The objective of this thesis was to better understand how the accumulation of different types of pathology during the course of multiple sclerosis ultimately drive cognitive decline. We aimed to find answers to the following questions: • How does the accumulation of grey matter atrophy relate to changes in brain function, protective capacity and, ultimately, cognitive decline during the course of multiple sclerosis? • Can the use of a network approach enable a further unravelling of the underlying mechanisms of cognitive decline in multiple sclerosis? • What are the main MR imaging predictors of future cognitive decline and do these predictors differ according to disease stage? In the current section, our main findings regarding these questions will be discussed and suggestions for future research will be provided. Frequency of cognitive impairment in multiple sclerosis As this thesis investigates determinants, mechanisms and predictors of cognitive decline in multiple sclerosis, a first step was to find ways to best characterize “impairment” and “decline”. Multiple sclerosis patients frequently suffer from cognitive deficits, which can have a severe impact on daily functioning. 3, 4 Estimations in literature of the prevalence of cognitive impairment in multiple sclerosis range from 40-70%, depending on patient populations and types of neuropsychological tests as well as the choice of threshold for “impairment”. 3 To measure cognitive function in the current thesis, all reported studies used the same extensive neuropsychological evaluation, based on an expanded Brief Repeatable Battery of Neuropsychological tests. 8 Executive functioning was assessed using the concept shifting test, 9 verbal memory using the selective reminding test, 10 verbal fluency using the word list generation test, 12 information processing speed using the symbol digit modalities test, 14 visuospatial memory using the spatial recall test, 12 attention using the Stroop colour-word test 16 and working memory using the memory comparison test. 17 Using these cognitive tests, the 332 patients that were part of the baseline measurement of our Amsterdam multiple sclerosis cohort showed a moderate global impairment in cognitive function of Z = -0.80 compared to a group of 96 matched healthy controls, at an average disease duration of 15 years. The most severely affected cognitive domain in the entire patient group was information processing speed (Z = -1.18 compared to the healthy control group), which is often reported to be one of the most common and worst affected cognitive domains in multiple sclerosis. 18, 19 Other severely affected cognitive domains in our cohort were working memory (Z = -1.02 compared to the healthy control group) and executive function (Z = -1.18 compared to the healthy control group).
27
Embed
Frequency of cognitive impairment in multiple sclerosis english.pdf · answers to the following questions: • How does the accumulation of grey matter atrophy relate to changes in
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
The objective of this thesis was to better understand how the accumulation of different types of
pathology during the course of multiple sclerosis ultimately drive cognitive decline. We aimed to find
answers to the following questions:
• How does the accumulation of grey matter atrophy relate to changes in brain function,
protective capacity and, ultimately, cognitive decline during the course of multiple sclerosis?
• Can the use of a network approach enable a further unravelling of the underlying mechanisms
of cognitive decline in multiple sclerosis?
• What are the main MR imaging predictors of future cognitive decline and do these predictors
differ according to disease stage?
In the current section, our main findings regarding these questions will be discussed and suggestions
for future research will be provided.
Frequency of cognitive impairment in multiple sclerosis
As this thesis investigates determinants, mechanisms and predictors of cognitive decline in multiple
sclerosis, a first step was to find ways to best characterize “impairment” and “decline”. Multiple
sclerosis patients frequently suffer from cognitive deficits, which can have a severe impact on daily
functioning.3, 4 Estimations in literature of the prevalence of cognitive impairment in multiple sclerosis
range from 40-70%, depending on patient populations and types of neuropsychological tests as well
as the choice of threshold for “impairment”.3 To measure cognitive function in the current thesis, all
reported studies used the same extensive neuropsychological evaluation, based on an expanded Brief
Repeatable Battery of Neuropsychological tests.8 Executive functioning was assessed using the concept
shifting test,9 verbal memory using the selective reminding test,10 verbal fluency using the word list
generation test,12 information processing speed using the symbol digit modalities test,14 visuospatial
memory using the spatial recall test,12 attention using the Stroop colour-word test16 and working
memory using the memory comparison test.17 Using these cognitive tests, the 332 patients that were
part of the baseline measurement of our Amsterdam multiple sclerosis cohort showed a moderate
global impairment in cognitive function of Z = -0.80 compared to a group of 96 matched healthy
controls, at an average disease duration of 15 years. The most severely affected cognitive domain in
the entire patient group was information processing speed (Z = -1.18 compared to the healthy control
group), which is often reported to be one of the most common and worst affected cognitive domains
in multiple sclerosis.18, 19 Other severely affected cognitive domains in our cohort were working
memory (Z = -1.02 compared to the healthy control group) and executive function (Z = -1.18 compared
to the healthy control group).
