Neural Correlates of Predictive Saccades.

Every day we generate motor responses that are timed with external cues. This phenomenon of sensorimotor synchronization has been simplified and studied extensively using finger tapping sequences that are executed in synchrony with auditory stimuli. The predictive saccade paradigm closely resembles the finger tapping task. In this paradigm, participants follow a visual target that steps between two fixed locations on a visual screen at predictable ISIs. Eventually, the time from target appearance to saccade initiation (i.e., saccadic RT) becomes predictive with values nearing 0 msec. Unlike the finger tapping literature, neural control of predictive behavior described within the eye movement literature has not been well established and is inconsistent, especially between neuroimaging and patient lesion studies. To resolve these discrepancies, we used fMRI to investigate the neural correlates of predictive saccades by contrasting brain areas involved with behavior generated from the predictive saccade task with behavior generated from a reactive saccade task (saccades are generated toward targets that are unpredictably timed). We observed striking differences in neural recruitment between reactive and predictive conditions: Reactive saccades recruited oculomotor structures, as predicted, whereas predictive saccades recruited brain structures that support timing in motor responses, such as the crus I of the cerebellum, and structures commonly associated with the default mode network. Therefore, our results were more consistent with those found in the finger tapping literature

The utility of multivariate outlier detection techniques for data quality evaluation in large studies: an application within the ONDRI project

Abstract Background Large and complex studies are now routine, and quality assurance and quality control (QC) procedures ensure reliable results and conclusions. Standard procedures may comprise manual verification and double entry, but these labour-intensive methods often leave errors undetected. Outlier detection uses a data-driven approach to identify patterns exhibited by the majority of the data and highlights data points that deviate from these patterns. Univariate methods consider each variable independently, so observations that appear odd only when two or more variables are considered simultaneously remain undetected. We propose a data quality evaluation process that emphasizes the use of multivariate outlier detection for identifying errors, and show that univariate approaches alone are insufficient. Further, we establish an iterative process that uses multiple multivariate approaches, communication between teams, and visualization for other large-scale projects to follow. Methods We illustrate this process with preliminary neuropsychology and gait data for the vascular cognitive impairment cohort from the Ontario Neurodegenerative Disease Research Initiative, a multi-cohort observational study that aims to characterize biomarkers within and between five neurodegenerative diseases. Each dataset was evaluated four times: with and without covariate adjustment using two validated multivariate methods – Minimum Covariance Determinant (MCD) and Candès’ Robust Principal Component Analysis (RPCA) – and results were assessed in relation to two univariate methods. Outlying participants identified by multiple multivariate analyses were compiled and communicated to the data teams for verification. Results Of 161 and 148 participants in the neuropsychology and gait data, 44 and 43 were flagged by one or both multivariate methods and errors were identified for 8 and 5 participants, respectively. MCD identified all participants with errors, while RPCA identified 6/8 and 3/5 for the neuropsychology and gait data, respectively. Both outperformed univariate approaches. Adjusting for covariates had a minor effect on the participants identified as outliers, though did affect error detection. Conclusions Manual QC procedures are insufficient for large studies as many errors remain undetected. In these data, the MCD outperforms the RPCA for identifying errors, and both are more successful than univariate approaches. Therefore, data-driven multivariate outlier techniques are essential tools for QC as data become more complex

The utility of multivariate outlier detection techniques for data quality evaluation in large studies: An application within the ONDRI project

© 2019 The Author(s). Background: Large and complex studies are now routine, and quality assurance and quality control (QC) procedures ensure reliable results and conclusions. Standard procedures may comprise manual verification and double entry, but these labour-intensive methods often leave errors undetected. Outlier detection uses a data-driven approach to identify patterns exhibited by the majority of the data and highlights data points that deviate from these patterns. Univariate methods consider each variable independently, so observations that appear odd only when two or more variables are considered simultaneously remain undetected. We propose a data quality evaluation process that emphasizes the use of multivariate outlier detection for identifying errors, and show that univariate approaches alone are insufficient. Further, we establish an iterative process that uses multiple multivariate approaches, communication between teams, and visualization for other large-scale projects to follow. Methods: We illustrate this process with preliminary neuropsychology and gait data for the vascular cognitive impairment cohort from the Ontario Neurodegenerative Disease Research Initiative, a multi-cohort observational study that aims to characterize biomarkers within and between five neurodegenerative diseases. Each dataset was evaluated four times: with and without covariate adjustment using two validated multivariate methods - Minimum Covariance Determinant (MCD) and Candès\u27 Robust Principal Component Analysis (RPCA) - and results were assessed in relation to two univariate methods. Outlying participants identified by multiple multivariate analyses were compiled and communicated to the data teams for verification. Results: Of 161 and 148 participants in the neuropsychology and gait data, 44 and 43 were flagged by one or both multivariate methods and errors were identified for 8 and 5 participants, respectively. MCD identified all participants with errors, while RPCA identified 6/8 and 3/5 for the neuropsychology and gait data, respectively. Both outperformed univariate approaches. Adjusting for covariates had a minor effect on the participants identified as outliers, though did affect error detection. Conclusions: Manual QC procedures are insufficient for large studies as many errors remain undetected. In these data, the MCD outperforms the RPCA for identifying errors, and both are more successful than univariate approaches. Therefore, data-driven multivariate outlier techniques are essential tools for QC as data become more complex

sj-pdf-1-cpa-10.1177_07067437221147443 - Supplemental material for Neuropsychiatric Symptom Burden across Neurodegenerative Disorders and its Association with Function

