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126,115 results for “Patients”
Olfactory event-related potentials in a functionally anosmic patient with arrested hydrocephalus
<p>Hydrocephalus is one of the lesser known causes of central olfactory loss. The pathogenesis of hydrocephalus involves the olfactory bulbs or tracts, and more rarely, other frontotemporal cortical regions. We describe a case of olfactory dysfunction in a macrocephalic 63-year-old female patient with arrested hydrocephalus. Her olfactory function was assessed by using the Sniffin' Sticks test, olfactory event-related potentials (OERPs), and 3-Tesla magnetic resonance imaging (MRI). An OERP examination suggested partial impairment of the central olfactory pathways and central parietal regions where OERP amplitude is maximal. Indeed, we found an evident olfactory potential trace with an increased latency only on Pz derivation. However, structural MRI showed important cortical brain thinning and large expansion of the third ventricle, with evident damage of the olfactory frontotemporal areas. The Sniffin' Sticks test and MRI supported the diagnosis of anosmia, while OERP findings indicated partial preservation of olfactory function, likely due to an adaptation of the central olfactory system. These findings highlight the importance of a multi-integrated approach to detect olfactory impairment.</p>
Neurophysiological assessment in a patient affected by Marfan syndrome
<p>Marfan syndrome (MS) is a hereditary connective tissue disorder characterized by different multiorgan patterns. The guidelines for MS diagnosis do not highlight the usefulness—or even the use—of any neurophysiological techniques for diagnosing this disease. Moreover, few neurophysiological studies assessing the central and peripheral nervous systems in MS subjects have been reported to date. Case presentation: We describe a male patient affected by MS. To assess sensory and nociceptive pathways in this patient, a neurophysiological assessment was performed using electroencephalogram, nerve conduction studies, and somatosensory and laser-evoked potentials. To the best of our knowledge, this is the first published case report to evaluate the role of evoked potential assessments for the study of sensory and nociceptive pathways in MS. Future studies should investigate the use of a complete neurophysiological approach for the clinical and therapeutic management of MS patients in a large sample.</p>
Effects of a Vibro-Tactile P300 Based Brain-Computer Interface on the Coma Recovery Scale-Revised in Patients With Disorders of Consciousness
<p>Persons diagnosed with disorders of consciousness (DOC) typically suffer from motor and cognitive disabilities. Recent research has shown that non-invasive brain-computer interface (BCI) technology could help assess these patients’ cognitive functions and command following abilities. 20 DOC patients participated in the study and performed 10 vibro-tactile P300 BCI sessions over 10 days with 8–12 runs each day. Vibrotactile tactors were placed on the each patient’s left and right wrists and one foot. Patients were instructed, via earbuds, to concentrate and silently count vibrotactile pulses on either their left or right wrist that presented a target stimulus and to ignore the others. Changes of the BCI classification accuracy were investigated over the 10 days. In addition, the Coma Recovery Scale-Revised (CRS-R) score was measured before and after the 10 vibro-tactile P300 sessions. In the first run, 10 patients had a classification accuracy above chance level (>12.5%). In the best run, every patient reached an accuracy ≥60%. The grand average accuracy in the first session for all patients was 40%. In the best session, the grand average accuracy was 88% and the median accuracy across all sessions was 21%. The CRS-R scores compared before and after 10 VT3 sessions for all 20 patients, are showing significant improvement (<em>p</em> = 0.024). Twelve of the twenty patients showed an improvement of 1 to 7 points in the CRS-R score after the VT3 BCI sessions (mean: 2.6). Six patients did not show a change of the CRS-R and two patients showed a decline in the score by 1 point. Every patient achieved at least 60% accuracy at least once, which indicates successful command following. This shows the importance of repeated measures when DOC patients are assessed. The improvement of the CRS-R score after the 10 VT3 sessions is an important issue for future experiments to test the possible therapeutic applications of vibro-tactile and related BCIs with a larger patient group.</p>