Summary and general discussion
Nonetheless, there was considerable heterogeneity in cognitive performance in our patient
sample, as is common in multiple sclerosis. Therefore, for part of the studies performed in this thesis,
the patient group was subdivided into subgroups based on the severity of cognitive impairment, as
was done previously.20, 21. Patients who scored one and a half standard deviations (i.e., Z ≤ -1.5) below
the average of the healthy control group on at least two cognitive domains were classified as
cognitively impaired, classifying 46% of our sample (N=152) as impaired. In chapter 3, we further
subdivided this cognitively impaired patient group into a mildly cognitive impaired group (i.e., Z ≤ -1.5)
and a more severely cognitively impaired group (i.e., Z ≤ -2.0) to be able to analyse underlying
mechanisms in these groups separately. Using these criteria, 65 patients were labelled as mildly
cognitively impaired and 87 patients as severely cognitively impaired, percentages comparable to
previous literature.3, 22 While the neuropsychological tests used in the studies part of this thesis are
commonly applied in multiple sclerosis, there are still cognitive domains that are underrepresented,
like multi-tasking and theory of mind.19, 23 Another concern is that this battery is most likely too long
for clinical implementation. Therefore, future studies could consider using either the long but
potentially more sensitive Minimal Assessment of Cognitive Function In Multiple Sclerosis (MACFIMS)24
or the shorter but extensively validated Brief International Cognitive Assessment for Multiple Sclerosis
(BICAMS).25
Acceleration of cognitive decline during the course of multiple sclerosis
While cognitive impairment can already occur in early disease, the frequency tends to increase with
disease progression.3, 4, 22, 26 This was also seen in our patient sample at baseline, where 39% of
relapsing-remitting patients and 64% of progressive patients were cognitively impaired. Before the
initiation of this research, however, it was unclear whether the rate of cognitive decline also differed
between early and progressive stages of multiple sclerosis, or whether differences between these
groups were simply due to a longer average disease duration in progressive patients with a steady rate
of decline during the disease. For instance, the high prevalence of cognitive deficits that has been
reported in some studies during the clinically isolated syndrome (i.e., the very first stage of multiple
sclerosis) or around the time of clinically definite multiple sclerosis diagnosis has sometimes been
interpreted as an indication that cognitive decline could demonstrate an initial drop, followed by a
subsequent slowing of the rate of decline or even stabilization.22, 26-28 Since some of these (cross-
sectional) studies relied on relaxed cognitive impairment classification (e.g., one standard deviation on
one test)28 and since the duration between disease onset and appearance of first symptoms is
unclear,29, 30 these conclusions of an initial drop in cognitive function should be interpreted with
caution until longitudinally confirmed.
It is therefore essential that such research questions are addressed using longitudinal study
designs that preferably also include longitudinal control samples. We therefore addressed this
question in chapter 2.1 by looking at the longitudinal data of our aforementioned cohort, of whom
230 patients and 59 healthy controls had returned after five years with identical cognitive evaluations.
To assess cognitive decline in patients with multiple sclerosis during the follow-up period, one major
challenge was how to deal with normal aging and learning effects. We decided to deal with these
effects by computing the modified practice adjusted reliable change index31 for each cognitive domain
separately. This method corrects for practice effects based on longitudinal changes observed in the
healthy control group. Individual cognitive domain reliable change index scores were then divided by
each individual subject’s time interval, to remove effects of variability in follow-up duration, and
averaged across domains to obtain a yearly rate of cognitive decline. We observed that the yearly
cognitive decline rate was around three times higher in progressive compared to relapsing-remitting
patients, which indicates that cognitive impairment is not only more severe in progressive patients due
to a longer average disease duration, but also due to a higher yearly rate of cognitive decline.
The cognitive decline rates that we observed were highly similar between primary and
secondary progressive patients as shown in chapter 4.2. While an early study reported more severe
cognitive deficits in secondary compared to primary progressive patients,32 other studies have shown
similar levels of cognitive impairment, which suggests that observed differences could be due to study-
specific heterogeneity regarding disease duration or overall disease burden.33, 34 The distinction
between a primary and a secondary form of disease progression has become somewhat less relevant
in recent years, as recent clinical guidelines moved towards a new model in which inflammation and
progression are classified separately.35, 36 Pathologically, this seems supported by accumulating
evidence that suggests at least partly similar underlying disease processes, such as the presence of
activated microglia and oxidative stress.37, 38 Given these pathological similarities and clinical
guidelines, we analysed the accelerated cognitive decline in primary and secondary progressive
patients together as a single group in chapter 2.1 to enable more robust statistical testing. On the
other side of the spectrum, however, we were unable to include patients with very early disease, i.e.
around or even before diagnosis, as the earliest patients in our sample had a disease duration of
around four years. We were therefore not able to assess the rate of cognitive decline at this earliest
stage of the disease and we encourage future longitudinal studies to investigate early inception
cohorts to address this remaining question.
Longitudinal structural pathology assessment using MR imaging – effects of scanner upgrade
What pathological process could be driving the continuous and even accelerated cognitive decline in
progressive multiple sclerosis is still unclear. The fact that the rate of cognitive decline varies according
Summary and general discussion
to disease stage, may even suggest that these underlying pathological substrates may change over
time.39-42 Several distinct cross-sectional pathological correlates of cognitive impairment have been
discovered, such as the amount of white matter lesions, diffuse white matter integrity loss and
especially the severity of grey matter atrophy.43-45 Using such cross-sectional study designs, it is,
however, difficult to assess shifts in the pathological drivers of cognitive decline with disease
progression since information on the ordering of pathology and accompanying changes in cognitive
function is missing. To longitudinally study pathological correlates of cognitive decline at different
stages of multiple sclerosis, we also obtained MR imaging in our cohort at both time points using a
General Electric 3-Tesla system. This enabled us to evaluate the rates of grey matter atrophy and white
matter lesion accumulation in relapsing and progressive patients separately as described in chapter
2.1. However, between the initial and follow-up visit, a scanner upgrade took place which is known to
have effects on volumetric measurements.46 To be able to correct for this effect, we employed a novel
approach in which we modelled the upgrade effect based on the volumetric changes observed in the
healthy control group. Normal aging-related atrophy was modelled using both the cross sectional
volumes at each time point and volumetric changes during follow-up in the healthy controls assuming
a non-linear and sex dependent relationship between age and volume.47-49 The differences between
volumes at initial visit and corrected follow-up volumes were then used to derive annual atrophy rates
in both the healthy control subjects and patients with multiple sclerosis. Although not without
limitations, such as the use of a single correction factor, we think this innovative approach could
provide a means of effectively dealing with scanner upgrade challenges and could also possibly enable
the longitudinal study of additional existing datasets.
