Brain Topography

, Volume 31, Issue 3, pp 346–363 | Cite as

Machine Learning EEG to Predict Cognitive Functioning and Processing Speed Over a 2-Year Period in Multiple Sclerosis Patients and Controls

  • Hanni Kiiski
  • Lee Jollans
  • Seán Ó. Donnchadha
  • Hugh Nolan
  • Róisín Lonergan
  • Siobhán Kelly
  • Marie Claire O’Brien
  • Katie Kinsella
  • Jessica Bramham
  • Teresa Burke
  • Michael Hutchinson
  • Niall Tubridy
  • Richard B. Reilly
  • Robert WhelanEmail author
Original Paper


Event-related potentials (ERPs) show promise to be objective indicators of cognitive functioning. The aim of the study was to examine if ERPs recorded during an oddball task would predict cognitive functioning and information processing speed in Multiple Sclerosis (MS) patients and controls at the individual level. Seventy-eight participants (35 MS patients, 43 healthy age-matched controls) completed visual and auditory 2- and 3-stimulus oddball tasks with 128-channel EEG, and a neuropsychological battery, at baseline (month 0) and at Months 13 and 26. ERPs from 0 to 700 ms and across the whole scalp were transformed into 1728 individual spatio-temporal datapoints per participant. A machine learning method that included penalized linear regression used the entire spatio-temporal ERP to predict composite scores of both cognitive functioning and processing speed at baseline (month 0), and months 13 and 26. The results showed ERPs during the visual oddball tasks could predict cognitive functioning and information processing speed at baseline and a year later in a sample of MS patients and healthy controls. In contrast, ERPs during auditory tasks were not predictive of cognitive performance. These objective neurophysiological indicators of cognitive functioning and processing speed, and machine learning methods that can interrogate high-dimensional data, show promise in outcome prediction.


Cognitive function Multiple sclerosis Electroencephalography Oddball paradigm Machine learning Longitudinal 



This study was partly funded by an Enterprise Ireland (eBiomed: eHealthCare based on Biomedical Signal Processing and ICT for Integrated Diagnosis and Treatment of Disease), a Science Foundation Ireland grant to R.B. Reilly (09/RFP/NE2382), an IRCSET grants to H. Kiiski and S. Ó. Donnchadha (, a Health Service Executive funding to M.C. O’Brien and a Science Foundation Ireland grant to R. Whelan (16/ERCD/3797). The study sponsors had no involvement in the collection, analysis and interpretation of data and in the writing of the manuscript.

Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee (the Ethics and Medical Research Committee of the St. Vincent’s Healthcare Group) and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent

Written informed consent was obtained from all individual participants included in the study on each testing occasion.

Supplementary material

10548_2018_620_MOESM1_ESM.tiff (312 kb)
Supplementary Fig. 1 Cognitive functioning composite score (z) in RRMS, SPMS and control participants. (TIFF 312 KB)
10548_2018_620_MOESM2_ESM.tiff (323 kb)
Supplementary Fig. 2 Processing speed and working memory composite score (z) in RRMS, SPMS and control participants. (TIFF 323 KB)
10548_2018_620_MOESM3_ESM.eegjob (7 kb)
Supplementary material 3 (EEGJOB 7 KB)
10548_2018_620_MOESM4_ESM.docx (13 kb)
Supplementary material 4 (DOCX 13 KB)
10548_2018_620_MOESM5_ESM.docx (15 kb)
Supplementary material 5 (DOCX 15 KB)
10548_2018_620_MOESM6_ESM.docx (13 kb)
Supplementary material 6 (DOCX 12 KB)
10548_2018_620_MOESM7_ESM.docx (16 kb)
Supplementary material 7 (DOCX 15 KB)

Supplementary Video 1 ERP activity over the scalp during visual 2-stimulus oddball task (0-700ms) that predicted cognitive functioning at Month 0. Higher beta choice frequency values denote better accuracy in predicting cognitive functioning score. (AVI 2023 KB)

