Medical and scientific advancement worldwide has led to a longer lifespan. With the population aging comes the risk of developing cognitive decline. The incorporation of neuroimaging measures in evaluating cognitive changes is limited in nursing research. The aim of this review is to introduce nurse scientists to neuroimaging measures employed to assess the association between brain and cognitive changes.
Methods
Relevant literature was identified by searching CINAHL, Web of Science, and PubMed databases using the following keywords: “neuroimaging measures,” “aging,” “cognition,” “qualitative scoring,” “cognitive ability,” “molecular,” “structural,” and “functional.”
Results
Neuroimaging measures can be categorized into structural, functional, and molecular imaging approaches. The structural imaging technique visualizes the anatomical regions of the brain. Visual examination and volumetric segmentation of select structural sequences extract information such as white matter hyperintensities and cerebral atrophy. Functional imaging techniques evaluate brain regions and underlying processes using blood-oxygen-dependent signals. Molecular imaging technique is the real-time visualization of biological processes at the cellular and molecular levels in a given region. Examples of biological measures associated with neurodegeneration include decreased glutamine level, elevated total choline, and elevated Myo-inositol.
Discussion
Nursing is at the forefront of addressing upstream factors impacting health outcomes across a lifespan of a population at increased risk of progressive cognitive decline. Nurse researchers can become more facile in using these measures both in qualitative and quantitative methodology by leveraging previously gathered neuroimaging clinical data for research purposes to better characterize the associations between symptom progression, disease risk, and health outcomes.
AppelmanA. P.ExaltoL. G.Van Der GraafY.BiesselsG. J.MaliW. P.GeerlingsM. I. (2009). White matter lesions and brain atrophy: More than shared risk factors? A systematic review. Cerebrovascular Diseases, 28(3), 227–242. https://doi.org/10.1159/000226774
2.
Armbruster-GencD. J.UeltzhofferK.FiebachC. J. (2016). Brain signal variability differentially affects cognitive flexibility and cognitive stability. The Journal of Neuroscience, 36(14), 3978–3987. https://doi.org/10.1523/JNEUROSCI.2517-14.2016
AtallaS. W.KalvasL. B.CampbellJ. L.AndersonA. R.CowanR. L.WrightK.HumbelA. C.MonroeT. B. (2020). Neuroimaging methods for nursing science. Nursing Research, 69(3), 219–226. https://doi.org/10.1097/NNR.0000000000000410
5.
AungW. Y.MarS.BenzingerT. L. (2013). Diffusion tensor MRI as a biomarker in axonal and myelin damage. Imaging Medicine, 5(5), 427–440. https://doi.org/10.2217/iim.13.49
6.
BarretoT.JettyA.EdenA. R.PettersonS.BazemoreA.PetersonL. E. (2021). Distribution of physician specialties by rurality. Journal of Rural Health, 37(4), 714–722. https://doi.org/10.1111/jrh.12548
7.
BatistaS.ZivadinovR.HoogsM.BergslandN.Heininen-BrownM.DwyerM. G.Weinstock-GuttmanB.BenedictR. H. (2012). Basal ganglia, thalamus and neocortical atrophy predicting slowed cognitive processing in multiple sclerosis. Journal of Neurology, 259(1), 139–146. https://doi.org/10.1007/s00415-011-6147-1
8.
BelliveauJ.KennedyD.McKinstryR.BuchbinderB.WeisskoffR.CohenM.VeveaJ.BradyT.RosenB. (1991). Functional mapping of the human visual cortex by magnetic resonance imaging. Science, 254(5032), 716–719. https://doi.org/10.1126/science.1948051
9.
