Skip to main content

Advertisement

SpringerLink
Log in

Retained executive abilities in mild cognitive impairment are associated with increased white matter network connectivity

  • Neuro
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Purpose

To describe structural network differences in individuals with mild cognitive impairment (MCI) with high versus low executive abilities, as reflected by measures of white matter connectivity using diffusion tensor imaging (DTI).

Materials and methods

This was a retrospective, cross-sectional study. Of the 128 participants from the Alzheimer’s Disease Neuroimaging Initiative database who had both a DTI scan as well as a diagnosis of MCI, we used an executive function score to classify the top 15 scoring patients as high executive ability, and the bottom-scoring 16 patients as low executive ability. Using a regions-of-interest-based analysis, we constructed networks and calculated graph theory measures on the constructed networks. We used automated tractography in order to compare differences in major white matter tracts.

Results

The high executive ability group yielded greater network size, density and clustering coefficient. The high executive ability group reflected greater fractional anisotropy bilaterally in the inferior and superior longitudinal fasciculi.

Conclusions

The network measures of the high executive ability group demonstrated greater white matter integrity. This suggests that white matter reserve may confer greater protection of executive abilities. Loss of this reserve may lead to greater impairment in the progression to Alzheimer’s disease dementia.

Key Points

• The MCI high executive ability group yielded a larger network.

• The MCI high executive ability group had greater FA in numerous tracts.

• White matter reserve may confer greater protection of executive abilities.

• Loss of executive reserve may lead to greater impairment in AD dementia.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

Abbreviations

AD:

Alzheimer’s Disease

ADNI:

Alzheimer’s Disease Neuroimaging Initiative

DTI:

Diffusion tensor imaging

EF:

Executive function

FA:

Fractional anisotropy

MCI:

Mild cognitive impairment

MCI-highEF:

Individuals with mild cognitive impairment with high executive abilities

MCI-lowEF:

Individuals with cognitive impairment with low executive abilities

MMSE:

Mini-Mental Status Examination

MRI:

Magnetic resonance imaging

PET:

Positron emission tomography

RD:

Radial diffusivity

ROI:

Region of interest

References

  1. Aretouli E, Brandt J (2010) Everyday functioning in mild cognitive impairment and its relationship with executive cognition. Int J Geriatr Psychiatry 25:224–233

    Article  PubMed  PubMed Central  Google Scholar 

  2. Gibbons LE, Carle AC, Mackin RS et al (2012) A composite score for executive functioning, validated in Alzheimer's Disease Neuroimaging Initiative (ADNI) participants with baseline mild cognitive impairment. Brain Imaging Behavior 6:517–527

    Article  PubMed  PubMed Central  Google Scholar 

  3. Bettcher BM, Mungas D, Patel N et al (2016) Neuroanatomical substrates of executive functions – Beyond prefrontal structures. Neuropsychologia 85:100–109

    Article  PubMed  PubMed Central  Google Scholar 

  4. Diamond A (2013) Executive functions. Annu Rev Psychol 64:135–168

    Article  PubMed  Google Scholar 

  5. Gold BT, Johnson NF, Powell DK, Smith CD (1822) White matter integrity and vulnerability to Alzheimer's disease: preliminary findings and future directions. Biochim Biophys Acta 2012:416–422

    Google Scholar 

  6. Nowrangi MA, Lyketsos CG, Leoutsakos JS et al (2013) Longitudinal, region-specific course of diffusion tensor imaging measures in mild cognitive impairment and Alzheimer's disease. Alzheimers Dement 9:519–528

    Article  PubMed  Google Scholar 

  7. Daianu M, Jahanshad N, Nir TM et al (2013) Breakdown of brain connectivity between normal aging and Alzheimer's disease: a structural k-core network analysis. Brain Connect 3:407–422

    Article  PubMed  PubMed Central  Google Scholar 

  8. Lo C, Wang P, Chou K, Wang J, He Y, Lin C (2010) Diffusion tensor tractography reveals abnormal topological organisation in structural cortical networks in Alzheimer's disease. J Neurosci 30:16876–16885

  9. Desikan R, Cabral H, Hess C et al (2009) Automated MRI measures identify individuals with mild cognitive impairment and Alzheimer's disease. Brain 132:2048–2057

    Article  PubMed  PubMed Central  Google Scholar 

  10. Choi M, Kim H, Gim S et al (2016) Differences in cognitive ability and hippocampal volume between Alzheimer’s disease, amnestic mild cognitive impairment, and healthy control groups, and their correlation. Neurosci Lett 620:115–120

    Article  CAS  PubMed  Google Scholar 

  11. Zheng D, Sun H, Dong X et al (2014) Executive dysfunction and gray matter atrophy in amnestic mild cognitive impairment. Neurobiol Aging 35:548–555

    Article  PubMed  Google Scholar 

  12. Fischl B, Salat D, Busa E et al (2002) Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33:341–355

    Article  CAS  PubMed  Google Scholar 

  13. Fischl B, Kouwe A, Destrieux C et al (2004) Automatically parcellating the human cerebral cortex. Cereb Cortex 14:11–22

