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Aging and disease    2016, Vol. 7 Issue (3) : 220-229     DOI: 10.14336/AD.2015.1110
Original Article |
Linear and Curvilinear Trajectories of Cortical Loss with Advancing Age and Disease Duration in Parkinson’s Disease
Claassen Daniel O.1,*, Dobolyi David G.2, Isaacs David A.1, Roman Olivia C.1, Herb Joshua3, Wylie Scott A.1, Neimat Joseph S.4, Donahue Manus J.1,5, Hedera Peter1, Zald David H.6, Landman Bennett A.5,7, Bowman Aaron B.1, Dawant Benoit M.7, Rane Swati5
1Department of Neurology, Vanderbilt University, Nashville, TN 37235, USA
2McIntire School of Commerce, University of Virginia, Charlottesville, VA 22904, USA
3Department of Medicine, University of Virginia, Charlottesville, VA 22904, USA
4Department of Neurosurgery, Vanderbilt University, Nashville, TN 37235, USA
5Department of Radiology, Vanderbilt University, Nashville, TN 37235, USA
6Department of Psychology, Vanderbilt University, Nashville, TN 37235, USA
7Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA
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Advancing age and disease duration both contribute to cortical thinning in Parkinson’s disease (PD), but the pathological interactions between them are poorly described. This study aims to distinguish patterns of cortical decline determined by advancing age and disease duration in PD. A convenience cohort of 177 consecutive PD patients, identified at the Vanderbilt University Movement Disorders Clinic as part of a clinical evaluation for Deep Brain Stimulation (age: M= 62.0, SD 9.3), completed a standardized clinical assessment, along with structural brain Magnetic Resonance Imaging scan. Age and gender matched controls (n=53) were obtained from the Alzheimer Disease Neuroimaging Initiative and Progressive Parkinson’s Marker Initiative (age: M= 63.4, SD 12.2). Estimated changes in cortical thickness were modeled with advancing age, disease duration, and their interaction. The best-fitting model, linear or curvilinear (2nd, or 3rd order natural spline), was defined using the minimum Akaike Information Criterion, and illustrated on a 3-dimensional brain. Three curvilinear patterns of cortical thinning were identified: early decline, late decline, and early-stable-late. In contrast to healthy controls, the best-fit model for age related changes in PD is curvilinear (early decline), particularly in frontal and precuneus regions. With advancing disease duration, a curvilinear model depicts accelerating decline in the occipital cortex. A significant interaction between advancing age and disease duration is evident in frontal, motor, and posterior parietal areas. Study results support the hypothesis that advancing age and disease duration differentially affect regional cortical thickness and display regional dependent linear and curvilinear patterns of thinning.

Keywords Parkinson’s disease      Cortex      MRI      Aging      Disease duration      Neurodegeneration     
Corresponding Authors: Claassen Daniel O.   
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These authors equally contribute this work

Issue Date: 09 January 2016
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Articles by authors
Claassen Daniel O.
Dobolyi David G.
Isaacs David A.
Roman Olivia C.
Herb Joshua
Wylie Scott A.
Neimat Joseph S.
Donahue Manus J.
Hedera Peter
Zald David H.
Landman Bennett A.
Bowman Aaron B.
Dawant Benoit M.
Rane Swati
Cite this article:   
Claassen Daniel O.,Dobolyi David G.,Isaacs David A., et al. Linear and Curvilinear Trajectories of Cortical Loss with Advancing Age and Disease Duration in Parkinson’s Disease[J]. Aging and disease, 2016, 7(3): 220-229.
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Parkinson’s Disease (n=177)Healthy Controls (n=53)
Gender (M:F)(121:56)(39:14)
Assessment Age (years)62.0 (9.3)63.68 (12.23)
Disease Duration10.3 (4.8)
UPDRS III Off40.8 (12.9)
UPDRS III On18.7 (10.0)
Table 1  Participant Demographics
Cognitive AssessmentNumber testedMean (S.D)
Scaled Score
Dementia Rating Scale[33]15312.0 (2.5)
Trails A[34,35]1777.6 (3.0)
Trails B[34,35]1778.1 (3.0)
Judgment of Line Orientation[36]17710.7 (2.9)
Letter Fluency[37]1779.4 (2.5)
Word List 1[38]1778.8 (3.4)
Word List 2[38]17711.3 (2.5)
Table 2  Cognitive Profile of PD patients
Figure 1.  Models of age and disease duration effects on cortical thickness