CollierD. C.BurnettS. S.AminM.BiltonS.BrooksC.RyanA.RonigerD.TranD.StarkschallG. (2003). Assessment of consistency in contouring of normal tissue anatomic structures. Journal of Applied Clinical Medical Physics, 4(1), 17–24. https://doi.org/10.1120/jacmp.v4i1.2538
CullerS. D.AtherlyA.WalczakS.DavisA.HawleyJ. N.RaskK. J.NaylorV.ThorpeK. E. (2006). Urban-Rural differences in the availability of hospital information technology applications: A survey of Georgia hospitals. Journal of Rural Health, 22(3), 242–247. https://doi.org/10.1111/j.1748-0361.2006.00039.x
12.
DebetteS.MarkusH. S. (2010). The clinical importance of white matter hyperintensities on brain magnetic resonance imaging: Systematic review and meta-analysis. British Medical Journal, 341, c3666. https://doi.org/10.1136/bmj.c3666
13.
DefrancescoM.MarksteinerJ.DeisenhammerE.KemmlerG.DjurdjevicT.SchockeM. (2013). Impact of white matter lesions and cognitive deficits on conversion from mild cognitive impairment to Alzheimer’s disease. Journal of Alzheimer’s Disease, 34(3), 665–672. https://doi.org/10.3233/JAD-122095
14.
De LeeuwF. E.de GrootJ. C.AchtenE.OudkerkM.RamosL. M.HeijboerR.HofmanA.JollesJ.van GijnJ.BretelerM. M. (2001). Prevalence of cerebral white matter lesions in elderly people: A population based magnetic resonance imaging study. The Rotterdam scan study. Journal of Neurology, Neurosurgery and Psychiatry, 70(1), 9–14. https://doi.org/10.1136/jnnp.70.1.9
15.
DeprezS.AmantF.SmeetsA.PeetersR.LeemansA.Van HeckeW.VerhoevenJ. S.ChristiaensM. R.VandenbergheJ.VandenbulckeM. (2012). Longitudinal assessment of chemotherapy-induced structural changes in cerebral white matter and its correlation with impaired cognitive functioning. Journal of Clinical Oncology, 30(3), 274–281. https://doi.org/10.1200/JCO.2011.36.8571
16.
DespotovicI.GoossensB.PhilipsW. (2015). MRI segmentation of the human brain: Challenges, methods, and applications. Computational and Mathematical Methods in Medicine, 2015, 450341. https://doi.org/10.1155/2015/450341
17.
DoluiS.WangZ.WangD. J.MattayR.FinkelM.ElliottM.DesiderioL.InglisB.MuellerB.StaffordR. B. (2016). Comparison of non-invasive MRI measurements of cerebral blood flow in a large multisite cohort. Journal of Cerebral Blood Flow & Metabolism, 36(7), 1244–1256. https://doi.org/10.1177/0271678X16646124
18.
DouaudG.JbabdiS.BehrensT. E.MenkeR. A.GassA.MonschA. U.RaoA.WhitcherB.KindlmannG.MatthewsP. M. (2011). DTI measures in crossing-fibre areas: Increased diffusion anisotropy reveals early white matter alteration in MCI and mild Alzheimer’s disease. Neuroimage, 55(3), 880–890. https://doi.org/10.1016/j.neuroimage.2010.12.008
19.
DufouilC.GodinO.ChalmersJ.CoskunO.MacMahonS.Tzourio-MazoyerN.BousserM. G.AndersonC.MazoyerB.TzourioC.InvestigatorsP. M. S. (2009). Severe cerebral white matter hyperintensities predict severe cognitive decline in patients with cerebrovascular disease history. Stroke: A Journal of Cerebral Circulation, 40(6), 2219–2221. https://doi.org/10.1161/STROKEAHA.108.540633
20.
DuncanR.PattersonJ.MacraeI. M. (1996). HMPAO as a regional cerebral blood flow tracer at high flow levels. Journal of Nuclear Medicine, 37(4), 661–664
21.
FazekasF.ChawlukJ. B.AlaviA.HurtigH. I.ZimmermanR. A. (1987). MR signal abnormalities at 1.5 T in Alzheimer’s dementia and normal aging. American Journal of Roentgenology, 149(2), 351–356. https://doi.org/10.2214/ajr.149.2.351
22.