    Article  PubMed  Google Scholar 

  14. Desikan RS, Segonne F, Fischl B 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

    Article  PubMed  Google Scholar 

  15. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B 57:289–300

    Google Scholar 

  16. Yendiki A, Panneck P, Srinivasan P et al (2011) Automated probabilistic reconstruction of white-matter pathways in health and disease using an atlas of the underlying anatomy. Front Neuroinform 5:1–12

    Article  Google Scholar 

  17. Grieve SM, Williams LM, Paul RH, Clark CR, Gordon E (2007) Cognitive aging, executive function, and fractional anisotropy: a diffusion tensor MR imaging study. Am J Neuroradiol 226–235

  18. Yeh F, Verstynen TD, Wang Y, Fernandez-Miranda JC, Tseng WI (2013) Deterministic diffusion fiber tracking improved by quantitative anisotropy. PLoS ONE 8:e80713

    Article  PubMed  PubMed Central  Google Scholar 

  19. Rubinov M, Sporns O (2010) Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52:1059–1069

    Article  PubMed  Google Scholar 

  20. Goveas J, O'Dwyer L, Mascalchi M et al (2015) Diffusion-MRI in neurodegenerative disorders. Magn Reson Imaging 33:853–876

    Article  PubMed  Google Scholar 

  21. Acosta-Cabronero J, Nestor PJ (2014) Diffusion tensor imaging in Alzheimer's disease: insights into the limbic-diencephalic network and methodological considerations. Front Aging Neurosci 6:143

    Article  Google Scholar 

  22. Nowrangi MA, Okonkwo O, Lyketsos C et al (2015) Atlas-based diffusion tensor imaging correlates of executive function. J Alzheimers Dis 44:585–598

    PubMed  PubMed Central  Google Scholar 

  23. Wardlaw J, Valdés-Hernández M, Muñoz-Maniega S (2015) What are white matter hyperintensities made of?: relevance to vascular cognitive impairment. J Am Heart Assoc: Cardiovasc Cerebrovasc Dis 4:e001140

    Article  Google Scholar 

  24. Kamali A, Flanders AE, Brody J, Hunter JV, Hasan KM (2014) Tracing superior longitudinal fasciculus connectivity in the human brain using high resolution diffusion tensor tractography. Brain Struct Funct 219:269–281

    Article  PubMed  Google Scholar 

  25. Duncan GW, Firbank MJ, Yarnall AJ et al (2016) Gray and white matter imaging: a biomarker for cognitive impairment in early Parkinson's disease? Mov Disord 31:103–110

    Article  PubMed  Google Scholar 

  26. Li X, Liang Y, Chen Y et al (2015) Disrupted frontoparietal network mediates white matter structure dysfunction associated with cognitive decline in hypertension patients. J Neurosci 35:10015–10024

    Article  CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Anatomy and Neurobiology, Boston University School of Medicine, 650 Albany St, Basement, Boston, MA, 02118, USA

    Danielle C. Farrar, Mark B. Moss, Bang Bon Koo & Ronald J. Killiany

  2. Department of Radiology, Boston University School of Medicine, Boston, MA, USA

    Asim Z. Mian

  3. VA Boston Healthcare System, Boston, MA, USA

    Andrew E. Budson

Authors
  1. Danielle C. Farrar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Asim Z. Mian
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Andrew E. Budson
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mark B. Moss
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Bang Bon Koo
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ronald J. Killiany
    View author publications

    You can also search for this author in PubMed Google Scholar

Consortia

for the Alzheimer’s Disease Neuroimaging Initiative

Corresponding author

Correspondence to Danielle C. Farrar.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Ron Killiany, PhD.

Conflict of interest

The conflicts of interest are as follows: Dr. Budson has been an investigator for clinical trials for the following companies: AstraZenica, Hoffmann-La Roche, Eli Lily, FORUM Pharmaceuticals, and Neuronetrix. Dr. Moss serves as a consultant for Pfizer, Inc. Dr. Killiany is funded on two research grants from Pfizer, Inc. Dr. Mian is a shareholder of Boston Imaging Core Lab.

Funding

Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organisation is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

Statistics and biometry

Danielle Farrar, MA performed the statistical analysis for this paper, with input from Dr. Ronald J. Killiany.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

This study was performed using data from the Alzheimer’s Disease Neuroimaging Initiative. Other studies published utilizing this dataset can be found at http://www.adni-info.org/Scientists/ADNIScientistsHome/ADNIPublications.html

Methodology

• retrospective

• cross-sectional study

• multicentre study

Additional information

Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Farrar, D.C., Mian, A.Z., Budson, A.E. et al. Retained executive abilities in mild cognitive impairment are associated with increased white matter network connectivity. Eur Radiol 28, 340–347 (2018). https://doi.org/10.1007/s00330-017-4951-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-017-4951-4

Keywords

Search

Navigation