Cerebellar-limbic neurocircuit is the novel biosignature of physio-cognitive decline syndrome

Li-Kuo Liu, Kun-Hsien Chou, Chih-Chin Heather Hsu, Li-Ning Peng, Wei-Ju Lee, Wei-Ta Chen, Ching-Po Lin, Chih-Ping Chung, Pei-Ning Wang, Liang-Kung Chen, Li-Kuo Liu, Kun-Hsien Chou, Chih-Chin Heather Hsu, Li-Ning Peng, Wei-Ju Lee, Wei-Ta Chen, Ching-Po Lin, Chih-Ping Chung, Pei-Ning Wang, Liang-Kung Chen

Abstract

Both physical and cognitive deficits occur in the aging process. We operationally defined the phenomenon as physio-cognitive decline syndrome (PCDS) and aimed to decipher its corresponding neuroanatomy patterns and neurocircuit. High resolution 3T brain magnetic resonance imaging (MRI) images from a community-dwelling longitudinal aging cohort were analysed. PCDS was defined as weakness (handgrip strength) and/or slowness (gait speed) concomitant with impairment in any cognitive domain (defined by 1.5 standard deviation below age, sex-matched norms), but without dementia or disability. Among 1196 eligible ≥ 50-year-old (62±9 years, 47.6%men) subjects, 15.9% had PCDS. Compared to the other participants, individuals with PCDS had significantly lower gray-matter volume (GMV) in the bilateral amygdala and thalamus, right hippocampus, right temporo-occipital cortex, and left cerebellum VI and V regions. The regions of reduced GMV in people with PCDS were similar between the middle-aged and older adults; whereas larger clusters with more extensive GMV-depleted regions were observed in ≥65-year-olds with PCDS. Diffusion-weighted tractography showed disrupted hippocampus-amygdala-cerebellum connections in subjects with PCDS. The neuroanatomic characteristics revealed by this study provide evidence for pathophysiological processes associated with concomitant physio-cognitive decline in the elderly. This neurocircuit might constitute a target for future preventive interventions.

Keywords: brain volume; cognitive impairment; diffusion-weighted tractography; frailty; magnetic resonance imaging.

Conflict of interest statement

CONFLICTS OF INTEREST: All authors have nothing to declare.

Figures

Figure 1
Figure 1
Study participant selection. ILAS = I-Lan Longitudinal Aging Study; MMSE = Mini-Mental State Examination.
Figure 2
Figure 2
Hot colour map of gray-matter volume-diminished regions in subjects with physio-cognitive decline syndrome. GMV = gray-matter volume; L = left; R = right; PCDS = physio-cognitive decline syndrome.
Figure 3
Figure 3
Group-wise probabilistic connections between cerebellar and bilateral amygdala/hippocampus areas. VBM = voxel-based morphometry.
Figure 4
Figure 4
Correlations between gray-matter volume in physio-cognitive decline syndrome-associated brain regions and each cognitive/physical domain. PCDS = physio-cognitive decline syndrome; ROI = region of interest; Rt. = right; Lt. = left; CVVLT = Chinese version Verbal Learning Test.

