 Hz) and downsampled to 250 Hz. Eye blink artifacts were removed using multiple source eye correction  ( Berg and Scherg, 1994 ).
We used an error tolerance of 0. 1, 100 maximum iterations, and unbiased cross-correlation. This new burst aligned sensor dataset was then used for the time-resolved laminar analyses. Burst amplitude was compared between pre-and post-movement epochs using a linear mixed model in R (v3. 6. 3; R Core  Team, 2020 )  with the lme4 package (v1. 1-21;  Bates et al. , 2014 ). Minimum beta amplitude in the 50 ms surrounding the burst peak was treated as the de-pendent measure, with epoch as a fixed effect (pre-or post-movement), and subject-specific intercepts as a random effect. Fixed effect significance was tested using a Type II Wald chi-square test.
Sliding time window source reconstruction We used an adaptive Woody filter to align the beta burst source time series across subjects  ( Woody, 1967 ). All subsequent analysis was based on these temporally aligned datasets. The sliding time window source reconstruction was performed using the Multiple Sparse Priors (MSP;  Friston et al. , 2008 )  algorithm as implemented in SPM12. This was applied to a 40 ms time window of the aligned average beta burst time course, with Hann windowing, with a frequency of interest of 1-256 Hz. As with the localizer source reconstruction, we used a spatial coherence prior with a FWHM of 5 mm, and the Nolte single shell model  ( Nolte, 2003 )  with link vector orientations  ( Bonaiuto et al. , 2020 ). The time window was advanced along the time course of the average beta burst in increments of one time step (4 ms), and the Bayesian model evidence (approximated by free energy) for the generative model was computed within each window.
Note that the distal drive synaptic weights provided to supra-and infra-granular pyramidal neurons were increased from HNN's default value to 6e-5 S. The distal and proximal current dipole moments were normalized by the maximum of the averaged amplitudes of each, then scaled to match the amplitudes of recorded data. All source code is provided online at https://hnn. brown. edu and https://github. com/jonescompneurolab/hnn , and a parameter file specific to the simulations here will be distributed upon publication.
Generation of simulated MEG datasets All simulations were based on a dataset acquired from one human participant, which was used to determine the sensor layout, sampling rate (1200 Hz, downsampled to 250 Hz), number of samples (51), and simulated dipole locations and orientations for the simulations. In each simulation, we specified spatially distributed source activity centered at a single vertex on the pial surface and the corresponding vertex on the white matter surface, approximating the deep and superficial ends of a cortical column  ( Bonaiuto et al. , 2020 ). The orientations of the simulated dipoles were defined by the vector connecting the two vertices (link vector) with opposite polarities: the pial dipole pointed toward the white matter dipole and vice versa. The time course of simulated activity at the pial vertex was given by the cumulative dipole moment from 50 beta bursts generated by the biophysical model run with only the distal drive applied, and that of the white matter vertex by the cumulative dipole moment from 50 bursts generated by the model run with only the proximal drive applied.
The amplitudes of these time courses were scaled to yield beta bursts matching those seen in the human data, with the pial and white matter vertex dipoles having mean magnitudes of 8 and 6 nAm, respectively. We then spatially smoothed these simulated dipole time courses with a Gaussian kernel (FWHM = 5 mm), to obtain two patches of spatially distributed activity. We then used a single shell forward model  ( Nolte, 2003 )  to generate a synthetic dataset from the simulated source activity. Unless otherwise specified, Gaussian white noise was added to the simulated data and scaled in order to yield a per-burst amplitude SNR level (averaged over all sensors) of -20 dB. Simulated data from the simplified synthetic model (Fig. S2 ) were generated in the same way as with the biophysical model, but the pial and white matter vertex dipoles had Gaussian activity time courses, centered within the epoch.
SNR and co -registration error simulations Beta bursts were generated using the simplified model and Gaussian white noise was added to the simulated data and scaled in order to yield per-trial amplitude SNR levels (averaged over all sensors) between -50 and -20 dB to generate synthetic datasets across a range of realistic SNRs (typically ranging from -40 to -20 dB;  Goldenholz et al. , 2009 ). This was repeated 50 times per SNR level and the minimum free energy difference during the tails of the burst ( -40 --10 ms and 10 -40 ms) and maximum free energy difference during the burst peak ( -10 -10 ms) was computed. Similarly, we simulated between-session co-registration error by introducing a linear transformation of the fiducial coil locations in random directions (0 mm translation and 0°rotation up to 2 mm translation and 2°rotation) to generate a realistic range of uncertainty concerning the location of the brain relative to the MEG sensors  ( Adjamian et al.
