Cognitive rehabilitation of memory for mild cognitive impairment: a methodological review and model for future research.

Journal of the International Neuropsychological Society (2014), 20, 135–151. Copyright E INS. Published by Cambridge University Press, 2013. doi:10.1017/S1355617713001306

CRITICAL REVIEW

Cognitive Rehabilitation of Memory for Mild Cognitive Impairment: A Methodological Review and Model for Future Research Benjamin M. Hampstead,1,2 M. Meredith Gillis,2 AND Anthony Y. Stringer2,3 1Rehabilitation

R&D Center of Excellence, Atlanta VAMC, Decatur, Georgia of Rehabilitation Medicine, Emory University, Atlanta, Georgia 3Department of Psychology, Emory University, Atlanta, Georgia 2Department

(RECEIVED August 20, 2013; FINAL REVISION November 4, 2013; ACCEPTED November 5, 2013; FIRST PUBLISHED ONLINE December 13, 2013)

Abstract Several recent reviews have suggested that cognitive rehabilitation may hold promise in the treatment of memory deficits experienced by patients with mild cognitive impairment. In contrast to the previous reviews that mainly focused on outcome, the current review examines key methodological challenges that are critical for designing and interpreting research studies and translating results into clinical practice. Using methodological details from 36 studies, we first examine diagnostic variability and how the use of cutoffs may bias samples toward more severely impaired patients. Second, the strengths and limitations of several common rehabilitative techniques are discussed. Half of the reviewed studies used a multi-technique approach that precludes the causal attribution between any specific technique and subsequent improvement. Third, there is a clear need to examine the dose-response relationship since this information was strikingly absent from most studies. Fourth, outcome measures varied widely and frequently depended on neuropsychological tests with little theoretical justification or ecological relevance. Fifth, we discuss how the variability in each of these other four areas complicates efforts to examine training generalization. Overall, future studies should place greater emphasis on ecologically relevant treatment approaches and outcome measures and we propose a hierarchical model that may aid in this pursuit. (JINS, 2014, 20, 135–151) Keywords: Memory training, Aging, Alzheimer’s disease, Dementia, Treatment, Intervention

prolong functioning in at-risk patients. This area is especially important since there is often a multi-year period of cognitive stability between conversion from ‘‘healthy’’ to MCI and subsequent progression to AD (Boyle, Wilson, Aggarwal, Tang, & Bennett, 2006; Manly et al., 2008; Smith et al., 2007). Debate continues on the extent to which pharmacological agents impact cognition and conversion rates (Allain, Bentue-Ferrer, & Akwa, 2007; Daviglus et al., 2010; Diniz et al., 2009; Farlow, 2009; Raschetti, Albanese, Vanacore, & Maggini, 2007) and is beyond the scope of the current study. Instead, we focus on non-pharmacologic approaches and specifically on techniques that are frequently used in the cognitive rehabilitation of explicit learning and memory, which represent the characteristic areas of impairment in MCI. Cognitive rehabilitation is considered a practice standard for some patient populations, such as traumatic brain injury and stroke (Cicerone et al., 2005, 2011). These practice guidelines emerged after decades of research and the development of a coherent and clinically oriented framework. Cognitive rehabilitation (and remediation) has also become a topic of

INTRODUCTION The rate of Alzheimer’s disease is expected to increase two- to three-fold in the coming decades (Brookmeyer, 2011), which threatens to overwhelm healthcare resources at both national and international levels. In response to this challenge, two separate but mutually informative lines of research emerged in recent years. The first attempts to identify at-risk patients before the onset of Alzheimer’s dementia (AD). This effort ultimately culminated in the National Institute on Aging’s and the Alzheimer’s Association’s (Albert et al., 2011) and the American Psychiatric Association’s (APA, 2013) acceptance of mild cognitive impairment (MCI) as a diagnosis that captures cognitively symptomatic individuals who are likely to convert to AD. The second line of research attempts to identify pharmacologic and non-pharmacologic interventions that can enhance, maximize, or otherwise Correspondence and reprint requests to: Benjamin M. Hampstead, 1441 Clifton Road NE Room 150, Atlanta, GA 30087. E-mail: [email protected] 135

136

B.M. Hampstead et al.

considerable interest in patients with schizophrenia (see review by Kurtz, 2012). At this point, the use of cognitive rehabilitation in those with MCI is comparatively understudied and more contentious. For example, a Cochrane Review of randomized controlled trials in those with MCI found no benefit of cognitively based interventions relative to control conditions (Martin, Clare, Altgassen, Cameron, & Zehnder, 2011). It is important to note that these conclusions rested on only three studies that used a wide variety of techniques. Over the past 3 years, there have been at least seven reviews (Belleville, 2008; Cotelli, Menenti, Zanetti, & Miniussi, 2012; Huckans et al., 2013; Jean, Bergeron, Thivierge, & Simard, 2010; Reijnders, van Heugten, & van Boxtel, 2013; Simon, Yokomizo, & Bottino, 2012; Stott & Spector, 2011) and one meta-analysis (Li et al., 2011) that examined whether the cognitive rehabilitation of memory can be effective in those with MCI. The results of all eight of these works generally indicated that patients could benefit from memory rehabilitation. Although the conclusions of these previous efforts are encouraging, the current review focuses on four critical methodological challenges that have yet to be examined in detail, each of which can have profound effects on this overall field of study. We contend that variability in the diagnostic criteria used, techniques investigated, dosage provided, and outcome measures selected render a coherent conclusion difficult at best. We also discuss how this variability and the nature of the rehabilitation affect generalization of the trained techniques. Finally, we provide a model that may prove especially useful in designing and comparing future studies. Selecting and developing control interventions is a major challenge in its own right that has previously been discussed in far more detail than is possible in the current review (see Hart, Fann, & Novack, 2008).

METHODS We reviewed and summarized the primary research articles from each of the eight previous reviews and meta-analytic

studies noted above (Belleville, 2008; Cotelli et al., 2012; Huckans et al., 2013; Jean, Simard, et al., 2010; Li et al., 2011; Reijnders et al., 2013; Simon et al., 2012; Stott & Spector, 2011). This resulted in 29 studies, 1 of which was excluded because it was written in Chinese (Chen et al., 2008). We used ISI Web of Knowledge to identify seven additional studies that targeted memory rehabilitation in patients with MCI with a publication date before April of 2013. Thus, a total of 36 studies were included in the current methodological review. The key features of these 36 studies, including behavioral outcome, can be seen in Supplemental Table 1. We refer the interested reader to the previous comprehensive reviews for a thorough discussion of treatment efficacy. We examined each of these studies according to: diagnostic criteria (Methodological Challenge 1), techniques used (Methodological Challenge 2), dosage provided (Methodological Challenge 3), and outcome measures used (Methodological Challenge 4). Several of the studies lacked detailed methodological details, especially surrounding the interventions. We were able to gather additional information by contacting study authors. When study authors failed to respond to inquiries, two of the current authors (B.M.H. & M.M.G.) re-reviewed the studies in question and arrived at a consensus decision using the available information.

Methodological Challenge 1: Diagnostic criteria The ability to compare treatment efficacy across studies fundamentally depends on the accurate and consistent diagnosis of MCI. As seen in Figure 1, however, there is variability in how previous research studies made this diagnosis. A seemingly encouraging finding was that approximately 81% of reviewed studies used some version of the Petersen et al. (e.g., Petersen et al., 1999; Petersen, 2004) or Winblad et al. (2004) criteria, which are comparable. However, closer examination of each study’s inclusion criteria revealed a split between a psychometrically based (11/36) and a clinically based (18/36) diagnosis. This split has become a topic of debate in the literature and primarily centers on the

Fig. 1. Flow chart of the diagnostic criteria used in the primary research studies (in superscript) that were included in the current review. AAMI 5 age associated memory impairment.

Memory rehabilitation in MCI

137

Table 1. Number of words that a 72 year-old would need to recall to meet standard cutoff scores of -1 or -1.5 SD on two common word list tests CVLT-II: Delayed Free Recall Full: 16 word (n 5 145) -1 SD -1.5 SD -2 SD -2.5 SD

5 3-4 2 0-1

RAVLT: Trial 7 (Delayed Recall) Short: 9 word (n 5 ?)

RBANS Word List Delayed Recall

Metanorms X 5 7 SD 5 2.4 (n 5 143)

MOANS Range 68-78; (n 5 214)

4 3 2 1

3 1 0 0

4-5 Missing 3 2

10 word list X 5 4.9 SD 5 2.5 (n 5 90) 2 1 0 0

CVLT-II: California Verbal Learning Test – II. CVLT norms are from the manual. The sample size for the CVLT Short is not provided, as the publisher (Pearson) considers this information a trade secret that cannot be published. RAVLT: Rey Auditory Verbal Learning Test. Metanorms from Table 10-61 and MOANS norms from Table 10-67 in Strauss, Sherman, & Spreen (2006). Note that the MOANS values are approximate given their use Scaled Scores. RBANS: Repeatable Battery for the Assessment of Neuropsychological Status. Normative data from RBANS post-publication update (Randolph, 2002).

confidence with which clinicians can diagnose MCI as a precursor for AD. For example, Lowenstein, Acevedo, Agron, Martinez, and Duara (2007) found substantial variability in the rate of reversion from MCI to ‘‘normal’’ that depended on the diagnostic criteria and psychometric cutoff values used. Reversion was more likely when patients were less impaired as defined by either the number of memory tests showing reduced performance or by the level of psychometric impairment. These and related findings (e.g., Albert, Moss, Tanzi, & Jones, 2001; Loewenstein, Acevedo, Agron, & Duara et al., 2007; Loewenstein et al., 2009; Teng, Tingus, Lu, & Cummings, 2009) suggest that psychometric cutoffs can increase diagnostic certainty, which should translate into a more homogeneous research population. Although we acknowledge the benefits of this approach for diagnostic purposes, we argue that cutoffs may be inappropriate for cognitive rehabilitation-based research for three primary reasons. First, cutoffs are inconsistent with the most commonly used (i.e., Petersen et al., 1999; Petersen, 2004; Winblad et al., 2004) and current criteria (Albert et al., 2011), which clearly advocate for a clinical diagnosis that is informed by neuropsychological (and other) data (see discussion of clinical vs. psychometric cutoffs on p. 307 of Petersen et al., 1999, p. 187 of Petersen, 2004, and p. 272–273 of Albert et al., 2011). The intermittent use of psychometric cutoffs in rehabilitation studies may ultimately increase inter-study variability and bias some samples toward more severely impaired patients. We return to this issue when discussing the next two problems. The second problem with the use of cutoffs relates to the variability in memory test selection and associated normative data. Simply put, there is no ‘‘gold standard’’ for assessing memory functioning and test selection varies considerably between and even within sites. Word list tests [e.g., Rey Auditory Verbal Learning Test (Rey, 1941; 1964), California Verbal Learning Test (Delis, Kramer, Kaplan, & Ober, 2000] are commonly used for clinical purposes and performance on such measures is sensitive to MCI/AD related decline (e.g., Estevez-Gonzalez, Kulisevsky, Boltes, Otermin, &