References

    1. World Health Organization. World Report on Ageing and Health. Geneva, Switzerland: World Health Organization, 2015. .
    1. Rosenberg A, Ngandu T, Rusanen M, Antikainen R, Bäckman L, Havulinna S, Hänninen T, Laatikainen T, Lehtisalo J, Levälahti E, Lindström J, Paajanen T, Peltonen M, et al.. Multidomain lifestyle intervention benefits a large elderly population at risk for cognitive decline and dementia regardless of baseline characteristics: the FINGER trial. Alzheimers Dement. 2018; 14:263–70. 10.1016/j.jalz.2017.09.006
    1. Ng TP, Feng L, Nyunt MS, Feng L, Niti M, Tan BY, Chan G, Khoo SA, Chan SM, Yap P, Yap KB. Nutritional, Physical, Cognitive, and Combination Interventions and Frailty Reversal Among Older Adults: A Randomized Controlled Trial. Am J Med. 2015; 128:1225–1236.e1. 10.1016/j.amjmed.2015.06.017
    1. Clegg A, Young J, Iliffe S, Rikkert MO, Rockwood K. Frailty in elderly people. Lancet. 2013; 381:752–62. 10.1016/S0140-6736(12)62167-9
    1. Robertson DA, Savva GM, Kenny RA. Frailty and cognitive impairment—a review of the evidence and causal mechanisms. Ageing Res Rev. 2013; 12:840–51. 10.1016/j.arr.2013.06.004
    1. Yu WC, Chou MY, Peng LN, Lin YT, Liang CK, Chen LK. Synergistic effects of cognitive impairment on physical disability in all-cause mortality among men aged 80 years and over: results from longitudinal older veterans study. PLoS One. 2017; 12:e0181741. 10.1371/journal.pone.0181741
    1. Kelaiditi E, Cesari M, Canevelli M, van Kan GA, Ousset PJ, Gillette-Guyonnet S, Ritz P, Duveau F, Soto ME, Provencher V, Nourhashemi F, Salvà A, Robert P, et al., and IANA/IAGG. Cognitive frailty: rational and definition from an (I.A.N.A./I.A.G.G.) international consensus group. J Nutr Health Aging. 2013; 17:726–34. 10.1007/s12603-013-0367-2
    1. Canevelli M, Cesari M. Cognitive frailty: far from clinical and research adoption. J Am Med Dir Assoc. 2017; 18:816–18. 10.1016/j.jamda.2017.07.004
    1. Lee WJ, Peng LN, Liang CK, Loh CH, Chen LK. Cognitive frailty predicting all-cause mortality among community-living older adults in Taiwan: a 4-year nationwide population-based cohort study. PLoS One. 2018; 13:e0200447. 10.1371/journal.pone.0200447
    1. Ruan Q, Yu Z, Chen M, Bao Z, Li J, He W. Cognitive frailty, a novel target for the prevention of elderly dependency. Ageing Res Rev. 2015; 20:1–10. 10.1016/j.arr.2014.12.004
    1. Verghese J, Wang C, Lipton RB, Holtzer R. Motoric cognitive risk syndrome and the risk of dementia. J Gerontol A Biol Sci Med Sci. 2013; 68:412–18. 10.1093/gerona/gls191
    1. Verghese J, Annweiler C, Ayers E, Barzilai N, Beauchet O, Bennett DA, Bridenbaugh SA, Buchman AS, Callisaya ML, Camicioli R, Capistrant B, Chatterji S, De Cock AM, et al.. Motoric cognitive risk syndrome: multicountry prevalence and dementia risk. Neurology. 2014; 83:718–26. 10.1212/WNL.0000000000000717
    1. Semba RD, Tian Q, Carlson MC, Xue QL, Ferrucci L. Motoric cognitive risk syndrome: integration of two early harbingers of dementia in older adults. Ageing Res Rev. 2020; 58:101022. 10.1016/j.arr.2020.101022
    1. Liu LK, Guo CY, Lee WJ, Chen LY, Hwang AC, Lin MH, Peng LN, Chen LK, Liang KY. Subtypes of physical frailty: latent class analysis and associations with clinical characteristics and outcomes. Sci Rep. 2017; 7:46417. 10.1038/srep46417