FerreiraL. K.BusattoG. F. (2013). Resting-state functional connectivity in normal brain aging. Neuroscience and Biobehavioral Reviews, 37(3), 384–400. https://doi.org/10.1016/j.neubiorev.2013.01.017
23.
FilippiM.RoccaM. A.CiccarelliO.De StefanoN.EvangelouN.KapposL.RoviraA.Sastre-GarrigaJ.TintorèM.FrederiksenJ. L. (2016). MRI criteria for the diagnosis of multiple sclerosis: MAGNIMS consensus guidelines. The Lancet Neurology, 15(3), 292–303. https://doi.org/10.1016/S1474-4422(15)00393-2
FischlB.SalatD. H.BusaE.AlbertM.DieterichM.HaselgroveC.Van der KouweA.KillianyR.KennedyD.KlavenessS.MontilloA.MakrisN.RosenB.DaleA. M. (2002). Whole brain segmentation: Automated labeling of neuroanatomical structures in the human brain. Neuron, 33(3), 341–355. https://doi.org/10.1016/s0896-6273(02)00569-x
26.
GlodzikL.SollbergerM.GassA.GokhaleA.RusinekH.BabbJ. S.HirschJ. G.AmannM.MonschA. U.GonenO. (2015). Global N-acetylaspartate in normal subjects, mild cognitive impairment and Alzheimer’s disease patients. Journal of Alzheimer’s Disease, 43(3), 939–947. https://doi.org/10.3233/JAD-140609
27.
GoodT. J.VillafuerteJ.RyanJ. D.GradyC. L.BarenseM. D. (2020). Resting state BOLD variability of the posterior medial temporal lobe correlates with cognitive performance in older adults with and without risk for cognitive decline. eNeuro, 7(3), 1-13. https://doi.org/10.1523/ENEURO.0290-19.2020.
28.
GriffithH. R.StewartC. C.den HollanderJ. A. (2009). Proton magnetic resonance spectroscopy in dementias and mild cognitive impairment. International Review of Neurobiology, 84, 105-131. https://doi.org/10.3389/fpsyt.2020.00769.
29.
HarrisT. C.De RooijR.KuhlE. (2019). The shrinking brain: Cerebral atrophy following traumatic brain injury. Annals of Biomedical Engineering, 47(9), 1941–1959. https://doi.org/10.1007/s10439-018-02148-2
30.
HasanT. F.BarrettK. M.BrottT. G.BadiM. K.LesserE. R.HodgeD. O.MeschiaJ. F.JenkinsonM.BeckmannC. F.BehrensT. E.WoolrichM. W.SmithS. M. (2019). Severity of white matter hyperintensities and effects on all-cause mortality in the mayo clinic Florida familial cerebrovascular diseases registry mayo clinic proceedings. Neuroimage, 62(2), 782–790. https://doi.org/10.1016/j.neuroimage.2011.09.015
JieC.TreyerV.SchibliR.MuL. (2021). Tauvid: The first FDA-approved PET tracer for imaging tau pathology in Alzheimer’s disease. Pharmaceuticals (Basel), 14(2), 110. https://doi.org/10.3390/ph14020110
33.
JohnsonL. C.RichardsT. L.ArchboldK. H.LandisC. A. (2006). Functional magnetic resonance imaging in nursing research. Biological Research for Nursing, 8(1), 43–54. https://doi.org/10.1177/1099800406289341
34.
JokinenH.LipsanenJ.SchmidtR.FazekasF.GouwA. A.van der FlierW. M.BarkhofF.MadureiraS.VerdelhoA.FerroJ. M.WallinA.PantoniL.InzitariD.ErkinjunttiT.GroupL. S. (2012). Brain atrophy accelerates cognitive decline in cerebral small vessel disease: The LADIS study. Neurology, 78(22), 1785–1792. https://doi.org/10.1212/WNL.0b013e3182583070
35.
