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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."]}

Recordings were carried out in 4-8 week old C57/Bl6 mice. SERT-Cre::Rosa-TdTomato and SOM-Cre::Rosa-TdTomatotransgenic lines were used to fluorescently label DRN 5-HT and SOM GABA neurons, respectively. All experiments were carried out in accordance with procedures approved by the University of Ottawa Animal Care and Veterinary Services. Experiments were carried out at room temperature using a potassium-gluconate-based internal solution, except for synaptic physiology experiments for which a cesium-based solution was used. In all cases, the external solution consisted of standard artificial cerebrospinal fluid. Detailed information is available in our related publication. The data is laid out as shown below, with recordings from DRN 5-HT, DRN GABA, and L5 mPFC pyramidal neurons stored in the corresponding directories. . ├── 5HT │ ├── current_steps │ ├── GABA_synapses │ ├── gating │ ├── heated_gating │ ├── heated_pharmacology │ ├── long_curr_steps │ ├── membrane_parameters │ ├── OU_noise │ ├── OU_noise_heated │ ├── pharmacology │ └── spk_time ├── GABA │ ├── current_steps │ ├── DRN393_firing_vsteps │ ├── DRN398_firing_vsteps │ ├── long_curr_steps │ ├── matched_I_V_steps │ ├── membrane_parameters │ ├── OU_noise │ ├── spk_time │ └── unmatched_V_steps └── mPFC ├── current_steps ├── gating └── OU_noise The names of subdirectories mainly reflect the different types of experiments carried out in each cell type: current_steps: Short (~1s) steps of current applied in current clamp. long_curr_steps: 20-30s current steps. spk_time: A 1s hyperpolarizing current step of variable amplitude followed by a short depolarizing step to evoke spiking. This protocol has been used in the past to investigate an effect of inactivating potassium currents (eg IA) on spike timing. OU_noise: Frozen Ornstein-Uhlenbeck noise with various timescale and amplitude characteristics applied in current clamp. This protocol provides a rich dataset for training spiking neuron models (see Related works). Each experiment is divided into training and test portions which we recommend using for training and testing models, respectively. gating: Voltage clamp protocol designed to characterize the voltage-dependence of ionic currents that activate near spike threshold. Consists of a hyperpolarizing pulse followed by depolarizing steps of varying amplitude. These experiments were carried out in the presence of tetrodotoxin (TTX) to block voltage-gated sodium channels. In the GABA dataset, these experiments are split across the unmatched_V_steps, matched_V_steps, and DRN39X_firing_vsteps directories because in some cases we were able to carry out spk_time and gating experiments in the same cells. In the case of DRN39X_firing_vsteps, we carried out the spk_timing protocol, applied TTX, then carried out the gating protocol. In the case of matched_I_V_steps, both protocols were carried out in the presence of TTX. pharmacology: Voltage clamp protocol designed to activate voltage-dependent ionic currents. 18411010.abf was recorded under baseline conditions, 18411013.abf was recorded in the presence of TEA, and 18411015.abf was recorded in the presence of TEA + 4AP. All three recordings were carried out in the same cell. membrane_parameters: Passive membrane parameters (resistance and capacitance) of DRN neurons. Most of the data files included in this package are electrophysiological recordings stored in Axon binary format (ABF) which can be opened in Python using neo or ez-ephys (which itself uses neo internally). Recordings are named according to the following convention: <experimenter_prefix><YYMDD><id>.abf where <experimenter_prefix> is an optional prefix with the initials of the person who collected the data, <YYMDD> is the date the experiment was carried out (M is either a number representing a month between January and September, or one of the letters o, n, or d for the remaining months), and <id> is a three digit number. For example, JF19121013.abf was collected by Jean-François Boucher on January 21, 2019. Metadata is included in files named index.csv. These tables include a unique ID for each neuron recorded (this can be used to determine which recordings were carried out in the same neuron), the passive membrane resistance in MOhm (R), membrane capacitance in pF (C), and the holding current at -70 mV or -60 mV in pA (I_hold). checksums.txt includes SHA256 checksums that can be used to verify data integrity. This dataset contains whole-cell electrophysiological recordings (patch-clamp recordings) from three cell types in mice: serotonin (5-HT) neurons, somatostatin (SOM)-expressing GABA interneurons, and layer 5 pyramidal neurons. 5-HT and GABA neurons were recorded in the dorsal raphe nucleus (DRN), which is the main source of serotonergic input to the forebrain. Together, they make up the majority of the neurons found in the DRN. This dataset can be used to investigate the intrinsic electrophysiological properties of these two types of DRN neurons and contrast them with another abundant and well-studied cell type, the L5 pyramidal neuron. This data was used in our paper describing a spiking neural network model of the dorsal raphe nucleus: Emerson F. Harkin, Michael B. Lynn, Alexandre Payeur, Jean-François Boucher, Léa Caya-Bissonnette, Dominic Cyr, Chloe Stewart, André Longtin, Richard Naud, and Jean-Claude Béïque. Temporal derivative computation in the dorsal raphe network revealed by an experimentally-driven augmented integrate-and-fire modeling framework. eLife, 2023. doi: 10.7554/eLife.72951 See the included README.md for detailed information about the dataset.