Garcia-Sanchez, 2003; Rabin et al., 2009). However, such lists vary in length (generally 9–16 words), semantic relatedness, number and structure of exposures, and other critical factors. While appropriate normative data should minimize concerns about test structure, the quality and nature of such data can also be a source of concern. For example, some tests use correction for factors like age, education, ethnicity, and gender while others do not. It is important to note that normative data are often only available or regularly updated for North American populations, which increases the likelihood of error in other parts of the world. Table 1 uses normative data (typically based on North American samples) for three common word list tests to highlight two potential concerns for a theoretical 72-year-old patient. First, the normative sample sizes vary considerably but are above the accepted number for establishing a normal curve (Fischer, 2010). However, it is possible that the lower end of this ‘‘normal’’ distribution actually represents those with preclinical Alzheimer’s disease since biomarkers have not been widely (if ever) used when collecting normative data. This possibility may mean that ‘‘normal’’ age-related variability in memory functioning is currently over-estimated, especially considering recent evidence that biomarker positive ‘‘normal’’ older adults demonstrate subtle memory decline relative to biomarker negative older adults (Sperling et al., 2013). Second, five or fewer words are needed to meet the -1 or -1.5 cutoff values in all cases in Table 1, indicating that floor effects are pervasive. This means that it will be extremely challenging, if not impossible (e.g., MOANS for the RAVLT; RBANS), to document further decline indicative of progression to AD. In essence, the theoretical distinction between lesser impairment in MCI and greater impairment with progression to AD has been compromised. Functionally, this basal level would typically suggest a frank amnestic profile—again inconsistent with the diagnosis of MCI. These concerns further highlight the probability that samples will be biased toward more severely impaired patients when cutoff scores are used. The third problem with using cutoff scores for cognitive rehabilitation research is that they fail to recognize the

138 continuum between ‘‘normal’’ aging and the ultimate diagnosis of AD. This is especially important because ‘‘early’’ MCI patients are more likely to be active and are often still employed. Conversely, ‘‘late’’ MCI patients can be virtually indistinguishable from those with AD, are less likely to be employed, and typically require greater assistance in everyday life. Thus, the treatment goals may vary considerably as a function of disease severity. This continuum certainly presents a challenge for cognitive rehabilitation research and requires careful selection of outcome measures that are widely applicable. We contend that rehabilitation-based research should include the full ‘‘MCI spectrum’’ and that disease severity should be included in the analysis plan. Approximately 53% of the studies reviewed identified patients as having single- or multi-domain MCI (e.g., Petersen, 2004); however, this distinction is problematic for two primary reasons: (1) it is no longer recognized in the Albert et al. (2011) criteria and (2) the designation of multi-domain provides little information about the actual domains affected or the severity of that impairment. A more informative approach may be to use neuropsychological performances as continuous variables and examine their relationship with treatment efficacy; an approach supported by our recent findings. For example, mnemonic-strategy based improvement was positively related to both executive (defined as the Trails B/A ratio) and memory functioning (Delayed Memory Index of the RBANS) as well as inversely with the volume of the inferior lateral ventricles (Hampstead, Sathian, Phillips, Amaraneni, Delaune, & Stringer, 2012). Our data suggest that training is appropriate for those with ‘‘early’’ MCI whereas improvement following mere repeated exposure (without strategy use) was unrelated to any cognitive or volumetric variable, suggesting it is appropriate for any stage of MCI. This type of approach will become increasingly important as biomarkers are more widely used in the early identification of Alzheimer’s pathology (e.g., Jack, 2012). Thus, using cutoffs would truncate the true range of MCI and negate the identification or use of potentially beneficial treatment methods. On a final note, there may be inherent differences between rehabilitation studies using patients drawn from clinical settings versus community-based recruitment efforts, a possibility that should be examined in future research. With the importance of consistent diagnostic criteria established, we now turn our attention toward factors that can promote perhaps the most common criticism of cognitive rehabilitation research, which is that the effects do not generalize beyond the trained condition. We discuss this issue below (Challenge 5), but contend that an inadequate delineation of the conditions under which the various techniques are successful (Challenge 2), lack of information about the doseresponse relationship (see Challenge 3), and questionable selection of outcome measures (see Challenge 4) can all impede generalization.

Methodological Challenge 2: Techniques Used Just as there are multiple medications available to treat most medical conditions (e.g., high cholesterol), so too are there


Fig. 2. Common categories and associated techniques that are used in cognitive rehabilitation and cognitive training research. Note that most of these techniques could use errorful or errorless learning methods.

multiple techniques that could be used, either alone or in combination, in the rehabilitation of memory. Figure 2 highlights several of the most commonly used memory rehabilitation techniques, which we grouped according to the primary ‘‘mechanism of action.’’ Previous research has distinguished between cognitive training (i.e., methods that seek to enhance specific cognitive abilities or areas of deficit) and cognitive rehabilitation (i.e., methods that seek to improve real-world functioning) (e.g., see Bahar-Fuchs, Clare & Woods, 2013). We acknowledge this distinction, but suggest that clearly delineating the conditions under which these classes and/or specific techniques are effective is vital for optimizing treatment outcome. Failure to do so gives rise to a common criticism of this field of research: that training ‘‘teaches the task.’’ As discussed below, this is more likely to occur with some techniques (e.g., computerized training programs/games) but it is critical to understand that this critique becomes less valid as the ecological relevance of the training techniques/tasks increases. For example, it cannot be considered problematic when a patient reports difficulty remembering faces and names and then demonstrates improvement after training/rehabilitation. However, this does not mean that the patient would also experience improvement remembering routes or upcoming appointments, so training may result in some degree of task specificity. Viewing this relative precision as a limitation would be analogous to viewing a statin as ineffective if it lowers some, but not necessarily all, measures of cholesterol (e.g., low density vs. very low density lipoprotein). Addressing this and similar criticisms requires an understanding of the strengths and weaknesses of the various techniques as well as the rational selection of outcome measures (see Challenge 4). The key is to demonstrate that laboratory-based improvement generalizes to everyday life, which is a challenge in its own right (see Challenge 5). In this section, we first discuss the likely benefits and limitations of these common approaches and then address the current state of research in this area. An important caveat is that almost any of these approaches could use errorful (i.e., allowing the patient to make errors when learning or

Memory rehabilitation in MCI recalling information) or errorless (i.e., preventing patients from making errors) learning approaches. The potential benefits of errorless learning in the rehabilitation context are discussed elsewhere (Wilson, 2009), and emerged following the recognition that mistakes interfered with memory disordered patients’ ability to encode and retain new information (see Clare & Jones, 2008, for a review).

Common Approaches As the name suggests, rehearsal-based approaches rely on the repetition of information over time. However, the nature of this repetition can vary considerably across these techniques. For example, spaced retrieval requires the patient to remember targeted information over progressively longer delays whereas the technique of subtracting cues gradually removes aspects of the target information (e.g., the letters of a name) over successive exposures. These techniques have demonstrated efficacy in patients who have progressed to AD (Cherry, Walvoord, & Hawley, 2010; Small, 2012); results that reinforce our above conclusion that rehearsal may be most appropriate for late MCI patients. These techniques are effective for teaching specific information (Hampstead, Sathian, et al., 2012; Sitzer, Twamley, & Jeste, 2006) (e.g., the names of new church members) but it is critical to understand that the effects are stimulus/information specific and unlikely to generalize (e.g., to other church members or new members of a Senior Center). Therefore, training needs to be repeated for each new piece of information that an individual wants to learn. Practically, this may take the form of a new church member’s name, the location of specific household objects, or a specific route to a new doctor’s office. So, although effective, the ultimate utility of these techniques is dependent on the situation and is stimulus specific. Rehearsal can also be time consuming and even complex (e.g., with spaced retrieval); factors that may further limit the clinical utility of these techniques. Although many of the available computerized training programs purport to improve cognitive abilities and there is one report of increased hippocampal activation after training (Rosen et al., 2011), such approaches have traditionally yielded conflicting evidence of generalization to standardized neuropsychological tests (e.g., Keuider, Parisi, Gross, & Rebok, 2012; Owen et al., 2010) or everyday functioning across patient populations (e.g., d’Amato et al., 2011; Lundqvist, Grundstrom, Samuelsson, & Ronnberg, 2010). This limitation again highlights the need for training to be functionally oriented. Compensatory techniques are designed to alter or augment memory processes, thereby changing the manner in which the patient learns, retains, or retrieves information. External compensatory aids are frequently used by even cognitively intact individuals (e.g., grocery lists; smartphones) and can clearly help improve everyday functioning. Such techniques are probably most effective for prospective tasks like remembering appointments and ‘‘to-do’’ lists. Memory notebooks have long been used in the rehabilitation of memory impairment due to traumatic brain injury (TBI) or stroke and