KantarciK.JackC.XuY.CampeauN.O’BrienP.SmithG.IvnikR.BoeveB.KokmenE.TangalosE. (2000). Regional metabolic patterns in mild cognitive impairment and alzheimer’s disease: A 1H MRS study. Neurology, 55(2), 210–217. https://doi.org/10.1212/wnl.55.2.210
36.
KapellerP.BarberR.VermeulenR. J.AderH.ScheltensP.FreidlW.AlmkvistO.MorettiM.Del SerT.VaghfeldtP.EnzingerC.BarkhofF.InzitariD.ErkinjuntiT.SchmidtR.FazekasF.European task force of age related white matter, C (2003). Visual rating of age-related white matter changes on magnetic resonance imaging: scale comparison, interrater agreement, and correlations with quantitative measurements. Stroke; A Journal of Cerebral Circulation, 34(2), 441–445. https://doi.org/10.1161/01.str.0000049766.26453.e9
37.
KhaliqA. A.DeyoD.DuszakR. (2015). The impact of hospital characteristics on the availability of radiology services at critical access hospitals. Journal of the American College of Radiology, 12(12), 1351–1356. https://doi.org/10.1016/j.jacr.2015.09.008
38.
KhaliqA. A.NsiahE.BilalN. H.HughesD. R.DuszakR. (2014). The scope and distribution of imaging services at critical access hospitals. Journal of the American College of Radiology, 11(9), 857–862. https://doi.org/10.1016/j.jacr.2014.02.013
39.
KoedamE. L.LehmannM.van der FlierW. M.ScheltensP.PijnenburgY. A.FoxN.BarkhofF.WattjesM. P. (2011). Visual assessment of posterior atrophy development of a MRI rating scale. European Radiology, 21(12), 2618–2625. https://doi.org/10.1007/s00330-011-2205-4
40.
KolanowskiA.Gilmore-BykovskyiA.HillN.MassimoL.MogleJ. (2019). Measurement challenges in research with individuals with cognitive impairment. Research in Gerontological Nursing, 12(1), 7–15. https://doi.org/10.3928/19404921-20181212-06
41.
KwongK. K.BelliveauJ. W.CheslerD. A.GoldbergI. E.WeisskoffR. M.PonceletB. P.KennedyD. N.HoppelB. E.CohenM. S.TurnerR. (1992). Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. Proceedings of the National Academy of Sciences, 89(12), 5675–5679. https://doi.org/10.1073/pnas.89.12.5675
42.
LandauS. M.HarveyD.MadisonC. M.KoeppeR. A.ReimanE. M.FosterN. L.WeinerM. W.JagustW. J.InitiativeA. S. D. N. (2011). Associations between cognitive, functional, and FDG-PET measures of decline in AD and MCI. Neurobiology of Aging, 32(7), 1207–1218. https://doi.org/10.1016/j.neurobiolaging.2009.07.002
43.
LiangP. P.XiangJ.LiangH.QiZ. G.LiK. C.Alzheimers Dis NeuroImagingI. (2014). Altered amplitude of low-frequency fluctuations in early and late mild cognitive impairment and Alzheimer’s disease. Current Alzheimer Research, 11(4), 389–398. https://doi.org/10.2174/1567205011666140331225335
44.
LiuT. T.BrownG. G. (2007). Measurement of cerebral perfusion with arterial spin labeling: Part 1. Methods. Journal of the International Neuropsychological Society, 13(3), 517–525. https://doi.org/10.1017/S1355617707070646
45.
Mac DonaldC. L.BarberJ.AndreJ.EvansN.PanksC.SunS.ZalewskiK.SandersR. E.TemkinN. (2017). 5-Year imaging sequelae of concussive blast injury and relation to early clinical outcome. Neuroimage: Clinical, 14, 371-378. https://doi.org/10.1016/j.nicl.2017.02.005.