Supplementary Video 2 ERP activity over the scalp during visual 2-stimulus oddball task (0-700ms) that predicted cognitive functioning at Month 13. Higher beta choice frequency values denote better accuracy in predicting cognitive functioning score. (AVI 2109 KB)

Supplementary Video 3 ERP activity over the scalp during visual 3-stimulus oddball task (0-700ms) that predicted cognitive functioning at Month 13. Higher beta choice frequency values denote better accuracy in predicting cognitive functioning score. (AVI 1947 KB)

Supplementary Video 4 ERP activity over the scalp during visual 2-stimulus oddball task (0-700ms) that predicted processing speed and working memory performance at Month 0. Higher beta choice frequency values denote better accuracy in predicting processing speed and working memory score. (AVI 1935 KB)

Supplementary Video 5 ERP activity over the scalp during visual 2-stimulus oddball task (0-700ms) that predicted processing speed and working memory performance at Month 13. Higher beta choice frequency values denote better accuracy in predicting processing speed and working memory score. (AVI 1990 KB)

Supplementary Video 6 ERP activity (µV) in multiple sclerosis and healthy control participants during visual 2-stimulus oddball task at Month 0. (AVI 1574 KB)


  1. Amato MP, Ponziani G, Pracucci G, Bracco L, Siracusa G, Amaducci L (1995) Cognitive impairment in early-onset multiple sclerosis. Pattern, predictors, and impact on everyday life in a 4-year follow-up. Arch Neurol 52(2):168–172CrossRefPubMedGoogle Scholar
  2. Amato MP, Ponziani G, Siracusa G, Sorbi S (2001) Cognitive dysfunction in early-onset multiple sclerosis: a reappraisal after 10 years. Arch Neurol 58(10):1602–1606CrossRefPubMedGoogle Scholar
  3. Amato MP, Zipoli V, Portaccio E (2006) Multiple sclerosis-related cognitive changes: a review of cross-sectional and longitudinal studies. J Neurol Sci 245(1–2):41–46. CrossRefPubMedGoogle Scholar
  4. Amato MP, Razzolini L, Goretti B, Stromillo ML, Rossi F, Giorgio A et al (2013) Cognitive reserve and cortical atrophy in multiple sclerosis: a longitudinal study. Neurology 80(19):1728–1733. CrossRefPubMedGoogle Scholar
  5. Azcarraga-Guirola E, Rodriguez-Agudelo Y, Velazquez-Cardoso J, Rito-Garcia Y, Solis-Vivanco R (2017) Electrophysiological correlates of decision making impairment in multiple sclerosis. Eur J Neurosc 45(2):321–329. CrossRefGoogle Scholar
  6. Bagnato F, Salman Z, Kane R, Auh S, Cantor FK, Ehrmantraut M et al (2010) T1 cortical hypointensities and their association with cognitive disability in multiple sclerosis. Mult Scler 16(10):1203–1212. CrossRefPubMedGoogle Scholar
  7. Barker-Collo SL (2005) Within session practice effects on the PASAT in clients with multiple sclerosis. Arch Clin Neuropsychol 20(2):145–152. CrossRefPubMedGoogle Scholar
  8. Beck AT, Epstein N, Brown G, Steer RA (1988) An inventory for measuring clinical anxiety: psychometric properties. J Consult Clin Psychol 56(6):893–897CrossRefPubMedGoogle Scholar
  9. Beck AT, Steer RA, Brown GK (1996) Beck depression inventory-II: manual. Psychological Corporation, San AntonioGoogle Scholar
  10. Benedict RH, Zivadinov R (2011) Risk factors for and management of cognitive dysfunction in multiple sclerosis. Nat Rev Neurol 7(6):332–342. CrossRefPubMedGoogle Scholar
