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363 results for “consciousness”
Evidence accumulation relates to perceptual consciousness and monitoring
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Modeling an auditory stimulated brain under altered states of consciousness using the generalized ising model
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Attention-based frontal-posterior coupling for visual consciousness in the human brain
<ol> <li>DataCode_Fig1_Attentional_Capture_Image_Detectability.m</li> <li>DataCode_Fig1_Attentional_Capture_Image_Detectability.mat</li> <li>DataCode_FigS2_Attentional_Capture_Image_Detecability.mat <ul> <li>.m Code (1) using .mat Data (2 and 3) illustrate main behavioral findings in our manuscript. Panel figures shown in Figure.1 and Figure.S2 could be well replicated using these materials.<br><br></li> </ul> </li> <li>Au_Step06_0601_unit_2C.m</li> <li>Au_Step06_0601_unit_mC.m</li> <li>Train_DSVM_xilei.m</li> <li>Classify_DSVM.m</li> <li>svmclassify.m</li> <li>svmtrain_xilei.m <ul> <li>.m Code (4) and .m code (5) using child .m functions (6, 7, 8 and 9) illustrate core codes used to discriminate neural pattern differences on a 2-class issue (image presence versus image absence) or a 3-class issue (animal, object or face), respectively. </li> </ul> </li> <li>Note_Location_activeChannels_distanceTest.m</li> <li>Note_Location_activeChannels_distanceTest.mat</li> <li>Note_Location_activeChannels.mat <ul> <li>.m Code (10) using .mat Data (11 and 12) illustrate our method used to calculate distance between responsive contacts. Based on that, we also made a statistical inference against a chance-level distribution. Panel figure shown in Figure.2F could be well replicated using these materials.<br><br></li> </ul> </li> <li>easy_ImgC.m <ul> <li>.m Code (13) illustrate our method used to calculate imaginary coherence between responsive contacts. A Rayleigh Z correction was also performed and outputed.<br><br></li> </ul> </li> <li>easy_visibility.m <ul> <li>.m Code (14) illustrate our method used to calculate an index of visibility from which measures of interest tied to an invisible image was subtracted from that of a visible image. </li> </ul> </li> </ol> <p> </p>
Journal Club "Only consciousness truly exists?"
<p>Journal Club from 22 November 2024, discussants: Qianchen Liang, Ellia Francesco and Matteo Grasso on:</p> <p>Cea, Negro and Signorelli (2024) "Only consciousness truly exists? Two Problems for IIT" PERSPECTIVE article Front. Psychol., 23 October 2024 Sec. Consciousness Research Volume 15 - 2024 | </p> <p>Link to the original article: https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2024.1485433/full</p> <p> </p> <p>Available online at: https://www.youtube.com/watch?v=36I8n3NPklc&t=10s</p>
Correlations between a Shintergy synchronized brain and a laser eld; a possible fractal structure of Consciousness (Part I of 7 – Local measure in time and space).
<p>Data set from Correlations between a Shintergy synchronized brain and a laser eld; a possible fractal structure of Consciousness (Part I of 7 – Local measure in time and space), and figures.</p>
Dataset: Ishares Climate Conscious & Transition MSCI USA ETF (USCL) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Global X Conscious Companies ETF (KRMA) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Common Data Elements for Disorders of Consciousness - Version 1.1
<p>In 2020, the Neurocritical Care Society’s <em>Curing Coma Campaign</em> launched an international initiative to create common data elements (CDEs) for disorders of consciousness (DoC). This CDE initiative is motivated by the recognition that ongoing progress in our field depends on the development of harmonized and uniform data elements. We formed multidisciplinary Work Groups with expertise in 1) Behavioral Phenotyping; 2) Hospital Course/Confounders/Medications; 3) Neuroimaging; 4) Electrophysiology; 5) Biospecimens; 6) Physiologic Data/Big Data; 7) Therapeutic Interventions; 8) Outcomes/Endpoints; and 9) Goals of Care/Family Data. Here, we disseminate the initial recommendations of this CDE development process and version 1.0 of the case report forms (CRFs) with CDEs that can be used in DoC studies. We aim for these CDEs to support progress in the field of DoC research and to facilitate multi-institutional collaboration. </p> <p>We welcome feedback and are committed to revising the CDEs and CRFs to ensure that they reflect developments in our field. To provide feedback about the current CDEs and CRFs, and to make recommendations about updates for future versions, please email <a href="mailto:cde.curingcoma@gmail.com">cde.curingcoma@gmail.com</a>.</p>