Breaking the ice to improve motor outcomes in patients with chronic stroke: a retrospective clinical study on neuromodulation plus robotics
<p>Stroke is one of the main causes of impairment affecting daily activities and quality of life. There is a growing effort to potentiate the recovery of functional gait and to enable stroke patients to walk independently. To estimate the effects of dual-site transcranial direct current stimulation (dstDCS) on gait recovery in chronic stroke patients provided with robot-aided gait training (RAGT). Thirty-seven patients were included in this retrospective clinical study. Nine patients were provided with dstDCS during the first 10 min of RAGT by using Lokomat®Pro (on-RAGT), 15 patients immediately after RAGT (post-RAGT), and 13 patients immediately before RAGT (pre-RAGT). Each group improved over time concerning disability burden and lower limb strength. on-RAGT and post-RAGT experienced better improvement in balance (p < 0.001) and, moderately, gait endurance (p = 0.04) as compared to pre-RAGT. Furthermore, all treatments decreased the facilitation of the unaffected hemisphere (p < 0.001) and the inhibition of the affected hemisphere (p < 0.001). The duration of such aftereffects was found to be greater for post-RAGT. This is the first trial with dstDCS coupled with RAGT in chronic stroke patients with gait impairment. When timely coupled with RAGT, dstDCS may be considered an effective tool for the recovery of lower limb function in patients with first unilateral stroke in the chronic phase. Moreover, our data suggest the ductility of dstDCS concerning RAGT timing, thus making this intervention suitable in a neurorehabilitation setting and well adaptable to patients’ needs. </p>
Objective evaluation of Nintendo Wii Fit plus balance program training on postural stability in Multiple Sclerosis patients
<p>The use of the Nintendo Wii system has become a common tool for balance rehabilitation in patients with multiple sclerosis, but few studies verified the effectiveness of such an approach using quantitative measures of postural control. We aimed to evaluate the impact of rehabilitation treatment using the Nintendo Wii Fit Plus balance program on objective stabilometric parameters in multiple sclerosis patients. We enrolled 36 multiple sclerosis patients, with mild-moderate disability, referring to the multiple sclerosis Centre of the University of Catania from September 2013 to June 2014. Twenty participants underwent 20 individual sessions of balance exercise using the Nintendo Wii Fit Plus. They were assessed at baseline (T0) and at the end of rehabilitation program (T1) by Neurocom Balance Manager. Functional independence measure, Barthel index, and Berg balance scale were also administered. At T1, we found a significant improvement in total path length-open eyes, sway area-open eyes, and mean sway velocity-open eyes. Patients showed significant improvement in functional independence measure motor score, Barthel index, and in Berg balance scale. No significant differences between T0 and T1 in closed eyes trials were found. A significant correlation between delta values between T0 and T1 of sway area-open eyes and the Berg balance scale (r = -0.76; P < 0.0001) was found. This study confirmed that balance rehabilitation training performed using the Nintendo Wii with balance board significantly reduced some postural sway parameters in multiple sclerosis patients. It could be a good support to standard rehabilitation program in multiple sclerosis patients.</p>
Hemangiomas of the tongue and the oral cavity in a myotonic dystrophy type 1 patient
