Attention Measures of Accuracy, Variability, and Fatigue Detect Early Response to Donepezil in Alzheimer’s Disease: A Randomized, Double-blind, Placebo-Controlled Pilot Trial

Attention Measures of Accuracy, Variability, and Fatigue Detect Early Response to Donepezil in... Abstract Objective Donepezil is widely used to treat Alzheimer’s disease (AD), but detecting early response remains challenging for clinicians. Acetylcholine is known to directly modulate attention, particularly under high cognitive conditions, but no studies to date test whether measures of attention under high load can detect early effects of donepezil. We hypothesized that load-dependent attention tasks are sensitive to short-term treatment effects of donepezil, while global and other domain-specific cognitive measures are not. Method This longitudinal, randomized, double-blind, placebo-controlled pilot trial (ClinicalTrials.gov Identifier: NCT03073876) evaluated 23 participants newly diagnosed with AD initiating de novo donepezil treatment (5 mg). After baseline assessment, participants were randomized into Drug (n = 12) or Placebo (n = 11) groups, and retested after approximately 6 weeks. Cognitive assessment included: (a) attention tasks (Foreperiod Effect, Attentional Blink, and Covert Orienting tasks) measuring processing speed, top-down accuracy, orienting, intra-individual variability, and fatigue; (b) global measures (Alzheimer’s Disease Assessment Scale-Cognitive Subscale, Mini-Mental Status Examination, Dementia Rating Scale); and (c) domain-specific measures (memory, language, visuospatial, and executive function). Results The Drug but not the Placebo group showed benefits of treatment at high-load measures by preserving top-down accuracy, improving intra-individual variability, and averting fatigue. In contrast, other global or cognitive domain-specific measures could not detect treatment effects over the same treatment interval. Conclusions The pilot-study suggests that attention measures targeting accuracy, variability, and fatigue under high-load conditions could be sensitive to short-term cholinergic treatment. Given the central role of acetylcholine in attentional function, load-dependent attentional measures may be valuable cognitive markers of early treatment response. Alzheimer dementia, Attention, Cholinesterase inhibitors, Donepezil, Treatment outcome Introduction Alzheimer’s disease (AD) is the most common cause of dementia in older adults (Alzheimer’s Association, 2016) with cholinesterase inhibitors (ChEIs) still the foremost treatment (Di Santo, Prinelli, Adorni, Caltagirone, & Musicco, 2013; Tan et al., 2014) targeting the cholinergic deficiency associated with the disease (Davies & Maloney, 1976; see reviews Dumas & Newhouse, 2011; Schliebs & Arendt, 2011). The mechanism of ChEIs prevents acetylcholinesterase enzymes from breaking down acetylcholine, leading to increased extracellular availability of the neurotransmitter. Despite continued use of these drugs, the degree of benefit is controversial (Bond et al., 2012; Deardorff, Feen, & Grossberg, 2015; Kobayashi, Ohnishi, Nakagawa, & Yoshizawa, 2015), and adverse side effects include bradycardia, gastrointestinal responses, and sleep disorders (Kröger et al., 2015). Clinical trials widely used the AD Assessment Scale-Cognitive Subscale (Rosen, Mohs, & Davis, 1984), which aggregates multiple cognitive domains, as the primary outcome measure, but this measure has had limited ability to detect the cognitive changes of treatment (Cano et al., 2010). Because acetylcholine is directly linked to the cognitive domain of attention (Hasselmo & Sarter, 2011), and ChEIs increase available acetylcholine, we proposed that attention tasks would be more sensitive to detect treatment response and do so within a short treatment period. Acetylcholine is linked to attentional functions both in animals and humans (see reviews Hasselmo & Sarter, 2011; Klinkenberg & Blokland, 2010). In a series of pivotal animal studies, Sarter, Bruno and colleagues (Himmelheber, Sarter, & Bruno, 2000; Kozak, Bruno, & Sarter, 2006) demonstrated a reciprocal relationship where, on the one hand, increasing attentional demands augmented acetylcholine availability, while on the other hand, taxing attentional demands under high-load conditions increased acetylcholine efflux. Yet, while the cognitive domain of attention is not identified as a primary domain of cognitive dysfunction in AD (McKhann et al., 2011), a wide variety of attentional deficits in processing speed, orienting, and divided attention are reported in AD (Baddeley, Baddeley, Bucks, & Wilcock, 2001; Chau, Herrmann, Eizenman, Chung, & Lanctôt, 2015; Coubard et al., 2011; Foldi, Lobosco, & Schaefer, 2002; Ishizaki, Meguro, Nara, Kasai, & Yamadori, 2013; Phillips, Rogers, Haworth, Bayer, & Tales, 2013; Sylvain-Roy, Bherer, & Belleville, 2010) as well as in mild cognitive impairment (MCI) (Saunders & Summers, 2011). Attentional deficits may possibly be one of the first cognitive domains to be affected in AD (Foldi et al., 2002; Grady et al., 1988; Reid et al., 1996). Thus, if AD is associated with depleted acetylcholine (Davies & Maloney, 1976) and acetylcholine is reciprocally linked to the attentional system, then the effect of ChEI treatment in AD should improve attentional function. Indeed, prior work clearly demonstrated treatment effects of ChEIs on attention in AD (Caramelli et al., 2004; Daiello et al., 2010; Foldi, White, & Schaefer, 2005; Galvin et al., 2008; Gorus, Lambert, De Raedt, & Mets, 2007; Lee et al., 2015; Palmqvist, Minthon, Wattmo, Londos, & Hansson, 2010; Shimizu, Kanetaka, Hirose, Sakurai, & Hanyu, 2015; Vellas et al., 2005; Wesnes et al., 1998, 2010; Wiig et al., 2010). Yet, none of these studies addressed whether there was a relationship between increased temporal demands (i.e., high-load) and acetylcholine availability, whether drug efficacy could be detected after a short treatment interval, or, whether attention would be more sensitive than global function or other specific cognitive domains. The current study aimed to address these questions using donepezil hydrochloride as the cholinergic agent. We hypothesized that (1) higher attentional demands would be sensitive to donepezil treatment in AD, (2) if load-dependent attention tasks were sensitive, they should detect treatment response even after a short treatment period once steady-state of the drug had been reached (Rogers et al., 1998), and (3) attention measures would be more sensitive than global or other domain-specific measures. Kahneman’s (1973) classic model of attention proposes that individuals have a finite capacity, and attention mechanisms serve to manage or allocate those limited resources. Tasks can vary in the amount of resources used, e.g., from high- to low-load demands. Our question posited whether cholinergic augmentation could improve performance under high load. We selected three attention tasks (Foreperiod Effect, Attentional Blink, and Covert Orienting) known to have deficits in AD, each of which allowed us to manipulate load levels within the task. Across these three tasks, we were able to address the attentional functions of processing speed, accuracy, orienting, variability, and fatigue, each under high- and low-load conditions. Processing speed to detect a stimulus (measured as reaction time, RT) slows in older adults (Salthouse, 1985) and in patients with AD (Pirozzolo, Christensen, Ogle, Hansch, & Thompson, 1981). Simple detection paradigms have already been used to reliably assess processing speed in ChEIs (Galvin et al., 2008; Palmqvist et al., 2010; Wiig et al., 2010). Moreover, Niemi and Näätänen (1981) demonstrated the sensitive Foreperiod Effect, where RT increases when the preparatory period before a subsequent stimulus decreases. To capture this foreperiod effect, we manipulated intervals between stimuli (stimulus onset asynchrony, SOA) in a simple detection task (Henderson & Dittrich, 1998), such that short intervals (SOA-350 and 500 ms) represented high-load conditions. Using this paradigm, Sylvain-Roy and colleagues (2010) have already demonstrated that patients with AD and MCI had delayed response time at short SOAs compared to healthy controls. Hence, we proposed that RT on a load-dependent simple detection task could capture early treatment effects of donepezil. To assess accuracy, we used the Attentional Blink paradigm. The blink phenomenon refers to the deficit identifying a second stimulus, when it is presented shortly after identification of a first stimulus (Broadbent & Broadbent, 1987; Duncan, Ward, & Shapiro, 1994; Raymond, Shapiro, and Arnell, 1992). The blink typically occurs when the interstimulus interval is short (between 200 and 500 ms; high load) compared to when it is long (SOAs > 500 ms; low load). A further manipulation of this paradigm is to provide a priori instructions about the target to the participant to improve accuracy, e.g., when presented with a number and a letter, the participant is asked to only report the letter. This instruction adds a top-down attentional control component that should facilitate identification of the second stimulus. Thus, above and beyond perceptual skill, the blink represents the ability to allocate attentional resources to the second of two rapidly presented stimuli. Perry and Hodges (2003) demonstrated that patients with MCI were less accurate than healthy controls identifying the second stimulus within the 200–500 ms SOA, suggesting that the deficits were not only due to the temporal influence of processing the first stimulus, but also the inefficiency allocating top-down attentional control resources. Our laboratory (Ly, 2013) previously demonstrated that patients with AD were less accurate than healthy older adults, who, in turn, showed worse performance than young adults. Importantly, top-down functions have been linked to cholinergic neuromodulator (Klinkenberg, Sambeth, & Blokland, 2011; Sarter, Gehring, & Kozak, 2006). Hence, we hypothesized that donepezil treatment would improve accuracy under high-load conditions of the blink task. Orienting (Posner, 1980) tested using the Covert Orienting task, demonstrates the attentional influence of pre-target spatial cues on RT. In this paradigm, the RT to detect a target is facilitated by a valid cue appearing in the same visual field as the target. Conversely, invalid cues presented in the opposite field as the target require disengagement and slows RT, representing greater task demands. Patients with AD show disproportionate slowing to invalid cues compared to healthy controls (Parasuraman, Greenwood, Haxby, & Grady, 1992). And, in animal models, lower acetylcholine levels disrupt the orienting network (Davidson & Marrocco, 2000), whereas nicotinic receptor agonists improve orienting RT in healthy controls (Hammersley, Gilbert, Rzetelny, & Rabinovich, 2016; Heishman, Kleykamp, & Singleton, 2010; Stewart, Burke, & Marrocco, 2001). These findings suggest that donepezil could improve orienting performance in patients with AD. Variability assesses inconsistency in performance. Within-subject variance (intra-individual variability, IIV) represents another important, secondary phenomenon of attention, which has been proposed as a construct to capture unique cognitive variance (Ram, Rabbitt, Stollery, & Nesselroade, 2005). Higher IIV has been documented in patients with MCI and AD (Troyer, Vandermorris, & Murphy, 2016) compared to healthy controls (Hultsch, MacDonald, Hunter, Levy-Bencheton, & Strauss, 2000), and was explained not only by depleted white matter integrity (Jackson, Balota, Duchek, & Head, 2012), but also increased neural noise due to dysfunctional modulation of acetylcholine (MacDonald, Cervenka, Farde, Nyberg, & Bäckman, 2009). Phillips and colleagues (2013) have also concluded that higher cognitive demands increased IIV in response latency tasks in both MCI and AD. Importantly, Gorus and colleagues (2007) demonstrated that treatment using a ChEI, galantamine, reduced IIV after 8 weeks in AD. In the current study, we evaluated IIV using trial-to-trial fluctuations of RT in the Foreperiod Effect task, and hypothesized that treatment with donepezil should reduce variability under high-load conditions (SOA < 500 ms). Lastly, fatigue is a common response to sustained attention and may manifest as increased RT over a sustained period (Kluger, Krupp, & Enoka, 2013). Acetylcholine levels are correlated with sustained performance (Passetti, Dalley, O’connell, Everitt, & Robbins, 2000), and heightened fatigue reduces attentional functions in non-demented older adults (Holtzer, Shuman, Mahoney, Lipton, & Verghese, 2011). Sustained attention tasks that increase load and lengthen task periods are commonly used to elicit fatigue (Claros-Salinas et al., 2013; Möller, Nygren de Boussard, Oldenburg, & Bartfai, 2014; Neumann et al., 2014; Sandry, Genova, Dobryakova, DeLuca, & Wylie, 2014). To our knowledge, no previous research has investigated cognitive fatigue in patients with AD, and we posited that cholinergic deficits would heighten their susceptibility to cognitive fatigue. We operationally defined fatigue as increase in RT over sustained performance in attention tasks, and proposed that variables of fatigue should improve with donepezil treatment. In summary, we proposed that high-load attention measures targeting speed, accuracy, orienting, variability, and fatigue should be sensitive indicators to short-term cholinergic treatment response in AD. We applied a randomized double-blind placebo-controlled pilot trial for patients with AD, who were initiating cholinergic treatment using donepezil. We hypothesized that after roughly 6-week donepezil treatment, the Drug but not the Placebo group would maintain their processing speed, reduce variability and fatigue, and improve orienting and top-down accuracy under high-load conditions. Moreover, we predicted that, in contrast to these attention measures, performance on global measures and other domain-specific cognitive measures would not detect treatment response during the same time interval. Methods The study was approved by the Institutional Review Boards of the City University of New York, Queens College, Queens, NY, and Winthrop-University Hospital, Stony Brook School of Medicine, Mineola, NY. Written consents were obtained from each participant and next of kin. This study is reported