46.
MarianiE.MonasteroR.MecocciP. (2007). Mild cognitive impairment: A systematic review. Journal of Alzheimer’s Disease, 12(1), 23–35. https://doi.org/10.3233/jad-2007-12104
47.
MerinoJ. G. (2019). White matter hyperintensities on magnetic resonance imaging: What is a clinician to do?Mayo Clinic Proceedings, 94(3), 380–382. https://doi.org/10.1016/j.mayocp.2019.01.016
48.
MillerB. L.MoatsR.ShonkT.ErnstT.WoolleyS.RossB. (1993). Alzheimer disease: Depiction of increased cerebral myo-inositol with proton MR spectroscopy. Radiology, 187(2), 433–437. https://doi.org/10.1148/radiology.187.2.8475286
49.
Mimenza-AlvaradoA.Aguilar-NavarroS. G.Yeverino-CastroS.Mendoza-FrancoC.Avila-FunesJ. A.RomanG. C. (2018). Neuroimaging characteristics of small-vessel disease in older adults with normal cognition, mild cognitive impairment, and alzheimer disease. Dementia and Geriatric Cognitive Disorders Extra, 8(2), 199–206. https://doi.org/10.1159/000488705
50.
ÖzG.AlgerJ. R.BarkerP. B.BarthaR.BizziA.BoeschC.BolanP. J.BrindleK. M.CudalbuC.DinçerA. (2014). Clinical proton MR spectroscopy in central nervous system disorders. Radiology, 270(3), 658–679. https://doi.org/10.1148/radiol.13130531
PasquierF.LeysD.WeertsJ. G.Mounier-VehierF.BarkhofF.ScheltensP. (1996). Inter- and intraobserver reproducibility of cerebral atrophy assessment on MRI scans with hemispheric infarcts. European Neurology, 36(5), 268–272. https://doi.org/10.1159/000117270
53.
PrinsN. D.Van StraatenE. C.Van DijkE. J.SimoniM.Van SchijndelR. A.VroomanH. A.KoudstaalP. J.ScheltensP.BretelerM. M.BarkhofF. (2004). Measuring progression of cerebral white matter lesions on MRI: Visual rating and volumetrics. Neurology, 62(9), 1533–1539. https://doi.org/10.1212/01.wnl.0000123264.40498.b6
54.
RaghavanS.PrzybelskiS. A.ReidR. I.Graff-RadfordJ.LesnickT. G.ZukS. M.KnopmanD. S.MachuldaM. M.MielkeM. M.PetersenR. C.JackC. R.Jr.VemuriP. (2020). Reduced fractional anisotropy of the genu of the corpus callosum as a cerebrovascular disease marker and predictor of longitudinal cognition in MCI. Neurobiology of Aging, 96, 176-183. https://doi.org/10.1016/j.neurobiolaging.2020.09.005.
55.
RoccaM. A.AmatoM. P.De StefanoN.EnzingerC.GeurtsJ. J.PennerI.-K.RoviraA.SumowskiJ. F.ValsasinaP.FilippiM. (2015). Clinical and imaging assessment of cognitive dysfunction in multiple sclerosis. The Lancet Neurology, 14(3), 302–317. https://doi.org/10.1016/S1474-4422(14)70250-9
56.
RupsinghR.BorrieM.SmithM.WellsJ.BarthaR. (2011). Reduced hippocampal glutamate in Alzheimer disease. Neurobiology of Aging, 32(5), 802–810. https://doi.org/10.3233/JAD-190773
57.
Sala-LlonchR.Bartrés-FazD.JunquéC. (2015). Reorganization of brain networks in aging: A review of functional connectivity studies. Frontiers in Psychology, 6(663). https://doi.org/10.3389/fpsyg.2015.00663.
58.