A) Age effects in the pars opercularis are different in PD (blue) compared to healthy controls (red). PD patients show a non-linear trajectory (blue) of ‘early decline’, which stabilizes. Rate of cortical thinning with age in PD is different in this frontal region, likely due to disease processes. However, cortical atrophy rate with respect to increasing disease durations was not significant. B) Age effects in the lateral occipital cortex conform to preservation of the cortex in the early years and an accelerated late decline in healthy controls (late decline), while in PD gray matter atrophies continuously with age (left panel). PD patients also showed a significant effect of disease duration alone, independent of age (right panel). C) Unlike Figure 1a, the parietal cortex shows a linear rate of cortical thinning with age in both controls and PD. However, cortical thinning in PD appears to be faster than in controls (left panel). Cortical thickness is also linearly dependent on duration of PD. Furthermore, increasing disease duration significantly increases the rate of cortical thinning (right panel). D) The interactions between age and disease duration in PD. Older patients with longer disease duration have a greater linear rate of cortical loss in the inferior parietal cortex.

Figure 2.  The effect of age on cortical thickness in healthy Ccontrols and Parkinson’s disease

Green highlighted regions represent those regions that follow a linear rate of atrophy. Red highlighted regions depict rates of early decline (decreasing quickly at first, then stabilizing), while regions in yellow depict regions that atrophy faster over time (stable at first, then decreasing quickly). Regions in orange depict early decline, stabilization, followed by late decline, which is notable in later decades of life (early-stable-late). PD patients show characteristic frontal cortical thinning with age.

Figure 3.  The effects of disease duration, and its interaction with age, on cortical thickness in Parkinson’s disease

Regions in yellow depict regions that atrophy faster over time (concave down, decreasing curve or late decline). Regions in blue shown in the right column are regions showing an interaction between age and disease.