<p>Myotonic dystrophy type 1 (DM1) is an autosomal dominant disease caused by a cytosine, guanine, thymine (CTG) trinucleotide repeat expansion in the non-coding region of dystrophia myotonica protein kinase gene, causing a multisystem involvement. To date, few studies have been performed to evaluate skin features in DM1 patients, but none reported on the possible association between the disease and tongue hemangiomas. We report a case of a 63-year-old woman affected by DM1 and presenting, at the intraoral examination, several swelling and buish lesions occurring on buccal and palatal mucosa, and in the anterior two-thirds and margins of the tongue. Multiple tongue hemangiomas in DM1 patient. Color Doppler ultrasound revealed hypoechoic lesions with intermittent color picking suggestive of vascular lesion. Surgical excision was performed under general anesthesia. Histopathological examination was compatible with the diagnosis of cavernous hemangiomas. At 6 months follow-up, a part from the cosmetic deformity, patient's hemangiomas did not bleed, but caused functional problems with speaking, mastication, and deglutition, in addition to the same symptoms induced by DM1. This case may add new details to better characterize the DM1 phenotype, suggesting that even tongue hemangiomas may be part of the DM1 multisystem involvement.</p>
IMC dataset: duodenal biopsies of COVID-19 and Control patients
<p>An Imaging Mass Cytometry dataset containing .mcd files of duodenal biopsies from COVID-19 and Control patients. The peer-reviewed publication for this dataset has been published in Mucosal Immunology, and can be accessed here: https://www.nature.com/articles/s41385-021-00437-z. Please cite this when using the dataset.</p>
Microbiome profile of patients in a referral gastrointestinal diseases centre in the Sudan
<p>Data set of 16S rRNA Nanopore sequencing of DNA extracted from faecal samples of Gastrointestinal tract diseases from Human. </p>
Pre-processed IgH receptor repertoire data from MS patients after aHSCT from BioProject PRJNA763367
<p><strong>Data Processing</strong></p> <p>Samples were demultiplexed via their Illumina indices, and processed using the Immcantation toolkit(1,2). Raw fastq files were filtered based on a quality score threshold of 20. Paired reads were joined if they had a minimum length of 10 nt, maximum error rate of 0.3 and a significance threshold of 0.0001. Reads with identical UMI were collapsed to a consensus sequence. Reads with identical full-length sequence and identical constant primer but differing UMI were further collapsed. Sequences were then submitted to IgBlast (3) for VDJ assignment and sequence annotation. Constant region sequences were mapped to germline using Stampy(4). The number and type of V gene mutations was calculated using the shazam R package.(2)</p> <p> </p> <p><strong>software_versions</strong> pRESTO:0.5.3,Change-O:0.3.4,IgBlast 1.6.1, stampy1.0.21. shazam0.1.8</p> <p><strong>quality_thresholds</strong> FilterSeq.py pRESTO Q>20</p> <p><strong>paired_reads_assembly</strong> AssemblePairs.py pRESTO minlen 10 maxerror 0.3 alpha 0.0001</p> <p><strong>primer_match_cutoffs</strong> MaskPrimers.py pRESTO C primer & V primer maxerror 0.2</p> <p><strong>consensus_building</strong> BuildConsensus.py pRESTO maxerror 0.1 maxgap 0.5</p> <p><strong>collapsing_method</strong> CollapseSeq.py pRESTO</p> <p><strong>germline_database </strong>IMGT</p> <p> </p> <p><strong>Format</strong></p> <p>Processed sequences are provided in a tab delimited file format, including the following annotations:</p> <p> </p> <p><strong>ISOTYPE_SUBCLASS </strong>Isotype subclass</p> <p><strong>SEQUENCE_ID </strong>Sequence identifier</p> <p><strong>JUNCTION_LENGTH </strong>Junction length</p> <p><strong>CONSCOUNT </strong>Raw read count from which UMI consensus sequences were generated, summed over all UMIs for the given unique sequence.