Stability of Satellite Planes in M31 II: Effects of the Dark Subhalo Population

The planar arrangement of nearly half the satellite galaxies of M31 has been a source of mystery and speculation since it was discovered. With a growing number of other host galaxies showing these satellite galaxy planes, their stability and longevity have become central to the debate on whether the presence of satellite planes are a natural consequence of prevailing cosmological models, or represent a challenge. Given the dependence of their stability on host halo shape, we look into how a galaxy plane's dark matter environment influences its longevity. An increased number of dark matter subhalos results in increased interactions that hasten the deterioration of an already-formed plane of satellite galaxies in spherical dark halos. The role of total dark matter mass fraction held in subhalos in dispersing a plane of galaxies present non trivial effects on plane longevity as well. But any misalignments of plane inclines to major axes of flattened dark matter halos lead to their lifetimes being reduced to < 3 Gyrs. Distributing > 40% of total dark mass in subhalos in the overall dark matter distribution results in a plane of satellite galaxies that is prone to change through the 5 Gyr integration time period.Comment: 11 pages, 9 figures, accepted to MNRAS September 22 201

Task Related Neural Activity at Time 2.

<p>Plotted values represent t-statistics from the emotional-faces-minus-shapes contrast during the second testing occasion. Default pipeline: data were modeled as a block-design with a canonical hemodynamic response function (HRF); Flexible HRF pipeline: data were modeled as a mixed block/event design and temporal and dispersion derivatives were added to the design matrix as additional basis functions; mCompCor pipeline: data were modeled as a block design and a additional regressor was added to the design matrix to account for physiological noise in the global signal; Combined Flexible HRF and mCompCor pipeline: data were modeled as a mixed block/event design and both the Flexible HRF and mCompCor components were implemented.</p

Proportion of Models for which the Assumption Test Failed.

<p>Pipeline(s) with the fewest failures compared to each of the other pipelines: ** at <i>p</i><0.01; *** at <i>p</i><0.001.</p>a<p>Number of models = 87744 per pipeline. Generalized linear mixed effects models indicated that the pipelines differed with regard to the proportion of models meeting each assumption (all <i>F</i>s (3, 141) >358.50, <i>p</i>s<0.001).</p>b<p>The mCompCor and the Combined HRF + mCompCor models did not differ at p<0.05 with respect to the normality assumption. Both are indicated as representing fewer model failures than the remaining pipelines.</p>c<p>Number of models = 8448 per pipeline. The pipelines differed with regard to the proportion of models meeting each assumption (all <i>F</i>s (3, 141) >82.65, <i>p</i>s<0.001).</p><p>Proportion of Models for which the Assumption Test Failed.</p

Voxelwise ICC Estimates in Task Related Regions at Time 1 in all Pipelines.

<p>Plotted values on the left represent voxelwise intraclass correlation coefficients (ICCs) in the regions that showed overlapping activity between the four pipelines at time 1. Plots on the right represent histograms of the voxelwise ICCs from the overlap mask, along with the Median ICC for the region. Default pipeline: data were modeled as a block-design with a canonical hemodynamic response function (HRF); Flexible HRF pipeline: data were modeled as a mixed block/event design and temporal and dispersion derivatives were added to the design matrix as additional basis functions; mCompCor pipeline: data were modeled as a block design and a additional regressor was added to the design matrix to account for physiological noise in the global signal; Combined Flexible HRF and mCompCor pipeline: data were modeled as a mixed block/event design and both the Flexible HRF and mCompCor components were implemented.</p

Model Specification and the Reliability of fMRI Results: Implications for Longitudinal Neuroimaging Studies in Psychiatry

<div><p>Functional Magnetic Resonance Imagine (fMRI) is an important assessment tool in longitudinal studies of mental illness and its treatment. Understanding the psychometric properties of fMRI-based metrics, and the factors that influence them, will be critical for properly interpreting the results of these efforts. The current study examined whether the choice among alternative model specifications affects estimates of test-retest reliability in key emotion processing regions across a 6-month interval. Subjects (N = 46) performed an emotional-faces paradigm during fMRI in which neutral faces dynamically morphed into one of four emotional faces. Median voxelwise intraclass correlation coefficients (mvICCs) were calculated to examine stability over time in regions showing task-related activity as well as in bilateral amygdala. Four modeling choices were evaluated: a default model that used the canonical hemodynamic response function (HRF), a flexible HRF model that included additional basis functions, a modified CompCor (mCompCor) model that added corrections for physiological noise in the global signal, and a final model that combined the flexible HRF and mCompCor models. Model residuals were examined to determine the degree to which each pipeline met modeling assumptions. Results indicated that the choice of modeling approaches impacts both the degree to which model assumptions are met and estimates of test-retest reliability. ICC estimates in the visual cortex increased from poor (mvICC = 0.31) in the default pipeline to fair (mvICC = 0.45) in the full alternative pipeline – an increase of 45%. In nearly all tests, the models with the fewest assumption violations generated the highest ICC estimates. Implications for longitudinal treatment studies that utilize fMRI are discussed.</p></div

Voxelwise ICC Estimates in Bilateral Amygdala.

<p>Plotted values on the left represent voxelwise intraclass correlation coefficients (ICCs) in bilateral amygdala. Plots on the right represent histograms of the voxelwise ICCs from the bilateral amygdala mask, along with the Median ICC for the region. Default pipeline: data were modeled as a block-design with a canonical hemodynamic response function (HRF); Flexible HRF pipeline: data were modeled as a mixed block/event design and temporal and dispersion derivatives were added to the design matrix as additional basis functions; mCompCor pipeline: data were modeled as a block design and a additional regressor was added to the design matrix to account for physiological noise in the global signal; Combined Flexible HRF and mCompCor pipeline: data were modeled as a mixed block/event design and both the Flexible HRF and mCompCor components were implemented.</p

Demographic Features and Severity of Depression.

*<p>HRSD-25∶25-item Hamilton Rating Scale for Depression.</p

Within-group decoding accuracy in Healthy Controls (HC) and Depressed Patients (DP).

*<p>Overall Accuracy is the mean between emotional accuracy (emotional correctly classified as emotional) and neutral accuracy (neutral correctly classified as neutral).</p

Emotional Faces N-Back Task Performance Among Individuals with Schizophrenia and Healthy Volunteers.

<p>Emotional Faces N-Back Task Performance Among Individuals with Schizophrenia and Healthy Volunteers.</p

Fronto-Limbic Differences in Brain Activity Between Individuals with Schizophrenia and Healthy Volunteers During Voluntary Attentional Control of Positive Versus Negative Emotions.

<p>Fronto-Limbic Differences in Brain Activity Between Individuals with Schizophrenia and Healthy Volunteers During Voluntary Attentional Control of Positive Versus Negative Emotions.</p