Human brain mapping
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Human brain mapping · Apr 2015
Dynamic shifts in brain network activation during supracapacity working memory task performance.
Despite significant advances in understanding how brain networks support working memory (WM) and cognitive control, relatively little is known about how these networks respond when cognitive capabilities are overtaxed. We used a fine-grained manipulation of memory load within a single trial to exceed WM capacity during functional magnetic resonance imaging to investigate how these networks respond to support task performance when WM capacity is exceeded. Analyzing correct trials only, we observed a nonmonotonic (inverted-U) response to WM load throughout the classic WM network (including bilateral dorsolateral prefrontal cortex, posterior parietal cortex, and presupplementary motor areas) that peaked later in individuals with greater WM capacity. ⋯ At the individual subject level, the inverted-U pattern was associated with poorer performance while expression of the early and late activating patterns was predictive of better performance. In addition, greater activation in bilateral fusiform gyrus and right occipital lobe at the highest WM loads predicted better performance. These results demonstrate dynamic and behaviorally relevant changes in the level of activation of multiple brain networks in response to increasing WM load that are not well accounted for by present models of how the brain subserves the cognitive ability to hold and manipulate information on-line.
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Human brain mapping · Mar 2015
Close-range blast exposure is associated with altered functional connectivity in Veterans independent of concussion symptoms at time of exposure.
Although there is emerging data on the effects of blast-related concussion (or mTBI) on cognition, the effects of blast exposure itself on the brain have only recently been explored. Toward this end, we examine functional connectivity to the posterior cingulate cortex, a primary region within the default mode network (DMN), in a cohort of 134 Iraq and Afghanistan Veterans characterized for a range of common military-associated comorbidities. Exposure to a blast at close range (<10 meters) was associated with decreased connectivity of bilateral primary somatosensory and motor cortices, and these changes were not different from those seen in participants with blast-related mTBI. ⋯ In contrast, differences in functional connectivity based on concussion history and blast exposures at greater distances were not apparent. Despite the limitations of a study of this nature (e.g., assessments long removed from injury, self-reported blast history), these data demonstrate that blast exposure per se, which is prevalent among those who served in Iraq and Afghanistan, may be an important consideration in Veterans' health. It further offers a clinical guideline for determining which blasts (namely, those within 10 meters) are likely to lead to long-term health concerns and may be more accurate than using concussion symptoms alone.
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Human brain mapping · Mar 2015
Review Meta AnalysisToward systems neuroscience in mild cognitive impairment and Alzheimer's disease: a meta-analysis of 75 fMRI studies.
Most of the previous task functional magnetic resonance imaging (fMRI) studies found abnormalities in distributed brain regions in mild cognitive impairment (MCI) and Alzheimer's disease (AD), and few studies investigated the brain network dysfunction from the system level. In this meta-analysis, we aimed to examine brain network dysfunction in MCI and AD. We systematically searched task-based fMRI studies in MCI and AD published between January 1990 and January 2014. ⋯ Both MCI-related and AD-related hyperactivation fell in frontoparietal, ventral attention, default, and somatomotor networks relative to healthy controls. MCI and AD presented different pathological while shared similar compensatory large-scale networks in fulfilling the cognitive tasks. These system-level findings are helpful to link the fundamental declines of cognitive tasks to brain networks in MCI and AD.
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Human brain mapping · Mar 2015
Large-scale functional brain network changes in taxi drivers: evidence from resting-state fMRI.
Driving a car in the environment is a complex behavior that involves cognitive processing of visual information to generate the proper motor outputs and action controls. Previous neuroimaging studies have used virtual simulation to identify the brain areas that are associated with various driving-related tasks. Few studies, however, have focused on the specific patterns of functional organization in the driver's brain. ⋯ The major finding of this study, however, was that the FNC between the cognitive and sensory RSNs became more positively or less negatively correlated in the drivers relative to that in the nondrivers. Notably, the strength of the FNC between the left frontoparietal and primary visual RSNs was positively correlated with the number of taxi-driving years. Our findings may provide new insight into how the brain supports driving behavior.
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Human brain mapping · Feb 2015
Multivariate decoding of cerebral blood flow measures in a clinical model of on-going postsurgical pain.
Recent reports of multivariate machine learning (ML) techniques have highlighted their potential use to detect prognostic and diagnostic markers of pain. However, applications to date have focussed on acute experimental nociceptive stimuli rather than clinically relevant pain states. These reports have coincided with others describing the application of arterial spin labeling (ASL) to detect changes in regional cerebral blood flow (rCBF) in patients with on-going clinical pain. ⋯ Using all data from all sessions, an independent Gaussian Process binary classifier successfully discriminated postsurgical from presurgical states with 94.73% accuracy; over 80% accuracy could be achieved using half of the data (equivalent to 15 min scan time). This work demonstrates the concept and feasibility of time-efficient, probabilistic prediction of clinically relevant pain at the individual level. We discuss the potential of ML techniques to impact on the search for novel approaches to diagnosis, management, and treatment to complement conventional patient self-reporting.