following the 2010 Consolidated Standards of Reporting Trials (CONSORT) guidelines (Begg et al., 1996), and is registered in ClinicalTrials.gov (ClinicalTrials.gov Identifier NCT03073876). Participants Inclusion criteria: a new diagnosis of probable AD (McKhann et al., 1984), Mini-Mental Status Examination (MMSE) scores between 15 and 26 to ensure ability to perform experimental attention tasks (Folstein, Folstein, & McHugh, 1975; Monsch et al., 1995), and normal or corrected vision. Diagnosis was reached by consensus of an interdisciplinary team including neuropsychology, geriatrics, neurology, and neuroradiology. Biofluid and PET neuroimaging markers were not available. Participants were allowed to be on concurrent treatments not affecting the cholinergic system (e.g., non-steroidal anti-inflammatory medications, Vitamin E, antidepressants). Exclusion criteria: Primary focal cerebrovascular disease or other dementia etiologies including Parkinson’s Disease, Lewy Body Dementia, or Fronto-temporal Dementia, concurrent use of memantine hydrochloride or other anticholinergic treatments, or prior use of any ChEI. A total of 26 individuals were recruited between 2005 and 2009 at the Memory and Cognitive Disorders Center at Winthrop-University Hospital, met eligibility criteria, and were randomly assigned to a group (Drug = 13, Placebo = 13; see Fig. 1 and Table 1). Power analysis calculations were based on preliminary data, which determined a proposed sample size of 40 participants. Unfortunately, recruitment was challenged by reluctance of patients to delay their treatment. Design We conducted a longitudinal, randomized, double-blind, placebo-controlled trial. All participants were tested at baseline (T1), then randomized into two groups, Drug (5 mg donepezil hydrochloride) or Placebo (gelatin capsules), and retested after approximately 6 weeks (T2; Average = 46 days) to ensure that steady-state was reached (Rogers et al., 1998). We selected donepezil hydrochloride (donepezil) among the ChEIs because its effective dose can be achieved early in the course of treatment, and required only once-daily administration, facilitating compliance. As participants were enrolled by the Memory and Cognitive Disorders Center team prior to publication of updated criteria (McKhann et al., 2011), 1984 criteria (McKhann et al., 1984) were used. The hospital pharmacist was solely apprised of group membership, which was generated using a computerized randomization list. Medication compliance was promoted by working closely with caregivers, and corroborated by the pharmacist. All measures were administered at T1 and T2 in a fixed sequence designed to avoid interference among tasks. All testing procedures were conducted in a quiet and well-lit room. Participants received 30 dollars after each session lasting approximately two and a half hours. Measures Table 2 lists the neuropsychological measures assessing global and domain-specific measures. Attention Tasks All attention tasks were administered on a 2.01 GHz, 960 MB RAM PC computer, with centered stimuli subtending a visual angle ranging between 0.75° and 8.0° (depending on the task), and presented on a 13½” monitor with a refresh rate of 75 Hz (Neath, Earle, Hallett, & Surprenant, 2011) placed 50 cm from the participant. Participants responded with a button press on a centrally placed Ergodex® keypad (Rix, 2003). The Foreperiod Effect task (see Van Dyk et al., 2015) measured the RT to detect a centrally placed asterisk presented at six randomized variable intervals (SOA: 350, 500, 650, 800, 1100, and 1400 ms), with 10 trials at each interval generating 60 trials. High load was defined as the two shortest SOAs, 350 and 500 ms. Database management included deletion of the first trial, anticipatory trials (RT < 100 ms), erroneous button click responses, and missed responses (total 4% trials). Speed was defined as the median RT at each SOA. Intra-individual variability was defined as the standard deviation of RT at each SOA. The task was administered twice, at the beginning (Block 1) and end of each testing session (Block 2). Fatigue was defined by comparing overall median RT (across all SOAs) at each block. The Attentional Blink task (Perry & Hodges, 2003) measured top-down accuracy under increasing levels of temporal load. Prior to any task administration, individualized stimulus duration was established such that each participant obtained ≥ 85% accuracy identifying a single alphanumeric character (Ly, 2013). The task consisted of two stimuli, a number (i.e., 2, 3, 5, 6, or 9) and a letter (i.e., A, D, E, N, or R) sequentially presented to the right or left of a fixation point, with each stimulus immediately followed by a mask. The interval between stimuli was varied randomly (SOA: 133, 266, 399, 532, or 655 ms). We defined high load as the intervals below 500 ms: SOAs 266 and 399 ms. SOA-133 ms was excluded, as it has been shown to be too challenging for patients with AD (Ly, 2013). The task had two counter-balanced conditions; participants were instructed to either “report the letter” (40 trials), or “report the number” (40 trials), totaling 80 trials. Verbal responses were recorded by the examiner. Accuracy of the second stimulus presented at each SOA was used as the dependent variable. The Covert Orienting task was used to evaluate orienting and fatigue. We measured the RT to detect a target following an exogenous spatial cue in the same visual field (valid), opposite field (invalid), or in both fields (neutral) (Posner, 1980). Participants fixated on a central red cross throughout each trial, and responded with a button press to the target (“X”). The cue-target intervals were set at 250 ms or, for catch trials, at 850 ms. A total of 170 trials (18% catch, 12% neutral, 12% invalid, 58% valid) were randomly presented over five sequential blocks each containing 34 trials. Prior to aggregating the data, catch trials were removed. Orienting was assessed using median RT to valid, invalid, and neutral cues across blocks. High load was defined as RT after an invalid cue. Fatigue was assessed comparing overall median RT across cues between Block 1 and Block 5. Analyses Analyses were carried out using SPSS Version 22.0 (2013). Initial Kolmogorov–Smirnov preliminary analyses revealed that the attention variables were not normally distributed, therefore, non-parametric Wilcoxon signed-rank analyses were selected to conduct within-group comparisons to increase power. Parametric analyses were applied to demographic, global, and domain-specific measures. First, we performed baseline group comparisons on all measures. Second, we analyzed treatment effects of global and domain-specific performance using a Group (Drug, Placebo) × Time (T1, T2) repeated measures Analyses of Variance (ANOVA). For the attention variables: (a) within-group differences compared T1 to T2 (Wilcoxon signed-rank tests) on measures of processing speed, accuracy, and variability; (b) treatment effects of fatigue compared performance across blocks at T2. Two-tailed tests, p < .05 level of significance, and effect sizes (partial eta squared and Cohen’s r) are reported (Cohen, 1988). One participant was excluded from the global and domain-specific measures analyses due to missing data at T2. One participant was excluded from the fatigue analyses on the Foreperiod Effect task at baseline because Block 2 was not administered. Results At baseline (T1), the Drug and Placebo groups were not significantly different on any demographic, global, domain-specific, or attentional variables (see Table 1). These comparisons indicated that any subsequent differences could be attributed to experimental manipulation of drug administration. Table 1. Baseline group comparisons on demographics, global and domain-specific measures Drug (n = 12) Placebo (n = 11) Mean (SD) Range Mean (SD) Range χ2/t statistic (df) p Demographics  Age (yrs) 79.3 (7.4) 61–88 81.7 (3.8) 75–87 −1 (21) .3  Education (yrs) 13.2 (4.1) 8–22 15.1 (4.0) 12–22 −1.1 (21) .3  Gender 4 males 4 males 1.3 (1) .2  GDS 6.7 (6.2) 0–16 6.5 (4.5) 2–15 0.1 (21) .9  NPI 16.7 (12.3) 2–40 15.4 (15.8) 0–59 0.2 (21) .8 Global Measures  MMSE† 24.3 (3.5) 18–29 25.4 (1.7) 23–28 −0.9 (16.4) .4  DRS 119.7 (10.4) 101–136 127.1 (7.2) 111–135 −2 (21) .1  CDR 1 (.4) 0.5–2.0 0.8 (.3) 0.5–1.0 1.3 (21) .2  ADAS-Cog 17.3 (6.6) 9.0–28.3 13.1 (3.8) 8.0–20 1.3 (21) .2 Domain-Specific Measures  Memory   HVLT Learning 11 (2.6) 7–16 11.4 (3.8) 5–19 −0.3 (21) .8   HVLT Recall 1.2 (1.0) 0–3 1.1 (1.4) 0–4 0.3 (21) .7   HVLT Recognition 5.3 (3.6) 0–10 6.7 (2.7) 3–11 −1.1 (21) .2  Verbal   Letter Fluency 9 (5.0) 3–17 9.4 (4.3) 2–15 −0.2 (21) .8   Category Fluency 7.4 (2.7) 3–12 7.9 (2.7) 5–13 −0.5 (21) .6  Visuospatial   Visual Form Discrimination 26.7 (3.9) 18–31 27.9 (2.5) 24–32 −0.8 (21) .4  Attention & Executive Function   Digit Span Forward (max) 5.9 (.9) 5–9 6 (.8) 5–7 −0.2 (21) .8   Digit Span Backward (max) 3.6 (.8) 2–5 3.6 (.8) 2–5 −0.2 (21) .9   DKEFS Trail Making Test–4† 12.2 (11.7) 2–12 6.7 (6.2) 2–10 1.4 (16.9) .2  Motor Speed   DKEFS Trail Making Test–5 62.7 (36.5) 26–150 54.3 (16.6) 34–93 0.7 (21) .5 Drug (n = 12) Placebo (n = 11) Mean (SD) Range Mean (SD) Range χ2/t statistic (df) p Demographics  Age (yrs) 79.3 (7.4) 61–88 81.7 (3.8) 75–87 −1 (21) .3  Education (yrs) 13.2 (4.1) 8–22 15.1 (4.0) 12–22 −1.1 (21) .3  Gender 4 males 4 males 1.3 (1) .2  GDS 6.7 (6.2) 0–16 6.5 (4.5) 2–15 0.1 (21) .9  NPI 16.7 (12.3) 2–40 15.4 (15.8) 0–59 0.2 (21) .8 Global Measures  MMSE† 24.3 (3.5) 18–29 25.4 (1.7) 23–28 −0.9 (16.4) .4  DRS 119.7 (10.4) 101–136 127.1 (7.2) 111–135 −2 (21) .1  CDR 1 (.4) 0.5–2.0 0.8 (.3) 0.5–1.0 1.3 (21) .2  ADAS-Cog 17.3 (6.6) 9.0–28.3 13.1 (3.8) 8.0–20 1.3 (21) .2 Domain-Specific Measures  Memory   HVLT Learning 11 (2.6) 7–16 11.4 (3.8) 5–19 −0.3 (21) .8   HVLT Recall 1.2 (1.0) 0–3 1.1 (1.4) 0–4 0.3 (21) .7   HVLT Recognition 5.3 (3.6) 0–10 6.7 (2.7) 3–11 −1.1 (21) .2  Verbal   Letter Fluency 9 (5.0) 3–17 9.4 (4.3) 2–15 −0.2 (21) .8   Category Fluency 7.4 (2.7) 3–12 7.9 (2.7) 5–13 −0.5 (21) .6  Visuospatial   Visual Form Discrimination 26.7 (3.9) 18–31 27.9 (2.5) 24–32 −0.8 (21) .4  Attention & Executive Function   Digit Span Forward (max) 5.9 (.9) 5–9 6 (.8) 5–7 −0.2 (21) .8   Digit Span Backward (max) 3.6 (.8) 2–5 3.6 (.8) 2–5 −0.2 (21) .9   DKEFS Trail Making Test–4† 12.2 (11.7) 2–12 6.7 (6.2) 2–10 1.4 (16.9) .2  Motor Speed   DKEFS Trail Making Test–5 62.7 (36.5) 26–150 54.3 (16.6) 34–93 0.7 (21) .5 Note: Statistics reported are: mean (M), standard deviation (SD), Pearson’s chi square test (χ2), t-statistic (t), degrees of freedom (df), and significance value (p). (†) indicates that Levene’s test was significant, p-value reported assuming non-equal variances. MEASURES: GDS = Geriatric Depression Scale; NPI = Neuropsychiatric Inventory; MMSE = Mini-Mental State Examination; DRS = Dementia Rating Scale; CDR = Clinical Dementia Rating Scale; ADAS-Cog = Alzheimer’s Disease Assessment Scale-cognitive subscale; HVLT Learning, Delayed, Recognition = Hopkins Verbal Learning Test – learning, delayed, and recognition raw scores; Letter Fluency = FAS Mean Fluency; Category Fluency = Mean Fluency across animals, fruits, and vegetables; Digit Span Forward: Longest Digit Span Forward, Digit Span Backward: Longest Digit Span Backward; DKEFS Trail Making Test 4, 5 = Delis–Kaplan Executive Function System Trail Making Test Conditions 4 (scaled score) and 5 (raw). Note: T-test analyses were conducted to compare the groups. Table 1. Baseline group comparisons on demographics, global and domain-specific measures Drug (n = 12) Placebo (n = 11) Mean (SD) Range Mean (SD) Range χ2/t statistic (df) p Demographics  Age (yrs) 79.3 (7.4) 61–88 81.7 (3.8) 75–87 −1 (21) .3  Education (yrs) 13.2 (4.1) 8–22 15.1 (4.0) 12–22 −1.1 (21) .3  Gender 4 males 4 males 1.3 (1) .2  GDS 6.7 (6.2) 0–16 6.5 (4.5) 2–15 0.1 (21) .9  NPI 16.7 (12.3) 2–40 15.4 (15.8) 0–59 0.2 (21) .8 Global Measures  MMSE† 24.3 (3.5) 18–29 25.4 (1.7) 23–28 −0.9 (16.4) .4  DRS 119.7 (10.4) 101–136 127.1 (7.2) 111–135 −2 (21) .1  CDR 1 (.4) 0.5–2.0 0.8 (.3) 0.5–1.0 1.3 (21) .2  ADAS-Cog 17.3 (6.6) 9.0–28.3 13.1 (3.8) 8.0–20 1.3 (21) .2 Domain-Specific Measures  Memory   HVLT Learning 11 (2.6) 7–16 11.4 (3.8) 5–19 −0.3 (21) .8   HVLT Recall 1.2 (1.0) 0–3 1.1 (1.4) 0–4 0.3 (21) .7   HVLT Recognition 5.3 (3.6) 0–10 6.7 (2.7) 3–11 −1.1 (21) .2  Verbal   Letter Fluency 9 (5.0) 3–17 9.4 (4.3) 2–15 −0.2 (21) .8   Category Fluency 7.4 (2.7) 3–12 7.9 (2.7) 5–13 −0.5 (21) .6  Visuospatial   Visual Form Discrimination 26.7 (3.9) 18–31 27.9 (2.5) 24–32 −0.8 (21) .4  Attention & Executive Function   Digit Span Forward (max) 5.9 (.9) 5–9 6 (.8) 5–7 −0.2 (21) .8   Digit Span Backward (max) 3.6 (.8) 2–5 3.6 (.8) 2–5 −0.2 (21) .9   DKEFS Trail Making Test–4† 12.2 (11.7) 2–12 6.7 (6.2) 2–10 1.4 (16.9) .2  Motor Speed   DKEFS Trail Making Test–5 62.7 (36.5) 26–150 54.3 (16.6) 34–93 0.7 (21) .5 Drug (n = 12) Placebo (n = 11) Mean (SD) Range Mean (SD) Range χ2/t statistic (df) p Demographics  Age (yrs) 79.3 (7.4) 61–88 81.7 (3.8) 75–87 −1 (21) .3  Education (yrs) 13.2 (4.1) 8–22 15.1 (4.0) 12–22 −1.1 (21) .3  Gender 4 males 4 males 1.3 (1) .2  GDS 6.7 (6.2) 0–16 6.5 (4.5) 2–15 0.1 (21) .9  NPI 16.7 (12.3) 2–40 15.4 (15.8) 0–59 0.2 (21) .8 Global Measures  MMSE† 24.3 (3.5) 18–29 25.4 (1.7) 23–28 −0.9 (16.4) .4  DRS 119.7 (10.4) 101–136 127.1 (7.2) 111–135 −2 (21) .1  CDR 1 (.4) 0.5–2.0 0.8 (.3) 0.5–1.0 1.3 (21) .2  ADAS-Cog 17.3 (6.6) 9.0–28.3 13.1 (3.8) 8.0–20 1.3 (21) .2 Domain-Specific Measures  Memory   HVLT Learning 11 (2.6) 7–16 11.4 (3.8) 5–19 −0.3 (21) .8   HVLT Recall 1.2 (1.0) 0–3 1.1 (1.4) 0–4 0.3 (21) .7   HVLT Recognition 5.3 (3.6) 0–10 6.7 (2.7) 3–11 −1.1 (21) .2  Verbal   Letter Fluency 9 (5.0) 3–17 9.4 (4.3) 2–15 −0.2 (21) .8   Category Fluency 7.4 (2.7) 3–12 7.9 (2.7) 5–13 −0.5 (21) .6  Visuospatial   Visual Form Discrimination 26.7 (3.9) 18–31 27.9 (2.5) 24–32 −0.8 (21) .4  Attention & Executive Function   Digit Span Forward (max) 5.9 (.9) 5–9 6 (.8) 5–7 −0.2 (21) .8   Digit Span Backward (max) 3.6 (.8) 2–5 3.6 (.8) 2–5 −0.2 (21) .9   DKEFS Trail Making Test–4† 12.2 (11.7) 2–12 6.7 (6.2) 2–10 1.4 (16.9) .2  Motor Speed   DKEFS Trail Making Test–5 62.7 (36.5) 26–150 54.3 (16.6) 34–93 0.7 (21) .5 Note: Statistics reported are: mean (M), standard deviation (SD), Pearson’s chi square test (χ2), t-statistic (t), degrees of freedom (df), and significance value (p). (†) indicates that