dataesr/scientific-paragraphs-categorization huggingface dataset:
Opportunistic stacking approach. The proposed method allows to learn from any case for which at least one modality is available. The stacking model first generates, separately for each modality, linear predictions of age for held-out data. 10-fold cross-validation with 10 repeats is used. This step based on ridge regression helps reduce the dimensionality of the data by finding the major directions of variance within each modality. The predicted age is then used as derived set of features in the following steps. First, missing value are handled by a coding-scheme that duplicates the second-level data and substitutes missing values with arbitrary small and large numbers. A random forest model is then trained with the missing-value coded age-predictions from each ridge model as features to predict the actual age, potentially improving the prediction performance by combining additive information and correcting the bias of the linear model on a lower-dimensional representation.
trophysiology and MRI including any case for which there was the opportunity to see at least one modality. We therefore call it opportunistic stacking model. For validation, we chose the currently largest public multimodal imaging and electrophysiology resource: The Cam-CAN dataset contains rich neuropsychological data, magnetic resonance imaging as well as non-invasive high-resolution electrophysiology in the form of magnetoencephalography (MEG) for more than 650 healthy subjects between 17 and 90 years  (Shafto et al. , 2014; Taylor et al. , 2017). The choice of MEG has the advantage of improved spatial and frequency resolution. This should help identify robust and translatable electrophysiology markers potentially suitable for clinical EEG. Therefore, our study focuses on the following questions: 1) Can MRI-based prediction of age be enhanced with electrophysiology? 2) Do fMRI and MEG carry non-redundant clinically relevant information? 3) What are the most informative electrophysiological markers of aging? 4) Can potential advantages of multimodal learning be maintained in the presence of missing values?.
Discussion We have demonstrated improved learning of surrogate biomarkers by combining electrophysiology, functional and anatomical MRI. Here, we have focused on the example of age-prediction by multimodal modeling on 674 subjects from the Cam-CAN dataset, the currently largest publicly available collection of MEG, fMRI and MRI data. Our results suggest that MEG and fMRI both substantially improved age-prediction when combined with anatomical MRI. We have then explored potential implications of the ensuing brain-age Δ as a surrogate-biomarker for cognitive and physical health. Our results suggest that MEG and fMRI convey non-redundant information on cognitive functioning and health, e. g. , fluid intelligence, memory, sleep quality, cognitive decline and depression. Moreover, combining all modalities has led to lower prediction errors.
Indeed, the strength of linear association was below 0. 5 in units of standard deviations of the normalized predictors and the target. On the other hand, it is important to consider that the Cam-CAN sample consists of healthy individuals only. It appears, thus, as rather striking that systematic and neuropsychologically plausible effects can be detected. The finding, therefore, argues in favor of the brain age Δ being a sensitive marker of normative aging. The effects are expected to be far more pronounced when applying the method in clinical settings, i. e. , in patients suffering from mild cognitive impairment, depression, neurodevelopmental or neurodegenerative disorders. This suggests that brain age Δ might be used as a screening tool for a wide array of clinical settings for which the Cam-CAN dataset could serve as a normative sample.
It will therefore be critical to benchmark and improve learning from power topographies in clinical settings  (Sabbagh et al. , 2019a). Finally, it is worthwhile to highlight that here we have focused on age, in the more specific context of the brain age Δ as surrogate biomarker in order to be able to benefit from a relatively large benchmark dataset. However, the proposed approach is fully compatible with any target of interest and may be easily applied directly to clinical end points, e. g. , drug dosage, survival or diagnosis. Moreover, the approach presented here can be easily adapted to work with classification problems, for instance, by exchanging ridge regression with logistic regression and using a random forest classifier in the stacking layer. We have provided all materials from our study in form of publicly available versioncontrolled code with the hope to help other teams of biomedical researchers to adapt our method to their prediction problem.
