1,213 research outputs found

    ICA model order selection of task co-activation networks

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    Independent component analysis (ICA) has become a widely used method for extracting functional networks in the brain during rest and task. Historically, preferred ICA dimensionality has widely varied within the neuroimaging community, but typically varies between 20 and 100 components. This can be problematic when comparing results across multiple studies because of the impact ICA dimensionality has on the topology of its resultant components. Recent studies have demonstrated that ICA can be applied to peak activation coordinates archived in a large neuroimaging database (i.e., BrainMap Database) to yield whole-brain task-based co-activation networks. A strength of applying ICA to BrainMap data is that the vast amount of metadata in BrainMap can be used to quantitatively assess tasks and cognitive processes contributing to each component. In this study, we investigated the effect of model order on the distribution of functional properties across networks as a method for identifying the most informative decompositions of BrainMap-based ICA components. Our findings suggest dimensionality of 20 for low model order ICA to examine large-scale brain networks, and dimensionality of 70 to provide insight into how large-scale networks fractionate into sub-networks. We also provide a functional and organizational assessment of visual, motor, emotion, and interoceptive task co-activation networks as they fractionate from low to high model-orders

    Thalamic medial dorsal nucleus atrophy in medial temporal lobe epilepsy: A VBM meta-analysis

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    Purpose: Medial temporal lobe epilepsy (MTLE) is associated with MTLE network pathology within and beyond the hippocampus. The purpose of this meta-analysis was to identify consistent MTLE structural change to guide subsequent targeted analyses of these areas. Methods: We performed an anatomic likelihood estimation (ALE) meta-analysis of 22 whole-brain voxel-based morphometry experiments from 11 published studies. We grouped these experiments in three ways. We then constructed a meta-analytic connectivity model (MACM) for regions of consistent MTLE structural change as reported by the ALE analysis. Key findings: ALE reported spatially consistent structural change across VBM studies only in the epileptogenic hippocampus and the bilateral thalamus; within the thalamus, the medial dorsal nucleus of the thalamus (MDN thalamus) represented the greatest convergence (Pb0.05 corrected for multiple comparisons). The subsequent MACM for the hippocampus and ipsilateral MDN thalamus demonstrated that the hippocampus and ipsilateral MDN thalamus functionally co-activate and are nodes within the same network, suggesting that MDN thalamic damage could result from MTLE network excitotoxicity. Significance: Notwithstanding our large sample of studies, these findings aremore restrictive thanprevious reports and demonstrate the utility of our inclusion filters and of recently modified meta-analyticmethods in approximating clinical relevance. Thalamic pathology is commonly observed in animal and human studies, suggesting it could be a clinically useful indicator. Thalamus-specific research as a clinical marker awaits further investigation

    Factors associated with spontaneous clearance of chronic hepatitis C virus infection

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    Background & Aims: Spontaneous clearance of chronic hepatitis C virus (HCV) infection (CHC) is rare. We conducted a retrospective case-control study to identify rates and factors associated with spontaneous clearance of CHC. Methods: We defined cases as individuals who spontaneously resolved CHC, and controls as individuals who remained chronically infected. We used data obtained on HCV testing between 1994 and 2013 in the West of Scotland to infer case/control status. Specifically, untreated patients with ⩾2 sequential samples positive for HCV RNA ⩾6 months apart followed by ⩾1 negative test, and those with ⩾2 positive samples ⩾6 months apart with no subsequent negative samples were identified. Control patients were randomly selected from the second group (4/patient of interest). Case notes were reviewed and patient characteristics obtained. Results: 25,113 samples were positive for HCV RNA, relating to 10,318 patients. 50 cases of late spontaneous clearance were identified, contributing 241 person-years follow-up. 2,518 untreated, chronically infected controls were identified, contributing 13,766 person-years follow-up, from whom 200 controls were randomly selected. The incidence rate of spontaneous clearance was 0.36/100 person-years follow-up, occurring after a median 50 months’ infection. Spontaneous clearance was positively associated with female gender, younger age at infection, lower HCV RNA load and co-infection with hepatitis B virus. It was negatively associated with current intravenous drug use. Conclusions: Spontaneous clearance of CHC occurs infrequently but is associated with identifiable host and viral factors. More frequent HCV RNA monitoring may be appropriate in selected patient groups. Lay summary: Clearance of hepatitis C virus infection without treatment occurs rarely once chronic infection has been established. We interrogated a large Scottish patient cohort and found that it was more common in females, patients infected at a younger age or with lower levels of HCV in the blood, and patients co-infected with hepatitis B virus. Patients who injected drugs were less likely to spontaneously clear chronic infection