Levene’s test was significant, p-value reported assuming non-equal variances. MEASURES: GDS = Geriatric Depression Scale; NPI = Neuropsychiatric Inventory; MMSE = Mini-Mental State Examination; DRS = Dementia Rating Scale; CDR = Clinical Dementia Rating Scale; ADAS-Cog = Alzheimer’s Disease Assessment Scale-cognitive subscale; HVLT Learning, Delayed, Recognition = Hopkins Verbal Learning Test – learning, delayed, and recognition raw scores; Letter Fluency = FAS Mean Fluency; Category Fluency = Mean Fluency across animals, fruits, and vegetables; Digit Span Forward: Longest Digit Span Forward, Digit Span Backward: Longest Digit Span Backward; DKEFS Trail Making Test 4, 5 = Delis–Kaplan Executive Function System Trail Making Test Conditions 4 (scaled score) and 5 (raw). Note: T-test analyses were conducted to compare the groups. Fig. 1. View largeDownload slide Participant flow diagram following CONSORT 2010 guidelines. Fig. 1. View largeDownload slide Participant flow diagram following CONSORT 2010 guidelines. Repeated measures analyses of all global and domain-specific cognitive measures revealed no significant Group × Time interaction effect indicating that none of these measures was sensitive to short-term treatment (see Table 2). Table 2. Global and domain-specific cognitive measures: descriptive statistics and repeated measures ANOVA Drug (n = 12) Placebo (n = 11) ANOVA T1 T2 T1 T2 Time × Group (Mean, SD) F p Partial Eta2 Global Measures  MMSE 24.3 (3.5) 24.2 (2.8) 25.4 (1.7) 24.3 (1.9) 1 .3 0.0  DRS 119.7 (10.4) 121.3 (9.5) 127.1 (7.2) 126.5 (8.1) 0.6 .4 0.0  ADAS-Cog 17.3 (6.7) 15.4 (4.4) 13.1 (3.8) 14.0 (4.4) 2.9 .1 0.1 Domain-Specific  Memory   HVLT Learning 11.0 (2.6) 10.8 (2.3) 11.4 (3.8) 11.6 (4.9) 0.1 .7 0.0   HVLT Recall 1.2 (1.0) 1.0 (1.6) 1.1 (1.4) 2.3 (1.9) 3 .1 0.1   HVLT Recognition 5.3 (3.6) 4.9 (3.3) 6.7 (2.7) 5.7 (2.4) 0.3 .6 0.0  Language   Fluency FAS (M) 9.0 (5.0) 9.0 (5.4) 9.4 (4.3) 9.5 (3.9) 0.0 .8 0.0   Fluency Category (M) 7.4 (2.7) 7.4 (2.6) 7.9 (2.7) 7.0 (2.2) 2.0 .2 0.1  Visuospatial   VFD 26.7 (3.9) 27.6 (3.5) 27.9 (2.5) 27.9 (2.3) 0.1 .7 0.0  Attention & Executive Function   Digit Span Forward (max) 5.9 (0.9) 5.3 (1.1) 6.0 (0.8) 5.6 (0.7) 0.3 .6 0.0   Digit Span Backward (max) 3.6 (0.8) 4.1 (0.8) 3.6 (0.8) 3.7 (0.7) 2.2 .1 0.1   DKEFs Trail Making Test Condition 4 12.2 (11.7) 10.8 (12.5) 6.7 (6.2) 9.3 (9.2) 0.8 .4 0.0  Motor Speed   DKEFs Trail Making Test Condition 5 62.7 (36.5) 64.2 (33.7) 54.3 (16.6) 59.2 (22.5) 0.1 .7 0.0 Drug (n = 12) Placebo (n = 11) ANOVA T1 T2 T1 T2 Time × Group (Mean, SD) F p Partial Eta2 Global Measures  MMSE 24.3 (3.5) 24.2 (2.8) 25.4 (1.7) 24.3 (1.9) 1 .3 0.0  DRS 119.7 (10.4) 121.3 (9.5) 127.1 (7.2) 126.5 (8.1) 0.6 .4 0.0  ADAS-Cog 17.3 (6.7) 15.4 (4.4) 13.1 (3.8) 14.0 (4.4) 2.9 .1 0.1 Domain-Specific  Memory   HVLT Learning 11.0 (2.6) 10.8 (2.3) 11.4 (3.8) 11.6 (4.9) 0.1 .7 0.0   HVLT Recall 1.2 (1.0) 1.0 (1.6) 1.1 (1.4) 2.3 (1.9) 3 .1 0.1   HVLT Recognition 5.3 (3.6) 4.9 (3.3) 6.7 (2.7) 5.7 (2.4) 0.3 .6 0.0  Language   Fluency FAS (M) 9.0 (5.0) 9.0 (5.4) 9.4 (4.3) 9.5 (3.9) 0.0 .8 0.0   Fluency Category (M) 7.4 (2.7) 7.4 (2.6) 7.9 (2.7) 7.0 (2.2) 2.0 .2 0.1  Visuospatial   VFD 26.7 (3.9) 27.6 (3.5) 27.9 (2.5) 27.9 (2.3) 0.1 .7 0.0  Attention & Executive Function   Digit Span Forward (max) 5.9 (0.9) 5.3 (1.1) 6.0 (0.8) 5.6 (0.7) 0.3 .6 0.0   Digit Span Backward (max) 3.6 (0.8) 4.1 (0.8) 3.6 (0.8) 3.7 (0.7) 2.2 .1 0.1   DKEFs Trail Making Test Condition 4 12.2 (11.7) 10.8 (12.5) 6.7 (6.2) 9.3 (9.2) 0.8 .4 0.0  Motor Speed   DKEFs Trail Making Test Condition 5 62.7 (36.5) 64.2 (33.7) 54.3 (16.6) 59.2 (22.5) 0.1 .7 0.0 Note: Statistics reported: mean (M), standard deviation (SD), F Ratio (F), significance value (p). Test abbreviations: MMSE = Mini-Mental State Examination; DRS = Dementia Rating Scale; ADAS-Cog = Alzheimer’s Disease Assessment Scale-cognitive subscale; HVLT Learning, Recall, Recognition = Hopkins Verbal Learning Test – learning, delayed, and recognition raw score; Fluency FAS = Mean Letter Fluency; Fluency Category = Mean Fluency across animals, fruits, and vegetables; VFD = Visual Form Discrimination; Digit Span Forward: Longest Digit Span Forward, Digit Span Backward: Longest Digit Span Backward; DKEFs = Delis–Kaplan Executive Function System: Trail Making, Conditions 4 and 5. Table 2. Global and domain-specific cognitive measures: descriptive statistics and repeated measures ANOVA Drug (n = 12) Placebo (n = 11) ANOVA T1 T2 T1 T2 Time × Group (Mean, SD) F p Partial Eta2 Global Measures  MMSE 24.3 (3.5) 24.2 (2.8) 25.4 (1.7) 24.3 (1.9) 1 .3 0.0  DRS 119.7 (10.4) 121.3 (9.5) 127.1 (7.2) 126.5 (8.1) 0.6 .4 0.0  ADAS-Cog 17.3 (6.7) 15.4 (4.4) 13.1 (3.8) 14.0 (4.4) 2.9 .1 0.1 Domain-Specific  Memory   HVLT Learning 11.0 (2.6) 10.8 (2.3) 11.4 (3.8) 11.6 (4.9) 0.1 .7 0.0   HVLT Recall 1.2 (1.0) 1.0 (1.6) 1.1 (1.4) 2.3 (1.9) 3 .1 0.1   HVLT Recognition 5.3 (3.6) 4.9 (3.3) 6.7 (2.7) 5.7 (2.4) 0.3 .6 0.0  Language   Fluency FAS (M) 9.0 (5.0) 9.0 (5.4) 9.4 (4.3) 9.5 (3.9) 0.0 .8 0.0   Fluency Category (M) 7.4 (2.7) 7.4 (2.6) 7.9 (2.7) 7.0 (2.2) 2.0 .2 0.1  Visuospatial   VFD 26.7 (3.9) 27.6 (3.5) 27.9 (2.5) 27.9 (2.3) 0.1 .7 0.0  Attention & Executive Function   Digit Span Forward (max) 5.9 (0.9) 5.3 (1.1) 6.0 (0.8) 5.6 (0.7) 0.3 .6 0.0   Digit Span Backward (max) 3.6 (0.8) 4.1 (0.8) 3.6 (0.8) 3.7 (0.7) 2.2 .1 0.1   DKEFs Trail Making Test Condition 4 12.2 (11.7) 10.8 (12.5) 6.7 (6.2) 9.3 (9.2) 0.8 .4 0.0  Motor Speed   DKEFs Trail Making Test Condition 5 62.7 (36.5) 64.2 (33.7) 54.3 (16.6) 59.2 (22.5) 0.1 .7 0.0 Drug (n = 12) Placebo (n = 11) ANOVA T1 T2 T1 T2 Time × Group (Mean, SD) F p Partial Eta2 Global Measures  MMSE 24.3 (3.5) 24.2 (2.8) 25.4 (1.7) 24.3 (1.9) 1 .3 0.0  DRS 119.7 (10.4) 121.3 (9.5) 127.1 (7.2) 126.5 (8.1) 0.6 .4 0.0  ADAS-Cog 17.3 (6.7) 15.4 (4.4) 13.1 (3.8) 14.0 (4.4) 2.9 .1 0.1 Domain-Specific  Memory   HVLT Learning 11.0 (2.6) 10.8 (2.3) 11.4 (3.8) 11.6 (4.9) 0.1 .7 0.0   HVLT Recall 1.2 (1.0) 1.0 (1.6) 1.1 (1.4) 2.3 (1.9) 3 .1 0.1   HVLT Recognition 5.3 (3.6) 4.9 (3.3) 6.7 (2.7) 5.7 (2.4) 0.3 .6 0.0  Language   Fluency FAS (M) 9.0 (5.0) 9.0 (5.4) 9.4 (4.3) 9.5 (3.9) 0.0 .8 0.0   Fluency Category (M) 7.4 (2.7) 7.4 (2.6) 7.9 (2.7) 7.0 (2.2) 2.0 .2 0.1  Visuospatial   VFD 26.7 (3.9) 27.6 (3.5) 27.9 (2.5) 27.9 (2.3) 0.1 .7 0.0  Attention & Executive Function   Digit Span Forward (max) 5.9 (0.9) 5.3 (1.1) 6.0 (0.8) 5.6 (0.7) 0.3 .6 0.0   Digit Span Backward (max) 3.6 (0.8) 4.1 (0.8) 3.6 (0.8) 3.7 (0.7) 2.2 .1 0.1   DKEFs Trail Making Test Condition 4 12.2 (11.7) 10.8 (12.5) 6.7 (6.2) 9.3 (9.2) 0.8 .4 0.0  Motor Speed   DKEFs Trail Making Test Condition 5 62.7 (36.5) 64.2 (33.7) 54.3 (16.6) 59.2 (22.5) 0.1 .7 0.0 Note: Statistics reported: mean (M), standard deviation (SD), F Ratio (F), significance value (p). Test abbreviations: MMSE = Mini-Mental State Examination; DRS = Dementia Rating Scale; ADAS-Cog = Alzheimer’s Disease Assessment Scale-cognitive subscale; HVLT Learning, Recall, Recognition = Hopkins Verbal Learning Test – learning, delayed, and recognition raw score; Fluency FAS = Mean Letter Fluency; Fluency Category = Mean Fluency across animals, fruits, and vegetables; VFD = Visual Form Discrimination; Digit Span Forward: Longest Digit Span Forward, Digit Span Backward: Longest Digit Span Backward; DKEFs = Delis–Kaplan Executive Function System: Trail Making, Conditions 4 and 5. The attention measures were analyzed to show within-group performance between T1 and T2 (see Tables 3A and 3B). Processing Speed. There were no treatment effects for processing speed within the Drug or Placebo group, as RT did not improve across any SOA on the Foreperiod Effect task. Accuracy. The Attentional Blink showed that, while both groups declined from baseline at the high-load interval (SOA-266 ms, r = 0.5), the Placebo group was significantly worse (Fig. 2). Of note, there were no treatment effects at SOA-399 ms, which we discuss below. Orienting. Contrary to our prediction, there was no treatment effect on covert orienting. Variability. The Drug group significantly reduced variability after treatment at the shortest SOA-350 ms on the Foreperiod Effect task (r = 0.4), while the Placebo group did not differ over time (see Fig. 3). On SOA-500 ms, variability remained stable for both groups. Fatigue. The Placebo group significantly increased RT both on the Foreperiod Effect between Block 1–2 (r = 0.5), as well as on Covert Orienting from Block 1 to 5 (r = 0.6). In contrast, the Drug group maintained speed across blocks on both tasks (see Table 3B; and Fig. 4). Table 3A. Attention measures: speed, accuracy, variability, and orienting. Accuracy and variability showed treatment effect at high-load conditions Descriptives Within Group Treatment Effects between T1 and T2 Drug Placebo Drug Placebo Processing Speed - Foreperiod Effect Task [Median RT (IQR)] SOA (ms) T1 T2 T1 T2 z p r z p r 350 539 (261.6) 497 (180.2) 415 (103.5) 402 (194.5) −0.6 .5 0.1 −0.2 .9 0.0 500 449 (282.6) 416 (105.9) 345 (240.5) 332 (231) −0.5 .6 0.1 −0.2 .9 0.0 Accuracy – Attentional Blink [Median (IQR)] SOA (ms) T1 T2 T1 T2 z p r z p r 266 75.0 (21.9) 56.3 (68.7) 62.5 (25.0) 37.5 (37.5) −1.8 .1 0.4 −2.2 .0* 0.5 399 81.2 (31.2) 75.0 (34.4) 87.5 (50.0) 62.5 (50.0) −0.3 .8 0.1 −0.4 .7 0.1 Variability – Foreperiod Effect Task (SD Reaction Time) SOA (ms) T1 T2 T1 T2 z p r z p r 350 134.3 107.6 112.7 141.1 −2.0 .0* 0.4 −0.5 .6 0.1 500 107.6 103.1 92.3 100.7 −0.2 .9 0.0 −0.5 .6 0.1 Covert Orienting [Median RT (IQR)] Cue Type T1 T2 T1 T2 z p r z p r Invalid 497 (185.4) 496 (142.3) 490 (92.3) 475 (111) −0.4 .7 0.1 −0.8 .4 0.2 Descriptives Within Group Treatment Effects between T1 and T2 Drug Placebo Drug Placebo Processing Speed - Foreperiod Effect Task [Median RT (IQR)] SOA (ms) T1 T2 T1 T2 z p r z p r 350 539 (261.6) 497 (180.2) 415 (103.5) 402 (194.5) −0.6 .5 0.1 −0.2 .9 0.0 500 449 (282.6) 416 (105.9) 345 (240.5) 332 (231) −0.5 .6 0.1 −0.2 .9 0.0 Accuracy – Attentional Blink [Median (IQR)] SOA (ms) T1 T2 T1 T2 z p r z p r 266 75.0 (21.9) 56.3 (68.7) 62.5 (25.0) 37.5 (37.5) −1.8 .1 0.4 −2.2 .0* 0.5 399 81.2 (31.2) 75.0 (34.4) 87.5 (50.0) 62.5 (50.0) −0.3 .8 0.1 −0.4 .7 0.1 Variability – Foreperiod Effect Task (SD Reaction Time) SOA (ms) T1 T2 T1 T2 z p r z p r 350 134.3 107.6 112.7 141.1 −2.0 .0* 0.4 −0.5 .6 0.1 500 107.6 103.1 92.3 100.7 −0.2 .9 0.0 −0.5 .6 0.1 Covert Orienting [Median RT (IQR)] Cue Type T1 T2 T1 T2 z p r z p r Invalid 497 (185.4) 496 (142.3) 490 (92.3) 475 (111) −0.4 .7 0.1 −0.8 .4 0.2 Note: Non-parametric analyses conducted using within group comparisons: Wilcoxon Signed Rank Test; Baseline (T1), 6-week follow-up (T2), Stimulus Onset Asynchrony (SOA), Interquartile Range (IQR), Z-score (z), significance value (p), Effect size (Cohen’s r); (*) indicates unadjusted p < 0.05. Table 3A. Attention measures: speed, accuracy, variability, and orienting. Accuracy and variability showed treatment effect at high-load conditions Descriptives Within Group Treatment Effects between T1 and T2 Drug Placebo Drug Placebo Processing Speed - Foreperiod Effect Task [Median RT (IQR)] SOA (ms) T1 T2 T1 T2 z p r z p r 350 539 (261.6) 497 (180.2) 415 (103.5) 402 (194.5) −0.6 .5 0.1 −0.2 .9 0.0 500 449 (282.6) 416 (105.9) 345 (240.5) 332 (231) −0.5 .6 0.1 −0.2 .9 0.0 Accuracy – Attentional Blink [Median (IQR)] SOA (ms) T1 T2 T1 T2 z p r z p r 266 75.0 (21.9) 56.3 (68.7) 62.5 (25.0) 37.5 (37.5) −1.8 .1 0.4 −2.2 .0* 0.5 399 81.2 (31.2) 75.0 (34.4) 87.5 (50.0) 62.5 (50.0) −0.3 .8 0.1 −0.4 .7 0.1 Variability – Foreperiod Effect Task (SD Reaction Time) SOA (ms) T1 T2 T1 T2 z p r z p r 350 134.3 107.6 112.7 141.1 −2.0 .0* 0.4 −0.5 .6 0.1 500 107.6 103.1 92.3 100.7 −0.2 .9 0.0 −0.5 .6 0.1 Covert Orienting [Median RT (IQR)] Cue Type T1 T2 T1 T2 z p r z p r Invalid 497 (185.4) 496 (142.3) 490 (92.3) 475 (111) −0.4 .7 0.1 −0.8 .4 0.2 Descriptives Within Group Treatment Effects between T1 and T2 Drug Placebo Drug Placebo Processing Speed - Foreperiod Effect Task [Median RT (IQR)] SOA (ms) T1 T2 T1 T2 z p r z p r 350 539 (261.6) 497 (180.2) 415 (103.5) 402 (194.5) −0.6 .5 0.1 −0.2 .9 0.0 500 449 (282.6) 416 (105.9) 345 (240.5) 332 (231) −0.5 .6 0.1 −0.2 .9 0.0 Accuracy – Attentional Blink [Median (IQR)] SOA (ms) T1 T2 T1 T2 z p r z p r 266 75.0 (21.9) 56.3 (68.7) 62.5 (25.0) 37.5 (37.5) −1.8 .1 0.4 −2.2 .0* 0.5 399 81.2 (31.2) 75.0 (34.4) 87.5 (50.0) 62.5 (50.0) −0.3 .8 0.1 −0.4 .7 0.1 Variability – Foreperiod Effect Task (SD Reaction Time) SOA (ms) T1 T2 T1 T2 z p r z p r 350 134.3 107.6 112.7 141.1 −2.0 .0* 0.4 −0.5 .6 0.1 500 107.6 103.1 92.3 100.7 −0.2 .9 0.0 −0.5 .6 0.1 Covert Orienting [Median RT (IQR)] Cue Type T1 T2 T1 T2 z p r z p r Invalid 497 (185.4) 496 (142.3) 490 (92.3) 475 (111) −0.4 .7 0.1 −0.8 .4 0.2 Note: Non-parametric analyses conducted using within group comparisons: Wilcoxon Signed Rank Test; Baseline (T1), 6-week follow-up (T2), Stimulus Onset Asynchrony (SOA), Interquartile Range (IQR), Z-score (z), significance value (p), Effect size (Cohen’s r); (*) indicates unadjusted p < 0.05. Table 3B. Attention measure: Fatigue at T2. Placebo group showed significantly greater fatigue than Drug group Foreperiod Effect Task Median (IQR) Block 1 Block 2 z p r Drug 400 (87.4) 395 (85.3) 1.02 .3 0.2 Placebo 331 (200.7) 385 (254.2) −2.2 .0* 0.5 Foreperiod Effect Task Median (IQR) Block 1 Block 2 z p r Drug 400 (87.4) 395 (85.3) 1.02 .3 0.2 Placebo 331 (200.7) 385 (254.2) −2.2 .0* 0.5 Covert Orienting Task Median (IQR) Block 1 Block 5 z p r Drug 415 (145.1) 451 (134.2) −0.7 .5 0.1 Placebo 402 (95.1) 487 (144.3) −2.9 .0* 0.6 Covert Orienting Task Median (IQR) Block 1 Block 5 z p r Drug 415 (145.1) 451 (134.2) −0.7 .5 0.1 Placebo 402 (95.1) 487 (144.3) −2.9 .0* 0.6 Note: Non-parametric analyses using Wilcoxon Signed Rank Test (Within group comparisons). Statistics reported are: Interquartile Range (IQR), Z-score (z), significance value (p), Effect size (Cohen’s r); (*) indicates p< .05. Table 3B. Attention measure: Fatigue at T2. Placebo group showed significantly greater fatigue than Drug group Foreperiod Effect Task Median (IQR) Block 1 Block 2 z p r Drug 400 (87.4) 395 (85.3) 1.02 .3 0.2 Placebo 331 (200.7) 385 (254.2) −2.2 .0* 0.5 Foreperiod Effect Task Median (IQR) Block 1 Block 2 z p r Drug 400 (87.4) 395 (85.3) 1.02 .3 0.2 Placebo 331 (200.7) 385 (254.2) −2.2 .0* 0.5 Covert Orienting Task Median (IQR) Block 1 Block 5 z p r Drug 415 (145.1) 451 (134.2) −0.7 .5 0.1 Placebo 402 (95.1) 487 (144.3) −2.9 .0* 0.6 Covert Orienting Task Median (IQR) Block 1 Block 5 z p r Drug 415 (145.1) 451 (134.2) −0.7 .5 0.1 Placebo 402 (95.1) 487 (144.3) −2.9 .0* 0.6 Note: Non-parametric analyses using Wilcoxon Signed Rank Test (Within group comparisons). Statistics reported are: Interquartile Range (IQR), Z-score (z), significance value (p), Effect size (Cohen’s r); (*) indicates p< .05. Fig. 2. View largeDownload slide Treatment effects on top-down accuracy comparing accuracy percentage at T1 to T2. Error bars denote adjusted standard error to reflect within-group error variance (Field, 2009). SOA = Stimulus Onset Asynchrony; ms = milliseconds; T1 = baseline; T2 = after ≈6 weeks of treatment with donepezil. *indicates p < .05. Fig. 2. View largeDownload slide Treatment effects on top-down accuracy comparing accuracy percentage at T1 to T2. Error bars denote adjusted standard error to reflect within-group error variance (Field, 2009). SOA = Stimulus Onset Asynchrony; ms = milliseconds; T1 = baseline; T2 = after ≈6 weeks of treatment with donepezil. *indicates p < .05. Fig. 3. View largeDownload slide Treatment effects on variability comparing RT standard deviation from T1 to T2. Error bars denote adjusted standard error to reflect within-group error variance (Field, 2009). RT = Response Time; SOA = Stimulus Onset Asynchrony; ms = milliseconds; T1 = baseline; T2 = after ≈6 weeks of treatment with donepezil. * indicates p < .05. Fig. 3. View largeDownload slide Treatment effects on variability comparing RT standard deviation from T1 to T2. Error bars denote adjusted standard error to reflect within-group error variance (Field, 2009). RT = Response Time; SOA = Stimulus Onset Asynchrony; ms = milliseconds; T1 = baseline; T2 = after ≈6 weeks of treatment with donepezil. * indicates p < .05. Fig. 4. View largeDownload slide Treatment effects of fatigue at T2 comparing Drug versus Placebo group RT mean across blocks in the Covert Orienting and Foreperiod Effect task. Error bars denote adjusted standard error to reflect within-group error variance (Field, 2009). RT = Response Time; SOA = Stimulus Onset Asynchrony; ms = milliseconds; T1 = baseline; T2 = after ≈6 weeks of treatment with donepezil. * indicates p < .05. Fig. 4. View largeDownload slide Treatment effects of fatigue at T2 comparing Drug versus Placebo group RT mean across blocks in the Covert Orienting and Foreperiod Effect task. Error bars denote adjusted standard error to reflect within-group error variance (Field, 2009). RT = Response Time; SOA = Stimulus Onset Asynchrony; ms = milliseconds; T1 = baseline; T2 = after ≈6 weeks of treatment with donepezil. * indicates p < .05. Discussion The aim of this study was to investigate whether high-load attention measures could detect early treatment effects of donepezil hydrochloride in patients newly diagnosed with AD. Since the advent of cholinergic treatment for AD (Davies & Maloney, 1976), the ability to detect a short-term cognitive response to cholinesterase inhibitors has remained difficult (Deardorff et al., 2015). First, we posited that if acetylcholine mediates attention (Hasselmo & Sarter, 2011), and if increased attentional load taxes the cholinergic system (Himmelheber et al., 2000; Klinkenberg et al., 2010; Kozak et al., 2006), then attention measures under high-load conditions could better capture treatment response. Second, we posited that high-load attention measures in particular would be sufficiently sensitive to detect treatment efficacy even after a short treatment period of approximately 6 weeks. Lastly, we hypothesized that neither other cognitive domains nor global cognitive measures would be as sensitive as these carefully designed load-dependent measures of attention. Supporting our hypotheses, high-load conditions of three of the five attention measures did detect the drug effect, namely accuracy, variability, and fatigue. Importantly, neither global measures nor any other domain-specific measure of memory, language, visuospatial or executive functions could detect treatment response over the same time course. To our knowledge, this is the first study assessing treatment response of a ChEI using attention measures in a randomized, double-blind placebo-controlled 6-week trial. Accuracy was sensitive to treatment effects under high load (Attentional Blink, SOA-266 ms), which helped the Drug group avert deterioration of top-down skills (large effect size). In contrast, accuracy of the Placebo group significantly worsened over the 6-week course. None of the low-load conditions showed group differences. These blink data corroborate the findings of Perry and Hodges (2003), who documented that patients with MCI could not avail themselves of top-down instructions. Our study adds support to their findings in AD, and additionally links top-down accuracy to cholinergic modulation as postulated by Klinkenberg and colleagues (2011). Our findings suggest that the attentional blink was sensitive to treatment precisely because it captured the demands needed to process the combination of high load, simultaneous semantic instructions, and rapid decision-making. Although hypothesized, we noted that no treatment differences were detected at SOA-399 ms. One explanation may be that only the more demanding SOA-266 ms was more sensitive to increased cholinergic availability (Himmelheber et al., 2000; Kozak et al., 2006). Alternatively, the short treatment period (6 weeks) may not have been sufficient to show a treatment effect at SOA-399 ms. Intra-individual variability was also sensitive to drug treatment at high load, as measured by within-subject trial-to-trial inconsistencies in RT on the Foreperiod Effect task (medium effect size). The Drug, but not the Placebo group, decreased variability at the shortest interval (SOA-350 ms), although not at the next interval as predicted (SOA-500 ms). Our findings using a single task corroborate Gorus and colleagues (2007), who also found reduced dispersion effects using another ChEI, galantamine across four different visual attention tasks of varying complexity. With evidence that patients with AD and MCI increase variability (Gorus, De Raedt, Lambert, Lemper, & Mets, 2008; Hultsch et al., 2000) under demanding load (Phillips et al., 2013), and with our additional result that cholinergic treatment may decrease variability, these findings support that appropriate high-load attention tasks capturing variability could be used to detect cholinergic treatment response. Moreover, these results implicate that acetylcholine could play a role in the neurobiological mechanisms underlying variability. Cognitive fatigue was assessed comparing speed across multiple blocks in two tasks, Covert Orienting and Foreperiod Effect Task (large effect sizes). We posited that if cholinergic treatment ameliorated attention, and attention represented a component of fatigue (Holtzer et al., 2011), then successful cholinergic treatment response could improve cognitive fatigue. This hypothesis was supported on both tasks, such that the Placebo group became more fatigued than the Drug group after 6 weeks. The current findings raise the possibility that cholinergic treatment in AD could help reduce the impact of cognitive fatigue on functional abilities. Two predicted measures of attention, speed and orienting, did not detect drug effects. The Drug and Placebo groups exhibited similar processing speed across all levels of load. And, contrary to prior robust research documenting impaired covert orienting in AD (Parasuraman et al., 1992), we did not demonstrate either improved benefits of a valid cue nor reduced costs from an invalid cue after drug treatment. This could be due to our small sample size and lack of power. Alternatively, different mechanisms of attention may be compromised at different stages of disease progression. We hypothesized that targeted attention measures would be more sensitive to treatment than other measures of cognition. In our study, neither any global (i.e., ADAS-Cog, MMSE, Dementia Rating Scale-2) nor any cognitive domain-specific measure (i.e., language, memory, visuospatial function) detected the short-term treatment effect. Even standardized measures of simple or complex attention, such as the Digit Span or the D-KEFs Trail Making Test Condition 4, could not detect a treatment effect. While these measures target important aspects of attention, they did not capture load-dependent performance. Our findings also support the prediction by Edmonds and colleagues (2018) who suggested that cholinergic efficacy may be mediated by attention. The authors reanalyzed the Alzheimer Disease Cooperative Study (ADCS) (Petersen et al., 2005) using more stringent statistically derived criteria to define MCI membership. They showed that donepezil treatment response improved in this MCI group at short and long treatment intervals. Our current study supports their findings showing that donepezil exerts its effect by improving attention. We now provide evidence that our load-dependent attention tasks can capture the drug’s response, and unlike global tasks or other cognitive domains, are better measures of cholinergic efficacy. The current study revisits the important relationship between attention and memory: how and at what point do attention deficits contribute to, interact with, or mediate the critical AD-related mnemonic dysfunctions of learning, consolidation and retrieval? There is increasing evidence that the noradrenergic system is heavily implicated in AD pathology (Braak & Del Tredici, 2011; Ehrenberg, et al., 2017). Degeneration in this noradrenergic system stems from impairment in the locus coeruleus (LC) with accumulation of hyperphosphorylated tau (Braak & Del Tredici, 2011; Ehrenberg et al., 2017) affecting memory, learning, and attention (Berridge & Waterhouse, 2003). One possibility may be that both cholinergic and noradrenergic systems project directly or indirectly to common critical brain regions, with the consequence that degeneration of both neurotransmitter systems results in neuromodulatory effects on critical AD-related brain structures. For instance, both nucleus basalis cholinergic (Mesulam, 2013) and LC-adrenergic efferent projections share disease-related endpoints in the dentate gyrus (Prince, Bacon, Tigaret, & Mellor, 2016) as well as forebrain structures (Chandler, Lamperski, & Waterhouse, 2013). Future research of these pathways and the interactive roles of demanding attentional, learning and delayed retrieval mechanisms are warranted. We recognize that the study has several limitations. Recruitment was compromised, first because of our strict inclusion criteria, for which patients had to be naïve to any prior cholinergic treatment, and second, patients and their families did not want to delay treatment. This resulted in the small sample size and limited the power of the analyses. Additionally, non-parametric analyses had to be applied because the attention variables were not normally distributed, which limited the analyses. We believe that despite the sample size and power limitations, the magnitude of the effect sizes encourage future replication. Generalization should also be cautioned given the high educational level and limited ethnical diversity of our sample. Lastly, these findings only pertain to one specific drug (donepezil hydrochloride) and not the other ChEIs (e.g., galantamine hydrobromide and rivastigmine), which have different mechanisms of action and dosing (Čolović, Krstić, Lazarević-Pašti, Bondžić, & Vasić, 2013). Despite limitations, results from this study point to the benefits of testing drug efficacy using cognitive measures directly implicated by treatment mechanisms, and warrant follow-up larger replication investigation. The present findings also have important clinical implications. Specifically, current tools used to detect treatment effects are insufficient (Cano et al., 2010), leaving clinicians and families with the difficult decision of whether to continue ChEI treatment. To aid clinical management of risks and benefits in patients undergoing cholinergic treatment, we suggest that efficacy measures should include load-dependent attention measures to detect early drug response. Lastly, an additional benefit of our attention tasks is their independence of any particular language, and adaptability for diverse populations. In conclusion, this pilot study applied a well-designed, randomized, double-blind placebo-controlled trial to investigate the effects of donepezil on attention in patients newly diagnosed with AD. Because of the known association between attention and acetylcholine, we targeted sensitive load-dependent attention measures, and, even in a small group of participants, we were could detect treatment effects after only about 6 weeks. The effect of the drug, but not the placebo, prevented deterioration of top-down accuracy, reduced variability, and lessened cognitive fatigue. Importantly, no other global measure or domain-specific cognitive measures of memory, language, visuospatial or executive functions could detect a change in function over the same time course. These graded, load-dependent types of attention measures may therefore be valuable in the development of future efficacy assessments of cholinergic treatment. Funding This work was supported by the Alzheimer’s Association (Grant #IIRG-05-13534); National Institute of General Medical Sciences of the National Institutes of Health (SC3GM122662 to NSF); support for this project was provided by PSC-CUNY Awards, jointly funded by The Professional Staff Congress and The City University of New York (67024-00-36, 68058-00-37, & 69024-00-38 to NSF and 69060-00-38 to LRK); CUNY Doctoral Student Research Grant to JJL; CV-C received financial support from La Caixa–USA Program 2014 Fellowship. Conflict of interest None declared. Acknowledgements The authors declare that they do not have any financial interest or benefit arising from direct applications of this research. We are also grateful for the contributions from Susan Boglia, Pharm.D, Barbara Eisenkraft, MD, Lawrence S. Honig, MD, Richard E. White, PhD, John Zhu, MA, and Joel Redfield, PhD and to patients and families for their participation. References Alzheimer’s Association . 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Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Archives of Clinical Neuropsychology Oxford University Press

Attention Measures of Accuracy, Variability, and Fatigue Detect Early Response to Donepezil in Alzheimer’s Disease: A Randomized, Double-blind, Placebo-Controlled Pilot Trial

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Abstract

Abstract Objective Donepezil is widely used to treat Alzheimer’s disease (AD), but detecting early response remains challenging for clinicians. Acetylcholine is known to directly modulate attention, particularly under high cognitive conditions, but no studies to date test whether measures of attention under high load can detect early effects of donepezil. We hypothesized that load-dependent attention tasks are sensitive to short-term treatment effects of donepezil, while global and other domain-specific cognitive measures are not. Method This longitudinal, randomized, double-blind, placebo-controlled pilot trial (ClinicalTrials.gov Identifier: NCT03073876) evaluated 23 participants newly diagnosed with AD initiating de novo donepezil treatment (5 mg). After baseline assessment, participants were randomized into Drug (n = 12) or Placebo (n = 11) groups, and retested after approximately 6 weeks. Cognitive assessment included: (a) attention tasks (Foreperiod Effect, Attentional Blink, and Covert Orienting tasks) measuring processing speed, top-down accuracy, orienting, intra-individual variability, and fatigue; (b) global measures (Alzheimer’s Disease Assessment Scale-Cognitive Subscale, Mini-Mental Status Examination, Dementia Rating Scale); and (c) domain-specific measures (memory, language, visuospatial, and executive function). Results The Drug but not the Placebo group showed benefits of treatment at high-load measures by preserving top-down accuracy, improving intra-individual variability, and averting fatigue. In contrast, other global or cognitive domain-specific measures could not detect treatment effects over the same treatment interval. Conclusions The pilot-study suggests that attention measures targeting accuracy, variability, and fatigue under high-load conditions could be sensitive to short-term cholinergic treatment. Given the central role of acetylcholine in attentional function, load-dependent attentional measures may be valuable cognitive markers of early treatment response. Alzheimer dementia, Attention, Cholinesterase inhibitors, Donepezil, Treatment outcome Introduction Alzheimer’s disease (AD) is the most common cause of dementia in older adults (Alzheimer’s Association, 2016) with cholinesterase inhibitors (ChEIs) still the foremost treatment (Di Santo, Prinelli, Adorni, Caltagirone, & Musicco, 2013; Tan et al., 2014) targeting the cholinergic deficiency associated with the disease (Davies & Maloney, 1976; see reviews Dumas & Newhouse, 2011; Schliebs & Arendt, 2011). The mechanism of ChEIs prevents acetylcholinesterase enzymes from breaking down acetylcholine, leading to increased extracellular availability of the neurotransmitter. Despite continued use of these drugs, the degree of benefit is controversial (Bond et al., 2012; Deardorff, Feen, & Grossberg, 2015; Kobayashi, Ohnishi, Nakagawa, & Yoshizawa, 2015), and adverse side effects include bradycardia, gastrointestinal responses, and sleep disorders (Kröger et al., 2015). Clinical trials widely used the AD Assessment Scale-Cognitive Subscale (Rosen, Mohs, & Davis, 1984), which aggregates multiple cognitive domains, as the primary outcome measure, but this measure has had limited ability to detect the cognitive changes of treatment (Cano et al., 2010). Because acetylcholine is directly linked to the cognitive domain of attention (Hasselmo & Sarter, 2011), and ChEIs increase available acetylcholine, we proposed that attention tasks would be more sensitive to detect treatment response and do so within a short treatment period. Acetylcholine is linked to attentional functions both in animals and humans (see reviews Hasselmo & Sarter, 2011; Klinkenberg & Blokland, 2010). In a series of pivotal animal studies, Sarter, Bruno and colleagues (Himmelheber, Sarter, & Bruno, 2000; Kozak, Bruno, & Sarter, 2006) demonstrated a reciprocal relationship where, on the one hand, increasing attentional demands augmented acetylcholine availability, while on the other hand, taxing attentional demands under high-load conditions increased acetylcholine efflux. Yet, while the cognitive domain of attention is not identified as a primary domain of cognitive dysfunction in AD (McKhann et al., 2011), a wide variety of attentional deficits in processing speed, orienting, and divided attention are reported in AD (Baddeley, Baddeley, Bucks, & Wilcock, 2001; Chau, Herrmann, Eizenman, Chung, & Lanctôt, 2015; Coubard et al., 2011; Foldi, Lobosco, & Schaefer, 2002; Ishizaki, Meguro, Nara, Kasai, & Yamadori, 2013; Phillips, Rogers, Haworth, Bayer, & Tales, 2013; Sylvain-Roy, Bherer, & Belleville, 2010) as well as in mild cognitive impairment (MCI) (Saunders & Summers, 2011). Attentional deficits may possibly be one of the first cognitive domains to be affected in AD (Foldi et al., 2002; Grady et al., 1988; Reid et al., 1996). Thus, if AD is associated with depleted acetylcholine (Davies & Maloney, 1976) and acetylcholine is reciprocally linked to the attentional system, then the effect of ChEI treatment in AD should improve attentional function. Indeed, prior work clearly demonstrated treatment effects of ChEIs on attention in AD (Caramelli et al., 2004; Daiello et al., 2010; Foldi, White, & Schaefer, 2005; Galvin et al., 2008; Gorus, Lambert, De Raedt, & Mets, 2007; Lee et al., 2015; Palmqvist, Minthon, Wattmo, Londos, & Hansson, 2010; Shimizu, Kanetaka, Hirose, Sakurai, & Hanyu, 2015; Vellas et al., 2005; Wesnes et al., 1998, 2010; Wiig et al., 2010). Yet, none of these studies addressed whether there was a relationship between increased temporal demands (i.e., high-load) and acetylcholine availability, whether drug efficacy could be detected after a short treatment interval, or, whether attention would be more sensitive than global function or other specific cognitive domains. The current study aimed to address these questions using donepezil hydrochloride as the cholinergic agent. We hypothesized that (1) higher attentional demands would be sensitive to donepezil treatment in AD, (2) if load-dependent attention tasks were sensitive, they should detect treatment response even after a short treatment period once steady-state of the drug had been reached (Rogers et al., 1998), and (3) attention measures would be more sensitive than global or other domain-specific measures. Kahneman’s (1973) classic model of attention proposes that individuals have a finite capacity, and attention mechanisms serve to manage or allocate those limited resources. Tasks can vary in the amount of resources used, e.g., from high- to low-load demands. Our question posited whether cholinergic augmentation could improve performance under high load. We selected three attention tasks (Foreperiod Effect, Attentional Blink, and Covert Orienting) known to have deficits in AD, each of which allowed us to manipulate load levels within the task. Across these three tasks, we were able to address the attentional functions of processing speed, accuracy, orienting, variability, and fatigue, each under high- and low-load conditions. Processing speed to detect a stimulus (measured as reaction time, RT) slows in older adults (Salthouse, 1985) and in patients with AD (Pirozzolo, Christensen, Ogle, Hansch, & Thompson, 1981). Simple detection paradigms have already been used to reliably assess processing speed in ChEIs (Galvin et al., 2008; Palmqvist et al., 2010; Wiig et al., 2010). Moreover, Niemi and Näätänen (1981) demonstrated the sensitive Foreperiod Effect, where RT increases when the preparatory period before a subsequent stimulus decreases. To capture this foreperiod effect, we manipulated intervals between stimuli (stimulus onset asynchrony, SOA) in a simple detection task (Henderson & Dittrich, 1998), such that short intervals (SOA-350 and 500 ms) represented high-load conditions. Using this paradigm, Sylvain-Roy and colleagues (2010) have already demonstrated that patients with AD and MCI had delayed response time at short SOAs compared to healthy controls. Hence, we proposed that RT on a load-dependent simple detection task could capture early treatment effects of donepezil. To assess accuracy, we used the Attentional Blink paradigm. The blink phenomenon refers to the deficit identifying a second stimulus, when it is presented shortly after identification of a first stimulus (Broadbent & Broadbent, 1987; Duncan, Ward, & Shapiro, 1994; Raymond, Shapiro, and Arnell, 1992). The blink typically occurs when the interstimulus interval is short (between 200 and 500 ms; high load) compared to when it is long (SOAs > 500 ms; low load). A further manipulation of this paradigm is to provide a priori instructions about the target to the participant to improve accuracy, e.g., when presented with a number and a letter, the participant is asked to only report the letter. This instruction adds a top-down attentional control component that should facilitate identification of the second stimulus. Thus, above and beyond perceptual skill, the blink represents the ability to allocate attentional resources to the second of two rapidly presented stimuli. Perry and Hodges (2003) demonstrated that patients with MCI were less accurate than healthy controls identifying the second stimulus within the 200–500 ms SOA, suggesting that the deficits were not only due to the temporal influence of processing the first stimulus, but also the inefficiency allocating top-down attentional control resources. Our laboratory (Ly, 2013) previously demonstrated that patients with AD were less accurate than healthy older adults, who, in turn, showed worse performance than young adults. Importantly, top-down functions have been linked to cholinergic neuromodulator (Klinkenberg, Sambeth, & Blokland, 2011; Sarter, Gehring, & Kozak, 2006). Hence, we hypothesized that donepezil treatment would improve accuracy under high-load conditions of the blink task. Orienting (Posner, 1980) tested using the Covert Orienting task, demonstrates the attentional influence of pre-target spatial cues on RT. In this paradigm, the RT to detect a target is facilitated by a valid cue appearing in the same visual field as the target. Conversely, invalid cues presented in the opposite field as the target require disengagement and slows RT, representing greater task demands. Patients with AD show disproportionate slowing to invalid cues compared to healthy controls (Parasuraman, Greenwood, Haxby, & Grady, 1992). And, in animal models, lower acetylcholine levels disrupt the orienting network (Davidson & Marrocco, 2000), whereas nicotinic receptor agonists improve orienting RT in healthy controls (Hammersley, Gilbert, Rzetelny, & Rabinovich, 2016; Heishman, Kleykamp, & Singleton, 2010; Stewart, Burke, & Marrocco, 2001). These findings suggest that donepezil could improve orienting performance in patients with AD. Variability assesses inconsistency in performance. Within-subject variance (intra-individual variability, IIV) represents another important, secondary phenomenon of attention, which has been proposed as a construct to capture unique cognitive variance (Ram, Rabbitt, Stollery, & Nesselroade, 2005). Higher IIV has been documented in patients with MCI and AD (Troyer, Vandermorris, & Murphy, 2016) compared to healthy controls (Hultsch, MacDonald, Hunter, Levy-Bencheton, & Strauss, 2000), and was explained not only by depleted white matter integrity (Jackson, Balota, Duchek, & Head, 2012), but also increased neural noise due to dysfunctional modulation of acetylcholine (MacDonald, Cervenka, Farde, Nyberg, & Bäckman, 2009). Phillips and colleagues (2013) have also concluded that higher cognitive demands increased IIV in response latency tasks in both MCI and AD. Importantly, Gorus and colleagues (2007) demonstrated that treatment using a ChEI, galantamine, reduced IIV after 8 weeks in AD. In the current study, we evaluated IIV using trial-to-trial fluctuations of RT in the Foreperiod Effect task, and hypothesized that treatment with donepezil should reduce variability under high-load conditions (SOA < 500 ms). Lastly, fatigue is a common response to sustained attention and may manifest as increased RT over a sustained period (Kluger, Krupp, & Enoka, 2013). Acetylcholine levels are correlated with sustained performance (Passetti, Dalley, O’connell, Everitt, & Robbins, 2000), and heightened fatigue reduces attentional functions in non-demented older adults (Holtzer, Shuman, Mahoney, Lipton, & Verghese, 2011). Sustained attention tasks that increase load and lengthen task periods are commonly used to elicit fatigue (Claros-Salinas et al., 2013; Möller, Nygren de Boussard, Oldenburg, & Bartfai, 2014; Neumann et al., 2014; Sandry, Genova, Dobryakova, DeLuca, & Wylie, 2014). To our knowledge, no previous research has investigated cognitive fatigue in patients with AD, and we posited that cholinergic deficits would heighten their susceptibility to cognitive fatigue. We operationally defined fatigue as increase in RT over sustained performance in attention tasks, and proposed that variables of fatigue should improve with donepezil treatment. In summary, we proposed that high-load attention measures targeting speed, accuracy, orienting, variability, and fatigue should be sensitive indicators to short-term cholinergic treatment response in AD. We applied a randomized double-blind placebo-controlled pilot trial for patients with AD, who were initiating cholinergic treatment using donepezil. We hypothesized that after roughly 6-week donepezil treatment, the Drug but not the Placebo group would maintain their processing speed, reduce variability and fatigue, and improve orienting and top-down accuracy under high-load conditions. Moreover, we predicted that, in contrast to these attention measures, performance on global measures and other domain-specific cognitive measures would not detect treatment response during the same time interval. Methods The study was approved by the Institutional Review Boards of the City University of New York, Queens College, Queens, NY, and Winthrop-University Hospital, Stony Brook School of Medicine, Mineola, NY. Written consents were obtained from each participant and next of kin. This study is reported following the 2010 Consolidated