Differential Stress-Induced Neuronal Activation Patterns in Mouse Lines Selectively Bred for High, Normal or Low Anxiety

There is evidence for a disturbed perception and processing of emotional information in pathological anxiety. Using a rat model of trait anxiety generated by selective breeding, we previously revealed differences in challenge-induced neuronal activation in fear/anxiety-related brain areas between high (HAB) and low (LAB) anxiety rats. To confirm whether findings generalize to other species, we used the corresponding HAB/LAB mouse model and investigated c-Fos responses to elevated open arm exposure. Moreover, for the first time we included normal anxiety mice (NAB) for comparison. The results confirm that HAB mice show hyperanxious behavior compared to their LAB counterparts, with NAB mice displaying an intermediate anxiety phenotype. Open arm challenge revealed altered c-Fos response in prefrontal-cortical, limbic and hypothalamic areas in HAB mice as compared to LAB mice, and this was similar to the differences observed previously in the HAB/LAB rat lines. In mice, however, additional differential c-Fos response was observed in subregions of the amygdala, hypothalamus, nucleus accumbens, midbrain and pons. Most of these differences were also seen between HAB and NAB mice, indicating that it is predominately the HAB line showing altered neuronal processing. Hypothalamic hypoactivation detected in LAB versus NAB mice may be associated with their low-anxiety/high-novelty-seeking phenotype. The detection of similarly disturbed activation patterns in a key set of anxiety-related brain areas in two independent models reflecting psychopathological states of trait anxiety confirms the notion that the altered brain activation in HAB animals is indeed characteristic of enhanced (pathological) anxiety, providing information for potential targets of therapeutic intervention

Deep sequencing analysis of the developing mouse brain reveals a novel microRNA

Extent: 15p.Background: MicroRNAs (miRNAs) are small non-coding RNAs that can exert multilevel inhibition/repression at a post-transcriptional or protein synthesis level during disease or development. Characterisation of miRNAs in adult mammalian brains by deep sequencing has been reported previously. However, to date, no small RNA profiling of the developing brain has been undertaken using this method. We have performed deep sequencing and small RNA analysis of a developing (E15.5) mouse brain. Results: We identified the expression of 294 known miRNAs in the E15.5 developing mouse brain, which were mostly represented by let-7 family and other brain-specific miRNAs such as miR-9 and miR-124. We also discovered 4 putative 22-23 nt miRNAs: mm_br_e15_1181, mm_br_e15_279920, mm_br_e15_96719 and mm_br_e15_294354 each with a 70-76 nt predicted pre-miRNA. We validated the 4 putative miRNAs and further characterised one of them, mm_br_e15_1181, throughout embryogenesis. Mm_br_e15_1181 biogenesis was Dicer1-dependent and was expressed in E3.5 blastocysts and E7 whole embryos. Embryo-wide expression patterns were observed at E9.5 and E11.5 followed by a near complete loss of expression by E13.5, with expression restricted to a specialised layer of cells within the developing and early postnatal brain. Mm_br_e15_1181 was upregulated during neurodifferentiation of P19 teratocarcinoma cells. This novel miRNA has been identified as miR-3099. Conclusions: We have generated and analysed the first deep sequencing dataset of small RNA sequences of the developing mouse brain. The analysis revealed a novel miRNA, miR-3099, with potential regulatory effects on early embryogenesis, and involvement in neuronal cell differentiation/function in the brain during late embryonic and early neonatal development.King-Hwa Ling, Peter J Brautigan, Christopher N Hahn, Tasman Daish, John R Rayner, Pike-See Cheah, Joy M Raison, Sandra Piltz Jeffrey R Mann, Deidre M Mattiske, Paul Q Thomas, David L Adelson and Hamish S Scot

Orthogonal Operators: Applications, Origin and Outlook

Orthogonal operators can successfully be used to calculate eigenvalues and eigenvector compositions in complex spectra. Orthogonality ensures least correlation between the operators and thereby more stability in the fit, even for small interactions. The resulting eigenvectors are used to transform the pure transition matrix into realistic intermediate coupling transition probabilities. Calculated transition probabilities for close lying levels illustrate the power of the complete orthogonal operator approach

Comparative Analysis of the Macroscale Structural Connectivity in the Macaque and Human Brain

The macaque brain serves as a model for the human brain, but its suitability is challenged by unique human features, including connectivity reconfigurations, which emerged during primate evolution. We perform a quantitative comparative analysis of the whole brain macroscale structural connectivity of the two species. Our findings suggest that the human and macaque brain as a whole are similarly wired. A region-wise analysis reveals many interspecies similarities of connectivity patterns, but also lack thereof, primarily involving cingulate regions. We unravel a common structural backbone in both species involving a highly overlapping set of regions. This structural backbone, important for mediating information across the brain, seems to constitute a feature of the primate brain persevering evolution. Our findings illustrate novel evolutionary aspects at the macroscale connectivity level and offer a quantitative translational bridge between macaque and human research

Human orbital and anterior medial prefrontal cortex:Intrinsic connectivity parcellation and functional organization