Consistent waveform shapes of beta bursts in human motor cortex We first identified bursts of precentral beta activity in MEG data from human participants performing a visually cued action decisionmaking task involving button press responses made using the right hand  ( Bonaiuto et al. , 2018a ). Bursts were identified at the source level as periods when beta amplitude in the hand area of left motor cortex exceeded an empirically defined threshold (burst duration: M = 94. 08, SD = 29. 19 ms;  Little et al. , 2019 ). We segmented each participant's preprocessed (not beta bandpass filtered) dataset into 200 ms epochs, centered on the peak of each burst, and split each dataset into bursts that occurred prior to ( M = 326. 8 SE = 133. 3 bursts per subject; 2614 total bursts), or after ( M = 722.
A) Pial and white matter surfaces are extracted from quantitative maps of proton density and T1 times from a multi-parameter mapping MRI protocol. B) Source inversion over the entire burst time course was used to localize the average beta burst sensor signal (inlay). The peak pial surface vertex and the corresponding vertex on the white matter surface were used as priors in the following sliding window inversion. C) Sliding time window source inversion was performed using a 40 ms wide window. For each iteration, source inversion was run using a pial generative model with the pial vertex from the localization inversion as a prior, and using a white matter generative model with the corresponding white matter vertex as a prior. The difference in free energy between the two models (F pial -F white matter ) was used to determine the laminar locus of dominant activity as the window advanced along the average time series of the beta burst (example data shown from a single human subject).
tation at that location  ( Bonaiuto et al. , 2020 ). Following the biophysical model prediction, the superficial current pointed in the direction of the white matter surface (i. e. into the cortex), whereas the deep current flow pointed in exactly the opposite direction (out of the cortex). The time course of each current in the generative MEG model was determined by the cumulative dipole moment of the biophysical model with only the distal (superficial dipole) or proximal (deep layer dipole) synaptic drive applied. This generative model was then used to create simulated MEG sensor data which had the same spatial and temporal features observed in the human MEG data, providing support for the model predictions ( Fig. 3 D ). This process was repeated 10 times to yield a dataset of virtual subjects.
Source inversion on these simulated datasets revealed same burst waveform shape in source space current density ( Fig. 3 E ). As expected, the sliding time window source identified the bilaminar pattern of simulated to generate the ( Fig. 3 F ): with significantly more evidence for the white matter (deep) model at the beginning and end of the burst ( -50 --18 ms and 6 -50 ms) and for the pial (superficial) model around the peak of the burst ( -14 -2 ms). These results generalized beyond the particular time series generated by the biophysical model, as we obtained the same results using a highly simplified model with deep and superficial currents given by scaled Gaussian time series (Fig. S2 ), as well as using the biophysical model with pink rather than white noise (Fig. S3 ). Having validated that the new temporally resolved laminar analysis could correctly reconstruct the time course of the relative strength of deep and superficial layer activity in simulated data, we next wanted to determine if the analysis could rule out a range of alternative synthetic generative models which differed in terms of their deep and superficial dipole moment waveforms and polarities.
We first tested a synthetic model in which the distal drive consisted of a temporally broad and weak signal, directed into the cortex, whilst the proximal drive consisted of a stronger and briefer signal, directed out of the cortex ( Fig. 4 A ). This resulted in a simulated sensor dataset with an oppositely oriented field and reversed polarity waveform to that observed in the human subject MEG data ( Fig. 4 B ). Importantly, the temporally resolved laminar analysis was able to correctly identify that this dataset was generated by a pattern of activity that was stronger in superficial layers at the beginning and end of the burst, and deep layers around the peak of the burst ( Fig. 4 C ). We next tested a model in which the deep and superficial layer dipoles pointed away rather than towards each other ( Fig.
