Logo of diaclinneuroLink to Publisher's site
Dialogues Clin Neurosci. Dec 2004; 6(4): 351–367.
PMCID: PMC3181818

Language: English | Spanish | French

Mild cognitive impairment: historical development and summary of research

Deterioro cognitivo leve: desarrollo histórico y resumen de investigaciones

Déficit cognitif léger: développement historique et résumé de la recherche

James Golomb, MD
James Golomb, Department of Neurology, William & Sylvia Silberstein Institute for Aging and Dementia, New York University Medical Center, New York, NY;
Alan Kluger, PhD
Alan Kluger, Department of Psychiatry, William & Sylvia Silberstein Institute for Aging and Dementia, New York University Medical Center, New York, NY; Department of Psychology, Lehman College, Bronx, NY, USA;

Abstract

This review article broadly traces the historical development, diagnostic criteria, clinical and neuropathological characteristics, and treatment strategies related to mild cognitive impairment (MCI), The concept of MCI is considered in the context of other terms that have been developed to characterize the elderly with varying degrees of cognitive impairment Criteria based on clinical global scale ratings, cognitive test performance, and performance on other domains of functioning are discussed. Approaches employing clinical, neuropsychological, neuroimaging, biological, and molecular genetic methodology used in the validation of MCI are considered, including results from cross-sectional, longitudinal, and postmortem investigations. Results of recent drug treatment studies of MCI and related methodological issues are also addressed.

Keywords: mild cognitive impairment, diagnostic criteria, treatment, prevalence, prediction, pathology, neuroimaging

Abstract

Este artículo de revisión investiga de manera general el desarrollo histórico, los criterios diagnósticos, las características clínicas y neuropatológicas, y las estrategias terapéuticas que se relacionan con el deterioro cognitivo leve (DCL). El concepto de DCL se incluye dentro del contexto de otros términos que han sido desarrollados para caracterizar la vejez con grados variables de deterioro cognitivo. Se discuten los criterios basados en evaluaciones de escalas clínicas globales, rendimiento en pruebas cognitivas y rendimiento en otros dominios del funcionamiento. También se revisan aproximaciones que utilizan metadología clínica, neuropsicológica, de neuroimágenes, biológicas y de genética molecular empleadas en la validación del DCL, incluyendo resultados de investigaciones transversales, longitudinales y postmortem. Además consignan resultados recientes de estudios farmacológicos en el DCL y temas metodológicos afines.

Résumé

Cet article retrace à grands traits le développement historique, les critères diagnostiques, les caractéristiques cliniques et neuropathologiques ainsi que les stratégies du traitement du déficit cognitif léger (mild cognitive impairment, MCI). Le concept de MCI est étudié conjointement avec d'autres termes créés pour qualifier les personnes âgées présentant des degrés variables de déficit cognitif. Les critères basés sur des échelles de comportement clinique global, les résultats de tests cognitifs et des bilans relatifs à d'autres domaines fonctionnels sont également passés en revue. Nous abordons les méthodes cliniques, neuropsychologiques, de neuro-imagerie, biologiques et de génétique moléculaire utilisées pour confirmer le MCI, y compris les résultats issus d'études transversales, longitudinales et post-mortem. Nous présentons également les résultats d'études sur de nouveaux médicaments pour traiter le MCI et les questions méthodologiques qui s'y rattachent.

The conceptual development of MCI

Families, caregivers, and physicians of persons with Alzheimer's disease (AD) generally find it difficult to pinpoint, even in retrospect, the precise onset of a patient's cognitive impairment. The development of dementia due to a degenerative neurological illness typically proceeds insidiously over several years from a state of cognitive normalcy to progressively severe stages of global intellectual dysfunction. While consensus criteria for diagnosing dementia and AD have been published and widely adopted (Diagnostic and Statistical Manual of Mental Disorders [DSM],1 National institute of Neurological and Communicative Diseases and Stroke/Alzheimer's Disease and Related Disorders Association [NINCDSADRDA]2), guidelines for distinguishing between normal age-related cognitive decline (ARCD) and the transitional levels of intellectual performance that precede the onset of dementia have been slow to emerge. In fact, clinical investigators have grappled with the problem of defining the boundaries of normal cognitive aging for over 40 years. In 1962, Kral3 coined the term “benign senescent forget-fulness” (BSF) to describe a population of nursing-home residents with mild memory deficits that were anticipated to remain stable over time. Subsequently, this concept has undergone many refinements resulting in a proliferation of proposed entities including age-associated memory impairment (AAMI),4 age-consistent memory impairment (ACMI),5 late-life forgetfulness (LLP),5 and ARCD.1 These constructs were intended to identify subjects whose cognitive performance had deteriorated below values established for young adults, but were not expected to undergo significant further decline and were not believed to harbor neuropathological changes. Nevertheless, a paucity of carefully collected follow-up data makes it impossible to validate this hypothesis and it remains unclear whether meeting diagnostic criteria for any of these syndromes really implies cognitive stability in contrast to these proposed definitions of “normal” brain aging, Levy's “aging-associated cognitive decline” (AACD)6 included subjects who performed below normative levels for their own age-group making a pathological basis more likely.

In the 1980s, global clinical staging scales for the study of AD were developed to more rigorously classify the broad spectrum of intellectual performance found in geriatric populations. Two of the most commonly used scales, the Global Deterioration Scale (GDS)7 and the Clinical Dementia Rating (CDR),8 both recognized the need to categorize subjects without dementia who nevertheless exhibited some evidence for cognitive dysfunction. Subjects classified as GDS stage 3 or CDR stage 0.5 were considered cases of “questionable,” “borderline ” or “preclinical” AD, whose cognitive status was intermediary between normal/AAMI/ARCD levels and mild dementia. Other global dementia scales have defined similar transitional stages, for example, “minimal dementia” from the Cambridge Mental Disorders of the Elderly Examination (CAMDEX)9 and “limited cognitive disturbance” from the Comprehensive Assessment and Referral Evaluation (CARE).10 Other constructs, such as isolated memory loss,11 mild cognitive disorder,12 mild neurocognitive disorder,1 and cognitive impairment-no dementia (CIND),13-45 were intended to capture similar levels of overall intellectual performance.

It was in this historical context that the expression “mild cognitive impairment” gradually entered the lexicon of the aging and dementia literature. In 1988, Reisberg et al16 used it as a descriptive term coinciding with the GDS stage 3. Three years later, the term appeared in the title of an article by Flicker et al describing GDS stage 3 subjects at risk for dementia.17 In 1995, Petersen et al18 used mild cognitive impairment (abbreviated as MCI) as an independent diagnostic category not linked to a previously defined rating scale. In this case, the diagnosis was applied to nondemented research subjects who retained normal global cognitive function without impairment on tasks of daily living, but had subjective memory complaints and scored below age-adjusted norms on memory tests. Subsequent years have witnessed further elaboration, refinement, and redefinition of the concept with interest growing markedly19 as exemplified by the exponential increase in published articles utilizing the term (Figure 1).

Figure 1.
Results of Medline searches for the number of citations detected for the term “mild cognitive impairment” between 1989 and 2003. Separate searches were conducted for the term as a keyword and as a title.

To a large extent, this explosion of interest reflects a shift of emphasis in dementia research away from established disease and toward early diagnosis with the recognition that effective therapy may be impossible once advanced neurodegenerative pathology and tissue loss ensues. Clearly, there are several conceptual advantages to the establishment of MCI as a diagnostic category for patients at risk for dementia. From the standpoint of clinical trials, access to samples of nondemented patients likely to undergo accelerated cognitive decline would greatly facilitate the testing of drugs aimed at arresting disease progression. Likewise, longitudinal studies designed to validate early biological or neuroimaging markers of AD pathology also require access to at-risk populations. Finally, the increase in public awareness of AD is driving more patients with mild memory complaints to physicians, who therefore need better diagnostic tools for estimating prognosis. This need will become increasingly acute as the population ages and as new treatments become available.

Criteria for diagnosis of MCI

While the notion of MCI as a transitional stage between cognitive normalcy and dementia is easy enough to grasp, it is presently unclear whether an operational definition can be made sufficiently precise to define a unique and useful diagnostic entity. Part of the difficulty lies in the concept itself. Should MCI be construed as a syndrome with multiple etiological explanations or should the concept be constrained to denote only patients with prodromal AD?20,21 Advocates of the former interpretation have proposed a multitude of MCI subtypes corresponding to the likely underlying neuropathological or psychiatric diagnosis. For example, some proponents of this view suggest vascular22 and frontotemporal23 subtypes of MCI. Such a strategy, however, may open the door to an unwieldy proliferation of subtypes that could weaken the concept by excessively widening its scope (eg, hypothyroid MCI, brain tumor MCI, etc). It is therefore unclear whether MCI should be considered the early stage of a specific disease, a syndrome, or a syndrome constrained by the exclusion of certain other diagnoses (Figure 2).24 The recognition that alternative neuropsychological presentations such as aphasia, ideomotor dyspraxia, or prominent behavioral and affective abnormalities may be relevant with respect to other neurodegenerative dementias has prompted additional MCI subtypes based on the principal form of cognitive deficit present. For example, Petersen22 has proposed a “multiple-domain MCI” for patients exhibiting dysfunction across a range of neuropsychologic modalities, “single nonmemory cognitive domain MCI” for patients whose cognitive symptoms reflect circumscribed impairment in a nonmemory domain, and “amnestic MCI” where memory loss is the predominate reason for impairment. Amnestic MCI has been proposed as the subtype most likely to portend a diagnosis of AD. Because memory symptoms are salient in most patients with early AD, this suggestion has certain face validity. Nevertheless, neuropsychological studies reveal that patients diagnosed with MCI have deficits in several cognitive domains25-29 casting suspicion on whether pure amnestic MCI, strictly speaking, actually exists. A recent European Alzheimer's Disease Consortium/ Alzheimer's Disease Cooperative Study (EADC/ADCS) consensus statement30,31 has expanded the initial concept of amnestic MCI to allow for the presence of other nonmemory deficits (Figure 3). In addition to eliminating cases that meet criteria for dementia, it has been suggested that MCI ought not include patients with impairments in activities of daily living (ADL).22 The stipulation that ADL impairment should be exclusionary, however, ignores the commonly observed subtle difficulties with complex tasks requiring organization and planning that MCI patients frequently experience.31 Thus, the EADC/ADCS revised criteria allow for mild decline in complex ADL.30,31 Requiring the presence of subjective memory complaints may also be too restrictive. Many patients with borderline dementia deny symptoms of memory loss and impaired awareness of cognitive deficits has been recently described in MCI32 In practice, reports of impairment from family members or other informants often substitute for subjective complaints by the patient.