Progression-related faster cortical atrophy associated with accelerated cognitive decline
The obtained cortical and deep grey matter atrophy rates were then compared between relapsing-
remitting and progressive groups. This analysis showed that while deep grey matter regions
demonstrated a stable (but high) atrophy rate in both relapsing-remitting and progressive groups,
cortical atrophy rates showed a clear acceleration with an about 1.8 times faster atrophy rate in
progressive compared to relapsing-remitting patients. What is causing this pattern of stable deep, but
accelerating cortical atrophy is still unclear, but it has been hypothesized that the close proximity of
the deep grey matter regions to the lesion-prone periventricular white matter could make them more
vulnerable for the impact of white matter lesions through axonal damage and Wallerian or dying-back
degeneration.50, 51 This would leave the deep grey matter regions vulnerable for the bouts of
inflammation typical for the relapsing-remitting stage of the disease.37 The acceleration in cortical
atrophy in the more neurodegenerative progressive phase of multiple sclerosis could then possibly be
explained by pathological processes within the (cortical) grey matter itself such as local microglial
activation,52 the presence of B cell follicles in the inner meningeal layers53 and the formation of cortical
lesions.54 However, given the absence of an association between cortical lesions and neuroaxonal
loss,55 an alternative explanation could be that initial white matter tract damage and neuroaxonal loss
in deep grey matter regions could lead to a ‘second-order network-mediated effect’ on connected
cortical regions.56, 57 To further investigate the impact of deep and cortical atrophy, as well as lesions
on cognitive decline, we correlated longitudinal changes in these imaging measures to longitudinal
rates of cognitive decline. Interestingly, longitudinal correlates of cognitive decline shifted from the
rate of lesion volume increase in stable relapsing-remitting patients, to the rate of deep grey matter
atrophy in converting relapsing-remitting patients to the rate of cortical atrophy in progressive
patients. With the transition from a more inflammatory towards a more neurodegenerative disease,37,
38 the substrate of cognitive decline, therefor, also seems to shift from white matter lesions and deep
grey matter in earlier disease stages towards cortical atrophy in late stage disease.
The aforementioned findings point towards an acceleration of cortical atrophy, with severe
cognitive implications. It is now of the utmost importance to further investigate, which of the proposed
or other underlying mechanisms primarily explains the accelerating cortical atrophy. These insights
could potentially delineate new neuroprotective treatment targets, which are unfortunately largely
lacking at the moment. Such neuroprotective strategies could include the stimulation of axonal
remyelination, restoration of trophic support, reduction of oxidative stress and the prevention of
sodium accumulation.58 In addition, future studies should aim to longitudinally acquire high quality
conventional as well as advanced MR imaging data such as diffusion-weighted, magnetic-transfer
weighted as well as double/phase-sensitive inversion recovery images to measure additional
pathological substrates such as white matter integrity, myelin content and cortical lesions
respectively.59 Finally, the inclusion of additional imaging time points (at least three) would enable a
more sophisticated measurement and modelling of within subject acceleration and deceleration in the
accumulation of different types of pathology, thereby reducing potential sources of bias and enabling
a more in-depth study of pathological correlates.
Depletion of cognitive reserve in patients with grey matter atrophy
In chapter 2.2 we used a different approach to unravel underlying mechanisms of cognitive
impairment in early and advanced stages of multiple sclerosis by using a radiological instead of a clinical
classification. Using the cross-sectional measurements of the first time-point of our cohort of 332
patients, we divided patients into either having atrophy or not within the deep and cortical grey matter
separately. Within each subgroup we investigated the frequency and correlates of cognitive
impairment. In patients without any type of grey matter atrophy (N=132) cognitive impairment was
still common (32%) and best explained by a lower level of education. In patients with deep as well as
Summary and general discussion
cortical grey matter atrophy (N=65), cognitive impairment (75%) was explained very differently,
namely by the severity of accompanying white matter damage, such as white matter lesion volume
and white matter integrity damage, but not by level of education. Interestingly, there were almost no
patients with isolated cortical atrophy (N=6), and patients with isolated deep grey matter atrophy
(N=125) showed a frequency of cognitive impairment that was in between those without any atrophy
and those with wide-spread atrophy. These cross sectional findings further substantiate the hypothesis
of an order of events, as was already indicated in chapter 2.1, where patients move from no atrophy
towards deep grey matter atrophy, and then towards atrophy in both deep and cortical grey matter.
