Verborgen vertraging: hoe hersenen in Multiple Sclerose informatieverwerking beïnvloeden

Olivier Daniel
Burta

Niet iedereen verwerkt informatie even snel. We staan daar niet bij stil, maar mensen met vertraagde informatieverwerking hebben meer moeite met het uitvoeren van alledaagse activiteiten. Het meest opvallend aan mensen met Multiple Sclerose (MS) zijn de zichtbare fysieke beperkingen. Maar wist je dat mensen met MS vaak last hebben van cognitieve beperkingen, waaronder vertraagde informatieverwerking?

Door het bestuderen van magnetische hersenactiviteit kon ingenieursstudent Olivier Burta achterhalen hoe de snelheid van informatieverwerking eigenlijk tot stand komt. Deze hersenactiviteit was opgemeten bij mensen met MS gedurende een cognitieve test, die routinematig in de kliniek wordt gebruikt. Ben je benieuwd hoe MS de snelheid van informatieverwerking concreet kan beïnvloeden, of wil je weten op welke manier dit werk kan bijdragen tot toekomstige MS behandelingen? Blijf dan zeker verder lezen! 

Levenslange beperkingen

Multiple sclerose (MS) is een ingewikkelde auto-immuunziekte met hersenschade als gevolg. Deze littekens kunnen zich vrijwel overal in het centrale zenuwenstelsel (hersenen en ruggenmerg) vormen, waardoor de mogelijke klachten heel divers zijn. Zo kan de ene persoon met MS spierzwakte ondervinden, terwijl een andere geheugenstoornissen ervaart. En dat laatste is belangrijk, aangezien de meesten onder ons een verkeerd beeld hebben over deze ziekte: “MS is een spierziekte en je belandt sowieso in een rolstoel.” Maar de cognitieve achteruitgang is minstens even belangrijk en mag absoluut niet worden verwaarloosd.

Cognitie omvat alles wat betreft kennis en informatie. Het verwerven, verwerken, onthouden en toepassen van informatie vallen allemaal onder deze noemer. Hoewel verschillende aspecten van cognitie aangetast zijn bij MS, staat één daarvan centraal: de snelheid van informatieverwerking. Stel dat je als persoon met MS een familiebijeenkomst organiseert. Het plannen van zo’n evenement vraagt om veel taken die elkaar snel opvolgen. Wanneer bij elke taak vertraging optreedt, stapelt dit zich op en kan het ervoor zorgen dat niet alles op tijd af is. Dit kan ook bijdragen tot extra stress en vermoeidheid. Bovendien is MS een levenslange ziekte die jongvolwassenen voor aanzienlijke uitdagingen kan stellen. Tegenwoordig zijn er maar zeer beperkte behandelingen die een effect hebben op cognitie, maar we kunnen MS nog niet genezen.

 

 

Hoe wordt die snelheid gemeten?

Om te bepalen in hoeverre informatieverwerking vertraagd is, wordt standaard een neuropsychologisch onderzoek (NPO) uitgevoerd. De meest gebruikte test is de Symbol Digit Modalities Test (SDMT). Het doel is om een rij symbolen om te zetten naar cijfers van 1 tot 9. De correcte antwoordkeuze bij elk symbool kan je in een legenda raadplegen. Hoe hoger het aantal correcte antwoorden binnen 90 seconden, hoe beter de SDMT score. In het algemeen zal een persoon met MS een lagere score behalen in vergelijking met een klinisch gezonde persoon. 

Ondanks de hoge gevoeligheid van deze test voor het vaststellen van vertraagde informatieverwerking, blijven er na afloop nog veel vragen onbeantwoord, zoals: “Wat vertelt dit over de mogelijke oorzaken van mijn vertraagde informatieverwerking?” of  “Wanneer loopt er iets fout als ik inkomende informatie verwerk?” Voor dit soort details moeten we verder gaan kijken. De oplossing? Laten we ons richten op de hersenen en hun activiteit, want het is juist daar dat alle cognitieve processen tot stand komen. 

Onze hersenen liegen nooit

In dit thesisonderzoek werd de breinactiviteit van personen met MS gemeten terwijl ze de SDMT uitvoeren. Deze activiteit bestaat uit elektrische signalen die worden opgewekt in specifieke delen van je hersenen, afhankelijk van de veranderingen in je omgeving. Stel dat je plotseling iets ziet verschijnen. In eerste instantie zullen je ogen deze informatie oppikken en verder doorgeven. Hierna wordt de occipitale kwab geactiveerd, het deel van de hersenen dat zich helemaal aan de achterkant bevindt. Het meten van deze elektromagnetische velden stelt ons in staat gegevens aan zeer hoge tijdsresolutie vast te leggen. Cognitie ontwikkelt zich over enkele milliseconden, waardoor deze aanpak optimaal is! 

Dankzij een methode gebaseerd op artificiële intelligentie (AI), konden we achterhalen hoeveel hersennetwerken activeren gedurende de SDMT. Een hersennetwerk is een groep regio’s in de hersenen die samenwerken om informatie te verwerken. Bovendien is elk verkregen netwerk gekenmerkt op 3 vlakken: in de tijd, in de ruimte, en in het frequentiedomein. Zeer gedetailleerd dus, wat ons nieuwe inzichten biedt.

3 factoren vormen een geheel

In totaal hebben we 4 unieke hersennetwerken geïdentificeerd. Bij elke nieuwe visuele stimulus gedurende de SDMT, activeren deze 4 netwerken in chronologische volgorde. Door het bestuderen van de kenmerken in elk netwerk, konden we deze verdelen onder 3 groepen: sensorische, cognitieve, en motorische snelheid. Dit betekent dat de snelheid van informatieverwerking moet worden gezien als de som van deze 3 factoren. Deze nieuwe beschrijving markeert een doorbraak in ons begrip van hoe de snelheid van informatieverwerking wordt geëvalueerd met de klinische SDMT test. Bij een psycholoog ligt de focus op het bepalen van de cognitieve snelheid van een patiënt. Om dit te bereiken, moeten dus zowel de sensorische als de motorische snelheid op een uiterst nauwkeurige manier worden bepaald.  