ScheltensP.BarkhofF.LeysD.PruvoJ. P.NautaJ. J.VermerschP.SteinlingM.ValkJ. (1993). A semiquantative rating scale for the assessment of signal hyperintensities on magnetic resonance imaging. Journal of the Neurological Sciences, 114(1), 7–12. https://doi.org/10.1016/0022-510x(93)90041-v
59.
ScheltensP.BlennowK.BretelerM. M.de StrooperB.FrisoniG. B.SallowayS.Van der FlierW. M. (2016). Alzheimer’s disease. Lancet, 388(10043), 505–517. https://doi.org/10.1016/S0140-6736(15)01124-1
60.
ScheltensP.LaunerL. J.BarkhofF.WeinsteinH. C.van GoolW. A. (1995). Visual assessment of medial temporal lobe atrophy on magnetic resonance imaging: Interobserver reliability. Journal of Neurology, 242(9), 557–560. https://doi.org/10.1007/BF00868807
61.
SchöllM.LockhartS. N.SchonhautD. R.O’NeilJ. P.JanabiM.OssenkoppeleR.BakerS. L.VogelJ. W.FariaJ.SchwimmerH. D. (2016). PET imaging of tau deposition in the aging human brain. Neuron, 89(5), 971–982. https://doi.org/10.1016/j.neuron.2016.01.028
StaffenW.BergmannJ.SchönauerU.ZaunerH.KronbichlerM.GolaszewskiS.LadurnerG. (2009). Cerebral perfusion (HMPAO-SPECT) in patients with depression with cognitive impairment versus those with mild cognitive impairment and dementia of alzheimer’s type: A semiquantitative and automated evaluation. European Journal of Nuclear Medicine and Molecular Imaging, 36(5), 801–810. https://doi.org/10.1007/s00259-008-1028-2
64.
UnschuldP. G.EddenR. A.CarassA.LiuX.ShanahanM.WangX.OishiK.BrandtJ.BassettS. S.RedgraveG. W. (2012). Brain metabolite alterations and cognitive dysfunction in early Huntington’s disease. Movement Disorders, 27(7), 895–902. https://doi.org/10.1002/mds.25010
65.
VillemagneV. L.BurnhamS.BourgeatP.BrownB.EllisK. A.SalvadoO.SzoekeC.MacaulayS. L.MartinsR.MaruffP. (2013). Amyloid β deposition, neurodegeneration, and cognitive decline in sporadic Alzheimer's disease: A prospective cohort study. The Lancet Neurology, 12(4), 357–367. https://doi.org/10.1016/S1474-4422(13)70044-9
66.
WangF.HuaS.ZhangY.YuH.ZhangZ.ZhuJ.LiuR.JiangZ. (2021). Association between small vessel disease markers, medial temporal lobe atrophy and cognitive impairment after stroke: A systematic review and meta-analysis. Journal of Stroke and Cerebrovascular Diseases: the Official Journal of National Stroke Association, 30(1), 105460. https://doi.org/10.1016/j.jstrokecerebrovasdis.2020.105460
67.
WoltersF. J.ZonneveldH. I.HofmanA.van der LugtA.KoudstaalP. J.VernooijM. W.IkramM. A. (2017a). Cerebral perfusion and the risk of dementia: a population-based study. Circulation, 136(8), 719. https://doi.org/10.1161/circulationaha.117.027448
68.
WoltersF. J.ZonneveldH. I.HofmanA.Van der LugtA.KoudstaalP. J.VernooijM. W.IkramM. A. (2017b). Cerebral perfusion and the risk of dementia: A population-based study. Circulation, 136(8), 719–728. https://doi.org/10.1161/CIRCULATIONAHA.117.027448
YoonH. J.ParkK. W.JeongY. J.KangD. Y. (2012). Correlation between neuropsychological tests and hypoperfusion in MCI patients: Anatomical labeling using XJ view and talairach daemon software. Annals of Nuclear Medicine, 26(8), 656–664. https://doi.org/10.1007/s12149-012-0625-0