4 D ). The simulated sensor data from this model also had oppositely oriented spatial and temporal features compared to those observed in human subject MEG data ( Fig. 4 E ), and the temporally resolved laminar analysis again correctly identified the time course of laminar activity ( Fig. 4 F ). We tested a range of other synthetic models including two models containing single deep or superficial dipoles in isolation, and models where dipole currents were oriented in the same direction. In each of these alternative models, the analysis correctly identified the time course of dominant laminar activity (Fig. S4 ). The only model whose simulated dataset matched that of the human subject data in terms of field direction and waveform shape was the original biophysical model in which strong superficial layer activity was temporally aligned with broad, weaker activity in deep layers.
Temporally resolved estimates of lamina -specific currents require high precision MEG data The spatial precision of MEG data is classically limited by its signalto-noise ratio (SNR) and error in localization of the brain relative to the C) The generative model included two oppositely oriented currents positioned at corresponding locations on the pial and white matter surfaces in motor cortex, which represent the superficial and deep cortical layers, with source activity given by the model run with the distal and proximal synaptic drives, respectively. D) Simulated sensor data generated by the model has the same spatial and temporal features as beta bursts observed in the human subject MEG data used to determine the simulated current locations and orientations (inlay). E) Time course of source current density resulting from the localizer source inversion on the simulated sensor datasets.
, 2017 ; Troebinger et al. , 2014b ). Before applying the new analysis to human MEG data, we first wanted to determine if the SNR and co-registration accuracy achieved by this approach were within the range required for accurate estimates of lamina-specific currents. We therefore used the simplified beta burst model (Fig. S2 ) to generate simulated MEG datasets with a range of SNR and co-registration error levels, and examined the resulting laminar biases during the tails and peak of the simulated bursts (see Methods). At low SNR levels, no significant laminar biases could be detected, but as SNR increased above -30 dB, the deep bias at the tail ends of the burst and superficial bias at the peak of the burst could be resolved ( Fig. 5 A ). Moreover, we saw that the ability to resolve this laminar bias slowly decreased with increasing co-registration error, and deep and superficial biases could no longer be reliably detected when this error increased beyond 2 mm and 2°( Fig.
Acknowledgments JB was supported by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (ERC consolidator grant  864550  to  J Bonaiuto ). SL was funded by a clinical research training grant from the  Wellcome Trust  (  105804/Z/14/Z  ). SAN was funded by an  NIDCD  ( R01DC012947-06A1 ) and an  Army Research Office Grant  ( W911NF-19-1-0402 ). SRJ and SAN were supported by  NIH   R01EB022889  and  NIH   R01MH106174. The  Wellcome centre for Human Neuroimaging  is supported by a strategic award from  Wellcome  ( 091593/Z/10/Z ). The funders had no role in the preparation of the manuscript.
All MEG analysis code is available at https://github. com/jbonaiuto/beta_burst_layers and biophysical model code at https://github. com/jonescompneurolab/hnn. MEG preprocessing code is available at http://github. com/jbonaiuto/meglaminar. Data are archived at the Open MEG Archive (OMEGA; Nisoet al. , 2016) and may be accessed via http://dx. doi. org/10. 23686/0015896 (Nisoet al. , 2018) after registration at https://www. mcgill. ca/bic/resources/omega.
, 2015a; Lauerwald et al. , 2015)  due to high terrestrial NPP and precipitation, and a recent study used an upscaling approach based on observations to estimate present-day CO 2 evasion from the rivers of the Congo Basin at 251 ± 46 Tg C yr -1 and the lateral C (TOC + DIC) export to the coast at 15. 5 (13-18) Tg C yr -1  (Borges at al. , 2015a (Borges at al. , , 2019)). To put this into context, the estimate of aquatic CO 2 evasion represents 39 % of the global value estimated by  Lauerwald et al. (2015, 650  Tg C yr -1 ) or 14 % of the global estimate of  Raymond et al. (2013 Raymond et al. ( , 1800  Tg C yr -1 ). Note that while  Lauerwald et al. (2015)  and  Raymond et al. (2013)  relied largely on the same database of partial pressure of CO 2 (pCO 2 ) measurements (GloRiCh;  Hartmann et al.
, 2019 , for further discussion). Moreover, it was recently demonstrated that this model could recreate observed seasonal and interannual variation in Amazon Basin aquatic and terrestrial C fluxes  (Hastie et al. , 2019). In order to accurately simulate aquatic C fluxes, it is crucial to provide a realistic representation of the hydrological dynamics of the Congo River, including its wetlands. Here, we develop new wetland forcing files for the OR-CHILEAK model from the high-resolution dataset of  Gumbricht et al. (2017)  and apply the model to the Congo Basin. After validating the model against observations of discharge, flooded area, DOC concentrations, and pCO 2 for the present day, we then use the model to understand and quantify the long-term (1861-2099) temporal trends in both the terrestrial and aquatic C fluxes of the Congo Basin.
, 2019 Bowring et al. , , 2020)) , we run the model at a spatial resolution of 1 • and use the default time step of 30 min for all vertical transfers of water, energy, and C between vegetation, soil, and the atmosphere, as well as the daily time step for the lateral routing of water. Until now, in the tropics, ORCHILEAK has been parameterized and calibrated only for the Amazon River Basin  (Lauerwald et al. , 2017; Hastie et al. , 2019). To adapt and apply OR-CHILEAK to the specific characteristics of the Congo River Basin (Sect. 2. 1), we had to establish new forcing files rep- resenting the maximal fraction of floodplains (MFF) and the maximal fraction of swamps (MFS) (Sect. 2. 2) and to recalibrate the river-routing module of ORCHILEAK (Sect. 2. 3). All of the processes represented in ORCHILEAK remain identical to those previously represented for the Amazon OR-CHILEAK  (Lauerwald et al.
Land use is mixed with tropical broadleaved evergreen (PFT2, Fig. 1c ), https://doi. org/10. 5194/esd-12-37-2021 Earth Syst. Dynam. , 12, 37-62, 2021 tropical broadleaved rain green (PFT3, Fig. 1d ), C 3 grass (PFT10, Fig. 2e ), and C 4 grass (PFT11, Fig. 2f ) covering a maximum of 26 %, 35 %, 8 %, and 25 % of the basin area, respectively. Most published estimates for land cover follow national boundaries, so we can make broad comparisons with published estimates for the Democratic Republic of Congo (DRC). For example, our value for total forest cover for the DRC (65 %) is close to the 67 % and 68 % values estimated by the Congo Basin Forest Partnership  (CBFP, 2009)  and  Potapov et al. (2012) , respectively. Agriculture covers only a small proportion of the basin according to the LUH dataset that is based on FAO cropland area statistics, with C 3 (PFT12, Fig.
2g ) and C 4 (PFT13, Fig. 2h ) agriculture making up a maximum basin area of 0. 5 % and 2 %, respectively. In reality, a larger fraction of the basin is composed of small-scale and rotational agriculture  (Tyukavina et al. , 2018). The ORCHILEAK model also has a "poor soils" forcing file (Fig. 2j ) which prescribes reduced decomposition rates in soils with low-nutrient and low-pH soils such as Podzols and Arenosols  (Lauerwald et al. , 2017). This file is developed from the Harmonized World Soil Database (FAO/IIASA/ISRIC/ISS-CAS/JRC, 2009).
, 2012). See also  Lauerwald et al. (2017)  and  Hastie et al. (2019)  for further details. We created an MFF forcing file for the Congo Basin derived from the Global Wetlands v3 database, which includes the 232 m resolution tropical wetland map of  Gumbricht et al. (2017)  (Fig. 3a  and b ). We firstly amalgamated all the categories of wetland (which include floodplains and swamps) before aggregating them to a resolution of 0. 5 • (the resolution at which the floodplain and swamp forcing files are read by ORCHILEAK), assuming that this represents the maximum extent of inundation in the basin. This results in a mean MFF of 10 %; i. e. a maximum of 10 % of the surface area of the Congo Basin can be inundated with water. This is identical to the mean MFF value of 10 % produced with the Global Lakes and Wetlands Database, GLWD  (Lehner and Döll, 2004; Borges et al.
, 2015b). We also created an MFS forcing file from the same dataset (Fig. 3c  and d ), merg-ing the "swamps" and "fens" wetland categories (although note that there are virtually no fens in the Congo Basin) from the Global Wetlands v3 database  (Gumbricht et al. , 2017)  and again aggregating them to a 0. 5 • resolution. Please see Table  1  of  Gumbricht et al. (2017)  for further details.