139 are considered a practice standard as a result (Cicerone et al., 2011). Several well-documented approaches exist, including one described in the Cognitive Rehabilitation Manual published by the American Congress of Rehabilitation Medicine (Haskins et al., 2012). Greenaway, Duncan, and Smith (2013) modified this traditional notebook approach and have shown that a regimented training process improves activities of daily living and memory self-efficacy in patients with MCI while also reducing caregiver distress. Although such external aids can be effective, several limitations exist. The main limitation is that the individual is highly dependent on these external aids, meaning that task failure is almost guaranteed if the aid is lost or forgotten. Additionally, the use of such aids may not always be appropriate. For example, social norms dictate that an individual’s name be recalled relatively quickly, so using an external aid would be especially cumbersome and awkward for this purpose. Likewise, retrieving specific information will become especially challenging with the passage of time given the accumulation of pages (digital or physical), notes, or bookmarks. Although patients may be able to locate such information by referencing key events (e.g., holidays), patients with MCI have difficulty retaining the temporal aspects of information (Gillis, Quinn, Phillips, & Hampstead, 2013) and associating information (Hampstead et al., 2011; for a review see Sperling, 2007); findings that suggest that such temporal or associative referencing will be especially challenging. Mnemonic strategies comprise the internal compensatory category and are cognitive ‘‘tools’’ that facilitate the organization and association of new information, thereby facilitating a deeper level of processing. These techniques include processes like semantic organization, semantic elaboration, and mental imagery. The ‘‘internal’’ (i.e., cognitive) nature of these techniques means that the patient can use them virtually anywhere. Mnemonic strategies are considered a practice standard for treatment of those with mild memory deficits after traumatic brain injury (Cicerone et al., 2011); a fact consistent with our previous finding that these techniques may be most beneficial in early MCI (Hampstead, Sathian, Phillips, et al., 2012). Although comparatively less work has been performed in the field of aging and dementia, a meta-analysis (Verhaeghen, Marcoen, & Goossens, 1992) and several large-scale studies that included mnemonic strategies within larger programs (Craik et al., 2007; Oswald, Rupprecht, Cunzelmann, & Tritt, 1996; Willis et al., 2006) revealed that these techniques facilitate learning and memory in healthy older adults. Our previous findings (Hampstead, Sathian, Moore, Nalisnick, & Stringer, 2008; Hampstead, Sathian, et al., 2012) and those of other groups (Belleville et al., 2011) indicate that mnemonic strategy training can be effective in MCI patients. Because such strategies engage several cognitive processes, it is possible that they can restore the use of ‘‘normal’’ brain regions (or networks) and/or engage alternative compensatory regions (or networks) to achieve behavioral improvement. In fact, we demonstrated increased fMRI-based activation in, and effective connectivity between, several key prefrontal and parietal

140


Fig. 3. Number of studies (references in superscript) using each category (or approach) of techniques (percent of total in parentheses). Note that these designations refer to the primary intervention group. Several studies included active control conditions that used one or more of these techniques. The description of the intervention was vague in eight studies, in which case the intervention was assigned to the most appropriate category based on the information available. Studies in the ‘‘Other’’ category generally involved some variation of psychosocial education or psychotherapy.

regions (Hampstead, Stringer, Stilla, Deshpande, et al., 2011) as well as a partial restoration of hippocampal activation in those with MCI (Hampstead, Stringer Stilla, Giddens, & Sathian, 2012). Belleville and colleagues (2011) also suggested that the right temporoparietal junction may play a compensatory role during strategy use in MCI patients. These findings and the associated behavioral improvements reinforce the notion that mnemonic strategies adaptively alter the manner in which patients process information. As such, they may hold particular promise for generalizing to other situations and everyday life since they involve general ‘‘rules’’ that, once learned, can be applied across settings. As with the other approaches, however, several limitations exist. First, these techniques are time consuming and effortful; facts that are especially important to consider when selecting outcome measures that provide limited exposure time (e.g., one word per second as in most word lists) or a large amount of information that exceeds attentional/working memory capacity. Second, the cognitive demands may render this approach too complex for more cognitively compromised patients (Hampstead, Sathian, et al., 2012). Third, using strategies may be more cumbersome than the task warrants. For example, it is far more efficient to make a grocery list (i.e., rely on an external aid) than to mentally picture and associate all of the items on that list. This last limitation again reinforces the need to select techniques that most appropriately match the patient’s concerns.

Current Literature Figure 3 reveals the variability in techniques used for the primary experimental group in the 36 memory rehabilitation studies we reviewed. We contend that understanding the conditions under which the approaches/techniques are effective is a critical first step for developing effective, individualized cognitive rehabilitation programs. Nearly half (47%) of the reviewed studies used a single treatment approach. Among these, rehearsal based approaches were slightly more often investigated than were compensatory techniques. These findings reveal that there is a relatively

small, but comparatively precise, body of research that can be used to develop more comprehensive programs. As discussed below, however, it is difficult to directly compare the outcome of these studies given the highly variable dose (Challenge 3) and wide array of outcome measures used, many of which are unrelated to the primary intervention techniques (see Challenge 4). Two-thirds of all the studies had total samples of 30 or fewer patients (24/36; 66.7%), which raises additional concerns about statistical power and applicability to the larger MCI population (Figure 4). Half of the studies (50%) used multiple approaches to intervention. This format is more analogous to clinical practice than are the single treatment approaches discussed above and may achieve beneficial effects by exposing patients to a range of options that are adopted based on personal preference (i.e., a ‘‘buffet’’ approach). It is also possible that the combination and duration (see Challenge 3) of these programs facilitates synergistic effects between the various techniques. However, the combination of techniques negates any potential causal inference between a specific technique and an observed cognitive change. Rather, it only allows for the general conclusion that the specific combination of approaches resulted in improvement. This could result in the inclusion of ineffective or unnecessary techniques within a larger rehabilitation program, thereby wasting valuable resources. This limitation would be especially problematic when the clinician is faced with a limited number of billable sessions, particularly with the current trend toward empirically based treatments that may ultimately require the clinician to justify each technique used. It is also difficult to quantify the amount of exposure to, or experience with, each technique. This limitation is addressed in more detail below (see Challenge 3) but raises the possibility that the amount of training, and not the techniques themselves, is responsible for any lack of observed benefit or generalization. Given the variability in techniques used, we have proposed a hierarchical model that will help identify effective techniques and the conditions under which they are successful (see Future Directions).


Fig. 4. Total sample size (experimental 1 control groups) for each of the 36 studies reviewed based on type of intervention.

Methodological Challenge 3: Dose Knowing the proper dose is a fundamental precondition for any treatment, yet such knowledge is lacking in the field of cognitive rehabilitation compared to other disciplines. For instance, the motor rehabilitation literature has demonstrated that functional improvement and the associated neuroplastic change requires of thousands upon thousands of trials. This includes 2500 repetitions of hand movements (Boyd & Winstein, 2003), 18,000 for a finger tracking task (Carey et al., 2002), and 31,500 for sequenced finger movements

141 (Karni et al., 1995). Likewise, constraint induced movement therapy provides patients with hundreds of hours of use with the affected limb (Wolf et al., 2006, 2008). Cognitive processes are far more complex than many of these basic motor activities, so it may be reasonable to assume that comparable or even greater exposure is necessary to fully address these complex processes. It is also important to recognize the differences in clinical approaches that generally provide effectiveness data versus research studies that generally provide efficacy data. For example, clinically oriented cognitive rehabilitation often uses a patient-specific approach where the amount of treatment (i.e., sessions, hours, trials) varies depending on the targeted area(s)/abilities and patient’s adherence to the treatment regimen. One example of this approach is the Ecologically Oriented Neurorehabilitation of Memory (EON-Mem; Stringer, 2007) that is tailored to real-world memory tasks that are both important to, and challenging for, each individual patient. Nightly homework is given and patients continue performing characteristic exercises with each memory task until they have demonstrated a reasonable level of mastery. This approach can be clinically effective (e.g., Stringer, 2011); however, it is largely inconsistent with the research setting where manualized, inflexible, and time limited randomized controlled trials (RCT) are considered the gold standard. As such, RCTs are unlikely to fully accommodate an individual patient’s needs. Such differences have led to calls for more clinically oriented research methods without a strict reliance on RCTs (e.g., Whyte, Gordon, & Gonzalez Rothi, 2009). As noted, there have been few attempts to identify doseresponse relationships in studies of cognitive rehabilitation with MCI. This may relate to the difficulty in defining ‘‘dose’’ since it could refer to the exposure (i.e., number of trials or amount of time) necessary to learn specific information, the exposure necessary to learn to use a given technique or techniques, or the exposure necessary to demonstrate either short- or long-term improvement in functioning. The most common way of expressing dose in the 36 studies we reviewed was in the number of sessions and hours per session. The number of sessions in these studies ranged from 1 to 103 and Figure 5 shows the total number of hours (rounded to the nearest hour) for each study by technique category. It is noteworthy that the duration of intervention did not appear to be empirically based since justification was not typically provided. In essence, the research seemed to use a ‘‘best guess’’ approach. As could be expected, studies using multiple techniques provided more training (i.e., hours) than did those using only one type of intervention. Rehearsalbased studies using computerized programs provided 40–67 hr of training, only two of the six provided justification for the amount of training. Barnes et al. (2009) and Rosen et al. (2011) provided variable lengths of training that depended on patients achieving either asymptotic performance or over 80% completion on a given portion of the program. However, a dose-response relationship has not been established using these programs despite the relative ease with which it could be performed.

142

B.M. Hampstead et al. and these benefits persisted over additional trials and subsequent sessions (see data presented in Table 3 from Hampstead, Sathian, et al., 2012). Given the lack of information in this area, we recommend that future research (1) provide rationale for the dose provided and (2) consider using a trial-based as opposed to a gross timeor session-based measure of dose. Of course, determining the dose at which a given technique or combination of techniques is effective is only relevant if the outcome measures are appropriately selected.

Methodological Challenge 4: Outcome Measures

Fig. 5. Total hours of treatment for each of the reviewed studies based on type of intervention.