Sample Here, we included MEG (task & rest), fMRI (rest), MRI and neuropsychological data (cognitive tests, home-interview, questionnaires) from the CAM-Can dataset  (Shafto et al. , 2014). Our sample comprised 674 (340 female) healthy individuals between 18 (female = 18) to 88 (female = 87) years with an average of 54. 2 (female = 53. 7) and a standard deviation of 18. 7 (female = 18. 8) years. We included data according to availability and did not apply an explicit criterion for exclusion. When automated processing resulted in errors, we did not manually repair the computation. This induced additional missing data for some cases. A summary of available cases by input modality is reported in Table  1  in the appendix. For technical details regarding the MEG, fMRI, and MRI data acquisition, please consider the Cam-CAN reference publications  (Shafto et al.
MRI features cortical thickness Aging-related brain atrophy has been related to thinning of the cortical tissue, e. g. ,  (Thambisetty et al. , 2010). Here, we extracted cortical thickness estimates on from the Freesurfer  (Fischl, 2012)  segmentation on a grid of 5,124 vertices in fsaverage4 space obtained from the mris_preproc script (index 11, Table  3 ). No further reduction was computed. cortical surface area Aging is also reflected in shrinkage of the cortical surface itself, e. g. ,  (Lemaitre et al. , 2012). Hence, we also extracted cortical surface area estimates on from the Freesurfer segmentation on a grid of 5,124 vertices in fsaverage4 space obtained from the mris_preproc script (index 12, Table  3 ). No further reduction was computed. subcortical volumes The volume of subcortical structures has been linked to aging  (Murphy et al.
They are based on two principles: regression trees and bagging (bootstrapping and aggregating). Regression trees are non-parametric methods and recursively subdivide the input data by finding combinations of thresholds that relate value ranges of the input variables to the target. The principle is illustrated at the right bottom of Fig. 1. For a fully grown tree, each sample falls into one leaf of the tree which is defined by its unique path through combinations of input-variable thresholds through the tree. However, regression trees tend to easily overfit. This is counteracted by randomly generating alternative trees from bootstrap replica of the dataset and randomly selecting subset of variables for each tree. Importantly, thresholds are by default optimized with regard to a so-called impurity criterion such as entropy or mutual information.
Stacked cross-validation We used a 10-fold cross-validation scheme. To mitigate bias due to the actual order of the data, we repeated the procedure 10 times while reshuffling the data at each repeat. We then generated age-predictions from each Inputlayer model on the left-out folds, such that we had for each case one age-prediction per repeat. We then stored the indices for each fold to make sure the random forest was trained on left-out predictions for the ridge models. This ensured that the input-layer train-test splits where carried forward to the stacking-layer and that the stacking model was always evaluated on left-out folds in which the input ages are actual predictions and the targets have not been seen by the model. Here, we customized the stacking procedure to be able to unbox and analyze the input-age predictions and implement opportunistic handling of missing values.
To implement opportunistic stacking, we considered the full dataset with missing values and then kept track of missing data while training the input-layer. This yielded the stacking-data consisting of the age-predictions and missing values. Stacking was then performed after applying the feature-coding of missing values. This procedure made sure that all training and test splits were defined with regard to the full cases and, hence, the stacking model could be applied to all cases after feature-coding of missing values.
Analysis of brain-behavior correlation To explore the cognitive implications of the brain age Δ, we computed correlations with the neurobehavioral score from the Cam-CAN dataset. Table  4  lists the scores we considered. The measures fall into three broad classes: neuropsychology, physiology and questionnaires ('Type' columns in Table  4 ). Extraction of neuropsychological scores sometimes required additional computation, which followed the description in  Shafto et al. 2014, (see also 'Variables' column in Table 4). For some neuropsychological tasks, the Cam-CAN dataset provided multiple scores and sometimes the final score of interest as described in  Shafto et al. 2014 , had yet to be computed. At times, this amounted to computing ratios, averages or differences between different scores.
In other scores, it was not obvious how to aggregate multiple interrelated sub-scores, hence, we computed summaries by extracting the first principal component. In total, we included 38 variables. All neuropsychology and physiology scores (up to #17) were the scores available in the 'cc770-scored' folder from release 001 of the Cam-CAN dataset. We selected the additional questionnaire scores (#18-23) on theoretical grounds to provide an assessment To obtain comparable coefficients across scores, we standardized both the age and the scores.