Figure 2.
A conceptual model of mild cognitive impairment (MCI) as prodromal dementia. A minority of persons diagnosed with MCI may remain stable or even improve over time. Although individuals with MCI may decline to vascular or other forms of dementia, the majority ...
Figure 3.
European Alzheimer's Disease Consortium (EADC)/Alzheimer's Disease Cooperative Study (ADCS) consensus on mild cognitive impairment (MCI) subtypes.30 In this scheme, amnestic MCI consists of cases either with memory impairment alone, or accompanied with ...

Regardless of how these conceptual and taxonomic problems are resolved, the successful implementation of MCI as a diagnostic category would seem to depend on the development of a precise set of definitional rules. Nevertheless, despite nearly 10 years of clinical research, a single universally recognized standard has yet to emerge. In general, the difficulty in formulating an operational definition for MCI reflects tension between precisely enumerated rules using cut-scores on staging instruments or psychometric tests and broader criteria that are more conceptual in nature. The former strategy results in a diagnosis that can be established more reliably, but may be too narrow in scope and too complex for routine clinical purposes. The latter strategy, however, may allow too much flexibility of interpretation and result in criteria that are harder to implement consistently inevitably, a compromise solution will need to be reached, but some investigators may argue that existing constructs based on semistructured clinical interviews such as GDS stage 3 or CDR stage 0.5 should form the main basis for diagnosis. Despite the lack of universally accepted diagnostic criteria, an increasing number of groups have been reporting research on MCI populations defined using the classification schemes described above or variations of these methods. The diagnosis is typically made when the clinical context, imaging data, and laboratory results exclude structural, toxic/metabolic, ischemic, or primary psychiatric factors in favor of neurodegenerative processes as the most likely causative mechanism. Regardless of the specific criteria employed, clinicians with experience diagnosing dementia are probably more in agreement than not when characterizing such patients as nondemented, but cognitively impaired. It is therefore likely that samples of MCI patients, particularly when defined in dementia research centers, share enough attributes to give the diagnosis overall “face validity.”

Prevalence of MCI

For a comprehensive treatment of epidemiological characteristics of MCI see the article by Ritchie in this issue.33 The prevalence of MCI in older adults has been difficult to determine. This is due, in part, to the lack of consensus on diagnostic criteria for MCI that can be applied in epidemiological studies, the discrepancies in the age ranges examined, and the demographic characteristics of the samples employed. Due to the protracted time course of MCI and because the population of persons with dementia undergoes an accelerated rate of attrition due to death, the prevalence of persons with MCI at risk for AD is expected to outnumber cases actually diagnosed with AD. A review of population-based investigations of MCI prevalence has observed widely varying rates across studies.34 An estimate of the prevalence rate of MCI can be derived from data reported on elderly from the Canadian Study of Health and Ageing.15 On the basis of pooled samples of community and institutional Canadian elderly aged 65 years and older, the estimated prevalence of CIND was 16.8%. This compared with a prevalence of 8.0% for all types of dementia combined. Since CIND is comprised of a number of categories, including circumscribed memory impairment, depression, drug use, mental retardation, etc, it is likely that it is more inclusive than current definitions of MCI. The category of circumscribed memory impairment (the most frequent category of CIND) is probably less inclusive than current definitions of MCI, and has a prevalence of 5.2%. Therefore, the prevalence rate of MCI can be estimated to be between 5.2% and 16.8%. Yesavage et al35 have employed a Markov model to estimate the most likely prevalence of MCI at specific ages. MCI prevalence increased as a function of age: 1% at age 60; 6% at age 65; 12% at age 70; 20% at age 75; 30% at age 80; and 42% at age 85.

Validation of MCI

Establishing the validity of a clinically defined condition such as MCI depends on it having properties that are distinct from those used to establish the diagnosis. Several strategies have been used to validate the concept of MCI including the following:

  • Longitudinal studies demonstrating that MCI groups are at increased risk for dementia.
  • Cross-sectional studies demonstrating that MCI patients exhibit psychometric, neuroimaging, and biomarker characteristics that are intermediary between normal subjects and those with dementia.
  • Neuropathological studies demonstrating that MCI patients evidence either unique brain changes that would justify a new diagnostic category, or brain changes consistent with an early stage of a dementing disorder.

Longitudinal outcome in MCI

Several studies have examined rates of conversion to dementia among clinical samples diagnosed with MCI. Despite the use of different diagnostic criteria, these studies all demonstrate conversion rates that are higher than the incidence of dementia in the general population, thus lending overall validity to the notion that MCI patients are at increased risk for significant cognitive decline. Bruscoli and Lovestone36 identified 19 longitudinal studies published between 1991 and 2001 that reported conversion rates from MCI to dementia.11,17,21,31,37-51 Although large differences in conversion were observed across these studies (2% to 31%), the calculated mean annual conversion rate was 10.24% (95% confidence interval [CI] 6.9%-11.9%). This figure was slightly more than five times the mean incidence of dementia for similarly aged individuals (estimated to be 1.82%; 95% CI 1.38%-2.38%), based on results from previously published reports.52,53

The highly disparate conversion rates across studies most likely reflect several confounding factors including (i) differences in definitional criteria for MCI; (ii) cross-rater and cross-center reliability differences in the implementation of criteria for both MCI and dementia; (iii) differences in study populations (eg, community versus research clinic); (iv) differences in follow-up interval; and (v) variable use of cholinesterase inhibitors and other potentially protective drugs. In the series reviewed by Bruscoli and Lovestone,36 the single largest factor accounting for variability in decline was the source of the MCI subjects: research clinic subjects had higher conversion rates than community-living volunteers. The impact of subtle differences in definitional criteria on conversion rate is highlighted by a report by Morris et al,21 who subdivided CDR=0.5 patients into three groups based on the CDR subscale scores. These groups, defined as (i) uncertain dementia of the Alzheimer type (DAT), (ii) incipient DAT, and (iii) DAT, represented increasing degrees of clinical confidence that prodromal AD was present. Results of survival analyses indicated that the 5-year rates of progression to dementia (defined as a CDR>1 at follow-up) were 19.9% for the uncertain DAT group, 35.7% for the incipient DAT group, and 60.5% for the DAT group. This compares with a 5-year rate of progression of 6.8% for controls classified as having a CDR=0 at baseline.

Cross-sectional neurpsychological differences in MCI

For a thorough review neuropsychological methods used in MCI see the article by Hahn-Barma et al in this issue.54 A number of studies have compared neuropsychological test performance in subjects diagnosed as cognitively normal, MCI, and AD. In general, MCI patients have been found to perform more poorly than normal subjects on a variety of tests that also separate mildly demented patients from normal individuals. Results from several of these studies are summarized in Table I.16,17,21,25,40,55-59

Table I.
Studies examining cross-sectional psychometric differences between normal and mild cognitive impairment (MCI) elderly people. GDS, Global Deterioration Scale; CDR, Clinical Dementia Rating; DAT, dementia of the Alzheimer's type. Updated from reference ...

While mean neuropsychologic test score differences are found to separate groups of normal, MCI, and mild dementia subjects, significant overlap has been noted.25,55 These results highlight the inherent heterogeneity of MCI as a diagnostic entity comprised of both patients with early neurodegenerative disease and more benign forms of ARCD. Interest has therefore focused on the use of neuropsychological test instruments to predict longitudinal outcome in MCI.

Psychometric prediction of dementia in MCI

The following review is meant to be representative rather than exhaustive, concentrating on studies that have reported on the predictive accuracies of cognitive/psychometric instruments. A number of studies have assessed longitudinal decline in MCI groups. Rubin et al60 followed 16 individuals with MCI (CDR=0.5) over 7 years and found that 69% had declined to dementia by the end of the third year; no other cases converted beyond that time. No formal neuropsychological test data were reported, but the memory subscale of the CDR at baseline predicted 100% of the nondecliners and 64% of the décliners. Similarly, Daly et al43 studied 123 MCI elderly over a 3-year interval and found that 18.7% declined to AD. The sum of six subscales from the CDR (along with information from a clinical interview) correctly identified 90% of the nondecliners and 83% of the decliners.