Objective: To generate a national multiple sclerosis (MS) prevalence estimate for the United States by applying a validated algorithm to multiple administrative health claims (AHC) datasets. Methods: A validated algorithm was applied to private, military, and public AHC datasets to identify adult cases of MS between 2008 and 2010. In each dataset, we determined the 3-year cumulative prevalence overall and stratified by age, sex, and census region. We applied insurance-specific and stratum-specific estimates to the 2010 US Census data and pooled the findings to calculate the 2010 prevalence of MS in the United States cumulated over 3 years. We also estimated the 2010 prevalence cumulated over 10 years using 2 models and extrapolated our estimate to 2017. Results: The estimated 2010 prevalence of MS in the US adult population cumulated over 10 years was 309.2 per 100,000 (95% confidence interval [CI] 308.1–310.1), representing 727,344 cases. During the same time period, the MS prevalence was 450.1 per 100,000 (95% CI 448.1–451.6) for women and 159.7 (95% CI 158.7–160.6) for men (female:male ratio 2.8). The estimated 2010 prevalence of MS was highest in the 55- to 64-year age group. A US north-south decreasing prevalence gradient was identified. The estimated MS prevalence is also presented for 2017. Conclusion: The estimated US national MS prevalence for 2010 is the highest reported to date and provides evidence that the north-south gradient persists. Our rigorous algorithm-based approach to estimating prevalence is efficient and has the potential to be used for other chronic neurologic conditions. Prev of MS in the US-E-Appendix-Feb-19-2018

IV-tPA consent OSCE scriptThis is the actual IV-tPA consent OSCE script that was used for the simulation session for first year neurology residents.OSCE script.pdf [No abstract entered]

Objective: To evaluate progressive white matter (WM) degeneration in ALS. Methods: Sixty-six patients with ALS and 43 healthy controls were enrolled in a prospective, longitudinal, multicentre study in the Canadian ALS Neuroimaging Consortium (CALSNIC). Participants underwent a harmonized neuroimaging protocol across 4 centres including diffusion tensor imaging (DTI) for assessment of WM integrity. Three visits were accompanied by clinical assessments of disability (ALSFRS-R) and upper motor neuron (UMN) function. Voxel-wise whole brain and quantitative tractwise DTI assessments were done at baseline and longitudinally. Correction for site variance incorporated data from healthy controls and from healthy volunteers that underwent the DTI protocol at each centre. Results: ALS patients had a mean progressive decline in fractional anisotropy (FA) of the corticospinal tract (CST) and frontal lobes. Tractwise analysis revealed reduced FA in the CST, corticopontine/corticorubral and corticostriatal tracts. CST FA correlated with UMN function and frontal lobe FA with the ALSFRS-R. A progressive decline in CST FA correlated with a decline in the ALSFRS-R and worsening UMN signs. Patients with fast vs slow progression had a greater reduction in FA of the CST and upper frontal lobe. Conclusions: Progressive WM degeneration in ALS is most prominent in the CST and frontal lobes, and to a lesser degree in the corticopontine/corticorubral tracts and the corticostriatal pathways. With the use of a harmonized imaging protocol and incorporation of analytical methods to address site-related variances, this study is an important milestone towards developing DTI biomarkers for cerebral degeneration in ALS.