 

 

Verder waren we in staat te ontdekken welke netwerken verstoord zijn bij mensen met MS. De correcte werking van het eerste en tweede netwerk, verantwoordelijk voor respectievelijk stimulusperceptie en uitvoerende functies, wordt aangetast door MS. Deze kennis kan worden gebruikt om toekomstige behandelingen voor MS te ontwerpen. Enerzijds kan dezelfde analysemethode worden gebruikt om de effecten van cognitieve revalidatie op te volgen. Anderzijds kan deze studie aanleiding geven tot meer gerichte neurostimulatie, waarbij de focus ligt op het herstellen van de integriteit in één van de hersennetwerken. Bij neurostimulatie worden de hersenen gestimuleerd aan de hand van externe elektrische stromen, wat in dit geval kan leiden tot een verbeterde snelheid van informatieverwerking. Dit allemaal heeft één overkoepelend doel: het verbeteren van de levenskwaliteit van mensen met MS die te maken hebben met cognitieve beperkingen. 

Bibliografie

[1]       A. Compston and A. Coles, ‘Multiple sclerosis’, Lancet Lond. Engl., vol. 372, no. 9648, pp. 1502–1517, Oct. 2008.

[2]       L. J. Julian, ‘Cognitive Functioning in Multiple Sclerosis’, Neurol. Clin., vol. 29, no. 2, pp. 507–525, May 2011.

[3]       N. D. Chiaravalloti and J. DeLuca, ‘Cognitive impairment in multiple sclerosis’, Lancet Neurol., vol. 7, no. 12, pp. 1139–1151, Dec. 2008.

[4]       J. Van Schependom et al., ‘Reduced information processing speed as primum movens for cognitive decline in MS’, Mult. Scler. J., vol. 21, no. 1, pp. 83–91, Jan. 2015.

[5]       D. R. Denney, S. G. Lynch, and B. A. Parmenter, ‘A 3-year longitudinal study of cognitive impairment in patients with primary progressive multiple sclerosis: Speed matters’, J. Neurol. Sci., vol. 267, no. 1, pp. 129–136, Apr. 2008.

[6]       L. Strober, J. Englert, F. Munschauer, B. Weinstock-Guttman, S. Rao, and R. H. B. Benedict, ‘Sensitivity of conventional memory tests in multiple sclerosis: comparing the Rao Brief Repeatable Neuropsychological Battery and the Minimal Assessment of Cognitive Function in MS’, Mult. Scler. Houndmills Basingstoke Engl., vol. 15, no. 9, pp. 1077–1084, Sep. 2009.

[7]       J. Van Schependom et al., ‘The Symbol Digit Modalities Test as sentinel test for cognitive impairment in multiple sclerosis’, Eur. J. Neurol., vol. 21, no. 9, pp. 1219–1225, e71-72, Sep. 2014.

[8]       R. H. Benedict, J. DeLuca, G. Phillips, N. LaRocca, L. D. Hudson, and R. Rudick, ‘Validity of the Symbol Digit Modalities Test as a cognition performance outcome measure for multiple sclerosis’, Mult. Scler. Houndmills Basingstoke Engl., vol. 23, no. 5, pp. 721–733, Apr. 2017.

[9]       R. M. Middleton et al., ‘A Rapid Electronic Cognitive Assessment Measure for Multiple Sclerosis: Validation of Cognitive Reaction, an Electronic Version of the Symbol Digit Modalities Test’, J. Med. Internet Res., vol. 22, no. 9, p. e18234, Sep. 2020.

[10]     V. M. Leavitt, G. Wylie, H. M. Genova, N. D. Chiaravalloti, and J. DeLuca, ‘Altered effective connectivity during performance of an information processing speed task in multiple sclerosis’, Mult. Scler. J., vol. 18, no. 4, pp. 409–417, Apr. 2012.

[11]     M. Grothe, M. Domin, K. Hoffeld, G. Nagels, and M. Lotze, ‘Functional representation of the symbol digit modalities test in relapsing remitting multiple sclerosis’, Mult. Scler. Relat. Disord., vol. 43, p. 102159, Aug. 2020.

[12]     A. Smith, ‘The Symbol-Digit Modalities Test: A Neuropsychologic Test of Learning and Other Cerebral Disorders’, Spec. Child Publ., pp. 83–91, 1968.

[13]     A. J. Quinn, M. W. J. van Es, C. Gohil, and M. W. Woolrich, ‘OHBA Software Library in Python (OSL)’. Zenodo, Nov. 14, 2022.

[14]     J. Bourgeois and W. Minker, Eds., ‘Linearly Constrained Minimum Variance Beamforming’, in Time-Domain Beamforming and Blind Source Separation: Speech Input in the Car Environment, Boston, MA: Springer US, 2009, pp. 27–38.

[15]     A. Gramfort et al., ‘MEG and EEG data analysis with MNE-Python’, Front. Neurosci., vol. 7, Dec. 2013.

[16]     A. J. Quinn, D. Vidaurre, R. Abeysuriya, R. Becker, A. C. Nobre, and M. W. Woolrich, ‘Task-Evoked Dynamic Network Analysis Through Hidden Markov Modeling’, Front. Neurosci., vol. 12, 2018.

[17]     C. Rossi et al., ‘A novel description of the network dynamics underpinning working memory’. bioRxiv, p. 2023.01.20.524895, Jan. 20, 2023.

[18]     C. Gohil et al., ‘osl-dynamics, a toolbox for modeling fast dynamic brain activity’, eLife, vol. 12, p. RP91949, Jan. 2024.

[19]     D. Vidaurre et al., ‘Discovering dynamic brain networks from big data in rest and task’, NeuroImage, vol. 180, pp. 646–656, Oct. 2018.

[20]     D. Vidaurre et al., ‘Spontaneous cortical activity transiently organises into frequency specific phase-coupling networks’, Nat. Commun., vol. 9, no. 1, p. 2987, Jul. 2018.

[21]     J. Foong, L. Rozewicz, W. K. Chong, A. J. Thompson, D. H. Miller, and M. A. Ron, ‘A comparison  of neuropsychological deficits  in primary and secondary  progressive multiple sclerosis’, J. Neurol., vol. 247, no. 2, pp. 97–101, Feb. 2000.

