Skip to main content

Advertisement

SpringerLink
Log in

Physiological separation of Alzheimer’s disease and Alzheimer’s disease with significant levels of cerebrovascular symptomology and healthy controls

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

Most dementia patients with a mixed dementia (MxD) diagnosis have a mix of Alzheimer’s disease (AD) and vascular dementia. Electrovestibulography (EVestG) records vestibuloacoustic afferent activity. We hypothesize EVestG recordings of AD and MxD patients are different. All patients were assessed with the Montreal cognitive assessment (MoCA) and Hachinski ischemic scale (HIS) (> 4 HIS score < 7 is representative of MxD cerebrovascular symptomology). EVestG recordings were made from 26 AD, 21 MxD and 44 healthy (control) participants. Features were derived from the EVestG recordings of the average field potential and field potential interval histogram to classify the AD, MxD and control groups. Multivariate analysis was used to test the features’ significance. Using a leave-one-out cross-validated linear discriminant analysis with 3 EVestG features yielded accuracies > 80% for separating pairs of AD/MxD/control. Using the MoCA assessment and 2 EVestG features, a best accuracy of 81 to 91% depending on the classifier was obtained for the 3-way identification of AD, MxD and controls. EVestG measures provide a physiological basis for identifying AD from MxD. EVestG measures are hypothesized to be partly related to channelopathies and changes in the descending input to the vestibular periphery. Four of the five AD or MxD versus control features used had significant correlations with the MoCA. This supports assertions that the pathologic changes associated with AD impact the vestibular system and further are suggestive that the postulated physiological changes behind these features have an association with cognitive decline severity.

Graphical abstract

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
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data availability

The analysed datasets for this study can be accessed by requesting permission from Charles Hider at NeuralDX. charles.hider@icloud.com.

References

  1. Beach TG et al (2012) Accuracy of the clinical diagnosis of Alzheimer disease at national institute on aging Alzheimer disease centers, 2005–2010. J Neuropathol Exp Neurol 13(4):266–273

    Article  Google Scholar 

  2. (2020) 2020 Alzheimer's disease facts and figures. Alzheimer's Dementia 16(3):391–460. https://doi.org/10.1002/alz.12068

  3. Rizzi L, Rosset I, M R-C (2014) Global Epidemiology of dementia: Alzheimer's and vascular types. Biomed Res Int

  4. Brenowitz WD et al (2017) Mixed neuropathologies and estimated rates of clinical progression in a large autopsy sample. Alzheimers Dementia 13(6):654–662

    Article  Google Scholar 

  5. Custodio N et al (2017) Mixed dementia: a review of the evidence. Dement Neuropsychol 11(4):364–370

    Article  PubMed  PubMed Central  Google Scholar 

  6. Hachinski VC et al (1975) Cerebral blood flow in dementia. Arch Neurol 32:632–637

    Article  CAS  PubMed  Google Scholar 

  7. Molsa PK et al (1985) Validity of clinical diagnosis in dementia: a prospective clinicopathological study. J Neurol Neurosurg and Psychiat 48:1085–1090

    Article  CAS  Google Scholar 

  8. Dorsey J, White M et al (2018) Vascular dementia: signs, symptoms, prognosis and support. https://www.helpguide.org/harvard/whats-causing-your-memory-loss.htm. Accessed 2 Sep 2018

  9. Roman GC et al (1993) Vascular dementia: diagnostic criteria for research studies. Report of the NINDS-AIREN International Workshop. Neurology 43(2):250–60

    Article  CAS  PubMed  Google Scholar 

  10. Khvostikov A, Aderghal K, Benois-Pineau J, Krylov A, Catheline G (2018) 3D CNN-based classification using sMRI and MD-DTI images for Alzheimer disease studies. arXiv preprint arXiv:1801.05968

  11. Grundman M et al (2016) Effect of amyloid imaging on the diagnosis and management of patients with cognitive decline: impact of appropriate use criteria. Dement Geriatr Cogn Disord 41(1–2):80–92

