dataesr/scientific-paragraphs-categorization huggingface dataset:
In contrast behavioral, structural, and functional connectivity were obtained at all three time points in both groups (see Methods sections for details about the stroke dataset, lesion analysis, diffusion-weighted imaging (DWI), and resting-state Functional magnetic resonance imaging). The whole-brain is described as a network of N = 324 nodes (i. e. , cortical brain regions), linked with symmetric and weighted connections obtained from DWI scans and reconstructed with spherical deconvolution  56, 57. The weights of the structural connectivity matrix, W ij , describe the connection density, i. e, the number of white-matter fiber tracts connecting a given pair of regions of interest (ROIs) normalized by the product of their average surface and average fiber length  58. The ROIs are derived from a functional atlas of the cerebral cortex  59.
At criticality the simulated BOLD activity shows the highest correlation (Fig. 1d , red time-series)  38. In the next sections, first, we present simulations of the wholebrain model with homeostatic plasticity for stroke and control individuals, as well as at the group level. Next, we study the structural connectivity correlates of neural dynamics alterations induced by stroke lesions. Third, we identify components of the structural networks that are most strongly related to criticality and its recovery over time. Fourth, we compare the simulations output with empirical functional networks, and with behavioral data obtained in multiple domains (e. g. , language, motor, memory) from an extensive neuropsychological battery (see Methods section and Supplementary Note 2). Abnormal neural dynamics in stroke.
The healthy participant exhibits features of criticality at both time points with small variability across time points and within the variability of the healthy group. At the same threshold T, both S 1 and S 2 sharply change their size. For the stroke patient under consideration, the flattened shape at t 1 of both, S 1 and S 2 , combined with the monotonic behavior of S 2 indicates lack of criticality, which, however, returns at t 2 as depicted by the emergence of a peak in S 2. The black vertical dashed line depicts the critical point of healthy controls. Source data are provided as a Source Data file. at the critical point for healthy participants, but was significantly attenuated in patients. We statistically assessed how the neural variables evaluated at the critical point shown in Fig.
Methods This research complies with all relevant ethical regulations. All studies with human participants were approved by the Washington University Institutional Review Board. Stroke patients and healthy controls provided informed consent according to procedures approved by the Washington University Institutional Review Board. Stroke dataset. All data came from a large prospective longitudinal stroke study described in previous publications  35, 51, 55. We provide here a brief description of the dataset and refer the reader to those articles for a more comprehensive description. Clinical sample: The dataset includes 132 stroke patients (mean age 54, standard deviation 11, range 19-83; 71 males; 68 left side lesions) at the sub-acute stage (2 weeks post-stroke). The inclusion/exclusion criteria were as follows: first symptomatic stroke, ischemic or hemorrhagic, and clinical evidence of any neurological deficit.
We used data from the subset of 103 patients who returned for clinical and imaging assessments at 3 months post-stroke, as well as the data from the 88 patients who returned for 1 year post-stroke assessment (for details see Corbetta et al. 51  ). The control group, formed by 28 individuals, was matched with the stroke sample for age, gender, and years of education. Data was collected twice in the healthy controls, 3 months apart. The neuropsychological battery included 44 behavioral scores across five behavioral domains: language, memory, motor, attention, and visual function. These domains were chosen to represent a wide range of the most commonly identified deficits in people after a stroke. Resting state functional scans were acquired with a gradient echo EPI sequence (TR = 2000 ms, TE = 27 ms, 32 contiguous 4-mm slices, 4 × 4 mm in-plane resolution) during which participants were instructed to fixate on a small cross in a low luminance environment.