Regardless of the approach, it could be argued that the number of hours is an insensitive method since it provides little information about how much practice patients actually receive using the trained technique. Conversely, a trial based method may be more beneficial in this regard. For example, we found an inverse relationship between ‘‘long-term’’ memory of face-name associations and the number of trials needed to learn these stimuli using mnemonic strategies (Hampstead et al., 2008). Our recent RCT showed that mnemonic strategies enhanced memory after a single trial (Cohen’s d 5 1.18 in healthy older adults; d 5 0.97 in MCI)

Most medical research targets a specific condition or disease (e.g., blood pressure, cancer) so precise outcome measures can be obtained and directly compared across studies then translated into clinical practice. However, the study of cognition is far less precise. Just as there are no ‘‘gold standards’’ for assessing memory impairment, none exist for assessing outcome following memory rehabilitation. This is an obvious problem when attempting to compare the results across studies, especially when taking Challenges 1–3 into account. We have identified four general types of outcome measures based on a review of the 36 studies and have summarized some of the benefits and limitations of each type (Table 2). We contend that outcome measures need to be carefully selected based on both theoretical and practical rationale and that these rationale need to be clearly conveyed in the research report. Although seemingly straightforward, such information was rarely provided in the 36 studies we reviewed. In fact, only approximately 64% of the studies provided justification for a general domain (e.g., memory, attention) while a mere 28% justified the use of specific tests/tasks. Table 3 demonstrates the profound variability in outcome measures used across the 36 studies. Multiple outcome measures of the same type (e.g., neuropsychological tests) appeared to be the rule rather than the exception within studies; a finding that reinforces concern about the theoretical justification of these measures as well as the statistical power of the studies themselves. Neuropsychological measures were, by far, the most commonly used with total of 49 different measures across 31 studies. The Mini-Mental Status Exam (Folstein, Folstein, & McHugh, 1975) was the most often used individual measure (12 times); the utility of which could certainly be questioned given its non-specific nature and potential ceiling effects in mildly impaired populations. Across studies the inferred rationale was that neuropsychological tests should be sensitive to rehabilitative efforts since they targeted cognitive processes. This assumption is fundamentally linked to the nature of the training program and is limited by the psychometric properties of the outcome measures. These assumptions will be incorrect in several instances. For example, some version of a word list task was used in 17 studies, yet such tasks are more likely to demonstrate change when patients are taught ways of semantically organizing information whereas change is less likely when


143

Table 2. General types of outcome measures and some benefits and limitations associated with each Type of Measure

Benefits

Limitations

Neuropsychological

- Widely recognized - Standardized - Validated - Normative data (see Challenge 1) - Measures patient’s perception - Good for assessing self-efficacy & willingness to adopt techniques - Subjective (objective?) measure of patient’s functioning - Helpful for examining & predicting caregiver stress, burden, burnout - Best reflection of everyday functioning

- Ecological relevance/validity - Design (e.g., exposure time)

Self-Report

Informant-Report

Ecological

- Increases likelihood of transfer - Increases patient buy-in & motivation

the intervention focuses on the acquisition of specific information/content. The design of most neuropsychological tests (including word lists) is inappropriate for measuring the efficacy of external aids since their use would violate standardization. Furthermore, the limited stimulus exposure (e.g., one word per second) may mitigate the use of internal mnemonic strategies or rehearsal based approaches, especially in bradyphrenic patients. The ultimate ecological relevance of such measures is tenuous (Bjornebekk, Westlye, Walhovd, & Fjell, 2010; Farias, Harrell, Neumann, & Houtz, 2003; Ruff, 2003). These criticisms do not mean that previous research is critically flawed. For instance, Belleville and colleagues (2011) used a word list format to elucidate the neural mechanisms underlying treatment success in patients with MCI. Instead, these criticisms highlight the need for researchers to justify the inclusion of such tests while also considering more ecologically relevant/valid methods as primary outcome variables. Self- (36 measures in 23 studies) and informant(13 measures in 8 studies) report measures were the next most commonly used, respectively, but there was again little consistency across studies. Measures assessing activities of daily living (ADLs) and emotional functioning, especially anxiety and depression, were most frequently used. Using measures of ADLs is debatable depending on how strictly one adheres to the diagnostic criteria for MCI, which require patients to generally be independent. However, research has demonstrated that patients with MCI often show some functional limitations, the severity of which appears to increase as patients progress toward AD (Brown et al., 2011; Burton, Strauss, Bunce, Hunter, & Hultsch, 2009). Therefore, the potential range for improvement is likely to be far smaller in ‘‘early’’ MCI than in ‘‘late’’ MCI, resulting in bias toward the latter. Although administered in only three studies, the Multifactorial Memory Questionnaire (MMQ) may be an especially promising self-report measure given its focus on clinically relevant aspects of memory, strong psychometric

- Requires insight into deficits - Reliability & validity may be questionable - Subject to rater bias (e.g., halo effect, recency effect, caregiver mood) - May rely on inference (e.g., use of ‘‘internal’’ techniques difficult to assess) - Few measures have been validated or standardized (relevance vs. valid) - How many are reasonable to target and test?

properties (see Troyer & Rich, 2002, for a full description), and sensitivity to change after treatment in both healthy older adults (Carretti, Borella, Zavagnin, & De Beni, 2011) and MCI (Kinsella et al., 2009; Troyer, Murphy, Anderson, Moscovitch, & Craik, 2008). Despite the importance of targeting ecologically relevant tasks/abilities, only 10 of the 36 studies (27.8%) used such measures to assess outcome. The limited use of such measures may be yet another reason why generalization is rarely observed and can likely be traced back to the facts that (1) there is a paucity of measures from which to select and (2) the few available measures are rarely used in clinical practice. However, a case could be made for including the Rivermead Behavioural Memory Test (RBMT; RBMT-Extended; Wilson, Cockburn, Baddeley, & Hiorns, 1989; Wilson et al., 1998; Wilson et al., 2008) in this category given its demonstrated ecological validity (Wilson et al., 1989) and multiple test versions that make it attractive for rehabilitation research. In this case a total of 18 studies (50%) used ecological tasks. Despite its strengths, the RBMT may be relatively insensitive to change given the small number of items within each subtest (i.e., ceiling effects) (Wester, Leenders, Egger, & Kessels, 2013) and limitations in the normative data (Strauss, Sherman, & Spreen, 2006; p. 843–845). Wills, Clare, Shiel, and Wilson (2000) suggested that combining the different forms may help overcome the former limitation and the RBMT-Extended is another viable possibility for this purpose, but these options do not address concerns about the normative data. Other measures like the Ecologic Memory Simulations (Stringer, 2011) assess memory functioning across several ecologically based tasks, have multiple versions, and have also shown clinical benefit following cognitive rehabilitation in a mixed neurological sample (Stringer, 2011) but have not been used in an aged population. Given the relative lack of tests and growing importance of such measures, it is clear that the development of ecologically valid tests would benefit rehabilitation research as well as the general field of Neuropsychology.

144

B.M. Hampstead et al. Table 3. Specific tests used as outcome measures in the 36 reviewed studies Outcome measures Neuropsychological Tests A quick test of cognitive speed (AQT)22 AD Assessment Scale (ADAS-cog)7, 25 Analogies23 Animal Fluency26 Attentional marices23 Batterie d’Efficience Mnesique 144 (BEM-144)15 Bell Test23 Boston Diagnostic Aphasia Examination (BDAE)33 Boston Naming Test (BNT)2,33 Bourdon test23 Brown-Peterson task3 California Trail Making Test2 California Verbal Learning Test- Second Edition (CVLT-II)2,12,17,21 Cambridge Automated Neuropsychological Test Battery (CANTAB)9 Cattell Test (Culture Fair Test, scale 3)6 Clock drawing31 Controlled Oral Word Association Test (COWAT)2 Dementia Rating Scale (DRS)10,11,17,24 Design fluency2 Digit span6,15,22,31 Digit symbol coding7,22,31,33 Doors and People15 Dot Matrix6 Hierarchic Dementia Scale (HDS)27 Hopkins Verbal Learning Test (HVLT)34 Letter/word reasoning34 Mini-Mental State Exam (MMSE)5,7,15,16,17,18,24,25,27,30,31,33 Narrative memory (other)3,23,28,30 Nuremberg Aging Inventory (NAI)12 Pattern Comparison task (processing speed)6 Phonemic fluency (FAS)26,31 Raven progressive matrices30 Repeatable Battery for the Assessment of Neuropsychological Status (RBANS)2,5,29 Rey Auditory Verbal Learning Test (RAVLT)18,23,26,33,34 REY-Osterrieth Complex Figure Test (ROCFT)15,21,22,30,31,33 Rivermead Behavioural Memory Test (RBMT)7,8,16,17,26,27,31,33,34 Span tasks (other)23 Spatial span2,22 Stroop test23 Test of everyday attention (TEA)33 The Montreal Cognitive Assessment (MOCA)33 Tower of London23 Trail Making Test A and/or B5,6,23,33,36 Useful field of view34 Verbal fluency (other)3,7,23,30,31,36 Visual search (other)6,31 Wechsler Memory Scale Logical Memory Subtest35,36 Wechsler Memory Scale Word Pairs36 Word list (other)1,3,4,5,15,28,29,32 Self-Report AD-Related Quality of Life scale (ADRQL)25 Advanced activity of daily living (AADL)7 Barthel ADL Index 2024 Basic ADL (BADL)30, 31 Beck Depression Inventory (BDI)21 Canadian Occupational Performance Measure8

Frequency # 1 2 1 1 1 1 1 1 2 1 1 1 4 1 1 1 1 4 1 4 4 1 1 1 1 1 12 4 1 1 2 1 3 5 6 9 1 2 1 1 1 1 5 1 6 2 2 1 8 Frequency # 1 1 1 2 1 1 (Continued )


145

Table 3. Continued Outcome measures Neuropsychological Tests

Frequency # 11

The Center for Epidemiological Studies-Depression (CES-D) Cornell Scale for Depression in Dementia25 Depression Anxiety and Stress Scale9 The measurement of everyday cognition (E-Cog)10,11 Functional Activities Questionnaire (FAQ)25 Geriatric Depression Scale (GDS)5,16,17,18,24,25,30,31 Goldberg scale36 Hospital Anxiety and Depression Scale (HADS)8 Instrumental Activities of Daily Living Scale (IADL)24,31 Illness Cognition Questionnaire (ICQ)18,19 Index of Independence in Activities of Daily Living25 Informant Questionnaire on Cognitive Decline in the Elderly – Short Form (adjusted for self-report)18 Judgments of learning1 Memory Assessment Clinics questionnaire (MAC)35 Maudsley Marital Questionnaire18 Memory controllability inventory (MCI)9,11,28 Montgomery and Asberg Depression Rating Scale (MADRS)5 The Mood and Feelings Questionnaire (MFQ)9,28 Multifactorial Memory Questionnaire (MMQ)17,20,32 Neuropsychiatric Inventory (NPI)24,30,31 Profile of Mood States (POMS)28 Questionnaire d’autoe´valuation de la me´moire3 Quality of Life in Alzheimer’s disease questionnaire (QoLAD)5,10,22 RAND-3618,19 Record of independent living (ROIL)10 Self-esteem scale17 State-trait anxiety inventory6,31 Strategy knowledge repertoire20 Well being questionnaire3 Work related behavior pattern (AVEM)35 Informant Report Bayer-ADL Scale21 Burden Interview25 Caregiver Burden10,11 Chinese Zarit Carer Stress Index24 Swedish version of the Canadian Occupational Performance Measure (COPM)22 The Center for Epidemiological Studies-Depression (CES-D)11 The measurement of everyday cognition (E-Cog)10,11 Instrumental Activities of Daily Living Scale (IADL)21 Informant Questionnaire on Cognitive Decline in the Elderly – Short Form18 Quality of Life in Alzheimer’s disease questionnaire (QoLAD)11 Record of independent living (ROIL)10 Revised Memory and Behaviour Problems Checklist19 Sense of Competence Questionnaire19 Ecological Appointment test35 Face-name associations3,10,15,19,28,32 Functional Cognitive Assessment Scale (FUCAS)32 Grocery list28 Number recall31 Memory-strategy knowledge and behaviour31 Object location associations14 Prospective memory20 Rivermead Behavioural Memory Test (RBMT)7,8,16,17,26,27,31,33,34