, 2015b; Bouillon et al. , 2012 Bouillon et al. , , 2014 ; locations shown in Fig. 1 ), with the river DOC and CO 2 concentration being regarded as an integrator of the C transport at the terrestrial-aquatic interface. We first ran the model for the present-day period, defined as 1990 to 2005-2010 depending on which climate forcing data were applied, using four climate forcing datasets, namely ISIMIP2b  (Frieler et al. , 2017) , Princeton GPCC  (Sheffield et al. , 2006) , GSWP3  (Kim, 2017) , and CRUN-CEP  (Viovy, 2018). We used ISIMIP2b for the historical and future simulations as it is the only climate forcing dataset to cover the full period (1861-2099). However, we compared it to other climate forcing datasets for the present day in order to gauge its ability to simulate observed discharge on the Congo River at Brazzaville (Table  A1 ).
Without calibration, the majority of the different climate forcing model runs performed poorly and were unable to accurately represent the seasonality and mean monthly discharge at Brazzaville (Table  A1 ). The best-performing climate forcing dataset was ISIMIP2b, followed by Princeton GPCC, with root mean square errors (RMSEs) of 29 % and 40 % and a Nash-Sutcliffe efficiency (NSE) of 0. 20 and -0. 25, respectively. NSE is a statistical coefficient specifically used to test the predictive skill of hydrological models  (Nash and Sutcliffe, 1970). For ISIMIP2b we further calibrated key hydrological model parameters, namely the constants (tau, τ ) which help to control the water residence time of the groundwater (slow reservoir), headwaters (fast reservoir), and floodplain reservoirs in order to improve the simulation of observed discharge at Brazzaville and Oubangui (Table  2 ).
, 2006; Fig. 1). This procedure was repeated iteratively with the ISIMIP2b climate forcing with modifying the τ value of each reservoir in order to find the best-performing parametrization. We firstly compared simulated versus observed discharge at Brazzaville (NSE, RMSE; Table  2 ), before using the data of  Bouillon et al. (2014)  to further validate discharge at Bangui (Fig. 1 ) on the main tributary Oubangui. In addition, we compared the simulated seasonality of flooded area against the satellite-derived dataset GIEMS  (Prigent et al. , 2007; Becker et al. , 2018)  within the Cuvette Centrale wetlands (Fig. 1 ).
Soil carbon spin-up ORCHILEAK includes a soil module, primarily derived from ORCHIDEE-SOM  (Camino-Serrano et al. , 2018). The soil module has three different pools of soil DOC: the passive, slow, and active pool, and these are defined by their source material and residence times (τ carbon ). ORCHILEAK https://doi. org/10. 5194/esd-12-37-2021 Earth Syst. Dynam. , 12, 37-62, 2021 also differentiates between flooded and non-flooded soils as well as the decomposition rates of DOC, soil organic carbon (SOC), and litter being reduced (3 times lower) in flooded soils. In order for the soil C pools to reach steady state, we spun up the model for around 9000 years with fixed land use representative of 1861 and looping over the first 30 years of the ISMSIP2b climate forcing data (1861-1890). During the first 2000 years of spin-up, we ran the model with an atmospheric CO 2 concentration of 350 µ atm and default soil C residence times (τ carbon ) halved, which allowed it to approach steady state more rapidly.
(2017)  revised the area of this type of agriculture downwards, with generally low values in the Congo except in the northeast and southeast, but suggested a shorter turnover of agriculture of only 2 years. In view of such uncertainties, we did not include shifting agriculture in the model. Moreover, there is considerable uncertainty associated with the effect of future land use change in Africa  (Hurtt et al. , 2011). We chose RCP6. 0 as it represents a no mitigation (middle to high emissions) scenario. Moreover, the ISIMIP2b data only provided two Representative Concentration Pathways (RCPs) at the time we performed the simulations: RCP2. 6 (low emission) and RCP6. 0. With the purpose of separately evaluating the effects of land use change, climate change, and rising atmospheric CO 2 , we ran a series of factorial simulations.