Applying Quantitative Neuroimaging in Cognitive Decline and Alzheimer’s

by Cyrus Raji, MD PhD

Original source Journal of Alzheimer’s Disease

Alzheimer’s disease (AD) is the most common cause of dementia and remains incurable. Its prevalence is rising, current afflicting 5 million Americans with projections to affect millions more as the population ages [1]. Yet, there are other diseases associated with cognitive impairment and memory loss such as depression and long standing vascular disease. In one sample of patients with vascular dementia, 6% of a sample of over 9,000 patients had been misdiagnosed with AD leading to a delay in diagnosis by 2 years [2]. Treatable risk factors for dementia such as late life depression and traumatic brain injury may also be under-recognized [3, 4].

All of these factors highlight the need for improved diagnosis of memory disorders. Imaging remains an important component of diagnosis for AD but traditionally limited to excluding “organic” causes of dementia such as brain tumors and strokes [5]. However, newer tools exist to extract additional information from brain MRI scans with quantitative analysis. The main tool being used at UCLA is a program called Neuroreader. Neuroreader is an FDA cleared software program that computes the physical volumes of different brain regions on MRI scans. These volumes can be of entire lobes of the brain, such as the frontal lobes or of specific structures known to be abnormal in Alzheimer’s disease such as the hippocampus. Neuroreader was validated in the computation of hippocampal volumes [6] compared to gold standard expert anatomical tracings. Unlike expert anatomical hippocampal tracings, which can take at least 30 minutes to do, Neuroreader can compute hippocampal volumes in under 5 minutes and multiple structures of the entire brain in under 10 minutes. This allows for rapidly quantitative information that clinicians can use in tailoring their diagnostic and therapeutic approaches.

One example of this application at UCLA is the innovative approach to the care of memory loss disorders being done at the Cognitive Health Clinic (CHC) directed by Dr. David Merrill—an MD/PhD Geriatric Psychiatrist. Initial assessments of patients include combination of mental status examination, computerized neuropsychology tests, genomic directed psychopharmacology, quantitative electroencephalography with event related potential testing [7] [8], and quantitative MRI volumetrics with Neuroreader. The imaging component is a key part of initial assessment—not only for identifying hippocampal volume loss that is a key imaging feature of Alzheimer’s disease [9] but more importantly for identifying other potential causes of memory loss. The importance of this technology is pertinent given the difficulty of identifying either cross sectional or longitudinal atrophy [10].

One such case is of a 62-year old man who came to the CHC convinced that he “has Alzheimer’s.” He had been experiencing progressive memory loss over several years and was worried it had been due to Alzheimer’s. Upon scanning him with an MRI of the brain no visually apparent structural or other abnormalities were seen. Neuroreader analysis did not show atrophy in the hippocampus. This was important because it made us assured that Alzheimer’s as a cause of his memory loss was very unlikely. In fact, all of the Neuroreader measured volumes were normal except one structure—the cerebellum which had abnormally low volumes compared to a normative database. Upon seeing these volumes, Dr. Merrill asked the patient about his alcohol use and the patient stated that he drank one bottle of wine a night for 15 years. This is important information because the cerebellum can specifically shrink as a function of chronic alcohol abuse [11]. The patient’s cognition improved once his alcohol use was discontinued. In this case, quantitative imaging with Neuoreader was especially helpful in clarifying the etiology of the patient’s cognitive decline.

Another case demonstrating Neuroreader’s utility is a case of a 51 year old former high school football player who sustained 900+ blows to his head in the course of his playing career. One of these blows lead to at least a 30 second loss of consciousness. Plagued by subtle but progressive attentional issues beginning in his 40s, the patient saw other physicians and was given medications for bipolar and ADHD in attempt to ameliorate his symptoms. However, these approaches were not fully successful and the patient continued to have attentional problems on neuropsychological testing. Neuroreader analysis of two MRI scans four years apart a showed a progressive gray matter volume loss of 14% with the majority of these decreases occurring in the brainstem and ventral diencephalon. These findings are important because these regions are also implicated in the pathology of a football related neurodegenerative disease called Chronic Traumatic Encephalopathy (CTE) [12]. While we do not have confirmation of CTE in this patient, the convergence of findings raises the possibility of our identification of an MRI signature for the disorder. This is possible due to Neuroreader’s ability measure the volume of non-hippocampal structures such as the brainstem.

Related to this same case is the patient’s hippocampal volumes. His first MRI showed a hippocampal volume of 9.02 cc and his second MRI showed an increase in this volume to 9.49 cc—an increase of 5.2% in four years. The patient had MRI scans of the brain at both times points with the exact same scanner specifications and Neuroreader’s variability between scans is less than 0.5%. Therefore, these results are not due to technical error. Additionally, while the patient had attentional defects he did not exhibit memory loss. This may have been vital in helping him maintain overall neuropsychological function in the face of his other deficits.