1 1 1 2 1 8 1 1 2 2 1 1 1 1 1 3 1 2 3 3 1 1 3 2 1 1 2 1 1 1 Frequency # 1 1 2 1 1 1 2 1 1 1 1 1 1 Frequency # 1 6 1 1 1 1 1 1 9

146


Fig. 6. Hierarchical approach for establishing empirical support for memory rehabilitation techniques (Hampstead, B. M., Sathian, K., Phillips, P. A., Amaraneni, A., Delaune, W. R., & Stringer, A.Y. Mnemonic strategy training improves memory for object location associations in both healthy elderly and patients with amnestic mild cognitive impairment: A randomized, single blind study (Neuropsychology, 26, 385–399, 2012, APA, adapted with permission).

In light of these concerns, we suggest that (1) the strengths and limitations of each type of outcome measure be carefully considered during the study design phase, (2) the rationale for each outcome measure be clearly stated and theoretically and/or functionally related to the treatment approach, and (3) ecological measures be included whenever possible. Care must also be taken, however, to avoid pitfalls associated with the overuse of the same outcome measures across studies in the absence of a control group since this was found to potentially bias results in the TBI and stroke cognitive rehabilitation literature (Rohling, Faust, Beverly, & Demakis, 2009). Ultimately, designing the study and outcome measures to assess patient-specific goals may hold particular promise since it would increase patient motivation and adherence to the treatment regimen.

Methodological Challenge 5: Generalization The ultimate goal of any rehabilitation program is to improve functioning within the patient’s everyday life. Achieving this goal is easier when there is a definitive task that is applicable across a wide range of activities. Returning to the motor rehabilitation field, it is clear that regaining the use of an affected limb within the laboratory or clinical setting can have widespread impact on ‘‘real-world’’ activities (e.g., Wolf et al., 2006). Again, cognition is less precise and individuals must learn and remember a vast array of information to function well in everyday life. Each of the Challenges discussed above can affect generalization, whether due to disease progression, an unknown dose-response relationship, and/or outcome measures that have minimal relation to everyday life. Perhaps most important are the issues raised in Challenge 2. It is clear that no single technique or even combination of techniques is sufficient for meeting all of the possible situations in everyday life. Therefore, it is inappropriate to

criticize cognitive rehabilitation research (or clinical practice) for a lack of widespread or global memory improvement; however, concerns about the ecological relevance/validity of training and failure to demonstrate improvement in relevant ‘‘real world’’ tasks are unquestionably valid and should be the focus of future research. These issues were highlighted by Dr. Barbara Wilson (2009), a pioneer in the field of cognitive rehabilitation, when she wrote, ‘‘We need to take into account individual preferences and styles because different people may prefer different strategies and, as far as possible, we should focus on things that the person with memory impairments wants and needs to learn. This means we should work on material that will be useful in everyday life. Finally, generalization or the transfer to real life must be built into the training program’’ (p. 81). Using the existing literature to guide future studies, the key question is how to build such generalization into the training paradigm. In the next section, we offer a model that may be helpful in this regard.

Future Directions The marked variability in the literature reviewed above leads us to the conclusion that additional work investigating the benefits of individual categories and/or techniques is appropriate at this point in time (see Challenge 2). We previously presented a hierarchal model for establishing the efficacy of internal mnemonic strategies (Hampstead, Sathian, et al., 2012). In light of the current review, we believed it appropriate to revise this model to integrate the Challenges discussed above (Figure 6). At the first and most basic stage of the hierarchy, the technique should help patients learn and remember specific information (e.g., the names of specific church members). If the goal is to improve specific cognitive abilities, as in cognitive training, the approach should improve the targeted abilities. Failure to demonstrate such basic benefits means that the technique likely

Memory rehabilitation in MCI holds minimal value for those with MCI and should not be included in a comprehensive rehabilitation program. The second stage should demonstrate that patients are capable of independently using the technique to learn information similar to the training conditions (e.g., using the technique to learn the names of a social group). Failure at this stage suggests that the technique is primarily useful under the conditions of Stage 1. Stage 3 requires the critical leap from experimental to a broader range of ecological situations (e.g., learning a new route) or broader cognitive abilities. Basic behavioral (Skinnerian) principles for promoting generalization can be implemented in this process. For example, training could start with experimental stimuli (e.g., specific face-name pairs), progress to laboratory/ clinic-based analogues of real-world tasks (e.g., a mock cocktail party), and finally focus on real-world environments (e.g., having the patient use the technique(s) at church, social events, and other similar situations). Explicit instruction should be provided in how the patients should apply the technique to other types of information (e.g., routes) and the generalization process reinforced. While the persistence of any improvements can and perhaps should be addressed at each stage, this information will be especially critical during Stage 3 because it would dictate when ‘‘booster’’ sessions are provided in the clinical program. Failure to generalize across situations would indicate the need to alter training processes and/or focus training efforts on areas that are most important to the patient (i.e., reverting to Stage 2). In essence, the hierarchy progresses from a content-based to a rule-based approach to learning. Consistent with the major challenges identified in this review, we recommend that the effects of disease severity and dose be considered at each stage. Outcome measures should be selected to answer stagespecific questions. We posit that this approach will facilitate direct comparisons across studies and clarify how a given technique (or category of techniques) is most appropriately used for those with MCI. Once established, the empirically supported techniques could be combined into more comprehensive programs that target an individual patient’s needs.

Biomarkers and Neuroimaging Given the advances in biomarkers of Alzheimer’s disease, future studies should integrate these measures into their inclusion criteria and analysis plans (e.g., compare intervention effects in biomarker positive vs. negative participants). This may be especially effective when examining methods to prolong functioning of older adults who are biomarker positive yet cognitively asymptomatic. However, additional guidelines are needed to ensure that the various biomarkers are collected, analyzed, and reported in a standardized manner across sites. This is a challenge unto itself but should be aided through large scale efforts like the Alzheimer’s Disease Neuroimaging Initiative (ADNI; http://www.adni-info.org). These same biomarkers could then be used to predict treatment success (e.g., Hampstead, Sathian, et al., 2012). In a recent review, Belleville and Bherer (2012) reported evidence of cognitivetraining induced changes in both structural and functional

147 neuroimaging measures. We are especially supportive of such efforts and believe that this type of multi-method approach will facilitate the understanding, development, and selection of techniques that are most effective in those with MCI. Such data could also provide a priori rationale for pairing cognitive training techniques with specific pharmacologic agents or other non-pharmacologic approaches (e.g., exercise).

Summary While there has been considerable progress in the early identification of those at risk of AD and other forms of dementia, treatment options are lacking. Several novel pharmacological agents are under development; however, these are years away from being widely available under the best circumstances. Even if such agents completely arrested the disease process, patients would presumably experience residual cognitive impairment. Thus, there is a clear need to identify non-pharmacologic interventions that can help maintain and/or maximize functioning in those with MCI, yet success depends on improved methodological rigor. We have highlighted four methodological challenges that are critical to consider in designing and interpreting research studies, each of which can impact the fifth challenge of promoting generalization to everyday life. The proposed model may provide a useful framework for establishing the conditions under which different techniques are effective. This model may also inform other, more holistic, approaches to rehabilitation (Huckans et al., 2013) and have synergistic interactions with other non-pharmacologic interventions like exercise, diet, and general cognitive stimulation. Given the available evidence, a well-developed and empirically supported, multi-method approach likely holds the most promise for maintaining functioning in this growing population.

ACKNOWLEDGMENTS This work arose from an invited lecture given at the XVII International Symposium in Geriatric Psychiatry, sponsored by the Old Age Research Group (PROTER), University of Sa˜o Paulo, Sa˜o Paulo, Brazil. We thank Dr. Cassio Bottino for his support in this regard. Grant support from the Department of Veterans Affairs, Veterans Health Administration, Office of Research and Development, and Rehabilitation Research and Development Service [B6366W to BMH] is acknowledged. The contents of this manuscript do not represent the views of the Department of Veterans Affairs or the United States Government. Dr. Anthony Y. Stringer is the author of the Ecologically Oriented Neurorehabilitation of Memory (EON-Mem) program and receives royalties from its sale. There are no other conflicts of interest or financial disclosures. Each author provided significant intellectual contribution to warrant authorship and declares that he/she has seen and approved this manuscript. Dr. Benjamin M. Hampstead had full access to all the data in the study and had final responsibility for the decision to submit for publication.