    1. Liu LK, Chen CH, Lee WJ, Wu YH, Hwang AC, Lin MH, Shimada H, Peng LN, Loh CH, Arai H, Chen LK. Cognitive frailty and its association with all-cause mortality among community-dwelling older adults in Taiwan: results from I-Lan longitudinal aging study. Rejuvenation Res. 2018; 21:510–17. 10.1089/rej.2017.2038
    1. Wu YH, Liu LK, Chen WT, Lee WJ, Peng LN, Wang PN, Chen LK. Cognitive function in individuals with physical frailty but without dementia or cognitive complaints: results from the I-Lan longitudinal aging study. J Am Med Dir Assoc. 2015; 16:899.e9–16. 10.1016/j.jamda.2015.07.013
    1. Chen WT, Chou KH, Liu LK, Lee PL, Lee WJ, Chen LK, Wang PN, Lin CP. Reduced cerebellar gray matter is a neural signature of physical frailty. Hum Brain Mapp. 2015; 36:3666–76. 10.1002/hbm.22870
    1. Nishita Y, Nakamura A, Kato T, Otsuka R, Iwata K, Tange C, Ando F, Ito K, Shimokata H, Arai H. Links between physical frailty and regional gray matter volumes in older adults: a voxel-based morphometry study. J Am Med Dir Assoc. 2019; 20:1587–92.e7. 10.1016/j.jamda.2019.09.001
    1. Shimada H, Doi T, Lee S, Makizako H, Chen LK, Arai H. Cognitive frailty predicts incident dementia among community-dwelling older people. J Clin Med. 2018; 7:250. 10.3390/jcm7090250
    1. Sugimoto T, Sakurai T, Ono R, Kimura A, Saji N, Niida S, Toba K, Chen LK, Arai H. Epidemiological and clinical significance of cognitive frailty: a mini review. Ageing Res Rev. 2018; 44:1–7. 10.1016/j.arr.2018.03.002
    1. Chen LK, Hwang AC, Lee WJ, Peng LN, Lin MH, Neil DL, Shih SF, Loh CH, Chiou ST, and Taiwan Health Promotion Intervention Study for Elders research group. Efficacy of multidomain interventions to improve physical frailty, depression and cognition: data from cluster-randomized controlled trials. J Cachexia Sarcopenia Muscle. 2020; 11:650–62. 10.1002/jcsm.12534
    1. Chen LK, Arai H. Physio-cognitive decline as the accelerated aging phenotype. Arch Gerontol Geriatr. 2020; 88:104051. 10.1016/j.archger.2020.104051
    1. Halliday G. Pathology and hippocampal atrophy in Alzheimer’s disease. Lancet Neurol. 2017; 16:862–64. 10.1016/S1474-4422(17)30343-5
    1. Buckner RL. The cerebellum and cognitive function: 25 years of insight from anatomy and neuroimaging. Neuron. 2013; 80:807–15. 10.1016/j.neuron.2013.10.044
    1. Nadkarni NK, Nunley KA, Aizenstein H, Harris TB, Yaffe K, Satterfield S, Newman AB, Rosano C, and Health ABC Study. Association between cerebellar gray matter volumes, gait speed, and information-processing ability in older adults enrolled in the health ABC study. J Gerontol A Biol Sci Med Sci. 2014; 69:996–1003. 10.1093/gerona/glt151
    1. Koziol LF, Budding D, Andreasen N, D’Arrigo S, Bulgheroni S, Imamizu H, Ito M, Manto M, Marvel C, Parker K, Pezzulo G, Ramnani N, Riva D, et al.. Consensus paper: the cerebellum’s role in movement and cognition. Cerebellum. 2014; 13:151–77. 10.1007/s12311-013-0511-x
    1. Bernard JA, Seidler RD. Moving forward: age effects on the cerebellum underlie cognitive and motor declines. Neurosci Biobehav Rev. 2014; 42:193–207. 10.1016/j.neubiorev.2014.02.011
    1. Keser Z, Hasan KM, Mwangi BI, Kamali A, Ucisik-Keser FE, Riascos RF, Yozbatiran N, Francisco GE, Narayana PA. Diffusion tensor imaging of the human cerebellar pathways and their interplay with cerebral macrostructure. Front Neuroanat. 2015; 9:41. 10.3389/fnana.2015.00041