[22]     N. Akbar, K. Honarmand, N. Kou, and A. Feinstein, ‘Validity of a computerized version of the Symbol Digit Modalities Test in multiple sclerosis’, J. Neurol., vol. 258, no. 3, pp. 373–379, Mar. 2011.

[23]     H. M. Genova, F. G. Hillary, G. Wylie, B. Rypma, and J. Deluca, ‘Examination of processing speed deficits in multiple sclerosis using functional magnetic resonance imaging’, J. Int. Neuropsychol. Soc., vol. 15, no. 3, pp. 383–393, May 2009.

[24]     R. J. Huster, M. S. Messel, C. Thunberg, and L. Raud, ‘The P300 as marker of inhibitory control – Fact or fiction?’, Cortex, vol. 132, pp. 334–348, Nov. 2020.

[25]     G. Bush, P. Luu, and M. I. Posner, ‘Cognitive and emotional influences in anterior cingulate cortex’, Trends Cogn. Sci., vol. 4, no. 6, pp. 215–222, Jun. 2000.

[26]     R. E. Sakai, D. J. Feller, K. M. Galetta, S. L. Galetta, and L. J. Balcer, ‘Vision in Multiple Sclerosis: The Story, Structure-Function Correlations, and Models for Neuroprotection’, J. Neuroophthalmol., vol. 31, no. 4, p. 362, Dec. 2011.

[27]     I. Gabilondo et al., ‘The influence of posterior visual pathway damage on visual information processing speed in multiple sclerosis’, Mult. Scler. J., vol. 23, no. 9, pp. 1276–1288, Aug. 2017.

[28]     A. Figueroa-Vargas et al., ‘Frontoparietal connectivity correlates with working memory performance in multiple sclerosis’, Sci. Rep., vol. 10, p. 9310, Jun. 2020.

[29]     E. Dobryakova, S. L. Costa, G. R. Wylie, J. DeLuca, and H. M. Genova, ‘Altered Effective Connectivity during a Processing Speed Task in Individuals with Multiple Sclerosis’, J. Int. Neuropsychol. Soc., vol. 22, no. 2, pp. 216–224, Feb. 2016.

[30]     M. Lobier, J. M. Palva, and S. Palva, ‘High-alpha band synchronization across frontal, parietal and visual cortex mediates behavioral and neuronal effects of visuospatial attention’, NeuroImage, vol. 165, pp. 222–237, Jan. 2018.

[31]     S. L. Costa, H. M. Genova, J. DeLuca, and N. D. Chiaravalloti, ‘Information processing speed in multiple sclerosis: Past, present, and future’, Mult. Scler. J., vol. 23, no. 6, pp. 772–789, May 2017.

[32]     C. Baiano and M. Zeppieri, ‘Visual Evoked Potential’, in StatPearls, Treasure Island (FL): StatPearls Publishing, 2024.

[33]     H. S. M. Kiiski et al., ‘Delayed P100-Like Latencies in Multiple Sclerosis: A Preliminary Investigation Using Visual Evoked Spread Spectrum Analysis’, PLOS ONE, vol. 11, no. 1, p. e0146084, Jan. 2016.

[34]     L.-T. Hsieh and C. Ranganath, ‘Frontal Midline Theta Oscillations during Working Memory Maintenance and Episodic Encoding and Retrieval’, NeuroImage, vol. 85, no. 0 2, p. 10.1016/j.neuroimage.2013.08.003, Jan. 2014.

[35]     T. J. Buschman and E. K. Miller, ‘Top-Down Versus Bottom-Up Control of Attention in the Prefrontal and Posterior Parietal Cortices’, Science, vol. 315, no. 5820, pp. 1860–1862, Mar. 2007.

[36]     R. A. Andersen and H. Cui, ‘Intention, Action Planning, and Decision Making in Parietal-Frontal Circuits’, Neuron, vol. 63, no. 5, pp. 568–583, Sep. 2009.

[37]     C. Rossi et al., ‘Impaired activation of the prefrontal executive network during working memory processing in multiple sclerosis’. bioRxiv, p. 2023.12.22.573051, Dec. 27, 2023.

[38]     D. J. Kravitz, K. S. Saleem, C. I. Baker, and M. Mishkin, ‘A new neural framework for visuospatial processing’, Nat. Rev. Neurosci., vol. 12, no. 4, pp. 217–230, Apr. 2011.

[39]     J. Fielding, T. Kilpatrick, L. Millist, and O. White, ‘Antisaccade performance in patients with multiple sclerosis’, Cortex, vol. 45, no. 7, pp. 900–903, Jul. 2009.

[40]     C. J. Stam, G. Nolte, and A. Daffertshofer, ‘Phase lag index: Assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources’, Hum. Brain Mapp., vol. 28, no. 11, pp. 1178–1193, Jan. 2007.

[41]     C. Gohil et al., ‘Mixtures of large-scale dynamic functional brain network modes’, NeuroImage, vol. 263, p. 119595, Nov. 2022.

[42]     L. Włodarczyk, N. Cichon, J. Saluk-Bijak, M. Bijak, A. Majos, and E. Miller, ‘Neuroimaging Techniques as Potential Tools for Assessment of Angiogenesis and Neuroplasticity Processes after Stroke and Their Clinical Implications for Rehabilitation and Stroke Recovery Prognosis’, J. Clin. Med., vol. 11, no. 9, Art. no. 9, Jan. 2022.

[43]     A. Thukral, F. Ershad, N. Enan, Z. Rao, and C. Yu, ‘Soft Neural Interfaces for Ultrathin Electronics’, IEEE Nanotechnol. Mag., vol. PP, pp. 1–1, Jan. 2018.

[44]     S. Fan, Q. Zhou, K.-M. Lei, P.-I. Mak, and R. P. Martins, ‘Miniaturization of a Nuclear Magnetic Resonance System: Architecture and Design Considerations of Transceiver Integrated Circuits’, IEEE Trans. Circuits Syst. Regul. Pap., vol. 69, no. 8, pp. 3049–3060, Aug. 2022.