Flicker et al17 followed 32 normal (GDS=l-2) and 32 MCI cases (GDS=3) over a 2-year follow-up interval and found that 72% of the mildly impaired group progressed to a dementia diagnosis. Classification analyses of the four cognitive tests that showed poorer scores at baseline among the decliners yielded high levels of specificity and sensitivity These four tests assessed verbal recall, visuospatial recall, and two aspects of language function. The verbal recall test (learning a shopping list) was the best single predictor, correctly classifying 95% of the nondecliners and 90% of the decliners. Kluger et al48 studied 213 nondemented elderly (GDS=l-3) over an average follow-up interval of 3.8 years. Of the 87 MCI (GDS=3) cases followed, 68% declined to dementia. Cut-scores from a paragraph delayed recall test assessing recent memory correctly identified 92% of the decliners and 79% of the nondecliners, yielding an overall predictive accuracy of 87%. A diagnostically more restrictive subset of this MCI sample (N=71) was also examined, of whom 66% declined to a diagnosis of probable AD. This same paragraph cutscore correctly identified 96% of the decliners and 83% of the nondecliners, providing an overall accuracy of 92%. Similar findings have been reported by Tierney et al41 for a cognitively diverse sample of research clinic-based, nondemented elderly individuals (GDS=2-3), by Devanand et al38 for individuals with scores of CDR=0 to 0.5, as well as by Masur et al28 for nondemented, healthy communityresiding elderly, who are likely to be comprised of both normal and MCI individuals.

An overview of relatively largesample longitudinal studies (N>70) that have reported predictive accuracies of either individual or small sets of baseline neuropsycho logical test scores for predicting subsequent decline to dementia is provided in Table II.28,38,41,44,48,59,61,62 These studies are organized according to the composition of the nondemented samples at baseline: (i) primarily normal/AAMI/ARCD elderly; (ii) various combinations of normal and MCI cases; or (iii) only MCI cases. One general pattern that emerges from this organizational scheme is “the greater the proportion of MCI cases in the nondemented sample, the greater the subsequent rates of decline.” The reported predictive accuracies include specificity versus sensitivity and/or negative predictive value versus positive predictive value. The specificity of a test signifies the percentage of all truly nondeclining cases accurately classified by the predictor variable, while the sensitivity indicates the percentage of all truly déclining cases accurately classified by the predictor variable. The negative predictive value denotes the percentage of all cases classified by the predictor variable as nondedining cases that actually do not decline, while the positive predictive value indicates the percentage of all cases classified by the predictor variable as declining cases that actually do decline. The overall accuracy identifies the total percentage of subjects (true nondecliners plus true decliners) accurately classified by the predictor variable. The results of these studies assessing putative cognitive predictors of dementia indicate that a small set of psychometric measures can relatively accurately detect pathological decline in nondemented (especially MCI) elderly people. The best single predictors were measures of recent verbal/visuospatial learning and memory, espedaily from tests of delayed recall. Other predictors that have been frequently identified include assessments of language function and psychomotor integration.

Table II.
Summary of relatively large-sample studies (N>70) examining the accuracy of neuropsychological measures in predicting decline to dementia. MCI, mild cognitive impairment. *Decline to Alzheimer's disease. Reproduced from reference 59: Kluger A, ...

It is apparent that not all elderly who are classified as MCI eventually decline to dementia, at least over follow-up intervals of several years. If the definition of MCI at baseline is based on global staging scales (CDR=0.5 or GDS=3), a trade-off can be observed between the added strictness in the definition imposed by additional psychometric criteria and the proportion of decliners observed at follow-up. But this added sensitivity comes at a cost: some decliners will not be identified. Illustrating this point are data described in Table III, representing a recalculation of results from a previous longitudinal report/8 if MCI is defined as all elderly with a baseline GDS=3 (a relatively lax criterion), 68% (59 of 87 cases) of this group will decline at follow-up, roughly 4 years later. If additional criteria are imposed on top of the global scale scores (ie, progressively poorer performance on a test of delayed paragraph recall), the percentage of this group that will eventually decline increases substantially For example, if the definition of MCI is based on GDS=3 as well as a recall score of <4 at baseline, 98% (45 of 46 cases) of this group will decline, but nearly one-quarter of the future decliners (14 of the 59 decliners) will be missed using this relatively strict definition. It is very likely that similar patterns of trade-offs will occur with any sensitive psychometric, biological, or imaging marker when combined with a global scale score definition of MCI. For example, as has been seen, the stratification of the CDR stage 0.5 by the additional clinical criteria suggested by Morris21 results in divergent expectations with respect to rapidity of decline to dementia. Knowledge of these trade-offs has been helpful in selecting enriched MCI samples for drug-treatment trials. Often, only those MCI cases (identified initially by global rating scale classifications) with heightened risk of future decline based on poor memory scores are included in the treatment studies. The strictness of the criterion can be adjusted, depending on the degree of risk associated with the particular investigational compound.

Table III.
Trade-off between strictness of mild cognitive impairment (MCI) criterion (based on New York University [NYU] delayed paragraph recall) and decliners missed. GDS, Global Deterioration Score. Recalculated from data in Kluger et al.48

Pathological basis of MCI

Most MCI patients identified in research clinics who decline to dementia can be retrospectively diagnosed with probable early stage AD. Such patients may therefore already harbor neuritic plaques and neurofibrillary tangles (NFTs), the classically recognized histolopathological hallmarks of AD. In a large study of 109 community-dwelling older adults without dementia,63 33% were found at autopsy to have neocortical neuritic plaques and NFTs suggesting a pathological diagnosis of AD. Methodological considerations preclude knowing how many of these cases actually had MCI, but the findings prompt speculation that gradations of AD-related pathology could explain the milder degrees of intellectual dysfunction prevalent in nondemented populations. The nature of the brain changes that distinguish pathological from normal aging and constitute the basis for MCI are now becoming less obscure.

On the basis of a large autopsy series of 2661 cases, Braak and Braak64 identified six age-associated stages of neurofibrillary change where early NFT formation is restricted to the entorhinal and transentorhinal regions of the medial temporal lobe and occurs in the absence of amyloid plaques. In autopsy studies of normal subjects without any cognitive impairment (CDR=0), investigators have found NFTs to be ubiquitous, but generally confined to the entorhinal cortex and hippocampus65 with densities, particularly for the CA1 region, that increase exponentially with advancing age.66 While most of these cognitively normal cases had either no amyloid deposition or only diffuse nonfibrillar plaques, between 18% and 45% may also exhibit neuritic plaques that are predominately concentrated in the limbic regions of the medial temporal lobe.65-67 It is therefore apparent that some cognitively normal subjects harbor “preclinical” brain changes consistent with a pathological diagnosis of early AD; presumably, such individuals will eventually develop MCI and dementia upon longitudinal observation. Virtually all CDR=0.5 (MCI) subjects studied by Price and Morris were found to have neuritic plaques distributed more diffusely, involving neocortical as well as limbic regions.21,66 These data indicate that MCI, defined as CDR=0.5, may represent early AD more often than previously believed. Such observations, however, must be reconciled with the widely disparate rates of longitudinal decline exhibited by MCI subjects. As discussed previously, the etiological heterogeneity of MCI is most likely influenced by clinical diagnostic criteria as well as the characteristics of the population sampled.

Current research therefore supports the view that a slow progressive increase in medial temporal (entorhinal, perirhinal, and hippocampal) neurofibrillary pathology is the histopathological signature of normal brain aging and generally occurs with minimal or no cognitive consequences. These changes may however, underlie the more subtle and benign memory deficits observed in normal aging and could represent the pathologic basis for AAMI/ARCD. The emergence of neuritic plaques within the medial temporal lobe and neocortex, however, may be the pathological substrate of MCI and signal the onset of AD Figure 1. Why some persons with medial temporal AD pathology are unimpaired (CDR=0), while others exhibit MCI is at present uncertain, although the explanation may, in part, reflect the emergence of neuronal loss within the entorhinal cortex,68,69 a more widespread neocortical localization of plaques and tangles,66 and, perhaps, changes in synaptic morphology and density70 Although they are less pronounced, neurofibrillary changes also affect the nucleus basalis of Meynert in aging and become more pronounced with MCI.71 While cholinergic deficiency could therefore also account for the symptoms of MCI, this has been called into question due to the lack of associated reductions in cortical choline acetyltransferase activity72

Figure 4.
Schematic representation of the distinction between normal (upper curve) and pathologic (lower curve) brain aging. This view, supported by recent clinical pathological studies, suggests that minimal cognitive decline is associated with an age-dependent ...

Neuroimaging findings in MCI

Structural imaging

Given the clinical and pathological results described above, it is understandable that neuroimaging research in MCI has focused on the medial temporal lobe, with particular emphasis on such structures as the hippocampus and entorhinal cortex. The accumulation of AD pathology affecting this anatomy is reflected in volume loss73 and, although hippocampal atrophy is not specific to AD,74-77magnetic resonance imaging (MRI) studies conducted on postmortem brains have shown hippocampal volume reductions that correlate with the Braak stage of neurofibrillary degeneration.78,79 In vivo studies confirm that hippocampal atrophy is a frequent characteristic of MCI80-83 and can predict the occurrence of subsequent dementia.46,84,85 Hippocampal atrophy has also been demonstrated in nondemented subjects destined to develop AD due to the amyloid precursor protein (APP) 717Val-Gly mutation.86 Up to one-third of highly functioning cognitively normal older adults exhibit milder degrees of hippocampal atrophy that correlate with diminished delayed recall performance.87,88 Hippocampal volume loss in these cases may not always reflect the presence of AD pathology,74 but might correspond to benign age-associated neurofibrillary changes. More recent MRI studies have found atrophy of the entorhinal cortex in MCI patients89-91 with greater volume reductions in cases that decline to dementia.92 Nevertheless, it is unknown whether entorhinal atrophy precedes hippocampal atrophy during the pathogenesis of AD or whether MRI measurements of the entorhinal cortex correlate better than volume measurements of the hippocampus with a diagnosis of MCI.93 It is likewise unclear if either measure is a better predictor of risk for subsequent decline.88,91

Structural MRI studies have begun to examine medial temporal lobe volumes as predictors of MCI. An earlier study of highly functioning cognitively normal subjects found baseline measurements of hippocampal size to predict subsequent changes in memory performance and the development of MCI.94 More contemporary studies have analyzed scans at two or more time points to calculate volumetric rates of change.95-98 These studies confirm that higher rates of atrophy affecting medial temporal lobe structures can predict longitudinal cognitive decline and the emergence of MCI. Such results also highlight the potential for using structural MRI as outcome measures in pharmacological trials targeting MCI subjects. At present, however, it is uncertain whether neuropsychological decline can be more robustly detected over a shorter time interval than structural radiographical change.

