The present studies sought to (1) identify hubs within the human cerebral cortex, (2) determine the stability of hubs across subject groups and task states, and (3) explore whether the locations of hubs correlated with one component of AD pathology (Aβ deposition). The basic analytic strategy was to compute an estimate of the functional connectivity of each voxel within the brain. Regions showing a high degree of connectivity across participants were considered candidate hubs. Our primary measure of connectivity -- degree centrality or degree -- was defined as the number of voxels across the brain that showed strong correlation with the target voxel. Using this procedure, a map of candidate hubs was computed for an average of 24 participants (Data Set 1) and replicated in a second group of 24 participants (Data Set 2). Data Sets 1 and 2 were acquired while participants fixated on a cross-hair. As the results will reveal, the locations of cortical hubs were highly similar between participant groups. To explore in more detail the connectivity patterns of the identified hubs, we employed seed-based and formal network analyses on the combined data set (n=48). To explore whether the identified hubs reflect a stable property of cortex or were task dependent, maps of hubs were estimated in a third group of 12 participants (Data Set 3) that varied the task performed during data collection (passive visual fixation versus continuous task performance). Similar hubs were present across task states. To provide a consensus estimate of the locations of cortical hubs, the data across 127 participants were combined. The consensus estimate was compared to a map of Aβ deposition in early-stage AD obtained using PiB positron emission tomography (PET) imaging to explore whether hub regions are preferentially associated with the locations of Aβ accumulation. To aid visualization, all image maps were projected on to the left and right cerebral hemispheres of the inflated PALS surface using Caret software (Van Essen, 2005 (link)).
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Procedures
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Therapeutic or Preventive Procedure
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Task Performance
Task Performance
Task Performance refers to an individual's ability to carry out specific cognitive or physical tasks effectively and efficiently.
This encompasses factors such as accuracy, speed, and precision in completing assigned duties or goals.
Task Performance is influenced by a variety of physiological, psychological, and environmental variables, including motivation, fatigue, training, and task complexity.
Optimizing Task Performance is crucial for enhancing research reproducibility and productivity across diverse domains, from scientific experimentation to industrial applications.
By leveraging advanced AI-driven tools like PubCompare.ai, researchers can streamline their workflows, identify best practices, and make more informed decisions to improve Task Performance and drive scientific discovery.
This encompasses factors such as accuracy, speed, and precision in completing assigned duties or goals.
Task Performance is influenced by a variety of physiological, psychological, and environmental variables, including motivation, fatigue, training, and task complexity.
Optimizing Task Performance is crucial for enhancing research reproducibility and productivity across diverse domains, from scientific experimentation to industrial applications.
By leveraging advanced AI-driven tools like PubCompare.ai, researchers can streamline their workflows, identify best practices, and make more informed decisions to improve Task Performance and drive scientific discovery.
Most cited protocols related to «Task Performance»
Brain
Cerebral Hemisphere, Right
Cortex, Cerebral
Fixation, Ocular
Hair
Homo sapiens
Microtubule-Associated Proteins
Papillon-Lefevre Disease
Task Performance
BAD protein, human
Cognition
Patients
Physical Therapist
Task Performance
As a general purpose language representation model, BERT was pre-trained on English Wikipedia and BooksCorpus. However, biomedical domain texts contain a considerable number of domain-specific proper nouns (e.g. BRCA1, c.248T>C) and terms (e.g. transcriptional, antimicrobial), which are understood mostly by biomedical researchers. As a result, NLP models designed for general purpose language understanding often obtains poor performance in biomedical text mining tasks. In this work, we pre-train BioBERT on PubMed abstracts (PubMed) and PubMed Central full-text articles (PMC). The text corpora used for pre-training of BioBERT are listed in Table 1 , and the tested combinations of text corpora are listed in Table 2 . For computational efficiency, whenever the Wiki + Books corpora were used for pre-training, we initialized BioBERT with the pre-trained BERT model provided by Devlin et al. (2019) . We define BioBERT as a language representation model whose pre-training corpora includes biomedical corpora (e.g. BioBERT (+ PubMed)).
