Semin Speech Lang 2016; 37(01): 003-009
DOI: 10.1055/s-0036-1572385
Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

The Rise of Big Data in Neurorehabilitation

Yasmeen Faroqi-Shah
1   Department of Hearing and Speech Sciences, University of Maryland, College Park, Maryland
› Author Affiliations
Further Information

Publication History

Publication Date:
16 February 2016 (online)

Abstract

In some fields, Big Data has been instrumental in analyzing, predicting, and influencing human behavior. However, Big Data approaches have so far been less central in speech-language pathology. This article introduces the concept of Big Data and provides examples of Big Data initiatives pertaining to adult neurorehabilitation. It also discusses the potential theoretical and clinical contributions that Big Data can make. The article also recognizes some impediments in building and using Big Data for scientific and clinical inquiry.

 
  • References

  • 1 Akushevich I, Kravchenko J, Ukraintseva S, Arbeev K, Yashin AI. Time trends of incidence of age-associated diseases in the US elderly population: Medicare-based analysis. Age Ageing 2013; 42 (4) 494-500
  • 2 National Institute on Aging Genetics of Alzheimer's Disease Data Storage Site (NIAGADS) . University of Pennsylvania. Available at: https://www.niagads.org/ . Accessed December 12, 2015
  • 3 Weiner MW. Alzheimer's Disease Neuroimaging Initiative (ADNI). Available at: http://adni.loni.ucla.edu/ . Accessed December 12, 2015
  • 4 Alzheimer's Disease Big Data Dream Challenge 1 . Available at: https://www.synapse.org/#!Synapse:syn2290704/wiki/60828 . Accessed January 5, 2016
  • 5 Kuwano R, Miyashita A, Arai H , et al; Japanese Genetic Study Consortium for Alzeheimer's Disease. Dynamin-binding protein gene on chromosome 10q is associated with late-onset Alzheimer's disease. Hum Mol Genet 2006; 15 (13) 2170-2182
  • 6 Beck T, Hastings RK, Gollapudi S, Free RC, Brookes AJ. GWAS Central: a comprehensive resource for the comparison and interrogation of genome-wide association studies. Eur J Hum Genet 2014; 22 (7) 949-952
  • 7 Miyashita A, Koike A, Jun G , et al; Alzheimer Disease Genetics Consortium. SORL1 is genetically associated with late-onset Alzheimer's disease in Japanese, Koreans and Caucasians. PLoS ONE 2013; 8 (4) e58618
  • 8 Human Connectome Project (HCP) . Available at: http://www.humanconnectome.org/ . Accessed December 12, 2015
  • 9 Gorgolewski KJ, Varoquaux G, Rivera G , et al. NeuroVault.org: a web-based repository for collecting and sharing unthresholded statistical maps of the human brain. Front Neuroinform 2015; 9: 8
  • 10 Yarkoni T, Poldrack RA, Nichols TE, Van Essen DC, Wager TD. Large-scale automated synthesis of human functional neuroimaging data. Nat Methods 2011; 8 (8) 665-670
  • 11 Eickhoff SB, Laird AR, Grefkes C, Wang LE, Zilles K, Fox PT. Coordinate-based activation likelihood estimation meta-analysis of neuroimaging data: a random-effects approach based on empirical estimates of spatial uncertainty. Hum Brain Mapp 2009; 30 (9) 2907-2926
  • 12 Mirman D, Strauss TJ, Brecher A , et al. A large, searchable, web-based database of aphasic performance on picture naming and other tests of cognitive function. Cogn Neuropsychol 2010; 27 (6) 495-504
  • 13 Schwartz MF, Kimberg DY, Walker GM , et al. Anterior temporal involvement in semantic word retrieval: voxel-based lesion-symptom mapping evidence from aphasia. Brain 2009; 132 (Pt 12): 3411-3427
  • 14 Schwartz MF, Faseyitan O, Kim J, Coslett HB. The dorsal stream contribution to phonological retrieval in object naming. Brain 2012; 135 (Pt 12): 3799-3814
  • 15 Dell GS, Schwartz MF, Nozari N, Faseyitan O, Branch Coslett H. Voxel-based lesion-parameter mapping: identifying the neural correlates of a computational model of word production. Cognition 2013; 128 (3) 380-396
  • 16 Macwhinney B, Fromm D, Forbes M, Holland A. AphasiaBank: methods for studying discourse. Aphasiology 2011; 25 (11) 1286-1307
  • 17 Forbes MM, Fromm D, Macwhinney B. AphasiaBank: a resource for clinicians. Semin Speech Lang 2012; 33 (3) 217-222
  • 18 MacWhinney B. The TalkBank Project. In Beal JC, Corrigan KP, Moisl HL, Eds. Creating and Digitizing Language Corpora: Synchronic Databases, Vol. 1. Houndmills, Basingstoke, England: Palgrave-Macmillan; 2007: 163-180
  • 19 MacWhinney B. The CHILDES project: The database, Psychology Press. 2000
  • 20 Granger S, Gilquin G, Meunier F , Eds. The Cambridge Handbook of Learner Corpus Research. Cambridge, UK: Cambridge University Press; 2015
  • 21 Collaboration of Aphasia Trialists (CATs) . Available at: http://www.aphasiatrials.org/ . Accessed December 12, 2015
  • 22 American Speech-Language Hearing Association . National Outcomes Measurement System: Adults in Healthcare–Acute Hospital National Data Report. Rockville, MD: 2011
  • 23 Center for Rehabilitation Research for Large Databases (CRRLD) . Available at: http://rehabsciences.utmb.edu/cldr/ . Accessed on December 12, 2015
  • 24 Holland AL, Weinberg P, Dittelman J. How to use apps clinically in the treatment of aphasia. Semin Speech Lang 2012; 33 (3) 223-233
  • 25 Constant Therapy . Available at: https://constanttherapy.com/ . Accessed January 8, 2016
  • 26 Talkpath, Lingraphica . Available at: http://www.aphasia.com/ . Accessed January 12, 2016
  • 27 Morrison GE, Simone CM, Ng NF, Hardy JL. Reliability and validity of the NeuroCognitive Performance Test, a web-based neuropsychological assessment. Front Psychol 2015; 6: 1652
  • 28 Basso A. Prognostic factors in aphasia. Aphasiology 1992; 6: 337-348
  • 29 Morrison GE, Simone CM, Ng NF, Hardy JL. Reliability and validity of the NeuroCognitive Performance Test, a web-based neuropsychological assessment. Front Psychol 2000; 6