    1. Heath RG, Harper JW. Ascending projections of the cerebellar fastigial nucleus to the hippocampus, amygdala, and other temporal lobe sites: evoked potential and histological studies in monkeys and cats. Exp Neurol. 1974; 45:268–87. 10.1016/0014-4886(74)90118-6
    1. Blatt GJ, Oblak AL, Schmahmann JD. Cerebellar connections with limbic circuits: anatomy and functional implications. Handbook of the cerebellum and cerebellar disorders. Netherlands: Springer, 2013. 10.1007/978-94-007-1333-8_22
    1. Guo W, Liu F, Zhang Z, Liu J, Yu M, Zhang J, Xiao C, Zhao J. Unidirectionally affected causal connectivity of cortico-limbic-cerebellar circuit by structural deficits in drug-naive major depressive disorder. J Affect Disord. 2015; 172:410–16. 10.1016/j.jad.2014.10.019
    1. Tournier JD, Calamante F, Connelly A. Improved probabilistic streamlines tractography by 2nd order integration over fibre orientation distributions. Proc Int Soc Magn Reson Med. 2010; 18:1670 .
    1. Beauchet O, Allali G, Annweiler C, Verghese J. Association of motoric cognitive risk syndrome with brain volumes: results from the GAIT study. J Gerontol A Biol Sci Med Sci. 2016; 71:1081–88. 10.1093/gerona/glw012
    1. Blumen HM, Allali G, Beauchet O, Lipton RB, Verghese J. A gray matter volume covariance network associated with the motoric cognitive risk syndrome: a multicohort MRI study. J Gerontol A Biol Sci Med Sci. 2019; 74:884–89. 10.1093/gerona/gly158
    1. la Fougère C, Zwergal A, Rominger A, Förster S, Fesl G, Dieterich M, Brandt T, Strupp M, Bartenstein P, Jahn K. Real versus imagined locomotion: a [18F]-FDG PET-fMRI comparison. Neuroimage. 2010; 50:1589–98. 10.1016/j.neuroimage.2009.12.060
    1. Zwergal A, Linn J, Xiong G, Brandt T, Strupp M, Jahn K. Aging of human supraspinal locomotor and postural control in fMRI. Neurobiol Aging. 2012; 33:1073–84. 10.1016/j.neurobiolaging.2010.09.022
    1. Leisman G, Moustafa AA, Shafir T. Thinking, walking, talking: integratory motor and cognitive brain function. Front Public Health. 2016; 4:94. 10.3389/fpubh.2016.00094
    1. Lockhart SN, DeCarli C. Structural imaging measures of brain aging. Neuropsychol Rev. 2014; 24:271–89. 10.1007/s11065-014-9268-3
    1. Moran C, Phan TG, Chen J, Blizzard L, Beare R, Venn A, Münch G, Wood AG, Forbes J, Greenaway TM, Pearson S, Srikanth V. Brain atrophy in type 2 diabetes: regional distribution and influence on cognition. Diabetes Care. 2013; 36:4036–42. 10.2337/dc13-0143
    1. Cox SR, Lyall DM, Ritchie SJ, Bastin ME, Harris MA, Buchanan CR, Fawns-Ritchie C, Barbu MC, de Nooij L, Reus LM, Alloza C, Shen X, Neilson E, et al.. Associations between vascular risk factors and brain MRI indices in UK biobank. Eur Heart J. 2019; 40:2290–300. 10.1093/eurheartj/ehz100
    1. Beauchet O, Celle S, Roche F, Bartha R, Montero-Odasso M, Allali G, Annweiler C. Blood pressure levels and brain volume reduction: a systematic review and meta-analysis. J Hypertens. 2013; 31:1502–16. 10.1097/HJH.0b013e32836184b5
    1. American Psychiatric Association. Diagnostic and statistical manual of mental disorders. 3rd ed. Revised DSM-III-R. Washington DC: American Psychiatric Press, 1987.
    1. Liu HC, Lin KN, Teng EL, Wang SJ, Fuh JL, Guo NW, Chou P, Hu HH, Chiang BN. Prevalence and subtypes of dementia in Taiwan: a community survey of 5297 individuals. J Am Geriatr Soc. 1995; 43:144–49. 10.1111/j.1532-5415.1995.tb06379.x
    1. Huang CY, Hwang AC, Liu LK, Lee WJ, Chen LY, Peng LN, Lin MH, Chen LK. Association of dynapenia, sarcopenia, and cognitive impairment among community-dwelling older Taiwanese. Rejuvenation Res. 2016; 19:71–78. 10.1089/rej.2015.1710
