Skip to main content Accessibility help

Memory Performance and Quantitative Neuroimaging Software in Mild Cognitive Impairment: A Concurrent Validity Study

  • Laura Glass Umfleet (a1), Alissa M. Butts (a1), Julie K. Janecek (a1), Katherine Reiter (a1), Mohit Agarwal (a1), Benjamin L. Brett (a1), Joseph J. Ryan (a2), James Reuss (a3), Andrew Klein (a1), Anthony N. Correro (a1) and Malgorzata Franczak (a1)...



This study examined the relationship between patient performance on multiple memory measures and regional brain volumes using an FDA-cleared quantitative volumetric analysis program – Neuroreader™.


Ninety-two patients diagnosed with mild cognitive impairment (MCI) by a clinical neuropsychologist completed cognitive evaluations and underwent MR Neuroreader™ within 1 year of testing. Select brain regions were correlated with three widely used memory tests. Regression analyses were conducted to determine if using more than one memory measures would better predict hippocampal z-scores and to explore the added value of recognition memory to prediction models.


Memory performances were most strongly correlated with hippocampal volumes than other brain regions. After controlling for encoding/Immediate Recall standard scores, statistically significant correlations emerged between Delayed Recall and hippocampal volumes (rs ranging from .348 to .490). Regression analysis revealed that evaluating memory performance across multiple memory measures is a better predictor of hippocampal volume than individual memory performances. Recognition memory did not add further predictive utility to regression analyses.


This study provides support for use of MR Neuroreader™ hippocampal volumes as a clinically informative biomarker associated with memory performance, which is a critical diagnostic feature of MCI phenotype.


Corresponding author

*Correspondence and reprint requests to: Laura Glass Umfleet, Department of Neurology, Division of Neuropsychology, Medical College of Wisconsin, 9200 W Wisconsin Ave, Milwaukee, WI53226, USA. Phone: +1 414 955 0664, Fax: +1 414 955 0076. E-mail:


Hide All
Ahdidan, J., Raji, C.A., DeYoe, E.A., Mathis, J., Noe, K., Rimestad, J.,…Lopez, O. (2016). Quantitative neuroimaging software for clinical assessment of hippocampal volumes on MR Imaging. Journal of Alzheimer’s Disease, 49, 723732.
Barbeau, E.J., Ranjeva, J.P., Didic, M., Confort-Gouny, S., Felician, O., Soulier, E.,…Poncet, M. (2008). Profile of memory impairment and gray matter loss in amnestic mild cognitive impairment. Neuropsychologia, 46, 10091019.
Baron, J., Chetelat, G., Desgranges, B., Perchey, G., Landeau, B., de la Sayette, V., & Eustache, F. (2001). In vivo mapping of gray matter loss with voxel-based morphometry in mild Alzheimer’s disease. Neuroimage, 14, 298309.
Brandt, J. & Benedict, R.H.B. (2001). Hopkins Verbal Learning Test-Revised. Lutz, FL: Psychological Assessment Resources.
Bondi, M.W. & Smith, G.E. (2014). Mild cognitive impairment: A concept and diagnostic entity in need of input from neuropsychology. Journal of the International Neuropsychological Society, 20, 129134.
Bottino, C.M, Castro, C.C., Gomes, R.L., Buchpiguel, C.A., Marchetti, R.L., & Neto, M.R. (2002). Volumetric MRI measurements can differentiate Alzheimer’s disease, mild cognitive impairment, and normal aging. International Psychogeriatrics, 14, 5972.
Brooks, B.L., Iverson, G.L., Holdnack, J.A., & Feldman, H.H. (2008). Potential for misclassification of mild cognitive impairment: A study of memory scores on the Wechsler Memory Scale-III in healthy older adults. Journal of the International Neuropsychological Society, 14, 463478.
Busatto, G., Garrido, G., Almeida, O., Castro, C., Camargo, C., Cid, C.,…Bottino, C.M. (2003). A voxel-based morphometry study of temporal lobe gray matter reductions in Alzheimer’s disease. Neurobiology of Aging, 24, 221231.
Busse, A., Hensel, A., Guhne, U, Angermeyer, M.C., & Riedel-Heller, S.G. (2006). Mild cognitive impairment: Long-term course of four clinical subtypes. Neurology, 67, 21762785.
Chetelat, G., Desgranges, B., De La Sayette, V., Viader, F., Eustache, F., & Baron, J-C. (2002). Mapping gray matter loss with voxel-based morphometry in mild cognitive impairment. Neuroreport, 13, 19391943.
Chetelat, G., Landeau, B., Eustache, F., Mezenge, F., Viader, F., de la Sayette, V.,…Baron, J. C. (2005). Using voxel-based morphometry to map the structural changes associated with rapid conversion in MCI: A longitudinal MRI study. Neuroimage, 27, 934946.
Clark, L.R., Delano-Wood, L., Libon, D.J., McDonald, C.R., Nation, D.A., Bangen, K.J., … Bondi, M.W. (2013). Are empirically-derived subtypes of mild cognitive impairment consistent with conventional subtypes? Journal of the International Neuropsychological Society, 19, 635645.
deToledo-Morrell, L., Stoub, T., Bulgakova, M., Wilson, R., Bennett, D., Leurgans, S.,…Turner, D.A. (2004). MRI-derived entorhinal volume is a good predictor of conversion from MCI to AD. Neurobiology of Aging, 25, 11971203.
DeVivo, R., Zajac, L., Mian, A., Cervantes-Arslanian, A., Steinberg, E., Alosco, M.,…Killany, R. (2019). Differentiating between healthy control participants and those with mild cognitive impairment using volumetric MRI data. JINS, 25, 800810.
Du, A.T., Schuff, N., Amend, D., Lasskso, M.P., Hsu, Y.Y., Jagust, W.J.,…Weiner, M.W. (2001). Magnetic resonance imaging of the entorhinal cortex and hippocampus in mild cognitive impairment and Alzheimer’s disease. Journal of Neurology, Neurosurgery, and Psychiatry, 71, 441447.
Edmonds, E.C., Delano-Wood, L., Clark, L.R., Jak, A.J., Nation, D.A., McDonald, C.R.for the Alzheimer’s Disease Neuroimaging Initiative. (2015). Susceptibility of the conventional criteria for MCI to false positive diagnostic errors. Alzheimer’s & Dementia, 11, 415424.
Fischer, P., Jungwirth, S., Zehetmayer, S., Weissgram, S., Hoeningschnabl, S., Gelpi, E., & Tragl, K. H. (2007). Conversion from subtypes of mild cognitive impairment to Alzheimer’s dementia. Neurology, 68, 288291.
Gauthier, S., Reisberg, B., Zaudig, M., Petersen, R.C., Ritchie, K., Broich, K.,…Winblad, B. (2006). Mild cognitive impairment. The Lancet, 367, 12621270.
Hirata, Y., Matsuda, H., Nemoto, K., Ohnishi, R., Hirao, K., Yamashita, F.,…Samejima, H. (2005). Voxel-based morphometry to discriminate early Alzheimer’s disease from controls. Neuroscience Letters, 382, 269274.
Jack, C.R., Petersen, R.C., O’Brien, P.C., & Tangalos, E.G. (1992). MR-based hippocampal volumetry in the diagnosis of Alzheimer’s disease, Neurology, 42, 183188.
Jack, C.R., Therneau, R.M., Wiste, H.J., Weigand, S.D., Knopman, D.S., Lowe, V.J….Petersen, R.C. (2016). Transition rates between amyloid and neurodegeneration biomarker states and to dementia: A population-based, longitudinal cohort study. Lancet Neurology, 15, 5664.
Jak, A.J., Urban, S., McCauley, A., Bangen, K.J., Delano-Wood, L., Corey-Bloom, J., & Bondi, M.W. (2009). Profile of hippocampal volumes and stroke risk varies by neuropsychological definition of mild cognitive impairment. Journal of the International Neuropsychological Society, 2, 18.
Karas, G.B., Scheltens, P., Rombouts, S.A., Visser, P.J., van Schijndel, R.A., Fox, N.C., & Barkhof, N.C. (2004). Global and local gray matter loss in mild cognitive impairment and Alzheimer’s disease. Neuroimage, 23, 708716.