  11. Benedict RHB, Schretlen D, Groninger L, Dobraski M, Shpritz B (1996) Revision of the brief visuospatial memory test: studies of normal performance, reliability, and validity. Psychol Assess 8(2):145–153CrossRefGoogle Scholar
  12. Benedict RH, Fischer JS, Archibald CJ, Arnett PA, Beatty WW, Bobholz J et al (2002) Minimal neuropsychological assessment of MS patients: a consensus approach. Clin Neuropsychol 16(3):381–397. CrossRefPubMedGoogle Scholar
  13. Benedict RH, Bruce JM, Dwyer MG, Abdelrahman N, Hussein S, Weinstock-Guttman B et al (2006) Neocortical atrophy, third ventricular width, and cognitive dysfunction in multiple sclerosis. Arch Neurol 63(9):1301–1306. CrossRefPubMedGoogle Scholar
  14. Benedict RH, Morrow SA, Weinstock Guttman B, Cookfair D, Schretlen DJ (2010) Cognitive reserve moderates decline in information processing speed in multiple sclerosis patients. J Int Neuropsychol Soc 16(5):829–835. CrossRefPubMedGoogle Scholar
  15. Benton AL, Hamsher K (1989) Multilingual aphasia examination. AJA Associates, Iowa CityGoogle Scholar
  16. Bergendal G, Fredrikson S, Almkvist O (2007) Selective decline in information processing in subgroups of multiple sclerosis: an 8-year longitudinal study. Eur Neurol 57(4):193–202. CrossRefPubMedGoogle Scholar
  17. Cawley GC, Talbot NLC (2010) On over-fitting in model selection and subsequent selection bias in performance evaluation. J Mach Learn Res 11:2079–2107Google Scholar
  18. Chiaravalloti ND, DeLuca J (2008) Cognitive impairment in multiple sclerosis. Lancet Neurol 7(12):1139–1151. CrossRefPubMedGoogle Scholar
  19. Costa SL, Genova HM, DeLuca J, Chiaravalloti ND (2017) Information processing speed in multiple sclerosis: past, present, and future. Mult Scler 23(6):772–789. CrossRefPubMedGoogle Scholar
  20. Covey TJ, Shucard JL, Shucard DW (2016) Evaluation of cognitive control and distraction using event-related potentials in healthy individuals and patients with multiple sclerosis. In: International conference on augmented cognition. Springer International Publishing, pp 165–176Google Scholar
  21. Covey TJ, Shucard JL, Shucard DW (2017) Event-related brain potential indices of cognitive function and brain resource reallocation during working memory in patients with Multiple Sclerosis. Clin Neurophysiol 128(4):604–621. CrossRefPubMedGoogle Scholar
  22. Crawford JR (1992) Current and premorbid intelligence measures in neuropsychological assessment. In: Crawford JR, McKinlay WW (eds) A handbook of neuropsychological assessment. Erlbaum, London, pp 21–49Google Scholar
  23. De Sonneville LM, Boringa JB, Reuling IE, Lazeron RH, Ader HJ, Polman CH (2002) Information processing characteristics in subtypes of multiple sclerosis. Neuropsychologia 40(11):1751–1765CrossRefPubMedGoogle Scholar
  24. Delis DC, Kramer JH, Kaplan E, Ober BA (2000) California verbal learning test: second edition (CVLT-II). The Psychological Corporation, San AntonioGoogle Scholar
  25. Delorme A, Makeig S (2004) EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods 134(1):9–21. CrossRefPubMedGoogle Scholar
  26. Doyle OM, Mehta MA, Brammer MJ (2015) The role of machine learning in neuroimaging for drug discovery and development. Psychopharmacology 232(21–22):4179–4189. CrossRefPubMedGoogle Scholar
  27. Filippi M, Rocca MA, Benedict RH, DeLuca J, Geurts JJ, Rombouts SA, Ron M, Comi G (2010) The contribution of MRI in assessing cognitive impairment in multiple sclerosis. Neurology 75(23):2121–2128CrossRefPubMedPubMedCentralGoogle Scholar
