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This archive contains sample output files for the sample data accompanying the Princeton Handbook for Reproducible Neuroimaging. Outputs include the NIfTI images converted using HeuDiConv (v0.8.0) and organized according to the BIDS standard, quality control evaluation using MRIQC (v0.15.1), data preprocessed using fMRIPrep (v20.2.0), and other auxiliary files. All outputs were created according to the procedures outlined in the handbook, and are intended to serve as a didactic reference for use with the handbook. The sample data from which the outputs are derived were acquired (with informed consent) using the ReproIn naming convention on a Siemens Skyra 3T MRI scanner. The sample data include a T1-weighted anatomical image, four functional runs with the “prettymouth” spoken story stimulus, and one functional run with a block design emotional faces task, as well as auxiliary scans (e.g., scout, soundcheck). The “prettymouth” story stimulus created by Yeshurun et al., 2017 and is available as part of the Narratives collection, and the emotional faces task is similar to Chai et al., 2015. The brain data are contributed by author S.A.N. and are authorized for non-anonymized distribution.

This repository contains information about submitted solutions and resulting analysis metrics of the 2019 Quantitative Susceptibility Mapping Reconstruction Challenge. The original susceptibility maps submitted for participation in the challenge are available here and here. The package contains seven Comma-Separated Values (CSV) files and two PDF files: master_stage1_anonymized.csv: Results of stage 1 of the challenge at the time of presentation at the workshop (fully-blinded); master_stage2_snr1_anonymized.csv: Results of stage 2 of the challenge using the high noise dataset at the time of presentation at the workshop (fully-blinded); master_stage2_snr2_anonymized.csv: Results of stage 2 of the challenge using the low noise dataset at the time of presentation at the workshop (fully-blinded); submission_form_stage1.pdf: PDF export of the online form used in stage 1; submission_form_stage2.pdf: PDF export of the online form used in stage 2. For the manuscript, we analyzed these CSV files with scripts reported here. Each csv file contains metrics for all submitted solutions along with detailed information about the algorithm used, provided by the participant at the time of submission. The very first record in each file is a header containing a list of field names: normalized rmse: Whole-brain root-mean-squared error relative to ground truth; rmse_detrend_tissue: Root-mean-squared error relative to ground truth (after detrending) in grey and white matter mask; rmse_detrend_blood: Root-mean-squared error relative to ground truth (after detrending) using a one-pixel dilated vein mask; rmse_detrend_DGM: Root-mean-squared error relative to ground truth (after detrending) in a deep gray matter mask (substantia nigra & subthalamic nucleus, red nucleus, dentate nucleus, putamen, globus pallidus and caudate); DeviationFromLinearSlope: Absolute difference between the slope of the average value of the six deep gray matter regions vs. the prescribed mean value and 1.0; CalcStreak: Estimation of the impact of the streaking artifact in a region of interest surrounding the calcification through the standard deviation of the difference map between reconstruction and the ground truth; DeviationFromCalcMoment: Absolute deviation from the volumetric susceptibility moment of the reconstructed calcification, compared to the ground truth (computed at in the high-resolution model); Submission Identifier: Self-chosen unique identifier of the submission; Submission Identifier of the corresponding Stage 1 submission: This is the Submission Identifier of the solution submitted to Stage 2 that was calculated with a similar algorithm in Stage 1; Changes with respect to Stage 1 submission: Self-reported information about modifications made to the algorithm for Stage 2; Number of submissions in Stage 2: The number of solutions that were submitted to Stage 2 with a similar algorithm; Sim1/Sim2: Filename of the submitted solutions for Stage 1; File name of the zip-file you are going to upload: Filename of the file uploaded to Stage 2; Full name of the algorithm: Self-reported full name of the algorithm used; Preferred Acronym: Self-reported acronym of the algorithm used; Algorithm-type: Self-reported type of algorithm used; Does your algorithm incorporate information derived from magnitude images?: Self-reported Yes/No; Regularization terms: Self-reported types of regularization terms involved; Did your algorithm use the provided frequency map or the four individual echo phase images?: Self-reported information about involved magnitude information; Publication-ready description of the reconstruction technique: Self-reported description of the algorithm; Publications that describe the algorithm: Self-reported literature reference; Algorithm publicly available?: Self-reported public availability of the algorithm; If your algorithm is not yet publicly available, would you be willing to make it available at the end of the challenge?: Self-reported willingness to share the algorithm code with the public; Specific information about this solution: Self-reported detailed information about the solution; Herewith, I permit the QSM Challenge committee to publish my uploaded files (calculated maps) after the completion of the challenge: Self reported agreement with publication of submitted solution; Ground truth was not explicitly or implicitly incorporated into your algorithm or solution: Self-reported confirmation that the ground truth was not incorporated in the solution.