For tokenization, BioBERT uses WordPiece tokenization (Wu et al., 2016 ), which mitigates the out-of-vocabulary issue. With WordPiece tokenization, any new words can be represented by frequent subwords (e.g. Immunoglobulin => I ##mm ##uno ##g ##lo ##bul ##in). We found that using cased vocabulary (not lower-casing) results in slightly better performances in downstream tasks. Although we could have constructed new WordPiece vocabulary based on biomedical corpora, we used the original vocabulary of BERTBASE for the following reasons: (i) compatibility of BioBERT with BERT, which allows BERT pre-trained on general domain corpora to be re-used, and makes it easier to interchangeably use existing models based on BERT and BioBERT and (ii) any new words may still be represented and fine-tuned for the biomedical domain using the original WordPiece vocabulary of BERT.
For tokenization, BioBERT uses WordPiece tokenization (Wu et al., 2016 ), which mitigates the out-of-vocabulary issue. With WordPiece tokenization, any new words can be represented by frequent subwords (e.g. Immunoglobulin => I ##mm ##uno ##g ##lo ##bul ##in). We found that using cased vocabulary (not lower-casing) results in slightly better performances in downstream tasks. Although we could have constructed new WordPiece vocabulary based on biomedical corpora, we used the original vocabulary of BERT
BRCA1 protein, human
Conditioning, Psychology
Immunoglobulins
Microbicides
Task Performance
Transcription, Genetic
Cognitive Testing
Crossing Over, Genetic
Fingers
High Blood Pressures
N-(4-hydroxyphenethyl)cotinine carboxamide
Neoplasm Metastasis
neuro-oncological ventral antigen 2, human
Neuropsychological Tests
Pharmaceutical Preparations
Psychotropic Drugs
Task Performance
Thumb
Vascular Diseases
Vision
All participants had normal or corrected vision, no history of neurological, psychiatric, or vascular disease, and were not taking any psychotropic or hypertension medications. In addition, they were considered ‘non-gamers’ given that they played less than 2 hours of any type of video game per month. For NeuroRacer, each participant used their left thumb for tracking and their right index finger for responding to signs on a Logitech (Logitech, USA) gamepad controller. Participants engaged in three 3-minute runs of each condition in a randomized fashion. Signs were randomly presented in the same position over the fixation cross for 400 msec every 2, 2.5, or 3 seconds, with the speed of driving dissociated from sign presentation parameters. The multitasking cost index was calculated as follows: [(‘Sign & Drive’ performance - ‘Sign Only’ performance) / ‘Sign Only’ performance] * 100. EEG data for 1 MTT Post-training participant and 1 STT Pre-training participant were corrupted during acquisition. 2 MTT participants, 2 STT participants, and 4 NCC participants were unable to return to complete their 6-month follow-up assessments. Critically, no between-group differences were observed for neuropsychological assessments (p= .52) or Pre-training data involving: i) NeuroRacer thresholding for both Road (p= .57) and Sign (p= .43), ii) NeuroRacer component task performance (p> .10 for each task), iii) NeuroRacer multitasking costs (p= .63), iv) any of the cognitive tests (all ANOVAs at Pre-training: p≥ .26), v) ERSP power for either condition (p≥ .12), and, vi) coherence for either condition (p≥ .54).
