Data_Sheet_1_Functional Hyperconnectivity and Task-Based Activity Changes Associated With Neuropathic Pain After Spinal Cord Injury: A Pilot Study.docx by Shana R. Black (10950318)

“…We developed a novel somatosensory attention task to identify short term fluctuations in neural activity related to NP vs. non-painful somatosensation using functional magnetic resonance imaging (fMRI). We also collected high-resolution resting state fMRI to identify connectivity-based correlations over time between the two groups. …”

Table_1_The Impact of Spatial Normalization Strategies on the Temporal Features of the Resting-State Functional MRI: Spatial Normalization Before rs-fMRI Features Calculation May R... by Zhao Qing (8041313)

“…Our results indicated that Prenorm may induce algorithmic intersubject variability on tSNR and reduce its reliability, which also significantly affected ALFF and ReHo. We suggest using Postnorm instead of Prenorm for future rs-fMRI studies using ALFF/ReHo.…”

Presentation_1_Key considerations for child and adolescent MRI data collection.pdf by Brittany R. Davis (13782070)

“…In this discussion, we focus on collecting magnetic resonance imaging (MRI) data, as it is one of the more complex protocols used with children and youth. …”

Detection of Arterial Stenosis Based on Synchronized Signals from Wearable Pulse and Blood Flow Velocity Sensors by Pengrui Zhu (10024714)

Subjects:

Percent difference in Precision-Recall AUC, sensitivity, and specificity for pooled models relative to eBird-only models. by Reid Rumelt (21680956)

“…<p>Precision-Recall AUC and sensitivity were higher in all species (positive values); decreases in specificity, present in all species, were more minor (negative values, note differences in x-axis scales between panels).…”

Table_1_Verbal Training Induces Enhanced Functional Connectivity in Japanese Healthy Elderly Population.docx by Fan-Pei Gloria Yang (12187121)

“…<p>This study employs fMRI to examine the neural substrates of response to cognitive training in healthy old adults. …”

AUC statistics as calculated from simulated time series. Each statistical metric was calculated within sliding windows, throughout the pre-critical interval. We considered five-, fifteen-, and thirty-day sliding windows. Given that the temperature of the system increased to 12°C on day sixty, we also considered three pre-critical intervals: Days 1 to 60, Days 20 to 60, and Days 30 to 60. To evaluate trends in these metrics, we calculated Kendall’s rank correlation coefficient during the pre-critical interval, and compared control (constant temperature, non-epidemic) and warming (warming treatment, epidemic emergence) coefficients across simulations and experimental populations by calculating the area under the curve (AUC) statistic. Values less than 0.5 suggest that a decrease in the statistical metric indicates emergence, while values greater than 0.5 suggest that an increase in the statistical metric indicates emergence, with more extreme values indicating stronger tre by Madeline Jarvis-Cross (22394247)

“…To evaluate trends in these metrics, we calculated Kendall’s rank correlation coefficient during the pre-critical interval, and compared control (constant temperature, non-epidemic) and warming (warming treatment, epidemic emergence) coefficients across simulations and experimental populations by calculating the area under the curve (AUC) statistic. Values less than 0.5 suggest that a decrease in the statistical metric indicates emergence, while values greater than 0.5 suggest that an increase in the statistical metric indicates emergence, with more extreme values indicating stronger tre</p>…”

Image_1_Effects of variability in manually contoured spinal cord masks on fMRI co-registration and interpretation.eps by Mark A. Hoggarth (8921624)

“…Although group-level activation maps differed between raters, no systematic bias was identified. Increasing consistency in manual contouring of spinal cord fMRI data improved co-registration and inter-rater agreement in activation mapping, however our results suggest that improvements in image acquisition and post-processing are also critical to address.…”

Image_3_Effects of variability in manually contoured spinal cord masks on fMRI co-registration and interpretation.eps by Mark A. Hoggarth (8921624)