Six to eight resting-state fMRI runs, each including 128 volumes (30 min total), were acquired. fMRI Data Preprocessing of fMRI data included: (i) compensation for asynchronous slice acquisition using sinc interpolation; (ii) elimination of odd/even slice intensity differences resulting from interleaved acquisition; (iii) whole-brain intensity normalization to achieve a mode value of 1000; (iv) removal of distortion using synthetic field map estimation and spatial realignment within and across fMRI runs; and (v) resampling to 3-mm cubic voxels in atlas space including realignment and atlas transformation in one resampling step. Cross-modal (e. g. , T2 weighted to T1 weighted) image registration was accomplished by aligning image gradients. Cross-model image registration in patients was checked by comparing the optimized voxel similarity measure to the 97.
In order to optimize the contrast of acquisition, this sequence was repeated twice. MRI and lesion analysis. Individual T1 MRI images were registered to the Montreal Neurological Institute brain using FSL (FMRIB Software Library) FNIRT (FMRIB nonlinear imaging registration tool). Lesions were manually segmented on individual structural MRI images (T1-weighted MPRAGE, T2-weighted spin echo images, and FLAIR images obtained 1-3 week post-stroke) using the Analyze biomedical imaging software system (http://www. mayoclinic. org). Two boardcertified neurologists (M. C. and Alexandre Carter) reviewed all segmentations. Special attention was given to distinguish lesion from cerebral spinal fluid (CSF), hemorrhage from surrounding edema, and to identify the degree of periventricular white matter damage present.
Streamlines were halted when a voxel without fiber orientation was reached or when the curvature between two steps exceeded a threshold of 60 ∘. Normalization to the MNI152 space was performed after reconstructing the streamline in the native space of the patients  111. We co-registered the structural connectome data to the standard MNI 2 mm space using the following steps: first, whole-brain streamline tractography was converted into streamline density volumes where the intensities corresponded to the number of streamlines crossing each voxel. Second, individual streamline density volumes were registered to the streamline density template in the MNI152 space template derived from  60  masking for the lesion  112, 113  and the same transformation was applied to the individual whole-brain streamline tractography using the trackmath tool distributed with the software package Tract Querier  114.
Acknowledgements We would like to express our gratitude to  Professor Sergio Souto Rocha  (in memoriam) for his insightful advice and discussions during all stages of this research. R. P. R. was supported by the  Research, Innovation and Dissemination Center for Neuromathematics (FAPESP  grant no. 2018/08609-8 ) and by the  National Council for Scientific and Technological Development  ( CNPq Grant  No. 201241/2015-3 ). M. D. F. and M. Z. were supported by the  Italian Ministry of Health  (Grants  RF-2013-02359306  and  RF-2019-12369300  to M. Z. ; Ricerca Corrente). M. T. d. S. has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement no. 818521 ). M. C. was supported by the  Italian Ministry of Research Departments of Excellence  ( 2017-2022 ), CARIPARO foundation (Grant # 55403 ),  Italian Ministry of Health  (Grant No.
Data availability Data to replicate all the figures and tables are provided as Source data with this paper and are also deposited in Github (https://github. com/CorbettaLab/Rodrigo2022NatComm) and Zenodo  123  (https://doi. org/10. 5281/zenodo. 6459955). Individual structural connectivity matrices for controls and patients have been deposited in the Github and Zenodo repositories. Raw neuroimaging and neuropsychological data from  35, 51  are publicly available at cnda. wustl. edu and require controlled access as they contain sensitive patients' data. The person requesting the data must sign a confidentiality agreement provided by Washington University stipulating that they will make no attempt to identify the patients and to use data only for research purposes. Correspondence and requests for materials should be addressed to R.