Supplementary material To view supplementary material for this article, please visit http:// dx.doi.org/10.1017/S1355617713001306

148

REFERENCES Albert, M. S., DeKosky, S. T., Dickson, D., Dubois, B., Feldman, H. H., Fox, N. C., y Phelps, C. H. (2011). The diagnosis of mild cognitive impairment due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s & Dementia, 7, 270–279. Albert, M. S., Moss, M. B., Tanzi, R., & Jones, K. (2001). Preclinical prediction of AD using neuropsychological tests. Journal of the International Neuropsychological Society, 7, 631–639. Allain, H., Bentue-Ferrer, D., & Akwa, Y. (2007). Treatment of the mild cognitive impairment (MCI). Human Psychopharmacology: Clinical and Experimental, 22, 189–197. 1 Akhtar, S., Moulin, C. J., & Bowie, P. C. (2006). Are people with mild cognitive impairment aware of the benefits of errorless learning? Neuropsychological Rehabilitation, 16, 329–346. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: American Psychiatric Publishing. Bahar-Fuchs, A., Clare, L., & Woods, B. (2013). Cognitive training and cognitive rehabilitation for mild to moderate Alzheimer’s disease and vascular dementi. Cochrane Database Systematic Reviews, 6, 1–100. 2 Barnes, D. E., Yaffe, K., Belfor, N., Jagust, W. J., DeCarli, C., Reed, B. R., & Kramer, J. H. (2009). Computer-based cognitive training for mild cognitive impairment results from a pilot randomized, controlled trial. Alzheimer Disease and Associated Disorders, 23, 205–210. Belleville, S. (2008). Cognitive training for persons with mild cognitive impairment. International Psychogeriatric, 20, 57–66. Belleville, S., & Bherer, L. (2012). Biomarkers of cognitive training effects in aging. Current Translational Geriatrics & Experimental Gerontology Reports, 1(2), 104–110. 3 Belleville, S., Gilbert, B., Fontaine, F., Gagnon, L., Menard, E., & Gauthier, S. (2006). Improvement of episodic memory in persons with mild cognitive impairment and healthy older adults: Evidence from a cognitive intervention program. Dementia and Geriatric Cognitive Disorders, 22, 486–499. 4 Belleville, S., Clement, F., Mellah, S., Gilbert, B., Fontane, F., & Gauthier, S. (2011). Training related brain plasticity in subjects at risk of developing Alzheimer’s disease. Brain, 134(4), 1623–1634. Boyd, L. A., & Winstein, C. J. (2003). Impact of explicit information on implicit motorsequence learning following middle cerebral artery stroke. Physical Therapy, 83, 976–989. Bjornebekk, A., Westlye, L. T., Walhovd, K. B., & Fjell, A. M. (2010). Everyday memory: Self-perception and structural brain correlates in a healthy elderly population. Journal of the International Neuropsychological Society, 16, 1115–1126. Boyle, P. A., Wilson, R. S., Aggarwal, N. T., Tang, Y., & Bennett, D. A. (2006). Mild cognitive impairment: Risk of Alzheimer disease and rate of cognitive decline. Neurology, 67, 441–445. Brookmeyer, R., Evans, D. A., Herbert, L., Langa, K. M., Heeringa, S. G., Plassman, B. L., & Kukull, W. A. (2011). National estimates of the prevalence of Alzheimer’s disease in the United States. Alzheimer’s Dementia, 7(1), 61–73. Brown P. J., Devanand D. P., Liu X., Caccappolo E., & the Alzheimer’s Disease Neuroimaging Initiative. (2011). Functional impairment in elderly patients with mild cognitive impairment and mild Alzheimer’s disease. Archives of General Psychiatry, 68(6), 617–626.

B.M. Hampstead et al. Burton, C. L., Strauss, E., Bunce, D., Hunter, M. A., & Hultsch, D. F. (2009). Functional abilities in older adults with mild cognitive impairment. Gerontology, 55, 570–581. 5 Buschert, V. C., Friese, U., Teipel, S. J., Schneider, P., Merensky, W., Rujescu, D., y Buerger, K. (2011). Effects of a newly developed cognitive intervention in amnestic mild cognitive impairment and mild Alzheimer’s disease: A pilot study. Journal of Alzheimer’s Disease, 25, 679–694. Carey, J. R., Kimberley, T. J., Lewis, S. M., Auerbach, E. J., Dorsey, L., Rundquist, P., & Ugurbil, K. (2002). Analysis of fMRI and finger tracking training in subjects with chronic stroke. Brain, 125(4), 773–788. 6 Carretti, B., Borella, E., Zavagnin, M., & De Beni, R. (2011). Impact of metacognition and motivation on the efficacy of strategic memory training in older adults. Archives of Gerontology and Geriatrics, 52(3), e192–e197. Chen, H., Gu, Y., Wang, C., Lv, M., Wang, L., & Gu, J. (2008). Community based intervention for mild cognitive impairment (in Chinese). Chinese Primary Health Care, 22, 17–19. Cherry, K. E., Walvoord, A. A. G., & Hawley, K. S. (2010). Spaced retrieval enhances memory for a name-face-occupation association in older adults with probable Alzheimer’s disease. The Journal of Genetic Psychology, 171(2), 168–181. Cicerone, K. D., Dahlberg, C., Malec, J. F., Langenbahn, D. M., Felicetti, T., Kneipp, S., y Catanese, J. (2005). Evidence-based cognitive rehabilitation: Updated review of the literature from 1998 through 2002. Archives of Physical Medicine and Rehabilitation, 86, 1681–1692. Cicerone, K. D., Langenbahn, D. M., Braden, C., Malec, J. F., Kalmar, K., Fraas, M., y Ashman, T. (2011). Evidence-based cognitive rehabilitation: Updated review of the literature from 2003 through 2008. Archives of Physical Medicine and Rehabilitation, 92, 519–530. 7 Cipriani, G., Bianchetti, A., & Trabucchi, M. (2006). Outcomes of a computer-based cognitive rehabilitation program on Alzheimer’s disease patients compared with those on patients affected by mild cognitive impairment. Archives of Gerontology and Geriatrics, 43, 327–335. Clare, L., & Jones, R. S. P. (2008). Errorless learning in the rehabilitation of memory impairment: A critical review. Neuropsychology Review, 18(1), 1–23. 8 Clare, L., van Paasschen, J., Evans, S. J., Parkinson, C., Woods, R. T., & Linden, D. E. J. (2009). Goal-oriented cognitive rehabilitation for an individual with mild cognitive impairment: Behavioural and neuroimaging outcomes. Neurocase, 16, 1–14. Cotelli, M., Menenti, R., Zanetti, O., & Miniussi, C. (2012). Nonpharmacological intervention for memory decline. Frontiers in Human Neuroscience, 6(article 46), 1–17. Craik, F. I. M., Winocur, G., Palmer, H., Binns, M. A., Edwards, M., Bridges, K., y Stuss, D. T. (2007). Cognitive rehabilitation in the elderly: Effects on memory. Journal of the International Neuropsychological Society, 13, 132–142. D’Amato, T., Bation, R., Cochet, A., Jalenques, I., Galland, F., & Giraud-Baro, E. (2011). A randomized, controlled trial of computer-assisted cognitive remediation for schizophrenia. Schizophrenia Research, 125, 284–290. Daviglus, M. L., Bell, C. C., Berrettini, W., Bowen, P. E., Connolly, E. S., Cox, N. J., y Trevisan, M. (2010). National Institutes of Health State-of-the-Science Conference Statement: Preventing Alzheimer’s disease and cognitive decline. NIH Consens State Sci Statements, 27(4), 1–30.

Memory rehabilitation in MCI Delis, D. C., Kramer, J. H., Kaplan, E., & Ober, B. A. (2000). California Verbal Learning Test (CVLT) (2nd ed.). San Antonio, TX: The Psychological Corporation. Diniz, B. S., Pinto, J. A., Jr., Gonzaga, M. L., Guimaraes, F. M., Gattaz, W. F., & Forlenza, O. V. T. (2009). To treat or not to treat? A meta-analysis of the use of cholinesterase inhibitors in mild cognitive impairment for delaying progression to Alzheimer’s disease. European Archives of Psychiatry and Clinical Neuroscience, 259(4), 248–256. Estevez-Gonzalez, A., Kulisevsky, J., Boltes, A., Otermin, P., & Garcia-Sanchez, C. (2003). Rey verbal learning test is a useful tool for differential diagnosis in the preclinincal phase of Alzheimer’s disease: Comparison with mild cognitive impairment and normal aging. International Journal of Geriatric Psychiatry, 18, 1021–1028. Farias, S. T., Harrell, E., Neumann, C., & Houtz, A. (2003). The relationship between neuropsychological performance and daily functioning in individuals with Alzheimer’s disease: Ecological validity of neuropsychological tests. Archives of Clinical Neuropsychology, 18, 655–672. Farlow, M. R. (2009). Treatment of mild cognitive impairment (MCI). Current Alzheimer’s Research, 6(4), 362–367. 9 Finn, M., & McDonald, S. (2011). Computerised cognitive training for older persons with mild cognitive impairment: A pilot study using a randomised controlled trial design. Brain Impairment, 12(3), 187–189. Fischer, H. (2010). A history of the central limit theorem: From classical to modern probability theory. New York: Springer. Folstein, M., Folstein, S. E., & McHugh, P. R. (1975). Mini-Mental State: A practical method for grading the cognitive status of patients for the clinician. Journal of Psychiatric Research, 12, 189–198. Gillis, M. M., Quinn, K. M., Phillips, P. A. T., & Hampstead, B. M. (2013). Impaired retention is responsible for temporal order memory deficits in mild cognitive impairment. Acta Psychologica, 143, 88–95. 10 Greenaway, M. C., Hanna, S. M., Lepore, S. W., & Smith, G. E. (2008). A behavioral rehabilitation intervention for amnestic mild cognitive impairment. American Journal of Alzheimer’s Disease and Other Dementias, 23, 451–461. 11 Greenaway, M. C., Duncan, N. L., & Smith, G. E. (2013). The memory support system for mild cognitive impairment: Randomized trial of a cognitive rehabilitation intervention. International Journal of Geriatric Psychiatry, 28, 402–409. 12 Gunther, V. K., Schafer, P., Holzner, B. J., & Kemmler, G. W. (2003). Long-term improvements in cognitive performance through computer-assisted cognitive training: A pilot study in a residential home for older people. Aging & Mental Health, 7(3), 200–206. 13 Hampstead, B. M., Sathian, K., Moore, A. B., Nalisnick, C., & Stringer, A. Y. (2008). Explicit memory training leads to improved memory for face-name pairs in patients with mild cognitive impairment: Results of a pilot investigation. Journal of the International Neuropsychological Society, 14, 883–889. 14 Hampstead, B. M., Sathian, K., Phillips, P. A., Amaraneni, A., Delaune, W. R., & Stringer, A. Y. (2012). Mnemonic strategy training improves memory for object location associations in both healthy elderly and patients with amnestic mild cognitive impairment: A randomized, single blind study. Neuropsychology, 26(3), 385–399. Hampstead, B. M., Stringer, A. Y., Stilla, R. F., Amaraneni, A., & Sathian, K. (2011). Where did I put that? Patients with amnestic mild cognitive impairment demonstrate widespread reductions in