“…Although group-level activation maps differed between raters, no systematic bias was identified. Increasing consistency in manual contouring of spinal cord fMRI data improved co-registration and inter-rater agreement in activation mapping, however our results suggest that improvements in image acquisition and post-processing are also critical to address.…”

Image_4_Effects of variability in manually contoured spinal cord masks on fMRI co-registration and interpretation.eps by Mark A. Hoggarth (8921624)

“…Although group-level activation maps differed between raters, no systematic bias was identified. Increasing consistency in manual contouring of spinal cord fMRI data improved co-registration and inter-rater agreement in activation mapping, however our results suggest that improvements in image acquisition and post-processing are also critical to address.…”

Image_2_Effects of variability in manually contoured spinal cord masks on fMRI co-registration and interpretation.eps by Mark A. Hoggarth (8921624)

“…Although group-level activation maps differed between raters, no systematic bias was identified. Increasing consistency in manual contouring of spinal cord fMRI data improved co-registration and inter-rater agreement in activation mapping, however our results suggest that improvements in image acquisition and post-processing are also critical to address.…”

Data Sheet 1_Adolescent brain maturation associated with environmental factors: a multivariate analysis.pdf by Bhaskar Ray (18575134)

Prolonged inflammation leads to ongoing damage after spinal cord injury - Fig 2 by Jacek M. Kwiecien (8599929)

A forward modelling approach for the estimation of oxygen extraction fraction by calibrated fMRI by Michael Germuska (19080245)

“…One potential MRI method for the measurement of CMRO2 is via the combination of fMRI and cerebral blood flow (CBF) data acquired during periods of hypercapnic and hyperoxic challenges. …”

Video_9_Sampling Rate Effects on Resting State fMRI Metrics.AVI by Niko Huotari (6533783)

“…In this study, we examine the effect of sampling rate on currently used rs-fMRI FC metrics. Ultra-fast fMRI magnetic resonance encephalography (MREG) data, sampled with TR 0.1 s, was downsampled to different subsampled repetition times (sTR, range 0.3–3 s) for comparisons. …”

Video_2_Sampling Rate Effects on Resting State fMRI Metrics.AVI by Niko Huotari (6533783)

“…In this study, we examine the effect of sampling rate on currently used rs-fMRI FC metrics. Ultra-fast fMRI magnetic resonance encephalography (MREG) data, sampled with TR 0.1 s, was downsampled to different subsampled repetition times (sTR, range 0.3–3 s) for comparisons. …”

Video_3_Sampling Rate Effects on Resting State fMRI Metrics.AVI by Niko Huotari (6533783)

“…In this study, we examine the effect of sampling rate on currently used rs-fMRI FC metrics. Ultra-fast fMRI magnetic resonance encephalography (MREG) data, sampled with TR 0.1 s, was downsampled to different subsampled repetition times (sTR, range 0.3–3 s) for comparisons. …”

Video_1_Sampling Rate Effects on Resting State fMRI Metrics.AVI by Niko Huotari (6533783)

“…In this study, we examine the effect of sampling rate on currently used rs-fMRI FC metrics. Ultra-fast fMRI magnetic resonance encephalography (MREG) data, sampled with TR 0.1 s, was downsampled to different subsampled repetition times (sTR, range 0.3–3 s) for comparisons. …”

Video_4_Sampling Rate Effects on Resting State fMRI Metrics.AVI by Niko Huotari (6533783)

“…In this study, we examine the effect of sampling rate on currently used rs-fMRI FC metrics. Ultra-fast fMRI magnetic resonance encephalography (MREG) data, sampled with TR 0.1 s, was downsampled to different subsampled repetition times (sTR, range 0.3–3 s) for comparisons. …”

Video_7_Sampling Rate Effects on Resting State fMRI Metrics.AVI by Niko Huotari (6533783)

“…In this study, we examine the effect of sampling rate on currently used rs-fMRI FC metrics. Ultra-fast fMRI magnetic resonance encephalography (MREG) data, sampled with TR 0.1 s, was downsampled to different subsampled repetition times (sTR, range 0.3–3 s) for comparisons. …”