[45]     L.-M. Lacroix, F. Delpech, C. Nayral, S. Lachaize, and B. Chaudret, ‘New generation of magnetic and luminescent nanoparticles for in vivo real-time imaging’, Interface Focus, vol. 3, no. 3, p. 20120103, Jun. 2013.

[46]     B. A. Jung and M. Weigel, ‘Spin echo magnetic resonance imaging’, J. Magn. Reson. Imaging, vol. 37, no. 4, pp. 805–817, 2013.

[47]     Y. Jo et al., ‘Guideline for Cardiovascular Magnetic Resonance Imaging from the Korean Society of Cardiovascular Imaging—Part 1: Standardized Protocol’, Korean J. Radiol., vol. 20, no. 9, pp. 1313–1333, Sep. 2019.

[48]     A. Ray and S. M. Bowyer, ‘Clinical applications of magnetoencephalography in epilepsy’, Ann. Indian Acad. Neurol., vol. 13, no. 1, p. 14, Mar. 2010.

[49]     F. L. da Silva, ‘EEG and MEG: relevance to neuroscience’, Neuron, vol. 80, no. 5, pp. 1112–1128, 2013.

[50]     S. P. Singh, ‘Magnetoencephalography: Basic principles’, Ann. Indian Acad. Neurol., vol. 17, no. Suppl 1, pp. S107–S112, Mar. 2014.

[51]     E. Tamilia, J. R. Madsen, P. E. Grant, P. L. Pearl, and C. Papadelis, ‘Current and Emerging Potential of Magnetoencephalography in the Detection and Localization of High-Frequency Oscillations in Epilepsy’, Front. Neurol., vol. 8, Jan. 2017.

[52]     P. Van Mierlo, S. Vandenberghe, and V. Keereman, Neuroengineering Science. 2022.

[53]     R. Hari et al., ‘IFCN-endorsed practical guidelines for clinical magnetoencephalography (MEG)’, Clin. Neurophysiol., vol. 129, no. 8, pp. 1720–1747, Aug. 2018.

[54]     S. Supek and C. J. Aine, Magnetoencephalography: From Signals to Dynamic Cortical Networks. Cham: Springer International Publishing, 2019.

[55]     P. Marcon and K. Ostanina, ‘Overview of Methods for Magnetic Susceptibility Measurement’, 2012.

[56]     L. Sörnmo and P. Laguna, ‘Chapter 4 - Evoked Potentials’, in Bioelectrical Signal Processing in Cardiac and Neurological Applications, L. Sörnmo and P. Laguna, Eds., in Biomedical Engineering. , Burlington: Academic Press, 2005, pp. 181–336.

[57]     E. Niedermeyer, 9. The Normal EEG of the Waking Adult, 3rd ed. in Electroencephalography: Basic principles, clinical applications, and related fields. Balitmore, MD: Williams and Wilkins, 1993.

[58]     G. F. Woodman, ‘A Brief Introduction to the Use of Event-Related Potentials (ERPs) in Studies of Perception and Attention’, Atten. Percept. Psychophys., vol. 72, no. 8, p. 10.3758/APP.72.8.2031, Nov. 2010.

[59]     L. SJ, ‘An Introduction to the Event-Related Potential Technique’, MIT Press.

[60]     J. W. Rohrbaugh, W. C. McCallum, A. W. Gaillard, R. F. Simons, N. Birbaumer, and D. Papakostopoulos, ‘ERPs associated with preparatory and movement-related processes. A review’, Electroencephalogr. Clin. Neurophysiol. Suppl., vol. 38, pp. 189–229, 1986.

[61]     A. Z. Snyder and M. E. Raichle, ‘A Brief History of the Resting State: the Washington University Perspective’, Neuroimage, vol. 62, no. 2, pp. 902–910, Aug. 2012.

[62]     M. Coles and M. Rugg, Event-related brain potentials: An introduction. in Oxford psychology series, no. 25. New York: Oxford University Press, 1995.

[63]     L. Zhang, Z. Li, F. Zhang, R. Gu, W. Peng, and L. Hu, ‘Demystifying signal processing techniques to extract task-related EEG responses for psychologists’, Brain Sci. Adv., vol. 6, no. 3, pp. 171–188, Sep. 2020.

[64]     A.-L. Paradis, S. Morel, P. Seriès, and J. Lorenceau, ‘Speeding up the brain: when spatial facilitation translates into latency shortening’, Front. Hum. Neurosci., vol. 6, Dec. 2012.

[65]     A. M. Bastos and J.-M. Schoffelen, ‘A Tutorial Review of Functional Connectivity Analysis Methods and Their Interpretational Pitfalls’, Front. Syst. Neurosci., vol. 9, Jan. 2016.

[66]     R. Salvador, N. Verdolini, E. Jiménez, E. Vilella, and A. N. Voineskos, ‘Multivariate Brain Functional Connectivity Through Regularized Estimators’, Front. Neurosci., vol. 14, Dec. 2020.

[67]     S. P. van den Broek, F. Reinders, M. Donderwinkel, and M. J. Peters, ‘Volume conduction effects in EEG and MEG’, Electroencephalogr. Clin. Neurophysiol., vol. 106, no. 6, pp. 522–534, Jun. 1998.

[68]     A. Hillebrand, G. R. Barnes, J. L. Bosboom, H. W. Berendse, and C. J. Stam, ‘Frequency-dependent functional connectivity within resting-state networks: An atlas-based MEG beamformer solution’, Neuroimage, vol. 59, no. 4–2, pp. 3909–3921, Feb. 2012.

[69]     M. J. Brookes et al., ‘Measuring temporal, spectral and spatial changes in electrophysiological brain network connectivity’, NeuroImage, vol. 91, pp. 282–299, May 2014.

[70]     J. Gonzalez-Castillo and P. A. Bandettini, ‘Task-based Dynamic Functional Connectivity: recent findings and open questions’, NeuroImage, vol. 180, no. Pt B, pp. 526–533, Oct. 2018.

[71]     D. Lombardo et al., ‘Modular slowing of resting-state dynamic functional connectivity as a marker of cognitive dysfunction induced by sleep deprivation’, NeuroImage, vol. 222, p. 117155, Nov. 2020.