Cognitive Testing
Crossing Over, Genetic
Fingers
High Blood Pressures
N-(4-hydroxyphenethyl)cotinine carboxamide
Neoplasm Metastasis
neuro-oncological ventral antigen 2, human
Neuropsychological Tests
Pharmaceutical Preparations
Psychotropic Drugs
Task Performance
Thumb
Vascular Diseases
Vision
Most recents protocols related to «Task Performance»
The Sea Hero Quest (SHQ) application features two main tasks, wayfinding, and path integration [28 (link), 29 (link)]. The wayfinding tasks presented via the SHQ application start with an allocentric view of a map that displays the participant’s current starting location and the locations of the goals to find once the game commences. There were no time restrictions for the duration that participants may study the map. The wayfinding task entails navigating a boat along a river to the goal locations in the order indicated by the map (e.g., goal one must be found first, then goal two, and so on). Goals are identifiable as buoys with flags marking the goal number. The task is complete when all goals have been located. These tasks have been shown to require cognitive processes such the interpretation of a map, planning a multi–stop route, learning and remembering the navigation route, monitoring progression along the route, as well as the transformation from an allocentric to an egocentric perspective needed for navigation [12 (link), 31 ]. The real–world ecological validity of the SHQ way finding tasks has been demonstrated by a significant correlation (r = 0.44) between navigation performance on the virtual wayfinding tasks and real–world city street finding tasks [12 (link)]. More specifically, Coutrot and colleagues [12 (link)] report a strong correlation between the distance participants travelled in the video game (in pixels) and in the real–world street network (in metres, measured by a GPS device). Wayfinding performance metrics derived from SHQ include duration as measured by time taken and distance quantified as the Euclidean distance travelled within the goals. The first two levels completed by a participant were treated as training levels, as they were only designed to assess ability to control the boat (i.e., tap left to turn left, tap right to turn right, swipe up to speed up and swipe down to halt). A subset of five levels that varied in difficulty were selected to evaluate wayfinding performance, each with increasing levels of difficulty.
Performance on the virtual wayfinding and path integration tasks are argued to be dependent on different cognitive processes, and this therefore accounts for the previous finding that performance on the virtual path integration tasks (as measured by flare accuracy) was not significantly correlated with performance in a real–world street network [12 (link)]. Specifically, the path integration tasks are thought to rely upon the perception of ego–motion during navigation, which serves to update one’s orientation to the virtual environment and is typically dependent on spatial and working memory [32 (link)]. The path integration tasks presented via the SHQ app entails navigating along a river with bends until the participant identifies a flare gun, at which point the boat rotates by 180°. The participant then chooses to shoot the flare in one of three directions (right, front, and left) that they believe points to the correct starting location. They are awarded stars for this choice, three stars for the correct answer (correctly selecting the direction referencing the starting point), two stars for the second closest direction, and one star for the third closest direction. A subset of three levels that varied in route complexity and thus difficulty was selected to evaluate path integration performance, with each level increasing in the number of transverse bends (one, three and four). SeeFig 2 for visual examples of wayfinding and path integration tasks presented by the SHQ application.
Performance on the virtual wayfinding and path integration tasks are argued to be dependent on different cognitive processes, and this therefore accounts for the previous finding that performance on the virtual path integration tasks (as measured by flare accuracy) was not significantly correlated with performance in a real–world street network [12 (link)]. Specifically, the path integration tasks are thought to rely upon the perception of ego–motion during navigation, which serves to update one’s orientation to the virtual environment and is typically dependent on spatial and working memory [32 (link)]. The path integration tasks presented via the SHQ app entails navigating along a river with bends until the participant identifies a flare gun, at which point the boat rotates by 180°. The participant then chooses to shoot the flare in one of three directions (right, front, and left) that they believe points to the correct starting location. They are awarded stars for this choice, three stars for the correct answer (correctly selecting the direction referencing the starting point), two stars for the second closest direction, and one star for the third closest direction. A subset of three levels that varied in route complexity and thus difficulty was selected to evaluate path integration performance, with each level increasing in the number of transverse bends (one, three and four). See
Courage
Decompression Sickness
Disease Progression
Medical Devices
Memory, Short-Term
Mental Processes
Motion Perception
Rivers
Stars, Celestial
Task Performance
Non-participatory observation and investigation were conducted from June 1st to October 31st, 2021. A total of 152 nurses (response rate 98.70%) were initially permitted to participate and were observed in all EHR tasks during their shifts (8 h). Day shifts were scheduled from 8:00 A.M. to 3:30 P.M. or 8:00 A.M. to 5:00 P.M., and the night shift was from 5:00 P.M. to 1:00 A.M. Questionnaires including several scales were distributed at the end of the EHR task observation to measure nurses’ mental workload for the task and to explore the potential influencing factors on mental workload and task performance. Initial permission was obtained from various department heads and hospital administrators before the study was conducted. Prior to each observation, individual nurses were informed about the study objectives and procedure, and written consent was obtained.