# Prediction of Individual Autism Diagnostic Observation Schedule (ADOS) scores based on neural responses during live eye-to-eye contact Included in the compressed folder ("ASDSVM.zip") are the fNIRS data collected from typically developing and autistic participants engaged in a face-to-face task in which direct eye contact is made on and off for 3-second intervals during a 30-second block design in which 18 seconds are task (face looking alternating with LED looking) and a rest phase (only LED looking) for 12 seconds. The zipped folder also included the 3D localizer information for each participant. ## Description of the Data and file structure The data file includes the following file structures: There is one folder named ASDSVM. In this folder are all the files needed. ADOS Scores for each participant are defined in the file ADOS4ASD.txt. The onset times of each face-to-face interaction are defined in the onset.txt file. The paradigm was defined as follows * 0-10 seconds - baseline rest period * 10-12 seconds - look at faces * 13-15 seconds - look at LED Light * 16-18 seconds - look at faces * 19-21 seconds - look at LED Light * 22-24 seconds - Look at faces * 25-28 seconds - Look at LED light * 29-39 seconds - Look at LED light This pattern repeats 6 times with onsets at 10, 40, 70, 100, 130 and 160 seconds marking the initial face-looking onset. For every TD and ASD subject, a series of files exists. These files are pre-pended with TD\_SXX or ASD\_SXX depending on the Subject Number (XX). For each subject, there are expected to be a total of 5 files. As described above, each file is prepended with the SubjectID. Each file name is subsequently named according to the run name or the 3D placement of the optodes on the head. Runs are defined as either EyeEye (direct face-to-face interaction with live partner) or EyeVideo (Gaze at eyes of prerecorded video of partner). There are expected to be 2 runs per interaction. In each of these text files the structure is as follows: The first column is time. The following columns are increasing channel numbers that contain OxyHb and DeOxyHb values per channel. The channel locations are defined in the xyz file as described below. The 3D digitized optode locations are in MNI coordinates for the optodes on the subject in the file that has xyz in the filename. The file is a CSV text file in which each row is a channel number and the channel location is defined as (X, Y, Z) ## Sharing/access Information Links to other publicly accessible locations of the data: N/A Was data derived from another source? No If yes, list source(s): These data were orginally recorded and used in the following publication: https://doi.org/10.1371/journal.pone.0265798 Hirsch J, Zhang X, Noah JA, Dravida S, Naples A, Tiede M, Wolf JM, McPartland JC. Neural correlates of eye contact and social function in autism spectrum disorder. Plos one. 2022 Nov 9;17(11):e0265798. Social difficulties are impactful in autism spectrum disorder (ASD), and links between these difficulties and underlying neural processes are active research questions. We present a multivariate classification method for neural data acquired from 36 participants during a live eye-to-eye contact task. Participants were either typically developed (TD) or diagnosed as ASD through gold-standard Autism Diagnostic Observation Schedule (ADOS) evaluation. We hypothesized multivariate classification could discriminate TD vs. ASD based on neural responses. Support vector machine (SVM) classification was able to discriminate between groups during the eye-contact interaction. In addition, it was found that underlying neural patterns contributing to binary classification also predicted measured ADOS scores with high correlation even though ADOS scores were not used for training. The correlation between observed and predicted ADOS scores was 0.72 (p < 0.002) for eye-to-eye contact. These findings suggest neural responses to live eye-to-eye contact are predictive of social symptomatology in ASD. Functional near-infrared spectroscopic brain imaging data were collected from a Shimadzu LABNIRS during a face to face interaction task from participants that were categorized as either TD or ASD by a trained clinician. Univaraite and Multivariate analyses were used to determine classification of participants as TD or ASD using only FNIRS responses. Typical GLM methods were used for univariate classification. For multivariate classification, an SVM classifier was used to predict ADOS values for the ASD group and categorize all participants as either TD or ASD.