149 activity during the encoding of ecologically relevant objectlocation associations. Neuropsychologia, 49, 2349–2361. Hampstead, B. M., Stringer, A. Y., Stilla, R. F., Deshpande, G., Hu, X. P., Moore, A. B., & Sathian, K. (2011). Activation and effective connectivity changes following explicit-memory training for face-name pairs in patients with mild cognitive impairment: A pilot study. Neurorehabilitation and Neural Repair, 25, 210–222. Hampstead, B. M., Stringer, A. Y., Stilla, R. F., Giddens, M., & Sathian, K. (2012). Mnemonic strategy training partially restores hippocampal activity in patients with mild cognitive impairment. Hippocampus, 22(8), 1652–1658. Hart, T., Fann, J. R., & Novack, T. A. (2008). The dilemma of the control condition in experience-based cognitive and behavioural treatment research. Neuropsychological Rehabilitation, 18(1), 1–21. Haskins, E. C., Cicerone, K., Darns-O’Connor, K. D., Eberle, R., Langenbahn, D., & Shapiro-Ronsenbaum, A. (2012). Cognitive rehabilitation manual: Translating evidence-based recommendations into practice. Reston, VA: American Congress of Rehabilitation Medicine. 15 Herrera, C., Chambon, C., Michel, B. F., Paban, V., & Alescio-Lautier, B. (2012). Positive effects of computer-based cognitive training in adults with mild cognitive impairment. Neuropsychologia, 20, 1871–1881. Huckans, M., Hutson, L., Twamley, E., Jak, A., Kaye, J., & Storzbach, D. (2013). Efficacy of cognitive rehabilitation therapies for mild cognitive impairment (MCI) in older adults: Working toward a theoretical model and evidence-based interventions. Neuropsychological Review, 23, 63–80. Jack, C. R. (2012). Alzheimer’s disease: New concepts on its neurobiology and the clinical role imaging will play. Radiology, 263(2), 344–361. Jean, L., Bergeron, M. -E., Thivierge, S., & Simard, M. (2010). Cognitive intervention programs for individuals with mild cognitive impairment: Systematic review of the literature. The American Journal of Geriatric Psychiatry, 18(4), 281–296. 16 Jean, L., Simard, M., van Reekum, R., & Bergeron, M. -E. (2007). Towards a cognitive stimulation program using an errorless learning paradigm in amnestic mild cognitive impairment. Neuropsychiatric Disease and Treatment, 3(6), 975–985. 17 Jean, L., Simard, M., Wiederkehr, S., Bergeron, M. -E., Turgeon, Y., Hudon, C., y van Reekum, R. (2010). Efficacy of a cognitive training programme for mild cognitive impairment: Results of a randomised controlled study. Neuropsychological Rehabilitation, 20(3), 377–405. 18 Joosten-Weyn Banning, L. W. A., Kessels, R. P., Rikkert, M. G. M. O., Geleijns-Lanting, C. E., & Kraaimaat, F. W. (2008). A cognitive behavioral group therapy for patients diagnosed with mild cognitive impairment and their significant others: Feasibility and preliminary results. Clinical Rehabilitation, 22, 731–740. 19 Joosten-Weyn Banningh, L. W. A., Prins, J. B., Vernooij-Dassen, J. F. J. M., Wijnen, H. H., Olde Rikkert, M. G. M., & Kessels, R. P. C. (2010). Group therapy for patients with mild cognitive impairment and their significant others: Results of a waiting-list controlled trial. Gerontology, 57, 444–454. Karni, A., Meyer, G., Jezzard, P., Adams, M. M., Turner, R., & Ungerleider, L. G. (1995). Functional MRI evidence for adult motor cortex plasticity during motor skill learning. Nature, 377(6545), 155–158. Keuider, A. M., Parisi, J. M., Gross, A. L., & Rebok, G. W. (2012). Computerized cognitive training with older adults: A systematic review. PLoS One, 7(7), e40588.

150 20

Kinsella, G. J., Mullaly, E., Rand, E., Ong, B., Burton, C., Price, S., y Storey, E. (2009). Early intervention for mild cognitive impairment: A randomized controlled trial. Journal of Neurology, Neurosurgery, and Psychiatry, 80(7), 730–736. Kurtz, M. A. (2012). Cognitive remediation for schizophrenia: Current status, biological correlates and predictors of response. Expert Reviews Neurotherapy, 12(7), 813–821. 21 Kurz, A., Pohl, C., Ramsenthaler, M., & Sorg, C. (2009). Cognitive rehabilitation in patients with mild cognitive impairment. International Journal of Geriatric Psychiatry, 24, 163–168. Li, H., Li, J., Li, N., Li, B., Wang, P., & Zhou, T. (2011). Cognitive intervention for persons with mild cognitive impairment: A meta-analysis. Ageing Research Reviews, 10, 285–296. Loewenstein, D. A., Acevedo, A., Agron, J., & Duara, R. (2007). Stability of neurocognitive impairment in different subtypes of mild cognitive impairment. Dementia and Geriatric Cognitive Disorders, 23, 82–86. Loewenstein, D. A., Acevedo, A., Agron, J., Martinez, G., & Duara, R. (2007). The use of amnestic and nonamnestic composite measures at different thresholds in the neuropsychological diagnosis of MCI. Journal of Clinical and Experimental Neuropsychology, 29, 300–307. Loewenstein, D. A., Acevedo, A., Small, B. J., Agron, J., Crocco, E., & Duara, R. (2009). Stability of different subtypes of mild cognitive impairment among the elderly over a 2- to 3- year follow-up period. Dementia and Geriatric Cognitive Disorders, 27, 418–423. 22 Londos, E., Boschian, K., Linden, A., Persson, C., Minthon, L., & Lexell, J. (2008). Effects of a goal-oriented rehabilitation program in mild cognitive impairment: A pilot study. American Journal of Alzheimer’s Disease and Other Dementias, 23, 177–183. Lundqvist, A., Grundstrom, K., Samuelsson, K., & Ronnberg, J. (2010). Computerized training of working memory in a group of patients suffering from acquired brain injury. Brain Injury, 24(10), 1173–1183. Manly, J. J., Tang, M. X., Schupf, N., Stern, Y., Vonsattel, J. P. G., & Mayeux, R. (2008). Frequency and course of mild cognitive impairment in a multiethnic community. Annals of Neurology, 63(4), 494–506. Martin, M., Clare, L., Altgassen, A. M., Cameron, M. H., & Zehnder, F. (2011). Cognition-based interventions for healthy older people and people with mild cognitive impairment. Cochrane Database of Systematic Reviews, 19(1), 1–51. 23 Moro, V., Condoleo, M. T., Sala, F., Pernigo, S., Moretto, M. D., & Gambina, G. (2012). Cognitive stimulation in a-MCI: An experimental study. American Journal of Alzheimer’s Disease & Other Dementias, 27(2), 1210130. 24 Ng, S., Lo, A., Lee, G., Lam, M., Yeong, E., Koo, M., y Lau, V. (2006). Report of the outcomes of occupational therapy programmes for elderly persons with mild cognitive impairment (MCI) in community elderly centres. Hong Kong Journal of Occupational Therapy, 16, 16–22. 25 Olazaran, J., Muniz, R., Reisberg, B., Pena-Casanova, J., del Ser, T., Cruz-Jentoft, A. J., y Sevilla, C. (2004). Benefits of cognitive-motor intervention in MCI and mild to moderate Alzheimer’s disease. Neurology, 63, 2348–2353. 26 Olchik, M. R., Farina, J., Steibel, N., Teixeira, A. R., & Yassuda, M. S. (2013). Memory training (MT) in mild cognitive impairment (MCI) generates change in cognitive performance. Archives of Gerontology and Geriatrics, 56, 442–447.

B.M. Hampstead et al. Oswald., W. D., Rupprecht, R., Cunzelmann, T., & Tritt, K. (1996). The SIMA-project: Effects of 1 year cognitive and psychomotor training on cognitive abilities of the elderly. Behavioural Brain Research, 78, 67–72. Owen, A. M., Hampshire, A., Grahn, J. A., Dajani, S., Burns, A. S., Howard, R. J., & Ballard, C. G. (2010). Putting brain training to the test. Nature, 465(7299), 775–778. Petersen, R. C. (2004). Mild cognitive impairment as a diagnostic entity. Journal of International Medicine, 256, 183–194. Petersen, R. C., Smith, G. E., Waring, S. C., Ivnik, R. J., Tangalos, E. G., & Kokmen, E. (1999). Mild cognitive impairment – clinical characterization and outcome. Archives of Neurology, 56, 303–308. 27 Poon, P., Hui, E., Dai, D., Kwok, T., & Woo, J. (2005). Cognitive intervention for community-dwelling older persons with memory problems: Telemedicine versus face-to-face treatment. International Journal of Geriatric Psychiatry, 20, 285–286. Rabin, L. A., Pare, N., Saykin, A. J., Brown, M. J., Wishart, H. A., Flashman, L. A., & Santulli, R. B. (2009). Differential memory test sensitivity for diagnosing amnestic mild cognitive impairment and predicting conversion to Alzheimer’s disease. Neuropsychology, Development, and Cognition. Section B, Aging, Neuropsychology and Cognition, 16(3), 357–376. 28 Rapp, S., Brenes, G., & Marsh, A. P. (2002). Memory enhancement training for older adults with mild cognitive impairment: A preliminary study. Aging & Mental Health, 6(1), 5–11. Raschetti, R., Albanese, E., Vanacore, N., & Maggini, M. (2007). Cholinesterase inhibitors in mild cognitive impairment: A systematic review of randomised trials. PLos Medicine, 4(11), 1818–1828. Reijnders, J., van Heugten, C., & van Boxtel, M. (2013). Cognitive interventions in healthy older adults and people with mild cognitive impairment: A systematic review. Ageing Research Reviews, 12, 263–275. Rey, A. (1941). L’examen psychologique dans les cas d’ence0 phalopathie traumatique. Archives de Psychologie, 28, 21. Rey, A. (1964). L ‘examen clinique en psychologie [Clinical tests in psychology]. Paris: Presses Universitaires de France. Rohling, M. L., Faust, M. E., Beverly, B., & Demakis, G. (2009). Effectiveness of cognitive rehabilitation following acquired brain injury: A meta-analytic re-examination of Cicerone et al. ’s (2000, 2005) systematic reviews. Neuropsychology, 23(1), 20–39. 29 Rosen, A. C., Sugiura, L., Kramer, J. H., Whitfield-Gabrieli, S., & Gabrieli, J. D. (2011). Cognitive training changes hippocampal function in mild cognitive impairment: A pilot study. Journal of Alzheimer’s Disease, 26, 349–357. 30 Rozzini, L., Costardi, D., Chilovi, B. V., Trabucchi, M., & Padovani, A. (2007). Efficacy of cognitive rehabilitation in patients with mild cognitive impairment treated with cholinesterase inhibitors. International Journal of Geriatric Psychiatry, 22, 356–360. Ruff, R. M. (2003). A friendly critique of neuropsychology: Facing the challenges of our future. Archives of Clinical Neuropsychology, 18, 847–864. Sitzer, D. I., Twamley, E. W., & Jeste, D. V. (2006). Cognitive training in Alzheimer’s disease: A meta-analysis of the literature. Acta Psychiatrica Scandinavica, 114, 75–90. Small, J. A. (2012). A new frontier in spaced retrieval memory training for persons with Alzheimer’s disease. Neuropsychological Rehabilitation, 22(3), 329–361.