    Article  CAS  PubMed  Google Scholar 

  12. Menéndez-González M (2014) Routine lumbar puncture for the early diagnosis of Alzheimer’s disease. Is it safe? Front Aging Neurosci. 6:65

    PubMed  PubMed Central  Google Scholar 

  13. Wang L et al (2016) Evaluation of Tau imaging in staging Alzheimer disease and revealing interactions between β-amyloid and tauopathy. JAMA Neurol 73(9):1070–1077

    Article  PubMed  PubMed Central  Google Scholar 

  14. MRC_CFAS (2001) Pathological correlates of late-onset dementia in a multicentre, community-based population in England and Wales. Lancet 357:169–175

    Article  Google Scholar 

  15. Group-Discussion (2013) Coverage denial for amyloid scans riles Alzheimer’s community. [cited 2018 15-aug-2018]; part 2 of 14]

  16. Perneczky R et al (2011) CSF soluble amyloid precursor proteins in the diagnosis of incipient Alzheimer disease. Neurology 77:35–38

    Article  CAS  PubMed  Google Scholar 

  17. Engelborghs S et al (2008) Diagnostic performance of a CSF-biomarker panel in autopsy-confirmed dementia. Neurobiol Aging 29(8):1143–1159

    Article  PubMed  Google Scholar 

  18. Sperlinga RA, Aisenb PS et al (2011) Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the national institute on aging-Alzheimer’s association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s Dementia 7:280–292

    Article  Google Scholar 

  19. Mapstone M, Cheema AK, Fiandaca MS, Zhong X, Mhyre TR, Macarthur LH, Hall WJ, Fisher SG, Peterson DR, Haley JM, Nazar MD, Rich SA, Berlau DJ, Peltz CB, Tan MT, Kawas CH, Federoff HJ (2014) Plasma phospholipids identify antecedent memory impairment in older adults. Nat Med 20:415–418

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Nakamura A et al (2018) High performance plasma amyloid-β biomarkers for Alzheimer’s disease. Nat Commun 554:249–254

    Article  CAS  Google Scholar 

  21. Taia SK, Leung LS (2012) Vestibular stimulation enhances hippocampal long-term potentiation via activation of cholinergic septohippocampal cells. Behav Brain Res 232:174–182

    Article  CAS  Google Scholar 

  22. Navratilova Z, Lan T et al (2012) Experience-dependent firing rate remapping generates directional selectivity in hippocampal place cells. Front Neural Circuits 6(6):1–14

    Google Scholar 

  23. Apostolova LG, Dinov ID et al (2006) 3D comparison of hippocampal atrophy in amnestic mild cognitive impairment and AD. Brain 129:2867–2873

    Article  PubMed  Google Scholar 

  24. Du AT, Schuff Nea (2001) Magnetic resonance imaging of the entorhinal cortex and hippocampus in mild cognitive impairment and Alzheimer’s disease. J Neurol Neurosurg Psychiatry 71(4):441–447

    Article  CAS  PubMed  Google Scholar 

  25. Cronin T, Arshad Q, Seemungal BM (2017) Vestibular deficits in neurodegenerative disorders: balance, dizziness, and spatial disorientation. Front Neurol 8:538

    Article  PubMed  PubMed Central  Google Scholar 

  26. Xu DE et al (2014) Amyloid precursor protein at node of Ranvier modulates nodal formation. Cell Adh Migr 8:396–403

    Article  PubMed  PubMed Central  Google Scholar 

  27. Liu C et al (2015) Amyloid precursor protein enhances Nav1.6 sodium channel cell surface expression. J. Biol. Chem. 290:12048–57

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Rissman RA, De Blas AL, Armstrong DM (2007) GABAA receptors in aging and Alzheimer’s disease. J Neurochem 103:1285–1292

    Article  CAS  PubMed  Google Scholar 

  29. Marczynski TJ (1998) GABAergic deafferentation hypothesis of brain aging and Alzheimer’s disease revisited. Brain Res Bull 45:341–379