[72]     W. Liu, X. Wang, T. Ristaniemi, and F. Cong, ‘Identifying Task-Based Dynamic Functional Connectivity Using Tensor Decomposition’, in Neural Information Processing, H. Yang, K. Pasupa, A. C.-S. Leung, J. T. Kwok, J. H. Chan, and I. King, Eds., Cham: Springer International Publishing, 2020, pp. 361–369.

[73]     S. Sadaghiani and A. Kleinschmidt, ‘Functional interactions between intrinsic brain activity and behavior’, NeuroImage, vol. 80, pp. 379–386, Oct. 2013.

[74]     J. Gonzalez-Castillo et al., ‘Tracking ongoing cognition in individuals using brief, whole-brain functional connectivity patterns’, Proc. Natl. Acad. Sci., vol. 112, no. 28, pp. 8762–8767, Jul. 2015.

[75]     V. Christopher, ‘Markov and Hidden Markov Model [Online]. Available: https://towardsdatascience.com/markov-and-hidden-markov-model-3eec42298…, 2020’, Medium.

[76]     L. R. Rabiner, ‘A tutorial on hidden Markov models and selected applications in speech recognition’, Proc. IEEE, vol. 77, no. 2, pp. 257–286, Feb. 1989.

[77]     T. Jack, ‘Hidden Markov Model - Warwick [Online]. Available: https://warwick.ac.uk/fac/sci/masdoc/current/msc-modules/ma916/mlia/hmm/, 2018’.

[78]     M. Gales and S. Young, ‘The Application of Hidden Markov Models in Speech Recognition’, Found. Trends® Signal Process., vol. 1, no. 3, pp. 195–304, 2007.

[79]     Encyclopedia of bioinformatics and computational biology, vol. 1–3. Amsterdam; Oxford; Cambridge: Elsevier, 2019.

[80]     A. P. Baker et al., ‘Fast transient networks in spontaneous human brain activity’, eLife, vol. 3, p. e01867, Mar. 2014.

[81]     D. Vidaurre, A. J. Quinn, A. P. Baker, D. Dupret, A. Tejero-Cantero, and M. W. Woolrich, ‘Spectrally resolved fast transient brain states in electrophysiological data’, NeuroImage, vol. 126, pp. 81–95, Feb. 2016.

[82]     C. Fauchon et al., ‘A Hidden Markov Model reveals magnetoencephalography spectral frequency-specific abnormalities of brain state power and phase-coupling in neuropathic pain’, Commun. Biol., vol. 5, no. 1, pp. 1–12, Sep. 2022.

[83]     Z. A. Seedat et al., ‘The role of transient spectral “bursts” in functional connectivity: A magnetoencephalography study’, NeuroImage, vol. 209, p. 116537, Apr. 2020.

[84]     F. van Ede, A. J. Quinn, M. W. Woolrich, and A. C. Nobre, ‘Neural Oscillations: Sustained Rhythms or Transient Burst-Events?’, Trends Neurosci., vol. 41, no. 7, pp. 415–417, Jul. 2018.

[85]     S. R. Jones, ‘When brain rhythms aren’t “rhythmic”: implication for their mechanisms and meaning’, Curr. Opin. Neurobiol., vol. 40, pp. 72–80, Oct. 2016.

[86]     H. Shin, R. Law, S. Tsutsui, C. I. Moore, and S. R. Jones, ‘The rate of transient beta frequency events predicts behavior across tasks and species’, eLife, vol. 6, p. e29086, Nov. 2017.

[87]     R. Hari and L. Parkkonen, ‘The brain timewise: how timing shapes and supports brain function’, Philos. Trans. R. Soc. B Biol. Sci., vol. 370, no. 1668, p. 20140170, May 2015.

[88]     C. Walton et al., ‘Rising prevalence of multiple sclerosis worldwide: Insights from the Atlas of MS, third edition’, Mult. Scler. J., vol. 26, no. 14, pp. 1816–1821, Dec. 2020.

[89]     C. Stadelmann, C. Wegner, and W. Brück, ‘Inflammation, demyelination, and degeneration — Recent insights from MS pathology’, Biochim. Biophys. Acta BBA - Mol. Basis Dis., vol. 1812, no. 2, pp. 275–282, Feb. 2011.

[90]     C. C. Hemond and R. Bakshi, ‘Magnetic Resonance Imaging in Multiple Sclerosis’, Cold Spring Harb. Perspect. Med., vol. 8, no. 5, p. a028969, May 2018.

[91]     S. L. Hauser and J. R. Oksenberg, ‘The Neurobiology of Multiple Sclerosis: Genes, Inflammation, and Neurodegeneration’, Neuron, vol. 52, no. 1, pp. 61–76, Oct. 2006.

[92]     D. Kidd, F. Barkhof, R. McConnell, P. R. Algra, I. V. Allen, and T. Revesz, ‘Cortical lesions in multiple sclerosis’, Brain, vol. 122, no. 1, pp. 17–26, Jan. 1999.

[93]     J. W. Peterson, L. Bö, S. Mörk, A. Chang, and B. D. Trapp, ‘Transected neurites, apoptotic neurons, and reduced inflammation in cortical multiple sclerosis lesions’, Ann. Neurol., vol. 50, no. 3, pp. 389–400, Sep. 2001.

[94]     D. S. Goodin, P. Khankhanian, P.-A. Gourraud, and N. Vince, ‘The nature of genetic and environmental susceptibility to multiple sclerosis’, PLoS ONE, vol. 16, no. 3, p. e0246157, Mar. 2021.

[95]     A. Dhakal and B. Bobrin, ‘Cognitive Deficits [Online]. Available: https://www.ncbi.nlm.nih.gov/books/NBK559052/, 2024’.

[96]     R. H. Benedict et al., ‘Screening for multiple sclerosis cognitive impairment using a                self-administered 15-item questionnaire’, Mult. Scler. J., vol. 9, no. 1, pp. 95–101, Feb. 2003.

[97]     D. J. Zgaljardic and R. O. Temple, ‘Neuropsychological Assessment Battery (NAB): Performance in a sample of patients with moderate-to-severe traumatic brain injury’, Appl. Neuropsychol., vol. 17, no. 4, pp. 283–288, Oct. 2010.

