Correction: Modular brain network organization predicts response to cognitive training in older adults (PLoS ONE (2016) 11:12 (e0169015) DOI: 10.1371/journal.pone.0169015);Modular brain network organization predicts response to cognitive training in older adults

PLoS ONE

Volumn 11, Issue 12, 2016, Pages

  Gallen, Courtney L ^a   Baniqued, Pauline L ^a   Chapman, Sandra B ^a   Aslan, Sina ^b   Keebler, Molly ^b   Didehbani, Nyaz ^b   D'Esposito, Mark ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF TEXAS AT DALLAS   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADULT; AGED; ARTICLE; ASSOCIATION CORTEX; BRAIN CORTEX; CLINICAL EFFECTIVENESS; CLINICAL TRIAL; COGNITIVE THERAPY; CONNECTOME; FEMALE; FUNCTIONAL CONNECTIVITY; HUMAN; HUMAN EXPERIMENT; MALE; MENTAL PERFORMANCE; NERVE CELL PLASTICITY; NORMAL HUMAN; PREDICTION; SENSORIMOTOR CORTEX; SENSORIMOTOR FUNCTION; STRATEGIC MEMORY AND REASONING TRAINING; AGING; COGNITION; COGNITIVE AGING; INDIVIDUALITY; LEARNING; MIDDLE AGED; NERVE CELL NETWORK; NUCLEAR MAGNETIC RESONANCE IMAGING; PHYSIOLOGY;

AGED; AGING; COGNITION; COGNITIVE AGING; FEMALE; HUMANS; INDIVIDUALITY; LEARNING; MAGNETIC RESONANCE IMAGING; MALE; MIDDLE AGED; NERVE NET; NEURONAL PLASTICITY;

EID: 85007332885     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0174570     Document Type: Erratum

References (54)