Data acquisition and preparation 1 H-MR spectra were acquired with a single voxel sequence, TR = 1700 ms, TE= 135 ms, number of excitation ((NEX)= 160) with a voxel size of 28 × 28 × 20mm 3. To facilitate voxel placement, highresolution MR images were acquired with a T1-weighted Magnetization-Prepared Rapid Gradient-Echo (MPRAGE) with the following parameters: TR = 18,000 ms, TE= 2. 24 ms, TI= 900 ms, 176 axial slices and a spatial resolution of 1 mm 3. The jMRUI plugin 1 is used to process the MRS data. The data used in this work contain 3 Tesla 111 1 H-MRS spectra divided into three groups namely MCI, MAD, and NC with an average Mini-Mental State Examination (MMSE) score of 26. 73 ± 2. 00, 20. 54 ± 3. 06 and 29. 5 ± 1. 00 respectively. The individual labels of the data were achieved via the diagnosis of a practiced physician in which the clinical and radiological information of each patient were incorporated.
Synthetic data generation Data are generated from 1024-D spectra with the water peak following the data generation scheme described in (Section 3. 2. 4) where a set of spectra is used for training. For instance, given the 33 spectra of the NC class, we iteratively generate synthetics 1 H-MRS data from 28 spectra. Thus, after deploying the model, we can generate as many spectra as we want. The reserved 5 spectra will be used for testing the proposed classification model, i. e. , we test on the signals 1-5 in the first iteration, 6-10 in the second, and so on. By this way, we are performing crossvalidation to prove that our proposed method is generic and does not depend on training data. Fig. 6  shows some examples of real spectra for the three classes with the synthetics ones, which are very close to the original data.
Comparison with SOTA In order to evaluate the performance of the proposed framework against SOTA works. First, we compare our approach to  (Ahmed et al. , 2020)  which is the only work that has tackled the detection of AD vs NC using 1 H-MRS data using an end to end 1D-CNN approach. Indeed, we use their proposed model to perform multi-class classification using our 1 H-MRS dataset. However, we use three instead of two fully-connected neurons since we have three categories. Second, we evaluate the proposed MRS-based CAD approach for early AD against recent imaging-based approaches. Hence, we use the corresponding T1-weighted MRI of our MRS data for approaches evaluation and we perform multi-classification by updating the last model layer. For a fair comparison, we follow the same experimental protocol and training parameters.
Prosthetic devices that replace a lost limb have become increasingly performant in recent years. Recent advances in both software and hardware allow for the decoding of electroencephalogram (EEG) signals to improve the control of active prostheses with brain-computer interfaces (BCI). Most BCI research is focused on the upper body. Although BCI research for the lower extremities has increased in recent years, there are still gaps in our knowledge of the neural patterns associated with lower limb movement. Therefore, the main objective of this study is to show the feasibility of decoding lower limb movements from EEG data recordings. The second aim is to investigate whether well-known neuroplastic adaptations in individuals with an amputation have an influence on decoding performance. To address this, we collected data from multiple individuals with lower limb amputation and a matched able-bodied control group.
Using these data, we trained and evaluated common BCI methods that have already been proven e ective for upper limb BCI. With an average test decoding accuracy of % for both groups, our results show that it is possible to discriminate di erent lower extremity movements using EEG data with good accuracy. There are no significant di erences (p =. ) in the decoding performance of these movements between healthy subjects and subjects with lower extremity amputation. These results show the feasibility of using BCI for lower limb prosthesis control and indicate that decoding performance is not influenced by neuroplasticity-induced di erences between the two groups.
, 2017) , and even then, they only use BCI to lock/unlock the knee of the prosthesis. To the best of our knowledge, no open data are available for this setting. Furthermore, most studies on lower limb BCI focus on detecting the onset of movement or distinguishing movement of the left from the right limb  (Asanza et al. , 2022) , which limits the possible interactions that are supported by the control system. For this purpose, we chose to employ a data-driven approach to investigate the feasibility of lower limb BCI. Datadriven approaches focus on the data rather than the models used  (Brunton and Kutz, 2019). This approach was found successful in the field of machine learning (ML) as a method to identify bias in models  (Ntoutsi et al. , 2020)  and improve decoding performance with suitable data engineering  (Zhang and Duraisamy, 2015; Olson et al.
