Comparative cytogenetic analysis between Lonchorhina aurita and Trachops cirrhosus (Chiroptera, Phyllostomidae)

Phyllostomidae comprises the most diverse family of neotropical bats, its wide range of morphological features leading to uncertainty regarding phylogenetic relationships. Seeing that cytogenetics is one of the fields capable of providing support for currently adopted classifications through the use of several markers, a comparative analysis between two Phyllostomidae species was undertaken in the present study, with a view to supplying datasets for the further establishment of Phyllostomidae evolutionary relationships. Karyotypes of Lonchorhina aurita (2n = 32; FN = 60) and Trachops cirrhosus (2n = 30; FN = 56) were analyzed by G- and C-banding, silver nitrate staining (Ag-NOR) and base-specific fluorochromes. Chromosomal data obtained for both species are in agreement with those previously described, except for X chromosome morphology in T. cirrhosus, hence indicating chromosomal geographical variation in this species. A comparison of G-banding permitted the identification of homeologies in nearly all the chromosomes. Furthermore, C-banding and Ag-NOR patterns were comparable to what has already been observed in the family. In both species CMA3 /DA/DAPI staining revealed an R-banding-like pattern with CMA 3 , whereas DAPI showed uniform staining in all the chromosomes. Fluorochrome staining patterns for pericentromeric constitutive heterochromatin (CH) regions, as well as for nucleolar organizing regions (NORs), indicated heterogeneity regarding these sequences among Phyllostomidae species

Modeling Brain Resonance Phenomena Using a Neural Mass Model

Stimulation with rhythmic light flicker (photic driving) plays an important role in the diagnosis of schizophrenia, mood disorder, migraine, and epilepsy. In particular, the adjustment of spontaneous brain rhythms to the stimulus frequency (entrainment) is used to assess the functional flexibility of the brain. We aim to gain deeper understanding of the mechanisms underlying this technique and to predict the effects of stimulus frequency and intensity. For this purpose, a modified Jansen and Rit neural mass model (NMM) of a cortical circuit is used. This mean field model has been designed to strike a balance between mathematical simplicity and biological plausibility. We reproduced the entrainment phenomenon observed in EEG during a photic driving experiment. More generally, we demonstrate that such a single area model can already yield very complex dynamics, including chaos, for biologically plausible parameter ranges. We chart the entire parameter space by means of characteristic Lyapunov spectra and Kaplan-Yorke dimension as well as time series and power spectra. Rhythmic and chaotic brain states were found virtually next to each other, such that small parameter changes can give rise to switching from one to another. Strikingly, this characteristic pattern of unpredictability generated by the model was matched to the experimental data with reasonable accuracy. These findings confirm that the NMM is a useful model of brain dynamics during photic driving. In this context, it can be used to study the mechanisms of, for example, perception and epileptic seizure generation. In particular, it enabled us to make predictions regarding the stimulus amplitude in further experiments for improving the entrainment effect

Connecting mean field models of neural activity to EEG and fMRI data.

Contains fulltext : 89286reid.pdf (publisher's version ) (Closed access)Progress in functional neuroimaging of the brain increasingly relies on the integration of data from complementary imaging modalities in order to improve spatiotemporal resolution and interpretability. However, the usefulness of merely statistical combinations is limited, since neural signal sources differ between modalities and are related non-trivially. We demonstrate here that a mean field model of brain activity can simultaneously predict EEG and fMRI BOLD with proper signal generation and expression. Simulations are shown using a realistic head model based on structural MRI, which includes both dense short-range background connectivity and long-range specific connectivity between brain regions. The distribution of modeled neural masses is comparable to the spatial resolution of fMRI BOLD, and the temporal resolution of the modeled dynamics, importantly including activity conduction, matches the fastest known EEG phenomena. The creation of a cortical mean field model with anatomically sound geometry, extensive connectivity, and proper signal expression is an important first step towards the model-based integration of multimodal neuroimages.1 juni 201