1. Grady C. The cognitive neuroscience of ageing. Nat Rev Neurosci. 2012; 13: 491-505. doi: 10.1038/ nrn3256 PMID: 22714020
2. Mattar MG, Cole MW, Thompson-Schill SL, Bassett DS. A Functional Cartography of Cognitive Systems. PLoS Comput Biol. 2015; 11: e1004533. doi: 10.1371/journal.pcbi.1004533 PMID: 26629847
3. Medaglia JD, Lynall ME, Bassett DS. Cognitive Network Neuroscience. Journal of Cognitive Neuroscience. 2015; 27: 1471-1491. doi: 10.1162/jocn-a-00810 PMID: 25803596
4. Newman ME, Girvan M. Finding and evaluating community structure in networks. Phys Rev E. 2004; 69: 026113.
5. Geerligs L, Renken RJ, Saliasi E, Maurits NM, Lorist MM. A Brain-Wide Study of Age-Related Changes in Functional Connectivity. Cerebral Cortex. 2015; 25:1987-1999. doi: 10.1093/cercor/bhu012 PMID: 24532319
6. Onoda K, Yamaguchi S. Small-worldness and modularity of the resting-state functional brain network decrease with aging. Neuroscience Letters. 2013; 556: 104-108. doi: 10.1016/j.neulet.2013.10.023 PMID: 24157850
7. Chen ZJ, He Y, Rosa-Neto P, Gong G, Evans AC. Age-related alterations in the modular organization of structural cortical network by using cortical thickness from MRI. NeuroImage. 2011; 56: 235-245. doi: 10.1016/j.neuroimage.2011.01.010 PMID: 21238595
8. Betzel RF, Byrge L, He Y, Goñi J, Zuo XN, Sporns O. Changes in structural and functional connectivity among resting-state networks across the human lifespan. NeuroImage. 2014; 102: 345-357. doi: 10. 1016/j.neuroimage.2014.07.067 PMID: 25109530
9. Chan MY, Park DC, Savalia NK, Petersen SE, Wig GS. Decreased segregation of brain systems across the healthy adult lifespan. Proceedings of the National Academy of Sciences. 2014; 111: E4997-E5006.
10. Lustig C, Shah P, Seidler R, Reuter-Lorenz PA. Aging, Training, and the Brain: A Review and Future Directions. Neuropsychol Rev. 2009; 19: 504-522. doi: 10.1007/s11065-009-9119-9 PMID: 19876740
11. Bherer L. Cognitive plasticity in older adults: effects of cognitive training and physical exercise. Annals of the New York Academy of Sciences. 2015; 1337: 1-6. doi: 10.1111/nyas.12682 PMID: 25773610
12. Ballesteros S, Kraft E, Santana S, Tziraki C. Maintaining older brain functionality: A targeted review. Neuroscience and Biobehavioral Reviews. 2015; 55: 453-477. doi: 10.1016/j.neubiorev.2015.06.008 PMID: 26054789
13. Brehmer Y, Kalpouzos G, Wenger E, Lövdén M. Plasticity of brain and cognition in older adults. Psychological Research. 2014; 78: 790-802. doi: 10.1007/s00426-014-0587-z PMID: 25261907
14. Kelly ME, Loughrey D, Lawlor BA, Robertson IH, Walsh C, Brennan S. The impact of cognitive training and mental stimulation on cognitive and everyday functioning of healthy older adults: a systematic review and meta-analysis. Ageing Research Reviews. 2014; 15: 28-43. doi: 10.1016/j.arr.2014.02.004 PMID: 24607830
15. Taya F, Sun Y, Babiloni F, Thakor N, Bezerianos A. Brain enhancement through cognitive training: a new insight from brain connectome. Front Syst Neurosci. 2015; 9.
16. Chapman SB, Aslan S, Spence JS, Hart JJ, Bartz EK, Didehbani N, et al. Neural Mechanisms of Brain Plasticity with Complex Cognitive Training in Healthy Seniors. Cerebral Cortex. 2015; 25: 396-405. doi: 10.1093/cercor/bht234 PMID: 23985135
17. Engvig A, Fjell AM, Westlye LT, Moberget T, Sundseth ∅, Larsen VA, et al. Memory training impacts short-term changes in aging white matter: A Longitudinal Diffusion Tensor Imaging Study. Hum Brain Mapp. 2012; 33: 2390-2406. doi: 10.1002/hbm.21370 PMID: 21823209
18. Lövdén M, Bodammer NC, Kühn S, Kaufmann J, Schütze H, Tempelmann C, et al. Experience-dependent plasticity of white-matter microstructure extends into old age. Neuropsychologia. 2010; 48: 3878-3883. doi: 10.1016/j.neuropsychologia.2010.08.026 PMID: 20816877
19. Sridharan D, Levitin DJ, Menon V. A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks. Proceedings of the National Academy of Sciences. 2008; 105: 12569-12574.
20. Seeley WW, Menon V, Schatzberg AF, Keller J, Glover GH, Kenna H, et al. Dissociable Intrinsic Connectivity Networks for Salience Processing and Executive Control. Journal of Neuroscience. 2007; 27: 2349-2356. doi: 10.1523/JNEUROSCI.5587-06.2007 PMID: 17329432
21. Cao W, Cao X, Hou C, Li T, Cheng Y, Jiang L, et al. Effects of Cognitive Training on Resting-State Functional Connectivity of Default Mode, Salience, and Central Executive Networks. Frontiers in Aging Neuroscience. 2016; 8: 1-11.
22. Arnemann KL, Chen AJW, Novakovic-Agopian T, Gratton C, Nomura EM, D'Esposito M. Functional brain network modularity predicts response to cognitive training after brain injury. Neurology. 2015; 84: 1568-1574. doi: 10.1212/WNL.0000000000001476 PMID: 25788557
23. Vas AK, Chapman SB, Cook LG, Elliott AC, Keebler M. Higher-Order Reasoning Training Years After Traumatic Brain Injury in Adults. Journal of Head Trauma Rehabilitation. 2011; 26: 224-239. doi: 10. 1097/HTR.0b013e318218dd3d PMID: 21552071
24. Wechsler D. WAIS-III, Wechsler adult intelligence scale: Administration and scoring manual. Psychological Corporation. 1997.
25. Vas AK, Spence JS, Eschler B, Chapman SB. Sensitivity and Specificity of Abstraction using Gist Reasoning Measure in Adults with Traumatic Brain Injury. Journal of Applied Biobehavioral Research. 2016; in press.
26. sgiavasis. C-PAC: CPAC Version 0.3.9 Alpha. Zenodo. 10.5281/zenodo.16557
27. Friston KJ, Williams S, Howard R, Frackowiak RSJ, Turner R. Movement-Related effects in fMRI timeseries. Magn Reson Med. 1996; 35: 346-355. PMID: 8699946