[98]     R. H. Benedict et al., ‘Brief International Cognitive Assessment for MS (BICAMS): international standards for validation’, BMC Neurol., vol. 12, no. 1, p. 55, Jul. 2012.

[99]     J. Van Schependom and G. Nagels, ‘Targeting Cognitive Impairment in Multiple Sclerosis—The Road toward an Imaging-based Biomarker’, Front. Neurosci., vol. 11, Jun. 2017.

[100]    A. J. Thompson et al., ‘Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria’, Lancet Neurol., vol. 17, no. 2, pp. 162–173, Feb. 2018.

[101]    C. Granziera et al., ‘Quantitative magnetic resonance imaging towards clinical application in multiple sclerosis’, Brain, vol. 144, no. 5, pp. 1296–1311, May 2021.

[102]    R. A. Colorado, K. Shukla, Y. Zhou, J. S. Wolinsky, and P. A. Narayana, ‘Multi-task functional MRI in multiple sclerosis patients without clinical disability’, NeuroImage, vol. 59, no. 1, pp. 573–581, Jan. 2012.

[103]    M. M. Schoonheim, K. A. Meijer, and J. J. G. Geurts, ‘Network collapse and cognitive impairment in multiple sclerosis’, Front. Neurol., vol. 6, p. 82, 2015.

[104]    P. Tewarie et al., ‘Cognitive and Clinical Dysfunction, Altered MEG Resting-State Networks and Thalamic Atrophy in Multiple Sclerosis’, PLOS ONE, vol. 8, no. 7, p. e69318, Jul. 2013.

[105]    B. Nucera et al., ‘Quantitative EEG differentiate Multiple Sclerosis with and without Cognitive Impairment from healthy controls at the beginning of the disease: 4th Congress of the European-Academy-of-Neurology (EAN)’, Eur. J. Neurol., vol. 25, no. Suppl. 2, pp. 255–255, Jun. 2018.

[106]    E. Neuromag, ‘Elekta Neuromag® System Hardware Technical manual’. 2005.

[107]    S. Taulu and J. Simola, ‘Spatiotemporal signal space separation method for rejecting nearby interference in MEG measurements’, Phys. Med. Biol., vol. 51, no. 7, p. 1759, Mar. 2006.

[108]    V. N. Kiroy, L. V. Warsawskaya, and V. B. Voynov, ‘EEG after prolonged mental activity’, Int. J. Neurosci., vol. 85, no. 1–2, pp. 31–43, Mar. 1996.

[109]    L. J. Trejo, K. Kubitz, R. Rosipal, R. L. Kochavi, and L. D. Montgomery, ‘EEG-Based Estimation and Classification of Mental Fatigue’, Psychology, vol. 6, no. 5, Art. no. 5, Apr. 2015.

[110]    L. Parkkonen, ‘Instrumentation and Data Preprocessing’, in MEG: An Introduction to Methods, P. Hansen, M. Kringelbach, and R. Salmelin, Eds., Oxford University Press, 2010, p. 0.

[111]    B. Rosner, ‘Percentage Points for a Generalized ESD Many-Outlier Procedure’, Technometrics, vol. 25, no. 2, pp. 165–172, 1983.

[112]    ‘Coregistration with SPM and RHINO [Online]. Available: https://ohba-analysis.github.io/osl-docs/matlab/osl_example_coregistrat…, 2018’.

[113]    P. J. Besl and N. D. McKay, ‘A method for registration of 3-D shapes’, IEEE Trans. Pattern Anal. Mach. Intell., vol. 14, no. 2, pp. 239–256, Feb. 1992.

[114]    F. Cao et al., ‘Co-registration Comparison of On-Scalp Magnetoencephalography and Magnetic Resonance Imaging’, Front. Neurosci., vol. 15, p. 706785, Aug. 2021.

[115]    D. Pantazis and A. Adler, ‘MEG Source Localization via Deep Learning’, Sensors, vol. 21, no. 13, Art. no. 13, Jan. 2021.

[116]    J. Van Schependom et al., ‘Altered transient brain dynamics in multiple sclerosis: Treatment or pathology?’, Hum. Brain Mapp., vol. 40, no. 16, pp. 4789–4800, Nov. 2019.

[117]    G. L. Colclough, M. J. Brookes, S. M. Smith, and M. W. Woolrich, ‘A symmetric multivariate leakage correction for MEG connectomes’, NeuroImage, vol. 117, pp. 439–448, Aug. 2015.

[118]    H. B. Mann and D. R. Whitney, ‘On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other’, Ann. Math. Stat., vol. 18, no. 1, pp. 50–60, Mar. 1947.

[119]    K. Pearson, ‘X. On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling’, Lond. Edinb. Dublin Philos. Mag. J. Sci., vol. 50, no. 302, pp. 157–175, Jul. 1900.

[120]    G. J. G. Upton, ‘Fisher’s Exact Test’, J. R. Stat. Soc. Ser. A Stat. Soc., vol. 155, no. 3, pp. 395–402, 1992.

[121]    J. F. Kurtzke, ‘A New Scale for Evaluating Disability in Multiple Sclerosis’, Neurology, vol. 5, no. 8, pp. 580–580, Aug. 1955.

[122]    C. P. Kamm, B. M. Uitdehaag, and C. H. Polman, ‘Multiple Sclerosis: Current Knowledge and Future Outlook’, Eur. Neurol., vol. 72, no. 3–4, pp. 132–141, Jul. 2014.

[123]    M. Sjøgård et al., ‘Brain dysconnectivity relates to disability and cognitive impairment in multiple sclerosis’, Hum. Brain Mapp., vol. 42, no. 3, pp. 626–643, 2021.

[124]    C. Rossi and J. Van Schependom, ‘Two approaches to tackle the sign ambiguity of beamformed MEG source-reconstructed data: International Conference on Biomagnetism’, Apr. 2022.

[125]    OHBA, Oxford University, ‘OHBA workshop OSL sensorspace - Sensor Space Analysis [Online]. Available: https://ohba-analysis.github.io/osl-docs/downloads/Session_2_ohba_works…, 2017’. 2017.