, 2017). Furthermore, we investigate possible neuroplasticity-induced differences in EEG decoding between individuals with an amputation and able-bodied individuals that are possibly relevant to designing a lower limb prosthetic BCI control system. Neuroplasticity resulting from lower limb amputation is a well-known phenomenon  (Schwenkreis et al. , 2003; Draganski et al. , 2006; Jiang et al. , 2015; Bramati et al. , 2019). However, the effect of these changes on EEG data and subsequent movement decoding from this data are currently unknown. By identifying the methods that provide optimal decoding performance and investigating differences between individuals with an amputation and able-bodied individuals, our objective is to identify possible approaches to including EEG as a control signal for lower limb prostheses.
Data acquisition At the beginning of each session the participant's head circumference was measured to select the correct EEG cap size followed by an anamnesis, biometrical measurements and the EuroQol-5D questionnaire measuring quality of life  (Herdman et al. , 2011). The participant remained seated for the whole session in a chair that was adjusted to ensure optimal comfort. After applying the EEG acquisition hardware, the room was darkened and the participant was instructed to keep their eyes closed to avoid external stimuli as much as possible. Before starting the experiment, a baseline EEG measurement was recorded for 3 min while the participant remained seated and kept their eyes closed. Following preparation, the participant was instructed to perform multiple repetitions of a complete knee extension and ankle dorsiflexion at their own pace with their dominant leg.
For participants with an amputation, this corresponded to their healthy leg. The participant was not informed about the number of expected repetitions that they had to perform to avoid that they would count their movements. They were informed to take approximately 5 s to rest between each movement and that the entire session should take approximately 15 min. The experimenter counted the number of movements performed until 30 repetitions were performed for each movement. When a participant reached the expected number of movement repetitions, they were told that the experiment was over. No additional feedback was provided. An overview of the experimental procedure is shown in Figure  1. Markers were placed in the data during the experiment with a label that indicates the movement performed. After the experiment, the timing of each marker was updated based on auxiliary EMG data from sensors (Ambu®Neuroline 710, Ambu A/S, Denmark) that were placed on the rectus femoris and tibialis anterior.
This should highlight promising avenues of research for future work and enable the design of future general-purpose BCI systems. To compare the EEG activity between the groups of individuals with an amputation and able-bodied individuals, epochs of both movements were averaged for each participant. For this analysis, the preprocessing of EEG was different to focus more on the frequencies in which MRCPs typically occur. The data was filtered to only keep frequencies between 0. 1 and 5 Hz and the resampling was set to 250 Hz to allow for higher temporal resolution in this analysis. Additionally, bad epochs were rejected or repaired by using an automated method  (Jas et al. , 2016). Subsequently, the grand average was calculated over the individuals in each group. The resulting grand averages were then compared by aggregating sensor data and taking the average global field power (GFP;  Lehmann and Skrandies, 1980; Murray et al.
, 2008)  across each channel. Data were processed with the Python 3 programming language (Van Rossum and Drake, 2009, RRID:SCR_008394), using the MNE toolkit to read and filter EEG data  (Gramfort et al. , 2013, RRID:SCR_005972). MNE was also used to epoch the data and extract features from the epoched EEG. Automated rejection and repairing of epoch data was handled by the AutoReject software library  (Jas et al. , 2017). The employed ML models were provided by the Scikit-learn software library  (Pedregosa et al. , 2011 , RRID:SCR_002577), except for the neural network model, which was implemented using PyTorch  (Paszke et al. , 2019, RRID:SCR_018536). Custom code was written to perform the experiments and integrate the different software components of the software libraries mentioned above.
The hardware used to perform training and evaluation of the models was a desktop computer running on the Ubuntu 20. 04 operating system. The machine has an AMD Ryzen 9 3900X 12-Core Processor and has 66 GB of memory. The neural network models were run on the machine's NVIDIA GV100 (TITAN V) graphical processing unit by leveraging the CUDA software toolkit  (Luebke, 2008).