Head
Hospital Administrators
Nurses
Respiratory Diaphragm
Task Performance
Data from the real switch task were used to exclude subjects who had trouble detecting real stimulus direction switches from further analyses (i.e., of their bi-stable task data). We defined correct responses in the real switch task as those that matched the stimulus rotation direction and occurred within 4 s after a direction change. This was based on our examination of the average reaction time for correct responses (Supplemental Figure 2 ), the average percept durations towards and away from the physical rotation direction (Supplemental Figure 3 ), the distribution of reaction times during the real switch task (Supplemental Figure 4 ), and the total number of reported switches (Supplemental Figure 5 ). We defined poor real switch task performance as having 6 or fewer correct responses (≤ 63.6% accuracy) made within 4 s of a stimulus direction change (Supplemental Figure 4B ). We chose these thresholds after visually inspecting the data and determining that the large majority of participants and responses fell above these thresholds. Of the 147 total data sets with real switch data, including re-test sessions, we excluded a total of 30 data sets who did not meet the above post-hoc criteria (6 controls, 7 relatives, 17 PwPP).
Physical Examination
Task Performance
Neurochemical concentrations in the mid-occipital lobe were collected as part of a 7 T magnetic resonance spectroscopy (MRS) scan on the same day as behavioral SFM data. For full scanning details, see (Schallmo et al., 2023 (link)). Data were acquired on a Siemens MAGNETOM 7 T scanner with a custom surface radio frequency head coil using a STEAM sequence (Marjańska et al., 2017 (link)) with the following parameters: TR = 5000 ms, TE = 8 ms, volume size = 30 mm (left-right) × 18 mm (anterior-posterior) × 18 mm (inferior-superior), 3D outer volume suppression interleaved with VAPOR water suppression (Tkáč et al., 2001 (link)), 2048 complex data points with a 6000 Hz spectral bandwidth, chemical shift displacement error = 4% per ppm. B0 shimming was performed using FAST(EST) MAP to ensure a linewidth of water within the occipital voxel ≤ 15 Hz (Gruetter, 1992 ).
We processed our MRS data using the matspec toolbox (github.com/romainVala/matspec ) in MATLAB, including frequency and phase correction. Concentrations for 18 different metabolites including glutamate, glutamine, and GABA were quantified in each scanning session using LCModel. We scaled metabolite concentrations relative to an unsuppressed water signal reference, after correcting for differences in gray matter, white matter, and CSF fractions within each subject’s MRS voxel, the proportion of water in these different tissue types, and the different T1 and T2 relaxation times of the different tissue types. Tissue fractions within the voxel were quantified in each subject using individual gray matter and white matter surface models from FreeSurfer (Fischl, 2012 (link)). MRS data sets were excluded based on the following data quality criteria: H2O line width > 15, LCModel spectrum line width > 5 Hz or LCModel SNR < 40. Out of a total of 193 MRS datasets (54 controls, 44 relatives, and 95 PwPP), 10 sets (1 control, 4 relatives, 5 PwPP) were excluded in this way, leaving 183 total MRS datasets. In addition to subjects whose SFM data we excluded for having poor real switch task performance, and excluding re-test sessions, this left a total of 114 participants with usable SFM and MRS data (37 controls, 33 relatives, and 44 PwPP).
In order to probe the possible role of excitatory and inhibitory markers during bi-stable perception in PwPP, we examined relationships between metabolite concentrations from MRS and our bi-stable SFM behavioral measures. Specifically, we used Spearman rank correlation to test for correlations between metabolite levels from MRS (i.e., GABA, glutamate, and glutamine) and average switch rates across participants from all three groups. As in our other correlational analyses, data from retest sessions were excluded, as Spearman correlations assume independence across data points.