The protective effect of higher level of education against cognitive impairment that we
observed has been observed previously in multiple sclerosis,60 and a similar protective effect has been
observed for intellectual enrichment. This effect has also been observed in other neurological
disorders,61 and is commonly referred to as cognitive reserve, which seemingly serves to protect
against cognitive decline in the face of accumulating disease-related pathology.62, 63 Our finding that
this protective effect of cognitive reserve is primarily relevant in early stages of multiple sclerosis has
been confirmed recently using a disease duration based classification.64 This depletion of cognitive
reserve could perhaps, in conjunction with the acceleration in cortical atrophy described in chapter
2.1, at least partly explain the acceleration in cognitive decline observed in progressive multiple
sclerosis, where this buffer is seemingly lost. This protective effect of cognitive reserve and how it
could relate to the accumulation of different types of pathology during different disease stages is
illustrated in Figure 1.
Regional disturbance in brain function as an imaging marker of cognitive impairment
In addition to examining how structural pathology and protective mechanisms such as cognitive
reserve could determine cognitive decline at different disease stages, we also explored disturbances
in brain function in chapter 2.2, which will be more thoroughly discussed in chapter 3. By comparing
cognitively impaired and preserved patients in the groups without and with atrophy separately, we
showed that a disturbance in the connectedness of the posterior cingulate cortex characterized both
cognitively impaired patient groups. The presence of this functional disturbance in cognitively impaired
patients without grey matter atrophy was an interesting findings since white matter damage (i.e.
lesions, integrity damage) was also still limited in this group and did not differ between cognitively
impaired and preserved patients. Based on these findings we hypothesized that individuals with a low
cognitive reserve might be more vulnerable to develop such a functional disturbance in the presence
of only limited structural pathology. In this theoretical model, the accumulating pathology is buffered
by cognitive reserve and when this pathology exceeds an individual’s cognitive reserve buffer, both
functional network disturbances and cognitive decline could set in. Future research should now further
investigate whether such functional measures are indeed more closely related to clinical dysfunction
compared to structural measures (which are to a variable extent buffered by cognitive reserve) and
could serve as more specific imaging markers of cognitive impairment.
Figure 1. Theoretical model of the evolution of imaging measures of pathology and cognitive decline.
Patients with low cognitive reserve (red) and high cognitive reserve (green) have different trajectories, but in both cognitive
decline is driven by the accumulation of different types of pathology; white matter lesions (orange), deep grey matter atrophy
(light blue) and cortical grey matter atrophy (dark blue). Note: atrophy measures are expressed as the inverse of deep and
cortical grey matter volumes and indicate atrophy severity. Abbreviations: GM = grey matter, WM = white matter, IS =
o Is the acceleration of cortical atrophy primarily driven by a network-guided process
that spreads between (cortical) brain regions or by local pathological processes
occurring within the cortex?
o Can we detect novel neuroprotective treatment targets that slow the progression of
cortical atrophy and thereby preserve cognitive function?
o Can we delineate additional relevant pathological processes of cognitive decline by
longitudinally acquiring more advanced MR imaging measures?
o Can we further elucidate the neural mechanisms that protect patients with a high
cognitive reserve from cognitive decline in early, but not late, multiple sclerosis?
2. The relation between functional network disturbances and cognitive decline
o Can we unravel whether the ‘stuck’ default-mode network in a more central position
during rest and its impaired attenuation during task, are primarily due to a local or
network-mediated process?
o Can we delineate whether the functional network disturbances in the posterior
cingulate cortex indeed precede the strong atrophy in this region, possibly suggesting
hub failure?
o Can we more precisely unravel the mechanisms underlying specific cognitive deficits
by studying functional network dynamics during these cognitive task states?
3. MR imaging predictors of future cognitive decline
o Can we further improve the prediction of future cognitive decline by incorporating
functional network disturbances in addition to structural pathology?
o Can we integrate information provided by independent predictors of future cognitive
decline in multiple sclerosis and their interactions to predict an individual patient’s
cognitive trajectory?