Standards of Reporting Trials (CONSORT) guidelines (Begg et al., 1996), and is registered in ClinicalTrials.gov (ClinicalTrials.gov Identifier NCT03073876). Participants Inclusion criteria: a new diagnosis of probable AD (McKhann et al., 1984), Mini-Mental Status Examination (MMSE) scores between 15 and 26 to ensure ability to perform experimental attention tasks (Folstein, Folstein, & McHugh, 1975; Monsch et al., 1995), and normal or corrected vision. Diagnosis was reached by consensus of an interdisciplinary team including neuropsychology, geriatrics, neurology, and neuroradiology. Biofluid and PET neuroimaging markers were not available. Participants were allowed to be on concurrent treatments not affecting the cholinergic system (e.g., non-steroidal anti-inflammatory medications, Vitamin E, antidepressants). Exclusion criteria: Primary focal cerebrovascular disease or other dementia etiologies including Parkinson’s Disease, Lewy Body Dementia, or Fronto-temporal Dementia, concurrent use of memantine hydrochloride or other anticholinergic treatments, or prior use of any ChEI. A total of 26 individuals were recruited between 2005 and 2009 at the Memory and Cognitive Disorders Center at Winthrop-University Hospital, met eligibility criteria, and were randomly assigned to a group (Drug = 13, Placebo = 13; see Fig. 1 and Table 1). Power analysis calculations were based on preliminary data, which determined a proposed sample size of 40 participants. Unfortunately, recruitment was challenged by reluctance of patients to delay their treatment. Design We conducted a longitudinal, randomized, double-blind, placebo-controlled trial. All participants were tested at baseline (T1), then randomized into two groups, Drug (5 mg donepezil hydrochloride) or Placebo (gelatin capsules), and retested after approximately 6 weeks (T2; Average = 46 days) to ensure that steady-state was reached (Rogers et al., 1998). We selected donepezil hydrochloride (donepezil) among the ChEIs because its effective dose can be achieved early in the course of treatment, and required only once-daily administration, facilitating compliance. As participants were enrolled by the Memory and Cognitive Disorders Center team prior to publication of updated criteria (McKhann et al., 2011), 1984 criteria (McKhann et al., 1984) were used. The hospital pharmacist was solely apprised of group membership, which was generated using a computerized randomization list. Medication compliance was promoted by working closely with caregivers, and corroborated by the pharmacist. All measures were administered at T1 and T2 in a fixed sequence designed to avoid interference among tasks. All testing procedures were conducted in a quiet and well-lit room. Participants received 30 dollars after each session lasting approximately two and a half hours. Measures Table 2 lists the neuropsychological measures assessing global and domain-specific measures. Attention Tasks All attention tasks were administered on a 2.01 GHz, 960 MB RAM PC computer, with centered stimuli subtending a visual angle ranging between 0.75° and 8.0° (depending on the task), and presented on a 13½” monitor with a refresh rate of 75 Hz (Neath, Earle, Hallett, & Surprenant, 2011) placed 50 cm from the participant. Participants responded with a button press on a centrally placed Ergodex® keypad (Rix, 2003). The Foreperiod Effect task (see Van Dyk et al., 2015) measured the RT to detect a centrally placed asterisk presented at six randomized variable intervals (SOA: 350, 500, 650, 800, 1100, and 1400 ms), with 10 trials at each interval generating 60 trials. High load was defined as the two shortest SOAs, 350 and 500 ms. Database management included deletion of the first trial, anticipatory trials (RT < 100 ms), erroneous button click responses, and missed responses (total 4% trials). Speed was defined as the median RT at each SOA. Intra-individual variability was defined as the standard deviation of RT at each SOA. The task was administered twice, at the beginning (Block 1) and end of each testing session (Block 2). Fatigue was defined by comparing overall median RT (across all SOAs) at each block. The Attentional Blink task (Perry & Hodges, 2003) measured top-down accuracy under increasing levels of temporal load. Prior to any task administration, individualized stimulus duration was established such that each participant obtained ≥ 85% accuracy identifying a single alphanumeric character (Ly, 2013). The task consisted of two stimuli, a number (i.e., 2, 3, 5, 6, or 9) and a letter (i.e., A, D, E, N, or R) sequentially presented to the right or left of a fixation point, with each stimulus immediately followed by a mask. The interval between stimuli was varied randomly (SOA: 133, 266, 399, 532, or 655 ms). We defined high load as the intervals below 500 ms: SOAs 266 and 399 ms. SOA-133 ms was excluded, as it has been shown to be too challenging for patients with AD (Ly, 2013). The task had two counter-balanced conditions; participants were instructed to either “report the letter” (40 trials), or “report the number” (40 trials), totaling 80 trials. Verbal responses were recorded by the examiner. Accuracy of the second stimulus presented at each SOA was used as the dependent variable. The Covert Orienting task was used to evaluate orienting and fatigue. We measured the RT to detect a target following an exogenous spatial cue in the same visual field (valid), opposite field (invalid), or in both fields (neutral) (Posner, 1980). Participants fixated on a central red cross throughout each trial, and responded with a button press to the target (“X”). The cue-target intervals were set at 250 ms or, for catch trials, at 850 ms. A total of 170 trials (18% catch, 12% neutral, 12% invalid, 58% valid) were randomly presented over five sequential blocks each containing 34 trials. Prior to aggregating the data, catch trials were removed. Orienting was assessed using median RT to valid, invalid, and neutral cues across blocks. High load was defined as RT after an invalid cue. Fatigue was assessed comparing overall median RT across cues between Block 1 and Block 5. Analyses Analyses were carried out using SPSS Version 22.0 (2013). Initial Kolmogorov–Smirnov preliminary analyses revealed that the attention variables were not normally distributed, therefore, non-parametric Wilcoxon signed-rank analyses were selected to conduct within-group comparisons to increase power. Parametric analyses were applied to demographic, global, and domain-specific measures. First, we performed baseline group comparisons on all measures. Second, we analyzed treatment effects of global and domain-specific performance using a Group (Drug, Placebo) × Time (T1, T2) repeated measures Analyses of Variance (ANOVA). For the attention variables: (a) within-group differences compared T1 to T2 (Wilcoxon signed-rank tests) on measures of processing speed, accuracy, and variability; (b) treatment effects of fatigue compared performance across blocks at T2. Two-tailed tests, p < .05 level of significance, and effect sizes (partial eta squared and Cohen’s r) are reported (Cohen, 1988). One participant was excluded from the global and domain-specific measures analyses due to missing data at T2. One participant was excluded from the fatigue analyses on the Foreperiod Effect task at baseline because Block 2 was not administered. Results At baseline (T1), the Drug and Placebo groups were not significantly different on any demographic, global, domain-specific, or attentional variables (see Table 1). These comparisons indicated that any subsequent differences could be attributed to experimental manipulation of drug administration. Table 1. Baseline group comparisons on demographics, global and domain-specific measures Drug (n = 12) Placebo (n = 11) Mean (SD) Range Mean (SD) Range χ2/t statistic (df) p Demographics  Age (yrs) 79.3 (7.4) 61–88 81.7 (3.8) 75–87 −1 (21) .3  Education (yrs) 13.2 (4.1) 8–22 15.1 (4.0) 12–22 −1.1 (21) .3  Gender 4 males 4 males 1.3 (1) .2  GDS 6.7 (6.2) 0–16 6.5 (4.5) 2–15 0.1 (21) .9  NPI 16.7 (12.3) 2–40 15.4 (15.8) 0–59 0.2 (21) .8 Global Measures  MMSE† 24.3 (3.5) 18–29 25.4 (1.7) 23–28 −0.9 (16.4) .4  DRS 119.7 (10.4) 101–136 127.1 (7.2) 111–135 −2 (21) .1  CDR 1 (.4) 0.5–2.0 0.8 (.3) 0.5–1.0 1.3 (21) .2  ADAS-Cog 17.3 (6.6) 9.0–28.3 13.1 (3.8) 8.0–20 1.3 (21) .2 Domain-Specific Measures  Memory   HVLT Learning 11 (2.6) 7–16 11.4 (3.8) 5–19 −0.3 (21) .8   HVLT Recall 1.2 (1.0) 0–3 1.1 (1.4) 0–4 0.3 (21) .7   HVLT Recognition 5.3 (3.6) 0–10 6.7 (2.7) 3–11 −1.1 (21) .2  Verbal   Letter Fluency 9 (5.0) 3–17 9.4 (4.3) 2–15 −0.2 (21) .8   Category Fluency 7.4 (2.7) 3–12 7.9 (2.7) 5–13 −0.5 (21) .6  Visuospatial   Visual Form Discrimination 26.7 (3.9) 18–31 27.9 (2.5) 24–32 −0.8 (21) .4  Attention & Executive Function   Digit Span Forward (max) 5.9 (.9) 5–9 6 (.8) 5–7 −0.2 (21) .8   Digit Span Backward (max) 3.6 (.8) 2–5 3.6 (.8) 2–5 −0.2 (21) .9   DKEFS Trail Making Test–4† 12.2 (11.7) 2–12 6.7 (6.2) 2–10 1.4 (16.9) .2  Motor Speed   DKEFS Trail Making Test–5 62.7 (36.5) 26–150 54.3 (16.6) 34–93 0.7 (21) .5 Drug (n = 12) Placebo (n = 11) Mean (SD) Range Mean (SD) Range χ2/t statistic (df) p Demographics  Age (yrs) 79.3 (7.4) 61–88 81.7 (3.8) 75–87 −1 (21) .3  Education (yrs) 13.2 (4.1) 8–22 15.1 (4.0) 12–22 −1.1 (21) .3  Gender 4 males 4 males 1.3 (1) .2  GDS 6.7 (6.2) 0–16 6.5 (4.5) 2–15 0.1 (21) .9  NPI 16.7 (12.3) 2–40 15.4 (15.8) 0–59 0.2 (21) .8 Global Measures  MMSE† 24.3 (3.5) 18–29 25.4 (1.7) 23–28 −0.9 (16.4) .4  DRS 119.7 (10.4) 101–136 127.1 (7.2) 111–135 −2 (21) .1  CDR 1 (.4) 0.5–2.0 0.8 (.3) 0.5–1.0 1.3 (21) .2  ADAS-Cog 17.3 (6.6) 9.0–28.3 13.1 (3.8) 8.0–20 1.3 (21) .2 Domain-Specific Measures  Memory   HVLT Learning 11 (2.6) 7–16 11.4 (3.8) 5–19 −0.3 (21) .8   HVLT Recall 1.2 (1.0) 0–3 1.1 (1.4) 0–4 0.3 (21) .7   HVLT Recognition 5.3 (3.6) 0–10 6.7 (2.7) 3–11 −1.1 (21) .2  Verbal   Letter Fluency 9 (5.0) 3–17 9.4 (4.3) 2–15 −0.2 (21) .8   Category Fluency 7.4 (2.7) 3–12 7.9 (2.7) 5–13 −0.5 (21) .6  Visuospatial   Visual Form Discrimination 26.7 (3.9) 18–31 27.9 (2.5) 24–32 −0.8 (21) .4  Attention & Executive Function   Digit Span Forward (max) 5.9 (.9) 5–9 6 (.8) 5–7 −0.2 (21) .8   Digit Span Backward (max) 3.6 (.8) 2–5 3.6 (.8) 2–5 −0.2 (21) .9   DKEFS Trail Making Test–4† 12.2 (11.7) 2–12 6.7 (6.2) 2–10 1.4 (16.9) .2  Motor Speed   DKEFS Trail Making Test–5 62.7 (36.5) 26–150 54.3 (16.6) 34–93 0.7 (21) .5 Note: Statistics reported are: mean (M), standard deviation (SD), Pearson’s chi square test (χ2), t-statistic (t), degrees of freedom (df), and significance value (p). (†) indicates that Levene’s test was significant, p-value reported assuming non-equal variances. MEASURES: GDS = Geriatric Depression Scale; NPI = Neuropsychiatric Inventory; MMSE = Mini-Mental State Examination; DRS = Dementia Rating Scale; CDR = Clinical Dementia Rating Scale; ADAS-Cog = Alzheimer’s Disease Assessment Scale-cognitive subscale; HVLT Learning, Delayed, Recognition = Hopkins Verbal Learning Test – learning, delayed, and recognition raw scores; Letter Fluency = FAS Mean Fluency; Category Fluency = Mean Fluency across animals, fruits, and vegetables; Digit Span Forward: Longest Digit Span Forward, Digit Span Backward: Longest Digit Span Backward; DKEFS Trail Making Test 4, 5 = Delis–Kaplan Executive Function System Trail Making Test Conditions 4 (scaled score) and 5 (raw). Note: T-test analyses were conducted to compare the groups. Table 1. Baseline group comparisons on demographics, global and domain-specific measures Drug (n = 12) Placebo (n = 11) Mean (SD) Range Mean (SD) Range χ2/t statistic (df) p Demographics  Age (yrs) 79.3 (7.4) 61–88 81.7 (3.8) 75–87 −1 (21) .3  Education (yrs) 13.2 (4.1) 8–22 15.1 (4.0) 12–22 −1.1 (21) .3  Gender 4 males 4 males 1.3 (1) .2  GDS 6.7 (6.2) 0–16 6.5 (4.5) 2–15 0.1 (21) .9  NPI 16.7 (12.3) 2–40 15.4 (15.8) 0–59 0.2 (21) .8 Global Measures  MMSE† 24.3 (3.5) 18–29 25.4 (1.7) 23–28 −0.9 (16.4) .4  DRS 119.7 (10.4) 101–136 127.1 (7.2) 111–135 −2 (21) .1  CDR 1 (.4) 0.5–2.0 0.8 (.3) 0.5–1.0 1.3 (21) .2  ADAS-Cog 17.3 (6.6) 9.0–28.3 13.1 (3.8) 8.0–20 1.3 (21) .2 Domain-Specific Measures  Memory   HVLT Learning 11 (2.6) 7–16 11.4 (3.8) 5–19 −0.3 (21) .8   HVLT Recall 1.2 (1.0) 0–3 1.1 (1.4) 0–4 0.3 (21) .7   HVLT Recognition 5.3 (3.6) 0–10 6.7 (2.7) 3–11 −1.1 (21) .2  Verbal   Letter Fluency 9 (5.0) 3–17 9.4 (4.3) 2–15 −0.2 (21) .8   Category Fluency 7.4 (2.7) 3–12 7.9 (2.7) 5–13 −0.5 (21) .6  Visuospatial   Visual Form Discrimination 26.7 (3.9) 18–31 27.9 (2.5) 24–32 −0.8 (21) .4  Attention & Executive Function   Digit Span Forward (max) 5.9 (.9) 5–9 6 (.8) 5–7 −0.2 (21) .8   Digit Span Backward (max) 3.6 (.8) 2–5 3.6 (.8) 2–5 −0.2 (21) .9   DKEFS Trail Making Test–4† 12.2 (11.7) 2–12 6.7 (6.2) 2–10 1.4 (16.9) .2  Motor Speed   DKEFS Trail Making Test–5 62.7 (36.5) 26–150 54.3 (16.6) 34–93 0.7 (21) .5 Drug (n = 12) Placebo (n = 11) Mean (SD) Range Mean (SD) Range χ2/t statistic (df) p Demographics  Age (yrs) 79.3 (7.4) 61–88 81.7 (3.8) 75–87 −1 (21) .3  Education (yrs) 13.2 (4.1) 8–22 15.1 (4.0) 12–22 −1.1 (21) .3  Gender 4 males 4 males 1.3 (1) .2  GDS 6.7 (6.2) 0–16 6.5 (4.5) 2–15 0.1 (21) .9  NPI 16.7 (12.3) 2–40 15.4 (15.8) 0–59 0.2 (21) .8 Global Measures  MMSE† 24.3 (3.5) 18–29 25.4 (1.7) 23–28 −0.9 (16.4) .4  DRS 119.7 (10.4) 101–136 127.1 (7.2) 111–135 −2 (21) .1  CDR 1 (.4) 0.5–2.0 0.8 (.3) 0.5–1.0 1.3 (21) .2  ADAS-Cog 17.3 (6.6) 9.0–28.3 13.1 (3.8) 8.0–20 1.3 (21) .2 Domain-Specific Measures  Memory   HVLT Learning 11 (2.6) 7–16 11.4 (3.8) 5–19 −0.3 (21) .8   HVLT Recall 1.2 (1.0) 0–3 1.1 (1.4) 0–4 0.3 (21) .7   HVLT Recognition 5.3 (3.6) 0–10 6.7 (2.7) 3–11 −1.1 (21) .2  Verbal   Letter Fluency 9 (5.0) 3–17 9.4 (4.3) 2–15 −0.2 (21) .8   Category Fluency 7.4 (2.7) 3–12 7.9 (2.7) 5–13 −0.5 (21) .6  Visuospatial   Visual Form Discrimination 26.7 (3.9) 18–31 27.9 (2.5) 24–32 −0.8 (21) .4  Attention & Executive Function   Digit Span Forward (max) 5.9 (.9) 5–9 6 (.8) 5–7 −0.2 (21) .8   Digit Span Backward (max) 3.6 (.8) 2–5 3.6 (.8) 2–5 −0.2 (21) .9   DKEFS Trail Making Test–4† 12.2 (11.7) 2–12 6.7 (6.2) 2–10 1.4 (16.9) .2  Motor Speed   DKEFS Trail Making Test–5 62.7 (36.5) 26–150 54.3 (16.6) 34–93 0.7 (21) .5 Note: Statistics reported are: mean (M), standard deviation (SD), Pearson’s chi square test (χ2), t-statistic (t), degrees of freedom (df), and significance value (p). (†) indicates that Levene’s test was