  28. Friedman D, Cycowicz YM, Gaeta H (2001) The novelty P3: an event-related brain potential (ERP) sign of the brain’s evaluation of novelty. Neurosci Biobehav Rev 25(4):355–373CrossRefPubMedGoogle Scholar
  29. Genova HM, DeLuca J, Chiaravalloti N, Wylie G (2013) The relationship between executive functioning, processing speed and white matter integrity in multiple sclerosis. J Clin Exp Neuropsychol 35(6):631CrossRefPubMedPubMedCentralGoogle Scholar
  30. Ghaffar O, Fiati M, Feinstein A (2012) Occupational attainment as a marker of cognitive reserve in multiple sclerosis. PLoS ONE 7(10):e47206. CrossRefPubMedPubMedCentralGoogle Scholar
  31. Gillan CM, Whelan R (2017) What big data can do for treatment in psychiatry. Curr Opin Behav Sci 31(18):34–42CrossRefGoogle Scholar
  32. Glanz BI, Healy BC, Hviid LE, Chitnis T, Weiner HL (2012) Cognitive deterioration in patients with early multiple sclerosis: a 5-year study. J Neurol Neurosurg Psychiatry 83(1):38–43. CrossRefPubMedGoogle Scholar
  33. Gronwall DMA (1977) Paced auditory serial-addition task: measure of recovery from concussion. Percept Motor Skill 44:367–373CrossRefGoogle Scholar
  34. Hamalainen P, Rosti-Otajarvi E (2016) Cognitive impairment in MS: rehabilitation approaches. Acta Neurol Scand 134(Suppl 200):8–13. CrossRefPubMedGoogle Scholar
  35. Hoffmann S, Tittgemeyer M, von Cramon DY (2007) Cognitive impairment in multiple sclerosis. Curr Opin Neurol 20(3):275–280. CrossRefPubMedGoogle Scholar
  36. Holdnack HA (2001) Wechsler test of adult reading: WTAR. The Psychological Corporation, San AntonioGoogle Scholar
  37. Jollans L, Whelan R (2016) The clinical added value of imaging: A perspective from outcome prediction. Biol Psychiatry Cogn Neurosci Neuroimaging 1(5):423–432CrossRefPubMedGoogle Scholar
  38. Jollans L, Whelan R (2017) Neuromarkers for mental disorders: Harnessing population neuroscience. In: Werdecker A (ed) Biomarkers for demographic research. Springer (In press)Google Scholar
  39. Jollans L, Zhipeng C, Icke I, Greene C, Kelly C, Banaschewski T et al (2016) Ventral striatum connectivity during reward anticipation in adolescent smokers. Dev Neuropsychol 41(1–2):6–21CrossRefPubMedPubMedCentralGoogle Scholar
  40. Jollans L, Whelan R, Venables L, Turnbull OH, Cella M, Dymond S (2017) Computational EEG modelling of decision making under ambiguity reveals spatio-temporal dynamics of outcome evaluation. Behav Brain Res 321:28–35CrossRefPubMedGoogle Scholar
  41. Kalmar JH, Halper J, Gaudino EA, Moore NB, DeLuca J (2008) The relationship between cognitive deficits and everyday functional activities in multiple sclerosis. Neuropsychology 22(4):442–449. CrossRefPubMedGoogle Scholar
  42. Kendler KS (2012) The dappled nature of causes of psychiatric illness: replacing the organic-functional/hardware-software dichotomy with empirically based pluralism. Mol Psychiatry 17(4):377–388. CrossRefPubMedPubMedCentralGoogle Scholar
  43. Key AP, Dove GO, Maguire MJ (2005) Linking brainwaves to the brain: an ERP primer. Dev Neuropsychol 27(2):183–215. CrossRefPubMedGoogle Scholar
  44. Kiiski H, Reilly RB, Lonergan R, Kelly S, O’Brien M, Kinsella K et al (2011) Change in PASAT performance correlates with change in P3 ERP amplitude over a 12-month period in multiple sclerosis patients. J Neurol Sci 305(1–2):45–52. CrossRefPubMedGoogle Scholar