[126]    A. Barman, P. Prabhu, V. G. Mekhala, K. Vijayan, and N. Swapna, ‘Auditory Processing in Children with Specific Language Impairment: A FFR Based Study’, Indian J. Otolaryngol. Head Neck Surg., vol. 74, no. 1, pp. 368–373, Aug. 2022.

[127]    E. Neuromag, ‘Elekta Neuromag®’. 2008.

[128]    J. A. Nelder and R. W. M. Wedderburn, ‘Generalized Linear Models’, J. R. Stat. Soc. Ser. Gen., vol. 135, no. 3, pp. 370–384, 1972.

[129]    G. E. Thomas, ‘Resampling-Based Multiple Testing: Examples and Methods for p-Value Adjustment’, J. R. Stat. Soc. Ser. Stat., vol. 43, no. 2, pp. 347–348, Jun. 1994.

[130]    P. Ranganathan, C. S. Pramesh, and M. Buyse, ‘Common pitfalls in statistical analysis: The perils of multiple testing’, Perspect. Clin. Res., vol. 7, no. 2, pp. 106–107, 2016.

[131]    T. Nichols and S. Hayasaka, ‘Controlling the familywise error rate in functional neuroimaging: a                 comparative review’, Stat. Methods Med. Res., vol. 12, no. 5, pp. 419–446, Oct. 2003.

[132]    S. M. Bowyer, ‘Coherence a measure of the brain networks: past and present’, Neuropsychiatr. Electrophysiol., vol. 2, no. 1, p. 1, Jan. 2016.

[133]    S. Torres-Ramos, R. A. Salido-Ruiz, A. Espinoza-Valdez, F. R. Gómez-Velázquez, A. A. González-Garrido, and I. Román-Godínez, ‘A brain connectivity characterization of children with different levels of mathematical achievement based on graph metrics’, PLOS ONE, vol. 15, no. 1, p. e0227613, Jan. 2020.

[134]    K. Pearce, ‘Understanding Brain Waves: Beta, Alpha, Theta, Delta + Gamma [Online]. Available: https://www.diygenius.com/the-5-types-of-brain-waves/, 2022’.

[135]    T. Steven Waterstone et al., ‘Functional Connectivity Analysis on Resting-State Electroencephalography Signals Following Chiropractic Spinal Manipulation in Stroke Patients’, Brain Sci., vol. 10, no. 9, Art. no. 9, Sep. 2020.

[136]    Y. Benjamini and Y. Hochberg, ‘Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing’, J. R. Stat. Soc. Ser. B Methodol., vol. 57, no. 1, pp. 289–300, 1995.

[137]    I. Rezek and S. Roberts, ‘Ensemble Hidden Markov Models with Extended Observation Densities for Biosignal Analysis’, in Probabilistic Modeling in Bioinformatics and Medical Informatics, D. Husmeier, R. Dybowski, and S. Roberts, Eds., London: Springer, 2005, pp. 419–450.

[138]    L. E. Baum and T. Petrie, ‘Statistical Inference for Probabilistic Functions of Finite State Markov Chains’, Ann. Math. Stat., vol. 37, no. 6, pp. 1554–1563, Dec. 1966.

[139]    D. P. Kingma and J. Ba, ‘Adam: A Method for Stochastic Optimization’. arXiv, Jan. 29, 2017.

[140]    C. M. Bishop and N. M. Nasrabadi, ‘Pattern Recognition and Machine Learning’, J. Electron. Imaging, vol. 16, p. 049901, Jan. 2007.

[141]    J. Van Schependom et al., ‘Increased brain atrophy and lesion load is associated with stronger lower alpha MEG power in multiple sclerosis patients’, NeuroImage Clin., vol. 30, p. 102632, Mar. 2021.

[142]    D. Vidaurre, ‘A new model for simultaneous dimensionality reduction and time-varying functional connectivity estimation’, PLOS Comput. Biol., vol. 17, no. 4, p. e1008580, Apr. 2021.

[143]    M. López-Góngora, L. Querol, and A. Escartín, ‘A one-year follow-up study of the Symbol Digit Modalities Test (SDMT) and the Paced Auditory Serial Addition Test (PASAT) in relapsing-remitting multiple sclerosis: an appraisal of comparative longitudinal sensitivity’, BMC Neurol., vol. 15, p. 40, Mar. 2015.

[144]    K. T. Olkkola and J. Ahonen, ‘Midazolam and Other Benzodiazepines’, in Modern Anesthetics, J. Schüttler and H. Schwilden, Eds., Berlin, Heidelberg: Springer, 2008, pp. 335–360.

[145]    S. A. Stewart, ‘The effects of benzodiazepines on cognition’, J. Clin. Psychiatry, vol. 66 Suppl 2, pp. 9–13, 2005.

[146]    P. H. R. Silva, C. T. Spedo, A. A. Barreira, and R. F. Leoni, ‘Symbol Digit Modalities Test adaptation for Magnetic Resonance Imaging environment: A systematic review and meta-analysis’, Mult. Scler. Relat. Disord., vol. 20, pp. 136–143, Feb. 2018.

[147]    C. Forn et al., ‘A Symbol Digit Modalities Test version suitable for functional MRI studies’, Neurosci. Lett., vol. 456, no. 1, pp. 11–14, May 2009.

[148]    S. H. Patel and P. N. Azzam, ‘Characterization of N200 and P300: Selected Studies of the Event-Related Potential’, Int. J. Med. Sci., vol. 2, no. 4, pp. 147–154, Oct. 2005.

[149]    R. Vlieger et al., ‘The use of event-related potentials in the investigation of cognitive performance in people with Multiple Sclerosis: Systematic review’, Brain Res., vol. 1832, p. 148827, Jun. 2024.

[150]    S. Caffarra, S. J. Joo, D. Bloom, J. Kruper, A. Rokem, and J. D. Yeatman, ‘Development of the visual white matter pathways mediates development of electrophysiological responses in visual cortex’, Hum. Brain Mapp., vol. 42, no. 17, pp. 5785–5797, Dec. 2021.