Knopman, D.S., DeKosky, S.T., Cummings, J.L., Chui, H., Corey-Bloom, J., Relkin, N.,…Stevens, J.C. (2001). Practice parameter: Diagnosis of dementia (an evidence based review): Report of the Quality Standards Subcommittee of the American Academy of Neurology. Neurology, 56, 11431153.
Kovacevic, S., Rafii, M.S., Brewer, J.B., & the Alzheimer’s Disease Neuroimaging Initiative. (2009). High-throughput, fully-automated volumetry for prediction of MMSE and CDR decline in mild cognitive impairment. Alzheimer’s Disease & Associated Disorders, 23, 139145.
Lange, K.L., Bondi, M.W., Salmon, D.P., Galasko, D., Delis, D.C., Thomas, R.G., & Thal, L. J. (2002). Decline in verbal memory during preclinical Alzheimer’s disease: Examination of the effect of APOE genotype. Journal of the International Neuropsychological Society, 8, 943955.
Marra, C., Ferraccioli, M., Vita, M.G., Quaranta, D., & Gainotti, G. (2011). Patterns of cognitive decline and rates of conversion to dementia in patients with degenerative and vascular forms of MCI. Current Alzheimer Research, 8, 2431.
Mitchell, A.J. & Shiri-Feshki, M. (2009). Rate of progression of mild cognitive impairment to dementiameta-analysis of 41 robust inception cohort studies. Acta Psychiatrica Scandinavica, 119, 252265.
Momenan, R., Rawlings, R., Fong, G., Knutson, B., & Hommer, D. (2004). Voxel-based homogeneity probability maps of gray matter in groups: Assessing the reliability of functional effects. Neuroimage, 21, 965972.
Nordlund, A., Rolstad, S., Hellstrom, P., Sjogren, M., Hansen, S., & Wallin, A. (2005). The Goteborg MCI study: Mild cognitive impairment is a heterogeneous condition. Journal of Neurology, Neurosurgery, and Psychiatry, 76, 14851490.
Ochs, A.L., Ross, D.E., Zannoni, M.D., Abildskov, T.J., & Bigler, E.D. (2015). Comparison of automated brain volume measures obtained with NeuroQuant(R) and FreeSurfer. Journal of Neuroimaging, 25, 721727.
Petersen, R.C., Smith, G.E., Waring, S.C., Ivnik, R.J., Tangalos, E.G., & Kokmen, E. (1999). Mild cognitive impairment: Clinical characterization and outcome. Archives of Neurology, 56, 303308.
Rabin, L., Paré, N., Saykin, A., Brown, M.J., Wishart, H.A., Flashman, L.A., & Santulli, R. B. (2009). Differential memory test sensitivity for diagnosing amnestic mild cognitive impairment and predicting conversion to Alzheimer’s disease. Aging, Neuropsychology, and Cognition, 16, 357376.
Ringman, J.M., Pope, W., & Salamon, N. (2010). Insensitivity of visual assessment of hippocampal atrophy in familial Alzheimer’s disease. Journal of Neurology, 257, 839842.
Rombouts, S., Barkhof, F., Witter, M., & Scheltens, P. (2000). Unbiased whole-brain analysis of gray matter in Alzheimer’s disease. Neuroscience Letters, 285, 231233.
Scahill, R.I., Schott, J.M., Stevens, J.M., Rossor, M.N., & Fox, N.C. (2002). Mapping the evolution of regional atrophy in Alzheimer’s disease: Unbiased analysis of fluidregistered serial MRI. Proceedings of the National Academy of Sciences of the United States of America, 99, 47034707.
Schretlen, D.J., Testa, S.M., Winicki, J.M., Pearlson, G.D., & Gordon, B. (2008). Frequency and bases of abnormal performance by healthy adults on neuropsychological testing. Journal of the International Neuropsychological Society, 14, 436445.
Sperling, R. & Johnson, K. (2013). Biomarkers of Alzheimer’s disease: Current and future applications to diagnostic criteria. Continuum, 19, 325338.
Squire, L.R., Stark, C.E.L., & Clark, R.E. (2004). The medial temporal lobe. Annual Review of Neuroscience, 27, 279306.
Tabert, M.H., Manly, J.J., Liu, X., Pelton, G.H., Rosenblum, S., Jacobs, M.,…Devanand, D. P. (2006). Neuropsychological prediction of conversion to Alzheimer’s disease in patients with mild cognitive impairment. Archives of General Psychiatry , 63, 913924.
Tanpitukpongse, T.P., Mazurowski, M.A., Ikhena, J., Petrella, J.R., & for the Alzheimer’s Disease Neuroimaging Initiative (2017). Predictive utility of marketed volumetric software tools in subjects at risk for Alzheimer’s disease: Do regions outside the hippocampus matter? AJNR, 38, 546552.
Varon, D., Barker, W., Loewenstein, D., Greig, M., Bohorquez, A., Santos, I.,…Alzhiemer’s Disease Neuroimaging Initiative. (2015). Visual rating and volumetric measurement of medial temporal atrophy in the Alzhiemer’s Disease Neuroimaging Initiative (ADNI) cohort: Baseline diagnosis and the prediction of MCI outcome. International Journal of Geriatric Psychiatry, 30, 192200.
Wechsler, D. (2009). Wechsler Memory Scale–Fourth Edition (4th ed.). San Antonio, TX: Pearson.
Whitwell, J.L., Shiung, M.M., Przybelski, S.A., Weigand, S.D., Knopman, D.S., Boeve, B.F.,…Jack, C.R. (2008). MRI patterns of atrophy associated with progression to AD in amnestic mild cognitive impairment. Neurology, 70, 512520.
Wolf, H., Hensel, A., Kruggel, F., Riedel-Heller, S.G., Arendt, T., Wahlund, L.-O., & Gertz, H.-J. (2004). Structural correlates of mild cognitive impairment. Neurobiology of Aging, 25, 913924.


Memory Performance and Quantitative Neuroimaging Software in Mild Cognitive Impairment: A Concurrent Validity Study

  • Laura Glass Umfleet (a1), Alissa M. Butts (a1), Julie K. Janecek (a1), Katherine Reiter (a1), Mohit Agarwal (a1), Benjamin L. Brett (a1), Joseph J. Ryan (a2), James Reuss (a3), Andrew Klein (a1), Anthony N. Correro (a1) and Malgorzata Franczak (a1)...


Altmetric attention score

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed.