Individual Differences in Mental Imagery Modulate Effective Connectivity of Scene-Selective Regions During Resting State

Successful navigation relies on the ability to identify, perceive, and correctly process the spatial structure of a scene. It is well known that visual mental imagery plays a crucial role in navigation. Indeed, cortical regions encoding navigationally relevant information are also active during mental imagery of navigational scenes. However, it remains unknown whether their intrinsic activity and connectivity reflect the individuals’ ability to imagine a scene. Here, we primarily investigated the intrinsic causal interactions among scene-selective brain regions such as Parahipoccampal Place Area (PPA), Retrosplenial Complex (RSC), and Occipital Place Area (OPA) using Dynamic Causal Modelling (DCM) for resting-state functional magnetic resonance (rs-fMRI) data. Second, we tested whether resting-state effective connectivity parameters among scene-selective regions could reflect individual differences in mental imagery in our sample, as assessed by the self-reported Vividness of Visual Imagery Questionnaire (VVIQ). We found an inhibitory influence of occipito-medial on temporal regions, and an excitatory influence of more anterior on more medial and posterior brain regions. Moreover, we found that a key role in imagery is played by the connection strength from OPA to PPA, especially in the left hemisphere, since the influence of the signal between these scene-selective regions positively correlated with good mental imagery ability. Our investigation contributes to the understanding of the complexity of the causal interaction among brain regions involved in navigation and provides new insight in understanding how an essential ability, such as mental imagery, can be explained by the intrinsic fluctuation of brain signal.

INTRINSIC FLUCTUATION OF CHUA SYSTEM

Intrinsic fluctuation of Chua system is studied with master equation method. Our results have shown that the intrinsic noise indeed exerted considerable influence on Chua system. In contrast to that of deterministic equation the patterns of time evolution and attractor have been greatly altered under the influence of intrinsic noise.

Modeling Atomistic Ion-Implantation and Diffusion for Simulating Intrinsic Fluctuation in MOSFETs arising from Line-Edge Roughness

ABSTRACTWe have developed new simulation tools to enable more precise design of sub-100nm MOSFETs. Intrinsic fluctuations in the characteristics of these devices occur as part of their statistical nature. Our three-dimensional atomistic approach to both process and device simulations enabled us to examine the coupling effects of the most significant sources of fluctuation, i.e. line-edge-roughness and random discrete dopants, considering practical fabrication processes.

Observations of the CMB on Scales of 2° to 15°

Beamswitching has been used at 10, 15 and 33 GHz to map the microwave background over the Declination range 30° to 45°, covering more than one steradian of the sky. The beamwidth is 5° and the beam-throw is ±8° at each frequency. The three data sets are used to separate Galactic emission from intrinsic CMB emission. For the scan at Dec = 40° the intrinsic fluctuation level is ΔTrms = 48+21−15 μK on a coherence scale of 4°; the equivalent analysis for a Harrison-Zeldovich model gives a power spectrum normalisation of Qrms = 22+10−6 μK. The value of the fluctuation amplitude calculated from the likelihood analysis of the two-dimensional data set is ΔTrms = 54 ± 13 μK at 10 GHz and 39+8−7 μK at 15 GHz.