</p> <p><strong>DUPCOUNT </strong>UMI count for the given unique sequence</p> <p><strong>ISOTYPE </strong>Constant region primer (isotype)</p> <p><strong>MUT_TOTAL </strong>Total number of mutations in V gene </p> <p><strong>SAMPLE </strong>Sample identifier, linking back to raw data</p> <p><strong>JUNCTION </strong>Junction nucleotide sequence</p> <p><strong>Protein_seq </strong>Amino acid sequence</p> <p><strong>CDR3_AA_GRAVY </strong>CDR3 hydrophobicity index</p> <p><strong>CDR3_AA_BULK </strong>CDR3 bulkiness</p> <p><strong>CDR3_AA_ALIPHATIC </strong>CDR3 aliphatic index</p> <p><strong>CDR3_AA_POLARITY </strong>CDR3 polarity</p> <p><strong>CDR3_AA_CHARGE </strong>CDR3 normalized net charge</p> <p><strong>CDR3_AA_BASIC </strong>CDR3 basic side chain residue content</p> <p><strong>CDR3_AA_ACIDIC </strong>CDR3 acidic side chain residue content</p> <p><strong>CDR3_AA_AROMATIC </strong>CDR3 aromatic side chain content</p> <p><strong>Subset </strong>Defined B cell subset </p> <p><strong>Repertoire </strong>Defined B cell repertoire (Naive, Memory IgM/IgD, IgA, IgG)</p> <p><strong>R_SCDR </strong>R/S ratio in CDR region</p> <p><strong>R_SFWR </strong>R/S ratio in FWR region</p> <p><strong>V_GENE </strong>V segment gene</p> <p><strong>D_GENE </strong>D segment gene</p> <p><strong>J_GENE </strong>J segment gene</p> <p><strong>V_FAM </strong>V family gene</p> <p><strong>Clust_REPRES </strong>Cluster representative</p> <p><strong>Clust_SIZE </strong>Cluster size</p> <p><strong>Sex </strong>Sex of the Subject</p> <p><strong>UNIQUE_ID </strong>Sample identifier </p> <p><strong>Bcellno </strong>Input B cell number</p> <p><strong>Days_posttx </strong>Sampling time point relative to transplantation</p> <p><strong>Age_at_tx </strong>Age of the subject (at aHSCT)</p> <p><strong>Disease </strong>MS subtype</p> <p><strong>Last_therapy </strong>Last therapy prior to aHSCT</p> <p><strong>Disease_duration </strong>Disease duration</p> <p><strong>CMV_reactivation </strong>Cytomegalovirus reactivation</p> <p><strong>Month_label </strong>Month post-aHSCT inverval bin</p> <p><strong>Patient_label </strong>Subject identifier</p> <pre> </pre> <p><strong>References</strong></p> <p>1. Vander Heiden, J. A., G. Yaari, M. Uduman, J. N. H. Stern, K. C. O’Connor, D. A. Hafler, F. Vigneault, and S. H. Kleinstein. 2014. PRESTO: A toolkit for processing high-throughput sequencing raw reads of lymphocyte receptor repertoires. <em>Bioinformatics</em>30: 1930–1932.</p> <p>2. Gupta, N. T., J. A. Vander Heiden, M. Uduman, D. Gadala-Maria, G. Yaari, and S. H. Kleinstein. 2015. Change-O: A toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data. <em>Bioinformatics</em>31: 3356–3358.</p> <p>3. Ye, J., N. Ma, T. L. Madden, and J. M. Ostell. 2013. IgBLAST: an immunoglobulin variable domain sequence analysis tool. <em>Nucleic Acids Res.</em>41.</p> <p>4. Lunter, G., and M. Goodson. 2011. Stampy: A statistical algorithm for sensitive and fast mapping of Illumina sequence reads. <em>Genome Res.</em>21: 936–939.</p>
Diagnostic performance of LI-RADS in adult patients with rare hepatic tumors.
<p>We uploadedthe images of the manuscript "Diagnostic performance of LI-RADS in adult patients with rare hepatic tumors" pubished on European Review for Medical and Pharmacological Sciences 2021.</p>
Survey of patient advocacy groups regarding the use of AI in cancer screening
<p>This file contains the results of a survey of members of patient advocacy groups affiliated with Washington University in St Louis, Stanford University and Johns Hopkins University. The survey asks about attitudes regarding different ways in which AI could be involved in the process of cancer screening. IRB approval was obtained from Washington University in St Louis.</p>
Long-Term Survival and Curative-Intent Treatment in Hepatitis B or C Virus-Associated Hepatocellular Carcinoma Patients Diagnosed during Screening
<p>We uploaded the image of the mansucript <strong>Long-Term Survival and Curative-Intent Treatment in Hepatitis B or C Virus-Associated Hepatocellular Carcinoma Patients Diagnosed during Screening </strong>accepted on Biology.</p>
Labelled and unlabelled hand acceleration data captured unobtrusively from PD patients and Healthy Controls
