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      Interpreting and Utilising Intersubject Variability in Brain Function

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          Abstract

          We consider between-subject variance in brain function as data rather than noise. We describe variability as a natural output of a noisy plastic system (the brain) where each subject embodies a particular parameterisation of that system. In this context, variability becomes an opportunity to: (i) better characterise typical versus atypical brain functions; (ii) reveal the different cognitive strategies and processing networks that can sustain similar tasks; and (iii) predict recovery capacity after brain damage by taking into account both damaged and spared processing pathways. This has many ramifications for understanding individual learning preferences and explaining the wide differences in human abilities and disabilities. Understanding variability boosts the translational potential of neuroimaging findings, in particular in clinical and educational neuroscience.

          Highlights

          A wealth of scientifically and clinically relevant information is hidden, and potentially invalidated, when data are averaged across subjects.

          There is growing interest in using neuroimaging to explain differences in human abilities and disabilities. Progress in this endeavour requires us to treat intersubject variability as data rather than noise.

          Our plastic and noisy brains intrinsically change the parameterisation of each individual’s brain, providing a rich opportunity to understand differences in brain function.

          Normal variability can be used to decode different neural pathways that can sustain the same task (degeneracy).

          This is of paramount importance for understanding why patients have variable outcomes after damage to seemingly similar brain regions.

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          Most cited references115

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          Noise in the nervous system.

          Noise--random disturbances of signals--poses a fundamental problem for information processing and affects all aspects of nervous-system function. However, the nature, amount and impact of noise in the nervous system have only recently been addressed in a quantitative manner. Experimental and computational methods have shown that multiple noise sources contribute to cellular and behavioural trial-to-trial variability. We review the sources of noise in the nervous system, from the molecular to the behavioural level, and show how noise contributes to trial-to-trial variability. We highlight how noise affects neuronal networks and the principles the nervous system applies to counter detrimental effects of noise, and briefly discuss noise's potential benefits.
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            Structural and functional brain networks: from connections to cognition.

            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.
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              Unity and diversity of executive functions: Individual differences as a window on cognitive structure.

              Executive functions (EFs) are high-level cognitive processes, often associated with the frontal lobes, that control lower level processes in the service of goal-directed behavior. They include abilities such as response inhibition, interference control, working memory updating, and set shifting. EFs show a general pattern of shared but distinct functions, a pattern described as "unity and diversity". We review studies of EF unity and diversity at the behavioral and genetic levels, focusing on studies of normal individual differences and what they reveal about the functional organization of these cognitive abilities. In particular, we review evidence that across multiple ages and populations, commonly studied EFs (a) are robustly correlated but separable when measured with latent variables; (b) are not the same as general intelligence or g; (c) are highly heritable at the latent level and seemingly also highly polygenic; and (d) activate both common and specific neural areas and can be linked to individual differences in neural activation, volume, and connectivity. We highlight how considering individual differences at the behavioral and neural levels can add considerable insight to the investigation of the functional organization of the brain, and conclude with some key points about individual differences to consider when interpreting neuropsychological patterns of dissociation.
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                Author and article information

                Contributors
                Journal
                Trends Cogn Sci
                Trends Cogn. Sci. (Regul. Ed.)
                Trends in Cognitive Sciences
                Elsevier Science
                1364-6613
                1879-307X
                1 June 2018
                June 2018
                : 22
                : 6
                : 517-530
                Affiliations
                [1 ]Cognitive Neuroimaging Unit, Emirates College for Advanced Education, PO Box 126662, Abu Dhabi, United Arab Emirates
                [2 ]Wellcome Centre for Human Neuroimaging, University College London, Institute of Neurology, WC1N 3BG, London, UK
                Author notes
                Article
                S1364-6613(18)30064-0
                10.1016/j.tics.2018.03.003
                5962820
                29609894
                44913cb0-8328-4f94-9851-30f357bb3956
                © 2018 The Authors

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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                Categories
                Article

                Neurosciences
                neuroimaging,functional variability,brain structure,cognitive strategies,individualised predictions,covariance

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