Inter-subject registration of cortical areas is necessary in functional imaging (fMRI) studies for making inferences about equivalent brain function across a population. However, many high-level visual brain areas are defined as peaks of functional contrasts whose cortical position is highly variable. As such, most alignment methods fail to accurately map functional regions of interest (ROIs) across participants. To address this problem, we propose a locally optimized registration method that directly predicts the location of a seed ROI on a separate target cortical sheet by maximizing the functional correlation between their time courses, while simultaneously allowing for non-smooth local deformations in region topology. Our method outperforms the two most commonly used alternatives (anatomical landmark-based AFNI alignment and cortical convexity-based FreeSurfer alignment) in overlap between predicted region and functionally-defined LOC. Furthermore, the maps obtained using our method are more consistent across subjects than both baseline measures. Critically, our method represents an important step forward towards predicting brain regions without explicit localizer scans and deciphering the poorly understood relationship between the location of functional regions, their anatomical extent, and the consistency of computations those regions perform across people.
A recent method incorporates functional connectivity constraints in the mapping @cite_15 and shows improved ability to align intertwined networks in the brain (i.e. default mode network ). However, many functional areas are not usually a strong part of these networks and thus receive little benefit from this approach.
@cite_15
2005885249
Functional imaging studies have shown that certain brain regions, including posterior cingulate cortex (PCC) and ventral anterior cingulate cortex (vACC), consistently show greater activity during resting states than during cognitive tasks. This finding led to the hypothesis that these regions constitute a network supporting a default mode of brain function. In this study, we investigate three questions pertaining to this hypothesis: Does such a resting-state network exist in the human brain? Is it modulated during simple sensory processing? How is it modulated during cognitive processing? To address these questions, we defined PCC and vACC regions that showed decreased activity during a cognitive (working memory) task, then examined their functional connectivity during rest. PCC was strongly coupled with vACC and several other brain regions implicated in the default mode network. Next, we examined the functional connectivity of PCC and vACC during a visual processing task and show that the resultant connectivity maps are virtually identical to those obtained during rest. Last, we defined three lateral prefrontal regions showing increased activity during the cognitive task and examined their resting-state connectivity. We report significant inverse correlations among all three lateral prefrontal regions and PCC, suggesting a mechanism for attenuation of default mode network activity during cognitive processing. This study constitutes, to our knowledge, the first resting-state connectivity analysis of the default mode and provides the most compelling evidence to date for the existence of a cohesive default mode network. Our findings also provide insight into how this network is modulated by task demands and what functions it might subserve. Brain-computer interfaces (BCIs) use brain signals to convey a user's intent. Some BCI approaches begin by decoding kinematic parameters of movements from brain signals, and then proceed to using these signals, in absence of movements, to allow a user to control an output. Recent results have shown that electrocorticographic (ECoG) recordings from the surface of the brain in humans can give information about kinematic parameters (e.g., hand velocity or finger flexion). The decoding approaches in these demonstrations usually employed classical classification regression algorithms that derive a linear mapping between brain signals and outputs. However, they typically only incorporate little prior information about the target kinematic parameter. In this paper, we show that different types of anatomical constraints that govern finger flexion can be exploited in this context. Specifically, we incorporate these constraints in the construction, structure, and the probabilistic functions of a switched non-parametric dynamic system (SNDS) model. We then apply the resulting SNDS decoder to infer the flexion of individual fingers from the same ECoG dataset used in a recent study. Our results show that the application of the proposed model, which incorporates anatomical constraints, improves decoding performance compared to the results in the previous work. Thus, the results presented in this paper may ultimately lead to neurally controlled hand prostheses with full fine-grained finger articulation. How rich functionality emerges from the invariant structural architecture of the brain remains a major mystery in neuroscience. Recent applications of network theory and theoretical neuroscience to large-scale brain networks have started to dissolve this mystery. Network analyses suggest that hierarchical modular brain networks are particularly suited to facilitate local (segregated) neuronal operations and the global integration of segregated functions. Although functional networks are constrained by structural connections, context-sensitive integration during cognition tasks necessarily entails a divergence between structural and functional networks. This degenerate (many-to-one) function-structure mapping is crucial for understanding the nature of brain networks. The emergence of dynamic functional networks from static structural connections calls for a formal (computational) approach to neuronal information processing that may resolve this dialectic between structure and function. For the first time, this paper presents an analysis of the performance of the IEEE 802.15.4 low power, low data rate wireless standard in relation to medical sensor body area networks. This is an emerging application of wireless sensor networking with particular performance constraints, including power consumption, physical size, robustness and security. In the analysis presented, the star network configuration of the 802.15.4 standard at 2.4 GHz was considered for a body area network consisting of a wearable or desk mounted coordinator outside of the body with up to 10 body implanted sensors. The main consideration in this work was the long-term power consumption of devices, since for practical reasons, implanted medical devices and sensors must function for at least 10 to 15 years without battery replacement. The results show that when properly configured, 802.15.4 can be used for medical sensor networking when configured in non-beacon mode with low data rate asymmetric traffic. Beacon mode may also be used, but with more severe restrictions on data rate and crystal tolerance.