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

Demographic and Clinical Characteristics of Healthy Offspring Having a Parent with Bipolar Disorder and Age- and Sex- Matched Control Offspring of Healthy Parents.

<p>Abbreviations: HBO = healthy offspring having a parent diagnosed with bipolar disorder; HC = healthy control offspring of healthy parents; MFQ, Mood and Feelings Questionnaire (range, 0–68); SCARED, Screen for Childhood Anxiety and Related Disorders (range, 0–82); CALS, Child Affect Lability Scale (range, 0–80).</p

Summary of pattern recognition analyses.

<p>(1) Feature Extraction: the information from the beta images were transformed into an input vector. (2) Nested leave one out (LOO) Approach. We employed a nested (3-way) cross-validation, where we first excluded one matched pair of subjects to comprise the test set (test loop in light blue). We then performed a second split (validation loop in dark blue), where we removed 5000 voxels each iteration and repeatedly repartitioned the remaining 15 subject pairs into a validation set (1 pair) and training set (14 pairs) to compute the mean accuracy on the validation set. This procedure (removing voxels and computing mean accuracy) was repeated until all voxels were removed. We then selected the number of voxels that produced maximal accuracy on the validation set before applying it to the test set. The final accuracy was the mean accuracy over all test subjects (outer test loop in light blue). (3) We then generated a map training the GPC with all subjects and removing voxels until we obtained the mean number of voxels.</p

We used the predictive probabilities from the classifier at-risk adolescents vs. controls as a score for the at-risk adolescents.

<p>An ROC curve was used to evaluate if this score could be used to predict which of at-risk adolescents developed a future mood disorder. Each point on the ROC curve represents a sensitivity/specificity pair corresponding to a particular decision threshold. A test with perfect discrimination has a ROC curve that passes through the upper left corner (100% sensitivity, 100% specificity). The area under the ROC curve (AUC) was 0.78 (p<0.05).</p

Summary of results from pattern recognition analyses.

<p>A. Decision boundary and individual predictive probabilities. B. GPC weights overlaid on an anatomical template. The color code shows the relative weight of each voxel for the decision boundary (red scales: higher weights for healthy bipolar offspring and blue scales: higher weights for healthy controls). The discriminating pattern included clusters with higher weights for healthy bipolar offspring in the superior temporal sulcus (STS; x, y, z: -50, 11, -5) and in a posterior region of the ventromedial prefrontal cortex (VMPFC(p); x, y, z,: 0, 29, -14) and a cluster with higher weights for healthy controls in the anterior region of the ventromedial prefrontal cortex (VMPFC (a); x, y, z: -2, 51, -19) (x, y, z, are in Talairach coordinates).</p

Mean predictive probabilities (with standard error to the mean) for the comparison between healthy bipolar offspring with and without future onset of Axis I disorder, which in this sample was major depressive disorder and anxiety disorders.

<p>Mean predictive probabilities (with standard error to the mean) for the comparison between healthy bipolar offspring with and without future onset of Axis I disorder, which in this sample was major depressive disorder and anxiety disorders.</p

The impact of treatment expectations on exposure process and treatment outcome in childhood anxiety disorders

This study examined the relationship between caregivers’ and youths’ treatment expectations and characteristics of exposure tasks (quantity, mastery, compliance) in cognitive-behavioral therapy (CBT) for childhood anxiety. Additionally, compliance with exposure tasks was tested as a mediator of the relationship between treatment expectations and symptom improvement. Data were from youth (N = 279; 7–17 years old) enrolled in the Child/Adolescent Anxiety Multimodal Study (CAMS) and randomized to cognitive-behavioral therapy (CBT) or the combination of CBT and sertraline for the treatment of separation anxiety disorder, generalized anxiety disorder, and social phobia. Caregivers and youth independently reported treatment expectations prior to randomization, anxiety was assessed pre- and post-treatment by independent evaluators blind to treatment condition, and exposure characteristics were recorded by the cognitive-behavioral therapists following each session. For both caregivers and youths, more positive expectations that anxiety would improve with treatment were associated with greater compliance with exposure tasks, and compliance mediated the relationship between treatment expectations and change in anxiety symptoms following treatment. Additionally, more positive parent treatment expectations were related to a greater number and percentage of sessions with exposure. More positive youth treatment expectations were associated with greater mastery during sessions focused on exposure. Findings underscore the importance of addressing parents’ and youths’ treatment expectations at the outset of therapy to facilitate engagement in exposure and maximize therapeutic gains.</p

The interaction and main effects of genetic (risk calculator score) and environmental (negative stressful life events score) factors on activity and functional connectivity of reward processing task.

The interaction and main effects of genetic (risk calculator score) and environmental (negative stressful life events score) factors on activity and functional connectivity of reward processing task.</p

Interaction effects of risk calculator score and negative stressful life events schedule (nSLES) score on whole-brain functional connectivity to bilateral amygdala during emotion processing (top).

Positive interactions between risk calculator score, nSLES score and activity were found within 2 clusters after correction for multiple comparisons. A graphical representation of this interaction in the right lateral occipital cortex is presented here (bottom). Higher risk calculator score showed a greater positive association between functional connectivity and nSLES score, which was not present at low risk calculator score. A full set of these interaction plots can be found in the supplementary. * au = arbitrary units All results were corrected for using Z-statistic threshold at z>2.3, pFWE<0.0017. A contrast of all emotions versus shape conditions was used. Functional connectivity values were mean adjusted using a healthy control sample.</p

Demographic and clinical information.

Demographic and clinical information.</p