Supplemental material, sj-pdf-1-cpa-10.1177_07067437221147443 for Neuropsychiatric Symptom Burden across Neurodegenerative Disorders and its Association with Function by Daniel Kapustin, Shadi Zarei, Wei Wang, Malcolm A. Binns, Paula M. McLaughlin, Agessandro Abrahao, Sandra E. Black, Michael Borrie, David Breen, Leanna Casaubon, Dar Dowlatshahi, Elizabeth Finger, Corinne E Fischer, Andrew Frank, Morris Freedman, David Grimes, Ayman Hassan, Mandar Jog, Donna Kwan, Anthony Lang, Brian Levine, Jennifer Mandzia, Connie Marras, Mario Masellis, Joseph B. Orange, Stephen Pasternak, Alicia Peltsch, Bruce G. Pollock, Tarek K. Rajji, Angela Roberts, Demetrios Sahlas, Gustavo Saposnik, Dallas Seitz, Christen Shoesmith, Alisia Southwell, Thomas D.L. Steeves, Kelly Sunderland, Richard H Swartz, Brian Tan, David F. Tang-Wai, Maria Carmela Tartaglia, Angela Troyer, John Turnbull, Lorne Zinman, and Sanjeev Kumar in The Canadian Journal of Psychiatry</p

sj-docx-2-cpa-10.1177_07067437221147443 - Supplemental material for Neuropsychiatric Symptom Burden across Neurodegenerative Disorders and its Association with Function

Supplemental material, sj-docx-2-cpa-10.1177_07067437221147443 for Neuropsychiatric Symptom Burden across Neurodegenerative Disorders and its Association with Function by Daniel Kapustin, Shadi Zarei, Wei Wang, Malcolm A. Binns, Paula M. McLaughlin, Agessandro Abrahao, Sandra E. Black, Michael Borrie, David Breen, Leanna Casaubon, Dar Dowlatshahi, Elizabeth Finger, Corinne E Fischer, Andrew Frank, Morris Freedman, David Grimes, Ayman Hassan, Mandar Jog, Donna Kwan, Anthony Lang, Brian Levine, Jennifer Mandzia, Connie Marras, Mario Masellis, Joseph B. Orange, Stephen Pasternak, Alicia Peltsch, Bruce G. Pollock, Tarek K. Rajji, Angela Roberts, Demetrios Sahlas, Gustavo Saposnik, Dallas Seitz, Christen Shoesmith, Alisia Southwell, Thomas D.L. Steeves, Kelly Sunderland, Richard H Swartz, Brian Tan, David F. Tang-Wai, Maria Carmela Tartaglia, Angela Troyer, John Turnbull, Lorne Zinman, and Sanjeev Kumar in The Canadian Journal of Psychiatry</p

Soluble Epoxide Hydrolase Derived Linoleic Acid Oxylipins, Small Vessel Disease Markers, and Neurodegeneration in Stroke

Background Cerebral small vessel disease is associated with higher ratios of soluble-epoxide hydrolase derived linoleic acid diols (12,13-dihydroxyoctadecenoic acid [DiHOME]&nbsp;and 9,10-DiHOME) to their parent epoxides (12(13)-epoxyoctadecenoic acid [EpOME]&nbsp;and 9(10)-EpOME); however, the relationship has not yet been examined in stroke. Methods and Results Participants with mild to moderate small vessel stroke or large vessel stroke were selected based on clinical and imaging&nbsp;criteria. Metabolites were quantified by ultra-high-performance liquid chromatography-mass spectrometry. Volumes of stroke, lacunes, white matter hyperintensities, magnetic resonance imaging visible perivascular spaces, and free water diffusion were quantified from structural and diffusion magnetic resonance imaging (3 Tesla). Adjusted linear regression models were used for analysis. Compared with&nbsp;participants with large vessel stroke (n=30), participants with small vessel stroke (n=50) had a higher 12,13-DiHOME/12(13)-EpOME ratio (β=0.251, P=0.023). The 12,13-DiHOME/12(13)-EpOME ratio was associated with more lacunes (β=0.266, P=0.028) but not with large vessel stroke volumes. Ratios of 12,13-DiHOME/12(13)-EpOME and 9,10-DiHOME/9(10)-EpOME were associated with greater volumes of white matter hyperintensities (β=0.364, P&lt;0.001; β=0.362, P&lt;0.001) and white matter MRI-visible perivascular spaces (β=0.302, P=0.011; β=0.314, P=0.006). In small vessel stroke, the 12,13-DiHOME/12(13)-EpOME ratio was associated with higher white matter free water diffusion (β=0.439, P=0.016), which was specific to the temporal lobe in exploratory regional analyses. The 9,10-DiHOME/9(10)-EpOME ratio was associated with temporal lobe atrophy (β=-0.277, P=0.031). Conclusions Linoleic acid markers of cytochrome P450/soluble-epoxide hydrolase activity were associated with small versus large vessel stroke, with small vessel disease markers consistent with blood brain barrier and neurovascular-glial disruption, and temporal lobe atrophy. The findings may indicate a novel modifiable risk factor for small vessel disease and related neurodegeneration