Functional imaging

Functional imaging research in MCI has included studies using positron emission tomography pgx), single photon emission computed tomography (SPECT), functional magnetic resonance imaging (fMRI), and magnetic resonance spectroscopy (MRS).

Positron emission tomography

PET studies using the radiotracer 2-deoxy-2[18F]fluoro-D-glucose (18FDG) have been employed for over 20 years to study regional rates of glucose utilization in the brain. AD patients tend to exhibit characteristic metabolic reductions in the temporal and parietal association cortices99-101; a distribution that coincides with the neuropathological distribution of AD pathology FDG studies in patients with MCI have demonstrated similar topographic patterns, as well as metabolic reductions in the posterior cingulate gyrus.102-106 Subjects at high genetic risk for AD (due to apolipoprotein E4 [ApoE-4] homozygocity) also exhibit glucose utilization reductions in regions similar to those that become involved in AD.107 Evidence is conflicting concerning the presence of metabolic reductions within the medial temporal anatomy affected in early AD. Some groups have not found differences,108 while others have reported decreased glucose utilization rates affecting the hippocampus and other limbic structures including the mammilary bodies, amygdala, and medial thalamus.109,110 One study found metabolic reductions within the entorhinal cortex to be associated with longitudinal decline to MCI and AD.111 These studies, however, draw their conclusions from small samples and purport to measure structures that challenge the spatial resolving power of the equipment. Despite statistical adjustments for atrophy, it seems possible that tissue loss may be confounding these results, particularly given the findings from numerous structural imaging studies reviewed previously. Cerebral perfusion imaging using SPECT may also be useful in predicting subsequent dementia among patients with MCI.112,113

Functional magnetic resonance imaging

Brain activity following a stimulus can be localized with fMRI, a technique that is sensitive to the small changes in blood oxygenation associated with increased regional metabolic demand. Using visual memory tests to activate the medial temporal lobe, MCI subjects were found to exhibit a smaller fMRI response than cognitively normal subjects, though differences between MCI and AD were not detected.114 Another fMRI study found poor activation within the hippocampus in all MCI subjects, while some had normal entorhinal cortex responses suggesting anatomical heterogeneity with respect to memory processing.115 A recent MCI study116 found that visual memory test performance correlated with medial temporal lobe activation but, surprisingly, activation was more extensive in patients who developed dementia compared with those who remained stable. Like PET and structural MRI studies, nondemented patients at high genetic risk for dementia may exhibit decreased patterns of brain activation compared with controls.117

Magnetic resonance spectroscopy

Using proton MRS (1H-MRS), several groups have found brain metabolite concentrations for 7V-acetylaspartate (NAA) and myoinositol (MI) to distinguish AD patients from controls although conflicting results have been reported for choline.118 Decreased NAA concentration relative to creatine (NAA/Cr) is considered to be an MRS marker of diminished neuronal density and viability. Elevations in Ml/Cr ratios are less specific, but may be associated with glial activation and other neurochemical processes; it is unclear how this may relate to AD pathogenesis. Compared with normal controls, some investigators have found increased Ml/Cr in the posterior cingulate gyrus and white matter of MCI patients.119,120

Nondemented Down's syndrome patients at high risk for AD also have elevated Ml/Cr ratios.121 A recent study observed that decreased medial temporal lobe NAA/H2O ratios distinguished MCI patients from normal controls, while increased parietotemporal MI/H2O distinguished MCI cases from AD.122 Further research will determine whether MRS can identify a specific metabolite signature that differentiates early AD pathology. Some evidence, however, suggests that while NAA/Cr may be a nonspecific marker for age-related neuronal dysfunction and cognitive decline, MI elevations may be a better index of neuropathology.123

Imaging AD pathology

Recently developed amyloid imaging tracers for PET have resulted in pilot studies with promising initial findings.124,125 The positron-emitting [11C]benzothiazole derivative known as Pittsburgh compound-B (PIB) has been shown to effectively discriminate a group of 16 mild AD patients from cognitively normal controls in a recently published PET study.124 The absence of PIB retention within white matter or cerebellar regions (the latter an area of nonfibrillar β-amyloid [Aβ] accumulation) suggests that this agent may specifically image the neuritic plaque deposits that characterize early AD. Although MCI cases were not included, 7 patients were very mildly impaired, as evidenced by Mini-Mental State Examination (MMSE) scores ≥27. The patterns of PIB uptake for 3 of these mildly impaired cases were indistinguishable from control values casting some early doubt on the sensitivity of this technique for identifying MCI cases with AD pathology. Further research will undoubtedly clarify the potential of PIB and other amyloid imaging techniques for making an early diagnosis of AD and monitoring progression of pathology over time.

Biological markers of AD pathology in MCI

Over the past decade, several groups have compared cerebral spinal fluid (CSF) from AD patients with fluid from cognitively normal controls in an effort to identify biological markers indicative of AD pathology Although a large number of candidate markers have been examined, recent interest has focused on observations that CSF concentrations of tau, a microtubule_associated protein comprising NFTs, is elevated in AD,126,127 while levels of the 42 residue form of the Aβ peptide (Aβ1-42) are decreased.128 As reviewed in this issue by Hampel and Blennow,129 multiple studies over recent years have confirmed that these biomarkers can effectively discriminate control subjects from demented patients with a clinical diagnosis of AD. Averaging across 43 studies while fixing diagnostic specificity at 90%, these authors130 found mean sensitivities of over 80% for CSF measurements of total tau and Aβ1-42. Overall discrimination may be somewhat improved by detecting the abnormally phosphorylated forms of tau (phospho-tau) that occur in neurons undergoing neurofibrillary degeneration in AD.131,132 Nearly all groups who have studied CSF tau and Aβ1-42 in MCI populations have found mean concentrations to be intermediary between AD and control values, but closer to the AD levels in patients who decline to dementia.133-138 These results highlight the biological heterogeneity of MCI and suggest that phospho-tau measurements, in particular, could be useful in identifying cases of prodromal AD. As a potential index of AD pathological burden, tau and Aβ1-42 concentrations could be useful outcome measures in treatment studies. Some preliminary evidence, however, suggests that repeated measurements may not always correlate with disease progression.136 It also remains to be determined whether these CSF markers are better predictors of cognitive decline than the structural and functional imaging techniques reviewed previously. Clearly, longitudinal studies in MCI using combinations of brain imaging, psychometric testing, and CSF sampling need to be performed before these questions can be addressed.

Genetic markers of AD pathology in MCI

It is now well recognized that persons carrying the ε4 isoform of ApoE are at increased risk for developing lateonset AD.139 While overexpression of the ε4 allele might be expected in MCI compared with normal controls, its frequency would not be likely to reach the levels seen in AD, since MCI cases comprise not only preclinical AD, but also other more benign conditions predisposing to cognitive impairment. In one large study, the prevalence of nondemented persons with at least one copy of the £4 allele was 22%, while in AD the frequency was 64%.140 Values intermediary between these estimates were found in several studies of nondemented memory impaired individuals who appear to satisfy criteria for the diagnosis of MCI14,18,141,142 Two large population-based studies found that ε4 status was a significant risk factor for MCI.143,144

Most studies have found e4 to exert a cognitive impact on nondemented older adults. In community samples of nondemented elderly, although one cross_sectional study did not find a significant relationship between £4 status and cognition,145 other longitudinal studies found ε4 to be a predictor of accelerated cognitive decline.146-148 According to one report,149 nondemented subjects who carried an e4 allele were more likely to have subjective memory complaints than those without ε4. In studies of cognitively normal persons with high MMSE scores, the impact of age on memory performance (and memory change over time) was more pronounced in e4 homozygotes relative to those without ε4.150-152 These latter reports indicate that ε4 may subtly influence cognitive performance even before the onset of MCI; it is unknown whether this influence can precede the emergence of AD pathology. Although epidemiological and longitudinal clinical data support ε4 as a risk factor for dementia and cognitive decline, its utility as a predictor of clinical outcome in MCI populations needs to be compared with imaging, biomarker, and neuropsychological variables.

Treatment of MCI

The treatment of MCI is reviewed in detail by Gauthier later in this issue.153 Currently, there are no pharmacological treatments for MCI with proven efficacy or regulatory approval. However, clinically there appears to be growing use in MCI of the marketed AD treatments, donepezil, rivastigmine, galantamine, and memantine. A number of clinical trials in MCI patients have been conducted, thus far with mixed results. For example, a 6-month, placebo-controlled trial of donepezil failed to show significant efficacy on the primary end points, but did show efficacy on some secondary cognitive measures.154

Since a high proportion of “amnestic” MCI patients (presumably representing cases of prodromal AD) progress to an AD diagnosis within several years, 2- to 4-year “survival” clinical trial designs have been conducted with MCI patients in which “conversion” to AD is the primary outcome. Such studies are used to determine if a treatment can slow the progression of symptoms. For example, a 3-year trial of vitamin E, donepezil, or placebo failed to show an effect on conversion of vitamin E, but did demonstrate a benefit of donepezil at 6, 12, and 18 months.155 Since there was no benefit at 2 to 3 years, these results for donepezil are consistent with a symptomatic effect that lasts for up to 18 months. A similar 2-year trial of galantamine in MCI failed to show a benefit on the primary end points, but there was some benefit on a secondary cognitive measure.156 Results of a 3- to 4-year conversion trial of rivastigmine have not as yet been reported, but a similar 4-year trial of the anti-inflammatory drug rofecoxib failed to show any clinical efficacy157 Despite the mixed and generally disappointing results of these initial MCI clinical trials, an important general finding is that when the patients progressed to dementia over the course of the trial, the specific diagnosis was almost always AD. This result provides some validation for the operational criteria used to select cases with “amnestic/AD type” MCI.