Memory rehabilitation in MCI Simon, S. S., Yokomizo, J. E., & Bottino, C. M. C. (2012). Cognitive intervention in amnestic mild cognitive impairment: A sustematic review. Neuroscience and Biobehavioral Reviews, 36, 1163–1178. Smith, G. E., Pankratz, V. S., Negash, S., Machulda, M. M., Petersen, R. C., Boeve, B. F., y Ivnik, R. J. (2007). A plateau in pre-Alzheimer’s memory decline: Evidence for compensatory mechanisms? Neurology, 69, 133–139. Sperling, R. (2007). Functional MRI studies of associative encoding in normal aging, mild cognitive impairment, and Alzheimer’s disease. Annals of the New York Academy of Sciences, 1097, 146–155. Sperling, R. A., Johnson, K. A., Daoraiswamy, P. M., Reiman, E. M., Fleisher, A. S., Sabbagh, M. N., y Pontecorvo, M. J. (2013). Amyloid deposition detected with florbetapir F 18 (F-18-AV-45) is related to lower episodic memory performance in clinically normal older individuals. Neurobiology of Aging, 34(3), 822–831. Strauss, E., Sherman, E. M. S., & Spreen, O. (2006). A compendium of neuropsychological tests. New York: Oxford University Press. Stott, J., & Spector, A. (2011). A review of the effectiveness of memory interventions in mild cognitive impairment (MCI). International Psychogeriatrics, 23(4), 526–538. Stringer, A. Y. (2007). Ecologically oriented neurorehabilitation of memory therapist manual. Los Angeles: Western Psychological Services. Stringer, A. Y. (2011). Ecologically-oriented neurorehabilitation of memory: Robustness of outcome across diagnosis and severity. Brain Injury, 25(2), 169–178. 31 Talassi, E., Guerreschi, M., Feriani, M., Fedi, V., Bianchetti, A., & Trabucchi, M. (2007). Effectiveness of a cognitive rehabilitation program in mild dementia (MD) and mild cognitive impairment (MCI): A case control study. Archives of Gerontology and Geriatrics, 44(Suppl. 1), 391–399. Teng, W., Tingus, K. D., Lu, P. H., & Cummings, J. L. (2009). Persistence of neuropsychological testing deficits in mild cognitive impairment. Dementia and Geriatric Cognitive Disorders, 28, 168–178. 32 Troyer, A. K., Murphy, K. J., Anderson, N. D., Moscovitch, M., & Craik, F. I. M. (2008). Changing everyday memory behaviour in amnestic mild cognitive impairment: A randomised controlled trial. Neuropsychological Rehabilitation, 18(1), 65–88. Troyer, A. K., & Rich, J. B. (2002). Psychometric properties of a new metamemory questionnaire for older adults. The Journals of Gerontology Series B—Psychological Sciences and Social Sciences, 57(1), 19–27. 33 Tsolaki, M., Kounti, F., Agogiatou, C., Poptsi, E., Bakoglidou, E., Zafeiropoulou, M., y Vasiloglou, M. (2011). Effectiveness of nonpharmacological approaches in patients with mild cognitive impairment. Neurodegenerative Diseases, 8(3), 138–145. 34 Unverzagt, F. W., Kasten, L., Johnson, K. E., Rebok, G. W., Marsiske, M., Koepke, K. M., y Tennstedt, S. L. (2007). Effect of memory impairment on training outcomes in ACTIVE. Journal of the International Neuropsychological Society, 13, 953–960. Verhaeghen, P., Marcoen, A., & Goossens, L. (1992). Improving memory performance in the aged through mnemonic training: A meta-analytic study. Psychology and Aging, 7, 242–251. 35 Wagner, S., Kaschel, R., Paulsenm, S., Bleichner, F., Knickenberg, R. J., & Beutel, M. E. (2008). Does a cognitivetraining programme improve the performance of middle-aged

151 employees undergoing in-patient psychosomatic treatment? Disability and Rehabilitation, 30(23), 1786–1793. 36 Wenisch, E., Cantegreil-Kallen, I., De Rotrou, J., Garrigue, P., Moulin, F., Batouche, F., y Rigaud, A. S. (2007). Cognitive stimulation intervention for elders with mild cognitive impairment compared with normal aged subjects: Preliminary results. Aging Clinical and Experimental Research, 19, 316–322. Wester, A. J., Leenders, P., Egger, J. I. M., & Kessels, R. P. (2013). Ceiling and floor effects on the Rivermead Behavioural Memory Test in patients with alcohol-related memory disorders and healthy participants. International Journal of Psychiatry in Clinical Practice, 17, 286–291. Whyte, J., Gordon, W., & Gonzalez Rothi, L. J. (2009). A phased developmental approach to neurorehabilitation research: The science of knowledge building. Archives of Physical Medicine and Rehabilitation, 90(11 Suppl. 1), S3–S10. Willis, S. L., Tennstedt, S. L., Marsiske, M., Elias, J., Koepke, K. M., Morris, J. N., y Wright, E. (2006). Long-term effects of cognitive training on everyday functional outcomes in older adults. Journal of the American Medical Association, 296(23), 2805–2814. Wills, P., Clare, L., Shiel, A., & Wilson, B. (2000). Assessing subtle memory impairments in the everday memory performnace of brain injured people: Exploring the potential of the Extended Rivermean Behavioural Memory Test. Brain Injury, 14(8), 693–704. Wilson, B. (2009). Memory rehabilitation integrating theory and practice. NewYork: Guilford Press. Wilson, B., Cockburn, J., Baddeley, A., & Hiorns, R. (1989). The development and validation of a test battery for detecting and monitoring everyday memory problems. Journal of Clinical and Experimental Neuropsychology, 11(6), 855–870. Wilson, B. A., Clare, L., Baddeley, A. D., Cockburn, J., Watson, P., & Tate, R. (1998). The Rivermead Behavioural Memory Test – Extended version. Bury St. Edmunds, UK: Thames Valley Test Company. Wilson, B. A., Greenfield, E., Clare, L., Baddeley, A., Cockburn, J., Watson, P., y Nannery, R. (2008). The Rivermead Behavioural Memory Test – Third Edition (RBMT-3). London, UK: Pearson Assessment; 2008. Winblad, B. I., Palmer, K., Kivipelto, M., Jelic, V., Fratiglioni, L., Wahlund, L. O., y Petersen, R. C. (2004). Mild cognitive impairment — beyond controversies, towards a consensus: Report of the International Working Group on Mild Cognitive Impairment. Journal of Internal Medicine, 256(3), 240–246. Wolf, S. L., Winstein, C. J., Miller, J. P., Taub, E., Uswatte, G., Morris, D., y Nichols-Larsen, D. (2006). Effect of constraintinduced movement therapy on upper extremity function 3 to 9 months after stroke. Journal of the American Medical Association, 296(17), 2095–2104. Wolf, S. L., Winstein, C. J., Miller, J. P., Thompson, P. A., Taub, E., Uswatte, G., y Nichols-Larsen, D. (2008). The EXCITE trial: Retention of improved upper extremity function among stroke survivors receiving CI movement therapy. Lancet Neurology, 7(1), 33–40.

Copyright of Journal of the International Neuropsychological Society is the property of Cambridge University Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.

Cognitive rehabilitation multi-family group intervention for individuals with mild cognitive impairment and their care-partners.

Cognitive rehabilitation for mild cognitive impairment: developing and piloting an intervention.

From mild cognitive impairment to subjective cognitive decline: conceptual and methodological evolution.

Profile of cognitive complaints in vascular mild cognitive impairment and mild cognitive impairment.

Risk Factors for Mild Cognitive Impairment (MCI).

Mild Cognitive Impairment: A Brief Review and Suggested Clinical Algorithm.

Sleep disturbances and mild cognitive impairment: A review.

Mild Cognitive Impairment.

Mild cognitive impairment.

Alterations in working memory networks in amnestic mild cognitive impairment.

Working memory span in mild cognitive impairment. Influence of processing speed and cognitive reserve.

Potential for a "Memory Gym" Intervention to Delay Conversion of Mild Cognitive Impairment to Dementia.

Interference Impacts Working Memory in Mild Cognitive Impairment.

Everyday Impact of Cognitive Interventions in Mild Cognitive Impairment: a Systematic Review and Meta-Analysis.

Memory for Public Events in Mild Cognitive Impairment and Alzheimer's Disease: The Importance of Rehearsal.

Predicting decline in mild cognitive impairment: a prospective cognitive study.

A Review of Self-Management Interventions for People With Dementia and Mild Cognitive Impairment.

Translating cognitive and everyday activity deficits into cognitive interventions in mild dementia and mild cognitive impairment.

Power spectra for screening parkinsonian patients for mild cognitive impairment.

Memory for the 2008 presidential election in healthy ageing and mild cognitive impairment.

Explicit (semantic) memory for music in patients with mild cognitive impairment and early-stage Alzheimer's disease.

Risk for Mild Cognitive Impairment Is Associated With Semantic Integration Deficits in Sentence Processing and Memory.

Source Memory for Self and Other in Patients With Mild Cognitive Impairment due to Alzheimer's Disease.

Frequent and discriminative subnetwork mining for mild cognitive impairment classification.