significant, p-value reported assuming non-equal variances. MEASURES: GDS = Geriatric Depression Scale; NPI = Neuropsychiatric Inventory; MMSE = Mini-Mental State Examination; DRS = Dementia Rating Scale; CDR = Clinical Dementia Rating Scale; ADAS-Cog = Alzheimer’s Disease Assessment Scale-cognitive subscale; HVLT Learning, Delayed, Recognition = Hopkins Verbal Learning Test – learning, delayed, and recognition raw scores; Letter Fluency = FAS Mean Fluency; Category Fluency = Mean Fluency across animals, fruits, and vegetables; Digit Span Forward: Longest Digit Span Forward, Digit Span Backward: Longest Digit Span Backward; DKEFS Trail Making Test 4, 5 = Delis–Kaplan Executive Function System Trail Making Test Conditions 4 (scaled score) and 5 (raw). Note: T-test analyses were conducted to compare the groups. Fig. 1. View largeDownload slide Participant flow diagram following CONSORT 2010 guidelines. Fig. 1. View largeDownload slide Participant flow diagram following CONSORT 2010 guidelines. Repeated measures analyses of all global and domain-specific cognitive measures revealed no significant Group × Time interaction effect indicating that none of these measures was sensitive to short-term treatment (see Table 2). Table 2. Global and domain-specific cognitive measures: descriptive statistics and repeated measures ANOVA Drug (n = 12) Placebo (n = 11) ANOVA T1 T2 T1 T2 Time × Group (Mean, SD) F p Partial Eta2 Global Measures  MMSE 24.3 (3.5) 24.2 (2.8) 25.4 (1.7) 24.3 (1.9) 1 .3 0.0  DRS 119.7 (10.4) 121.3 (9.5) 127.1 (7.2) 126.5 (8.1) 0.6 .4 0.0  ADAS-Cog 17.3 (6.7) 15.4 (4.4) 13.1 (3.8) 14.0 (4.4) 2.9 .1 0.1 Domain-Specific  Memory   HVLT Learning 11.0 (2.6) 10.8 (2.3) 11.4 (3.8) 11.6 (4.9) 0.1 .7 0.0   HVLT Recall 1.2 (1.0) 1.0 (1.6) 1.1 (1.4) 2.3 (1.9) 3 .1 0.1   HVLT Recognition 5.3 (3.6) 4.9 (3.3) 6.7 (2.7) 5.7 (2.4) 0.3 .6 0.0  Language   Fluency FAS (M) 9.0 (5.0) 9.0 (5.4) 9.4 (4.3) 9.5 (3.9) 0.0 .8 0.0   Fluency Category (M) 7.4 (2.7) 7.4 (2.6) 7.9 (2.7) 7.0 (2.2) 2.0 .2 0.1  Visuospatial   VFD 26.7 (3.9) 27.6 (3.5) 27.9 (2.5) 27.9 (2.3) 0.1 .7 0.0  Attention & Executive Function   Digit Span Forward (max) 5.9 (0.9) 5.3 (1.1) 6.0 (0.8) 5.6 (0.7) 0.3 .6 0.0   Digit Span Backward (max) 3.6 (0.8) 4.1 (0.8) 3.6 (0.8) 3.7 (0.7) 2.2 .1 0.1   DKEFs Trail Making Test Condition 4 12.2 (11.7) 10.8 (12.5) 6.7 (6.2) 9.3 (9.2) 0.8 .4 0.0  Motor Speed   DKEFs Trail Making Test Condition 5 62.7 (36.5) 64.2 (33.7) 54.3 (16.6) 59.2 (22.5) 0.1 .7 0.0 Drug (n = 12) Placebo (n = 11) ANOVA T1 T2 T1 T2 Time × Group (Mean, SD) F p Partial Eta2 Global Measures  MMSE 24.3 (3.5) 24.2 (2.8) 25.4 (1.7) 24.3 (1.9) 1 .3 0.0  DRS 119.7 (10.4) 121.3 (9.5) 127.1 (7.2) 126.5 (8.1) 0.6 .4 0.0  ADAS-Cog 17.3 (6.7) 15.4 (4.4) 13.1 (3.8) 14.0 (4.4) 2.9 .1 0.1 Domain-Specific  Memory   HVLT Learning 11.0 (2.6) 10.8 (2.3) 11.4 (3.8) 11.6 (4.9) 0.1 .7 0.0   HVLT Recall 1.2 (1.0) 1.0 (1.6) 1.1 (1.4) 2.3 (1.9) 3 .1 0.1   HVLT Recognition 5.3 (3.6) 4.9 (3.3) 6.7 (2.7) 5.7 (2.4) 0.3 .6 0.0  Language   Fluency FAS (M) 9.0 (5.0) 9.0 (5.4) 9.4 (4.3) 9.5 (3.9) 0.0 .8 0.0   Fluency Category (M) 7.4 (2.7) 7.4 (2.6) 7.9 (2.7) 7.0 (2.2) 2.0 .2 0.1  Visuospatial   VFD 26.7 (3.9) 27.6 (3.5) 27.9 (2.5) 27.9 (2.3) 0.1 .7 0.0  Attention & Executive Function   Digit Span Forward (max) 5.9 (0.9) 5.3 (1.1) 6.0 (0.8) 5.6 (0.7) 0.3 .6 0.0   Digit Span Backward (max) 3.6 (0.8) 4.1 (0.8) 3.6 (0.8) 3.7 (0.7) 2.2 .1 0.1   DKEFs Trail Making Test Condition 4 12.2 (11.7) 10.8 (12.5) 6.7 (6.2) 9.3 (9.2) 0.8 .4 0.0  Motor Speed   DKEFs Trail Making Test Condition 5 62.7 (36.5) 64.2 (33.7) 54.3 (16.6) 59.2 (22.5) 0.1 .7 0.0 Note: Statistics reported: mean (M), standard deviation (SD), F Ratio (F), significance value (p). Test abbreviations: MMSE = Mini-Mental State Examination; DRS = Dementia Rating Scale; ADAS-Cog = Alzheimer’s Disease Assessment Scale-cognitive subscale; HVLT Learning, Recall, Recognition = Hopkins Verbal Learning Test – learning, delayed, and recognition raw score; Fluency FAS = Mean Letter Fluency; Fluency Category = Mean Fluency across animals, fruits, and vegetables; VFD = Visual Form Discrimination; Digit Span Forward: Longest Digit Span Forward, Digit Span Backward: Longest Digit Span Backward; DKEFs = Delis–Kaplan Executive Function System: Trail Making, Conditions 4 and 5. Table 2. Global and domain-specific cognitive measures: descriptive statistics and repeated measures ANOVA Drug (n = 12) Placebo (n = 11) ANOVA T1 T2 T1 T2 Time × Group (Mean, SD) F p Partial Eta2 Global Measures  MMSE 24.3 (3.5) 24.2 (2.8) 25.4 (1.7) 24.3 (1.9) 1 .3 0.0  DRS 119.7 (10.4) 121.3 (9.5) 127.1 (7.2) 126.5 (8.1) 0.6 .4 0.0  ADAS-Cog 17.3 (6.7) 15.4 (4.4) 13.1 (3.8) 14.0 (4.4) 2.9 .1 0.1 Domain-Specific  Memory   HVLT Learning 11.0 (2.6) 10.8 (2.3) 11.4 (3.8) 11.6 (4.9) 0.1 .7 0.0   HVLT Recall 1.2 (1.0) 1.0 (1.6) 1.1 (1.4) 2.3 (1.9) 3 .1 0.1   HVLT Recognition 5.3 (3.6) 4.9 (3.3) 6.7 (2.7) 5.7 (2.4) 0.3 .6 0.0  Language   Fluency FAS (M) 9.0 (5.0) 9.0 (5.4) 9.4 (4.3) 9.5 (3.9) 0.0 .8 0.0   Fluency Category (M) 7.4 (2.7) 7.4 (2.6) 7.9 (2.7) 7.0 (2.2) 2.0 .2 0.1  Visuospatial   VFD 26.7 (3.9) 27.6 (3.5) 27.9 (2.5) 27.9 (2.3) 0.1 .7 0.0  Attention & Executive Function   Digit Span Forward (max) 5.9 (0.9) 5.3 (1.1) 6.0 (0.8) 5.6 (0.7) 0.3 .6 0.0   Digit Span Backward (max) 3.6 (0.8) 4.1 (0.8) 3.6 (0.8) 3.7 (0.7) 2.2 .1 0.1   DKEFs Trail Making Test Condition 4 12.2 (11.7) 10.8 (12.5) 6.7 (6.2) 9.3 (9.2) 0.8 .4 0.0  Motor Speed   DKEFs Trail Making Test Condition 5 62.7 (36.5) 64.2 (33.7) 54.3 (16.6) 59.2 (22.5) 0.1 .7 0.0 Drug (n = 12) Placebo (n = 11) ANOVA T1 T2 T1 T2 Time × Group (Mean, SD) F p Partial Eta2 Global Measures  MMSE 24.3 (3.5) 24.2 (2.8) 25.4 (1.7) 24.3 (1.9) 1 .3 0.0  DRS 119.7 (10.4) 121.3 (9.5) 127.1 (7.2) 126.5 (8.1) 0.6 .4 0.0  ADAS-Cog 17.3 (6.7) 15.4 (4.4) 13.1 (3.8) 14.0 (4.4) 2.9 .1 0.1 Domain-Specific  Memory   HVLT Learning 11.0 (2.6) 10.8 (2.3) 11.4 (3.8) 11.6 (4.9) 0.1 .7 0.0   HVLT Recall 1.2 (1.0) 1.0 (1.6) 1.1 (1.4) 2.3 (1.9) 3 .1 0.1   HVLT Recognition 5.3 (3.6) 4.9 (3.3) 6.7 (2.7) 5.7 (2.4) 0.3 .6 0.0  Language   Fluency FAS (M) 9.0 (5.0) 9.0 (5.4) 9.4 (4.3) 9.5 (3.9) 0.0 .8 0.0   Fluency Category (M) 7.4 (2.7) 7.4 (2.6) 7.9 (2.7) 7.0 (2.2) 2.0 .2 0.1  Visuospatial   VFD 26.7 (3.9) 27.6 (3.5) 27.9 (2.5) 27.9 (2.3) 0.1 .7 0.0  Attention & Executive Function   Digit Span Forward (max) 5.9 (0.9) 5.3 (1.1) 6.0 (0.8) 5.6 (0.7) 0.3 .6 0.0   Digit Span Backward (max) 3.6 (0.8) 4.1 (0.8) 3.6 (0.8) 3.7 (0.7) 2.2 .1 0.1   DKEFs Trail Making Test Condition 4 12.2 (11.7) 10.8 (12.5) 6.7 (6.2) 9.3 (9.2) 0.8 .4 0.0  Motor Speed   DKEFs Trail Making Test Condition 5 62.7 (36.5) 64.2 (33.7) 54.3 (16.6) 59.2 (22.5) 0.1 .7 0.0 Note: Statistics reported: mean (M), standard deviation (SD), F Ratio (F), significance value (p). Test abbreviations: MMSE = Mini-Mental State Examination; DRS = Dementia Rating Scale; ADAS-Cog = Alzheimer’s Disease Assessment Scale-cognitive subscale; HVLT Learning, Recall, Recognition = Hopkins Verbal Learning Test – learning, delayed, and recognition raw score; Fluency FAS = Mean Letter Fluency; Fluency Category = Mean Fluency across animals, fruits, and vegetables; VFD = Visual Form Discrimination; Digit Span Forward: Longest Digit Span Forward, Digit Span Backward: Longest Digit Span Backward; DKEFs = Delis–Kaplan Executive Function System: Trail Making, Conditions 4 and 5. The attention measures were analyzed to show within-group performance between T1 and T2 (see Tables 3A and 3B). Processing Speed. There were no treatment effects for processing speed within the Drug or Placebo group, as RT did not improve across any SOA on the Foreperiod Effect task. Accuracy. The Attentional Blink showed that, while both groups declined from baseline at the high-load interval (SOA-266 ms, r = 0.5), the Placebo group was significantly worse (Fig. 2). Of note, there were no treatment effects at SOA-399 ms, which we discuss below. Orienting. Contrary to our prediction, there was no treatment effect on covert orienting. Variability. The Drug group significantly reduced variability after treatment at the shortest SOA-350 ms on the Foreperiod Effect task (r = 0.4), while the Placebo group did not differ over time (see Fig. 3). On SOA-500 ms, variability remained stable for both groups. Fatigue. The Placebo group significantly increased RT both on the Foreperiod Effect between Block 1–2 (r = 0.5), as well as on Covert Orienting from Block 1 to 5 (r = 0.6). In contrast, the Drug group maintained speed across blocks on both tasks (see Table 3B; and Fig. 4). Table 3A. Attention measures: speed, accuracy, variability, and orienting. Accuracy and variability showed treatment effect at high-load conditions Descriptives Within Group Treatment Effects between T1 and T2 Drug Placebo Drug Placebo Processing Speed - Foreperiod Effect Task [Median RT (IQR)] SOA (ms) T1 T2 T1 T2 z p r z p r 350 539 (261.6) 497 (180.2) 415 (103.5) 402 (194.5) −0.6 .5 0.1 −0.2 .9 0.0 500 449 (282.6) 416 (105.9) 345 (240.5) 332 (231) −0.5 .6 0.1 −0.2 .9 0.0 Accuracy – Attentional Blink [Median (IQR)] SOA (ms) T1 T2 T1 T2 z p r z p r 266 75.0 (21.9) 56.3 (68.7) 62.5 (25.0) 37.5 (37.5) −1.8 .1 0.4 −2.2 .0* 0.5 399 81.2 (31.2) 75.0 (34.4) 87.5 (50.0) 62.5 (50.0) −0.3 .8 0.1 −0.4 .7 0.1 Variability – Foreperiod Effect Task (SD Reaction Time) SOA (ms) T1 T2 T1 T2 z p r z p r 350 134.3 107.6 112.7 141.1 −2.0 .0* 0.4 −0.5 .6 0.1 500 107.6 103.1 92.3 100.7 −0.2 .9 0.0 −0.5 .6 0.1 Covert Orienting [Median RT (IQR)] Cue Type T1 T2 T1 T2 z p r z p r Invalid 497 (185.4) 496 (142.3) 490 (92.3) 475 (111) −0.4 .7 0.1 −0.8 .4 0.2 Descriptives Within Group Treatment Effects between T1 and T2 Drug Placebo Drug Placebo Processing Speed - Foreperiod Effect Task [Median RT (IQR)] SOA (ms) T1 T2 T1 T2 z p r z p r 350 539 (261.6) 497 (180.2) 415 (103.5) 402 (194.5) −0.6 .5 0.1 −0.2 .9 0.0 500 449 (282.6) 416 (105.9) 345 (240.5) 332 (231) −0.5 .6 0.1 −0.2 .9 0.0 Accuracy – Attentional Blink [Median (IQR)] SOA (ms) T1 T2 T1 T2 z p r z p r 266 75.0 (21.9) 56.3 (68.7) 62.5 (25.0) 37.5 (37.5) −1.8 .1 0.4 −2.2 .0* 0.5 399 81.2 (31.2) 75.0 (34.4) 87.5 (50.0) 62.5 (50.0) −0.3 .8 0.1 −0.4 .7 0.1 Variability – Foreperiod Effect Task (SD Reaction Time) SOA (ms) T1 T2 T1 T2 z p r z p r 350 134.3 107.6 112.7 141.1 −2.0 .0* 0.4 −0.5 .6 0.1 500 107.6 103.1 92.3 100.7 −0.2 .9 0.0 −0.5 .6 0.1 Covert Orienting [Median RT (IQR)] Cue Type T1 T2 T1 T2 z p r z p r Invalid 497 (185.4) 496 (142.3) 490 (92.3) 475 (111) −0.4 .7 0.1 −0.8 .4 0.2 Note: Non-parametric analyses conducted using within group comparisons: Wilcoxon Signed Rank Test; Baseline (T1), 6-week follow-up (T2), Stimulus Onset Asynchrony (SOA), Interquartile Range (IQR), Z-score (z), significance value (p), Effect size (Cohen’s r); (*) indicates unadjusted p < 0.05. Table 3A. Attention measures: speed, accuracy, variability, and orienting. Accuracy and variability showed treatment effect at high-load conditions Descriptives Within Group Treatment Effects between T1 and T2 Drug Placebo Drug Placebo Processing Speed - Foreperiod Effect Task [Median RT (IQR)] SOA (ms) T1 T2 T1 T2 z p r z p r 350 539 (261.6) 497 (180.2) 415 (103.5) 402 (194.5) −0.6 .5 0.1 −0.2 .9 0.0 500 449 (282.6) 416 (105.9) 345 (240.5) 332 (231) −0.5 .6 0.1 −0.2 .9 0.0 Accuracy – Attentional Blink [Median (IQR)] SOA (ms) T1 T2 T1 T2 z p r z p r 266 75.0 (21.9) 56.3 (68.7) 62.5 (25.0) 37.5 (37.5) −1.8 .1 0.4 −2.2 .0* 0.5 399 81.2 (31.2) 75.0 (34.4) 87.5 (50.0) 62.5 (50.0) −0.3 .8 0.1 −0.4 .7 0.1 Variability – Foreperiod Effect Task (SD Reaction Time) SOA (ms) T1 T2 T1 T2 z p r z p r 350 134.3 107.6 112.7 141.1 −2.0 .0* 0.4 −0.5 .6 0.1 500 107.6 103.1 92.3 100.7 −0.2 .9 0.0 −0.5 .6 0.1 Covert Orienting [Median RT (IQR)] Cue Type T1 T2 T1 T2 z p r z p r Invalid 497 (185.4) 496 (142.3) 490 (92.3) 475 (111) −0.4 .7 0.1 −0.8 .4 0.2 Descriptives Within Group Treatment Effects between T1 and T2 Drug Placebo Drug Placebo Processing Speed - Foreperiod Effect Task [Median RT (IQR)] SOA (ms) T1 T2 T1 T2 z p r z p r 350 539 (261.6) 497 (180.2) 415 (103.5) 402 (194.5) −0.6 .5 0.1 −0.2 .9 0.0 500 449 (282.6) 416 (105.9) 345 (240.5) 332 (231) −0.5 .6 0.1 −0.2 .9 0.0 Accuracy – Attentional Blink [Median (IQR)] SOA (ms) T1 T2 T1 T2 z p r z p r 266 75.0 (21.9) 56.3 (68.7) 62.5 (25.0) 37.5 (37.5) −1.8 .1 0.4 −2.2 .0* 0.5 399 81.2 (31.2) 75.0 (34.4) 87.5 (50.0) 62.5 (50.0) −0.3 .8 0.1 −0.4 .7 0.1 Variability – Foreperiod Effect Task (SD Reaction Time) SOA (ms) T1 T2 T1 T2 z p r z p r 350 134.3 107.6 112.7 141.1 −2.0 .0* 0.4 −0.5 .6 0.1 500 107.6 103.1 92.3 100.7 −0.2 .9 0.0 −0.5 .6 0.1 Covert Orienting [Median RT (IQR)] Cue Type T1 T2 T1 T2 z p r z p r Invalid 497 (185.4) 496 (142.3) 490 (92.3) 475 (111) −0.4 .7 0.1 −0.8 .4 0.2 Note: Non-parametric analyses conducted using within group comparisons: Wilcoxon Signed Rank Test; Baseline (T1), 6-week follow-up (T2), Stimulus Onset Asynchrony (SOA), Interquartile Range (IQR), Z-score (z), significance value (p), Effect size (Cohen’s r); (*) indicates unadjusted p < 0.05. Table 3B. Attention measure: Fatigue at T2. Placebo group showed significantly greater fatigue than Drug group Foreperiod Effect Task Median (IQR) Block 1 Block 2 z p r Drug 400 (87.4) 395 (85.3) 1.02 .3 0.2 Placebo 331 (200.7) 385 (254.2) −2.2 .0* 0.5 Foreperiod Effect Task Median (IQR) Block 1 Block 2 z p r Drug 400 (87.4) 395 (85.3) 1.02 .3 0.2 Placebo 331 (200.7) 385 (254.2) −2.2 .0* 0.5 Covert Orienting Task Median (IQR) Block 1 Block 5 z p r Drug 415 (145.1) 451 (134.2) −0.7 .5 0.1 Placebo 402 (95.1) 487 (144.3) −2.9 .0* 0.6 Covert Orienting Task Median (IQR) Block 1 Block 5 z p r Drug 415 (145.1) 451 (134.2) −0.7 .5 0.1 Placebo 402 (95.1) 487 (144.3) −2.9 .0* 0.6 Note: Non-parametric analyses using Wilcoxon Signed Rank Test (Within group comparisons). Statistics reported are: Interquartile Range (IQR), Z-score (z), significance value (p), Effect size (Cohen’s r); (*) indicates p< .05. Table 3B. Attention measure: Fatigue at T2. Placebo group showed significantly greater fatigue than Drug group Foreperiod Effect Task Median (IQR) Block 1 Block 2 z p r Drug 400 (87.4) 395 (85.3) 1.02 .3 0.2 Placebo 331 (200.7) 385 (254.2) −2.2 .0* 0.5 Foreperiod Effect Task Median (IQR) Block 1 Block 2 z p r Drug 400 (87.4) 395 (85.3) 1.02 .3 0.2 Placebo 331 (200.7) 385 (254.2) −2.2 .0* 0.5 Covert Orienting Task Median (IQR) Block 1 Block 5 z p r Drug 415 (145.1) 451 (134.2) −0.7 .5 0.1 Placebo 402 (95.1) 487 (144.3) −2.9 .0* 0.6 Covert