Conclusion

The concept of MCI in the elderly has evolved over the past 40 years to the point where study of MCI is at the cutting edge of research on the early pathology, early diagnosis, and early treatment of AD. The broad syndrome of MCI, defined clinically as a state of mild impairment that is intermediate between the decline associated with brain aging and the clear deficits that occur in dementia, is clearly heterogeneous with respect to outcome and underlying etiology. However, it is apparent that the major MCI subgroup consists of individuals destined to progress to a diagnosis of AD. As reviewed above, this conclusion is supported by growing number of cross-sectional and longitudinal studies, as well as by studies examining postmortem neuropathology and in vivo neuroimaging and biomarker correlates of AD. Furthermore, since it is feasible clinically to operationalize the identification “amnestic” MCI cases who are likely to have very early AD, such individuals have become an important research group for inclusion in clinical trials designed to examine agents that may slow the progression of AD.

Although clearly valuable as a research tool, it may be debated whether physicians in clinical practice should consider a diagnosis of MCI for individual patients. Because MCI is a heterogeneous entity comprising a variety of neuropathological and psychiatric disorders, and because dementia is not an inevitable outcome, the term may carry too little prognostic and diagnostic weight to legitimize its widespread use on a case-by-case basis. Furthermore, the lack of universally agreed upon criteria and the public's unfamiliarity with the concept could result in increasing uncertainty, anxiety, and misunderstanding. Rather than invoking MCI, patients might be better served if their physicians simply conveyed an opinion regarding the most likely underlying pathological mechanism. For example, a patient with progressive memory loss and poor neuropsychological test performance might be told that early AD pathology is likely, while a patient with minimal objective memory impairment could be informed that such an explanation is less plausible. If medical, neurological, or brain imaging evidence supports other etiologically relevant conditions, this too could be imparted to patients as alternative or contributing factors. It might therefore be asked whether any additional information is gained by adding MCI to the diagnosis. On the other hand, patients and families might be comforted by the MCI label, provided that it is properly explained as a “risk” condition, rather than as a definitive diagnosis of “early AD.” Regardless of the unresolved issues and possibly premature nature of MCI as a psychiatric or neurological “diagnosis” in a patient care setting, the MCI concept has had, and will continue to have, great relevance and importance to research on the causes, early diagnosis, and early treatment of AD.

Selected abbreviations and acronyms

AAMI
age-associated memory impairment
AD
Alzheimer's disease
ARCD
age-related cognitive decline
CDR
Clinical Dementia Rating
CIND
cognitive impairment-no dementia
DAT
dementia of the Alzheimer's type
FMRI
functional magnetic resonance imaging
GDS
Global Deterioration Scale
MCI
mild cognitive impairment
MRS
magnetic resonance spectroscopy
PET
positron emission tomography
SPECT
single photon emission computed tomography

Notes

This work was supported in part by NIH grants P30 AG 08051 and S06 GM 08225.