Data Acquisition MEG recorded at a single site using a 306 VectorView system (Elekta Neuromag, Helsinki). This system is equipped with 102 magnetometers and 204 orthogonal planar gradiometers is placed light magnetically shielded room. During acquisition, an online filter was applied between around 0. 03 Hz and 1000Hz. To support offline artifact correction, vertical and horizontal electrooculogram (VEOG, HEOG) as well as electrocardiogram (ECG) signal was concomitantly recorded. Four Head-Position Indicator (HPI) coils were used to track head motion. All types of recordings, i. e. , resting-state, passive stimulation and the active task lasted about 8 minutes. For additional details on MEG acquisition, please consider the reference publications of the CAM-Can  (Taylor et al. , 2017; Shafto et al.
Rejection of residual artifacts To avoid contamination with artifacts that were not removed by SSS or SSP, we used the 'global' option from autoreject  (Jas et al. , 2017). This yielded a data-driven selection of the amplitude range above which data segments were excluded from the analysis. Temporal Filtering To study band-limited brain dynamics, we applied bandpass-filtering using the frequency band definitions in Table  2. We used default filter settings from the MNE software (development version 0. 19) with a windowed time-domain design (firwin) and Hamming taper. Filter length and transition band-width was set using the 'auto' option and depended on the data. Epoching For the active and passive tasks, we considered time windows between -200 to 700 milliseconds around stimulus-onset and decimated the signal by retaining every eighth time sample.
Channel selection It is important to highlight that after SSS, the magnetometer and gradiometer data are reprojected from a common lower dimensional SSS coordinate system that typically spans between 64 and 80 dimensions. As a result, both sensor types contain highly similar information, which also modifies the inter-channel correlation structure  (Garcés et al. , 2017). The MNE software, by default, treats them as a single sensor type in many of the analyses that follow and uses as degrees of freedom the number of underlying SSS dimensions. To simplify computation, we constrained the analysis to magnetometers. For some aspects of feature engineering in sensor space, i. e. , extraction of peak latency, we used the gradiometers as they tend to yield a cleaner view on the signal.
We used spatial whitening to approximate the model assumption of Gaussian noise. The whitening operator was based on the empty room noise covariance and applied to the MEG signal and the forward model. We applied no noise normalization and used the default depth weighting  (Lin et al. , 2006)  as implemented in the MNE software  (Gramfort et al. , 2014)  with weighting factor of 0. 8  (Lin et al. , 2006)  and a loose-constraint of 0. 2. The regularization parameter  2 was expressed with regard to the signal-to-noise ratio and kept at the default value of 1 SNR 2 with SNR = 3.
structural MRI For preprocessing of structural MRI data we used the FreeSurfer software (http:// surfer. nmr. mgh. harvard. edu/)) (Fischl, 2012). Reconstruction included the following steps (adapted from the methods citation recommended by the authors of FreeSurfer): motion correction and average of multiple volumetric T1-weighted images  (Reuter et al. , 2010) , removal of non-brain tissue  (Segonne et al. , 2004) , automated Talairach transformation, segmentation of the subcortical white matter and deep gray matter volumetric structures  (Fischl et al. , 2002 (Fischl et al. , , 2004) )  intensity normalization  (Sled et al. , 1998) , tessellation of the gray-matter / white-matter boundary, automated topology correction  (Fischl et al. , 2001; Segonne et al. , 2004) , and surface deformation following intensity gradients  (Dale et al.
fMRI The available fMRI data were visually inspected. The volumes were excluded from the study provided they had severe imaging artifacts or head movements with amplitude larger than 2 mm. After the rejection of corrupted data we obtained a subset of 626 subjects for further investigation. The fMRI volumes underwent slice timing correction and motion correction to the mean volume. Following that, co-registration between anatomical and function volumes was done for every subject. Finally, brain tissue segmentation was done for every volume and the output data were morphed to the MNI space.