Orienting Task Median (IQR) Block 1 Block 5 z p r Drug 415 (145.1) 451 (134.2) −0.7 .5 0.1 Placebo 402 (95.1) 487 (144.3) −2.9 .0* 0.6 Note: Non-parametric analyses using Wilcoxon Signed Rank Test (Within group comparisons). Statistics reported are: Interquartile Range (IQR), Z-score (z), significance value (p), Effect size (Cohen’s r); (*) indicates p< .05. Fig. 2. View largeDownload slide Treatment effects on top-down accuracy comparing accuracy percentage at T1 to T2. Error bars denote adjusted standard error to reflect within-group error variance (Field, 2009). SOA = Stimulus Onset Asynchrony; ms = milliseconds; T1 = baseline; T2 = after ≈6 weeks of treatment with donepezil. *indicates p < .05. Fig. 2. View largeDownload slide Treatment effects on top-down accuracy comparing accuracy percentage at T1 to T2. Error bars denote adjusted standard error to reflect within-group error variance (Field, 2009). SOA = Stimulus Onset Asynchrony; ms = milliseconds; T1 = baseline; T2 = after ≈6 weeks of treatment with donepezil. *indicates p < .05. Fig. 3. View largeDownload slide Treatment effects on variability comparing RT standard deviation from T1 to T2. Error bars denote adjusted standard error to reflect within-group error variance (Field, 2009). RT = Response Time; SOA = Stimulus Onset Asynchrony; ms = milliseconds; T1 = baseline; T2 = after ≈6 weeks of treatment with donepezil. * indicates p < .05. Fig. 3. View largeDownload slide Treatment effects on variability comparing RT standard deviation from T1 to T2. Error bars denote adjusted standard error to reflect within-group error variance (Field, 2009). RT = Response Time; SOA = Stimulus Onset Asynchrony; ms = milliseconds; T1 = baseline; T2 = after ≈6 weeks of treatment with donepezil. * indicates p < .05. Fig. 4. View largeDownload slide Treatment effects of fatigue at T2 comparing Drug versus Placebo group RT mean across blocks in the Covert Orienting and Foreperiod Effect task. Error bars denote adjusted standard error to reflect within-group error variance (Field, 2009). RT = Response Time; SOA = Stimulus Onset Asynchrony; ms = milliseconds; T1 = baseline; T2 = after ≈6 weeks of treatment with donepezil. * indicates p < .05. Fig. 4. View largeDownload slide Treatment effects of fatigue at T2 comparing Drug versus Placebo group RT mean across blocks in the Covert Orienting and Foreperiod Effect task. Error bars denote adjusted standard error to reflect within-group error variance (Field, 2009). RT = Response Time; SOA = Stimulus Onset Asynchrony; ms = milliseconds; T1 = baseline; T2 = after ≈6 weeks of treatment with donepezil. * indicates p < .05. Discussion The aim of this study was to investigate whether high-load attention measures could detect early treatment effects of donepezil hydrochloride in patients newly diagnosed with AD. Since the advent of cholinergic treatment for AD (Davies & Maloney, 1976), the ability to detect a short-term cognitive response to cholinesterase inhibitors has remained difficult (Deardorff et al., 2015). First, we posited that if acetylcholine mediates attention (Hasselmo & Sarter, 2011), and if increased attentional load taxes the cholinergic system (Himmelheber et al., 2000; Klinkenberg et al., 2010; Kozak et al., 2006), then attention measures under high-load conditions could better capture treatment response. Second, we posited that high-load attention measures in particular would be sufficiently sensitive to detect treatment efficacy even after a short treatment period of approximately 6 weeks. Lastly, we hypothesized that neither other cognitive domains nor global cognitive measures would be as sensitive as these carefully designed load-dependent measures of attention. Supporting our hypotheses, high-load conditions of three of the five attention measures did detect the drug effect, namely accuracy, variability, and fatigue. Importantly, neither global measures nor any other domain-specific measure of memory, language, visuospatial or executive functions could detect treatment response over the same time course. To our knowledge, this is the first study assessing treatment response of a ChEI using attention measures in a randomized, double-blind placebo-controlled 6-week trial. Accuracy was sensitive to treatment effects under high load (Attentional Blink, SOA-266 ms), which helped the Drug group avert deterioration of top-down skills (large effect size). In contrast, accuracy of the Placebo group significantly worsened over the 6-week course. None of the low-load conditions showed group differences. These blink data corroborate the findings of Perry and Hodges (2003), who documented that patients with MCI could not avail themselves of top-down instructions. Our study adds support to their findings in AD, and additionally links top-down accuracy to cholinergic modulation as postulated by Klinkenberg and colleagues (2011). Our findings suggest that the attentional blink was sensitive to treatment precisely because it captured the demands needed to process the combination of high load, simultaneous semantic instructions, and rapid decision-making. Although hypothesized, we noted that no treatment differences were detected at SOA-399 ms. One explanation may be that only the more demanding SOA-266 ms was more sensitive to increased cholinergic availability (Himmelheber et al., 2000; Kozak et al., 2006). Alternatively, the short treatment period (6 weeks) may not have been sufficient to show a treatment effect at SOA-399 ms. Intra-individual variability was also sensitive to drug treatment at high load, as measured by within-subject trial-to-trial inconsistencies in RT on the Foreperiod Effect task (medium effect size). The Drug, but not the Placebo group, decreased variability at the shortest interval (SOA-350 ms), although not at the next interval as predicted (SOA-500 ms). Our findings using a single task corroborate Gorus and colleagues (2007), who also found reduced dispersion effects using another ChEI, galantamine across four different visual attention tasks of varying complexity. With evidence that patients with AD and MCI increase variability (Gorus, De Raedt, Lambert, Lemper, & Mets, 2008; Hultsch et al., 2000) under demanding load (Phillips et al., 2013), and with our additional result that cholinergic treatment may decrease variability, these findings support that appropriate high-load attention tasks capturing variability could be used to detect cholinergic treatment response. Moreover, these results implicate that acetylcholine could play a role in the neurobiological mechanisms underlying variability. Cognitive fatigue was assessed comparing speed across multiple blocks in two tasks, Covert Orienting and Foreperiod Effect Task (large effect sizes). We posited that if cholinergic treatment ameliorated attention, and attention represented a component of fatigue (Holtzer et al., 2011), then successful cholinergic treatment response could improve cognitive fatigue. This hypothesis was supported on both tasks, such that the Placebo group became more fatigued than the Drug group after 6 weeks. The current findings raise the possibility that cholinergic treatment in AD could help reduce the impact of cognitive fatigue on functional abilities. Two predicted measures of attention, speed and orienting, did not detect drug effects. The Drug and Placebo groups exhibited similar processing speed across all levels of load. And, contrary to prior robust research documenting impaired covert orienting in AD (Parasuraman et al., 1992), we did not demonstrate either improved benefits of a valid cue nor reduced costs from an invalid cue after drug treatment. This could be due to our small sample size and lack of power. Alternatively, different mechanisms of attention may be compromised at different stages of disease progression. We hypothesized that targeted attention measures would be more sensitive to treatment than other measures of cognition. In our study, neither any global (i.e., ADAS-Cog, MMSE, Dementia Rating Scale-2) nor any cognitive domain-specific measure (i.e., language, memory, visuospatial function) detected the short-term treatment effect. Even standardized measures of simple or complex attention, such as the Digit Span or the D-KEFs Trail Making Test Condition 4, could not detect a treatment effect. While these measures target important aspects of attention, they did not capture load-dependent performance. Our findings also support the prediction by Edmonds and colleagues (2018) who suggested that cholinergic efficacy may be mediated by attention. The authors reanalyzed the Alzheimer Disease Cooperative Study (ADCS) (Petersen et al., 2005) using more stringent statistically derived criteria to define MCI membership. They showed that donepezil treatment response improved in this MCI group at short and long treatment intervals. Our current study supports their findings showing that donepezil exerts its effect by improving attention. We now provide evidence that our load-dependent attention tasks can capture the drug’s response, and unlike global tasks or other cognitive domains, are better measures of cholinergic efficacy. The current study revisits the important relationship between attention and memory: how and at what point do attention deficits contribute to, interact with, or mediate the critical AD-related mnemonic dysfunctions of learning, consolidation and retrieval? There is increasing evidence that the noradrenergic system is heavily implicated in AD pathology (Braak & Del Tredici, 2011; Ehrenberg, et al., 2017). Degeneration in this noradrenergic system stems from impairment in the locus coeruleus (LC) with accumulation of hyperphosphorylated tau (Braak & Del Tredici, 2011; Ehrenberg et al., 2017) affecting memory, learning, and attention (Berridge & Waterhouse, 2003). One possibility may be that both cholinergic and noradrenergic systems project directly or indirectly to common critical brain regions, with the consequence that degeneration of both neurotransmitter systems results in neuromodulatory effects on critical AD-related brain structures. For instance, both nucleus basalis cholinergic (Mesulam, 2013) and LC-adrenergic efferent projections share disease-related endpoints in the dentate gyrus (Prince, Bacon, Tigaret, & Mellor, 2016) as well as forebrain structures (Chandler, Lamperski, & Waterhouse, 2013). Future research of these pathways and the interactive roles of demanding attentional, learning and delayed retrieval mechanisms are warranted. We recognize that the study has several limitations. Recruitment was compromised, first because of our strict inclusion criteria, for which patients had to be naïve to any prior cholinergic treatment, and second, patients and their families did not want to delay treatment. This resulted in the small sample size and limited the power of the analyses. Additionally, non-parametric analyses had to be applied because the attention variables were not normally distributed, which limited the analyses. We believe that despite the sample size and power limitations, the magnitude of the effect sizes encourage future replication. Generalization should also be cautioned given the high educational level and limited ethnical diversity of our sample. Lastly, these findings only pertain to one specific drug (donepezil hydrochloride) and not the other ChEIs (e.g., galantamine hydrobromide and rivastigmine), which have different mechanisms of action and dosing (Čolović, Krstić, Lazarević-Pašti, Bondžić, & Vasić, 2013). Despite limitations, results from this study point to the benefits of testing drug efficacy using cognitive measures directly implicated by treatment mechanisms, and warrant follow-up larger replication investigation. The present findings also have important clinical implications. Specifically, current tools used to detect treatment effects are insufficient (Cano et al., 2010), leaving clinicians and families with the difficult decision of whether to continue ChEI treatment. To aid clinical management of risks and benefits in patients undergoing cholinergic treatment, we suggest that efficacy measures should include load-dependent attention measures to detect early drug response. Lastly, an additional benefit of our attention tasks is their independence of any particular language, and adaptability for diverse populations. In conclusion, this pilot study applied a well-designed, randomized, double-blind placebo-controlled trial to investigate the effects of donepezil on attention in patients newly diagnosed with AD. Because of the known association between attention and acetylcholine, we targeted sensitive load-dependent attention measures, and, even in a small group of participants, we were could detect treatment effects after only about 6 weeks. The effect of the drug, but not the placebo, prevented deterioration of top-down accuracy, reduced variability, and lessened cognitive fatigue. Importantly, no other global measure or domain-specific cognitive measures of memory, language, visuospatial or executive functions could detect a change in function over the same time course. These graded, load-dependent types of attention measures may therefore be valuable in the development of future efficacy assessments of cholinergic treatment. Funding This work was supported by the Alzheimer’s Association (Grant #IIRG-05-13534); National Institute of General Medical Sciences of the National Institutes of Health (SC3GM122662 to NSF); support for this project was provided by PSC-CUNY Awards, jointly funded by The Professional Staff Congress and The City University of New York (67024-00-36, 68058-00-37, & 69024-00-38 to NSF and 69060-00-38 to LRK); CUNY Doctoral Student Research Grant to JJL; CV-C received financial support from La Caixa–USA Program 2014 Fellowship. Conflict of interest None declared. Acknowledgements The authors declare that they do not have any financial interest or benefit arising from direct applications of this research. We are also grateful for the contributions from Susan Boglia, Pharm.D, Barbara Eisenkraft, MD, Lawrence S. Honig, MD, Richard E. White, PhD, John Zhu, MA, and Joel Redfield, PhD and to patients and families for their participation. References Alzheimer’s Association . 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Archives of Clinical NeuropsychologyOxford University Press

Published: Apr 9, 2018

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