    Article  CAS  PubMed  Google Scholar 

  30. Theofilas P et al (2017) Locus coeruleus volume and cell population changes during Alzheimer’s disease progression: a stereological study in human postmortem brains with potential implication for early- stage biomarker discovery. Alzheimers Dement 13(3):236–246

    Article  PubMed  Google Scholar 

  31. Cortes C et al (2013) Excitatory actions of GABA in developing chick vestibular afferents: effects on resting electrical activity. Synapse 67(7):374–381

    Article  CAS  PubMed  Google Scholar 

  32. Moroney JT et al (1997) Meta-analysis of the Hachinski ischemic score in pathologically verified dementias. Neurology 49:1096–1105

    Article  CAS  PubMed  Google Scholar 

  33. Nasreddine ZS, Philipps N, Bédirian V, Charbonneau S, Whitehead V, Collin I, Cummings JL, Chertkow H (2005) The Montreal cognitive assessment (MoCA): a brief screening tool for mild cognitive impairment. J Am Geriatr Soc. 53:695–699

    Article  PubMed  Google Scholar 

  34. Knopman DS et al (2001) Practice parameter: diagnosis of dementia (An evidence based Review) report of the quality standards subcommittee of the American Academy of Neurology. Neurology 56:1143–1153

    Article  CAS  PubMed  Google Scholar 

  35. Lithgow BJ et al (2018) Bipolar disorder in the balance. Europ Arch Psychiat Clin Neurosci 269(7):761–775

    Article  Google Scholar 

  36. Lithgow BJ et al (2015) Major Depression and electrovestibulography. World J Biol Psychiatry 16(5):334–350

    Article  PubMed  Google Scholar 

  37. Lithgow BJ (2012) A methodology for detecting field potentials from the external ear canal: NEER and EVestG. Ann BME 40(8):1835–1850

    Google Scholar 

  38. Blakley B et al (2018) EVestG recordings are vestibuloacoustic signals. J Med Biol Eng 39(2):213–217

    Article  Google Scholar 

  39. Lithgow BJ, Shoushtarian M (2015) Parkinson’s disease: disturbed vestibular function and levodopa. J Neurol Sci 353(1–2):49–58

    Article  CAS  PubMed  Google Scholar 

  40. Moussavi Z et al (2019) A pilot double-blind study of the tolerability and efficacy of repetitive transcranial magnetic stimulation on persistent post-concussion syndrome. Sci Rep 9:5498

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Suleiman A et al (2017) Quantitative measurement of post-concussion syndrome (PCS) using electrovestibulography (EVestG). Sci Rep 7:1–10

    Article  CAS  Google Scholar 

  42. Suleiman A et al (2018) Investigating the validity and reliability of electrovestibulography (EVestG) for detecting postconcussion syndrome (PCS) with and without comorbid depression. Sci Rep 8:1–11

    Article  CAS  Google Scholar 

  43. Blakley B et al (2014) Preliminary report: neural firing patterns specific for Meniere’s Disease. J Otolaryngol Head Neck Surg 43(52):5

    Google Scholar 

  44. Dastgheib Z et al (2014) A new diagnostic vestibular evoked response. J Otolaryngol Head Neck Surg 44(1):14

    Article  Google Scholar 

  45. Dastgheib ZA et al (2016) Application of vestibular spontaneous response as a diagnostic aid for Meniere’s disease. Ann Biomed Eng 44:1672–1684

    Article  CAS  PubMed  Google Scholar 

  46. Lithgow BJ, Moussavi Z, Fitzgerald PB (2019) Quantitative separation of the depressive phase of bipolar disorder and major depressive disorder using electrovestibulography. World J Biol Psychiatry 20(10):799–812

    Article  PubMed  Google Scholar 

  47. Marlinsky V (1995) The effect of somatosensory stimulation on second-order and efferent vestibular neurons in the decerebrate decerebellate guina-pig. Neuroscience 69:661–669

    Article  CAS  PubMed  Google Scholar 

  48. McLachlan GJ (1992) Discriminant analysis and statistical pattern recognition. Applied Probability and Statistics. John Wiley & Sons, Inc, New York, p 526