We processed our MRS data using the matspec toolbox (
In order to probe the possible role of excitatory and inhibitory markers during bi-stable perception in PwPP, we examined relationships between metabolite concentrations from MRS and our bi-stable SFM behavioral measures. Specifically, we used Spearman rank correlation to test for correlations between metabolite levels from MRS (i.e., GABA, glutamate, and glutamine) and average switch rates across participants from all three groups. As in our other correlational analyses, data from retest sessions were excluded, as Spearman correlations assume independence across data points.
gamma Aminobutyric Acid
Glutamate
Glutamine
Gray Matter
Head
Histocompatibility Testing
Magnetic Resonance Spectroscopy
Occipital Lobe
Psychological Inhibition
Steam
Task Performance
Tissues
Water Vapor
White Matter
In order to assess the stability of ambiguous motion direction percepts in this task, we compared the distribution of durations for all reported percepts across the three groups using two sample Kolmogorov-Smirnov tests. This allows us to examine how the durations of reported percepts may have differed between groups.
Next, we calculated the switch rate (switches reported per second) and average percept duration (average length of percept dominance) during each task for each subject. To quantify switch rates, we measured the total number of switches in each block and divided by the total time (2 min per block). Because the switch rate data were highly skewed (based upon visual inspection), we then normalized the data by performing a log10 transformation. As it is possible to not respond during a 2 min block in the bi-stable task, some participants can have switch rates of 0 Hz, which when normalized using log10, become values of negative infinity. Therefore, we replaced values of 0 switches per block with 0.5 switches / 120 s when normalizing. This occurred for a total of 2 participants (1 PwPP, 1 control). Although we measured both switch rate and percept duration, both of these measures displayed the same trends, and were highly correlated with one another. Therefore, to avoid repetition, we present results of the switch rate analysis in the main text, while percept duration data are presented in theSupplemental Information . We note that although the switch rate and percept duration data were very similar, they were not identical. This is because the percept duration is calculated only after the participant’s initial response in each block, which occurs a short time after the stimulus onset, whereas the switch rate was defined based on the full two minute duration of the block. As the data in both the real switch and bi-stable tasks did not have equal variance across groups and were skewed (even after log10 transformation), we performed a Kruskal-Wallis one-way non-parametric ANOVAs to assess group differences in switch rates during the real switch task.
We also measured the stability of bi-stable perception dynamics over time by examining longitudinal variability in a subset of our participants. To do so, we compared switch rates in the bi-stable task measured across two different experimental sessions (months apart) within the same individuals (49 individuals with re-test data; 10 controls, 0 relative, 39 PwPP). Of these individuals with re-test data, 9 participants (1 control and 8 PwPP) had 1 or more of their data sets excluded based on poor real switch task performance. This left a total of 40 individuals (9 controls and 31 PwPP) with usable re-test data. Information about the amount of time between testing sessions and number of participants who completed a second test session is provided inTable 1 . We calculated the intraclass correlation coefficient (ICC(3,k)) between switch rates for the first and second sessions (Shrout & Fleiss, 1979 (link)).
Finally, we sought to identify any differences between clinical diagnoses within our PwPP group. Specifically, we compared participants with schizophrenia (n = 25) and bipolar disorder (n = 16) to healthy controls (n = 37). People with schizoaffective disorder were not included in this analysis, due to a smaller sample size (n = 7).