[1] Lyoo CH, Ryu YH, Lee MS (2010). Topographical distribution of cerebral cortical thinning in patients with mild Parkinson’s disease without dementia. Mov Disord, 25: 496-499
[2] Braak H, Braak E (2000). Pathoanatomy of Parkinson’s disease. J Neurol, 247 Suppl 2: II3-10
[3] Braak H, Del Tredici K, Rub U, de Vos RA, Jansen Steur EN, Braak E (2003). Staging of brain pathology related to sporadic Parkinson’s disease. Neurobiol Aging, 24: 197-211
[4] Braak H, Ghebremedhin E, Rub U, Bratzke H, Del Tredici K (2004). Stages in the development of Parkinson’s disease-related pathology. Cell Tissue Res, 318: 121-134
[5] Braak H, Rub U, Jansen Steur EN, Del Tredici K, de Vos RA (2005). Cognitive status correlates with neuropathologic stage in Parkinson disease. Neurology, 64: 1404-1410
[6] Salat DH, Buckner RL, Snyder AZ, Greve DN, Desikan RS, Busa E, et al. (2004). Thinning of the cerebral cortex in aging. Cereb Cortex, 14: 721-730
[7] Storsve AB, Fjell AM, Tamnes CK, Westlye LT, Overbye K, Aasland HW, et al. (2014). Differential longitudinal changes in cortical thickness, surface area and volume across the adult life span: regions of accelerating and decelerating change. J Neurosci, 34: 8488-8498
[8] Hietanen M, Teravainen H (1988). The effect of age of disease onset on neuropsychological performance in Parkinson’s disease. J Neurol Neurosurg Psychiatry, 51: 244-249
[9] Jellinger K, Riederer P (1984). Dementia in Parkinson’s disease and (pre) senile dementia of Alzheimer type: morphological aspects and changes in the intracerebral MAO activity. Adv Neurol, 40: 199-210
[10] Jellinger KA, Attems J (2010). Prevalence of dementia disorders in the oldest-old: an autopsy study. Acta Neuropathol, 119: 421-433
[11] Han X, Jovicich J, Salat D, van der Kouwe A, Quinn B, Czanner S, et al. (2006). Reliability of MRI-derived measurements of human cerebral cortical thickness: the effects of field strength, scanner upgrade and manufacturer. Neuroimage, 32: 180-194
[12] Wonderlick JS, Ziegler DA, Hosseini-Varnamkhasti P, Locascio JJ, Bakkour A, van der Kouwe A, et al. (2009). Reliability of MRI-derived cortical and subcortical morphometric measures: effects of pulse sequence, voxel geometry, and parallel imaging. Neuroimage, 44: 1324-1333
[13] Lehmann M, Douiri A, Kim LG, Modat M, Chan D, Ourselin S, et al. (2010). Atrophy patterns in Alzheimer’s disease and semantic dementia: a comparison of FreeSurfer and manual volumetric measurements. Neuroimage, 49: 2264-2274
[14] Team RC (2015) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria
[15] Anderson DRB, Burnham KP (2002) Model selection and multimodel inference, Springer-Verlag New York, New York
[16] Desikan RS, Segonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, et al. (2006). An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage, 31: 968-980
[17] Charles PD, Dolhun RM, Gill CE, Davis TL, Bliton MJ, Tramontana MG, et al. (2012). Deep brain stimulation in early Parkinson’s disease: enrollment experience from a pilot trial. Parkinsonism Relat Disord, 18: 268-273
[18] Burn DJ, Rowan EN, Allan LM, Molloy S, O’Brien JT, McKeith IG (2006). Motor subtype and cognitive decline in Parkinson’s disease, Parkinson’s disease with dementia, and dementia with Lewy bodies. J Neurol Neurosurg Psychiatry, 77: 585-589
[19] Verbaan D, Marinus J, Visser M, van Rooden SM, Stiggelbout AM, Middelkoop HA, et al. (2007). Cognitive impairment in Parkinson’s disease. J Neurol Neurosurg Psychiatry, 78: 1182-1187
[20] Zhao YJ, Wee HL, Chan YH, Seah SH, Au WL, Lau PN, et al. (2010). Progression of Parkinson’s disease as evaluated by Hoehn and Yahr stage transition times. Mov Disord, 25: 710-716
[21] Factor SA, Steenland NK, Higgins DS, Molho ES, Kay DM, Montimurro J, et al. (2011). Postural instability/gait disturbance in Parkinson’s disease has distinct subtypes: an exploratory analysis. J Neurol Neurosurg Psychiatry, 82: 564-568
[22] Reid WG, Hely MA, Morris JG, Loy C, Halliday GM (2011). Dementia in Parkinson’s disease: a 20-year neuropsychological study (Sydney Multicentre Study). J Neurol Neurosurg Psychiatry, 82: 1033-1037
[23] Thambisetty M, Wan J, Carass A, An Y, Prince JL, Resnick SM (2010). Longitudinal changes in cortical thickness associated with normal aging. Neuroimage, 52: 1215-1223
[24] Morrison JH, Hof PR (1997). Life and death of neurons in the aging brain. Science, 278: 412-419
[25] Hanganu A, Bedetti C, Degroot C, Mejia-Constain B, Lafontaine AL, Soland V, et al. (2014). Mild cognitive impairment is linked with faster rate of cortical thinning in patients with Parkinson’s disease longitudinally. Brain, 137: 1120-1129
[26] Segura B, Baggio HC, Marti MJ, Valldeoriola F, Compta Y, Garcia-Diaz AI, et al. (2014). Cortical thinning associated with mild cognitive impairment in Parkinson’s disease. Mov Disord, 29: 1495-1503
[27] Zarei M, Ibarretxe-Bilbao N, Compta Y, Hough M, Junque C, Bargallo N, et al. (2013). Cortical thinning is associated with disease stages and dementia in Parkinson’s disease. J Neurol Neurosurg Psychiatry, 84: 875-881
[28] Bohnen NI, Muller ML, Koeppe RA, Studenski SA, Kilbourn MA, Frey KA, et al. (2009). History of falls in Parkinson disease is associated with reduced cholinergic activity. Neurology, 73: 1670-1676
[29] Bohnen NI, Albin RL (2011). The cholinergic system and Parkinson disease. Behav Brain Res, 221: 564-573
[30] Bohnen NI, Frey KA, Studenski S, Kotagal V, Koeppe RA, Constantine GM, et al. (2014). Extra-nigral pathological conditions are common in Parkinson’s disease with freezing of gait: an in vivo positron emission tomography study. Mov Disord, 29: 1118-1124
[31] Watanabe H, Senda J, Kato S, Ito M, Atsuta N, Hara K, et al. (2013). Cortical and subcortical brain atrophy in Parkinson’s disease with visual hallucination. Mov Disord, 28: 1732-1736
[32] Williams-Gray CH, Mason SL, Evans JR, Foltynie T, Brayne C, Robbins TW, et al. (2013). The CamPaIGN study of Parkinson’s disease: 10-year outlook in an incident population-based cohort. J Neurol Neurosurg Psychiatry, 84: 1258-1264
[33] Mattis S (1988) Dementia rating scale: professional manual. Florida: Psychological Assessment Resources. Inc
[34] Army U (1944). Army individual test battery. Manual of Directions and Scoring
[35] Strauss E, Sherman EM, Spreen O (2006) A compendium of neuropsychological tests: Administration, norms, and commentary, Oxford University Press, USA
[36] Benton AL, Varney NR, deS Hamsher K (1978). Visuospatial judgment: A clinical test. Arch Neurol, 35: 364-367
[37] Tombaugh TN, Kozak J, Rees L (1999). Normative data stratified by age and education for two measures of verbal fluency: FAS and animal naming. Arch Clin Neuropsychol, 14: 167-177
[38] Wechsler D (1997) The Wechsler Memory Scale, San Antonio, Tex, Psychological Corp. Harcourt
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