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

Story time turbocharger? Child engagement during shared reading and cerebellar activation and connectivity in preschool-age children listening to stories

<div><p>Expanding behavioral and neurobiological evidence affirms benefits of shared (especially parent-child) reading on cognitive development during early childhood. However, the majority of this evidence involves factors under caregiver control, the influence of those intrinsic to the child, such as interest or engagement in reading, largely indirect or unclear. The cerebellum is increasingly recognized as playing a “smoothing” role in higher-level cognitive processing and learning, via feedback loops with language, limbic and association cortices. We utilized functional MRI to explore the relationship between child engagement during a mother-child reading observation and neural activation and connectivity during a story listening task, in a sample of 4-year old girls. Children exhibiting greater interest and engagement in the narrative showed increased activation in right-sided cerebellar association areas during the task, and greater functional connectivity between this activation cluster and language and executive function areas. Our findings suggest a potential cerebellar “boost” mechanism responsive to child engagement level that may contribute to emergent literacy development during early childhood, and synergy between caregiver and child factors during story sharing.</p></div

Tri-planar view of neural activation (stories>tones) for the story listening task with child engagement score as explanatory variable.

<p>Orthogonal tri-planar view (origin x = 6, y = -78, z = -26, MNI coordinate space; right Crus II/Vermis VI) of cerebellar activation for the story listening task (stories>tones; n = 22), with child engagement score as explanatory variable. Total cluster size 1164 voxels (p<0.05, FDR corrected). Color scale <i>t</i> = 1.25 (cooler) to 4 (hotter). Radiological orientation, left = right, right = left, sagittal plane viewed from the right.</p

Observed child engagement scores and behaviors.

<p>Observed child engagement scores and behaviors.</p

Observed child engagement scores.

<p>Histogram and density curve for observed child engagement scores, including mean (dashed line) and standard deviation (sd). Total score for each child was the mean of 3 reviewer scores.</p

Regression map for the story listening task (stories>tones activation) with child engagement score applied as explanatory variable.

<p>Regression map for the story listening task (stories>tones; n = 22), with child engagement score as the explanatory variable. Total cluster size 1164 voxels (p<0.05, FDR corrected), with center of gravity at (x = 21, y = -67, z = -25; R posterior cerebellum) in MNI coordinate space and z-score local maxima 3.22–3.93. Shown as 5 mm slices from <i>z</i> = -64 to <i>z</i> = -12 in MNI coordinate space. Color scale from <i>t</i> = 1.25 (cooler) to 4 (hotter). Radiological orientation, left = right, right = left.</p

Demographic characteristics.

<p>Demographic characteristics.</p

Using machine learning and surface reconstruction to accurately differentiate different trajectories of mood and energy dysregulation in youth

<div><p>Difficulty regulating positive mood and energy is a feature that cuts across different pediatric psychiatric disorders. Yet, little is known regarding the neural mechanisms underlying different developmental trajectories of positive mood and energy regulation in youth. Recent studies indicate that machine learning techniques can help elucidate the role of neuroimaging measures in classifying individual subjects by specific symptom trajectory. Cortical thickness measures were extracted in sixty-eight anatomical regions covering the entire brain in 115 participants from the Longitudinal Assessment of Manic Symptoms (LAMS) study and 31 healthy comparison youth (12.5 y/o;-Male/Female = 15/16;-IQ = 104;-Right/Left handedness = 24/5). Using a combination of trajectories analyses, surface reconstruction, and machine learning techniques, the present study aims to identify the extent to which measures of cortical thickness can accurately distinguish youth with higher (n = 18) from those with lower (n = 34) trajectories of manic-like behaviors in a large sample of LAMS youth (n = 115; 13.6 y/o; M/F = 68/47, IQ = 100.1, R/L = 108/7). Machine learning analyses revealed that widespread <i>cortical thickening</i> in portions of the left dorsolateral prefrontal cortex, right inferior and middle temporal gyrus, bilateral precuneus, and bilateral paracentral gyri and <i>cortical thinning</i> in portions of the right dorsolateral prefrontal cortex, left ventrolateral prefrontal cortex, and right parahippocampal gyrus accurately differentiate (Area Under Curve = 0.89;p = 0.03) youth with <i>different</i> (higher vs lower) trajectories of positive mood and energy dysregulation over a period up to 5years, as measured by the Parent General Behavior Inventory-10 Item Mania Scale. Our findings suggest that specific patterns of cortical thickness may reflect transdiagnostic neural mechanisms associated with different temporal trajectories of positive mood and energy dysregulation in youth. This approach has potential to identify patterns of neural markers of future clinical course.</p></div

Line plot shows main class-trajectories identified in the 115 LAMS youth study participants.

<p>The red line represents the class-trajectory of LAMS youth with initially high and subsequently improving PGBI-10M scores, the blue line represents the class-trajectory of LAMS youth with intermediate PGBI-10 M scores and the green line represents the class-trajectory of LAMS youth with initially low and subsequently improving PGBI-10M scores in the pre-imaging follow-up period (5 years). The pink area represents the clinically significant range of PGBI-10M (>12).</p

Demographic and clinical variable in 3 LAMS sites (Cleveland, Cincinnati and Pittsburgh).

<p>Demographic and clinical variable in 3 LAMS sites (Cleveland, Cincinnati and Pittsburgh).</p