<p>The dataset contains acceleration signals captured in-the-wild via the IMU sensor embedded in modern smartphones, for the purpose of detecting tremorous episodes, related to Parkinson's Disease (PD). It contains two different groups of subjects:</p> <ul> <li>tremor_sdata.pickle --> A group of 45 subjects that have been subjected to neurological examination (the same dataset as https://zenodo.org/record/3519213)</li> <li>tremor_gdata.pickle --> A group of 454 subjects who just self-reported their PD status</li> </ul> <p>All subjects contributed accelerometer data using their personal smartphones, for a period spanning many months. Tri-axial acceleration values were recorded automatically whenever a phone call was realized. The recording lasted for 75 seconds at the most. Each phone call thus resulted in one recorded accelerometer signal, also referred to as session. Each subject contributed a different amount of sessions depending on the number of phone calls they realized during the data collection period as well as their participation time (they were free to drop-out at any time). A detailed description of the capturing process as well as analysis results, can be found in the related research article.</p> <p>The data is presented as a python dictionary, indexed by the subject ids. Each element of the dictionary is a list with the following significance:</p> <table> <thead> <tr> <th scope="col">Index</th> <th scope="col">Meaning</th> </tr> </thead> <tbody> <tr> <td>0</td> <td>List of np.arrays, Each array contains the power spectral density for an acceleration segment of 5s duration</td> </tr> <tr> <td>1</td> <td>Dictionary, Denotes subject updrs</td> </tr> <tr> <td>2</td> <td>List of str containing a unique identifier of the acceleration session that each segment in the other lists belongs to</td> </tr> <tr> <td>3</td> <td>List of np.arrays, Each array contains the pre-processed acceleration values for a 5s segment</td> </tr> </tbody> </table> <p> </p><p>The subject updrs is represented as dictionary containing the following tremor-related annotation values (FOR THE FIRST GROUP ONLY):<br> * updrs16: scalar int<br> The value related to tremor as described in item 16 of the part II of the MDS-UPDRS scale, as reported by the subject.</p> <p></p> <p>* updrs20_right: scalar int in range [0, 4]<br> The value related to rest tremor in the right hand as described in item 20 of the part III of the MDS-UPDRS scale, as reported by the attending neurologist.</p> <p>* updrs20_left: scalar int in range [0, 4]<br> Same as above but for left hand.</p> <p>* updrs21_right: scalar int in range [0, 4]<br> The value related to action/postural tremor in the right hand as described in item 21 of the part III of the MDS-UPDRS scale, as reported by the attending neurologist.</p> <p>* updrs21_left: scalar int in range [0, 4]<br> Same as above but for left hand.</p> <p>* sp_expert: scalar int in range [0, 1]<br> A binary tremor annotation created by a group of signal processing experts, upon visually examining the contributed signals in both time and frequency domain and taking into consideration the UDPRS scores of each subject. This was necessary due to the intermittent nature of tremor, as well as a number of considerations related to the in-the-wild nature of the data capturing process. For more details, we refer the reader to the dataset description in the related research article.<br> A '1' value indicates that the subject has tremor.<br> A '0' value indicates that the subject doesn't have tremor.</p> <p>* pd_status: scalar int in range [0, 1]<br> A '1' value indicates that the subject is a PD patient.<br> A '0' value indicates that the subject is a Healthy Control</p> <p>Note: Each annotation value refers to the subject as a whole, and not in any one session.</p>
A patient-centric modelling framework captures recovery from SARS-CoV-2 infection
<p>The biology driving individual patient responses to SARS-CoV-2 infection remains ill understood. Here, we developed a patient-centric framework leveraging detailed longitudinal phenotyping data and covering a year post-disease onset, from 215 SARS-CoV-2 infected subjects with differing disease severities. Our analyses revealed distinct “systemic recovery” profiles, with specific progression and resolution of the inflammatory, immune cell, metabolic and clinical responses. In particular, we found a strong inter- and intra-patient temporal covariation of innate immune cell numbers, kynurenine metabolites and lipid metabolites, which highlighted candidate immunologic and metabolic pathways influencing the restoration of homeostasis, the risk of death and that of long COVID. Based on these data, we identified a composite signature predictive of systemic recovery at the patient level, using a joint model on cellular and molecular parameters measured soon after disease onset. New predictions can be generated using the online tool <a href="https://aus01.safelinks.protection.outlook.com/?url=http%3A%2F%2Fshiny.mrc-bsu.cam.ac.uk%2Fapps%2Fcovid-19-systemic-recovery-prediction-app&data=05%7C01%7CJulien.Wist%40murdoch.edu.au%7C117789e168814a13808a08dabcf18a3e%7Cc00d4c1bcf7b4e93b7c710113a9bc230%7C1%7C0%7C638030042858394016%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=z7RJ8%2BGTWr84dYvWSoc%2F9Cj1zYRHV7%2BHfyr9MyCnX6M%3D&reserved=0">http://shiny.mrc-bsu.cam.ac.uk/apps/covid-19-systemic-recovery-prediction-app</a>, designed to test our findings prospectively.</p>