28. Behzadi Y, Restom K, Liau J, Liu TT. A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. NeuroImage. 2007; 37: 90-101. doi: 10.1016/j.neuroimage.2007.04. 042 PMID: 17560126
29. Power JD, Cohen AL, Nelson SM, Wig GS, Barnes KA, Church JA, et al. Functional Network Organization of the Human Brain. Neuron. 2011; 72: 665-678. doi: 10.1016/j.neuron.2011.09.006 PMID: 22099467
30. Gallen CL, Turner GR, Adnan A, D'Esposito M. Reconfiguration of brain network architecture to support executive control in aging. Neurobiology of Aging. 2016; 44: 42-52. doi: 10.1016/j.neurobiolaging.2016. 04.003 PMID: 27318132
31. Sadaghiani S, Poline JB, Kleinschmidt A, D'Esposito M. Ongoing dynamics in large-scale functional connectivity predict perception. Proceedings of the National Academy of Sciences. 2015; 112: 8463-8468.
32. Newman ME. Modularity and community structure in networks. Proceedings of the National Academy of Sciences. 2006; 103: 8577-8582.
33. Sporns O, Betzel RF. Modular Brain Networks. Annu Rev Psychol. 2016; 67: 613. doi: 10.1146/ annurev-psych-122414-033634 PMID: 26393868
34. Fortunato S, Hric D. Community detection in networks: A user guide. Physics Reports. 2016; 659: 1-44.
35. Cohen J, Cohen P, West SG, Aiken LS. Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. Erlbaum, Mahwah, NJ; 2003.
36. Myers L, Sirois MJ. Spearman Correlation Coefficients, Differences between. Encyclopedia of Statistical Sciences. John Wiley and Sons, New York; 2006.
37. Power JD, Barnes KA, Snyder AZ, Schlaggar BL, Petersen SE. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage. 2012; 59: 2142-2154. doi: 10.1016/j.neuroimage.2011.10.018 PMID: 22019881
38. Van Dijk KRA, Sabuncu MR, Buckner RL. The influence of head motion on intrinsic functional connectivity MRI. NeuroImage. 2012; 59: 431-438. doi: 10.1016/j.neuroimage.2011.07.044 PMID: 21810475
39. Satterthwaite TD, Wolf DH, Loughead J, Ruparel K, Elliott MA, Hakonarson H, et al. Impact of in-scanner head motion on multiple measures of functional connectivity: Relevance for studies of neurodevelopment in youth. NeuroImage. 2012; 60: 623-632. doi: 10.1016/j.neuroimage.2011.12.063 PMID: 22233733
40. Meunier D, Lambiotte R, Fornito A, Ersche KD, Bullmore ET. Hierarchical modularity in human brain functional networks. Frontiers in Neuroinformatics. 2010; 3.
41. Stevens AA, Tappon SC, Garg A, Fair DA. Functional Brain Network Modularity Captures Inter- and Intra-Individual Variation in Working Memory Capacity. PLoS ONE. 2012; 7: e30468. doi: 10.1371/ journal.pone.0030468 PMID: 22276205
42. Wen X, Zhang D, Liang B, Zhang R, Wang Z, Wang J, et al. Reconfiguration of the Brain Functional Network Associated with Visual Task Demands. PLoS ONE. 2015; 10: e0132518. doi: 10.1371/journal. pone.0132518 PMID: 26146993
43. Kashtan N, Alon U. Spontaneous evolution of modularity and network motifs. Proceedings of the National Academy of Sciences. 2005; 102: 13773-13778.
44. Schultz DH, Cole MW. Higher Intelligence Is Associated with Less Task-Related Brain Network Reconfiguration. Journal of Neuroscience. 2016; 36: 8551-8561. doi: 10.1523/JNEUROSCI.0358-16.2016 PMID: 27535904
45. Liang X, Zou Q, He Y, Yang Y. Topologically Reorganized Connectivity Architecture of Default-Mode, Executive-Control, and Salience Networks across Working Memory Task Loads. Cerebral Cortex. 2016; 26: 1501-1511. doi: 10.1093/cercor/bhu316 PMID: 25596593
46. Stanley ML, Dagenbach D, Lyday RG, Burdette JH, Laurienti PJ. Changes in global and regional modularity associated with increasing working memory load. Frontiers in Human Neuroscience. 2014; 8: 954. doi: 10.3389/fnhum.2014.00954 PMID: 25520639
47. Vatansever D, Menon DK, Manktelow AE, Sahakian BJ, Stamatakis EA. Default Mode Dynamics for Global Functional Integration. Journal of Neuroscience. 2015; 35: 15254-15262. doi: 10.1523/ JNEUROSCI.2135-15.2015 PMID: 26586814
48. Mathewson KE, Basak C, Maclin EL, Low KA, Boot WR, Kramer AF, et al. Different slopes for different folks: Alpha and delta EEG power predict subsequent video game learning rate and improvements in cognitive control tasks. Psychophysiology. 2012; 49: 1558-1570. doi: 10.1111/j.1469-8986.2012. 01474.x PMID: 23095124
49. Vo LTK, Walther DB, Kramer AF, Erickson KI, Boot WR, Voss MW, et al. Predicting Individuals' Learning Success from Patterns of Pre-Learning MRI Activity. PLoS ONE. 2011; 6: e16093. doi: 10.1371/ journal.pone.0016093 PMID: 21264257
50. Basak C, Voss MW, Erickson KI, Boot WR, Kramer AF. Regional differences in brain volume predict the acquisition of skill in a complex real-time strategy videogame. Brain and Cognition. 2011; 76: 407-414. doi: 10.1016/j.bandc.2011.03.017 PMID: 21546146
51. Erickson KI, Boot WR, Basak C, Neider MB, Prakash RS, Voss MW, et al. Striatal Volume Predicts Level of Video Game Skill Acquisition. Cerebral Cortex. 2010; 20: 2522-2530. doi: 10.1093/cercor/ bhp293 PMID: 20089946
52. Ellefsen KO, Mouret JB, Clune J. Neural Modularity Helps Organisms Evolve to Learn New Skills without Forgetting Old Skills. PLoS Comput Biol. 2015; 11: e1004128. doi: 10.1371/journal.pcbi.1004128 PMID: 25837826
53. Mattar M, Wymbs NF, Bock AS, Aguirre GK, Grafton ST, Bassett DS. Predicting future learning from baseline network architecture. BioRxiv. 2016;056861.
54. Bassett DS, Yang M, Wymbs NF, Grafton ST. Learning-induced autonomy of sensorimotor systems. Nature Neuroscience. 2015; 18: 744-751. doi: 10.1038/nn.3993 PMID: 25849989