Effects of Fast Simple Numerical Calculation Training on Neural Systems

Cognitive training, including fast simple numerical calculation (FSNC), has been shown to improve performance on untrained processing speed and executive function tasks in the elderly. However, the effects of FSNC training on cognitive functions in the young and on neural mechanisms remain unknown. We investigated the effects of 1-week intensive FSNC training on cognitive function, regional gray matter volume (rGMV), and regional cerebral blood flow at rest (resting rCBF) in healthy young adults. FSNC training was associated with improvements in performance on simple processing speed, speeded executive functioning, and simple and complex arithmetic tasks. FSNC training was associated with a reduction in rGMV and an increase in resting rCBF in the frontopolar areas and a weak but widespread increase in resting rCBF in an anatomical cluster in the posterior region. These results provide direct evidence that FSNC training alone can improve performance on processing speed and executive function tasks as well as plasticity of brain structures and perfusion. Our results also indicate that changes in neural systems in the frontopolar areas may underlie these cognitive improvements.

[1]  Yasuyuki Taki,et al.  Effects of multitasking‐training on gray matter structure and resting state neural mechanisms , 2013, Human brain mapping.

[2]  Yasuyuki Taki,et al.  Effects of working memory training on functional connectivity and cerebral blood flow during rest , 2013, Cortex.

[3]  Courtney Stevens,et al.  Family-based training program improves brain function, cognition, and behavior in lower socioeconomic status preschoolers , 2013, Proceedings of the National Academy of Sciences.

[4]  N. Kraus,et al.  Reversal of age-related neural timing delays with training , 2013, Proceedings of the National Academy of Sciences.

[5]  Yasuyuki Taki,et al.  A voxel-based morphometry study of gray and white matter correlates of a need for uniqueness , 2012, NeuroImage.

[6]  Heidi Johansen-Berg,et al.  Human Structural Plasticity at Record Speed , 2012, Neuron.

[7]  Yasuyuki Taki,et al.  Regional gray and white matter volume associated with Stroop interference: Evidence from voxel-based morphometry , 2012, NeuroImage.

[8]  Yasuyuki Taki,et al.  Brain Training Game Improves Executive Functions and Processing Speed in the Elderly: A Randomized Controlled Trial , 2012, PloS one.

[9]  Hiroshi Fukuda,et al.  Correlation between gray matter density‐adjusted brain perfusion and age using brain MR images of 202 healthy children , 2011, Human brain mapping.

[10]  Yasuyuki Taki,et al.  Cerebral Blood Flow during Rest Associates with General Intelligence and Creativity , 2011, PloS one.

[11]  Yasuyuki Taki,et al.  Regional gray matter density associated with emotional intelligence: Evidence from voxel‐based morphometry , 2011, Human brain mapping.

[12]  Yasuyuki Taki,et al.  Effects of Training of Processing Speed on Neural Systems , 2011, The Journal of Neuroscience.

[13]  Yasuyuki Taki,et al.  Working Memory Training Using Mental Calculation Impacts Regional Gray Matter of the Frontal and Parietal Regions , 2011, PloS one.

[14]  Brian Butterworth,et al.  Neural basis of mathematical cognition , 2011, Current Biology.

[15]  Matti Laine,et al.  Effects of Working-Memory Training on Striatal Dopamine Release , 2011, Science.

[16]  Bruce Fischl,et al.  Avoiding asymmetry-induced bias in longitudinal image processing , 2011, NeuroImage.

[17]  Susanne M. Jaeggi,et al.  Short- and long-term benefits of cognitive training , 2011, Proceedings of the National Academy of Sciences.

[18]  Yasuyuki Taki,et al.  Failing to deactivate: The association between brain activity during a working memory task and creativity , 2011, NeuroImage.

[19]  E. McAuley,et al.  Exercise training increases size of hippocampus and improves memory , 2011, Proceedings of the National Academy of Sciences.

[20]  Thomas E. Nichols,et al.  False positives in neuroimaging genetics using voxel-based morphometry data , 2011, NeuroImage.

[21]  Yasuyuki Taki,et al.  Regional gray matter volume of dopaminergic system associate with creativity: Evidence from voxel-based morphometry , 2010, NeuroImage.

[22]  Yasuyuki Taki,et al.  White matter structures associated with creativity: Evidence from diffusion tensor imaging , 2010, NeuroImage.

[23]  Yasuyuki Taki,et al.  Training of Working Memory Impacts Structural Connectivity , 2010, The Journal of Neuroscience.

[24]  Kim Mouridsen,et al.  The QUASAR reproducibility study, Part II: Results from a multi-center Arterial Spin Labeling test–retest study , 2010, NeuroImage.

[25]  Willie F. Tobin,et al.  Rapid formation and selective stabilization of synapses for enduring motor memories , 2009, Nature.

[26]  T. Asamizuya,et al.  Gray and white matter changes associated with tool-use learning in macaque monkeys , 2009, Proceedings of the National Academy of Sciences.

[27]  Timothy Edward John Behrens,et al.  Training induces changes in white matter architecture , 2009, Nature Neuroscience.

[28]  N. Raz,et al.  Aging white matter and cognition: Differential effects of regional variations in diffusion properties on memory, executive functions, and speed , 2009, Neuropsychologia.

[29]  D. Bavelier,et al.  Exercising your brain: a review of human brain plasticity and training-induced learning. , 2008, Psychology and aging.

[30]  Jordan Grafman,et al.  Damage to the Fronto-Polar Cortex Is Associated with Impaired Multitasking , 2008, PloS one.

[31]  Christian Büchel,et al.  Changes in Gray Matter Induced by Learning—Revisited , 2008, PloS one.