    Google Scholar 

  49. Govindpani K et al (2017) Towards a better understanding of GABAergic remodeling in Alzheimer’s disease. Int J Mol Sci 18:1813

    Article  PubMed Central  CAS  Google Scholar 

  50. Jin G-S et al (2018) Role of peripheral vestibular receptors in the control of blood pressure following hypotension. Korean J Physiol Pharmacol 22(4):363–368

    Article  PubMed  PubMed Central  Google Scholar 

  51. Balaban CD, Jacob RG, Furman JM (2011) Neurologic bases for comorbidity of balance disorders, anxiety disorders and migraine: neurotherapeutic implications. Expert Rev Neurother 11(3):379–394

    Article  PubMed  PubMed Central  Google Scholar 

  52. Heneka MT et al (2006) Locus ceruleus degeneration promotes Alzheimer pathogenesis in amyloid precursor protein 23 Transgenic Mice. J Neurosci 26(5):1343–1354

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Chalermpalanupap T, Weinshenker D, Rorabaugh JM (2017) Down but not out: the consequences of pretangle tau in the locus coeruleus. Neural Plasticity 2017:9. https://doi.org/10.1155/2017/7829507

    Article  CAS  Google Scholar 

  54. Brown DJ, Patuzzi RB (2010) Evidence that the compound action potential (CAP) from the auditory nerve is a stationary potential generated across dura mater. Hear Res 267:12–26

    Article  PubMed  Google Scholar 

  55. Mayordomo-Cava J et al (2015) Amyloid-β(25–35) Modulates the expression of GirK and KCNQ channel genes in the hippocampus. PLoS ONE 10(7):e0134385

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  56. DeTure MA, Dickson DW (2019) The neuropathological diagnosis of Alzheimer’s disease. Mol Neurodegener 14:32

    Article  PubMed  PubMed Central  Google Scholar 

  57. Koketsu K, Nishi S, Soeda H (1963) Effects of calcium ions on prolonged action potentials and hyperpolarizing responses. Nature 200:786–787

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

Abed Sulieman and Corey Boseke assisted in some patient recordings.

Funding

This study was supported by MITACS in partnership with the Riverview Foundation.

Author information

Authors and Affiliations

  1. Diagnostic and Neurosignal Processing Research Laboratory, Biomedical Engineering Program, University of Manitoba, Riverview Health Centre, Winnipeg, MB, Canada

    Brian J. Lithgow, Zeinab Dastgheib, Behzad Mansouri, Mehrangiz Ashiri & Zahra Moussavi

  2. Monash Alfred Psychiatry Research Centre, Victoria, Australia

    Brian J. Lithgow

  3. Vision Matter Research Ins., Brain, Vision and Concussion Clinic-iScope, Winnipeg, MB, Canada

    Neda Anssari & Behzad Mansouri

  4. Department of Otolaryngology, Health Sciences Center, Winnipeg, MB, Canada

    Brian Blakley

Authors
  1. Brian J. Lithgow
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zeinab Dastgheib
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Neda Anssari
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Behzad Mansouri
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Brian Blakley
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Mehrangiz Ashiri
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Zahra Moussavi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

B.L and Z.M supervised the entire project and contributed to the data and statistical analysis, writing the paper and discussion of the results; Z.D., A.S., M.A., B.B. contributed to discussion of the results; B.M. and N.A. examined and referred the patients and contributed to discussion of the results. All authors reviewed the manuscript.

Corresponding author

Correspondence to Brian J. Lithgow.

Ethics declarations

Conflict of interest

The author B.L has less than 0.5% shares in company NeuralDX Pty. Ltd. No other authors have any conflict of interest.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

ESM 1

(DOC 3.32 MB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lithgow, B.J., Dastgheib, Z., Anssari, N. et al. Physiological separation of Alzheimer’s disease and Alzheimer’s disease with significant levels of cerebrovascular symptomology and healthy controls. Med Biol Eng Comput 59, 1597–1610 (2021). https://doi.org/10.1007/s11517-021-02409-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-021-02409-8

Keywords

Search

Navigation