Next, we calculated the switch rate (switches reported per second) and average percept duration (average length of percept dominance) during each task for each subject. To quantify switch rates, we measured the total number of switches in each block and divided by the total time (2 min per block). Because the switch rate data were highly skewed (based upon visual inspection), we then normalized the data by performing a log10 transformation. As it is possible to not respond during a 2 min block in the bi-stable task, some participants can have switch rates of 0 Hz, which when normalized using log10, become values of negative infinity. Therefore, we replaced values of 0 switches per block with 0.5 switches / 120 s when normalizing. This occurred for a total of 2 participants (1 PwPP, 1 control). Although we measured both switch rate and percept duration, both of these measures displayed the same trends, and were highly correlated with one another. Therefore, to avoid repetition, we present results of the switch rate analysis in the main text, while percept duration data are presented in the
We also measured the stability of bi-stable perception dynamics over time by examining longitudinal variability in a subset of our participants. To do so, we compared switch rates in the bi-stable task measured across two different experimental sessions (months apart) within the same individuals (49 individuals with re-test data; 10 controls, 0 relative, 39 PwPP). Of these individuals with re-test data, 9 participants (1 control and 8 PwPP) had 1 or more of their data sets excluded based on poor real switch task performance. This left a total of 40 individuals (9 controls and 31 PwPP) with usable re-test data. Information about the amount of time between testing sessions and number of participants who completed a second test session is provided in
Finally, we sought to identify any differences between clinical diagnoses within our PwPP group. Specifically, we compared participants with schizophrenia (n = 25) and bipolar disorder (n = 16) to healthy controls (n = 37). People with schizoaffective disorder were not included in this analysis, due to a smaller sample size (n = 7).
Bipolar Disorder
Differential Diagnosis
neuro-oncological ventral antigen 2, human
Schizoaffective Disorder
Schizophrenia
Task Performance
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More about "Task Performance"
Task Efficiency, Cognitive Performance, Motor Skills, Workplace Productivity, Research Reproducibility, MATLAB Analysis, Presentation Slide Design, SPSS Statistical Modeling, Prism Data Visualization.
Task performance refers to an individual's capacity to execute specific cognitive or physical tasks effectively and efficiently.
This encompasses factors such as accuracy, speed, and precision in completing assigned duties or goals.
Task performance is influenced by a variety of physiological, psychological, and environmental variables, including motivation, fatigue, training, and task complexity.
Optimizing task performance is crucial for enhancing research reproducibility and productivity across diverse domains, from scientific experimentation to industrial applications.
By leveraging advanced AI-driven tools like PubCompare.ai, researchers can streamline their workflows, identify best practices, and make more informed decisions to improve task performance and drive scientific discovery.
MATLAB is a powerful software suite used for numerical computing, data analysis, and visualization, which can be leveraged to optimize task performance in research settings.
Presentation software, such as PowerPoint or Prezi, enables researchers to effectively communicate their findings and enhance the impact of their work.
SPSS, a statistical analysis software, provides advanced modeling capabilities to help researchers understand the factors influencing task performance.
By incorporating insights from these tools and leveraging the power of PubCompare.ai, researchers can enhance their task performance, improve research reproducibility, and accelerate scientific progress.
Whether you're conducting experiments, analyzing data, or presenting your findings, optimizing task performance is key to driving innovation and advancing knowledge in your field.
Task performance refers to an individual's capacity to execute specific cognitive or physical tasks effectively and efficiently.
This encompasses factors such as accuracy, speed, and precision in completing assigned duties or goals.
Task performance is influenced by a variety of physiological, psychological, and environmental variables, including motivation, fatigue, training, and task complexity.
Optimizing task performance is crucial for enhancing research reproducibility and productivity across diverse domains, from scientific experimentation to industrial applications.
By leveraging advanced AI-driven tools like PubCompare.ai, researchers can streamline their workflows, identify best practices, and make more informed decisions to improve task performance and drive scientific discovery.
MATLAB is a powerful software suite used for numerical computing, data analysis, and visualization, which can be leveraged to optimize task performance in research settings.
Presentation software, such as PowerPoint or Prezi, enables researchers to effectively communicate their findings and enhance the impact of their work.
SPSS, a statistical analysis software, provides advanced modeling capabilities to help researchers understand the factors influencing task performance.
By incorporating insights from these tools and leveraging the power of PubCompare.ai, researchers can enhance their task performance, improve research reproducibility, and accelerate scientific progress.
Whether you're conducting experiments, analyzing data, or presenting your findings, optimizing task performance is key to driving innovation and advancing knowledge in your field.