[32]  Lars Bäckman,et al.  Transfer of Learning After Updating Training Mediated by the Striatum , 2008, Science.

[33]  Susanne M. Jaeggi,et al.  Improving fluid intelligence with training on working memory: a meta-analysis , 2008, Psychonomic Bulletin & Review.

[34]  Christian Gaser,et al.  Gray Matter Increase Induced by Practice Correlates with Task-Specific Activation: A Combined Functional and Morphometric Magnetic Resonance Imaging Study , 2008, The Journal of Neuroscience.

[35]  Ryuta Kawashima,et al.  Reading and solving arithmetic problems improves cognitive functions of normal aged people: a randomized controlled study , 2008, AGE.

[36]  Christa Neuper,et al.  Individual differences in mathematical competence predict parietal brain activation during mental calculation , 2007, NeuroImage.

[37]  M. Posner,et al.  Short-term meditation training improves attention and self-regulation , 2007, Proceedings of the National Academy of Sciences.

[38]  Ramon Casanova,et al.  Biological parametric mapping: A statistical toolbox for multimodality brain image analysis , 2007, NeuroImage.

[39]  Y. Benjamini,et al.  Adaptive linear step-up procedures that control the false discovery rate , 2006 .

[40]  Sharona M. Atkins,et al.  Memory enhancement in healthy older adults using a brain plasticity-based training program: A randomized, controlled study , 2006, Proceedings of the National Academy of Sciences.

[41]  X Golay,et al.  Non-invasive Measurement of Perfusion: a Critical Review of Arterial Spin Labelling Techniques , 2022 .

[42]  Esben Thade Petersen,et al.  Model‐free arterial spin labeling quantification approach for perfusion MRI , 2006, Magnetic resonance in medicine.

[43]  Bruce D. McCandliss,et al.  Training, maturation, and genetic influences on the development of executive attention. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[44]  Masato Taira,et al.  Reading aloud and arithmetic calculation improve frontal function of people with dementia. , 2005, The journals of gerontology. Series A, Biological sciences and medical sciences.

[45]  H. Forssberg,et al.  Computerized training of working memory in children with ADHD--a randomized, controlled trial. , 2005, Journal of the American Academy of Child and Adolescent Psychiatry.

[46]  Susan Cooley,et al.  Cognitive enhancement therapy for schizophrenia: effects of a 2-year randomized trial on cognition and behavior. , 2004, Archives of general psychiatry.

[47]  Thomas E. Nichols,et al.  Nonstationary cluster-size inference with random field and permutation methods , 2004, NeuroImage.

[48]  Hiroshi Fukuda,et al.  A functional MRI study of simple arithmetic--a comparison between children and adults. , 2004, Brain research. Cognitive brain research.

[49]  Thomas E. Nichols,et al.  Validating cluster size inference: random field and permutation methods , 2003, NeuroImage.

[50]  C. S. Green,et al.  Action video game modifies visual selective attention , 2003, Nature.

[51]  K. Svoboda,et al.  Long-term in vivo imaging of experience-dependent synaptic plasticity in adult cortex , 2002, Nature.

[52]  H. Forssberg,et al.  Training of Working Memory in Children With ADHD , 2002 .

[53]  Gary N. McLean,et al.  Theories Supporting Transfer of Training. , 2001 .

[54]  J. Gabrieli,et al.  The frontopolar cortex and human cognition: Evidence for a rostrocaudal hierarchical organization within the human prefrontal cortex , 2000, Psychobiology.

[55]  Karl J. Friston,et al.  Voxel-Based Morphometry—The Methods , 2000, NeuroImage.

[56]  E. Spelke,et al.  Sources of mathematical thinking: behavioral and brain-imaging evidence. , 1999, Science.

[57]  P. Huttenlocher,et al.  Regional differences in synaptogenesis in human cerebral cortex , 1997, The Journal of comparative neurology.

[58]  Karl J. Friston,et al.  Detecting Activations in PET and fMRI: Levels of Inference and Power , 1996, NeuroImage.

[59]  箱田 裕司,et al.  集団用ストループ・逆ストループテスト 反応様式, 順序, 練習の効果 , 1990 .

[60]  R. Snow Toward Assessment of Cognitive and Conative Structures in Learning , 1989 .

[61]  R. C. Collins,et al.  Metabolic anatomy of brain: A comparison of regional capillary density, glucose metabolism, and enzyme activities , 1989, The Journal of comparative neurology.

[62]  W. Greenough,et al.  Differential rearing effects on rat visual cortex synapses. III. Neuronal and glial nuclei, boutons, dendrites, and capillaries , 1987, Brain Research.

[63]  R. C. Oldfield The assessment and analysis of handedness: the Edinburgh inventory. , 1971, Neuropsychologia.

[64]  Susanne M. Jaeggi,et al.  Improving fluid intelligence with training on working memory: a meta-analysis , 2015, Psychonomic bulletin & review.

[65]  Yasuyuki Taki,et al.  Effects of Working Memory Training on Cognitive Functions and Neural Systems , 2010, Reviews in the neurosciences.

[66]  A. May,et al.  Structural brain alterations following 5 days of intervention: dynamic aspects of neuroplasticity. , 2007, Cerebral cortex.

[67]  K. McGrew,et al.  The General (g), Broad, and Narrow CHC Stratum Characteristics of the WJ III and WISC-III Tests: A Confirmatory Cross-Battery Investigation. , 2005 .

[68]  F. Paas,et al.  Cognitive Architecture and Instructional Design , 1998 .

[69]  D. Scott Perceptual learning. , 1974, Queen's nursing journal.

[70]  J. Raven,et al.  Manual for Raven's progressive matrices and vocabulary scales , 1962 .