  45. Kiiski H, Reilly RB, Lonergan R, Kelly S, O’Brien MC, Kinsella K et al (2012) Only low frequency event-related EEG activity is compromised in multiple sclerosis: insights from an independent component clustering analysis. PLoS ONE 7(9):e45536. CrossRefPubMedPubMedCentralGoogle Scholar
  46. Kimiskidis VK, Papaliagkas V, Sotirakoglou K, Kouvatsou ZK, Kapina VK, Papadaki E et al (2016) Cognitive event-related potentials in multiple sclerosis: Correlation with MRI and neuropsychological findings. Mult Scler Relat Disord 10:192–197. CrossRefPubMedGoogle Scholar
  47. Kok A (2001) On the utility of P3 amplitude as a measure of processing capacity. Psychophysiology 38(3):557–577. CrossRefPubMedGoogle Scholar
  48. Kurtzke JF (2008) Historical and clinical perspectives of the expanded disability status scale. Neuroepidemiology 31(1):1–9. doi: CrossRefPubMedGoogle Scholar
  49. Lazeron RH, Rombouts SA, Scheltens P, Polman CH, Barkhof F (2004) An fMRI study of planning-related brain activity in patients with moderately advanced multiple sclerosis. Mult Scler 10(5):549–555. CrossRefPubMedGoogle Scholar
  50. Leocani L, Comi G (2000) Neurophysiological investigations in multiple sclerosis. Curr Opin Neurol 13(3):255–261CrossRefPubMedGoogle Scholar
  51. Lopez-Gongora M, Escartin A, Martinez-Horta S, Fernandez-Bobadilla R, Querol L, Romero S et al (2015) Neurophysiological evidence of compensatory brain mechanisms in early-stage multiple sclerosis. PLoS ONE 10(8):e0136786. CrossRefPubMedPubMedCentralGoogle Scholar
  52. Lowe C, Rabbitt P (1998) Test/re-test reliability of the CANTAB and ISPOCD neuropsychological batteries: theoretical and practical issues. Neuropsychologia 36(9):915–923CrossRefPubMedGoogle Scholar
  53. Luck SJ, Gaspelin N (2017) How to get statistically significant effects in any ERP experiment (and why you shouldn’t). Psychophysiology 54(1):146–157CrossRefPubMedPubMedCentralGoogle Scholar
  54. McCarthy M, Beaumont JG, Thompson R, Peacock S (2005) Modality-specific aspects of sustained and divided attentional performance in multiple sclerosis. Arch Clin Neuropsychol 20(6):705–718. CrossRefPubMedGoogle Scholar
  55. Moutoussis M, Eldar E, Dolan RJ (2016) Building a new field of computational psychiatry. Biol psychiatry 82(6):388–390. CrossRefPubMedGoogle Scholar
  56. Nolan H, Whelan R, Reilly RB (2010) FASTER: fully automated statistical thresholding for EEG artifact rejection. J Neurosci Methods 192(1):152–162. CrossRefPubMedGoogle Scholar
  57. Oostenveld R, Praamstra P (2001) The five percent electrode system for high-resolution EEG and ERP measurements. Clin Neurophysiol 112(4):713–719CrossRefPubMedGoogle Scholar
  58. Piras MR, Magnano I, Canu ED, Paulus KS, Satta WM, Soddu A et al (2003) Longitudinal study of cognitive dysfunction in multiple sclerosis: neuropsychological, neuroradiological, and neurophysiological findings. J Neurol Neurosurg Psychiatry 74(7):878–885CrossRefPubMedPubMedCentralGoogle Scholar
  59. Polich J (2007) Updating P300: an integrative theory of P3a and P3b. Clin Neurophysiol 118(10):2128–2148. CrossRefPubMedPubMedCentralGoogle Scholar
  60. Polman C, Barkhof F, Sandberg-Wollheim M, Linde A, Nordle O, Nederman T (2005) Treatment with laquinimod reduces development of active MRI lesions in relapsing MS. Neurology 64(6):987–991. CrossRefPubMedGoogle Scholar