Summary and general discussion
References 1. Bassett DS, Sporns O. Network neuroscience. Nat Neurosci 2017;20:353-364. 2. Hutchison RM, Womelsdorf T, Allen EA, et al. Dynamic functional connectivity: promise, issues, and interpretations. Neuroimage 2013;80:360-378. 3. Chiaravalloti ND, DeLuca J. Cognitive impairment in multiple sclerosis. Lancet Neurol 2008;7:1139-1151. 4. Benedict RHB, DeLuca J, Enzinger C, Geurts JJG, Krupp LB, Rao SM. Neuropsychology of Multiple Sclerosis: Looking Back and Moving Forward. J Int Neuropsychol Soc 2017;23:832-842. 5. Bullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 2009;10:186-198. 6. Calhoun VD, Miller R, Pearlson G, Adali T. The chronnectome: time-varying connectivity networks as the next frontier in fMRI data discovery. Neuron 2014;84:262-274. 7. Damaraju E, Allen EA, Belger A, et al. Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia. Neuroimage Clin 2014;5:298-308. 8. Rao SM. A Manual for the Brief Repeatable Battery of Neuropsychological Tests in Multiple Sclerosis: Medical College of Wisconsin. Milwaukee: Medical College of Wisconsin. 1990. 9. Van der Elst W, Van Boxtel MP, Van Breukelen GJ, Jolles J. The Concept Shifting Test: adult normative data. Psychol Assess 2006;18:424-432. 10. Buschke H. Selective Reminding for Analysis of Memory and Learning. J Verb Learn Verb Be 1973;12:543-550. 11. Bassett DS, Bullmore ET. Small-World Brain Networks Revisited. Neuroscientist 2017;23:499-516. 12. Boringa JB, Lazeron RH, Reuling IE, et al. The brief repeatable battery of neuropsychological tests: normative values allow application in multiple sclerosis clinical practice. Mult Scler 2001;7:263-267. 13. Sporns O, Betzel RF. Modular Brain Networks. Annu Rev Psychol 2016;67:613-640. 14. Smith A. The Symbol Digits Modalities Test Manual, revised. Los Angeles: Western Psychological Services, 1982. 15. van den Heuvel MP, Sporns O. Network hubs in the human brain. Trends Cogn Sci 2013;17:683-696. 16. Stroop JR. Studies of Interference in Serial Verbal Reactions (Reprinted from Journal Experimental-Psychology, Vol 18, Pg 643-662, 1935). J Exp Psychol Gen 1992;121:15-23. 17. Brand N, Jolles J. Information processing in depression and anxiety. Psychol Med 1987;17:145-153. 18. Benedict RH, DeLuca J, Phillips G, et al. Validity of the Symbol Digit Modalities Test as a cognition performance outcome measure for multiple sclerosis. Mult Scler 2017;23:721-733. 19. Sumowski JF, Benedict R, Enzinger C, et al. Cognition in multiple sclerosis: State of the field and priorities for the future. Neurology 2018;90:278-288. 20. Louapre C, Perlbarg V, Garcia-Lorenzo D, et al. Brain networks disconnection in early multiple sclerosis
cognitive deficits: an anatomofunctional study. Hum Brain Mapp 2014;35:4706-4717. 21. Schoonheim MM, Hulst HE, Brandt RB, et al. Thalamus structure and function determine severity of cognitive impairment in multiple sclerosis. Neurology 2015;84:776-783. 22. Amato MP, Zipoli V, Portaccio E. Multiple sclerosis-related cognitive changes: a review of cross-sectional and longitudinal studies. J Neurol Sci 2006;245:41-46. 23. Banati M, Sandor J, Mike A, et al. Social cognition and Theory of Mind in patients with relapsing-remitting multiple sclerosis. Eur J Neurol 2010;17:426-433. 24. Benedict RH, Cookfair D, Gavett R, et al. Validity of the minimal assessment of cognitive function in multiple sclerosis (MACFIMS). J Int Neuropsychol Soc 2006;12:549-558. 25. Langdon DW, Amato MP, Boringa J, et al. Recommendations for a Brief International Cognitive Assessment for Multiple Sclerosis (BICAMS). Mult Scler 2012;18:891-898. 26. Amato MP, Ponziani G, Siracusa G, Sorbi S. Cognitive dysfunction in early-onset multiple sclerosis: a reappraisal after 10 years. Arch Neurol 2001;58:1602-1606. 27. Amato MP, Ponziani G, Pracucci G, Bracco L, Siracusa G, Amaducci L. Cognitive impairment in early-onset multiple sclerosis. Pattern, predictors, and impact on everyday life in a 4-year follow-up. Arch Neurol 1995;52:168-172. 28. Achiron A, Barak Y. Cognitive impairment in probable multiple sclerosis. J Neurol Neurosurg Psychiatry 2003;74:443-446. 29. Cortese M, Riise T, Bjornevik K, et al. Preclinical disease activity in multiple sclerosis: A prospective study of cognitive performance prior to first symptom. Ann Neurol 2016;80:616-624. 30. Wijnands JM, Zhu F, Kingwell E, et al. Five years before multiple sclerosis onset: Phenotyping the prodrome. Mult Scler 2018:1352458518783662. 31. Iverson GL. Interpreting change on the WAIS-III/WMS-III in clinical samples. Arch Clin Neuropsychol 2001;16:183-191. 32. Comi G, Filippi M, Martinelli V, et al. Brain MRI correlates of cognitive impairment in primary and secondary progressive multiple sclerosis. J Neurol Sci 1995;132:222-227. 33. Foong J, Rozewicz L, Chong WK, Thompson AJ, Miller DH, Ron MA. A comparison of neuropsychological deficits in primary and secondary progressive multiple sclerosis. J Neurol 2000;247:97-101. 34. Camp SJ, Stevenson VL, Thompson AJ, et al. Cognitive function in primary progressive and transitional progressive multiple sclerosis: a controlled study with MRI correlates. Brain 1999;122 ( Pt 7):1341-1348. 35. Lublin FD, Reingold SC, Cohen JA, et al. Defining the clinical course of multiple sclerosis The 2013 revisions. Neurology 2014;83:278-286.
