5,546 research outputs found
Boundary detection in disease mapping studies
In disease mapping, the aim is to estimate the spatial pattern in disease
risk over an extended geographical region, so that areas with elevated risks
can be identified. A Bayesian hierarchical approach is typically used to
produce such maps, which models the risk surface with a set of spatially smooth
random effects. However, in complex urban settings there are likely to be
boundaries in the risk surface, which separate populations that are
geographically adjacent but have very different risk profiles. Therefore this
paper proposes an approach for detecting such risk boundaries, and tests its
effectiveness by simulation. Finally, the model is applied to lung cancer
incidence data in Greater Glasgow, Scotland, between 2001 and 2005
A Study of Single Pulses in the Parkes Multibeam Pulsar Survey
We reprocessed the Parkes Multibeam Pulsar Survey, searching for single
pulses out to a DM of 5000 pc cm with widths of up to one second. We
recorded single pulses from 264 known pulsars and 14 Rotating Radio Transients.
We produced amplitude distributions for each pulsar which we fit with
log-normal distributions, power-law tails, and a power-law function divided by
an exponential function, finding that some pulsars show a deviation from a
log-normal distribution in the form of an excess of high-energy pulses. We
found that a function consisting of a power-law divided by an exponential fit
the distributions of most pulsars better than either log-normal or power-law
functions. For pulsars that were detected in a periodicity search, we computed
the ratio of their single-pulse signal-to-noise ratios to their signal-to-noise
ratios from a Fourier transform and looked for correlations between this ratio
and physical parameters of the pulsars. The only correlation found is the
expected relationship between this ratio and the spin period. Fitting
log-normal distributions to the amplitudes of pulses from RRATs showed similar
behaviour for most RRATs. Here, however, there seem to be two distinct
distributions of pulses, with the lower-energy distribution being consistent
with noise. Pulse-energy distributions for two of the RRATS processed were
consistent with those found for normal pulsars, suggesting that pulsars and
RRATs have a common emission mechanism, but other factors influence the
specific emission properties of each source class.Comment: 11 pages, 6 figures, 3 tables, accepted for publication in MNRA
Response of the multiple-demand network during simple stimulus discriminations
The multiple-demand (MD) network is sensitive to many aspects of task difficulty, including such factors as rule complexity, memory load, attentional switching and inhibition. Many accounts link MD activity to top-down task control, raising the question of response when performance is limited by the quality of sensory input, and indeed, some prior results suggest little effect of sensory manipulations. Here we examined judgments of motion direction, manipulating difficulty by either motion coherence or salience of irrelevant dots. We manipulated each difficulty type across six levels, from very easy to very hard, and additionally manipulated whether difficulty level was blocked, and thus known in advance, or randomized. Despite the very large manipulations employed, difficulty had little effect on MD activity, especially for the coherence manipulation. Contrasting with these small or absent effects, we observed the usual increase of MD activity with increased rule complexity. We suggest that, for simple sensory discriminations, it may be impossible to compensate for reduced stimulus information by increased top-down control
Fluid Intelligence Predicts Novel Rule Implementation in a Distributed Frontoparietal Control Network
Fluid intelligence has been associated with a distributed cognitive control or multiple-demand (MD) network, comprising regions of lateral frontal, insular, dorsomedial frontal, and parietal cortex. Human fluid intelligence is also intimately linked to task complexity, and the process of solving complex problems in a sequence of simpler, more focused parts. Here, a complex target detection task included multiple independent rules, applied one at a time in successive task epochs. Although only one rule was applied at a time, increasing task complexity (i.e., the number of rules) impaired performance in participants of lower fluid intelligence. Accompanying this loss of performance was reduced response to rule-critical events across the distributed MD network. The results link fluid intelligence and MD function to a process of attentional focus on the successive parts of complex behavior.SIGNIFICANCE STATEMENTFluid intelligence is intimately linked to the ability to structure complex problems in a sequence of simpler, more focused parts. We examine the basis for this link in the functions of a distributed frontoparietal or multiple-demand (MD) network. With increased task complexity, participants of lower fluid intelligence showed reduced responses to task-critical events. Reduced responses in the MD system were accompanied by impaired behavioral performance. Low fluid intelligence is linked to poor foregrounding of task-critical information across a distributed MD system.This work was supported by the Medical Research Council (United Kingdom) intramural program MC_A060_5PQ10. N.T. was supported by a Research Fellowship from Girton College, University of Cambridge