Validation of Sepsis-3 using survival analysis and clinical evaluation of quick SOFA, SIRS, and burn-specific SIRS for sepsis in burn patients with suspected infection
<p><span><strong>Purpose</strong>: Sepsis-3 is a life-threatening organ dysfunction caused by dysregulated host responses to infection; and defined using the Sepsis-3 criteria, introduced in 2016, however, the criteria need to be validated in specific clinical fields. We investigated mortality prediction and compared the diagnostic performance of quick Sequential Organ Failure Assessment (qSOFA), systemic inflammatory response syndrome (SIRS), and burn-specific SIRS (bSIRS) in burn patients.</span></p> <p><span><strong>Methods</strong>: This single-center retrospective cohort study examined burn patients in Seoul, Korea during January 2010–December 2020. Overall, 1,391 patients with suspected infection were divided into four sepsis groups using SOFA, qSOFA, SIRS, and burn-specific SIRS. </span></p> <p><span><strong>Results</strong>: Hazard ratios (HRs) of all unadjusted models were statistically significant; however, the HR (0.726, p = 0.0080.001) in the SIRS ≥2 group is below 1. In the adjusted model, HRs of the SOFA ≥2 (2.426, p < 0.001), qSOFA ≥2 (7.198, p < 0.001), and SIRS ≥2 (0.575, p < 0.001) groups were significant. The diagnostic performance of dichotomized qSOFA, SIRS, and bSIRS for sepsis was defined by the Sepsis-3 criteria. The mean onset day was 4.13±2.97 according to Sepsis-3. The sensitivity of SIRS (0.989, 95% confidence interval [CI]: 0.982–0.994) was higher than that of qSOFA (0.841, 95% CI: 0.819–0.861) and bSIRS (0.803, 95% CI: 0.779–0.825). Specificities of qSOFA (0.929, 95% CI: 0.876–0.964) and bSIRS (0.922, 95% CI: 0.868–0.959) were higher than those of SIRS (0.461, 95% CI: 0.381–0.543).</span></p> <p><span><strong>Conclusion</strong>: Sepsis-3 is a good alternative diagnostic tool because it reflects sepsis severity without delaying diagnosis. SIRS showed higher sensitivity than qSOFA and bSIRS and may therefore more adequately diagnose sepsis.</span></p>
SPoD-Ex Videoabstract für Patient*innen - Demenzprävention durch Bewegung und Sport
<p>Eine unabhängige Patienteninformation zur Demenzprävention durch Bewegung. Ergebnisse des Forschungsprojekts SPoD-Ex.</p>
datset of "Networks and genes modulated by posterior hypothalamic stimulation in patients with aggressive behaviours: Analysis of probabilistic mapping, normative connectomics, and atlas-derived transcriptomics of the largest international multi-centre dataset"
<p>This dataset accompanies the manuscript:<br> "Networks and genes modulated by posterior hypothalamic stimulation in patients with aggressive behaviours: Analysis of probabilistic mapping, normative connectomics, and atlas-derived transcriptomics of the largest international multi-centre dataset."<br> DOI: (https://doi.org/10.1101/2022.10.29.22281666)</p> <p>by</p> <p>Flavia Venetucci Gouveia1,2,3*†,Jürgen Germann4,5†, Gavin JB Elias4,5, Alexandre Boutet4,6, Aaron Loh4,5, Adriana Lucia Lopez Rios7,8, Cristina V Torres Diaz9, William Omar Contreras Lopez10,11, Raquel CR Martinez3,12, Erich T Fonoff13, Juan C Benedetti-Isaac14, Peter Giacobbe 2,15,16, Pablo M Arango Pava17, Han Yan5,18, George M Ibrahim5, 18,19,20, Nir Lipsman2,5,15, Andres M Lozano4,5, Clement Hamani2,5,15*</p> <p>1. Neuroscience and Mental Health, Hospital for Sick Children Research Institute; Toronto, Canada <br> 2. Sunnybrook Research Institute; Toronto, Canada<br> 3. Division of Neuroscience, Sírio-Libanês Hospital; São Paulo, Brazil<br> 4. Division of Neurosurgery, Department of Surgery, University Health