So why did the patient’s hippocampal volume improve over time? In speaking with the patient and his family further we found that when his cognitive difficulties began he started to engage in several additional hours of aerobic physical activity a week and also started a diet high in omega-3 fatty acids from fish—most commonly salmon. We know from prior research that lifestyle can have an important relationship with hippocampal volume. One study found that weekly fish consumption is associated with larger gray matter volumes on MRI—particularly in the hippocampus [13]. Another study showed in 876 study participants that engaged in aerobic physical activities showed larger gray matter volumes on quantitative MRI—with strong main effects in the hippocampus [14]. Thus, even in this patient suffering from brain damage, volumetric MRI with Neuroreader not only identified brain areas that were doing progressively worse over time but also showed areas that were preserved or—in the case of the hippocampus – even improved.

These findings—although seen only in one case—do raise the possibility of using volumetric imaging not only to hone a differential diagnosis but also track responses of the brain to different treatment and prevention regimens. The need for additional longitudinal and randomized clinical trials to further develop the value of this approach is strong. However, if such trials are forthcoming one could envision a future where high risk individuals for Alzheimer’s dementia—either through genetics like APOE4 of lifestyle behaviors [15] may be scanned with volumetric MRI and quantified with Neuoreader identify early atrophy and then track these regions as therapeutic or prevention regimens are advances over time. For this to happen, geriatric psychiatrists will need to innovate a multidisciplinary approach for comprehensive care and radiologists will need to work closely with psychiatrists for the honing the imaging of cognitive disorders. UCLA’s CHC remains a model of this approach that can be applied throughout the country as the aging population continues to grow.

Such an approach is vital given the recognition that Alzheimer’s disease can and should be prevented and that lifestyle factor modification is part of this plan [16]. It has been estimated that a 10-25% reduction in the burden of preventable lifestyle factors (physical inactivity, obesity, smoking, hypertension, diabetes mellitus, depression, and low education) can cut the number of Alzheimer’s cases by one million worldwide [15]. Imaging will remain an important component of this strategy in terms of identifying persons at risk or early in the course of cognitive decline and tracking responses to brain rehabilitation programs. One example of where this might be effect was in a randomized clinical trial where persons assigned to an aerobic exercise regimen compared to a passive stretching group experiences a 2% increase in hippocampal volume in one year along with memory function [17]. The importance of this study is in its results and randomized clinical trial design—providing very strong evidence of a causal relationship between a lifestyle behavior—in this case physical activity—and improved brain structure and memory function. Such results were shown with volumetric MRI. Current studies underway are combining physical exercise with dietary improvement and/or cognitive training regimens.

Additionally, it is important to note that obtaining a volumetric MRI remains part of the standard of care in working up memory disorders [5]. While volumetric MRI is not yet recommended as part of this standard workup it can easily be applied to pre-existing scans to add value in ways similar to the cases described in this article. Ordering a volumetric MRI requires simple but specific instructions—mainly writing in order comments to “obtain a 3D MPRAGE or SPGR.” This informs the MRI technologist to acquire a fully volumetric sequence that allows for Neuroreader analysis. With respect to cost, a Neuroreader analysis per scan costs about 70 dollars and this is reimbursable by insurance with the CPT code of 76377. Additionally, no contrast agents are required for a Neuroreader MRI—an especially important consideration in elderly persons with renal failure. Additionally, MRI scans have no radiation and are cheaper in cost at around 437 dollars compared a PET scan that can cost as much as close to $3,000 [18]. This does not mean that PET scans lack value and it does not mean they should not be used but it does reinforce the concept of MRI a first line modality in memory loss and the need to extract as much information from this technique as possible, a function that Neuroreader is critical in providing. In terms of overall cost, Neuroreader MRI holds the promise of accurate diagnosis—an important cost consideration as the price of a misdiagnosis of AD ranges from $9,500 to $14,000 per year [19].

Why is brain volume important for memory loss disorders? It is important to think of brain volume as a “vital sign” for the brain. When brain volume is higher it suggests improved neuronal health. When brain volume declines—as in the case of Alzheimer’s disease—it is due to neurodegeneration [20]. In that case, brain volume declines due to a combination neurons dying and surviving neurons being reduced in size. This process of brain atrophy can be detected on MRI with quantitative methods up to 10 years before the onset of actual symptoms [21]. Thus, assessments of brain volume with a program—in this case Neuroreader – is a key approach towards identifying persons at risk for AD. Conversely, neurotrophic growth factors can improve brain structure and raise brain volume in relation to exercise—particularly in the hippocampus [22, 23].