In most animals, the brain makes behavioral decisions that are transmitted by descending neurons to the nerve cord circuitry that produces behaviors. In insects, only a few descending neurons have been associated with specific behaviors. To explore how descending neurons control an insect's movements, we developed a novel method to systematically assay the behavioral effects of activating individual neurons on freely behaving terrestrial D. melanogaster. We calculated a two-dimensional representation of the entire behavior space explored by these flies and we associated descending neurons with specific behaviors by identifying regions of this space that were visited with increased frequency during optogenetic activation. Applying this approach across a large collection of descending neurons, we found that (1) activation of most of the descending neurons drove stereotyped behaviors, (2) in many cases multiple descending neurons activated similar behaviors, and (3) optogenetically-activated behaviors were often dependent on the behavioral state prior to activation. Movies of optogenetically activated split-Gal4 linesEach movie contains 1 second before and after optogenetic stimulation for all experimental (retinal +) and control (retinal -) flies for all stimulation trials.opto_movies.zip

Individual variability in delay of gratification (DG) is associated with a number of important outcomes in both non-human and human primates. Using diffusion tensor imaging (DTI), this study describes the relationship between probabilistic estimates of white matter tracts projecting from the caudate to the prefrontal cortex (PFC) and DG abilities in a sample of 49 captive chimpanzees (Pan troglodytes). After accounting for time between collection of DTI scans and DG measurement, age and sex, higher white matter connectivity between the caudate and right dorsal PFC was found to be significantly associated with the acquisition (i.e. training phase) but not the maintenance of DG abilities. No other associations were found to be significant. The integrity of white matter connectivity between regions of the striatum and the PFC appear to be associated with inhibitory control in chimpanzees, with perturbations on this circuit potentially leading to a variety of maladaptive outcomes. Additionally, results have potential translational implications for understanding the pathophysiology of a number of psychiatric and clinical outcomes in humans. Latzman_et_al_DTI_DG

OBJECTIVE. To assess whether HS severity is mirrored at the level of large-scale networks. METHODS. We studied preoperative high-resolution anatomical and diffusion-weighted MRI of 44 TLE patients with histopathological diagnosis of HS (n=25; TLE-HS) and isolated gliosis (n=19; TLE-G), and 25 healthy controls. Hippocampal measurements included surface-based subfield mapping of atrophy and T2 hyperintensity indexing cell loss and gliosis, respectively. Whole-brain connectomes were generated via diffusion tractography and examined using graph theory along with a novel network control theory paradigm which simulates functional dynamics from structural network data. RESULTS. Compared to controls, we observed markedly increased path length and decreased clustering in TLE-HS compared to controls, indicating lower global and local network efficiency, while TLE-G showed only subtle alterations. Similarly, network controllability was lower in TLE-HS only, suggesting limited range of functional dynamics. Hippocampal imaging markers were positively associated with macroscale network alterations, particularly in ipsilateral CA1-3. Systematic assessment across several networks revealed maximal changes in the hippocampal circuity. Findings were consistent when correcting for cortical thickness, suggesting independence from grey matter atrophy. CONCLUSIONS. Severe HS is associated with marked remodeling of connectome topology and structurally-governed functional dynamics in TLE, as opposed to isolated gliosis which has negligible effects. Cell loss, particularly in CA1-3, may exert a cascading effect on brain-wide connectomes, underlining coupled disease processes across multiple scales. Data_phen_conn_dryadPhenotypic information and mean connectome feature data for Bernhardt et al. (2019) Temporal lobe epilepsy: hippocampal pathology modulates white matter connectome topology and controllability. Neurology