    1. Chen LK, Lee WJ, Peng LN, Liu LK, Arai H, Akishita M, and Asian Working Group for Sarcopenia. Recent advances in sarcopenia research in Asia: 2016 update from the Asian working group for sarcopenia. J Am Med Dir Assoc. 2016; 17:767.e1–7. 10.1016/j.jamda.2016.05.016
    1. Ashburner J, Friston KJ. Voxel-based morphometry—the methods. Neuroimage. 2000; 11:805–21. 10.1006/nimg.2000.0582
    1. Schmidt P, Gaser C, Arsic M, Buck D, Förschler A, Berthele A, Hoshi M, Ilg R, Schmid VJ, Zimmer C, Hemmer B, Mühlau M. An automated tool for detection of FLAIR-hyperintense white-matter lesions in multiple sclerosis. Neuroimage. 2012; 59:3774–83. 10.1016/j.neuroimage.2011.11.032
    1. Ashburner J. A fast diffeomorphic image registration algorithm. Neuroimage. 2007; 38:95–113. 10.1016/j.neuroimage.2007.07.007
    1. Panza F, Solfrizzi V, Barulli MR, Santamato A, Seripa D, Pilotto A, Logroscino G. Cognitive frailty: a systematic review of epidemiological and neurobiological evidence of an age-related clinical condition. Rejuvenation Res. 2015; 18:389–412. 10.1089/rej.2014.1637
    1. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, Mazoyer B, Joliot M. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage. 2002; 15:273–89. 10.1006/nimg.2001.0978
    1. Smith SM. Fast robust automated brain extraction. Hum Brain Mapp. 2002; 17:143–55. 10.1002/hbm.10062
    1. Zhang Y, Brady M, Smith S. Segmentation of brain MR images through a hidden markov random field model and the expectation-maximization algorithm. IEEE Trans Med Imaging. 2001; 20:45–57. 10.1109/42.906424
    1. Patenaude B, Smith SM, Kennedy DN, Jenkinson M. A bayesian model of shape and appearance for subcortical brain segmentation. Neuroimage. 2011; 56:907–22. 10.1016/j.neuroimage.2011.02.046
    1. Tournier JD, Calamante F, Gadian DG, Connelly A. Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution. Neuroimage. 2004; 23:1176–85. 10.1016/j.neuroimage.2004.07.037
    1. Tournier JD, Calamante F, Connelly A. Determination of the appropriate b value and number of gradient directions for high-angular-resolution diffusion-weighted imaging. NMR Biomed. 2013; 26:1775–86. 10.1002/nbm.3017
    1. Wang PN, Chou KH, Chang NJ, Lin KN, Chen WT, Lan GY, Lin CP, Lirng JF. Callosal degeneration topographically correlated with cognitive function in amnestic mild cognitive impairment and Alzheimer’s disease dementia. Hum Brain Mapp. 2014; 35:1529–43. 10.1002/hbm.22271
    1. Jenkinson M, Bannister P, Brady M, Smith S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage. 2002; 17:825–41. 10.1016/s1053-8119(02)91132-8
    1. Andersson JLR, Jenkinson M, Smith S. Non-linear registration, aka spatial normalisation FMRIB technical report TR07JA2. FMRIB Analysis Group, Oxford, 2007. .
    1. Smith RE, Tournier JD, Calamante F, Connelly A. Anatomically-constrained tractography: improved diffusion MRI streamlines tractography through effective use of anatomical information. Neuroimage. 2012; 62:1924–38. 10.1016/j.neuroimage.2012.06.005
    1. Hua K, Zhang J, Wakana S, Jiang H, Li X, Reich DS, Calabresi PA, Pekar JJ, van Zijl PC, Mori S. Tract probability maps in stereotaxic spaces: analyses of white matter anatomy and tract-specific quantification. Neuroimage. 2008; 39:336–47. 10.1016/j.neuroimage.2007.07.053

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