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Functional specialization within the inferior parietal lobes across cognitive domains

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TLDR
The inferior parietal lobe (IPL) is a key neural substrate underlying diverse mental processes, from basic attention to language and social cognition that define human interactions as mentioned in this paper, and its putative domain-global role appears to tie into poorly understood differences between cognitive domains in both hemispheres.
Abstract
The inferior parietal lobe (IPL) is a key neural substrate underlying diverse mental processes, from basic attention to language and social cognition, that define human interactions. Its putative domain-global role appears to tie into poorly understood differences between cognitive domains in both hemispheres. Across attentional, semantic, and social cognitive tasks, our study explored functional specialization within the IPL. The task specificity of IPL subregion activity was substantiated by distinct predictive signatures identified by multivariate pattern-learning algorithms. Moreover, the left and right IPL exerted domain-specific modulation of effective connectivity among their subregions. Task-evoked functional interactions of the anterior and posterior IPL subregions involved recruitment of distributed cortical partners. While anterior IPL subregions were engaged in strongly lateralized coupling links, both posterior subregions showed more symmetric coupling patterns across hemispheres. Our collective results shed light on how under-appreciated hemispheric specialization in the IPL supports some of the most distinctive human mental capacities.

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How does hemispheric specialization contribute to human-defining cognition?

TL;DR: In this paper, dual-processing theories of cognition have been used to explain human cognitive abilities, such as semantic understanding of world structure, logical reasoning, and communication via language, and they have been integrated with the global workspace theory to explain dynamic relay of information products between two systems.
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Enduring effect of abuse: Childhood maltreatment links to altered theory of mind network among adults

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Establishing a role of the semantic control network in social cognitive processing: A meta-analysis of functional neuroimaging studies

TL;DR: In this paper, the authors investigated whether the neural activation commonly found in social functional neuroimaging studies extends to these "semantic control" regions, namely the anterior inferior frontal gyrus (IFG) and posterior middle temporal gyrus.
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Generative lesion pattern decomposition of cognitive impairment after stroke.

TL;DR: In this paper, the authors integrated machine learning and Bayesian hierarchical modeling to enable a hemisphere-aware analysis of 1080 acute ischaemic stroke patients with deep profiling ∼3 months after stroke.
References
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Journal Article

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TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.
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The assessment and analysis of handedness: The Edinburgh inventory

TL;DR: An inventory of 20 items with a set of instructions and response- and computational-conventions is proposed and the results obtained from a young adult population numbering some 1100 individuals are reported.
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Control of goal-directed and stimulus-driven attention in the brain

TL;DR: Evidence for partially segregated networks of brain areas that carry out different attentional functions is reviewed, finding that one system is involved in preparing and applying goal-directed selection for stimuli and responses, and the other is specialized for the detection of behaviourally relevant stimuli.
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AFNI: software for analysis and visualization of functional magnetic resonance neuroimages

TL;DR: A package of computer programs for analysis and visualization of three-dimensional human brain functional magnetic resonance imaging (FMRI) results is described and techniques for automatically generating transformed functional data sets from manually labeled anatomical data sets are described.
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