36. Thompson AJ, Banwell BL, Barkhof F, et al. Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol 2018;17:162-173. 37. Lassmann H, van Horssen J, Mahad D. Progressive multiple sclerosis: pathology and pathogenesis. Nat Rev Neurol 2012;8:647-656. 38. Mahad DH, Trapp BD, Lassmann H. Pathological mechanisms in progressive multiple sclerosis. Lancet Neurol 2015;14:183-193. 39. Benedict RH, Weinstock-Guttman B, Fishman I, Sharma J, Tjoa CW, Bakshi R. Prediction of neuropsychological impairment in multiple sclerosis: comparison of conventional magnetic resonance imaging measures of atrophy and lesion burden. Arch Neurol 2004;61:226-230. 40. Benedict RH, Zivadinov R. Risk factors for and management of cognitive dysfunction in multiple sclerosis. Nat Rev Neurol 2011;7:332-342. 41. Barkhof F. The clinico-radiological paradox in multiple sclerosis revisited. Curr Opin Neurol 2002;15:239-245. 42. Chard D, Trip SA. Resolving the clinico-radiological paradox in multiple sclerosis. F1000Res 2017;6:1828. 43. Benedict RH, Weinstock-Guttman B, Fishman I, Sharma J, Tjoa CW, Bakshi R. Prediction of neuropsychological impairment in multiple sclerosis: comparison of conventional magnetic resonance imaging measures of atrophy and lesion burden. Arch Neurol 2004;61:226-230. 44. Hulst HE, Steenwijk MD, Versteeg A, et al. Cognitive impairment in MS: impact of white matter integrity, gray matter volume, and lesions. Neurology 2013;80:1025-1032. 45. Rocca MA, Amato MP, De Stefano N, et al. Clinical and imaging assessment of cognitive dysfunction in multiple sclerosis. Lancet Neurol 2015;14:302-317. 46. Steenwijk MD, Amiri H, Schoonheim MM, et al. Agreement of MSmetrix with established methods for measuring cross-sectional and longitudinal brain atrophy. Neuroimage Clin 2017;15:843-853. 47. Hedman AM, van Haren NE, Schnack HG, Kahn RS, Hulshoff Pol HE. Human brain changes across the life span: a review of 56 longitudinal magnetic resonance imaging studies. Hum Brain Mapp 2012;33:1987-2002. 48. Fotenos AF, Snyder AZ, Girton LE, Morris JC, Buckner RL. Normative estimates of cross-sectional and longitudinal brain volume decline in aging and AD. Neurology 2005;64:1032-1039. 49. Ziegler G, Dahnke R, Jancke L, Yotter RA, May A, Gaser C. Brain structural trajectories over the adult lifespan. Hum Brain Mapp 2012;33:2377-2389. 50. Steenwijk MD, Daams M, Pouwels PJ, et al. Unraveling the relationship between regional gray matter atrophy and pathology in connected white matter tracts in long-standing multiple sclerosis. Hum Brain Mapp 2015;36:1796-1807. 51. Azevedo CJ, Cen SY, Khadka S, et al. Thalamic atrophy in multiple sclerosis: A magnetic resonance imaging marker of neurodegeneration throughout disease. Ann Neurol 2018;83:223-234.
52. Prineas JW, Kwon EE, Cho ES, et al. Immunopathology of secondary-progressive multiple sclerosis. Ann Neurol 2001;50:646-657. 53. Franciotta D, Salvetti M, Lolli F, Serafini B, Aloisi F. B cells and multiple sclerosis. Lancet Neurol 2008;7:852-858. 54. Calabrese M, Filippi M, Gallo P. Cortical lesions in multiple sclerosis. Nat Rev Neurol 2010;6:438-444. 55. Klaver R, Popescu V, Voorn P, et al. Neuronal and axonal loss in normal-appearing gray matter and subpial lesions in multiple sclerosis. J Neuropathol Exp Neurol 2015;74:453-458. 56. Steenwijk MD, Geurts JJ, Daams M, et al. Cortical atrophy patterns in multiple sclerosis are non-random and clinically relevant. Brain 2016;139:115-126. 57. Calabrese M, Magliozzi R, Ciccarelli O, Geurts JJ, Reynolds R, Martin R. Exploring the origins of grey matter damage in multiple sclerosis. Nat Rev Neurosci 2015;16:147-158. 58. Franklin RJ, ffrench-Constant C, Edgar JM, Smith KJ. Neuroprotection and repair in multiple sclerosis. Nat Rev Neurol 2012;8:624-634. 59. Giorgio A, De Stefano N. Advanced Structural and Functional Brain MRI in Multiple Sclerosis. Semin Neurol 2016;36:163-176. 60. Sumowski JF, Chiaravalloti N, Wylie G, Deluca J. Cognitive reserve moderates the negative effect of brain atrophy on cognitive efficiency in multiple sclerosis. J Int Neuropsychol Soc 2009;15:606-612. 61. Stern Y. Cognitive reserve in ageing and Alzheimer's disease. Lancet Neurol 2012;11:1006-1012. 62. Benedict RH, Morrow SA, Weinstock Guttman B, Cookfair D, Schretlen DJ. Cognitive reserve moderates decline in information processing speed in multiple sclerosis patients. J Int Neuropsychol Soc 2010;16:829-835. 63. Sumowski JF, Rocca MA, Leavitt VM, et al. Brain reserve and cognitive reserve in multiple sclerosis: what you've got and how you use it. Neurology 2013;80:2186-2193. 64. Rimkus CM, Avolio IMB, Miotto EC, et al. The protective effects of high-education levels on cognition in different stages of multiple sclerosis. Mult Scler Relat Disord 2018;22:41-48. 65. Roosendaal SD, Schoonheim MM, Hulst HE, et al. Resting state networks change in clinically isolated syndrome. Brain 2010;133:1612-1621. 66. Rocca MA, Valsasina P, Absinta M, et al. Default-mode network dysfunction and cognitive impairment in progressive MS. Neurology 2010;74:1252-1259. 67. Hawellek DJ, Hipp JF, Lewis CM, Corbetta M, Engel AK. Increased functional connectivity indicates the severity of cognitive impairment in multiple sclerosis. Proc Natl Acad Sci U S A 2011;108:19066-19071. 68. Schoonheim MM, Meijer KA, Geurts JJ. Network collapse and cognitive impairment in multiple sclerosis. Front Neurol 2015;6:82. 69. Tona F, Petsas N, Sbardella E, et al. Multiple sclerosis: altered thalamic resting-state functional connectivity and its effect on cognitive function. Radiology 2014;271:814-821. 70. Hulst HE, Schoonheim MM, Roosendaal SD, et al. Functional adaptive changes within the hippocampal