  61. Polman CH, Reingold SC, Banwell B, Clanet M, Cohen JA, Filippi M et al (2011) Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann Neurol 69(2):292–302. CrossRefPubMedPubMedCentralGoogle Scholar
  62. Rabbitt P, Lowe C, Shilling V (2001) Frontal tests and models for cognitive ageing. Eur J Cogn Psychol 13:5–28CrossRefGoogle Scholar
  63. Rocca MA, Amato MP, De Stefano N, Enzinger C, Geurts JJ, Penner IK et al (2015) Clinical and imaging assessment of cognitive dysfunction in multiple sclerosis. Lancet Neurol 14(3):302–317. CrossRefPubMedGoogle Scholar
  64. Rogers JM, Panegyres PK (2007) Cognitive impairment in multiple sclerosis: evidence-based analysis and recommendations. J Clin Neurosci 14(10):919–927. CrossRefPubMedGoogle Scholar
  65. Santiago O, Guardia J, Casado V, Carmona O, Arbizu T (2007) Specificity of frontal dysfunctions in relapsing-remitting multiple sclerosis. Arch Clin Neuropsychol 22(5):623–629. CrossRefPubMedGoogle Scholar
  66. Scarpazza C, Braghittoni D, Casale B, Malagu S, Mattioli F, di Pellegrino G et al (2013) Education protects against cognitive changes associated with multiple sclerosis. Restor Neurol Neurosci 31(5):619–631. PubMedGoogle Scholar
  67. Smith A (1982) Symbol digit modalities test: manual. Western Psychological Services, Los AngelesGoogle Scholar
  68. Stern Y, Habeck C, Moeller J, Scarmeas N, Anderson KE, Hilton HJ et al (2005) Brain networks associated with cognitive reserve in healthy young and old adults. Cereb Cortex 15(4):394–402. CrossRefPubMedPubMedCentralGoogle Scholar
  69. Sumowski JF, Leavitt VM (2013) Cognitive reserve in multiple sclerosis. Mult Scler 19(9):1122–1127. CrossRefPubMedGoogle Scholar
  70. Sumowski JF, Chiaravalloti N, Leavitt VM, Deluca J (2012) Cognitive reserve in secondary progressive multiple sclerosis. Mult Scler 18(10):1454–1458. CrossRefPubMedGoogle Scholar
  71. Sumowski JF, Rocca MA, Leavitt VM, Riccitelli G, Comi G, DeLuca J et al (2013) Brain reserve and cognitive reserve in multiple sclerosis: what you’ve got and how you use it. Neurology 80(24):2186–2193. CrossRefPubMedPubMedCentralGoogle Scholar
  72. Sundgren M, Nikulin VV, Maurex L, Wahlin A, Piehl F, Brismar T (2015a) P300 amplitude and response speed relate to preserved cognitive function in relapsing-remitting multiple sclerosis. Clin Neurophysiol 126(4):689–697. CrossRefPubMedGoogle Scholar
  73. Sundgren M, Wahlin A, Maurex L, Brismar T (2015b) Event related potential and response time give evidence for a physiological reserve in cognitive functioning in relapsing-remitting multiple sclerosis. J Neurol Sci 356(1–2):107–112. CrossRefPubMedGoogle Scholar
  74. Titlic M, Mihalj M, Petrovic AB, Suljic E (2016) P300 as an auxiliary method in clinical practice: a review of literature. J Health Sci 6(3):143–148Google Scholar
  75. Trenerry MR, Crossan B, DeBoe J, Leber WR (1989) Stroop neuropsychological screening test: manual. Psychological Assessment Resources, FloridaGoogle Scholar
  76. Van Schependom J, Gielen J, Laton J, D’Hooghe MB, De Keyser J, Nagels G (2014) Graph theoretical analysis indicates cognitive impairment in MS stems from neural disconnection. Neuroimage Clin 4:403–410. CrossRefPubMedPubMedCentralGoogle Scholar
  77. Vazquez-Marrufo M, Gonzalez-Rosa JJ, Galvao-Carmona A, Hidalgo-Munoz A, Borges M, Pena JL et al (2013) Retest reliability of individual p3 topography assessed by high density electroencephalography. PLoS ONE 8(5):e62523. CrossRefPubMedPubMedCentralGoogle Scholar
