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Evoked Potentials and Neuropsychological Tests Validate Positron Emission Topography (PET) Brain Metabolism in Cognitively Impaired Patients

  • Eric R. Braverman ,

    Affiliations Department of Clinical Neurology, PATH Foundation NY, New York, New York, United States of America, Department of Psychiatry and McKnight Brain Institute, University of Florida, College of Medicine, Gainesville, Florida, United States of America

  • Kenneth Blum,

    Affiliations Department of Clinical Neurology, PATH Foundation NY, New York, New York, United States of America, Department of Psychiatry and McKnight Brain Institute, University of Florida, College of Medicine, Gainesville, Florida, United States of America, Global Integrated Services Unit, University of Vermont, Center for Clinical and Translational Science, College of Medicine, Burlington, Vermont, United States of America

  • Uma J. Damle,

    Affiliation Department of Clinical Neurology, PATH Foundation NY, New York, New York, United States of America

  • Mallory Kerner,

    Affiliation Department of Clinical Neurology, PATH Foundation NY, New York, New York, United States of America

  • Kristina Dushaj,

    Affiliation Department of Clinical Neurology, PATH Foundation NY, New York, New York, United States of America

  • Marlene Oscar-Berman

    Affiliation Departments of Psychiatry, Neurology, and Anatomy and Neurobiology, Boston University School of Medicine and Boston VA Healthcare System, Boston, Massachusetts, United States of America

Evoked Potentials and Neuropsychological Tests Validate Positron Emission Topography (PET) Brain Metabolism in Cognitively Impaired Patients

  • Eric R. Braverman, 
  • Kenneth Blum, 
  • Uma J. Damle, 
  • Mallory Kerner, 
  • Kristina Dushaj, 
  • Marlene Oscar-Berman


Fluorodeoxyglucose (FDG) Positron Emission Topography (PET) brain hypometabolism (HM) correlates with diminished cognitive capacity and risk of developing dementia. However, because clinical utility of PET is limited by cost, we sought to determine whether a less costly electrophysiological measure, the P300 evoked potential, in combination with neuropsychological test performance, would validate PET HM in neuropsychiatric patients. We found that patients with amnestic and non-amnestic cognitive impairment and HM (n = 43) evidenced significantly reduced P300 amplitudes, delayed latencies, and neuropsychological deficits, compared to patients with normal brain metabolism (NM; n = 187). Data from patients with missing cognitive test scores (n = 57) were removed from the final sample, and logistic regression modeling was performed on the modified sample (n = 173, p = .000004). The logistic regression modeling, based on P300 and neuropsychological measures, was used to validate membership in the HM vs. NM groups. It showed classification validation in 13/25 HM subjects (52.0%) and in 125/148 NM subjects (84.5%), correlating with total classification accuracy of 79.8%. In this paper, abnormal P300 evoked potentials coupled with cognitive test impairment validates brain metabolism and mild/moderate cognitive impairment (MCI). To this end, we cautiously propose incorporating electrophysiological and neuropsychological assessments as cost-effective brain metabolism and MCI indicators in primary care. Final interpretation of these results must await required additional studies confirming these interesting results.


Dementia is the sixth leading cause of death in the United States, increasing in incidence and prevalence as the “baby boomer” generation ages along with longer life expectancies [1]. With the economic burden of dementia consistently rising [2], early identification of cognitive decline in primary care settings is imperative [3]. Decades of research involving brain electrophysiology have shown that delayed latency in the P300 brain wave (the positive spike in an EEG wave 300 ms after a stimulus) and a lower amplitude in the voltage of the P300 wave, occur in both normal aging, and even more so, in dementia [4]. However, little is known about the relation of electrophysiological parameters (P300), HM of the brain, and MCI/Alzheimer's disease (AD) markers such as tau proteins, C-reactive protein, and hippocampal atrophy [5], [6], [7]. If a patient diagnosed with clinical MCI is positive for these markers, prodromal AD should be considered. Magnetic Resonance Imaging or Angiogram (MRI, MRA) and PET are useful techniques that permit us to track abnormalities that may be markers of MCI or AD [8][11]. Both P300 and PET can detect early functional changes in MCI before anatomical damage becomes evident on MRI/MRA or neuropsychological profiles. There is also a paucity of information linking scores on the Mini-Mental State Examination (MMSE) [12] and brain HM in early cognitive decline [13], [14]. Finally, there are no studies to our knowledge that have evaluated the validation ability of three common assessment tools for revealing brain HM: Central Nervous System Vital Signs Memory Test (CNSM); Test of Variables of Attention (TOVA); and Wechsler Memory Scale-III (WMS). Our hypothesis is that evoked potentials and neuropsychological tests can validate PET brain metabolism and MCI, or early stages of Alzheimer's disease [15]. Therefore, the current retrospective study systematically examined the sensitivity and specificity of using P300, TOVA, and memory tests (WMS, CNSM, and MMSE) as early indicators of HM as measured by PET, in a cohort of patients with amnestic and non-amnestic cognitive impairments presenting to a large medical practice [16].



Of the more than 9,000 outpatients who visited a neuropsychiatric private practice group in Manhattan (1998–2009), 662 receiving a fluorodeoxyglucose (FDG) PET scan expressed interest in enrollment in the study, and signed written informed consent forms. The study sample was further refined by selecting patients (n = 240) with data available from P300 [17] visual and auditory evoked potentials, TOVA, WMS, MMSE, and CNSM. Subjects enrolled in the study performed testing on arrival and were advised not to take medications 24 hours prior to testing and were asked to refrain from caffeine, nicotine, and alcohol as well. Subjects did not undergo any Magnetic Resonance (e.g., MRI, MRA). Subjects indicated if they had depressive symptoms (n = 124) prior to the study and were evaluated for depression using the Millon Clinical Multiaxial Inventory-III and the Myers-Briggs Type Indicator (MBTI). Fifty-three percent (53.2%) of these subjects (n = 66) were found to be clinically depressed. Subjects were excluded (n = 10) if they showed evidence of structural brain lesions (e.g., brain tumors, strokes, encephalomalacia) on concomitant computed tomography (CT) brain scans, other neurologic disorders affecting brain functioning (e.g., multiple sclerosis, head trauma), serious systemic illnesses affecting cognitive functioning, or serious psychiatric disorders affecting cognitive functioning (e.g., schizophrenia, bipolar disorder, brain damage or injury).

The study sample was first divided into two groups: positive for PET HM (n = 43) or PET NM (n = 187), as interpreted by visual inspection of PET scans by radiologists. Hypometabolic subjects were arranged in groups according to brain regions where HM was detected: Groups 1 (parietal), Groups 2 (parietal+temporal/frontal), Groups 3 (frontal), Groups 4 (temporal), Groups 5 (focal), and Groups 6 (none). The HM subjects were further categorized as amnestic single domain (n = 1), amnestic multi domain (n = 19), non-amnestic single domain (n = 7), non-amnestic multi domain (n = 14), or no signs of MCI (n = 2). The NM subjects were also categorized as amnestic single domain (n = 3), amnestic multi domain (n = 27), non-amnestic single domain (n = 50), non-amnestic multi domain (n = 88), or no signs of MCI (n = 19).


The narrative reports from the neuroradiology group were divided into six groups of hypometabolism: parietal, parietal plus temporal/frontal, frontal, temporal, focal, or none. The original reading of the neuroradiology group was reconfirmed with visual inspection of the DICOM PET images.

The PET scans completed by a private neuroradiology group (MedScan) were conducted with either a whole-body or brain-specific high-resolution PET (Siemens/CTI ECAT HR+, with 4.6×4.6×4.2 mm NEMA; National Electrical Manufacturers Association) using FDG. Methodological details for scanning have been published [18]. Prior to PET imaging, a diagnostic quality CT scan of the brain was performed without intravenous contrast, and the patient's blood glucose level was assessed as being within normal limits. After the CT scan, 14–18 mCi of FDG was administered intravenously. PET scan imaging was performed approximately 50 minutes after the administration of the radioisotope. Forty-seven slices were obtained at approximately 3.3 mm thickness, covering the entire brain parenchyma from the base of the cerebellum to the vertex.

CDs of the DICOM image data of the PET scans were converted to Analyze format utilizing MRIcro [18], which also anonymized the images to which blinded IDs were assigned. The analyzed formatted images were then imported to Statistical Parametric Mapping [19], where they were reviewed to exclude any scans with significant movement artifact, or areas of hypometabolism related to structural brain disorders not evident in the chart review, but observable on CT scan.

EEG, P300, and Evoked Potentials Data

The P300 potential was obtained using Lexicor and Cognitrace. Twenty electrodes were used (5 in frontal region, 2 frontal temporal, 3 occipital, 2 temporal, 2 temporal parietal, 3 parietal, and 3 along the central sulcus). The two machines were calibrated with repeat scans. Both Lexicor and Cognitrace use auditory stimuli of low and high beeps, and provide an output of latency and amplitude based on preprogrammed baselines based on age. The latency (in milliseconds) and voltage (in microvolts) from the waveform selected for analysis were calculated by the computer algorithm and documented in the patients' charts. All data were anonymized with confidential IDs matching those of the PET scans.

Cognitive Tests for MCI

Data were also collected regarding patients' memory complaints. Memory complaint data were used to determine whether the patient met clinical criteria for MCI: (1) the patient is neither normal nor demented; (2) evidence of cognitive deterioration indicated by subjective report of decline by self and/or informant in conjunction with objective cognitive deficits, or objectively measured cognitive decline over time; (3) and activities of daily living are either intact or only minimally impaired (Table 1) [20]. The CNSM test was computer-administered, and the resultant scores were recorded. The MMSE and WMS Immediate Memory (IM) Index (combining Immediate Verbal with Immediate Non-Verbal Memory scores) were recorded as well as the Wechsler Working Memory (WM) Index Score. The TOVA was computer-administered, and the Omission, Commission, Response Time, and Variability scores were recorded. Clinical and cognitive interviews also were conducted, and based upon combined assessments, the patients were categorized into four MCI domains: amnestic single domain, amnestic multi domain, non-amnestic single domain, or non-amnestic multi domain.

Statistical Analyses

Student's t-test was calculated between the HM group and the NM group, as well as between subjects with and without memory complaints, for P300 latency and amplitude, TOVA reaction time (RT), MMSE score, WMS IM, and CNSM.

It is noteworthy that TOVA RT measures continuous performance, which requires action, whereas P300 is an internal cerebral reaction performance test that does not require action. Consequently, the difference between P300 latency and TOVA RT correlates to the time difference between thought and action. Thus, we calculated two different scores using simple transformation mathematics. In one case, the difference between the P300 latency and TOVA RT (P300-TOVA Diff) was calculated, as well as the absolute value of this difference (Abs Val Diff). Moreover, to account for age differences in P300, a transformation of raw P300 latency was calculated as a difference score between the obtained latency and the predicted latency from the age adjustment [obtained latency−(300+age)], such that positive numbers reflect slower latencies and negative numbers reflect faster latencies (P300AgeDiff). Logistic regression was then utilized to classify patients into groups based on the neurophysiology and memory measures as predictors adjusted to the percent of the sample being predicted.


Correlation of PET with Evoked Potentials and Neuropsychological Test Results

Characteristics of the study sample, consisting of 43 patients with HM and 187 patients with NM, are listed in Table 2. The HM group was significantly older (t = 3.52, p = .007) than the NM group. Table 2 also displays statistics for the memory scores and neuropsychological (P300) measures between the HM and NM groups. As expected, the HM group had lower scores than the NM group for the MMSE (t = −2.73, p = .01) and CNSM (t = −2.82, p = .0001). The WMS IM Index (e.g., Verbal+Visual) and WMS WM Index were not significantly different between the two groups. Using a cutoff of −1.5, SD subjects (as recommended by the test details are considered to be borderline at the −1.5 SD) with impaired WMS IM Index scores did not differ significantly from those with intact scores when compared with hypometabolic and normal metabolic groups.

However, the comparison of NM/HM to non-impaired/impaired CNSM scores (−1.5SD) was significant (χ2 = 10.7, p = .003), with more HM patients presenting poor memory (n = 11/23) than those with NM (n = 19/156). Poor scores on the MMSE (−1.5SD) also differed significantly (χ2 = 5.54, p = .027) between HM patients (7/35) vs. NM (11/155).

Evoked potential and reaction time data for the P300 and the TOVA are also displayed in Table 2. The P300 latency was significantly longer (t = 4.10, p = .00006) and the amplitude significantly lower (t = 3.04, p = .007) in the HM group compared to the NM group. The TOVA RT was significantly longer (t = 3.82, p = .00006) in the HM group compared to the NM group. The difference score between the TOVA RT and P300 latency was significantly greater (t = 2.74, p<.002) in the HM than the NM group, as was the absolute value of this difference (AbsValDiff, t = 2.74, p = .009).

The presence of memory complaints also differentiated one memory variable and several neurophysiologic variables (Table 3). Patients with memory complaints had lower MMSE scores (t = −2.23, p = .027), but did not differ from those without memory complaints on the CNSM, WMS IM, or WMS WM Indexes. Patients with memory complaints were also older (t = 2.95, p = .003), showed prolonged latencies (t = 2.72, p = .007) and decreased amplitude (t = −2.83, p = .005) on the P300, and showed longer RTs on the TOVA (t = 2.72, p = .007). The difference was most marked between the TOVA RT and P300 latency (t = 5.41, p = .000001). Comparing the relationship of memory complaints to HM, 40 of the 43 HM patients (93%) complained of memory problems, whereas 102 of the 187 NM patients (54%) presented with memory complaints (χ2 = 21.9, p = .000001). Significant values were not found for P300AgeDiff, WMS indices, and CNSM indices.

Table 3. Means, SDs, and significance of subjects with and without memory complaints (Cx).

The presence or absence of memory complaints and HM was also examined by dividing the sample into four subgroups and performing one-way analyses of variance (ANOVA). The ANOVA of the P300 amplitude was significant (F = 4.46, p = .005), and post hoc analyses showed that the patients with memory complaints and HM showed significantly lower amplitudes than those without memory complaints and NM (p = .002). The analysis of AbsValDiff was significant (F = 6.97, p = .00017). Post hoc analyses showed the HM group with memory complaints had a significantly greater absolute value difference between TOVA and P300 (95.41 ms) than the NM subgroup with memory complaints (62.8 ms, p = .021) and the NM subgroup without memory complaints (43.5 ms, p = .00006). ANOVA was significant for the MMSE (F = 7.39, p = .0001) with HM patients with memory complaints scoring below NM patients with memory complaints (p = .0002) and NM patients without memory complaints (p = .0001). The ANOVA was also significant for CNSM (F = 6.10, p = .001); HM patients with memory complaints scored below NM patients both with (p = .0003) and without (p = .002) memory complaints.

Those patients missing TOVA, CNSM, and MMSE scores (n = 57) were removed from the final sample (n = 230) and logistic regression modeling was performed on the modified sample (n = 173). Logistic regression analysis was used to validate membership in the HM vs. NM groups using P300 latency (e.g., the difference score between the obtained latency and the predicted latency from the age adjustment [obtained latency−(300+age)]) and P300 amplitude, the absolute value of the difference score between the TOVA RT and P300 latency, and scores on the MMSE and CNSM. The best model was significant (F = 13.2, p = .000004) and retained the CNSM score (Wald = 6.41, p = .011) and the absolute value of the difference score between the TOVA RT and P300 latency (Wald = 4.88, p = .025). The MMSE and P300 amplitude were not retained as validators. The classification model correctly validated 13 of 25 HM subjects (52.0%) and 125 of 148 NM subjects (84.5%) for overall classification accuracy of 79.8%. We recognize that our model demonstrates high specificity with lower sensitivity. Because of the clinical interest in determining HM validating MCI or later dementia in patients with memory complaints, the HM group with memory complaints was compared to the NM group without memory complaints with Logistic Regression entering age, memory scores, and neurophysiologic measures as validators (same as above). This analysis was significant (F = 16.4, p = .000001) retaining CNSM (p = .011) and TOVA-P300 difference score (p = .001). The classification model was significant (χ2 = 28.8, p = .000001) and correctly classified 15 of the 28 HM patients (53.6%) with memory complaints and 72 of the 79 (91.1%) of the NM patients without memory complaints for an overall correct classification of 81.3%.

With regard to the HM subjects, and because of the small sample sizes in Groups 3 (frontal), 4 (temporal), 5 (focal), and 6 (none), only Groups 1 (parietal) and 2 (parietal+temporal/frontal) were compared. The majority of HM patients showed abnormalities in the parietal lobe (Table 4). Group 2 had significantly lower (p = .021) WMS IM scores (96.5, SD = 22.5) than Group 1 (112.75, SD = 19.6) as well as significantly lower scores (p = .047) on CNSM (74.4, SD = 31.5 vs. 101.6, SD = 22.3). The two groups did not differ on any of the P300 latency or amplitude variables. It is noteworthy that 66 participant subjects from the present cohort were diagnosed with depression. We found that 54 of these patients were classified as NM, while only 12 were classified as HM. We also found that 50 of these patients were classified as having amnestic MCI, while 16 were classified as having non-amnestic MCI. All depressed HM subjects (n = 12) have amnestic MCI.

As noted earlier, both HM and NM subjects were evaluated for signs of MCI according to four domains [21]; two HM subjects showed no signs of MCI. Of the HM subjects, 47% were amnestic, 49% non-amnestic and 4% showed no signs of MCI progression (Table 5). For convenience purposes, subjects were categorized into one table: HM/NM, exhibiting signs of MCI/no signs of MCI, and memory complaints/no memory complaints (Table 6).


To reiterate, in the present study using FDG PET scans, we found significantly impaired P300 evoked potentials in those subjects with HM compared to those subjects with NM. The P300 latency was significantly longer and the amplitude was significantly lower in the HM compared to the NM group. While other reports have suggested the importance of event related potentials of the brain as a biomarker for decline in cognitive processes including MCI and AD [22][25], this is the first report that has used FDG PET scans to demonstrate a significant association between brain metabolism and event related potentials. We are encouraged that with additional confirmation of these results, our initial hypothesis will be borne out.

There have been many patterns used to diagnose MCI. Ferris [2] classified multiple stages, but the most appropriate classification system is the subdivision into amnestic single or multi domain and non-amnestic single or multi domain. This four category subdivision is accomplished through the following series of tests: attention; reaction time; judgment; learning ability; delayed recall; linguistic function; verbal IQ; performance IQ; abstract IQ; processing speed; immediate memory; and general cognitive functioning [26], [27]. Subjects who are positive in more than one of these areas are placed into regions, either single or multi domain. The annual conversion rate of MCI to dementia is approximately 4.2% in the general population [28].

In the present study, 76.7% of the HM patients were multi domain. The most common brain region affected in these patients was the parietal lobe (n = 18), affecting 41.8% of the HM group. According to Jacobs, et al. [29], parietal lobe activity characterizes early MCI. With a new concept of the regional spread of Alzheimer's or tauopathy, it is likely that some MCI patients progress with abnormalities in parietal regions, toward abnormalities in the temporal and frontal lobes, similar to a disease that progresses forward [28]. Disease begins with the loss of electrophysiological processing speed followed by voltage, thought to action gaps (e.g., TOVA, P300), and several other forms of cognitive domains. Once progression occurs to hypometabolic loss in the parietal lobes, it is simultaneously marked by demyelination, atrophy, and micro stroke, as seen on MRI scans [30]. This progression is unpredictable because once the process begins, it is extremely difficult to reverse even with the incorporation of new neurogenesis approaches (including serotonin-norepinephrine reuptake inhibitors, insulin-like growth factor 1, selective serotonin re-uptake inhibitors, fish oil, dehydroepiandrosterone, and a variety of other neuroendocrine and nutritional advances). The correlation and supplementation of this primary care data with MRI hippocampal volume is still ongoing [31].

The value of using MMSE as a screening tool to identify cognitive decline has been praised [32] and questioned [33]. Our lab suggested that TOVA also showed promise as a validator of early cognitive decline and/or dementia. Our previous research correlated TOVA abnormalities with impaired WMS scores of early dementia. Coupling of TOVA assessment findings with results of P300, MMSE, and WMS-III may allow for enhanced accuracy in the diagnosis and evaluation of the complex pathways of failing attention, memory, and cognition that lead to dementia [16]. In addition, Gualtieri and Johnson reported on the reliability and validity of the computerized neurocognitive test battery, CNS Vital Signs (CNSVS) [34]. Among the cognitive scores, the MMSE, TOVA, and CNSM scores were significantly lower in the HM compared to the NM group, thus supporting earlier reports [16], [32]. Comparing NM/HM to non-impaired/impaired CNSM was significant, with more HM patients presenting poor memory than those with NM.

Logistic regression modeling significantly validated membership in the HM group with memory complaints vs. the NM group without memory complaints (correctly classifying 81.3% of all patients) using the absolute value of a difference score calculated between each subject's TOVA RT and P300 latency, and scores on the CNSM. Based on our earlier work [17], it was not surprising to find that logistic regression modeling did not retain MMSE as a valuable validator in this study. This supports other studies in the literature that suggest it has limited predictive validity [17], [35][40].

We are cognizant of certain limitations in the present study (e.g., MCI/dementia not specifically evaluated for the current population) [41]. It is tempting to speculate that coupling both the impaired electrophysiological and neuropsychological parameters may provide a less expensive test than FDG PET to assist the primary care physician in diagnosing memory deficits. Thus, large independent studies including additional work coupling impaired P300, validated neuropsychological memory tests, and activities for daily living with FDG PET is encouraged. Another limitation of the current study is that the interpretation of FDG PET scans as hypometabolic or normal were made by visual inspection of the reconstructed scans instead of quantitative analysis of the actual voxel values, either globally or refined by a region of interest analysis. The current study may be extended to include such a quantitative analysis. Since brain volume decreases after age thirty at 0.2% per year [42], [43], another relative weakness of the study was excluding other imaging modalities such as PET beta amyloid tracers, VILIP-1, F-ML-10 apoptosis measures, MRI hippocampal volume, cortical thickness, demyelination, and micro ischemia, which can be combined to earlier detect pre-clinical states of MCI [44][46]. We are cognizant that the depressed subjects showing NM (n = 54) being treated with anti-depressants may have skewed the data because of medication-induced normalization of brain metabolism.

Importantly, the finding of a high specificity of the variables in validating HM is not surprising. However, while the sensitivity appears low by comparison to the specificity, we propose that the sensitivity remains beneficial: if indeed HM is validated in half of the patients, it is highly likely to be present and warrants further investigation for the presence of MCI or dementia. Forty-eight percent (48%) of the HM subjects were false negatives. It is important to utilize the PET scan more selectively, indicating that these 48% receive a PET scan. Fifty-two percent (52%) are ruled out because they do not require the PET scan for early diagnosis.

Results of the present study demonstrated for the first time that electrophysiological parameters (e.g., P300) coupled with neuropsychological measures (e.g., TOVA and CNSM) validate brain HM with clinically useful sensitivity and specificity. The P300 latency was significantly longer and the amplitude was significantly lower in the HM compared to the NM group, and these effects were retained after age correction. Neurocognitive measures (e.g., the MMSE [p = .01], TOVA [p = .00006], and CNSM [p = .0001] scores) were also significantly lower in the HM compared to the NM group. NM/HM compared to non-impaired/impaired CNSM were significant (p = .003), with more HM patients presenting poor memory than patients with NM.

It also appears that patients with single domain cognitive decline have less progression to dementia, followed by those who are multi-domain amnestic or non-amnestic, causing the use of electrophysiology to deteriorate [29], [47], [48]. As MCI atrophy sets in and advances to dementia, positive hypometabolic FDG PET scans are helpful indicators. Brain metabolism then creates a cognitive tipping point that is indicative of dementia or AD [31]. Our subjects, although incompletely characterized, had MCI by a series of neuropsychological measures. Current characterizations suggest that MCI needs greater stratification considering the many patients who already have features of dementia. Most patients with hypometabolic FDG PET scans do have early dementia, even if their current symptoms resemble only MCI [15].

MCI is heterogeneous - with electrophysiological decline, memory and attention failure, multiple domains of cognitive deterioration with and without losses of hippocampal volume, and cortical atrophy - steering patients from MCI into dementia. There are numerous clinical variants of MCI that are antecedents to dementia, where progression is altered dependent on different cognitive phenotypes (e.g., individuals who emphasize working memory vs. auditory memory, abstract IQ vs. emotional IQ) [49]. A similar progression occurs in heart disease based on risk factors (e.g., cholesterol [HDL/LDL], electrophysiological dysfunction, valve and coronary artery disease, hormonal and vascular factors) which may occur in any combination dependent on genetic predispositions or environmental factors. Although no one system is perfectly predictive, an in-office model has been implemented, where electrophysiological decline, particularly, delays of processing speeds when moving from thought to action (e.g., TOVA and P300) seem to be validating PET hypometabolism.


A significant rise in AD is expected with a continuing demographic shift to a more elderly population. AD is predicted to increase from 4.5 million in 2000 to 13.2 million in 2050 as “baby boomers” age and life expectancy increases [50]. If primary care practices implement proper MCI checklists, P300, and TOVA testing done within an hour's time, physicians will be able to diagnose early MCI antecedents [51][55]. Sustaining our intellectual faculties with age may be possible with early diagnosis and treatment. These practices may also have economic advantages as a patient can receive the MCI domain assessment, electrophysiological markers and brain testing at a cost-effective price of $500, while PET scans still remain at an expensive price of $3000–6000 per patient. This proposal may aid in lessening the United States' $200 billion dementia burden by identifying high risk patients through multiple domains (e.g., P300 low voltage and slow speed and temporal differences between thought and action) [56]. These clinical implications may potentially impact the epidemic of dementia at a primary care level, similar to the ways an electrocardiography (ECG), cholesterol testing (HDL/LDL), and the echocardiogram lessened the cardiac burden worldwide.

Future work confirming this clinically relevant research may indeed provide sufficient evidence to suggest the incorporation of impaired electrophysiological and neuropsychological determinants as an efficient means for identifying and validating reduced brain metabolism and cognitive impairment in MCI care settings leading to an early hallmark identifier of patient progression to dementia. We must await further studies before any real interpretation can be garnished from this important preliminary study.


The following individuals researched relevant literature, conducted statistical analysis, and/or assisted with manuscript revisions: Kenneth R. Perrine, Ph.D. (Weill Cornell Medical College), Vivian Lau, Mona Li, Raquel Lohmann, Pooja Reddy, Pavan Reddy, and Swetha Yeldandi.

Author Contributions

Manuscript Preparation: UJD MK KD. Conceived and designed the experiments: ERB. Performed the experiments: ERB KB. Analyzed the data: KB UJD MK KD. Contributed reagents/materials/analysis tools: ERB. Wrote the paper: ERB KB MOB.


  1. 1. Alzheimer's Association (2010) Alzheimer's disease facts and figures. Alzheimers Dement 6: 158–194.
  2. 2. Doody RS, Ferris SH, Salloway S, Meuser TM, Murthy AK, et al. (2011) Identifying Amnestic Mild Cognitive Impairment in Primary Care: A Feasibility Study. Clin Drug Investig 31 (7) 483–491.
  3. 3. Alzheimer's Association (2011) Alzheimer's disease facts and figures. Alzheimers Dement 7 (2) 48–52.
  4. 4. Quiroz YT, Ally BA, Celone K, McKeever J, Ruiz-Rizzo AL, et al. (2011) Event-related potential markers of brain changes in preclinical familial Alzheimer disease. Neurology 77 (5) 469–475.
  5. 5. van Rossum IA, Vos SJB, Knol DL, Scheltens P, Soinninen H, et al. (2012) Injury markers predict time to dementia in subjects with MCI and amyloid pathology. Neurology 79 (17) 1809–1816.
  6. 6. Dlugaj M, Gerwig M, Wege N, Siegrist J, Mann K, et al. (2012) Elevated levels of high-sensitivity C-reactive protein are associated with mild cognitive impairment and its subtypes: results of a population-based case-control study. J Alzheimers Dis 28 (3) 503–14.
  7. 7. Apostolova LG, Thompson PM, Green AE, Hwang KS, Zoumalan C, et al. (2010) 3D Comparison of low, intermediate, and advanced hippocampal atrophy in MCI. Human Brain Mapping 31 (5) 786–97.
  8. 8. Kim SH, Seo SW, Yoon DS, Chin J, Lee BH, et al. (2010) Comparison of neuropsychological and FDG-PET findings between early- versus late-onset mild cognitive impairment: A five-year longitudinal study. Dement Geriatr Cogn Disord 29: 213–223.
  9. 9. Herholz K, Westwood S, Haense C, Dunn G (2011) Evaluation of a calibrated (18)F-FDG PET score as a biomarker for progression in Alzheimer Disease and Mild Cognitive Impairment. J Nucl Med 52 (8) 1218–26.
  10. 10. Aisen PS, Petersen RC, Donohue MC, Gamst A, Raman R, et al. (2010) Clinical core of the Alzheimer's disease neuroimaging initiative: progress and plans. Alzheimers Dement 6 (3) 239–46.
  11. 11. Silbert LC, Dodge HH, Perkins LG, Sherbakov L, Lahna D, et al. (2012) Trajectory of white matter hyperintensity burden preceding mild cognitive impairment. Neurology 79 (8) 741–747.
  12. 12. Folstein MF, Folstein SE, McHugh PR (1975) Mini-mental state: A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 12: 189–198.
  13. 13. Woo BK, Harwood DG, Melrose RJ, Mandelkern MA, Campa OM, et al. (2010) Executive deficits and regional brain metabolism in Alzheimer's disease. Int J Geriatr Psychiatry 25: 1150–1158.
  14. 14. Liepelt I, Reimold M, Maetzler W, Godau J, Reischl G, et al. (2009) Cortical Hypometabolism Assessed by a Metabolic Ratio in Parkinson's Disease Primarily Reflects Cognitive Deterioration – [18F]FDG-PET. Mov Disord 24: 1504–1511.
  15. 15. Koivunen J, Scheinin N, Virta JR, Aalto S, Vahlberg T, et al. (2011) Amyloid PET imaging in patients with mild cognitive impairment: A 2-year follow-up study. Neurology 76: 1085–1090.
  16. 16. Braverman ER, Chen TJ, Schoolfield J, Martinez-Pons M, Arcuri V, et al. (2006) Delayed P300 latency correlates with abnormal Test of Variables of Attention (TOVA) in adults and Predicts Early Cognitive Decline in a Clinical Setting. Advances in Therapy 23: 582–600.
  17. 17. Braverman ER, Blum K (2003) P300 (Latency) Event Related Potential: An Accurate Predictor of Memory Impairment. Clin Electroencephalogr 34: 124–139.
  18. 18. Del Sole A, Clerici F, Chiti A, Lecchi M, Mariani C, et al. (2008) Individual cerebral metabolic deficits in Alzheimer's disease and amnestic mild cognitive impairment: an FDG PET study. Eur J Nucl Med Mol Imaging 35: 1357–1366.
  19. 19. Penny WE, Friston KJ, Ashburner JT, Kiebel SJ, Nichols TE (2007) Statistical Parametric Mapping: The analysis of functional brain images. Burlington, MA: Elsevier.
  20. 20. Frank AR, Petersen RC (2008) Mild cognitive impairment. Handb Clin Neurol 89: 217–21.
  21. 21. Negash S, Geda YE, Petersen RC (2008) Neuropsychological characterization of mild cognitive impairment. Handb Clin Neurol 88: 499–509.
  22. 22. Jackson CE, Snyder PJ (2008) Electroencephalography and event-related potentials as biomarkers of mild cognitive impairment and mild Alzheimer's disease. Alzheimers Dement 4: 137–143.
  23. 23. Van Deursen JA, Vuurman EF, Smits LL, Verhey FR, Riedel WJ (2009) Response speed, contingent negative variation and P300 in Alzheimer's disease and MCI. Brain Cogn 69: 592–599.
  24. 24. Lai CL, Lin RT, Liou LM, Liu CK (2010) The role of event-related potentials in cognitive decline in Alzheimer's disease. Clin Neurophysiol 121: 194–199.
  25. 25. Golob EJ, Ringman JM, Irimajiri R, Bright S, Schaffer B, et al. (2009) Cortical event-related potentials in preclinical familial Alzheimer disease. Neurology 73 (20) 1649–1655.
  26. 26. Mattsson N, Rosen E, Hansson O, Andreasen N, Parnetti L, et al. (2012) Age and diagnostic performance of Alzheimer disease CSF biomarkers. Neurology 78: 468–476.
  27. 27. Wright DW, Nevarez H, Kilgo P, LaPlaca M, Robinson A, et al. (2011) A novel technology to screen for cognitive impairment in the elderly. American Journal of Alzheimer's Disease and Other Dementias 26 (6) 484–491.
  28. 28. Sachdev PS, Lipnicki DM, Crawford J, Reppermund S, Kochan NA, et al. (2012) Risk profiles of subtypes of mild cognitive impairment: the Sydney Memory and Aging Study. J Am Geriatr Soc 60: 24–33.
  29. 29. Jacobs HI, Van Boxtel MP, Heinecke A, Gronenschild EH, Backes WH, et al. (2012) Functional integration of parietal lobe activity in early Alzheimer disease. Neurology 78: 352–36.
  30. 30. Buchman AS, Bennett DA (2012) Amyloid pathology in persons with “normal” cognition. Neurology 78: 228–229.
  31. 31. Bis JC, Decarli C, Smith AV, van der Lijn F, Crivello F, et al. (2012) Common variants at 12q14 and 12q24 are associated with hippocampal volume. Nat Genet 44 (5) 545–551.
  32. 32. De Marchis GM, Foderaro G, Jemora J, Zanchi F, Altobianchi A, et al. (2010) Mild cognitive impairment in medical inpatients: the mini-mental state examination is a promising screening tool. Dement Geriatr Cogn Disord 29: 259–264.
  33. 33. Scazufca M, Almeida OP, Vallada HP, Tasse WA, Menezes PR (2009) Limitations of the Mini-Mental State Examination for screening dementia in a community with low socioeconomic status: results from the Sao Paulo Ageing & Health Study. Eur Arch Psychiatry Clin Neurosci 259: 8–15.
  34. 34. Gualtieri CT, Johnson LG (2008) Age-related cognitive decline in patients with mood disorders. Prog Neuropsychopharmacol Biol Psychiatry 32: 962–967.
  35. 35. Nieuwenhuis-Mark RE (2010) The death knoll for the MMSE: has it outlived its purpose? J Geriatr Psychiatry Neurol 23: 151–157.
  36. 36. McCarten JR, Rottunda SJ, Kuskowski MA (2004) Change in the mini-mental state exam in Alzheimer's disease over 2 years: the experience of a dementia clinic. J Alzheimers Dis 6: 11–15.
  37. 37. Tierney MC, Szalai JP, Dunn E, Geslani D, McDowell I (2000) Prediction of probable Alzheimer disease in patients with symptoms suggestive of memory impairment. Value of the Mini-Mental State Examination. Arch Fam Med 9: 527–532.
  38. 38. Tombaugh TN, McIntyre NJ (1992) The mini-mental state examination: a comprehensive review. J Am Geriatr Soc 40: 922–35.
  39. 39. Ashford JW, Kolm P, Colliver JA, Bekian C, Hsu LN (1989) Alzheimer patient evaluation and the mini-mental state: item characteristic curve analysis. J Gerontol 44: 139–146.
  40. 40. Vemuri P, Weigand SD, Knopman DS, Kantarci K, Boeve BF, et al. (2010) Time-to-event voxel-based techniques to assess regional atrophy associated with MCI risk of progression to AD. Neuroimage 54: 985–991.
  41. 41. Borroni B, Anchisi D, Paghera B, Vicini B, Kerrouche N, et al. (2006) Combined 99mTc-ECD SPECT and neuropsychological studies in MCI for the assessment of conversion to AD. Neurobiol Aging 27: 24–31.
  42. 42. Hedman AM, van Haren NEM, Schnack HG, Kahn RS, Hulshoff Pol HE (2012) Human brain changes across the life span: a review of 56 longitudinal magnetic resonance imaging studies. Hum Brain Mapp 33 (8) 1987–2002.
  43. 43. Jamie Talan (2012) Largest imaging study identifies genes that affect brain size, hippocampal volume: links found to intelligence and memory. Neurology Today 12 (11) 1.
  44. 44. Rodrigue KM, Kennedy KM, Devous MD, Rieck JR, Hebrank AC, et al. (2012) β-Amyloid burden in healthy aging: regional distribution and cognitive consequences. Neurology 78 (6) 387–95.
  45. 45. Tarawneh R, Lee JM, Ladenson JH, Morris JC, Holtzman DM (2012) CSF VILIP-1 predicts rates of cognitive decline in early Alzheimer disease. Neurology 78 (10) 709–19.
  46. 46. Dickerson BC, Wolk DA (2012) MRI cortical thickness biomarker predicts AD-like CSF and cognitive decline in normal adults. Neurology 78 (2) 84–90.
  47. 47. Smith EE, Salat DH, Jeng J, McCreary CR, Fischl B, et al. (2011) Correlations between MRI white matter lesion location and executive function and episodic memory. Neurology 76: 1492–99.
  48. 48. Petersen RC, Parisi JE, Dickson DW, Johnson KA, Knopman DS, et al. (2006) Neuropathologic features of amnestic mild cognitive impairment. Arch Neurol 63: 665–672.
  49. 49. Ridgway GR, Lehmann M, Barnes J, Rohrer JD, Warren JD, et al. (2012) Early-onset Alzheimer disease clinical variants: Multivariate analyses of cortical thickness. Neurology 79: 80–84.
  50. 50. Hathaway A (2012) Women, Estrogen, Cognition, and Alzheimer's disease. Townsend Letter 2012: 64.
  51. 51. Ahiskali M, Green D, Kounios J, Clark CM, Polikar R (2009) ERP based decision fusion for AD diagnosis across cohorts. Conf Proc IEEE Eng Med Biol Soc 2009: 2494–2497.
  52. 52. Lockwood AH, Wack DS, Benedict RHB, Coad ML, Sussman JE, et al. (2008) Multi-site phasic neural activity mediates the execution of an auditory continuous performance task: a PET and electrophysiological study. Journal of Neuroimaging 18 (4) 364–374.
  53. 53. Perrin F, Maquet P, Peigneux P, Ruby P, Degueldre C, et al. (2005) Neural mechanisms involved in the detection of our first name: a combined ERPs and PET study. Neuropsychologia 43: 12–19.
  54. 54. Reinsel RA, Veselis RA, Feshchenko A, Di Resta GR, Mawlawi O, et al. (1995) Target detection and the prefrontal cortex: a PET scan study of the P300 event-related potential. Annals New York Academy of Sciences 769: 393–397.
  55. 55. Marsh JT, Schubarth G, Brown WS, Riege W, Strandburg R, et al. (1990) PET and P300 relationships in early Alzheimer's Disease. Neurobiology of Aging 11: 471–476.
  56. 56. Correa F (2012) AD, Dementia cost $200 billion. Clinical Psychiatry News 40: 33.