This dataset contains synthetic data from simulations (for a total duration of 10 minutes) including the activity of one multi-unit and two single-units for different firing rates and signal-to-noise ratio levels. It is intended to be used as a standardized dataset to evaluate spike sorting algorithms. Recordings were taken using a sampling rate of 24 kHz, and are comprised of spikes from a database with 594 different average spike shapes, taken from real recordings from monkey neocortex and basal ganglia. This dataset is comprised of two files: data.npy and labels.csv. data.npy contains 14,400,000 sampled voltage values, from a single channel, taken at a sampling rate of 24 kHz. labels.csv contains the timestep, spike class, amplitude (SNR), and firing rate associated with each spiking event. The original samples used to construct this dataset where previously constructed and made available in [1]. This dataset is an amalgamation of simulation files, which were previously publicly accessible at: http://www2.le.ac.uk/departments/engineering/research/bioengineering/neuroengineering-lab/software. Consequently, when using or making modifications to this dataset, in addition to acknowledging this record, [1] must also be acknowledged, as per the original author's request. [1] J. Martinez, C. Pedreira, M. J. Ison, and R. Quian Quiroga, ���Realistic simulation of extracellular recordings,��� Journal of Neuroscience Methods, vol. 184, no. 2, pp. 285���293, Nov. 2009, doi: 10.1016/j.jneumeth.2009.08.017. {"references": ["J. Martinez, C. Pedreira, M. J. Ison, and R. Quian Quiroga, \"Realistic simulation of extracellular recordings,\" Journal of Neuroscience Methods, vol. 184, no. 2, pp. 285\u2013293, Nov. 2009, doi: 10.1016/j.jneumeth.2009.08.017."]}

Direct brain control of advanced robotic systems promises substantial improvements in health care, for example, to restore intuitive control of hand movements required for activities of daily living in quadriplegics, like holding a cup and drinking, eating with cutlery, or manipulating different objects. However, such integrated, brain- or neural-controlled robotic systems have yet to enter broader clinical use or daily life environments. We demonstrate full restoration of independent daily living activities, such as eating and drinking, in an everyday life scenario across six paraplegic individuals (five males, 30 ± 14 years) who used a noninvasive, hybrid brain/neural hand exoskeleton (B/NHE) to open and close their paralyzed hand. The results broadly suggest that brain/neural-assistive technology can restore autonomy and independence in quadriplegic individuals’ everyday life. Output dataset of EEG/EOG B/NHE controlOutput dataset of hybrid EEG/EOG brain/neural hand exoskeleton control.Soekadar2016_ZIP2.zipSource Codes and Software for EEG/EOG B/NHE control used in Soekadar et al. 2016This data container includes the custom-made modules for EEG/EOG-based B/NHE control that were embedded into the BCI2000 environment used in Soekadar et al. 2016. Please see the included tutorial for instructions on how to install and run the software.Soekadar2016_ZIP1.zip

Attending to a task-relevant location changes how neural activity oscillates in the alpha band (8–13Hz) in posterior visual cortical areas. However, a clear understanding of the relationships between top-down attention, changes in alpha oscillations in visual cortex, and attention performance are still poorly understood. Here, we tested the degree to which the posterior alpha power tracked the locus of attention, the distribution of attention, and how well the topography of alpha could predict the locus of attention. We recorded magnetoencephalographic (MEG) data while subjects performed an attention demanding visual discrimination task that dissociated the direction of attention from the direction of a saccade to indicate choice. On some trials, an endogenous cue predicted the target’s location, while on others it contained no spatial information. When the target’s location was cued, alpha power decreased in sensors over occipital cortex contralateral to the attended visual field. When the cue did not predict the target’s location, alpha power again decreased in sensors over occipital cortex, but bilaterally, and increased in sensors over frontal cortex. Thus, the distribution and the topography of alpha reliably indicated the locus of covert attention. Together, these results suggest that alpha synchronization reflects changes in the excitability of populations of neurons whose receptive fields match the locus of attention. This is consistent with the hypothesis that alpha oscillations reflect the neural mechanisms by which top-down control of attention biases information processing and modulate the activity of neurons in visual cortex. IkkaiDataUpload