REFERENCES

1. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders. 4th ed. Washington, DC: American Psychiatric Association; 1994
2. McKhann G., Drachman D., Folstein M., Katzman R., Price D., Stadlan EM. Clinical diagnosis of Alzheimer's disease. Report of the NINCDS-ADRDA work group under the auspices of Department of Health and Human Services Task Force on Alzheimer's disease. Neurology. 1984;34:939–944. [PubMed]
3. Kral VA. Senescent forgetfulness: benign and malignant. Can Med Assoc J. 1962;86:257–260. [PMC free article] [PubMed]
4. Crook T., Bartus RT., Ferris SH., Whitehouse P., Cohen GD., Gershon S. Age-associated memory impairment: proposed diagnostic criteria and measures of clinical change - Report of a NIMH Work Group. Dev Neuropsychol. 1986;2:261–276.
5. Blackford RC., La Rue A. Criteria for diagnosing age-associated memory impairment: proposed improvement from the field. Dev Neuropsychol. 1989;5:295–306.
6. Levy R. Aging-associated cognitive decline. Int Psychogeriatr. 1994;6:63–68. [PubMed]
7. Reisberg B., Ferris SH., de Leon MJ., Crook T. The global deterioration scale for assessment of primary degenerative dementia. Am J Psychiatry. 1982;139:1136–1139. [PubMed]
8. Hughes CP., Berg L., Danziger WL., Coben LA., Martin RL. A new clinical scale for the staging of dementia. Br J Psychiatry. 1982;140:566–572. [PubMed]
9. Roth M., Tym E., Mountjoy CQ., et al. CAMDEX: a standard instrument for the diagnosis of mental disorder in the elderly with special reference to the early detection of dementia. Br J Psychiatry. 1986;149:698–709. [PubMed]
10. Gurland BJ., Dean LL., Copeland J., Gurland R., Golden R. Criteria for the diagnosis of dementia in the community elderly. Gerontologist. 1982;22:180–186. [PubMed]
11. Bowen J., Teri L., Kukull W., McCormick W., McCurry SM., Larson EB. Progression to dementia in patients with isolated memory loss. Lancet. 1997;349:763–765. [PubMed]
12. World Health Organization. The ICD-10 Classification of Mental and Behavioral Disorders. Clinical descriptions and diagnostic guidelines. Geneva, Switzerland: World Health Organization; 1992
13. Ebly EM., Hogan DB., Parhad IM. Cognitive impairment in the nondemented elderly. Arch Neurol. 1995;52:612–619. [PubMed]
14. Tierney MC., Szalai JP., Snow WG., et al. A prospective study of the clinical utility of ApoE genotype in the prediction of outcome in patients with memory impairment. Neurology. 1996;46:149–154. [PubMed]
15. Graham JE., Rockwood K., Beattie EL., et al. Prevalence and severity of cognitive impairment with and without dementia in an elderly population. Lancet. 1997;349:1793–1796. [PubMed]
16. Reisberg B., Ferris SH., de Leon MJ., et al. Stage-specific behavioral, cognitive, and in vivo changes in community residing subjects with age-associated memory impairment (AAMI) and primary degenerative dementia of the Alzheimer type. Drug Dev Res. 1988;15:101–114.
17. Flicker C., Ferris SH., Reisberg B. Mild cognitive impairment in the elderly: predictors of dementia. Neurology. 1991;41:1006–1009. [PubMed]
18. Petersen RC., Smith GE., Ivnik RJ., et al. Apolipoprotein E status as a predictor of the development of Alzheimer's disease in memory-impaired individuals. JAMA. 1995;273:1274–1278. [PubMed]
19. Rivas-Vazquez RA., Mendez C., Rey GJ., Carrazana EJ. Mild cognitive impairment: new neuropsychological and pharmacological target. Arch Clin Neuropsychol. 2004;19:11–27. [PubMed]
20. Dubois B. “Prodromal Alzheimer's disease”: a more useful concept than mild cognitive impairment? Curr Opin Neurol. 2000;13:367–369. [PubMed]
21. Morris JC., Storandt M., Miller JP., et al. Mild cognitive impairment represents early-stage Alzheimer disease. Arch Neurol. 2001;58:397–405. [PubMed]
22. Petersen RC., Doody R., Kurz A., et al. Current concepts in mild cognitive impairment. Arch Neurol. 2001;58:1985–1992. [PubMed]
23. de Mendonca A., Ribeiro F., Guerreiro M., Garcia C. Frontotemporal mild cognitive impairment. J Alzheimer's Dis. 2004;6:1–9. [PubMed]
24. Golomb J., Kluger A., Garrard P., Ferris S. Clinician's Manual on Mild Cognitive Impairment. London, UK: Science Press; 2001
25. Kluger A., Gianutsos JG., Golomb J., et al. Patterns of motor impairment in normal aging, mild cognitive decline, and early Alzheimer's disease. J Gerontol Psychol Sci. 1997;52B:P28–P39. [PubMed]
26. Guarch J., Marcos T., Salamero M., Blesa R. Neuropsychological markers of dementia in patients with memory complaints. Int J Geriatr Psychiatry. 2004;19:352–358. [PubMed]
27. Jacobs DM., Sano M., Dooneief G., Marder K., Bell KL., Stern Y. Neuropsychological detection and characterization of preclinical Alzheimer's disease. Neurology. 1995;45:957–962. [PubMed]
28. Masur DM., Sliwinski M., Lipton RB., Blau AD., Crystal HA. Neuropsychological prediction of dementia and the absence of dementia in healthy elderly persons. Neurology. 1994;44:1427–1432. [PubMed]
29. Elias MF., Beiser A., Wolf PA., Au R., White RF., D'Agostino RB. The preclinical phase of Alzheimer disease. Arch Neurol. 2000;57:808–813. [PubMed]
30. Gauthier S., Touchon J. Subclassification of mild cognitive impairment in research and in clinical practice. In: Gauthier S, Scheltens P, Cummings J, eds. Alzheimer's Disease and Related Disorders. London, UK: Martin Dunitz; 2004:61–69.
31. Ritchie K., Artero S., Touchon J. Classification criteria for mild cognitive impairment: a population-based validation study. Neurology. 2001;56:37–42. [PubMed]
32. Vogel A., Stokholm J., Gade A., Andersen BB., Hejl AM., Waldemar G. Awareness of deficits in mild cognitive impairment and Alzheimer's disease: do MCI patients have impaired insight? Dement Geriatr Cogn Disord. 2004;17:181–187. [PubMed]
33. Ritchie K. Mild cognitive impairment: an epidemiological perspective. Dialogues Clin Neurosci. 2004;6:401–408. [PMC free article] [PubMed]
34. Bischkopf J., Busse A., Angermeyer MC. Mild cognitive impairment - a review of prevalence, incidence and outcome according to current approaches. Acta Psychiatr Scand. 2002;106:403–414. [PubMed]
35. Yesavage JA., O'Hara R., Kraemer H., et al. Modeling the prevalence and incidence of Alzheimer's disease and mild cognitive impairment. J Psychiatr Res. 2002;36:281–286. [PubMed]
36. Bruscoli M., Lovestone S. Is MCI really just early dementia? A systematic review of conversion studies. Int Psychogeriatr. 2004;16:129–140. [PubMed]
37. Bozoki A., Giordani B., Heidebrink JL., Berent S., Foster NL. Mild cognitive impairments predict dementia in nondemented elderly patients with memory loss. Arch Neurol. 2001;58:411–416. [PubMed]
38. Devanand DP., Folz M., Gorlyn M., Moeller JR., Stern Y. Questionable dementia: clinical course and predictors of outcome. J Am Geriatr Soc. 1997;45:321–328. [PubMed]
39. Huang C., Wahlund L., Dierks T., Julin P., Winblad B., Jelic V. Discrimination of Alzheimer's disease and mild cognitive impairment by equivalent EEG sources: a cross-sectional and longitudinal study. Clin Neurophysiol. 2000;111:1961–1967. [PubMed]
40. Petersen RC., Smith GE., Waring SC., Ivnik RJ., Tangalos EG., Kokmen E. Mild cognitive impairment: clinical characterization and outcome. Arch Neurol. 1999;56:303–308. [PubMed]
41. Tierney MC., Szalai JP., Snow WG., et al. Prediction of probable Alzheiemer's disease in memory-impaired patients: a prospective longitudinal study. Neurology. 1996;46:661–665. [PubMed]
42. Wolf H., Ecke GM., Bettin S., Dietrich J., Gertz HJ. Do white matter changes contribute to the subsequent development of dementia in patients with mild cognitive impairment? A longitudinal study. Int J Geriatr Psychiatry. 2000;15:803–812. [PubMed]
43. Daly E., Zaitchik D., Copeland M., Schmahmann J., Gunther J., Albert M. Predicting conversion to Alzheimer disease using standardized clinical information. Arch Neurol. 2000;57:675–680. [PubMed]
44. Grober E., Lipton RB., Hall C., Crystal H. Memory impairment on free and cued selective reminding predicts dementia. Neurology. 2000;54:827–832. [PubMed]
45. Hogan DB., Ebly EM. Predicting who will develop dementia in a cohort of Canadian seniors. Can J Neurol Sci. 2000;27:18–24. [PubMed]
46. Jack CRJ., Petersen RC., Xu YC., et al. Prediction of AD with MRI-based hippocampal volume in mild cognitive impairment. Neurology. 1999;52:1397–1403. [PMC free article] [PubMed]
47. Johansson B., Zarit SH. Early cognitive markers of the incidence of dementia and mortality: a longitudinal population-based study of the oldest old. Int J Geriatr Psychiatry. 1997;12:53–59. [PubMed]
48. Kluger A., Ferris SH., Golomb J., Mittelman MS., Reisberg B. Neuropsychological prediction of decline to dementia in nondemented elderly. J Geriatr Psychiatry Neurol. 1999;12:168–179. [PubMed]
49. Li YS., Meyer JS., Thornby J. Longitudinal follow-up of depressive symptoms among normal versus cognitively impaired elderly. Int J Geriatr Psychiatry. 2001;16:718–727. [PubMed]
50. Paykel ES., Huppert FA., Brayne C. Incidence of dementia and cognitive decline in over-75s in Cambridge: overview of cohort study. Soc Psychiatry Psychiatr Epidemiol. 1998;33:387–392. [PubMed]
51. Visser PJ., Scheltens P., Verby FRJ., et al. Medial temporal lobe atrophy and memory dysfunction as predictors for dementia in subjects with mild cognitive impairment. J Neurol. 1999;246:477–485. [PubMed]
52. Petersen RC., Stevens JC., Ganguli M., Tangalos EG., Cummings JL., DeKosky ST. Practice parameter: early detection of dementia: mild cognitive impairment (an evidence-based review). Report of the Quality Standards Subcommittee of the American Academy of Neurology. Neurology. 2001;56:1133–1142. [PubMed]
53. Bachman DL., Wolf PA., Linn RT., et al. Incidence of dementia and probable Alzheimer's disease in a general population: the Framingham Study. Neurology. 1993;43(3 Pt1):515–519. [PubMed]
54. Hahn-Barma V., Chamayou C., Rogan C., Sarazin M., Dubois B. Neuropsychological methods in mild cognitive impairment. Dialogues Clin Neurosci. 2004;6:396–399. [PMC free article] [PubMed]
55. Storandt M., Hill RD. Very mild senile dementia of the Alzheimer type. II. Psychometric test performance. Arch Neurol. 1989;46:383–386. [PubMed]
56. Mitrushina M., Satz P., Van Gorp W. Some putative cognitive precursors in subjects hypothesized to be at-risk for dementia. Arch Clin Neuropsychol. 1989;4:323–333. [PubMed]
57. Morris JC., McKeel DW., Storandt M., et al. Very mild Alzheimer's disease: informant-based clinical, psychometric, and pathological distinction from normal aging. Neurology. 1991;41:469–478. [PubMed]
58. Grundman M., Petersen RC., Ferris SH., et al. Mild cognitive impairment can be distinguished from Alzheimer disease and normal aging for clinical trials. Arch Neurol. 2004;61:59–66. [PubMed]
59. Kluger A., Golomb J., Ferris SH. Mild Cognitive Impairment. In: Nawab Qizilbash, ed. Evidence-Based Dementia Practice. Oxford, UK: Blackwell Science; 2002:341–354.
60. Rubin EH., Morris JC., Grant EA., Vendegna T. Very mild senile dementia of the Alzheimer type. I. Clinical assessment. Arch Neurol. 1989;46:379–382. [PubMed]
61. Fuld PA., David MM., Blau AD., Crystal H., Aronson MK. Object-memory evaluation for prospective detection of dementia in normal functioning elderly: predictive and normative data. J Clin Exp Neuropsychol. 1990;12:520–528. [PubMed]
62. Dal Forno G., Corrada M., Resnick S., Kawas C. Prediction of the risk of dementia in clinically normal subjects. Neurology. 1995;45(suppl 4):A171.
63. Neuropathology Group. Medical Research Council Cognitive Function and Aging Study. Pathological correlates of late-onset dementia in a multicentre, community-based population in England and Wales. Neuropathology Group of the Medical Research Council Cognitive Function and Ageing Study (MRC CFAS). Lancet. 2001;357:169–175. [PubMed]
64. Braak H., Braak E. Frequency of stages of Alzheimer-related lesions in different age categories. Neurobiol Aging. 1997;18:351–357. [PubMed]
65. Haroutunian V., Purohit DP., Perl DP., et al. Neurofibrillary tangles in nondemented elderly subjects and mild Alzheimer disease. Arch Neurol. 1999;56:713–718. [PubMed]
66. Price JL., Morris JC. Tangles and plaques in nondemented aging and “preclinical” Alzheimer's disease. Ann Neurol. 1999;45:358–368. [PubMed]
67. Hulette CM., Welsh-Bohmer KA., Murray MG., Saunders AM., Mash DC., Mclntyre LM. Neuropathological and neuropsychological changes in “normal” aging: evidence for preclinical Alzheimer disease in cognitively normal individuals. J Neuropathol Exp Neurol. 1998;57:1168–1174. [PubMed]
68. Price JL., Ko AI., Wade MJ., Tsou SK., McKeel DW., Morris JC. Neuron number in the entorhinal cortex and CA1 in preclinical Alzheimer disease. Arch Neurol. 2001;58:1395–1402. [PubMed]
69. Kordower JH., Chu Y., Stebbins GT., et al. Loss and atrophy of layer II entorhinal cortex neurons in elderly people with mild cognitive impairment. Ann Neurol. 2001;49:202–213. [PubMed]
70. Scheff SW., Price DA. Synaptic pathology in Alzheimer's disease: a review of ultrastructural studies. Neurobiol Aging. 2003;24:1029–1046. [PubMed]
71. Mesulam M., Shaw P., Mash D., Weintraub S. Cholinergic nucleus basalis tauopathy emerges early in the aging-MCI-AD continuum. Ann Neurol. 2004;55:815–828. [PubMed]
72. DeKosky ST., Ikonomovic MD., Styren SD., et al. Upregulation of choline acetyltransferase activity in hippocampus and frontal cortex of elderly subjects with mild cognitive impairment. Ann Neurol. 2002;51:145–155. [PubMed]
73. Bobinski M., Wegiel J., Wisniewski HM., et al. Neurofibrillary pathology - correlation with hippocampal formation atrophy in Alzheimer disease. Neurobiol Aging. 1996;17:909–919. [PubMed]
74. West MJ., Coleman PD., Flood DG., Troncoso JC. Differences in the pattern of hippocampal neuronal loss in normal ageing and Alzheimer's disease. Lancet. 1994;344:769–772. [PubMed]
75. Galton CJ., Gomez-Anson B., Antoun N., et al. Temporal lobe rating scale: application to Alzheimer's disease and frontotemporal dementia. J Neurol Neurosurg Psychiatry. 2001;70:165–173. [PMC free article] [PubMed]
76. Camicioli R., Moore MM., Kinney A., Corbridge E., Glassberg K., Kaye JA. Parkinson's disease is associated with hippocampal atrophy. Mov Disord. 2003;18:784–790. [PubMed]
77. Theodore WH., Gaillard WD. Neuroimaging and the progression of epilepsy. Prog Brain Res. 2002;135:305–313. [PubMed]
78. Gosche KM., Mortimer JA., Smith CD., Markesbery WR., Snowdon DA. Hippocampal volume as an index of Alzheimer neuropathology: findings from the Nun Study. Neurology. 2002;58:1476–1482. [PubMed]
79. Jack CR., Jr, Dickson DW., Parisi JE., et al. Antemortem MRI findings correlate with hippocampal neuropathology in typical aging and dementia. Neurology. 2002;58:750–757. [PMC free article] [PubMed]
80. Convit A., de Leon MJ., Golomb J., et al. Hippocampal atrophy in early Alzheimer's disease: anatomic specificity and validation. Psychiatr Q. 1993;64:371–387. [PubMed]
81. Jack CRJ., Petersen RC., Xu YC., et al. Medial temporal atrophy on MRI in normal aging and very mild Alzheimer's disease. Neurology. 1997;49:786–794. [PMC free article] [PubMed]
82. Krasuski JS., Alexander GE., Horwitz B., et al. Volumes of medial temporal lobe structures in patients with Alzheimer's disease and mild cognitive impairment (and in healthy controls). Biol Psychiatry. 1998;43:60–68. [PubMed]
83. Wolf H., Grunwald M., Kruggel F., et al. Hippocampal volume discriminates between normal cognition; questionable and mild dementia in the elderly. Neurobiol Aging. 2001;22:177–186. [PubMed]
84. de Leon MJ., Golomb J., George AE., et al. The radiologic prediction of Alzheimer's disease: the atrophic hippocampal formation. Am J Neuroradiol. 1993;14:897–906. [PubMed]
85. Killiany RJ., Gomez-Isla T., Moss M., et al. Use of structural magnetic resonance imaging to predict who will get Alzheimer's disease. Ann Neurol. 2000;47:430–439. [PubMed]
86. Fox NC., Warrington EK., Freeborough PA., et al. Presymptomatic hippocampal atrophy in Alzheimer's disease. A longitudinal MRI study. Brain. 1996;119(Pt6):2001–2007. [PubMed]
87. Golomb J., de Leon MJ., Kluger A., George AE., Tarshish C., Ferris SH. Hippocampal atrophy in normal aging: an association with recent memory impairment. Arch Neurol. 1993;50:967–973. [PubMed]
88. Golomb J., Kluger A., de Leon MJ., et al. Hippocampal formation size in normal human aging: a correlate of delayed secondary memory performance. Learn Mem. 1994;1:45–54. [PubMed]
89. Killiany RJ., Hyman BT., Gomez-Isla T., et al. MRI measures of entorhinal cortex vs hippocampus in preclinical AD. Neurology. 2002;58:1188–1196. [PubMed]
90. Xu Y., Jack CR., Jr, O'Brien PC., et al. Usefulness of MRI measures of entorhinal cortex versus hippocampus in AD. Neurology. 2000;54:1760–1767. [PubMed]
91. Bobinski M., de Leon MJ., Convit A., et al. MRI of entorhinal cortex in mild Alzheimer's disease. Lancet. 1999;353:38–40. [PubMed]
92. Dickerson BC., Goncharova I., Sullivan MP., et al. MRI-derived entorhinal and hippocampal atrophy in incipient and very mild Alzheimer's disease. Neurobiol Aging. 2001;22:747–754. [PubMed]
93. Pennanen C., Kivipelto M., Tuomainen S., et al. Hippocampus and entorhinal cortex in mild cognitive impairment and early AD. Neurobiol Aging. 2004;25:303–310. [PubMed]
94. Golomb J., Kluger A., de Leon MJ., et al. Hippocampal formation size predicts declining memory performance in normal aging. Neurology. 1996;47:810–813. [PubMed]
95. Mungas D., Reed BR., Jagust WJ., et al. Volumetric MRI predicts rate of cognitive decline related to AD and cerebrovascular disease. Neurology. 2002;59:867–873. [PMC free article] [PubMed]
96. Rusinek H., De Santi S., Frid D., et al. Regional brain atrophy rate predicts future cognitive decline: 6-year longitudinal MR imaging study of normal aging. Radiology. 2003;229:691–696. [PubMed]
97. Rodrigue KM., Raz N. Shrinkage of the entorhinal cortex over 5 years predicts memory performance in healthy adults. J Neurosci. 2004;24:956–963. [PubMed]
98. Jack CR., Shiung MM., Gunter JL., et al. Comparison of different MRI brain atrophy rate measures with clinical disease progression in AD. Neurology. 2004;62:591–600. [PMC free article] [PubMed]
99. Ferris SH., de Leon MJ., Wolf AP., et al. Positron emission tomography in the study of aging and senile dementia. Neurobiol Aging. 1980;1:127–131. [PubMed]
100. Friedland RP., Budinger TF., Ganz E., et al. Regional cerebral metabolic alterations in dementia of the Alzheimer type: positron emission tomography with [18F]fluorodeoxyglucose. J Comp Assisted Tomogr. 1983;7:590–598. [PubMed]
101. Smith GS., de Leon MJ., George AE., et al. Topography of cross-sectional and longitudinal glucose metabolic deficits in Alzheimer's disease. Pathophysiologic implications. Arch Neurol. 1992;49:1142–1150. [PubMed]
102. Pietrini P., Azari NP., Grady CL., et al. Pattern of cerebral metabolic interactions in a subject with isolated amnesia at risk for Alzheimer's disease: a longitudinal evaluation. Dementia. 1993;4:94–101. [PubMed]
103. Minoshima S., Giordani B., Berent S., Frey KA., Foster NL., Kuhl DE. Metabolic reduction in the posterior cingulate cortex in very early Alzheimer's disease. Ann Neurol. 1997;42:85–94. [PubMed]
104. Mielke R., Kessler J., Szelies B., Herholz K., Wienhard K., Heiss WD. Normal and pathological aging - findings of positron-emission-tomography. J Neural Transm (Budapest). 1998;105:821–837. [PubMed]
105. Berent S., Giordani B., Foster N., et al. Neuropsychological function and cerebral glucose utilization in isolated memory impairment and Alzheimer's disease. J Psychiatr Res. 1999;33:7–16. [PubMed]
106. Nestor PJ., Fryer TD., Ikeda M., Hodges JR. Retrosplenial cortex (BA 29/30) hypometabolism in mild cognitive impairment (prodromal Alzheimer's disease). Eur J Neurosci. 2003;18:2663–2667. [PubMed]
107. Reiman EM., Uecker A., Caselli RJ., et al. Hippocampal volumes in cognitively normal persons at genetic risk for Alzheimer's disease. Ann Neurol. 1998;44:288–291. [PubMed]
108. Ishii K., Sasaki M., Yamaji S., Sakamoto S., Kitagaki H., Mori E. Relatively preserved hippocampal glucose metabolism in mild Alzheimer's disease. Dement Geriatr Cogn Disord. 1998;9:317–322. [PubMed]
109. Nestor PJ., Fryer TD., Smielewski P., Hodges JR. Limbic hypometabolism in Alzheimer's disease and mild cognitive impairment. Ann Neurol. 2003;54:343–351. [PubMed]
110. De Santi S., de Leon MJ., Rusinek H., et al. Hippocampal formation glucose metabolism and volume losses in MCI and AD. Neurobiol Aging. 2001;22:529–539. [PubMed]
111. de Leon MJ., Convit A., Wolf OT., et al. Prediction of cognitive decline in normal elderly subjects with 2-[18F]fluoro-2-deoxy-D-glucose/poitron-emission tomography (FDG/PET). Proc Natl Acad Sci U S A. 2001;98:10966–10971. [PMC free article] [PubMed]
112. Johnson KA., Jones K., Holman BL., et al. Preclinical prediction of Alzheimer's disease using SPECT. Neurology. 1998;50:1563–1571. [PubMed]
113. Huang C., Wahlund LO., Svensson L., Winblad B., Julin P. Cingulate cortex hypoperfusion predicts Alzheimer's disease in mild cognitive impairment. BMC Neurol. 2002;2:9. [PMC free article] [PubMed]
114. Machulda MM., Ward HA., Borowski B., et al. Comparison of memory fMRI response among normal, MCI, and Alzheimer's patients. Neurology. 2003;61:500–506. [PMC free article] [PubMed]
115. Small SA., Perera GM., DeLaPaz R., Mayeux R., Stern Y. Differential regional dysfunction of the hippocampal formation among elderly with memory decline and Alzheimer's disease. Ann Neurol. 1999;45:466–472. [PubMed]
116. Dickerson RC., Salat DH., Bates JF., et al. Medial temporal lobe function and structure in mild cognitive impairment. Ann Neurol. 2004;56:27–35. [PubMed]
117. Bookheimer SY., Strojwas MH., Cohen MS., et al. Patterns of brain activation in people at risk for Alzheimer's disease. N Engl J Med. 2000;343:450–456. [PMC free article] [PubMed]
118. Valenzuela MJ., Sachdev P. Magnetic resonance spectroscopy in AD. Neurology. 2001;56:592–598. [PubMed]
119. Catani M., Cherubini A., Howard R., et al. 1H-MR spectroscopy differentiates mild cognitive impairment from normal brain aging. Neuroreport. 2001;12:2315–2317. [PubMed]
120. Kantarci K., Jack CR., Jr., Xu YC., et al. Regional metabolic patterns in mild cognitive impairment and Alzheimer's disease. A 1H-MRS study. Neurology. 2000;55:210–217. [PMC free article] [PubMed]
121. Huang W., Alexander GE., Daly EM., et al. High brain myoinositol levels in the predementia phase of Alzheimer's disease in adults with Down's syndrome: a 1H MRS study. Am J Psychiatry. 1999;156:1879–1886. [PubMed]
122. Chantai S., Braun CM., Bouchard RW., Labelle M., Boulanger Y. Similar 1H magnetic resonance spectroscopic metabolic pattern in the medial temporal lobes of patients with mild cognitive impairment and Alzheimer disease. Brain Res. 2004;1003:26–35. [PubMed]
123. Kantarci K., Smith GE., Ivnik RJ., et al. 1H magnetic resonance spectroscopy, cognitive function, and apolipoprotein E genotype in normal aging, mild cognitive impairment and Alzheimer's disease. J Int Neuropsychol Soc. 2002;8:934–942. [PMC free article] [PubMed]
124. Klunk WE., Engler H., Nordberg A., et al. Imaging brain amyloid in Alzheimer's disease with Pittsburgh compound-B. Ann Neurol. 2004;55:306–319. [PubMed]
125. Shoghi-Jadid K., Small GW., Agdeppa ED., et al. Localization of neurofibrillary tangles and β-amyloid plaques in the brains of living patients with Alzheimer disease. Am J Geriatr Psychiatry. 2002;10:24–35. [PubMed]
126. Vigo-Pelfrey C., Seubert P., Barbour R., et al. Elevation of microtubuleassociated protein tau in the cerebrospinal fluid of patients with Alzheimer's disease. Neurology. 1995;45:788–793. [PubMed]
127. Iqbal K., Grundke-lqbal I. Elevated levels of tau and ubiquitin in brain and cerebrospinal fluid in Alzheimer's disease. Int Psychogeriatr. 1997;9(suppl 1):289–296. [PubMed]
128. Motter R., Vigo-Pelfrey C., Kholodenko D., et al. Reduction of β-amyloid peptide42 in the cerebrospinal fluid of patients with Alzheimer's disease. Ann Neurol. 1995;38:643–648. [PubMed]
129. Hampel H., Blennow K. CSF tau and β-amyloid as biomarkers for mild cognitive impairment. Dialogues Clin Neurosci. 2004;6:379–390. [PMC free article] [PubMed]
130. Blennow K., Hampel H. CSF markers for incipient Alzheimer's disease. Lancet Neurol. 2003;2:605–613. [PubMed]
131. Itoh N., Arai H., Urakami K., et al. Large-scale, multicenter study of cerebrospinal fluid tau protein phosphorylated at serine 199 for the antemortem diagnosis of Alzheimer's disease. Ann Neurol. 2001;50:150–156. [PubMed]
132. Ishiguro K., Ohno H., Arai H., et al. Phosphorylated tau in human cerebrospinal fluid is a diagnostic marker for Alzheimer's disease. Neurosci Lett. 1999;270:91–94. [PubMed]
133. Andreasen N., Vanmechelen E., Vanderstichele H., Davidsson P., Blennow K. Cerebrospinal fluid levels of total-tau, phospho-tau and Aβ342 predicts development of Alzheimer's disease in patients with mild cognitive impairment. Acta Neurol Scand. 2003;(suppl 179):47–51. [PubMed]
134. Arai H., Ishiguro K., Ohno H., et al. CSF phosphorylated tau protein and mild cognitive impairment: a prospective study. Exp Neurol. 2000;166:201–203. [PubMed]
135. Riemenschneider M., Lautenschlager N., Wagenpfeil S., Diehl J., Drzezga A., Kurz A. Cerebrospinal fluid tau and beta-amyloid 42 proteins identify Alzheimer disease in subjects with mild cognitive impairment. Arch Neurol. 2002;59:1729–1734. [PubMed]
136. Andreasen N., Minthon L., Vanmechelen E., et al. Cerebrospinal fluid tau and Aβ42 as predictors of development of Alzheimer's disease in patients with mild cognitive impairment. Neurosci Lett. 1999;273:5–8. [PubMed]
137. Maruyama M., Matsui T., Tanji H., et al. Cerebrospinal fluid tau protein and periventricular white matter lesions in patients with mild cognitive impairment: implications for 2 major pathways. Arch Neurol. 2004;61:716–720. [PubMed]
138. Hampel H., Teïpel SJ., Fuchsberger T., et al. Value of CSF β-amyloid 1-42 and tau as predictors of Alzheimer's disease in patients with mild cognitive impairment. Mol Psychiatry. 2004;9:705–710. [PubMed]
139. Saunders AM., Strittmatter WJ., Schmechel D., et al. Association of apolipoprotein E allele ε4 with late-onset familial and sporadic Alzheimer's disease. Neurology. 1999;43:1467–1472. [PubMed]
140. Poirier J., Davignon J., Bouthïllier D., Kogan S., Bertrand P., Gauthier S. Apolipoprotein E polymorphism and Alzheimer's disease. Lancet. 1993;342:697–699. [PubMed]
141. Blesa R., Adroer R., Santacruz P., Ascaso C., Tolosa E., Oliva R. High apolipoprotein E ε4 Allele frequency in age-related memory decline. Ann Neurol. 1996;39:548–551. [PubMed]
142. Traykov L., Rigaud AS., Baudic S., Smagghe A., Boiler F., Forette F. Apolipoprotein E epsilon 4 allele frequency in demented and cognitively impaired patients with and without cerebrovascular disease. J Neurol Sci. 2002;203-204:177–181. [PubMed]
143. Lopez OL., Jagust WJ., Dulberg C., et al. Risk factors for mild cognitive impairment in the Cardiovascular Health Study Cognition Study. Part 2. Arch Neurol. 2003;60:1394–1399. [PubMed]
144. Tervo S., Kivipelto M., Hanninen T., et al. Incidence and risk factors for mild cognitive impairment: a population-based 3-year follow-up study of cognitively healthy elderly subjects. Dement Geriatr Cogn Disord. 2004;17:196–203. [PubMed]
145. Small BJ., Graves AB., McEvoy CL., Crawford FC., Mullan M., Mortimer JA. Is APOE-epsilon4 a risk factor for cognitive impairment in normal aging? Neurology. 2000;54:2082–2088. [PubMed]
146. Haan MN., Shemanski L., Jagust WJ., Manolio TA., Kuller L. The role of APOE epsilon4 in modulating effects of other risk factors for cognitive decline in elderly persons. JAMA. 1999;282:40–46. [PubMed]
147. Helkala EL., Koivisto K., Hanninen T., et al. Memory functions in human subjects with different apolipoprotein E phenotypes during a 3-year population-based follow-up study. Neurosci Lett. 1996;204:177–180. [PubMed]
148. Feskens EJ., Havekes LM., Kalmijn S., de Knijff P., Launer LJ., Kromhout D. Apolipoprotein e4 allele and cognitive decline in elderly men. BMJ. 1994;309:1202–1206. [PMC free article] [PubMed]
149. Small GW., Chen ST., Komo S., et al. Memory self-appraisal in middleaged and older adults with the apolipoprotein E-4 allele. Am J Psychiatry. 1999;156:1035–1038. [PubMed]
150. Caselli RJ., Graff-Radford NR., Reiman EM., et al. Preclinical memory decline in cognitively normal apolipoprotein E-epsilon4 homozygotes. Neurology. 1999;53:201–207. [PubMed]
151. Caselli RJ., Osborne D., Reiman EM., et al. Preclinical cognitive decline in late middle-aged asymptomatic apolipoprotein E-e4/4 homozygotes: a replication study. J Neurol Sci. 2001;189:93–98. [PubMed]
152. Baxter LC., Caselli RJ., Johnson SC., Reiman E., Osborne D. Apolipoprotein E epsilon 4 affects new learning in cognitively normal individuals at risk for Alzheimer's disease. Neurobiol Aging. 2003;24:947–952. [PubMed]
153. Gauthier S. Pharmacotherapy of mild cognitive impairment. Dialogues Clin Neurosci. 2004;6:391–395. [PMC free article] [PubMed]
154. Salloway S., Ferris S., Kluger A., et al. Efficacy of donepezil in mild cognitive impairment: a randomized placebo-controlled trial. Neurology. 2004;63:651–657. [PubMed]
155. Peterson R., Thomas R., Thai L. Donepezil and vitamin E as treatments for mild cognitive impairment. Paper presented at: 9th International Conference on Alzheimer's Disease and Related Disorders; July 17-22, 2004; Philadelphia, PA.
156. Gold M., Nye JS., Goldstein HR., Truyen L. Initial evaluation of galantamine for mild cognitive impairment: results from two double-blind, randomized, placebo-controlled studies. Paper presented at: 9th International Conference on Alzheimer's Disease and Related Disorders; July 17-22, 2004; Philadelphia, PA.
157. Visser H., Thai L., Ferris S., et al. A randomized, double-blind, placebocontrolled study of rofecoxib in patiens with mild cognitive impairment. Poster presented at: 42nd Annual Meeting of the American College of Neuropsychopharmacology; December 7-11, 2003; San Juan, Puerto Rico.

Articles from Dialogues in Clinical Neuroscience are provided here courtesy of Les Laboratoires Servier
PubReader format: click here to try

Formats:

Related citations in PubMed

See reviews...See all...

Cited by other articles in PMC

See all...

Links

  • Cited in Books
    Cited in Books
    PubMed Central articles cited in books
  • MedGen
    MedGen
    Related information in MedGen
  • PubMed
    PubMed
    PubMed citations for these articles

Recent Activity

Your browsing activity is empty.

Activity recording is turned off.

Turn recording back on

See more...