Scientific Computation and Software Computing environment For preprocessing and feature-extraction of MEG, MRI and fMRI we used a high-performance Linux server (72 cores, 376GB RAM) running Ubuntu Linux 18. 04. 1 LTS. For subsequent statistical modeling, we used a golden Apple Mac-Book 12'́(early 2016) running MacOS Mojave (8GB RAM). General purpose computation was carried out using the Python (3. 7. 3) language and the scientific Python stack including NumPy, SciPy, Pandas, and Matplotlib. For embarrassingly parallel processing we used the joblib library.
MRI & fMRI processing For anatomical reconstruction we used the shell-script based FreeSurfer software  Fischl et al. (2002). We used the pypreprocess package, which reimplements parts of the SPM12 software for the analysis of brain images (The Wellcome Centre for Human Neuroimaging, 2018), complemented by the Python-Matlab interface from Nipype  (Gorgolewski et al. , 2011). For feature extraction and processing related to predictive modeling with MRI and fMRI, we used the NiLearn package  (Abraham et al. , 2014).
Acknowledgments This work was partly supported by a 2018 'médecine numérique' (for digital medicine) thesis grant issued by  Inserm  ( French national institute of health and medical research) and Inria (French national research institute for the digital sciences ). It was also partly supported by the  European Research Council Starting  Grant  SLAB ERC-YStG-676943. We thank  Sheraz Khan  for help with the Freesurfer segmentation and data management of the Cam-CAN dataset. We thank  Mehdi Rahim  for advice with the model stacking framework and data management of the Cam-CAN dataset. We thank  Donald Krieger  and  Timothy Bardouille  for help with the MEG co-registration. We thank  Danilo Bzdok  for feedback on the first version of the preprint.
As effect sizes can be expected to be small in the curated and healthy population of the Cam-CAN dataset, we considered classical hypothesis testing for characterizing associations. Tra- ditional significance testing (panel A) suggests that the best stacking models supported discoveries for between 20% (7) and 25% (9) of the scores. Dominating associations concerned fluid intelligence, depression, sleep quality (PSQI), systolic and diastolic blood pressure (cardiac features 1,2 ), cognitive impairment (MMSE) and different types of memory performance (VSTM, PicturePriming, FamousFaces, EmotionalMemory). The model coefficients in panel B depict the strength and direction of association. One can see that stacking models not only tended to suggest more discoveries as their perfor- mance improves but also led to stronger effect sizes.
Acknowledgements We thank  H. Bellen ,  P. Heitzler ,  A. Preiss , the  Bloomington Drosophila Stock Center , the  Kyoto Drosophila Genetic Research Center , the  Developmental Studies Hybridoma Bank (DSHB) , and Flybase for flies, antibodies and other resources. We thank  V. Roca  for embryo injection and  M. Reyngoud  for technical help. We thank  L. Bally-Cuif ,  F. Bernard , and  D. Henrique  for critical reading and all lab members for discussion. This work was funded by  ANR-10-LABX-0073  and  ANR-16-CE13-0003  grants.
Behavioral Data Analysis For behavioral data analysis, a button press before target onset was considered as a false alarm (FA). A trial with no button-press after target onset and before the next cue onset was considered as a miss trial. A trial with no FA and with a button-press after target onset was counted as correct if the pressed button matched the response mapped to the target sound, and as incorrect if otherwise. Reaction-times (RTs) to targets were analysed in the correct trials only. The influence of (1) cue condition (2 levels: informative and uninformative) and (2) distractor condition (3 levels: NoDis, DIS1 and DIS2) on median reaction times (RTs) of correct responses and on percentage of incorrect responses was tested using a linear mixed-effects models using the lme4 package  (Bates et al.
Brain Signal Recordings Simultaneous EEG and MEG data were recorded, although the EEG data will not be presented here. The MEG data were acquired with a 275-sensor axial gradiometer system (CTF Systems Inc. , Port Coquitlam, Canada) with continuous sampling at a rate of 600Hz, a 0-150Hz filter bandwidth, and first-order spatial gradient noise cancellation. Moreover, eye-related movements were measured using vertical and horizontal EOG electrodes. Head position relative to the gradiometer array was acquired continuously using coils positioned at three fiducial points; nasion, left and right pre-auricular points. Head position was checked at the beginning of each block to control head movements. In addition to the MEG/EEG recordings, T1-weighted three-dimensional anatomical images were acquired for each participant using a 3T Siemens Magnetom whole-body scanner (Erlangen, Germany).