  78. Vazquez-Marrufo M, Galvao-Carmona A, Gonzalez-Rosa JJ, Hidalgo-Munoz AR, Borges M, Ruiz-Pena JL et al (2014) Neural correlates of alerting and orienting impairment in multiple sclerosis patients. PLoS ONE 9(5):e97226. CrossRefPubMedPubMedCentralGoogle Scholar
  79. Whelan R (2008) Effective analysis of reaction time data. Psychol Rec 58(3):475–482CrossRefGoogle Scholar
  80. Whelan R, Garavan H (2014) When optimism hurts: inflated predictions in psychiatric neuroimaging. Biol Psychiatry 75(9):746–748. CrossRefPubMedGoogle Scholar
  81. Whelan R, Lonergan R, Kiiski H, Nolan H, Kinsella K, Bramham J et al (2010a) A high-density ERP study reveals latency, amplitude, and topographical differences in multiple sclerosis patients versus controls. Clin Neurophysiol 121(9):1420–1426. CrossRefPubMedGoogle Scholar
  82. Whelan R, Lonergan R, Kiiski H, Nolan H, Kinsella K, Hutchinson M et al (2010b) Impaired information processing speed and attention allocation in multiple sclerosis patients versus controls: a high-density EEG study. J Neurol Sci 293(1):45–50. CrossRefPubMedGoogle Scholar
  83. Whelan R, Watts R, Orr CA, Althoff RR, Artiges E, Banaschewski T et al (2014) Neuropsychosocial profiles of current and future adolescent alcohol misusers. Nature 512(7513):185–189. CrossRefPubMedPubMedCentralGoogle Scholar
  84. Woo CW, Chang LJ, Lindquist MA, Wager TD (2017) Building better biomarkers: brain models in translational neuroimaging. Nat Neurosci 20(3):365–377. CrossRefPubMedGoogle Scholar
  85. Yarkoni T, Westfall J (2016) Choosing prediction over explanation in psychology: Lessons from machine learning. Unpublished manuscript. Retrieved from
  86. Zhao Y, Healy BC, Rotstein D, Guttmann CR, Bakshi R, Weiner HL, Brodley CE, Chitnis T (2017) Exploration of machine learning techniques in predicting multiple sclerosis disease course. PLoS ONE 12(4):e0174866CrossRefPubMedPubMedCentralGoogle Scholar
  87. Ziemann U, Wahl M, Hattingen E, Tumani H (2011) Development of biomarkers for multiple sclerosis as a neurodegenerative disorder. Prog Neurobiol 95(4):670–685. CrossRefPubMedGoogle Scholar
  88. Zou H, Hastie T (2005) Regularization and variable selection via the elastic net. J R Stat Soc Series B Stat Methodol 67:301–320CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Hanni Kiiski
    • 1
  • Lee Jollans
    • 1
  • Seán Ó. Donnchadha
    • 2
  • Hugh Nolan
    • 3
  • Róisín Lonergan
    • 4
  • Siobhán Kelly
    • 4
  • Marie Claire O’Brien
    • 2
  • Katie Kinsella
    • 4
  • Jessica Bramham
    • 2
  • Teresa Burke
    • 2
    • 5
  • Michael Hutchinson
    • 4
  • Niall Tubridy
    • 4
  • Richard B. Reilly
    • 3
    • 6
    • 7
  • Robert Whelan
    • 1
    • 8
    Email author
  1. 1.School of Psychology and Trinity College Institute of NeuroscienceTrinity College DublinDublinIreland
  2. 2.UCD School of Psychology, University College DublinDublinIreland
  3. 3.School of EngineeringTrinity College DublinDublinIreland
  4. 4.Department of Neurology, St. Vincent’s University HospitalDublinIreland
  5. 5.School of Nursing and Human SciencesDublin City UniversityDublinIreland
  6. 6.Trinity Centre for BioengineeringTrinity College DublinDublinIreland
  7. 7.School of MedicineTrinity College DublinDublinIreland
  8. 8.Global Brain Health InstituteTrinity College DublinDublinIreland

Personalised recommendations