Summary and general discussion
memory system of patients with multiple sclerosis. Hum Brain Mapp 2012;33:2268-2280. 71. Tewarie P, Steenwijk MD, Brookes MJ, et al. Explaining the heterogeneity of functional connectivity findings in multiple sclerosis: An empirically informed modeling study. Hum Brain Mapp 2018;39:2541-2548. 72. Leavitt VM, Paxton J, Sumowski JF. Default network connectivity is linked to memory status in multiple sclerosis. J Int Neuropsychol Soc 2014;20:937-944. 73. Faivre A, Rico A, Zaaraoui W, et al. Assessing brain connectivity at rest is clinically relevant in early multiple sclerosis. Mult Scler 2012;18:1251-1258. 74. Sporns O. The human connectome: origins and challenges. Neuroimage 2013;80:53-61. 75. Gamboa OL, Tagliazucchi E, von Wegner F, et al. Working memory performance of early MS patients correlates inversely with modularity increases in resting state functional connectivity networks. Neuroimage 2014;94:385-395. 76. Rocca MA, Valsasina P, Meani A, Falini A, Comi G, Filippi M. Impaired functional integration in multiple sclerosis: a graph theory study. Brain Struct Funct 2016;221:115-131. 77. Stam CJ. Modern network science of neurological disorders. Nat Rev Neurosci 2014;15:683-695. 78. Crossley NA, Mechelli A, Scott J, et al. The hubs of the human connectome are generally implicated in the anatomy of brain disorders. Brain 2014;137:2382-2395. 79. Lohmann G, Margulies DS, Horstmann A, et al. Eigenvector centrality mapping for analyzing connectivity patterns in fMRI data of the human brain. PLoS One 2010;5:e10232. 80. Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL. A default mode of brain function. Proc Natl Acad Sci U S A 2001;98:676-682. 81. Gusnard DA, Akbudak E, Shulman GL, Raichle ME. Medial prefrontal cortex and self-referential mental activity: relation to a default mode of brain function. Proc Natl Acad Sci U S A 2001;98:4259-4264. 82. de Haan W, Mott K, van Straaten EC, Scheltens P, Stam CJ. Activity dependent degeneration explains hub vulnerability in Alzheimer's disease. PLoS Comput Biol 2012;8:e1002582. 83. Rocca MA, Valsasina P, Hulst HE, et al. Functional correlates of cognitive dysfunction in multiple sclerosis: A multicenter fMRI Study. Hum Brain Mapp 2014;35:5799-5814. 84. Raichle ME. The brain's default mode network. Annu Rev Neurosci 2015;38:433-447. 85. Anticevic A, Cole MW, Murray JD, Corlett PR, Wang XJ, Krystal JH. The role of default network deactivation in cognition and disease. Trends Cogn Sci 2012;16:584-592. 86. Broyd SJ, Demanuele C, Debener S, Helps SK, James CJ, Sonuga-Barke EJ. Default-mode brain dysfunction in mental disorders: a systematic review. Neurosci Biobehav Rev 2009;33:279-296. 87. Gu H, Hu Y, Chen X, He Y, Yang Y. Regional excitation-inhibition balance predicts default-mode network deactivation via functional connectivity. Neuroimage 2018;185:388-397.
88. Buckner RL, Sepulcre J, Talukdar T, et al. Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer's disease. J Neurosci 2009;29:1860-1873. 89. Sporns O. Contributions and challenges for network models in cognitive neuroscience. Nat Neurosci 2014;17:652-660. 90. Leech R, Sharp DJ. The role of the posterior cingulate cortex in cognition and disease. Brain 2014;137:12-32. 91. Eshaghi A, Marinescu RV, Young AL, et al. Progression of regional grey matter atrophy in multiple sclerosis. Brain 2018;141:1665-1677. 92. Power JD, Schlaggar BL, Petersen SE. Recent progress and outstanding issues in motion correction in resting state fMRI. Neuroimage 2015;105:536-551. 93. Ciric R, Wolf DH, Power JD, et al. Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity. Neuroimage 2017;154:174-187. 94. Power JD, Mitra A, Laumann TO, Snyder AZ, Schlaggar BL, Petersen SE. Methods to detect, characterize, and remove motion artifact in resting state fMRI. Neuroimage 2014;84:320-341. 95. Parkes L, Fulcher B, Yucel M, Fornito A. An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI. Neuroimage 2018;171:415-436. 96. Pruim RHR, Mennes M, van Rooij D, Llera A, Buitelaar JK, Beckmann CF. ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data. Neuroimage 2015;112:267-277. 97. Gonzalez-Castillo J, Handwerker DA, Robinson ME, et al. The spatial structure of resting state connectivity stability on the scale of minutes. Front Neurosci 2014;8:138. 98. Shen K, Hutchison RM, Bezgin G, Everling S, McIntosh AR. Network structure shapes spontaneous functional connectivity dynamics. J Neurosci 2015;35:5579-5588. 99. Fox MD, Snyder AZ, Vincent JL, Corbetta M, Van Essen DC, Raichle ME. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc Natl Acad Sci U S A 2005;102:9673-9678. 100. Uddin LQ, Kelly AM, Biswal BB, Castellanos FX, Milham MP. Functional connectivity of default mode network components: correlation, anticorrelation, and causality. Hum Brain Mapp 2009;30:625-637. 101. Kelly AM, Uddin LQ, Biswal BB, Castellanos FX, Milham MP. Competition between functional brain networks mediates behavioral variability. Neuroimage 2008;39:527-537. 102. Gao W, Lin W. Frontal parietal control network regulates the anti-correlated default and dorsal attention networks. Hum Brain Mapp 2012;33:192-202. 103. Dixon ML, Andrews-Hanna JR, Spreng RN, et al. Interactions between the default network and dorsal attention network vary across default subsystems, time, and cognitive states. Neuroimage 2017;147:632-649. 104. Carter AR, Astafiev SV, Lang CE, et al. Resting interhemispheric functional magnetic resonance imaging connectivity predicts performance after stroke. Ann Neurol 2010;67:365-375.