Random Forest (RF;  Breiman, 2001)  consists of an ensemble of decision trees where each tree in the ensemble is constructed from a subset of data drawn with replacement (i. e. , a bootstrap sample) from the training data. Furthermore, either all input features or a random subset of fixed size, which is a hyperparameter of the model, are used to determine the split of each node in each decision tree. The purpose of these two sources of randomness is to decrease the variance of the forest estimator. This reduced variance sometimes comes at the cost of a slight increase in bias. In practice, the variance reduction is often significant, hence yielding an overall better model. In extremely randomized trees (ExtraTrees;  Geurts et al. , 2006) , randomness is further increased in the way the splits are calculated.
(2021) , we can also see that our decoding results are on par and that using more advanced methods could yield further performance improvements. However, it is important to note that accuracy results cannot be directly compared with models that are trained on different datasets and potentially use a different training regime. The results show that there is no single best pipeline to decode the movement of the lower extremities from EEG. This is in line with previous literature showing that interindividual variability makes it difficult to create a general-purpose BCI classifier  (Lotte et al. , 2018). For feature extraction, the best choice seems clear, as CSP is the feature extraction method in 13 of the 16 best pipelines. We have also observed that when using CSP as the feature extraction method, the median accuracy over all participants is higher.
Next to selecting the optimal pipeline components and filtering boundaries, the parameters of these components should also be calibrated to the user. As long as there is no general model that can perform well on all users, it would be more practical to determine the best pipeline and settings for each user on an individual basis. While timeconsuming, this process could be automated. This highlights the importance of developing methods for automated user-specific calibration to reduce the necessary calibration time and effort. Since the data consisted of matched trials for both individuals with an amputation and able-bodied individuals, a comparison could also be made between the two groups. For this study, the analysis was limited to a comparison of the BCI performance between the two groups and the trends in the optimal pipelines.
We found that there are no statistically significant differences in classification accuracy between the two groups. Similarly, no trend could be identified in terms of optimal pipeline composition. These results seem to indicate that the effect of neuroplasticity is limited when it comes to decoding movement intention from EEG in healthy limbs. To further compare the differences in EEG activity for individuals with an amputation and able-bodied individuals, the GFP was visualized in a grand average over both groups. The resulting figures display the typical activity related to movement for both movements and similar waveforms for both groups (Figure  3 ). One of the major limitations of this study pertains to the type of data that was collected. Since the participants only performed actual movements with their healthy limb, motor imagery in the amputated limbs is not represented.
Furthermore, the BCI methods used in this study were limited to basic methods to show the feasibility of discriminating different single-joint lower limb movements from EEG, and did not investigate state-of-theart methods. The evaluation of the methods was also limited to offline decoding accuracy for the best pipeline settings. Since the chosen time windows for the EEG input also include data from 2 s after movement onset, the models that were trained according to our method would not be suited for real-time decoding. This delay of 2 s would inhibit the user's sense of agency, removing the main advantage of using BCI. Furthermore, since the evaluation of the models was performed on a high-end desktop, the timing results would not be representative for the real-world setting. Another important aspect to note is that the code used was not optimized for fast execution as this was outside of the scope of this study.
Therefore, extensive benchmarks of state-of-the-art methods are necessary to assess which specific methods would be the most suitable for real-time decoding. Future research avenues include expanding the dataset with more data samples on the one hand and including imagined movement of the missing limb for individuals with an amputation on the other hand. This would allow for a more comprehensive comparison of the two populations, enabling more advanced analysis which takes into account neural activity related to actual movement and MI of the lower extremities. A better understanding and more data to train ML models would enable the development of more sophisticated BCI decoding pipelines. As we did not find significant differences in BCI decoding between the able-bodied group and the group of individuals with an amputation, it would be interesting to investigate whether it is possible to use a decoding pipeline that was trained with data from able-bodied individuals in a neuroprosthetic control system.