Figure2. Generation of negative feedbacks gets tuned once a TCR completes stimulation beyond the threshold l. A TCell generates activation signal to BCell once it gets stimulation of its k-TCRs.-AIDEN: A Density Conscious Artificial Immune System for Automatic Discovery of Arbitrary Shape Clusters in Spatial Patterns
<p>A TCR at position p is stimulated if rp (x) - rn(x) > l. Figure 1 depicts this process. When a T<br> Cell receives stimulations on more than k receptors, it generates activation signal to a B Cell, as<br> represented in Figure2.</p>
Figure1. Static stimulation of a single TCR- The kinetic proofreading by the receptor on input x Є X forwards the receptor position p toward l. The receptor will generate negative feedback if p > β. The receptor will generate success signal when p== l.-AIDEN: A Density Conscious Artificial Immune System for Automatic Discovery of Arbitrary Shape Clusters in Spatial Patterns
<p>A TCR at position p is stimulated if rp (x) - rn(x) > l. Figure 1 depicts this process. When a T<br> Cell receives stimulations on more than k receptors, it generates activation signal to a B Cell, as<br> represented in Figure2.</p>
FIGURES 2 a-g -AIDEN: A Density Conscious Artificial Immune System for Automatic Discovery of Arbitrary Shape Clusters in Spatial Patterns
<p>The program was implemented in Matlab and tested with several patterns. The first, dataset1<br> consisted of 2 patterns each comprised of 100 points falling on two concentric circles of radii 10<br> and 20 respectively. The second, dataset2 consisted of 3 patterns each of 100 points falling on three<br> concentric circles of radii 10, 15, and 20 respectively. The model was further tested for its<br> capability to find clusters in patterns of open spatial form using dataset3 and dataset4 consisting of<br> 200 and 300 points falling on 2 and 3 concentric semi circles respectively. As shown in the<br> Figure2.a and Figure2.b, the algorithm is capable of determining spatial association of a data point<br> with other data points belonging to its appropriate circle only. The results successfully demonstrated<br> the capability of our model to automatically detect clean clusters of arbitrary shapes in the input<br> data represented in closed spatial form. The model was found even capable of determining clusters<br> of open spatial forms also, as shown in Figure2.c and Figure2.e. However, the output of the<br> algorithm was found affected by the values of the algorithm parameters k and a. In the present<br> experiment, k =8 and a =10 was sufficient for performing correct cluster associations. On the other<br> hand, correct clustering for the dataset2, could be obtained with 10NN estimation i.e. k =10, with<br> a.=15. Moreover setting k =15, with a.=15 was required for dataset4, as clustering error was<br> observed with k =10, with a.=15, as in Figure2.d. Figure2.f and Figure2.g show the correct<br> clustering even in presence of combination of open and closed form of input patterns. In each<br> figure, the first sub-plot shows the original data and the second sub-plot shows the clusters<br> identified by our program.</p>
Figure3. APCs A1-A4 connected within range of cohesion-factor-threshold form members of one ARB-AIDEN: A Density Conscious Artificial Immune System for Automatic Discovery of Arbitrary Shape Clusters in Spatial Patterns
<p>Figure3 depicts this process. The<br> model with the above specification then effectively detects self or non-self pathogens. In terms of<br> its application to the task of clustering, this interpretation means making the affinities high within<br> clusters and low across clusters. A pathogen corresponding to an outlier would not stimulate a TCR<br> sufficiently and may not form part of any ARB.</p>
Data set for "Columnar clusters in the human motion complex reflect consciously perceived motion axis"