However, while the majority of this article has been devoted to AD it is notable to understand that regionally specific atrophy is a feature of other brain disorders as well. For example, persons with depression experience atrophy of the amygdala—the brain’s key emotional structure [24] and this is a brain structure also measured with Neuroreader. Indeed, a recent systematic review [25] showed that the basal ganglia, insula, and temporal cortex show greater atrophy in neurological disorders. Conversely, psychiatric disease are more likely to be related to atrophy in in different structures such as the cingulate, medial frontal, superior frontal, and occipital lobes. While simple, this classification scheme can be used to better distinguish neurological and psychiatric diseases using volumetric MRI while identifying possible areas of overlap. As with other questions raised in this paper, additional research will be needed to identify how best to add value for patients with these tools but simple applications – such as those described above can already be applied now.

From an evidence based medicine perspective, MRI volumetrics as applied to neurology and psychiatry has the best evidence in terms of number of systematic reviews. Research applying Neuroreader or Neuroreader like volumetric algorithms had 241 systematic reviews published between 2001 and 2010 [26], accounting for 28% for the total number of radiology and nuclear medicine systematic reviews written in this time period. However, while the clinical evidence is present and FDA cleared software exists, the actual clinical application and guidelines lag behind. Thus, the opportunity for collaboration between geriatric psychiatrist and neuroradiologists holds promise to continue the advancement of neuroimaging in psychiatry. This concept was articulated a recent paper promoting the idea of preventive neuroradiology in brain aging and cognitive decline [27].

Finally, it is important to remember that neuroimaging remains an important tool but ultimately a tool. At the core of any successful patient care endeavor is listening to the patient, taking the time and care to understand the clinical issues involved and most importantly treating the patient and not just a scan or lab value. It is with this mentality that all patient care of this vulnerable population of persons with memory loss disorders will succeed.

Cyrus A. Raji, MD, PhD
David A. Merrill, MD, PhD

Cyrus A. Raji, MD, PhD is a Clinical Neuroradiology fellow at UCSF with 10 years of experience in Research on Neurodegenerative Disease.
David A. Merrill, MD, PhD is an Assistant Clinical Professor of Psychiatry at UCLA Health Systems and the founder of the Cognitive Health Clinic.

This article was previously published in Today’s Geriatric Medicine (Vol. 9, No. 5, p. 16).

[1] Alzheimer’s Association (2016) 2016 Alzheimer’s Disease Facts and Figures., Accessed 7/27/2016.
[2] Happich M, Kirson NY, Desai U, King S, Birnbaum HG, Reed C, Belger M, Lenox-Smith A, Price D (2016) Excess costs associated with possible misdiagnosis of Alzheimer’s disease among patients with vascular dementia in a UK CPRD population. J Alzheimers Dis 53, 171-183.
[3] Barnes DE, Yaffe K, Byers AL, McCormick M, Schaefer C, Whitmer RA (2012) Midlife vs late-life depressive symptoms and risk of dementia: differential effects for Alzheimer disease and vascular dementia. Arch Gen Psychiatry 69, 493-498.
[4] Shively S, Scher AI, Perl DP, Diaz-Arrastia R (2012) Dementia resulting from traumatic brain injury: what is the pathology? Arch Neurol 69, 1245-1251.
[5] Knopman DS, DeKosky ST, Cummings JL, Chui H, Corey-Bloom J, Relkin N, Small GW, Miller B, Stevens JC (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.
[6] Ahdidan J, Raji CA, DeYoe EA, Mathis J, Noe KO, Rimestad J, Kjeldsen TK, Mosegaard J, Becker JT, Lopez O (2015) Quantitative neuroimaging software for clinical assessment of hippocampal volumes on MR imaging. J Alzheimers Dis 49, 723-732.
[7] Raicher I, Yasumasa Takahashi D, Afonso Mederios Kanda P, Nitrini R, Anghinah R (2008) qEEG spectral peak in Alzheimer’s disease A possible tool for treatment follow‐up. Dement Neuropsychol 2, 9-12.
[8] Cecchi M, Moore DK, Sadowsky CH, Solomon PR, Doraiswamy PM, Smith CD, Jicha GA, Budson AE, Arnold SE, Fadem KC (2015) A clinical trial to validate event-related potential markers of Alzheimer’s disease in outpatient settings. Alzheimers Dement (Amst) 1, 387-394.
[9] Laasko MP, Lehtovirta M, Partanen K, Riekkinen PJ, Soininen H (2000) Hippocampus in Alzheimer’s disease: a 3-year follow-up MRI study. Biol Psychiatry 47, 557-561.
[10] Ross DE, Ochs AL, DeSmit ME, Seabaugh JM, Havranek MD, Alzheimer’s Disease Neuroimaging Initiative (2015) Man versus machine part 2: comparison of radiologists’ interpretations and neuroquant measures of brain asymmetry and progressive atrophy in patients with traumatic brain injury. J Neuropsychiatry Clin Neurosci 27, 147-152.
[11] Oscar-Berman M, Marinkovic K (2007) Alcohol: effects on neurobehavioral functions and the brain. Neuropsychol Rev 17, 239-257.
[12] Barrio JR, Small GW, Wong KP, Huang SC, Liu J, Merrill DA, Giza CC, Fitzsimmons RP, Omalu B, Bailes J, Kepe V (2015) In vivo characterization of chronic traumatic encephalopathy using [F-18]FDDNP PET brain imaging. Proc Natl Acad Sci U S A 112, E2039-2047.
[13] Raji CA, Erickson KI, Lopez OL, Kuller LH, Gach HM, Thompson PM, Riverol M, Becker JT (2014) Regular fish consumption and age-related brain gray matter loss. Am J Prev Med 47, 444-451.
[14] Raji CA, Merrill DA, Eyre H, Mallam S, Torosyan N, Erickson KI, Lopez OL, Becker JT, Carmichael OT, Gach HM, Thompson PM, Longstreth WT, Kuller LH (2016) Longitudinal relationships between caloric expenditure and gray matter in the Cardiovascular Health Study. J Alzheimers Dis 52, 719-729.
[15] Barnes DE, Yaffe K (2011) The projected effect of risk factor reduction on Alzheimer’s disease prevalence. Lancet Neurol 10, 819-828.
[16] Smith AD, Yaffe K (2014) Dementia (including Alzheimer’s disease) can be prevented: statement supported by international experts. J Alzheimers Dis 38, 699-703.
[17] Erickson KI, Voss MW, Prakash RS, Basak C, Szabo A, Chaddock L, Kim JS, Heo S, Alves H, White SM, Wojcicki TR, Mailey E, Vieira VJ, Martin SA, Pence BD, Woods JA, McAuley E, Kramer AF (2011) Exercise training increases size of hippocampus and improves memory. Proc Natl Acad Sci U S A 108, 3017-3022.
[18] Desikan RS, Rafii MS, Brewer JB, Hess CP (2013) An expanded role for neuroimaging in the evaluation of memory impairment. AJNR Am J Neuroradiol 34, 2075-2082.
[19] Hunter CA, Kirson NY, Desai U, Cummings AK, Faries DE, Birnbaum HG (2015) Medical costs of Alzheimer’s disease misdiagnosis among US Medicare beneficiaries. Alzheimers Dement 11, 887-895.
[20] Braak H, Braak E (1991) Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol 82, 239-259.
[21] Dickerson BC, Stoub TR, Shah RC, Sperling RA, Killiany RJ, Albert MS, Hyman BT, Blacker D, Detoledo-Morrell L (2011) Alzheimer-signature MRI biomarker predicts AD dementia in cognitively normal adults. Neurology 76, 1395-1402.
[22] Cotman CW, Engesser-Cesar C (2002) Exercise enhances and protects brain function. Exerc Sport Sci Rev 30, 75-79.
[23] Cotman CW, Berchtold NC (2002) Exercise: a behavioral intervention to enhance brain health and plasticity. Trends Neurosci25, 295-301.
[24] Brendel M, Reinisch V, Kalinowski E, Levin J, Delker A, Darr S, Pogarell O, Forster S, Bartenstein P, Rominger A, Alzheimer’s Disease Neuroimaging Initiative (2016) Hypometabolism in brain of cognitively normal patients with depressive symptoms is accompanied by atrophy-related partial volume effects. Curr Alzheimer Res 13, 475-486.
[25] Crossley NA, Scott J, Ellison-Wright I, Mechelli A (2015) Neuroimaging distinction between neurological and psychiatric disorders. Br J Psychiatry 207, 429-434.
[26] Sardanelli F, Bashir H, Berzaczy D, Cannella G, Espeland A, Flor N, Helbich T, Hunink M, Malone DE, Mann R, Muzzupappa C, Petersen LJ, Riklund K, Sconfienza LM, Serafin Z, Spronk S, Stoker J, van Beek EJ, Vorwerk D, Leo GD (2014) The role of imaging specialists as authors of systematic reviews on diagnostic and interventional imaging and its impact on scientific quality: report from the EuroAIM Evidence-based Radiology Working Group. Radiology 272, 533-540.
[27] Raji CA, Eyre H, Wei SH, Bredesen DE, Moylan S, Law M, Small G, Thompson PM, Friedlander RM, Silverman DH, Baune BT, Hoang TA, Salamon N, Toga AW, Vernooij MW (2015) Hot topics in research: preventive neuroradiology in brain aging and cognitive decline. AJNR Am J Neuroradiol 36, 1803-1809.