Network, Toronto, Canada<br> 5. Division of Neurosurgery, Department of Surgery, University of Toronto; Toronto, Canada<br> 6. Joint Department of Medical Imaging, University of Toronto; Toronto, Canada<br> 7. Department of Functional and Stereotactic Neurosurgery, University Hospital San Vicente Fundación,<br> Medellín, Colombia<br> 8. Department of Functional and Stereotactic Neurosurgery, San Vicente Fundación, Rionegro, Colombia<br> 9. Department of Neurosurgery, University Hospital La Princesa; Madrid, Spain<br> 10. Nemod Research Group, Universidad Autónoma de Bucaramanga; Bucaramanga, Colombia<br> 11. Division of Functional Neurosurgery, Department of Neurosurgery, FOSCAL Clinic; Bucaramanga,<br> Colombia<br> 12. LIM 23, Institute of Psychiatry, School of Medicine, University of São Paulo; São Paulo, Brazil<br> 13. Department of Neurology, Integrated Clinic of Neuroscience, School of Medicine, University of São Paulo;<br> São Paulo, Brazil.<br> 14. Stereotactic and Functional Neurosurgery Division of the International Misericordia Clinic; Barranquilla,<br> Colombia<br> 15. Harquail Centre for Neuromodulation, Sunnybrook Health Sciences Centre; Toronto, Canada<br> 16. Department of Psychiatry, University of Toronto; Toronto, Canada<br> 17. Servicio de Neuocirugia Funcional y Esterotaxia, Clinica Comuneros Bucaramanga, Clinica Desa y Clinica<br> Dime Neurocardiovascular de Cali; Clinica Nueva del Lago, Bogota, Colombia.<br> 18. Division of Neurosurgery, The Hospital for Sick Children; Toronto, Canada<br> 19. Institute of Biomedical Engineering, University of Toronto; Toronto, Canada<br> 20. Institute of Medical Science, University of Toronto; Toronto, Canada<br> † Flavia Venetucci Gouveia and Jürgen Germann contributed equally to this work and share first authorship.</p> <p>* Corresponding Author: Dr. Flavia Venetucci Gouveia. Neuroscience and Mental Health, Hospital for Sick Children Research Institute. 686, Bay Street, Toronto, ON, M5G 0A4, Canada. flavia.venetuccigouveia@sickkids.ca<br> * Corresponding Author: Dr. Clement Hamani. Sunnybrook Research Institute. 2075 Bayview Ave, S126. Toronto, ON, M4N3M5, Canada. clement.hamani@sunnybrook.ca</p> <p>It contains a zip folder ("estimated_binary_Volume_of_Tissue_Activated.zip") with one file (in nii.gz format) per patient estimating the Volume of Activated Tissue for that patient (the estimated 'reach' of the active DBS stimulation) and a demographics file.<br> The case numbers are identical to Table 1 in the manuscript.</p>
Microelectrode register (MER) data from Deep Brain Stimulation (DBS) surgery in Parkinson's disease patients
<p>MER data consist of brain signal in different depths when DBS surgery is being done. In each depth, a data file is created, with different duration depending on the depth, and up to three channels.</p> <p>Data come from 14 patients (9 males and 5 females), they are anonymized and labelled from P1 to P14. They correspond to patients in age 65.1 +- 5.6 years.</p> <p>Data are organized in STN-IN and STN-OUT (different depths in each folder), subthalamus-in, and subthalamus-out since the STN area is the target area when implanting a DBS. Classification in STN-IN and STN-OUT was made by the neurophysiologists and surgeons.</p> <p>Data were recorded for left and right lobes, 8 patients in left and right lobe, 1 patient in right lobe, and 5 patients in left lobe.</p> <p>The format is mat file (MATLAB file)</p> <p>Data sampling frequency is 12kHz.</p> <p>No filtering or data processing was made, they are directly obtained from the MER acquisition system.</p> <p> </p>
Risk of Dementia and Its Associated Factors Among the Patients with Coronary Artery Disease Attending a Tertiary Cardiac Hospital of Dhaka City: A Cross-sectional Study
<p>This data set of research assessed the risk of dementia among patients with coronary artery disease.</p>
Supplementary materials for "SPAG9 expression predicts a good prognosis in patients with clear cell renal cell carcinoma: A bioinformatics integrative analysis"
<p>Supplementary materials for "SPAG9 expression predicts a good prognosis in patients with clear cell renal cell carcinoma: A bioinformatics integrative analysis".</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.