The orbital and medial prefrontal cortex (OMPFC) has been implicated in decision-making, reward and emotion processing, and psychopathology, such as depression and obsessive-compulsive disorder. Human and monkey anatomical studies indicate the presence of various cortical subdivisions and suggest that these are organized in two extended networks, a medial and an orbital one. Attempts have been made to replicate these neuroanatomical findings in vivo using MRI techniques for imaging connectivity. These revealed several consistencies, but also many inconsistencies between reported results. Here, we use fMRI resting-state functional connectivity (FC) and data-driven modularity optimization to parcellate the OMPFC to investigate replicability of in vivo parcellation more systematically. By collecting two resting-state data sets per participant, we were able to quantify the reliability of the observed modules and their boundaries. Results show that there was significantly more than chance overlap in modules and their boundaries at the level of individual data sets. Moreover, some of these consistent boundaries significantly co-localized across participants. Hierarchical clustering showed that the whole-brain FC profiles of the OMPFC subregions separate them in two networks, a medial and orbital one, which overlap with the organization proposed by Barbas and Pandya (J Comp Neurol 286:353-375, 1989) and Ongür and Price (Cereb Cortex 10:206-219, 2000). We conclude that in vivo resting-state FC can delineate reliable and neuroanatomically plausible subdivisions that agree with established cytoarchitectonic trends and connectivity patterns, while other subdivisions do not show the same consistency across data sets and studies

Orbital and Medial Prefrontal Cortex Functional Connectivity of Major Depression Vulnerability and Disease

Background: Pathophysiology models of major depression (MD) center on the dysfunction of various cortical areas within the orbital and medial prefrontal cortex. While independent structural and functional abnormalities in these areas are consistent findings in MD, the complex interactions among them and the rest of the cortex remain largely unexplored. Methods: We used resting-state functional magnetic resonance imaging connectivity to systematically map alterations in the communication between orbital and medial prefrontal cortex fields and the rest of the brain in MD. Functional connectivity (FC) maps from participants with current MD (n = 35), unaffected first-degree relatives (n = 36), and healthy control subjects (n = 38) were subjected to conjunction analyses to distinguish FC markers of MD vulnerability and FC markers of MD disease. Results: FC abnormalities in MD vulnerability were found for dorsal medial wall regions and the anterior insula and concerned altered communication of these areas with the inferior parietal cortex and dorsal posterior cingulate, occipital areas and the brainstem. FC aberrations in current MD included the anterior insula, rostral and dorsal anterior cingulate cortex, and lateral orbitofrontal areas and concerned altered communication with the dorsal striatum, the cerebellum, the precuneus, the anterior prefrontal cortex, somatomotor cortex, dorsolateral prefrontal cortex, and visual areas in the occipital and inferior temporal lobes. Conclusions: Functionally delineated parcellation maps can be used to identify putative connectivity markers in extended cortical regions such as the orbital and medial prefrontal cortex. The anterior insula and the rostral anterior cingulate cortex play a central role in the pathophysiology of MD, being consistently implicated both in the MD vulnerability and MD disease states

Branching Fractions and Transition Probabilities for UV Transitions in the Spectrum of Cr ii

We present transition probabilities for 268 spectral lines of singly ionized chromium (Cr ii ) in the wavelength region 208–414 nm. Branching fractions were measured in archival Fourier transform spectra of chromium-argon and chromium-neon hollow cathode lamps and a Penning discharge source. The branching fractions were combined with previously published experimental lifetimes of 14 levels, and with lifetimes from semiempirical calculations for 14 levels to give transition probabilities. The estimated uncertainties of the transition probabilities range from 10% to 26%. A comparison with previously published experimental transition probabilities shows discrepancies of up to a factor of 2.5 for lines around 213 nm

Schematic depiction of the main points of the analysis.

<p>A. The RM was used in order to construct the connectivity matrices of the two species, i.e. the MC and HC (panel B). The resulting connectivity matrices have i = 82 rows and j = 82 columns. B. Example of the calculation of the HCS for region i = 1. The entries of row 1 from the MC and row 1 from HC were used, resulting in entry HCS_i = 1. The same procedure was used for all regions, resulting in a 1×82 vector HCS containing the HCS indexes of all the regions. C. Example of calculation of the HMIS for region i = 1. The MC and HC were used for the separate calculation of the matching index matrices MI_ij (one for each species separately). The HMIS index for region i = 1 is calculated from the entries of row 1 of the matching index matrices of the two species. The procedure is repeated for all regions, resulting in a 1×82 vector HMIS containing the HMIS indexes of all the regions. D. Toy networks demonstrating differences of the HCS and the HMIS. Given two hypothetical “brains” that form a network with 4 regions and 4 connections the HCS and HMIS indexes are calculated for brain region A. On the one hand, the HCS index is equal to 1 (perfect similarity), since region A in both networks is connected exactly to the same regions, i.e. nodes B, C and D. On the other hand, the HMIS index is −0.5, indicating a divergence of the connectivity similarity profiles of region A in networks/brains 1 and 2. These discrepancies arise from the “rewiring” of the connection marked with an arrow.</p

Overlap of the MC and HC.

<p>A. Rendering of the MC and HC. B. Rendering of the overlap between the MC and HC. The two connectomes exhibit a statistically significant overlap (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003529#s3" target="_blank">Results</a>). The renderings were performed with BrainGL and a mean-shift edge bundling algorithm <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003529#pcbi.1003529-Bttger1" target="_blank">[52]</a>.</p