    ALE Meta-Analysis Workflows Via the Brainmap Database: Progress Towards A Probabilistic Functional Brain Atlas

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    With the ever-increasing number of studies in human functional brain mapping, an abundance of data has been generated that is ready to be synthesized and modeled on a large scale. The BrainMap database archives peak coordinates from published neuroimaging studies, along with the corresponding metadata that summarize the experimental design. BrainMap was designed to facilitate quantitative meta-analysis of neuroimaging results reported in the literature and supports the use of the activation likelihood estimation (ALE) method. In this paper, we present a discussion of the potential analyses that are possible using the BrainMap database and coordinate-based ALE meta-analyses, along with some examples of how these tools can be applied to create a probabilistic atlas and ontological system of describing function–structure correspondences

    Chronic cigarette smoking is linked with structural alterations in brain regions showing acute nicotinic drug-induced functional modulations

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    Background Whereas acute nicotine administration alters brain function which may, in turn, contribute to enhanced attention and performance, chronic cigarette smoking is linked with regional brain atrophy and poorer cognition. However, results from structural magnetic resonance imaging (MRI) studies comparing smokers versus nonsmokers have been inconsistent and measures of gray matter possess limited ability to inform functional relations or behavioral implications. The purpose of this study was to address these interpretational challenges through meta-analytic techniques in the service of clarifying the impact of chronic smoking on gray matter integrity and more fully contextualizing such structural alterations. Methods We first conducted a coordinate-based meta-analysis of structural MRI studies to identify consistent structural alterations associated with chronic smoking. Subsequently, we conducted two additional meta-analytic assessments to enhance insight into potential functional and behavioral relations. Specifically, we performed a multimodal meta-analytic assessment to test the structural?functional hypothesis that smoking-related structural alterations overlapped those same regions showing acute nicotinic drug-induced functional modulations. Finally, we employed database driven tools to identify pairs of structurally impacted regions that were also functionally related via meta-analytic connectivity modeling, and then delineated behavioral phenomena associated with such functional interactions via behavioral decoding. Results Across studies, smoking was associated with convergent structural decreases in the left insula, right cerebellum, parahippocampus, multiple prefrontal cortex (PFC) regions, and the thalamus. Indicating a structural?functional relation, we observed that smoking-related gray matter decreases overlapped with the acute functional effects of nicotinic agonist administration in the left insula, ventromedial PFC, and mediodorsal thalamus. Suggesting structural-behavioral implications, we observed that the left insula?s task-based, functional interactions with multiple other structurally impacted regions were linked with pain perception, the right cerebellum?s interactions with other regions were associated with overt body movements, interactions between the parahippocampus and thalamus were linked with memory processes, and interactions between medial PFC regions were associated with face processing. Conclusions Collectively, these findings emphasize brain regions (e.g., ventromedial PFC, insula, thalamus) critically linked with cigarette smoking, suggest neuroimaging paradigms warranting additional consideration among smokers (e.g., pain processing), and highlight regions in need of further elucidation in addiction (e.g., cerebellum). Electronic supplementary material The online version of this article (doi:10.1186/s12993-016-0100-5) contains supplementary material, which is available to authorized users