Towards systemic and individualized brain markers for learning difficulties

Abstract

Event Abstract Back to Event Towards systemic and individualized brain markers for learning difficulties Mohamed L. Seghier1*, Claudine Habak1 and Mohamed A. Fahim1 1 Emirates College for Advanced Education (ECAE), Cognitive Neuroimaging Unit, United Arab Emirates Variability in brain function is meaningful: It is known that there is a wide variation in learning ability: some individuals learn quickly, others struggle in a specific domain, and others have difficulties to learn in all domains. It is also known that there are many ways to learn, and each individual can rely on a different learning strategy that best matches his/her sills, expectations and prior knowledge. The sources of such differences in individual learning preference are not all well understood, including genetic and environmental factors. Both genetic and environmental factors inter-interact at every level, which makes their impact on learning very difficult to predict (Parasuraman and Jiang, 2012). Put another way, there is no single gene or environmental factor that taken alone would predict whether an individual is a good or a poor learner (Frith, 2011). However, what we know is that the interaction “genetic-by-environment” also leaves its signature on the individual’s brain because it constantly shapes and modifies the brain. In this context, cognitive neuroscience and functional neuroimaging can play a pivotal role in characterising and quantifying such individual differences in brain structure and function, with the aim of identifying credible biological factors that can be related to individual differences in behaviour (Miller et al., 2012). We argue here that this cannot be achieved until a shift is made in how between-subject variability is treated in multi-subject neuroimaging studies: effects of interest are commonly expressed as mean main effects that focus on what is consistent over subjects. In this framework, variance is treated as patternless and meaningless noise, and any individual contribution that deviate from the group is ignored, down-weighted, or even penalised. We argue that this framework is too reductionist, because it does not read enough into the rich neuroimaging and behavioural data. Specifically, it is important to treat inter-individual variability as data rather than noise (Thompson-Schill et al., 2005) because it can reflect the different learning preferences or cognitive strategies that each individual adopts to reach a particular outcome (Friston and Price, 2011). Researchers can then model variability between subjects in an informed way and work out how to decode such variability to make credible predictions about the most likely learning preference or strategy used by a given individual (Seghier and Price, 2009). This framework can boost the translational potential of functional neuroimaging findings in education. Heterogeneity in abnormal processing: One potential implication is the identification of brain markers of learning difficulties that are applicable at the individual level. There is a broad literature about abnormal processing in subjects with impaired learning such as dyslexia or dyscalculia; however, the majority of these studies were mainly concerned with average/mean effects in the population/group, and thus, we don’t know the level at which those average effects are representative of the individual pattern. To derive credible characterisations of abnormal processing at the individual level we must have a better understanding and accurate characterisation of the size of variance in normal processing. Put another way, to understand what constitutes atypical processing, we must first understand what can be considered as typical processing (i.e. estimation of the typical range of normal processing). The practical implication of the latter is to acknowledge that typical versus atypical processing may not be a categorical distinction with clear-cut thresholds; instead they are both on the same continuous dimension of the full spectrum of learning and cognitive processing. This leads to another important observation that groups are most likely heterogeneous, with for instance, the existence of different kinds of abnormal processing (e.g. different types of dyslexia). Brain markers of impaired learning at the individual level: The long-term goal is to build models with credible explanatory power, that is, the possibility to generate accurate predictions about individual learning abilities that are educationally useful. First, we must recognize that there is no single brain region or connection that makes an individual a good or a poor learner. Biomarkers must therefore be expressed at the system level. Second, we must acknowledge that the brain is constantly changing, and this time-dependent brain plasticity needs to be characterized and accounted for in current models. Third, when an individual is classified as a poor learner, this does not necessarily mean that this particular individual cannot learn. To the contrary, it means that this particular individual may struggle to learn through standard means but can be advised to adopt alternative learning strategies that rely on alternative intact brain processing pathways and not his/her abnormal ones (Seghier et al., 2012). This possibility is of great importance in the educational setting because it explicitly acknowledges that learning or educational difficulties may also have a biological basis, beyond parents’ or teachers’ control (Frith, 2011), which motivates the search for alternative teaching methods that can be tailored to the individual specific needs (e.g. using technology-based learning tools). Conclusion: We need to take every opportunity to bridge the gap between neuroscience and education. Neuroscience evidence can play a role in designing efficient teaching methods that appreciate how the brain processes information. On the other hand, neuroscience findings need to go beyond aggregate or average effects and start paying attention to the individual effect because individual differences could reflect the multitude of ways by which the brain learns and the different processing pathways that support each of those learning preferences. When an individual struggles with learning in one way, neuroscience can advise on the most efficient alternative, based on the available processing pathways in that individual. References Friston, K.J., Price, C.J., 2011. Modules and brain mapping. Cogn Neuropsychol 15, 1-10. Frith, U., 2011. Brain Waves Module 2: Neuroscience: implications for education and lifelong learning. . Science Policy Centre, The Royal Society. London. Miller, M.B., Donovan, C.L., Bennett, C.M., Aminoff, E.M., Mayer, R.E., 2012. Individual differences in cognitive style and strategy predict similarities in the patterns of brain activity between individuals. Neuroimage 59, 83-93. Parasuraman, R., Jiang, Y., 2012. Individual differences in cognition, affect, and performance: behavioral, neuroimaging, and molecular genetic approaches. Neuroimage 59, 70-82. Seghier, M.L., Neufeld, N.H., Zeidman, P., Leff, A.P., Mechelli, A., Nagendran, A., Riddoch, J.M., Humphreys, G.W., Price, C.J., 2012. Reading without the left ventral occipito-temporal cortex. Neuropsychologia 50, 3621-3635. Seghier, M.L., Price, C.J., 2009. Dissociating functional brain networks by decoding the between-subject variability. Neuroimage 45, 349-359. Thompson-Schill, S.L., Braver, T.S., Jonides, J., 2005. Individual differences. Cogn Affect Behav Neurosci 5, 115-116. Keywords: brain plasticity, Education, Cognitive neuroscience, variability, Learning Conference: International Conference - Educational Neuroscience, Abu Dhabi, United Arab Emirates, 28 Feb - 29 Feb, 2016. Presentation Type: Poster Presentation Topic: Educational Neuroscience Citation: Seghier ML, Habak C and Fahim MA (2016). Towards systemic and individualized brain markers for learning difficulties. Front. Neurosci. Conference Abstract: International Conference - Educational Neuroscience. doi: 10.3389/conf.fnins.2016.92.00016 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 11 Feb 2016; Published Online: 23 Mar 2016. * Correspondence: Dr. Mohamed L Seghier, Emirates College for Advanced Education (ECAE), Cognitive Neuroimaging Unit, Abu Dhabi, United Arab Emirates, mseghier@gmail.com Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. 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