<p>Accompanying data for manuscript “Columnar clusters in the human motion complex reflect consciously perceived motion axis” written by Marian Schneider, Valentin Kemper, Thomas Emmerling, Federico De Martino, Rainer Goebel, submitted, November 2018.</p> <p>Imaging files<br> -------------<br> * T1w and PDw images, only acquired in session 1<br> * 2 runs task-MotLoc, only acquired in session 2<br> * 5-6 runs task-ambiguous (called "Experiment 1" in accompanying manuscript, divided across 2 scanning sessions)<br> * 5-6 runs task-unambiguous (called "Experiment 2" in accompanying manuscript, divided across 2 scanning sessions)</p> <p><br> Acquisition details<br> -------------------<br> For visualization of the functional results, we acquired scans with structural information in the first scanning session. At high magnetic fields, MR images exhibit high signal intensity variations that result from heterogeneous RF coil profiles. We therefore acquired both T1w images and PDw images using a magnetization-prepared 3D rapid gradient-echo (3D MPRAGE) sequence (TR: 3100 ms (T1w) or 1440 ms (PDw), voxel size = 0.6 mm isotropic, FOV = 230 x 230 mm2, matrix = 384 x 384, slices = 256, TE = 2.52 ms, FA = 5°). Acquisition time was reduced by using 3× GRAPPA parallel imaging and 6/8 Partial Fourier in phase encoding direction (acquisition time (TA): 8 min 49 s (T1w) and 4 min 6 s (PDw)).</p> <p>To determine our region of interest, we acquired two hMT+ localiser runs. We used a 2D gradient echo (GE) echo planar imaging (EPI) sequence (1.6 mm isotropic nominal resolution; TE/TR = 18/2000 ms; in-plane field of view (FoV) 150×150 mm; matrix size 94 x 94; 28 slices; nominal flip angle (FA) = 69°; echo spacing = 0.71 ms; GRAPPA factor = 2, partial Fourier = 7/8; phase encoding direction head - foot; 240 volumes). We ensured that the area of acquisition had bilateral coverage of the posterior inferior temporal sulci, where we expected the hMT+ areas. Before acquisition of the first functional run, we collected 10 volumes for distortion correction - 5 volumes with the settings specified here and 5 more volumes with identical settings but opposite phase encoding (foot - head), here called "phase1" and "phase2".</p> <p>For the sub-millimetre measurements (Experiments 1: here called "task-ambiguous" and Experiments 2: here called "task-unambiguous"), we used a 2D GE EPI sequence (TE/TR = 25.6/2000 ms; in-plane FoV 148×148 mm; matrix size 186 x 186; slices = 28; nominal FA = 69°; echo spacing = 1.05 ms; GRAPPA factor = 3, partial Fourier = 6/8; phase encoding direction head - foot; 300 volumes), yielding a nominal resolution of 0.8 mm isotropic. Placement of the small functional slab was guided by online analysis of the hMT+ localizer data recorded immediately at the beginning of the first session. This allowed us to ensure bilateral coverage of area hMT+ for every subject. In the second scanning session, the slab was placed using Siemens auto-align functionality and manual corrections. Before acquisition of the first functional run, we collected 10 volumes for distortion correction (5 volumes with opposite phase encoding: foot - head). During acquisition, runs for the ambiguous and unambiguous motion experiments were interleaved.</p>
Data set: Can Stephen Curry really know? - Conscious access to outcome prediction of motor actions
<p>Data set associated with the following pre-print:</p> <p>Can Stephen Curry really know? - Conscious access to outcome prediction of motor actions. bioRxiv: 2021.03.30.437477</p>
10-Hz cross-spectral matrices from intracranial electrode recordings before and after loss of consciousness during propofol-induced general anesthesia
<p>Companion resources associated with the publication, "Propofol disrupts alpha dynamics in functionally distinct thalamocortical networks during loss of consciousness" (in press) (DOI: 10.1073/pnas.2207831120)</p> <p>https://www.biorxiv.org/content/10.1101/2022.04.05.487190v1.full.pdf</p> <p>Contents:</p> <p>1. 10-Hz cross-spectral matrices computed from pre- and post-loss of consciousness epochs (see manuscript for details)</p> <p>2. Channel labels of matrix rows and columns</p> <p>3. Channel coordinates and Freesurfer structural labels in MNI space.</p> <p>4. Structural segmentations of thalamic nuclei in MNI152 space. (Thanks to Fischl Lab, Martinos Center.)</p> <p>Patient demographics are listed in Supporting Information for the PNAS publication.</p> <p>For diffusion images used in the publication, please refer to the WU-Minn Human Connectome Project, using matches listed in the manuscript's Supporting Information.</p>
The eco-conscious wind turbine: design beyond purely economic metrics
<p>Figures from the publication <em>The eco-conscious wind turbine: design beyond purely economic<br> metrics</em>.</p>
Dataset of Intention to Whistleblow: Perception of Reporting Skill Mediates the Predicting Role of Class Consciousness and Perceived Probability of Revenge
<p><em>Dataset of </em><strong>Intention to Whistleblow: Perception of Reporting Skill Mediates the Predicting Role of Class Consciousness and Perceived Probability of Revenge</strong></p>
Slow and fast cortical cholinergic arousal is reduced in a mouse model of focal seizures with impaired consciousness