The ACLeeve is a portable and easy to use electromyography (EMG) device to be used during ACL injury recovery to aid in the rehabilitation process. The ACLeeve is wrapped around the user's leg using a knee sleeve and electrodes, and then wirelessly transfers data to the user's phone or laptop. To achieve this, the ACLeeve must be small and lightweight, with electronics and software capable of detecting EMG readings and then transmitting them over a secure wireless connection. As well, the ACLeeve must be safe for the user and conform to all relevant standards. ACLeeve will monitor the user's movements of both quadriceps using surface EMGs (SEMGs) and Strain Sensors. These devices will be actively transmitting the collected data to a microprocessor, which will then process and send the data to an external device for further software analysis. The ACLeeve will provide real-time feedback to the user (audio or visual) regarding the performance of their movements, which will allow the user to physically adjust in order to achieve better long-term results.Our software will perform long-term analyses which will evaluate the progress of obtaining the goal of 80-90% asymmetry between the user's ACL-injured quadricep and their other healthy quadricep.

L’objectif de cette thèse était la validation de l’existence ainsi que la découverte de nouveaux sous-types au sein de la maladie d’Alzheimer, première cause de démence au monde. Afin d’explorer son hétérogénéité, nous avons employé des méthodes d’apprentissage profond appliquées à une modalité de neuroimagerie, l’imagerie par résonance magnétique structurelle.Cependant, la découverte de biais méthodologiques importants dans de nombreuses études de notre domaine, ainsi que l’absence de consensus de la communauté sur la manière d’interpréter les résultats des méthodes d’apprentissage profond a fait en partie dévier la thèse de son objectif principal pour s’orienter d’avantage vers des problématiques de validation, de robustesse et d’interprétabilité de l’apprentissage profond. Ainsi, trois études expérimentales ont été menées pour s’assurer de la capacité des réseaux profonds de correctement détecter la maladie. La première est une étude expérimentale de méthodes d’apprentissage profond pour la classification de la maladie d’Alzheimer et a permis d’établir une juste comparaison des méthodes. La seconde étude a permis de constater un manque de robustesse de la classification avec l’apprentissage profond en termes de motifs d’atrophie découverts à l’aide de méthodes d’interprétabilité. Enfin, la dernière étude propose une méthode de découverte de sous-types aidée par l’augmentation de données. Bien que fonctionnant sur des données synthétiques, celle-ci ne généralise pas aux données réelles.Une contribution majeure de la thèse est la librairie ClinicaDL, grâce à laquelle les résultats expérimentaux de la thèse ont été produits de manière à être reproductibles. The goal of this PhD was the validation of the existence and the discovery of new subtypes of Alzheimer’s disease, the first cause of dementia worldwide. Indeed, despite its discovery more than a century ago, this disease is still not well defined and existing treatments are only weakly effective, possibly because several phenotypes exist within the disease. In order to explore its heterogeneity, we employed deep learning methods applied to a neuroimaging modality, structural magnetic resonance imaging.However, the discovery of important methodological biases in many studies in our field, as well as the lack of consensus regarding deep learning interpretability, partly changed the main objective of the PhD to focus more on issues of validation, robustness and interpretability of deep learning. Then, to correctly assess the ability of deep learning to detect Alzheimer’s disease, three experimental studies were conducted. The first one is a study of deep learning methods for Alzheimer’s classification and allowed a fair comparison of the methods. The second study found a lack of robustness of classification with deep learning in terms of atrophy patterns discovered using interpretability methods. Finally, the last study proposed a subtype discovery method aided by data augmentation. Although it works on synthetic data, it does not generalize to real data.Experimental results of this PhD were obtained thanks to ClinicaDL, one major contribution of this PhD. It is an open source Python library that was used to improve the reproducibility of deep learning experiments.