ACKNOWLODGEMENTS We would like to thank Sebastien DALIGAULT and Claude DELPUECH for technical assistance with the acquisition of electrophysiological data. This work was supported by the French National Research Agency ANR-14-CE30-0001-01 (ABC), by the Rhone-Alpes Region (HE) and by the LABEX CORTEX (ANR-11-LABX-0042). This work was performed within the framework of the LABEX CELYA (ANR-10-LABX-0060) of Université de Lyon, within the program ''Investissements d'Avenir'' (ANR-11-IDEX-0007) operated by the French ANR.
Another reference was used: a floral odor (phenylethyl alcohol or PEA) for which we have measures of general olfactory acuity with published standards  [50]. Detection abilities of men and women as well as their perceptual ratings (pleasantness, intensity etc. ) and verbal descriptions were examined. A second study was conducted in a large Malagasy sample (N = 280), using HMHA. Perceptual and threshold data were collected too using simplified methods due to the experimental constraints, which were to test a large sample of participants and to embed this study in another larger project on odor perception. Because of its sexual dimorphism, the male compound HMHA is likely to have a particular relevance to women: we therefore hypothesized that women may have more acute detection abilities of HMHA, and that -if we assume that this carboxylic acid is common to the human species -these differences may be independent of the cultural group.
Odorants Although the threshold and the perceptual rating task included a variety of odorants, here we chose to present only the data regarding HMHA and PEA also used in Study 1 (the other odorants were used for the purpose of another study, not presented here). In the threshold task, HMHA was the last of 6 different odors presented; threshold to PEA was not measured. In the perceptual rating task, HMHA and PEA were presented in random order among other different odors: 7 odors were presented to each participant, constituting one of three different sub-lists made from a total of seventeen different odors; HMHA was included in all sub-lists while PEA was included in only one and was therefore evaluated by a smaller sample of participants. For the threshold task, a series of 5 dilutions was prepared, using a ratio of 1:10 (v/v) between each dilution step, starting at 100 ppm for the highest concentration.
Whether this is limited to body odor compounds (versus other odor types) is a question that remains to be tested further before being generalizable; however, we provided a first piece of evidence by showing that intensity ratings of the same fertile 21 versus non-fertile participants differed for HMHA but not for a floral odor, PEA. Although in our study such a difference does not occur with HMHA thresholds, it could be revealed using a threshold test with higher sensitivity (such as the one used in the French sample). To conclude, this study provides new findings related to underexplored chemical compounds from human body odor. In particular, it offers new data about how carboxylic acids that are major compounds of human body odor, HMHA and 3MHA, are perceived. A strength of this study is that for HMHA, which proved to be more typical of body odor than 3MHA, data were obtained through different methodological approaches in two very different participants' samples.
K. was supported by the  Brain Research Program  and the  Bio & Medical Technology Development Program of the National Research Foundation  funded by the  Korean government  ( 2017M3C7A1043838  and  2019M3E5D2A01063794 ), and the  Korea Institute of Science and Technology Institutional Program  ( 2E29180  and  2V07340 ). We apologize to colleagues whose original work could not be cited in this review due to space constraints.
Statistical Analyses and Sample Size Data on all patients included in the study were analysed using SAS software version 9. 1 (SAS Institute, Cary, NC, USA). Statistical analyses were descriptive, and the 95% confidence intervals (CI) were given when necessary. Patient characteristics are reported as a number and percentage for categorical variables and as mean ± standard deviation (SD) and range for continuous variables. Quantitative variables were compared using twotailed Mann-Whitney U tests for group comparisons. Categorical variables were compared using either the chi-square test or Fisher's exact test. ONLS scores before Octagam initiation and at the time of evaluation were compared using signed tests. All tests, if performed, were conducted with a bilateral approach at the 5% threshold.