[151]    L. Costers et al., ‘Spatiotemporal and spectral dynamics of multi-item working memory as revealed by the n-back task using MEG’, Hum. Brain Mapp., vol. 41, no. 9, pp. 2431–2446, 2020.

[152]    A. U. Turken, S. Whitfield-Gabrieli, R. Bammer, J. Baldo, N. F. Dronkers, and J. D. E. Gabrieli, ‘COGNITIVE PROCESSING SPEED AND THE STRUCTURE OF WHITE MATTER PATHWAYS: CONVERGENT EVIDENCE FROM NORMAL VARIATION AND LESION STUDIES’, NeuroImage, vol. 42, no. 2, pp. 1032–1044, Aug. 2008.

[153]    P. Capotosto et al., ‘Dynamics of EEG Rhythms Support Distinct Visual Selection Mechanisms in Parietal Cortex: A Simultaneous Transcranial Magnetic Stimulation and EEG Study’, J. Neurosci., vol. 35, no. 2, pp. 721–730, Jan. 2015.

[154]    J. C. Rucker, C. Kennard, and R. J. Leigh, ‘Chapter 3 - The neuro-ophthalmological examination’, in Handbook of Clinical Neurology, vol. 102, C. Kennard and R. J. Leigh, Eds., in Neuro-ophthalmology, vol. 102. , Elsevier, 2011, pp. 71–94.

[155]    V. Romei, J. Gross, and G. Thut, ‘On the Role of Prestimulus Alpha Rhythms over Occipito-Parietal Areas in Visual Input Regulation: Correlation or Causation?’, J. Neurosci., vol. 30, no. 25, pp. 8692–8697, Jun. 2010.

[156]    R. Hari, R. Salmelin, J. P. Mäkelä, S. Salenius, and M. Helle, ‘Magnetoencephalographic cortical rhythms’, Int. J. Psychophysiol., vol. 26, no. 1, pp. 51–62, Jun. 1997.

[157]    J. J. Foxe and A. C. Snyder, ‘The Role of Alpha-Band Brain Oscillations as a Sensory Suppression Mechanism during Selective Attention’, Front. Psychol., vol. 2, Jul. 2011.

[158]    Y. Broche-Pérez, L. F. Herrera Jiménez, and E. Omar-Martínez, ‘Bases neurales de la toma de decisiones’, Neurología, vol. 31, no. 5, pp. 319–325, Jun. 2016.

[159]    K. A. Meijer, Q. van Geest, A. J. C. Eijlers, J. J. G. Geurts, M. M. Schoonheim, and H. E. Hulst, ‘Is impaired information processing speed a matter of structural or functional damage in MS?’, NeuroImage Clin., vol. 20, pp. 844–850, Jan. 2018.

[160]    M. Grothe et al., ‘Performance in information processing speed is associated with parietal white matter tract integrity in multiple sclerosis’, Front. Neurol., vol. 13, Nov. 2022.

[161]    M. Vázquez-Marrufo, E. Vaquero, M. Cardoso, and C. Gomez, ‘Temporal evolution of alpha and beta bands during visual spatial attention’, Brain Res. Cogn. Brain Res., vol. 12, pp. 315–20, Nov. 2001.

[162]    B. L. Roberg, J. M. Bruce, H. T. Feaster, S. R. O’Bryan, H. J. Westervelt, and M. Glusman, ‘Speedy eye movements in multiple sclerosis: Association with performance on visual and nonvisual cognitive tests’, J. Clin. Exp. Neuropsychol., vol. 37, no. 1, pp. 1–15, Jan. 2015.

[163]    M. Hardmeier et al., ‘Cognitive Dysfunction in Early Multiple Sclerosis: Altered Centrality Derived from Resting-State Functional Connectivity Using Magneto-Encephalography’, PLOS ONE, vol. 7, no. 7, p. e42087, Jul. 2012.

[164]    F. D. Lublin, S. C. Reingold, and National Multiple Sclerosis Society (USA) Advisory Committee on Clinical Trials of New Agents in Multiple Sclerosis*, ‘Defining the clinical course of multiple sclerosis’, Neurology, vol. 46, no. 4, pp. 907–911, Apr. 1996.

[165]    O. Jensen, P. Goel, N. Kopell, M. Pohja, R. Hari, and B. Ermentrout, ‘On the human sensorimotor-cortex beta rhythm: Sources and modeling’, NeuroImage, vol. 26, no. 2, pp. 347–355, Jun. 2005.

[166]    J. Sandry et al., ‘The Symbol Digit Modalities Test (SDMT) is sensitive but non-specific in MS: Lexical access speed, memory, and information processing speed independently contribute to SDMT performance’, Mult. Scler. Relat. Disord., vol. 51, p. 102950, Jun. 2021.

[167]    D. A. Spampinato, J. Ibanez, L. Rocchi, and J. Rothwell, ‘Motor potentials evoked by transcranial magnetic stimulation: interpreting a simple measure of a complex system’, J. Physiol., vol. 601, no. 14, pp. 2827–2851, 2023.

[168]    A. Sherstinsky, ‘Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network’, Phys. Nonlinear Phenom., vol. 404, p. 132306, Mar. 2020.

[169]    R. Pereira et al., ‘Ranking programming languages by energy efficiency’, Sci. Comput. Program., vol. 205, p. 102609, May 2021.

[170]    D. Costenaro and A. Duer, ‘The Megawatts behind Your Megabytes: Going from Data-Center to Desktop’, 2012.

[171]    T. Developers, ‘TensorFlow’. Zenodo, Mar. 08, 2024.

[172]    S. Mittal and J. S. Vetter, ‘A Survey of Methods for Analyzing and Improving GPU Energy Efficiency’, ACM Comput. Surv., vol. 47, no. 2, p. 19:1-19:23, Aug. 2014.

[173]    S. Dierickx, ‘Neural dynamics of information processing speed in MS: a magnetoencephalography study: MEG-modified SDMT in multiple sclerosis’, Vrije Universiteit Brussel, 2023.

Universiteit of Hogeschool
Vrije Universiteit Brussel
Thesis jaar
2024
Promotor(en)
Prof. Dr. Ir. Jeroen Van Schependom