105. MacDonald CL, Schwarze N, Vaishnavi SN, et al. Verbal memory deficit following traumatic brain injury: assessment using advanced MRI methods. Neurology 2008;71:1199-1201. 106. Andrews-Hanna JR. The brain's default network and its adaptive role in internal mentation. Neuroscientist 2012;18:251-270. 107. Buckner RL, Krienen FM, Yeo BT. Opportunities and limitations of intrinsic functional connectivity MRI. Nat Neurosci 2013;16:832-837. 108. Van Essen DC, Ugurbil K, Auerbach E, et al. The Human Connectome Project: a data acquisition perspective. Neuroimage 2012;62:2222-2231. 109. Glasser MF, Sotiropoulos SN, Wilson JA, et al. The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage 2013;80:105-124. 110. Smith SM, Beckmann CF, Andersson J, et al. Resting-state fMRI in the Human Connectome Project. Neuroimage 2013;80:144-168. 111. Smith SM, Vidaurre D, Beckmann CF, et al. Functional connectomics from resting-state fMRI. Trends Cogn Sci 2013;17:666-682. 112. Barch DM, Burgess GC, Harms MP, et al. Function in the human connectome: task-fMRI and individual differences in behavior. Neuroimage 2013;80:169-189. 113. Liegeois R, Laumann TO, Snyder AZ, Zhou J, Yeo BTT. Interpreting temporal fluctuations in resting-state functional connectivity MRI. Neuroimage 2017;163:437-455. 114. Laumann TO, Snyder AZ, Mitra A, et al. On the Stability of BOLD fMRI Correlations. Cereb Cortex 2017;27:4719-4732. 115. Summers M, Swanton J, Fernando K, et al. Cognitive impairment in multiple sclerosis can be predicted by imaging early in the disease. J Neurol Neurosurg Psychiatry 2008;79:955-958.
116. Summers M, Fisniku L, Anderson V, Miller D, Cipolotti L, Ron M. Cognitive impairment in relapsing-remitting multiple sclerosis can be predicted by imaging performed several years earlier. Mult Scler 2008;14:197-204. 117. Deloire MS, Ruet A, Hamel D, Bonnet M, Dousset V, Brochet B. MRI predictors of cognitive outcome in early multiple sclerosis. Neurology 2011;76:1161-1167. 118. Rimkus CM, Schoonheim MM, Steenwijk MD, et al. Gray matter networks and cognitive impairment in multiple sclerosis. Mult Scler 2018:1352458517751650. 119. Schoonheim MM, Popescu V, Rueda Lopes FC, et al. Subcortical atrophy and cognition: sex effects in multiple sclerosis. Neurology 2012;79:1754-1761. 120. Schoonheim MM, Popescu V, Rueda Lopes FC, et al. Subcortical atrophy and cognition: sex effects in multiple sclerosis. Neurology 2012;79:1754-1761. 121. Beatty WW, Aupperle RL. Sex differences in cognitive impairment in multiple sclerosis. Clin Neuropsychol 2002;16:472-480. 122. Filippi M, Preziosa P, Copetti M, et al. Gray matter damage predicts the accumulation of disability 13 years later in MS. Neurology 2013;81:1759-1767. 123. Bergsland N, Horakova D, Dwyer MG, et al. Subcortical and cortical gray matter atrophy in a large sample of patients with clinically isolated syndrome and early relapsing-remitting multiple sclerosis. AJNR Am J Neuroradiol 2012;33:1573-1578. 124. Minagar A, Barnett MH, Benedict RH, et al. The thalamus and multiple sclerosis: modern views on pathologic, imaging, and clinical aspects. Neurology 2013;80:210-219. 125. Damjanovic D, Valsasina P, Rocca MA, et al. Hippocampal and Deep Gray Matter Nuclei Atrophy Is Relevant for Explaining Cognitive Impairment in MS: A Multicenter Study. AJNR Am J Neuroradiol 2017;38:18-24.