Hierarchical Representation of Multistep Tasks in Multiple-Demand and Default Mode Networks
Task episodes consist of sequences of steps that are performed to achieve a goal. We used fMRI to examine neural representation of task identity, component items, and sequential position, focusing on two major cortical systems—the multipledemand (MD) and default mode networks (DMN). Human participants (20 males, 22 females) learned six tasks each consisting of four steps. Inside the scanner, participants were cued which task to perform and then sequentially identified the target
item of each step in the correct order. Univariate time course analyses indicated that intra-episode progress was tracked by a
tonically increasing global response, plus an increasing phasic step response specific to MD regions. Inter-episode boundaries
evoked a widespread response at episode onset, plus a marked offset response specific to DMN regions. Representational similarity analysis (RSA) was used to examine representation of task identity and component steps. Both networks represented
the content and position of individual steps, however the DMN preferentially represented task identity while the MD network
preferentially represented step-level information. Thus, although both MD and DMN networks are sensitive to step-level and
episode-level information in the context of hierarchical task performance, they exhibit dissociable profiles in terms of both
temporal dynamics and representational content. The results suggest collaboration of multiple brain regions in control of
multistep behavior, with MD regions particularly involved in processing the detail of individual steps, and DMN adding representation of broad task context
The Functional Convergence and Heterogeneity of Social, Episodic, and Self-Referential Thought in the Default Mode Network
The default mode network (DMN) is engaged in a variety of cognitive settings, including social, semantic, temporal, spatial, and self-related tasks. Andrews-Hanna et al. (2010; Andrews-Hanna 2012) proposed that the DMN consists of three distinct functional–anatomical subsystems—a dorsal medial prefrontal cortex (dMPFC) subsystem that supports social cognition; a medial temporal lobe (MTL) subsystem that contributes to memory-based scene construction; and a set of midline core hubs that are especially involved in processing self-referential information. We examined activity in the DMN subsystems during six different tasks: 1) theory of mind, 2) moral dilemmas, 3) autobiographical memory, 4) spatial navigation, 5) self/other adjective judgment, and 6) a rest condition. At a broad level, we observed similar whole-brain activity maps for the six contrasts, and some response to every contrast in each of the three subsystems. In more detail, both univariate analysis and multivariate activity patterns showed partial functional separation, especially between dMPFC and MTL subsystems, though with less support for common activity across the midline core. Integrating social, spatial, self-related, and other aspects of a cognitive situation or episode, multiple components of the DMN may work closely together to provide the broad context for current mental activity
The Functional Convergence and Heterogeneity of Social, Episodic, and Self-Referential Thought in the Default Mode Network.
The default mode network (DMN) is engaged in a variety of cognitive settings, including social, semantic, temporal, spatial, and self-related tasks. Andrews-Hanna et al. (2010; Andrews-Hanna 2012) proposed that the DMN consists of three distinct functional-anatomical subsystems-a dorsal medial prefrontal cortex (dMPFC) subsystem that supports social cognition; a medial temporal lobe (MTL) subsystem that contributes to memory-based scene construction; and a set of midline core hubs that are especially involved in processing self-referential information. We examined activity in the DMN subsystems during six different tasks: 1) theory of mind, 2) moral dilemmas, 3) autobiographical memory, 4) spatial navigation, 5) self/other adjective judgment, and 6) a rest condition. At a broad level, we observed similar whole-brain activity maps for the six contrasts, and some response to every contrast in each of the three subsystems. In more detail, both univariate analysis and multivariate activity patterns showed partial functional separation, especially between dMPFC and MTL subsystems, though with less support for common activity across the midline core. Integrating social, spatial, self-related, and other aspects of a cognitive situation or episode, multiple components of the DMN may work closely together to provide the broad context for current mental activity
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