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The impact of the SARS-COV2 infection on the disorder of consciousness rehabilitation unit
<p>Disorders of consciousness include coma (cannot be aroused, eye remain closed), vegetative state—VS (can appear to be awake, but unable to purposefully interact) and minimally conscious state—MCS (minimal but definite awareness). The objective of this study is to assess the impact of the SARS-CoV-2 infection on the Disorder of Consciousness (DOC) Rehabilitation Unit.This is a retrospective, longitudinal, descriptive, observational, pilot study. We consecutively enrolled 18 patients (age range: 40–72 years, 9 females and 9 males), from three to five months after a brain injury. They were grouped into VS (n = 8) and MCS (n = 10). A confirmed case of COVID-19 was defined as a positive result on high-throughput sequencing or real-time reverse-transcription polymerase chain reaction analysis of throat swab specimens. We collected data of lung Computed Tomography (CT) and laboratory exams. DOC patients who were positive for SARS-CoV-2 were classified into severe and no severe infected group, according to the American Thoracic Society guidelines.A total of 18 hospitalized patients with (16) and without confirmed (2) SARS-CoV-2 infection were included in the analysis. After one month, a follow-up clinical evaluation reported that one patient died, one patient was transferred from Covid Unit to Emergency Unit and 3 patients were resulted negative to double swab and they returned to Rehabilitative Unit. Significant differences were reported about hypertension, cardiac disease and respiratory problems between the patients with severe infection and patients without severe infection (P< 0.001). The laboratory findings, such as blood cell counts (<em>P</em> < 0.001), C-reactive protein, D-dimer, potassium and vitamin D levels, seemed to be considered as useful prognostic predictors.To our knowledge, this is the first longitudinal study on a sample of chronic DOC patients affected by SARS-CoV-2. This study may offer important new clinical information on COVID-19 for management of DOC patients. Our findings showed that for the subjects with severe infection due to COVID-19, rapid clinical deterioration or worsening could be associated with clinical and laboratory findings, which could contribute to high mortality rate. During the COVID-19 epidemic period, the clinicians should consider all the reported risk factors to avoid delayed diagnosis or misdiagnosis and to prevent the infection transmission in DOC Rehabilitation Unit.</p>
Larger capacity for unconscious versus conscious episodic memory
<p>Episodic memory is the memory for experienced events. A peak competence of episodic memory is the mental combination of events to infer commonalities. Inferring commonalities may proceed with and without consciousness of events. Yet, what distinguishes conscious from unconscious inference? This question inspired nine experiments that featured strongly and weakly masked cartoon clips presented for unconscious and conscious inference. Each clip featured a scene with a visually impenetrable hiding place. Five animals crossed the scene one-by-one consecutively. One animal trajectory represented one event. The animals moved through the hiding place, where they might linger or not. Participants' task was to observe the animals' entrances and exits to maintain a mental record of which animals hid simultaneously. We manipulated information load to explore capacity limits. Memory of inferences was tested immediately, 3.5 or 6 minutes following encoding. Participants retrieved inferences well when encoding was conscious. When encoding was unconscious, participants needed to respond intuitively. Only habitually intuitive decision-makers exhibited a significant delayed retrieval of inferences drawn unconsciously. Their unconscious retrieval performance did not drop significantly with increasing information load, while conscious retrieval performance dropped significantly. A working memory network, including hippocampus, was activated during both conscious and unconscious inference and correlated with retrieval success. An episodic retrieval network, including hippocampus, was activated during both conscious and unconscious retrieval of inferences and correlated with retrieval success. Only conscious encoding/retrieval recruited additional brain regions outside these networks. Hence, levels of consciousness influenced the memories' behavioral impact, memory capacity, and the neural representational code.</p>
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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.